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The impact of privatization of primary care programs in large county health department in florida

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Title:
The impact of privatization of primary care programs in large county health department in florida
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English
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Brock, Arlesia Lynn
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University of South Florida
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Tampa, Fla.
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Subjects / Keywords:
Health care cost /financing
Health care service availability
Health science research
Medically underserved populations
Outcomes research
Public health
Dissertations, Academic -- Public Health -- Doctoral -- USF   ( lcsh )
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government publication (state, provincial, terriorial, dependent)   ( marcgt )
bibliography   ( marcgt )
theses   ( marcgt )
non-fiction   ( marcgt )

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Summary:
ABSTRACT: Since the mid-1970s, top managers, politicians, and officials in both public and private institutions have promoted contracting-out services (privatization) as a means of increasing efficiency, flexibility, and quality. The privatization trend has occurred in many public sector organizations particularly in city services and prisons. Public health services are not immune to this trend. Today many county health departments have contracted the provision of public health services like womens health, primary care, and laboratory services. However, very few studies have analyzed the impact of these privatizations on cost, access, and health outcomes. Proponents favoring the private provision of these services argue that private providers are more efficient and can deliver these services at a lower cost. Also, because of better innovation, private providers can even improve quality.However, among opponents there is concern that a for-profit private provider might cut costs that adversely affects the quality of these services. The purpose of this dissertation is to analyze the effects of the privatization of primary care services on cost, access, and health outcomes in nine large counties in the state of Florida. In a survey of county health departments conducted in 1999, 61 out of 67 counties had outsourced at least one service. Primary care was the second most frequently privatized program. Womens health was the program most often privatized by counties. Using mixed models and logistic regression, a comparison was made between large counties that outsourced primary care services and counties that did not. Multiple years of data were obtained from federal and state sources for analysis.
Thesis:
Thesis (Ph.D.)--University of South Florida, 2005.
Bibliography:
Includes bibliographical references.
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Mode of access: World Wide Web.
Statement of Responsibility:
by Arlesia Lynn Brock.
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Title from PDF of title page.
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Document formatted into pages; contains 156 pages.

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aleph - 001670351
oclc - 62288484
usfldc doi - E14-SFE0001214
usfldc handle - e14.1214
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ABSTRACT: Since the mid-1970s, top managers, politicians, and officials in both public and private institutions have promoted contracting-out services (privatization) as a means of increasing efficiency, flexibility, and quality. The privatization trend has occurred in many public sector organizations particularly in city services and prisons. Public health services are not immune to this trend. Today many county health departments have contracted the provision of public health services like womens health, primary care, and laboratory services. However, very few studies have analyzed the impact of these privatizations on cost, access, and health outcomes. Proponents favoring the private provision of these services argue that private providers are more efficient and can deliver these services at a lower cost. Also, because of better innovation, private providers can even improve quality.However, among opponents there is concern that a for-profit private provider might cut costs that adversely affects the quality of these services. The purpose of this dissertation is to analyze the effects of the privatization of primary care services on cost, access, and health outcomes in nine large counties in the state of Florida. In a survey of county health departments conducted in 1999, 61 out of 67 counties had outsourced at least one service. Primary care was the second most frequently privatized program. Womens health was the program most often privatized by counties. Using mixed models and logistic regression, a comparison was made between large counties that outsourced primary care services and counties that did not. Multiple years of data were obtained from federal and state sources for analysis.
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The Impact of Privatization of Primary Care Programs in Large County Health Departments in Florida by Arlesia Lynn Brock A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Health Policy and Management College of Public Health University of South Florida Co Major Professor: Barbara L. Orban Ph.D. Co Major Professor: James Studnicki Sc.D. Ann L. Abbott J.D., Ph.D. M. R Francois M.D., Ph.D. Youg u i Wu Ph.D. Date of Approval: July 25, 2005 Keywords: health care cost /financing, health care service availab ility, health science research, medically underserved population s outcomes research, public health Copyright 2005 Arlesia Brock

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Dedication This dissertation is dedicated to the memory of my fath er, Willie Brock.

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i Table of Contents List of Tables i v List of Figures vi Abstract vii Preface i x THE INTRODUCTION 1 Historical Context 1 Defining Public Health 6 Defining Primary Care 9 Defining Privatization 12 Summary 14 THE STATEMENT OF THE PROBLEM 15 The Need for the Study 15 Statement of the Problem 15 Research Questions 17 Study Hypotheses 18 Limitations and Delimitations 19 Specific Aims 20 Summary 20 THE LITERATURE REVIEW 21 Overview 21 Privatization 21 Effic iency and Privatization 2 3 Theoretical Framework 25 Market Theory 25 Public Choice Theory 26 Privatization in State and Local Government 28 Refuse Collection 29 Transportation 29 Utility Services 29 Prisons/Jails 35 Public H ospitals 3 5

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ii State Government 36 Transportation 37 Corrections 38 Building Maintenance 38 WAGES 38 Human Resources 3 8 State Hospitals 39 Public Health Services 40 Privatization Trends in Floridas Public Health System 42 Measures o f Privatization 43 Cost 4 4 Access to Care 45 Health Outcomes/Status 46 Summary 49 THE RESEARCH DESIGN 51 Overview 51 Cost Data Sources and Variables 51 Access Data Sources and Variables 55 Health Outcomes Data Sources and Variables 59 Summ ary 62 THE RESULTS 63 Overview 63 Results of Costs Analyses 63 Results of Access Analyses 67 Results of Health Outcomes Analyses 68 Summary 70 THE CONCLUSIONS AND RECOMMENDATIONS 71 Discussion and Conclusions 71 Impact of Privatization o n Cost 71 Impact of Privatization on Access 72 Impact of Privatization on Health Outcomes 73 Implications and Recommendations 7 4 Summary 75 References 76 Bibliography 8 2 Appendices 8 3 Appendix A: Primary Care Access Scores 8 4 Appendix B: Var iable Specific Score by Zip Code 96 Appendix C: Descriptive Statistics for Population by Zip Code 12 0

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iii About the Author End Page

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iv List of Tables Table 1 Ten Great Public Health Achievement United States 1900 1999 7 Table 2 Alternati ve Approaches to Service Delivery 22 Table 3 Summary of Major Local Privatization Studies 31 Table 4 Privatization in the State of Florida 36 Table 5 Health Status Indicators Common to Most United States National Compendium 48 Table 6 Descriptions of Selected Primary Care Programs 52 Table 7 Variables Used in Cost Analysis 5 4 Table 8 Access Model Variables 5 6 Table 9 Data Sources for Access Variables 5 7 Table 10 Access Model Variable Weights 5 8 Table 11 Variables and Data Sources for Outcomes Ana lysis 60 Table 12 Health Status Models Using Primary Care Specific Indicators 61 Table 13 Descriptive Statistics for Primary Care Cost Analysis 64 Table 14 Cost Analysis Para meter Estimates for Mixed Model With Repeated Measures 65 Table 15 Chi Squar e and Z Statis tics Comparison for GEE Analysis 66 Table 16 GEE Analysis for Primary Care Access Scores 68

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v Table 17 Health Outcomes Mixed Model with Repeated Measures Solution of Fixed Effects 69

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vi List of Figures Figure 1. Health Outcomes Mode l 4 7

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vii The Impact of Privatization of Primary Care Programs in Large County Health Departments in Florida A rlesia Lynn Brock ABSTRACT Since the mid 1970s, top managers, politicians, and officials in both public and private institutions have promoted co ntracting out services (privatization) as a means of increasing efficiency, flexibility, and quality. The privatization trend has occurred in many public sector organizations particularly in city services and prisons. Public health services are not immun e to this trend. Today many county health departments have contracted the provision of public health services like womens health, primary care, and laboratory services. However, very few studies have analyzed the impact of these privatizations on cost, access, and health outcomes. Proponents favoring the private provision of these services argue that private providers are more efficient and can deliver these services at a lower cost. Also, because of better innovation, private providers can even improve quality. However, among opponents there is concern that a for profit private provider might cut costs that adversely affects the quality of these services. The purpose of this dissertation is to analyze the effects of the privatization of primary care s ervices on cost, access, and health outcomes in nine large counties in the state of Florida. In a survey of county health departments conducted in 1999, 61 out of 67 counties had outsourced at least one service. Primary care was the second most

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viii frequentl y privatized program. Womens health was the program most often privatized by counties. Using mixed models and logistic regression, a comparison was made between large counties that outsourced primary care services and counties that did not. Multiple year s of data were obtained from federal and state sources for analysis. Th is study answers the following research questions: 1) What are the costs of primary care services provided by contracted service provi ders compared to services provided by the public health department? 2) Where primary care services have been privatized, what is the effect on access to care for the Medi caid and uninsured patients? 3) What is the effect of privatization on health outcomes in privatized and non privatized counties?

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ix Preface Many capable people desire the attainment of their objectives and dream of doing something that might positively affect their community. However, high as pirations can only be attained with the help others who believe in your dreams. I was fortunate to receive more than my share of inspiration and encouragement from others. I received much encouragement from my family, especially from my mother, Lillian Ponton Brock, science and health teacher who blazed educational trails ahead of me. My brothers, Gary and Darryl Brock, and their wives, Sandra and Sheila, were also supportive at crucial times. My nieces, Taylor and Kaylyn, and my nephew, Julian, provided recreational relief. My fianc, Alaric (Ric) H. Mathis, backed his faith in this en deavor with resources as well as emotional support on a daily basis. (This was not a small feat on his part.) His resolute confidence in my ability to successfully complete the Ph .D. program energized me when I had run out of steam. And last but not lea st, Bee Frazier, my friend, who provided advice and comic relief when things seemed really dreary. The completion of this dissertation was made possible with the assistance of the faculty and staff of the Department of Health Policy and Management at the University of South Florida College of Public Health. Each member of the dissertation committee went beyond minimally required contributions. Dr. Barbara Orban, volunteered useful tips and allowed me to work on a project during a directed study that res ulted in my first publication entitled, Public Health, Primary Care, and Privatization. Dr. James

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x Studnicki, offered encouragement and took the extra time to advise me on a successful National Research Service Award application. Dr.Yougui Wu, provided h ours of input on complex statistical analyses. Dr. Ann Abbott, gave generous praise and encouragement as well as providing direction on making the dissertation the best that it could be. Dr. M. R. Francois, provided special encouragement and sacrifice to make the completion of this dissertation possible. Others who deserve acknowledgement for their assistance include: Dr. Stephen Luther, Dr. John Large, Dr. Etienne Pracht, Dr. Alan Sear, Dr. Laurence Branch and my fellow doctoral students who were willi ng to discuss my dissertation with me and offer helpful suggestions. My deepest gratitude extends to God for my life and health and for all the wonderful people he sent to encourage and guide me throughout thi s process. Finally, I would like to thank the Florida Education Fund and the National Institutes of Health, for providing the financial resources to make my dream a reality. This dissertation project was funded by the National Institute of Child Health and Human Development under NRSA (#1 F31 HD04642 4 01 ).

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1 THE INTRODUCTION Historical Context In many ways public health is a largely modern concept, however its roots began in antiquity. The history of public health can be traced back over 4,000 years to the ancient Indian cities of Mohenjo Daro and Harappa which first developed public sanitation. Throughout the history, major problems of health tha t humans faced have been concerned with community life -the control of transmissible disease, the control and improvement of the physical environment ( sanitation), the provision of medical care and the relief of disability and destitution. The relative emphasis placed on each of these problems has varied from time to time, but they have come to form the public health system as we know it today (Rosen, 1 958). The development of public health in Florida followed much the same course with its beginnings in the control of infectious or communicable disease and gradually incorporating aspects of personal health care The establishment of the public health s ystem in Florida occurred almost half a century after Florida became a State (Hardy and Pynchon, 1964). The State Board of Health was created in 1889 in response to a yellow fever epidemic in Jacksonville that killed over 400 and caused 40 percent of the population to flee the city. A previous attempt to create a state board of health was made in 1873, however the bill presented to the Legislature failed because the $200 appropriation was thought to be an exorbitant amount of money. In 1885 the State Co nstitution was approved, which provided for the

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2 State Board of Health and also authorized establishment of the county health departments. Although the constitution authorized the creation of health departments in each county and the State Health Officer sa w a need to have a key individual for public health in each county, health units at the county level did not begin operation until 1930 because of a lack of available funds. In 1931 an important legislative action, the County Health Unit Enabling Act, sp urred the creation of county health departments. Through this law, the administrative frame was created and in 1935 with the passage of Social Security legislation financial resources were available for the proliferation of county health departments thro ughout the state. Beginning in 1939 state funds were provided to support county health departments. Between 1889 and 1921, the public health system in Florida consisted of the State Board of Health. The funding, appropriated through the state legislature was unstable from year to year. In the early 1900s the budget gradually expanded reaching a high of $165,524 in 1916. These funds were aimed at treating malaria, hookworm and other infectious diseases. The public health program came under scrutiny fo r overexpansion as perceived by a new board appointed by the recently elected governor, Sidney J. Catts. In 1917, the board reduced the public health budget by 25 percent. The board quickly made changes all upon the basis of economy, efficiency, or ha rmony. However in 1918 maternal and child health were acknowledge d as a distinctive component of public health. The Bureau of Child Welfare was established to provide services to expectant mothers and to encourage the construction of maternity hospita ls. The examination of school children was also a major objective. Despite obvious needs and support of the

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3 Board of Health and the Federation of Floridas Womens Clubs, the Bureau of Child Welfare was abolished in 1921. In 1921, the Legislature reduced the State Board of Healths budget again by 50 percent. However, the effect on personal services was short lived. In 1921, the Federal Sheppard Towner Act provided $5,000 each year to states to improve maternal and child health. The U.S. Public Health S ervice Child Hygiene Unit visited Florida at the invitation of the State Board of Health and the Florida Federation of Womens Clubs. The U.S. Public Health Service made recommendations that led to the improvement of facilities for babies and mothers. Th e report also documented that of the 90,000 school children examined over 75 percent were suffering from some remedial or correctable defect. Indigent children received free treatment from local physicians. It was also during this time that the first can cer clinic was established in Jacksonville for the care of indigent patients. Radium treatment and physician services were contributed without cost. In 1935 passage of the Federal Social Security Act marked the beginning of the great expansion of person al care programs. The infusion of funds created the opportunity for the State Board of Health to focus on expanding the local health departments to provide services in areas with populations over 300,000. In the year following the passage of the Social Se curity Act, the number of county health departments went from three to eight. Within two years this number had doubled to sixteen. This expansion was also aided substantially through Hill Burton funds. From the time Federal funds became available under So cial Security, the development of local health services was considered to have the highest priority.

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4 During the 1940s the state enacted the Emergency Maternity and Infant Care Program. By 1943 over 40,000 babies were born in Florida. Many of the ba bies fathers were in the military which did not provide maternity medical or hospital care for the wives and infants. The Emergency Maternity and Infant Care Program provided these services. In 1944, there were 10,345 applications for hospital care. Th e rise in the number of military installations in Florida also led to a rise in Sexually Transmitted Infections. Health records from 1942 reported that five percent of white males and 40 percent of non white males tested positive for syphilis which was th e highest in the country. By 1943, the number of treatment clinics grew to 166, treating 33,601 cases of sexually transmitted infections. There were new and urgent demands on the State Board of Health because of the changing demographics and exploding p opulations. There was mounting concern for providing adequately for hospitalization of the indigent. In 1954, the Florida Medical Association requested the Governor to appoint a special committee to study the growing problem of hospital care for the indi gent in Florida. The committee recommended the establishment of a uniform system of hospitalization for acutely ill indigent patients with sharing of cost by the State and counties. The recommendation was enacted into law and an official program of hospi talization for the indigent began in 1956. The program was expanded in 1957 to include the categorically indigent, the indigent, and the medically indigent. The sudden arrival of 100,000 Cuban refugees on south Florida's shores in the early 1960's crea ted more stress on Floridas public health system. R esponsibility for their medical care was assigned to the S tate B oard of H ealth and the Dade C ounty H ealth

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5 D epartment. A special hospitalization program was developed that provided both in and outpatient m edical servic es to those in need. In the 1960s, the Federal government passed Title XIX of the Social Security Act. This act, more popularly known as Medicaid, provided funding to support the financing of medical services to indigent populations. The late 1960s also marked the end of the State Board of Health. The revised and new state constitution abolished the State Board of Health and created the Division of Health within the Department of Health and Rehabilitative Services (DHRS). The DHRS was a consolidation of 25 health and social services agencies. The county health departments remained essentially unchanged in this process. However, in 1975, the Legislature passed the HRS Reorganization Act to decentralize and unify the provision of services This mandate had tremendous impact on the existing public health organization as divisions within the agency were shifted or restructured. Several social issues changed public health programs and the delivery of local health services between 1975 and 19 88. Perhaps the most dramatic was precipitated by the legislature directing attention to the medical care needs of the indigent in the late 1970's. Th e Health Care Access Act of 1984 declared that access to health care was a right of every Floridian and di rected the Department of Health and Rehabilitative Services to provide "sick care" where there was manifest need. The Indigent Health Care Act of 1984 expanded the philosophy of provision of medical car e for the indigent through county health departments a nd provided funding. By 1988 primary care services were being provided in all 67 county health departments. During this time twenty five

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6 percent of the local effort of health departments was devoted to the delivery of primary care services. In the 1990 s, the Legislature split the Department of Health and Rehabilitative Services into two separate agencies -the Department of Health and the Department of Children and Families. As the Department of Health re emerged as a separate agency, other changes w ere also occurring in the delivery of health services. In the 1990s the Medicaid program witnessed a dramatic shift in the way that its populations were served. Managed care arrangements became the predominant service delivery mechanism, with managed ca re organizations assuming most of the Medicaid case load usually held by the health department. In addition to these market forces, the 1990s were characterized by governmental downsizing and budget cuts at all levels. These cuts compromised the ability of public health departments to provide all necessary services. As a result of the changes, public health and other governmental officials looked for more efficient ways to provide services. This dissertation will examine the changes in the delivery of primary care services in county health departments because of efforts to privatize these services. Privatization efforts are likely to continue and increase throughout the next decade. Defining Public Health The precise definition of public health is d ebated even by professionals in the field. Websters dictionary defines public health as an aspect of health s ervices concerned with threats to the overall health of the population of a community based o n population health analysis that generally include s inf ectious disease surveillance, infectious disease control a nd promotion of healthy behavio rs (health promotion) among

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7 members of the community. Public health is defined in medical/clinical terms as the approach to medicine that is concerned with the health of the community as a whole. The Institute of Medicine defines the three core functions of public health as assessment, policy development, and assurance (IOM, 1988). The public health system strives to prevent epidemics; protect the environment, workplace, housing, food, and water; promote healthy behavior; monitor the health status of the community; respond to disasters; ensure the quality of medical care; provide high risk persons with needed services; and provide leadership and research on he alth policy (CDC, 1991). It s mission is to fulfill society s interest in assuring conditions in which people can be healthy. The base of knowledge for public health comes from a variety of disciplines ranging from social sciences, to biological sciences and business. The notable public health achievements in the twentieth century according to the Centers for Disease Control are listed in Table 1. Table 1 Ten Great Public Health Achievements -United States, 1900 1999 Vaccination Vaccination has res ulted in the eradication of smallpox; elimination of poliomyletis in the Americas; and control of measles, rubella, tetanus, diptheria, Haemophilus influenzae type b, and other infectious diseases in the United States and other parts of the world. Motor vehicle safety Improvements in motor vehicle safety have resulted from engineering efforts to make both vehicles and highways safer and from successful efforts to change personal behavior (e.g., increased use of safety belts, child safety seats, and motorc ycle helmets and decreased drinking and driving). These efforts have contributed to large reductions in motor vehicle related deaths.

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8 Table 1 C ontinued Ten Great Public Health Achievements -United States, 1900 1999 Safer workplaces Work related he alth problems, such as coal workers' pneumoconiosis (black lung), and silicosis -common at the beginning of the century -have come under better control. Severe injuries and deaths related to mining, manufacturing, construction, and transportation also have decreased; since 1980, safer workplaces have resulted in a reduction of approximatel y 40% in the rate of fatal occupational injuries Control of infectious diseases Control of infectious diseases has resulted from clean water and improved sanitation. Infections such as typhoid and cholera transmitted by contaminated water, a major cause of illness and death early in the 20th century, have been reduced dramatically by improved sanitation. In addition, the discovery of antimicrobial therapy has been critical to successful public health efforts to control infections such as tuberculosis and sexually transmitted diseases (STDs). Decline in deaths from coronary heart disease and stroke Decline in deaths from coronary heart disease and stroke have resulted from risk factor modification, such as smoking cessation and blood pressure control coupled with improved access to early detection and better treatment. Since 1972, death rates for coronary heart disease have decreased 51%. Safer and healthier foods Since 1900, safer and healthier foods have resulted from decreases in microbial contamination and increases in nutritional content. Identifying essential micronutrients and establishing food fortification programs have almost eliminated major nutritional deficiency diseases such as rickets goiter and pellagra in the United States. Healthier mothers and babies Healt hier mothers and babies have resulted from better hygiene and nutrition, availability of antibiotics, greater access to health care, and technologic advances in maternal and neonatal medicine. Since 1900, infant mortality has decreased 90%, and maternal mortality has decreased 99%. Family planning Access to family planning and contraceptive services has altered social and economic roles of women. Family planning has provided health benefits such as smaller family size and longer interval between the birth of children; increased opportunities for preconceptional counseling and screening; fewer infant, child, and maternal deaths; and the use of barrier contraceptives to prevent pregnancy and transmission of human immunodeficiency virus and other STDs.

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9 Table 1 C ontinued Ten Great Public Health Achievements -United States, 1900 1999 Fluoridat ion of drinking water Fluoridation of drinking water began in 1945 and presently reaches an estimated 144 million persons in the United States. Fluoridation safely and inexpensively benefits both children and adults by effectively preventing tooth decay, regardless of socioeconomic status or access to care. Fluoridation has played an important role in the reductions in tooth decay (40% 70% in children) and of tooth loss in adults (40% 60%). Recognition of tobacco use as a health hazard R ecognition of to bacco use as a health hazard and subsequent public health anti smoking campaigns have resulted in changes in social norms to prevent initiation of tobacco use, promote cessation of use, and reduce exposure to environmental tobacco smoke. Since the 1964 Su rgeon General's report on the health risks of smoking, the prevalence of smoking among adults has decreased, and millions of smoking related deaths have been prevented. From the U.S. Centers for Disease Control Public health functions are carried out by all levels of government but the most visible activity occurs in the 3,000 county, city, and other municipal health department throughout the country. Staffs range from more than a thousand in large jurisdictions to one public health nurse or sanitarian i n the least populated areas (Wall, 1998). Defining Primary Care Since its introduction in 1961, the term primary care has been defined in various ways, often using one or more categories to describe what primary care is or who provides it. These categori es include: the care provided by clinicians in certain areas such as family medicine, pediatrics, obstetrics and gynecology; a set of activities whose functions define the boundaries of primary care such as curing or alleviating common illnesses and disab ilities; a level of care or a setting -an entry point to a system that includes secondary and tertiary care; a set of attributes as in the 1978 IOM definition

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10 care that is accessible, comprehensive, coordinated, continuous, and accountable; and finally a strategy for organizing the health care system as a whole such as community oriented primary care, which gives priority to and allocates resources to community based health care (IOM, 1996). The definition used by Barbara Starfield, a well known rese archer in the area of primary care incorporates many of these categories. She defines primary care as that level of the health service system that provides entry into the system for all new needs and problems, provides person focused (not disease oriented ) care over time provides care for all but very uncommon or unusual conditions, and coordinates or integrates care provided else where or by others (Starfield, 1998). When the term primary care first appeared, the health care system was organized in disc reet hierarchical levels. In most industrial countries such as the United Kingdom or Canada, primary care formed the basis for the provision of all health care services (Franks et al, 1993, Clancy, et al, 1998). These countries conform most closely to the IOM definition where primary care is first contact, longitudinal care that is comprehensive and person centered rather than disease specific. In the United States, primary care reflects the pluralistic nature of our society. There is no clearly defined m ode of primary care provision (Franks et al, Clancy et al) For over 25 years, primary care delivery has consisted of overlapping contributions in a variety of settings from at least three types of generalist physicians (general internists, general pediat ricians, and family physicians), nurse practitioners, and specialists (Franks et al, 1993). However many sub specialists such as geriatricians have defined themselves as primary care physicians (PCPs) and report that they deliver primary care consistent with the Institute of Medicine definition (IOM 1996). The participation of multiple parties in the primary

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11 care field evolved after the establishment of family medicine as a specialty in 1969 (Clancy and Cooper, 1998). The patchwork of provisional modes results in little identifiable system accountability. The complexity of the health care system also adds to the elusiveness of a satisfactory definition of primary care one that is not idealized, too vague, or prone to too many exceptions. It is esse ntial to distinguish three aspects that are confounded in many definitions of primary care: the patients perspective; the practitioners of primary care; and attributes such as coordination, accessibility, comprehensiveness, and continuity that may contri bute to the cont ent and quality of primary care (Franks et al, 1993). In 1978 Mendenhall developed an instrument for classifying patient services based on physician responses to questions about patient encounters. Based on the responses to these questi ons, encounters were characterized as first, episodic, principal, consultation, or specialized. This classification system provided some differentiation among the six physician groups examined. Two later studies, Aiken and Spiegel examined three different definitions of primary care provider. In 1983, Weiner and Starfield used encounter data from patients to measure comprehensiveness, accessibility, longitudinal care and family centeredness. Rosenblatt et al used diagnostic clusters to develop a definiti on for primary care. In 1995 Rosenblatt defined primary care as a non referred ambulatory visit for one of the top 20 diagnostic clusters of the 120 that were found to be mutually exclusive. In 1997, the Institute of Medicine revised their definition of primary care. In a report issued during 1997, the Institute of Medicine defined primary care as the provision of integrated, accessible health care services by clinicians who are

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12 accountable for addressing a large majority of personal health care needs, developing a sustained partnership with patients, and practicing in the context of family and community. The elements of the definiti on are further defined. Integrated is intended to encompass the following: comprehensiveness, implying first contact care both for any of the patients health problems and through the patients life cycle; coordination, including the rational selection of health services; and continuity, referring to care with time. Accessible refers to the ease with which a patient can ini tiate an interaction with a clinician, including efforts to eliminate barriers posed by geography and culture. Health care services refers to an array of services that promote, maintain or restore health. This definition was operationalized using multipl e year data from the National Ambulatory Care Survey (Franks, 1997) Defining Privatization Privatization is defined as the transfer of responsibility for services from governmental agencies to private providers. Privatization actually has many forms th at fall along a continuum. These forms range from contracting out to franchise agreements. The Public Health Foundation developed a broad working definition of privatization applied specifically to public health. According to this definition, privatiza tion encompasses those activities/services for which the state or local health department has reached a formal decision to withdraw from or contract out for provision of a public health service in whole or in part, and a non governmental entity has taken o ver responsibility for provision of that service. This may include development of formal

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13 partnerships with the private sector to offer public health activities/services not previously provided by the health department (PHF, 1999) . This working definition encompasses the most frequent types of privatization contracting and changes in ownership. The effectiveness of these two methods of privatization has been addressed by economists since the 1930s and 1940s. According to economist Andrei Shleifer (1998), changes in ownership from public to private is generally preferred when incentives to contain costs are strong and there are opportunities to innovate (improve quality). Contracting should be used when the government knows exactly what it wants th e producer to make and the contract can be enforced. Public ownership is preferred to private when opportunities for cost reductions that lead to non contractible deterioration of quality are significant; innovation is relatively unimportant; competition i s weak and consumer choice is ineffective; and reputational mechanisms are weak. Changes in ownership (divestiture) occur most frequently with public hospitals. This dissertation will focus on contracting of services because contracting is used most frequ ently to outsource public health services. Contracts are also developed between the county health department and local entities for the provision of services. In response to the Indigent Health Care Act of 1984, the county health departments were requir ed to provide services through annual contracts with the local county government. The contract details how public health services are delivered in local jurisdictions. Included in these annual contracts are the projected amounts of revenues by source and a detailed plan of the number of clients, services, staff positions, and expenditures. Expenditures consist of state and county contributions for the program

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14 provided by the local county health department. Non categorical funds are also included and assi gned to specific programs. Non categorical funds are revenue provided by the Florida Legislature that is not appropriated for a specific service. Non categorical revenue is particularly critical at the local level because of its ability to be redirected to areas of greatest need such as disease outbreaks or other threats to public health Several large counties contract with private providers for primary care services for clients of the health department. Summary This introduction provides the context for the development of primary care and its relationship to public health services. The history of the Florida Department of Health is used as a framework to show how public health services and primary care became intertwined. The chapter also includes definitions of the key items -public health, primary care and privatization. The next chapter, entitled Statement of the Problem, will identify the theoretical framework and research questions for this study. A comprehensive review of the literature w ill be presented in chapter three. The fourth chapter The Research Design, will provide details of the research methodological approach and study plan. The fifth chapter will include a th o rough analysis of data and results. The final chapter will offer conclusions, implications, and recommendations for further study.

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15 THE STATEMENT OF THE PROBLEM The Need for the Study Health care continues to be very expensive despite the introduction of managed care, downsizing, and restructuring. As the numb er of medical and pharmaceutical technologies increase so does the responsibility, demand, and price tag for services. Particularly vulnerable to increases in health care costs are the public health departments. These agencies provide numerous population based programs and activities, while targeting individuals who cannot afford to pay for services. An emphasis on cost containment has influenced reform efforts in the provision of government services. Three fundamental trends in health c are finance and organization have affected the provision of care over the last decade. These trends include: expanding managed care models, mergers and/or conversion of public hospitals to private or non profit hospitals, and outsourcing or privatizing public health de partment services. This study will focus on privatizing of public health department services. Statement of the Problem Since the 1960s, the public health department has been the provider of last resort for disadvantaged families and communities. Histor ically, health departments provided population based public health services, such as sanitation, while the private sector provided medical care services. However, when Medicaid was introduced in 1965,

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16 public health departments began to shift more of their attention to providing care for the chronically ill, disabled, and poor (Wall, 1998; PHF, 1999). In the 1990s, managed care arrangements became the predominant service delivery mechanism. This cost containment method caused a dramatic shift in the way populations were served in the Medicaid program (Dandoy, 1994). While cost containment and preventive care methods used by managed care organizations have definite advantages, the private provider networks have not shown that they have the capacity, infr astructure, or quality assurance mechanisms to assure access to needed services for disadvantaged populations (PHF, 1999). This is relevant for areas where there are large numbers of individuals lacking health insurance or with populations that contain la rge numbers of undocumented immigrants. Since Medicaid dollars diverted to private managed care organizations would no longer support the health department in providing services to these clients (Lipson and Naierman, 1996), access problems may be created and health disparities in these communities would be exacerbated. In addition to the changing market forces in the health delivery system over the past decade, governmenta l downsizing and budget cuts have also had a significant impact on health departmen ts (PHF, 1999; Wall, 1998). These cuts have compromised the ability of the local health department to provide essential public health services. As a result policymakers are exploring strategies to provide services more efficiently. Privatization is one strategy that is being explored as a potential community based approach for assuring the delivery of public health services. In 1993, the Council of State Governments conducted a comprehensive landmark study on privatization activities. The findings st ated that almost 50 percent of

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17 governmental health care agencies had privatized some aspect of their operations. In 1996, the Centers for Disease Control did an environmental scan of state health departments with the intention of building on the 1993 stud y. The CDC study focused specifically on public health. Seventy percent of those surveyed reported increases in privatization activities. In 1998, the Florida Association of County Health Officials recommended a survey of Florida county health department s to determine which services were previously and currently privatized and which models were used for privatization. This study, completed in 2000, reported that five of the nine largest county health departments had privatized primary care programs. This finding was similar to that of the 1993 Council of State governments study. However in 2001 Keane, Marx, and Ricci conducted a nation al survey of local health departments which found that three quarters of the local health departments had privatized som e public health services. These studies provide useful background information on privatization but more research is needed to understand the privatization trend and its impact on public health. Research Questions This dissertation will focus on the impac t of privatization on the delivery of primary care services in large counties (population greater than 500,000) in Florida. The following research questions will be examined: 1. What are the costs of primary care services provided by contracted service providers relative to services provided by the by public health department? 2. Where primary care services have been privatized, what is the effect on access to care for the Medicaid and uninsured patients?

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18 3. What is the effect of privatization on health outcomes in privatized and non privatized counties? Study Hypotheses Based on the research questions listed above, the following study hypotheses have been developed. 1. Hypothesis One: Contracting primary care to private providers reduces the costs o f providing services when compared to providing these services within the county health department. 2. Hypothesis Two: In counties where primary care programs have been privatized, potential access to primary care services will be significantly greater t han in non privatized areas. 3. Hypothesis Three: In counties where primary care programs have been privatized, health outcomes on primary care sensitive indicators (i.e. post neonatal mortality) will be significantly better when compared to non privati zed areas. These hypotheses are based on two economic frameworks most commonly used in discussions on privatization -market theory and public choice theory. According to market theory private providers can produce goods or services more efficiently bec ause of competition. And public choice theory proposes that government is inherently inefficient because it creates the natural characteristics of a monopoly. Over time programs will grow larger because the incentive structures work against the public a t large while serving those with concentrated interests in increasing public expenditures. These theories will be discussed in greater detail in the literature review section of this study.

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19 Delimitations and Limitations All studies have inherent delimitat ions and limitations. Delimitations describe the populations to which generalizations may be safely made. The generalizability of the study will be a function of the subject sample and the analysis employed. Delimit literally means to define the limits i nherent to the use of a particular construct or population. Limitations refer to limiting conditions or restrictive weaknesses. There are times when all factors cannot be controlled as part of a study design, or when the optimal number of observations si mply cannot be made because of problems involving ethics and feasibility. A delimitation of this study is that only counties with large populations were used because they were more likely to privatize services. Secondly, by choosing counties of similar size, characteristics affected by county size could be controlled (i.e. county expe n ditures, availability of providers, morbidity or mortality rates, etc.). The findings of this study can be generalized to larger or more populated counties. There were sev eral limitations in this study. First, the study used different units of analysis (program level, zip code level, county level) for measuring the three dimensions (cost, access, and he alth outcomes) because of the availability of data from some sources. S econdly, h ealth outcomes could not be directly measured for Department of Health clients. Performance indicators used by the Department of Health were used as proxy measures. Performance indicators for all primary care programs used in the cost analysis w ere not available. Some health status indicators used for evaluating primary care at population levels were assigned to other funding sources and therefore not

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20 selected for analysis. For example STDs are funded through communicable disease programs. Spec ific Aims The specific aims of the research study are to: 1. Examine the effect of privatization on the cost of providing primary care services to Medicaid and uninsured populations. 2. Examine the association between privatization and access to ca re. 3. Examine the association between privatization and health outcomes. 4. Contribute to the understanding of how alternative service delivery of primary care affects the health status of the general population. Summary In this chapter, the need for this study was addressed. The study will add to the literature specifically by providing empirical analysis of privatization of public health programs using three dimensions of effect measurement. This chapter also explained the conceptual framework, pre sented research questions and hypotheses, and provided specific aims. The next chapter will provide a comprehensive review of the literature on privatization of government services.

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21 THE LITERATURE REVIEW Overview Few public policy topics have drawn more attention or been more controversial than privatizing public services (Starr, 1998). For many years government officials, policy analysts economists, and others have struggled to determine the appropriate balance between government and the private sector. What are the tasks that should be performed by public agencies ? Which tasks are best performed by the private sector? What role should government play in regulating or reimbursing functions best implemented by the private sector? The purpose of thi s chapter is to take a thorough look at privatization. Privatization will be examined as a means of service delivery for local and state governments. Specific examples of privatization of governmental services in the state of Florida are discussed. The chapter also examines privatization in the public health system. Finally, the chapter concludes with a discussion of the outcomes (cost, access, and health status of the population) used to measure the impact of privatization of primary care programs in s elected county health departments in Florida. Privatization Privatization is the shifting of a function, either in whole or in part, from the public sector to the private sector (Butler, 1991; Bluestein, 1996). Increasingly, privatization is being exam ined by government officials as a strategy for improving public policy. These officials believe that through a combination of changing ownership, introducing competition from the private sector, and allowing consumer choice through

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22 vouchers and other appr oaches they may be able to achieve some public purpose more effectively and efficiently. The provision of services by the public sector is a complex issue affected by complicated factors (Finley, 1989) A form of privatization that works effectively in one situation may not work effectively in another (Butler, 1996) Likewise, some private sector approaches may not work in certain circumstances, while others may be very successful. There are various means of delivery ; however the most frequently used op t ions are listed in the Table 2 Table 2 Alternative Approaches to Service Delivery Contracting State or local governments contract with private firms either profit or nonprofit to provide goods or deliver services. Contracts may include all or a portion of the services to be provided by the private firm. Franchises State or governments award either an exclusive or nonexclusive franchise to private firms to provide a service within a certain geographical area. Under a franchise agreement, the citizen pa ys the firm directly for the service. Grants/subsidies Governments make a financial or in kind contribution to a private organization or individuals to encourage them to provide a service so that the government does not have to provide it. Vouchers Gover nments provide vouchers to citizens needing the service. The individuals are then free to choose the organization from which to buy the good or service. The government then reimburses the organization that provides the goods or services Volunteers Indiv iduals provide free help to a government agency. Self help Governments encourage individuals or groups to undertake self beneficial activities previously provided by the government. Service Shedding The government gives up responsibility for an activity but works with a private agency, either profit or non profit, who is willing to take over responsibility and provision of the service. Public Private Partnerships Government agencies join with businesses in the community to provide a good or service. N ote. Adapted from Review of Private Approaches for Delivery of Public Services, by H. P. Hatry, 1983, p.5 7.

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23 Efficiency and Privatization Providing public services through the p rivate sector is not a new idea; however, since the mid 1970s governments have turned increasingly to privatization. Government is frequently criticized for waste and inefficiency; but as the level of criticism increased, privatization emerged as an alternative for delivering public services. This was not surprising to many con sidering the dilemmas face d by many local and state jurisdictions in an era characterized by reinventing government (Osbourne and Gaebler, 1989) and the federal devolution of responsibilities to state and local governments (Brammer, 1997; Mahtesian, 1994). Despite the attention that privatization has attracted over the years, the concept is often misunderstood (NAPA, 1989) Privatization is difficult to define because it encompasses a variety of ideas and practices. However, these ideas and practices shar e a common ideal that involves increasing private sector participation in areas typically considered public sector responsi bilities (Greene, 2002) Privatization comes in many forms, which include simple contractual arrangements with private businesses and non profit organizations. In the purest form of privatization, the government divests itself of production and delivery of services. Privatization also includes a broad range of activities such as deregulation, tax reduction, voucher systems, and pu blic dives titure of government properties (Greene, 2002; Hatry, 1983) These activities are intended to enhance government efficiency and reduce government involvement. The ideology of privatization rests on the virtues of a freely functioning market eco nomy. Proponents believe that a market economy produces economic and technological progress, efficient utilization of resources, a rising standard

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24 of living, a reasonable and equitable distribution of wealth and a society characterized by social mobilit y and political freedom (Friedman, 1962). In this view, government intervention beyond its basic functions (those dealing with purely public goods, such as national defense) impairs efficient resource use. Proponents believe that government should confin e its activities only to those related to governing while the private sector is allowed to produce societys goods and services. Historically, the most common form of privatization (contracting) predates the Constituti on. The private sector performed man y functions which have eventually been taken over by the public sector (Swanstrom and Judd, 1994). Examples include subways and utilities (Ross & Levine, 2000; Swanstrom and Judd, 1994). As society became more complex because of industrialization, urbaniz ation, and changing values, the government assumed more eco nomic and social responsibility (Greene, 2002) For many, government action was viewed as the solution to societys problems and the government began providing an increasing assortment of services However, by the 1970s many services were returned to the private sector via contracting and public private partnerships because of costs and the perceived inefficiency of the public sector. The most prominent issue in privatization has been the allocat ion of resources to their best use. Efficiency has been one of the driving forces behind the reinventing government movement and the push for performance measures for government agencies and services. In addition to efficiency, there are also other conce rns like equity and public accountability. Much of the debate has been on whether privatization can actually deliver public services more economically than tradition government auspices.

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25 Theoretical Framework The theoretical foundations or frameworks for privatization have been provided by a variety of economic schools. The most prominent are market theory and public choice theory. Market theory looks at the nature of competitive markets while public choice theory deals with the nature of monopolies and non market decision making. These two theories provide the underlying assumptions that privatization is a better method for delivering services. Market Theory Market theory is based on an idealized model whereby firms seek to maximize profits but t heir ability to inflate prices is guarded by competition. In the competitive market the firms are small relative to their industries and there are no restrictions that prevent firms from entering or exiting any industry. Consumers in this market are wel l informed and have defined preferences about alternative good and services. Firms compete for a market share. This competition forces efficiency in the market. Efficiency is the ability to produce a product or service in a cost effective manner. If fi rms make unusually high profits then others will enter the market and cause decreases in the price of the good or service. Market theory is generally associated with private goods. Private goods inclu de those types of goods that are easy to exclude other s from using. Many public goods and services are difficult to exclude other s from using ; however, there is a general consensus that public or private organizations can provide or produce local or state services. Outcomes are judged by cost effectiveness or efficiency. If the market

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26 can provide a service at a lower cost than the government, or if it can provide a superior service at the same cost, then the market is considered more efficient (Wolf 1988). Market theory enters the privatization discussi ons because it provides an alternative arrangement with a long record of generating goods and services efficiently. It is believed that some of the markets power can be transferred through contracting. Market theorists believe that the problem with gover nment is that it is not an economically driven institution where efficiency is necessary for survival unlike th e marketplace where failing to maintain a competitive orientation and manage resources efficiently would result in the demise of the firm. In ad dition, government is a monopoly and monopolies are inefficient due to the lack of competition. Financing for public services is not directly connected to the actual services being produced, but are a result of the political process, unlike in a market wh ere the cost of producing a good or service is connected to the expense of perform ing the function. Because the revenues that sustain government activity usually come from taxes, government organizations are more likely to use budget size to measure perfo rmance. This in turn causes personnel to be rewarded for justifying costs rather than reducing them (Wolf, 198 8) Public Choice Theory Public choice theory has also had a noticeable impact on the privatization debate. Public choice theory is based on ra tional choice theory which assumes that all individuals act in a way that maximizes their own self interest. Within a theoretical framework, public choice theorists provide a rationale that suggests that public managers will take action that is in their o wn self interest. This rationale is the same as the motivation of

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27 managers in a competitive market but the incentive structures and the consequences of managers actions are different in the public sector. When applied to the public bureaucracy, governme nt managers will behave in a way that is in the publics interest only if it is also in their own self interest as well. Serving a greater good (the public interest) is secondary to serving ones own self interest (Greene, 2002) Public choice theory arg ues that the competitive marketplace produces goods and services efficiently while public monopolies are viewed as inefficient. Ine fficiency is seen as inherent in government agencies because the incentive structures encourage empire building and overprod uction of services. It is believed that public agencies encourage public personnel to advance their powers, budgets, and agency staffing levels. This theory explains why government budgets grow over time (Buchanan, 1978). The theory also states that int erest groups form to seek special advantages. According to public choice theory, in the public sector, citizens who are members of interest groups will demand too many services since increased quantities are n ot regulated by direct increase in costs for t hose people receiving the services. In situations where the public at large is paying to benefit a few, the cost of the service to the individual becomes so inexpensive that demand for the service increases resulting in an overly large demand and a bloat ed, wasteful government (Rubin, 1981) Public choice theory attributes the problem of inefficiency in government to the natural characteristics of monopolies. Within these government monopolies public managers behave in ways that are counterproductive t o the goal of efficiency. Public choice theory makes many recommendations regarding privatization but the main one relates to separating governmental financing from the production of services which can

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28 be accomplished through contracting. By altering t he delivery arrangements of public services, public choice theory argues that contracting will enhance efficiency and slow the pace of government growth (Wolf, 198 8). The theoretical foundations discussed above have had a powerful impact on the privatizat ion debate. The thrust of the theories may be summed up as markets versus monopolies. Both theories focus on the positive attributes of the competitive marketplace and emphasize inherent inefficiency in public monopolies. In reality, the choice of marke ts or monopolies are two i mperfect alternatives (Wolf, 198 8). In summary, proponents of privatization argue that government should turn over services to private firms to realize cost savings. Privatization is seen as a way of improving efficiency while o ffering new opportunities for private businesses. They also argue that this will result in greater satisfaction for the people being served. By allowing private firms to provide services, government can benefit from the power of the marketplace and free itself to govern. Privatization in State and Local Government Services in state and local government are provided through the private sector everyday. At the local level, these services include garbage collection, water and sewer systems, fire and buildi ng inspections, and sanitation inspections of food establishments. There is a long history of private companies providing public services. The following are examples of the most commonly privatized local services. Refuse Collection Garbage collection ha s received more attention in the privatization discussion than any other contracted service. How trash is collected and disposed of raises many public

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29 health and environmental concerns. Numerous studies have been conducted that suggest it is less costly t o contract out garbage collection (Greene, 2002; Hatry,1991); while privatization critics point to a similar number of other studies that suggest less than desirable results. Virtually all studies on garbage collection have found private contracting to be the most economical. Many of t he studies are listed in Table 3 However, contracting out these services has resulted in corruption and scandal (Bailey, 1991; Greene, 2002). Refuse collection also has the added dimension of disposal. The garbage has to be placed in a landfill, recycled or incinerated. Transportation Transportation has many dimensions including bus systems, rail systems, and highways. Cities encourage mass transportation to reduce pollution and congestion. Most mass transit systems originally operated under franchise agreements but in 1964, the Urban Mass Transportation Act was passed which allowed cities to purchase their own systems. Most of these systems are subsidized through federal grants. Although almost all rail systems rem ain publicly owned and operated, bus systems have been privatized in many communities. Sever al studies are listed in Table 3 Utility Services Privatization of water and wastewater facilities in the United States is not a new p henomenon. Converting gov ernment owned facilities to private ownership or management g oes back at least three decades (Beecher, 1995). Surveys in recent years by the National League of Cities, U.S. Conference of Mayors, and the International City/County Management Association amo ng other organizations find: Mo st local

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30 governments have been increasing their use of privatization in recent years, and plan to further increase privatization in coming years; Privatization grows the fastest in communities that have already made the most use of privatization Water and wastewat er service privatization follows these broader trends. More than 40 percent of drinking water systems nationwide are private, regulated utility systems. Of the 60 percent of systems owned by local governments, pri vatization by contracting for operations and management has grown rapidly in recent years. In 2001, nationwide privatization of water and wastewater services grew by 13 percent, after growing by 84 perce nt over the decade of the 1990s (Reinhardt, 2001). B y the end of 2001, ne arly 1300 local governments had privatized operation of wastewater systems, and over 1100 had privatized operations of water systems Several of the se studies are listed in Table 3 Like water and wastewater system s provision of electric power comes in several organizational forms including investor owned organizations, municipally owned and cooperatives. There have also been a number of studies of public and private operations. Some of the se studies are listed in Table 3 Non e of the studies found private power companies to be more efficient than publicly operated utilities. Because utility companies are natural monopolies, they do not face direct competition. However, in cases where power utilities face competition, there appears to be an average reduction in cost of 11 percent whether the companies are public or private (Greene, 2002)

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31 Table 3 Summary of Major Local Government Privatization Studies Government Activity Author Subject Findings 1. City Services Ferr is (1988) Multiple municipal services in 500 U.S. cities City expenditures decreased with the increased use of contracting. Carver (1989) Property tax assessment in 100 Massachusetts communities Public provision was found to be less costly than contracti ng. Pack (1992) Computer networking reliability for 55 public clients. Contracting with private firms resulted in a 30 percent increase in quality and reliability. 2. Transportation/Buses Morlok and Moseley (1986) Survey of 31 bus systems Average saving s of private contracted bus systems was 29 percent. Perry and Babitsky (1986) Private vs. cost plus private, Contract vs. public Private operators are significantly more efficient in all indicators. Teal et al. (1987) Study of 864 bus systems For la rge bus systems, private costs are 44 percent less than public cost. Contracting should save 36 50 percent for systems of more than 25 buses. Sherlock and Cox (1987) Study of 567 bus systems During a 13 year period, the cost per mile for private buses de creased by 3 percent while costs increased by 52 percent. Private bus service reduced costs by 32 percent.

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32 Table 3 C ontinued Summary of Major Local Government Privatization Studies Government Activity Author Subject Findings Walters (1987) Study of bus service in five large cities. Private operators were 50 to 65 percent less expensive than municipal bus systems. Feldman (1987) 68 U.S. bus organizations public and private comparison. Private operations were significantly more efficient. Musg rove (1988) Busing in 88 school districts in Missouri Contracting reduced transportation costs. Campbell (1988) Public v. private contracting for vehicle maintenance Contractors were 1 to 38 percent below in house municipal costs. Bails (1989) School t ransportation costs in six U.S. cities Contracting lowered transportation costs. 4.Refuse Collection Stevens and Savas (1976) Public v. private collection Cost of public collection 40 to 60 percent higher than private contracting. Edwards and Stevens (1 976) Public v. private collection Private monopolies were 5 percent higher than private nonfrachise collection. Savas (1977) Savas (1980) Public v. private collection Private contracting was found to be the most efficient collection method. Stevens (19 78) Public v. private collection Private contracting was found to be the most efficient collection method. Spann (1977) Survey of U.S. cities, municipal v. private collection Cost of public collection 45 percent higher.

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33 Table 3 C ontinued Summary of Major Local Government Privatization Studies Government Activity Author Subject Findings Bennett and Johnson (1979) Fai rfax County, VA, 29 private firms v. public collection authority Private firms in open competition were significantly more efficient. 5. Utilities ( Electric Power) Pescatrice and Trapani (1985) Public v. private operations Public operations are mo re efficient. Fare, Grosskopt, and Logan (1986) Public v. private operations No significant differences. Atkinson and Halvorsen (1986) Public v. private operations No significant differences. (Water) Bruggink (1982) Public v private operations Pub lic more efficient Feigenbaum, Temples, and Glyer (1986) Public v private operations No significant differences Byners, Grasskopt, and Hayes (1986) Public v private operations No significant differences Teeples and Glyer (1987) Public v private opera tions No significant differences Holcombe (1991) Public v. private operations for wastewater treatment in U. S. cities Higher costs associated with private provision. 6. Hospitals Clarkson (1972) Sample of U.S. hospitals, public nonprofit v private for profit Variations in input ratios greater in nonprofit hospitals. Higher cost found in nonprofit output indicators Lindsay (1976) Sample of U.S. hospitals v. Veterans Administration Cost per patient less in VA hospitals Wilson and Jadlow (1978) 1,200 U.S. hospitals producing nuclear medicine, government v. private units Deviation of private hospitals from a perfect efficiency index was less than public hospitals Wheeler, Zuckerman, Aderholt (1982) 10 hospitals under management contracts in 7 U.S. st ates Improved profitability occurred under private management

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34 Table 3 C ontinued Summary of Major Local Government Privatization Studies Government Activity Author Subject Findings Mennenmeyer and Olinger (1989) Medical care for Medicare patients in 267 California hospitals in the 1980s Contracting lowered costs between 11 and 23 percent From Cities and Privatization: Prospects for the New Century (2002)

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35 Prisons/Jails In recent years, there have been efforts to privatize significant segments of corrections (Savas, 1987). Private entrepreneurs have begun to play a major role in financing and building correctional facil ities, in supplying a variety of auxiliary services and in obtaining contracts to operate and administer prisons and jails. Advocates of privatization assert that privatization is efficient and cost effective. These proponents point to studies that found that private contractors when freed from cumbersome public personnel policies and unionized work forces are able to run correctional institutions and related progr ams more efficiently There is nearly a consensus that privatization of a number of correct ional functions using newer forms of financing and providing services is more effective and efficient that long standing conventional methods (Mullen, 1985). Despite generally favorabl e reviews, critics dismiss comparisons on cost alone. They argue that these studies ignore liability issues, do not account for long term costs and fail to compare identical prison populations. Public Hospitals In recent years, public hospitals have been more likely than either nonprofit or for profit hospitals to convert their ownership status. The favored conversion is from public to nonprofit status. The government simply converts the legal status of the public hospital to nonprofit status so that it can issue revenue bonds and escape "sunshine laws." In some cases, the government still retains ownership title to the buildings and land, and leases these to the nonprofit entity it created to operate the hospital. Although public hospitals are still more likely to become nonprofit tha n for profit, there is a recent trend to change

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36 to for profit status, if for no other reason than the increased access to capital enjoyed by such publicly traded firms. But from 1990 to 1993 only nineteen percent of conversions were to for profit status, and the for profit sector still has a limited market share of the industry (Greene, 2002) The conversion of a public hospital is often described as an effort to improve efficiency by freeing the hospital from civil service and hospital procurement rules, or a response to the unwillingness o f local governments to provide continued tax subsidies. State Government The Council of State Governments conducted a nation al survey of state government officials to identify recent privatization trends. The survey was sent to 450 state agencies dealing with personnel, education, health and human services, corrections, and transpo rtation. In the years surveyed (1998 2002), privatization remained the same or only increased slightly. Florida was a leader among states using contracting to deliver service s. These services included, road design and maintenance, toll operations, prisons, welfare employment services, and building maintenance. Table 4 Privatization in the State of Florida Agency Initiative Results Department of Transportation Board of Profe ssional Engineers Output increased but so did costs Toll Collections Cost reduction of $2.1 million annually Highway Maintenance Reduced costs by 15.3 percent or $83.7 million Department of Corrections Prison Operations Reduced costs by 7 percent Prison Food Service Reduced costs by $16.9 million over 3 years

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37 Table 4, C ontinued Privatization in the State of Florida Agency Initiative Results Inmate Health Care Reduced costs by $24.6 million over 4 years. Department of Management Services Janit orial Services Reduced costs by $1 million annually Personnel Functions Implementation behind schedule and did not result in estimated savings Department of Children and Families WAGES No difference between state and private operations Mental Health H ospitals Significant quality improvements and $110 million in savings ( Results from the Office of Program Policy Analysis and Government Accountability) Transportation Private contractors currently perform many activities at the Florida Department of Transportation including: construction engineering and inspection, design, planning, right of way, and materials testing research. Since many of the activities are commercial in nature they were targeted for privatization. In March 2001, the Office of Pr ogram Policy Analysis and Government Accountability suggested increased levels of contracting for toll collection operations. In addition, under direction from the Governors office contracts for highway maintenance were expanded. According to the Asset Management Program Summary from November 2003, the state saved $83.7 million through the life of the contracts. The agency states that the contractor is performing at better levels and the quality is the same if not superior to previous state delivered ma intenance.

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38 Corrections In 1995 the state of Florida began its venture to privatize prisons. Since that time the Florida Department of Corrections has contracted for the operations of numerous facilities. Several studies have evaluated the initiatives. The South Bay Correctional Facility achieved operational savings of 3.5 percent in 1997 98 and 10.6 percent in 1998 99, which exceeded the state mandated 7 percent. The report further noted that construction costs were 24 percent less than similar govern ment facilities. Other facilities similarly posted savings but not to such a great extent. Building Maintenance The Department of Management Services began contracting out some of its custodial services and reduced state costs. The department also r ealized cost savings by converting full time positions to part time positions, mostly through attrition. WAGES In the General Appropriations Act of 1997 98, the Florida Legislature created the Work and Gain Economic Self Sufficiency (WAGES) pilot project to determine the feasibility of contracting out all program services within a service area. There were no differences between the private pilot projects and the state programs. Human Resources The state entered into a contract with Convergys Corporation of Ohio to administer almost all of the routine personnel functions of the state. Initially, that contract was

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39 estimated to save $173 million over seven years. Eighty million was saved by avoiding the replacement of the aging computer system (COPES) and it was thought that millions would be saved from recurring fiscal expenses. The benefit of privatizing these functions was that it allowed the state to devote fewer internal resources to administrative tasks and instead concentrate resources on core miss ion, responsibilities and programs of state government. The implementation of this initiative has been problematic. The project was a year behind schedule diminishing some of the estimated savings. In addition, the functionality and user satisfaction wa s much lower than anticipated. During the product design phase of the project, an oversight by the contractor led to much of the dissatisfaction. Convergys designed the system to operate on computers with Windows 2000 platform or newer. In the private s ector, most computers have newer operating systems ; however in state government many of the computers operated on Windows 95 and 98. The new software was incompatible and did not work (Segal, 2005) State Hospitals In 1998, the Department of Children a nd Families entered into a public private partnership with Atlantic Shores Healthcare, a subsidiary of the GEO Group to manage South Florida State Hospital (SFSH). The 350 bed facility was completed and opened as the first fully private state mental hospi tal. Since the partnership, SFSH became fully accredited by the Joint Commission on Accreditation of Healthcare Organizations (JACHO) for the first time in its 40 year history. DCF s contract with Atlantic Shores stipulated that the facility must be full y accredited. The partnership also resulted in several other successes including higher admissions and discharges, lower re admissions,

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40 and decreased length of stay. Of the discharges 3.4 percent were readmitted within 30 days which was 50 percent below the nation al average. In addition, the average length of stay decreased from 8.27 years to 185 days. Considering the examples listed above, it appears that privatization works in many cases. Aside from the possibility of corru ption, some seem to endorse the use of private contractors to provide public services. Most of the examples of successful privatization have been in hard services such as garbage collection, construction, maintenance, etc. However, there are areas in which privatization has been less successful, mainly in soft services. Soft services refer to human services, such as health care, social services, and welfare services. These services are less mechanical, more unique and sometimes involve special needs. Soft services are less pro fitable for private firms than hard services. When these services are privatized, they are usually shifted to non profit organizations rather than for profit firms (Greene, 2002) Public Health Services The pressure to privatize public health servic es has occurred since the early 1980s when initiatives favoring privatization brought a 25% reduction to the budget of the Department of Health and Human Services. In 1993, the Council of State Governments conducted a comprehensive landmark study on pri vatization activities. The findings stated that almost 50 percent of state health departments had privatized some aspect of their operations. In 1996, the Centers for Disease Control and Prevention did an environmental scan of state health departments, w ith the intention of building upon the 1993 study looking specifically at public heath.

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41 The privatization of local health department services may be one of the most important transformations in the nation's public health system. Across the nation almost 3000 local health departments perform many essential public health services that private organizations may not have the authority, capacity, or incentive to provide (Lipson and Naierman, 1996; Keane, 2001). Therefore, it is worthy of concern that an incre asing array of services once performed directly by local health departments have been contracted out or in some way delegated to nongovernmental organizations. According to a study by Keane, Marx, and Ricci (2001) about three quarters of local health dep artments have privatized some public health services. Two general types of privatization are occurring. One form occurs when a service once directly performed by a local health department is contracted out to a private provider. Another less commonly reco gnized form of privatization occurs when a local health department becomes involved with a new service but contracts out (or otherwise delegates) the performance of the service from its inception. The most commonly privatized services are personal hea lth services, the largest proportion of which were performed by hospitals, physicians, and private clinics; but environmental health services, health education and community outreach services, an d data processing functions are also frequently privatized. A m ajority (57%) of all public health services that have been privatized have been contracted to investor owned, for profit organizations. Eighty four percent of environmental health services were privatized to for profit concerns (most often engineering comp anies) (Keane, 2001). The decision to privatize generally depends more on a communitys unique characteristics and service delivery system than on a specific type of needed service (CDC, 1998; PHF, 1999; Keane, Marx, and Ricci, 2001). The catalysts for privatization of health

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42 department services vary but generally revolve around four general themes : Medicaid Managed Care, cost savings and other fiscal concerns, improving the quality and efficiency of services, reorganizing state and/or local health depa rtment (Halverson et al, 1998; PHF, 1999; Keane, Marx, and Ricci, 2001). Privatization Trends in Floridas Public Health System In 1998 following national trends, the Florida Association of County Health Officials recommended a survey of Florida county he alth departments to determine which services were previously and currently privatized and models for privatization. This study was completed in 2000 by the Florida Department of Health. Using the methods outlined in the 1997 CDC study, the privatization c ommittee composed of member s from the Florida Association of Co unty Health Officials and the Florida Department of Health developed mail and telephone surveys for 67 counties. The survey identified sixty services that were currently privatized in the coun ty health departments. The type of service privatized varied between small counties and the medium and large counties. For the purposes of the study, small counties were defined as counties with populations between 7,000 and 112,000. Medium counties had populations of greater than 112,000 but less than 500,000. Large counties have populations of greater than 500,000. In small counties there were twenty four privatized services. The top three were womens health, radiology and pharmacy. These three acc ounted for 58% of the privatized services. In medium counties there were twenty privatized services. The top three were womens health, primary care and laboratory services. In large counties there were sixteen privatized services. The top three were wo mens health, primary care

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43 and HIV/AIDS. These three categories accounted for 81% of the privatized services. The decision to outsource these services was based on a variety of factors. These included the limited capacity of local health departments to fulfill public health obligations, availability of private organizations with which to contract, anticipated cost saving and efficiency improvements, and downsizing of government (Crockett & Rainhart, 2000) Measures of Privatization Cost Public health a ccounts for only a fractio n of national health spending -approximately 6.1 percent ($60 billion) in 2004 (UHF, 2004). These funds consist of federal, state, and local revenues, as well as Medicaid payments, patient fees and various regulatory fees. O ver time the relative importance of each of these sources has shifted. However, federal grants and state and local appropriations consistently account for the bulk of public health spending. The range of services provided by public health agencies vari es considerably across states and local jurisdictions; however, personal health services consume the largest share of the average local health departments staffing and funds (Eilbert, 1996). Florida is one of a few states most likely to deliver comprehen sive primary care through county health departments (Wall, 1998). A survey conducted by the National Association of County and City Health Officials found that public health systems in the South have traditionally considered personal health services as ce ntral to their mission because of the shortage of private providers in rural areas. Floridas public health system extends beyond maternal and child care to provide an even broader range of services to

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44 indigent patients and the uninsured, in part because of state legislation. However, in an environment dominated by Medicaid managed care, there is an on going debate among public health officials regarding the role of public health departments in the delivery of personal health services. In the early 1990s health departments started to develop cost systems during the shift to managed care which relied on cost based reimbursement from Medicaid. The old focus on revenue streams (categorical programmatic funding) began to shift toward measuring the actual c ost of health care services (Hadley et al, 2004). CPT codes, published annually by the American Medical Association in Current Procedural Terminology had become the universal language in the health care field and were used for managed care contracts, set ting reimbursement levels, and making comparisons among practice settings. In 2000, the Department of Health and Human Services (DHHS) designated the CPT code set as the national standard for financial and administrative electronic transactions related to health care professional services (HIPAA, Title II). From these codes public health departments determine the cost per service and per client. These codes also provide information on the number and types of service provided in each program area. Access to Care The issue of access has become a central concern for health care policy formulation and reform (Fos and Zuniga, 1999; Brandon, et al, 2003). Access to primary care, in particular, is very important in planning the future of health care delivery i n the United States and is viewed as a key to progressing toward the health objectives for Healthy People 2010. According to this report:

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45 Improving primary care across the Nation depends in part on ensuring that people have a usual source of care. Havin g a primary care provider as the usual source of care is especially important because of the beneficial attributes of primary care. These benefits include the provision of integrated, accessible health care services by clinicians who are accountable for ad dressing a large majority of personal health care needs, developing a sustained partnership with patients, and practicing in the context of family and community (DHHS, 1999) Access to health services including preventive care, primary care, and tertiary care often depends on whether a person has health insurance (Broyles et al, 1999; Broyles et al, 2002) Uninsured people are less than half as likely as people with health insurance to have a primary care provider; to have received appropriate preventive c are, such as recent mammograms or Pap tests; or to have had any recent medical visits (Broyles, 2002). In addition, access to care depends in part on access to an ongoing source of care. People with a usual source of health care are more likely than those without a usual source of care to receive a variety of preventive health care services. An estimated 15 percent of adults in the United States lack a usual source of care. Thus, more than 45 million persons have no particular doctors office, clinic, heal th center, or other place where they go for health care advice (Mongan and Lee, 2005). A usual source of primary care helps people clarify the nature of their health problems and can direct them to appropriate health services, including specialty care. Pr imary care also emphasizes continuity, which implies that individuals use their primary source of care over time for most of their health care needs (Franks, et al, 1997; Starfield, 1998) More after hours care, shorter travel time to a practice site, and shorter office waits have been associated

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46 with patients beginning an acute episode of care with primary care physicians. Greater continuity has been observed for individuals with shorter appointment waits, insurance, and access to more after hours care (F ranks, et al; Fos and Zuniga) Other advantages of primary care are that a primary care provider deals with all common health needs (comprehensiveness) and coordinates health care services, such as referrals to specialists (Starfield, 1998) Evidence sugge sts that first contact care provided by an individuals primary care provider leads to less costly medical care (Moy et al, 1998; Starfield, 1998) Health Outcomes/Health Status Although health outcomes and health status are equivalent concepts, the form er term is applied when assessing the clinical care of a group of patients while the latter is used when the focus is on populations or subpopulations. Historically, outcomes were initially measured by mortality rates. Decades later morbidity was added. In the 1980s a primary care friendly adaptation of assessments known as the International Classification of Health Problems in Primary Care was developed (World Organization of National Colleges, Academies, and Academic Associations, 1979). The most rec ent assessment focuses on the extent to which people can perform the activities of living -health related quality of life. This is a broad concept which takes into account how people feel and what they are able to do. Health is the result of personal be haviors, the environm ent of the community in which one live s the policies and practices of health care and prevention systems and the contributions of individual genetic predispositions to dise ase These three areas interact to

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47 creat e healthy outcomes i ncluding a long disease free and robust life unaffected by race, gender or socio economic status. Personal behaviors includ e the everyday decisions tha t affect personal health. They include habits and practices develop ed as individuals and fami lies that have an effect on personal health and the utilization of health resources. Community environment reflects the reality that the daily living conditions have a great effect on achieving optimal individual health. Health policies are indicative of the avail ability of resources and the extent of reach of public health programs into the general population. *Fr om Americas State Health Rankings 2004 (UHF, 2004) Figure 1 Health Outcomes Model*

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48 T hese elements influence each other a nd the resulting health outcomes of a population. Health status indicators measure the burden that disease and death place s on the overall health of a population. When the focus is on the mea sure of health status, whether viewed generically or with a focus on a particular disease, the challenge of that measurement is demanding because of the difficulty in determining specifically what aspects of health should be a responsibility of pr imary car e services (Starfield, 1998) Guidance in the selection of measures to evaluate the impact of primary care at the population level comes from four compendia, each resulting from extensive work by panels of national experts in the United States. The works include: the National Center for Health Statistics (Year 2000 National Objectives), the U.S. Centers for Communicable Disease (from Healthy People 2000) and the Bureau of Primary Care. Of the indicators proposed in the four compendia, no more than nine were proposed in all four and nine others were proposed in three of the compendia. These indicators are listed in Table 5 Table 5 Health Status Indicators Common to Most United States National Compendia Percent of the adult population who smoke Percent age of adults who are overweight Appropriate immunization status in childhood and over age 65 years Total mortality rates Rate of mortality from cardiovascular disease Rate of mortality from lung cancer Rate of mortality from breast cancer Rate of mo rtality from motor vehicle accidents Rate of mortality from suicide Rate of mortality from homicide Infant mortality rate Acquired immunodeficiency syndrome incidence Syphilis incidence Tuberculosis incidence Measles incidence

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49 Table 5 Continued H ealth Status Indicators Common to Most United States National Compendia Percentage of the population living in counties no t meeting standards for good air quality Percentage of deliveries with no prenatal care in the first trimester Percent of births to teenagers From Primary Care Starfield, 1998, p. 306 A primary care focused compendium was proposed by the Bureau of Primary Health Care in the U.S. Department of Health and Human Services which considered a variety of ways to evaluate primary care in the facilities under its jurisdiction. Most of the indicators listed above were included in the list of indicators for evaluating primary care at population levels. The Bureau of Primary Care added four other indicators that were particularly sensitive for measuring primary care -hospitalizations for ambulatory sensitive conditions, unwanted pregnancies, low incidence of adverse effects of medications, and low post neonatal mortality. Selected indicators are used by the Florida Department of Health as performance measures and will appear in this study to measure health outcomes. Summary This chapter provided a comprehensive literature review of privatization in state and local governments. Economic theories that provide the framework for privatization were also presented. Examples of privatization initiatives showed that privatization successes vary across government activities. The chapter also presented privatization trends in the public health system in Florida and across the nation. Researchers agree that privatization is widespread and will continue for the foreseeable future. The chapter concluded with an examination of the dimensions that will be used to study privatization

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50 in this dissertation. The next chapter, entitled, The Research Desi gn, explains in detail the study population, research methodology and data for this study.

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51 THE RESEARCH DESIGN Overview This chapter provides the research methodology for the study. This study builds on a 2000 review of the county health departmen t in Florida. In the 2000 survey of county health departments, five of the nine largest health departments privatized primary care programs. This study assesses the impact of privatization along three domains of effect measurement cost, access to care and health outcomes. The study approach for the three research hypotheses are explained in detail. The data sources and collection procedures are also discussed in addition to the dependent and independent variables. The methodology is divided into three sections corresponding with each measurement domain for clarity. Cost Data Sources and Variables This study is a retrospective, longitudinal population based analysis to examine the cost of privatized primary care programs versus the cost of primary ca re programs provided by the county health departments. Secondary data sources were obtained from the Florida Dep artment of Health. The data were extracted from the Contract Management System Variance Report. The C ontract M anagement S ystem Variance Repor t lists program components for Communicable Disease, Primary Care and Environmental Health. Within the primary care component, there are twelve programs -chronic disease prevention, tobacco, home health, WIC, family planning, maternal health,

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52 healthy st art prenatal, comprehensive child health, healthy start infants, school health, comprehensive adult health, and dental health. The programs included in this study are clinically based programs and include family planning, maternal health, healthy start pr enatal and infants, and comprehensive child and adult health. Table 6 Descriptions of Selected Primary Care Programs Program Description Chronic Disease Prevention Provides a range of services to prevent, detect, and reduce complications from chronic d iseases including: heart disease, stroke, diabetes and arthritis. Comprehensive Adult Health Makes available a range of basic medical care services and treatments that ensure access to essential health care and decrease unnecessary emergency room visits. Comprehensive Child Health Provides periodic physical examinations for infants and children who are about to enter school or pre school. Health and vision tests are also administered in kindergarten and first grade. Healthy Start Infants/ Healthy Start Prenatal/ Maternal Health IPO Provides universal risk screening for pregnant women and infants to identify those at risk for poor health and developmental outcomes such as low birth weight. Healthy start services include care coordination to assure acces s to needed services as well as the provision of services such as childbirth education. Family Planning Provides counseling, medical services, referral and follow up that will help individuals plan their family size. Multiple analyses were conducted wi th the variables in this part of the study. They include: 1) a descriptive analysis to provide information on the average cost, number of clients and number of services provided; 2) a correlational analysis to determine the relationship between the variab les of interest; 3) a mixed model analysis to test the hypothesis ; and 4) generalized estimating equations analysis to assess findings for spuriousness Spuriousness is the bias that arises from correlations between individual and cluster level variables. With clustered observational data, spuriousness is nearly

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53 always a s erious concern (Allison, 1999). GEE produces estimates that have less sampling variability than the mixed model. T he mixed model however corrects for heterogeneity shr inkage -attenuati on toward 0 in the presence of heterogeneity in the population. Both the mixed model and GEE are used in this study to provide greater insight into the data. The descriptive analy sis of the study variables provides the mean number of clients, services, a nd expenditures per program. The ave rage cost per client was calculated by dividing the reported expenditures by the number of clients served. The average cost per service for each program was determined by dividing the reported expenditures by the numbe r of services reported. The Variance Reports for years 2001 2004 were used to calculate the average cost per client for four years. The descriptive data analysis was conducted using Statistical Application Software (SAS 9.0), version 9.0. Since this d ata is longitudinal, all observations are not independent. R epeated measures occur on the same data over time In addition, there is likely correlation among programs within counties. Pearson correlations were used to measure the relationship between th e variables of interest. A bi variate analysis was conducted with PROC CORR using SAS 9.0 version 9. In general, moderate to strong correlation s between variables may cause confounding in a regression analysis. The mixed model and GEE adjusts for correl ations found with clustered data or repeated measures. Cost was used as the d ependent variable in the test of the hypothesis Cost is the actual expenditure per program for each county for fiscal years 2001 through 2004 as reported on the Contract Manageme nt System Variance Report. The independe nt variables used in the cost analysis included: unduplicated number of clients or units, the

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54 number of services performed, privatization status, the fiscal year, and the program. These variables except for priva tization status, appear on the Contract Management System Variance Report and we re selected as control variables to account for some of the variation of the dependent variable. Table 7 Variables Used in Cost Analysis Dependent Variable Independent Var iables Cost (Expenditure) Number of Clients, Number of Services Performed, Program, Privatization Status (Non privatized = 0) (Privatized = 1), Year (2000 2004) This analysis tests the following hypothesis: Contracting primary care to private provid ers reduces the costs of providing services when compared to providing these services within the county health department. The analysis was conducted using cost as a d ependent variable to determine if there was a difference in cost between privatized progr ams and non priva tized programs. The data were analyzed using a mixed model repeated measures design. The term repeated measures refers to data sets with multiple measurements of a response variable on the same experimental unit. In this case the variab le is time. There are three types of statistical analyses used for repeated measures. The method used in this analysis applies methods based on the mixed model with special parametric structure on the covariance matrices. The autoregressive order one was specified as the covariance matrix to account for correlation between programs. In order to reduce the possibility of e rror due to model sensitivity a second analysis was conducted using generalized estimating equations (GEE) According to

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55 Allison (1999 ), both GEE and the mixed model can be used to anal yze clustered data; however, GEE reduc es the possibility of spurious findings. Therefore, a GEE analysis was also conducted to determine if there is a difference among the privatized counties versus the n on privatized counties with regard to clients, services, or expenditures. GEE is used instead of the ordinary logit analysis for the following reason: I n ordinary logit analysis, the max imum likelihood estimates are obtained by iteratively re weighted least squares. In the GEE analysis, the algorithm used is generalized least squares. This means that the weight matrix has non zero off diagonal elements that are functions of the correlations among observations. These correlations are re estimated at e ach iteration based on correlations among the Pearson residuals (Allison, 1999 ) Access Data Sources and Variables The status of primary care access is unique to each specific geographic area. This analysis uses zip codes as the area for analysis. The methodology for analyzing primary care access uses a scoring system which assigns a numerical score to each zip code. This score represents the relative capacity to provide basic primary care services within the area. This method of assessing the capacit y of primary access is based on the model developed by Fos and Zuniga (1999) through a cooperative agreement from the Bureau of Primary Care. This study used three major categories of model variables: demographics and population characteristics, socioecon omics, and primary care resources. These variables and their effect on primary care are listed in Table 8. All variables used in the study are routinely collected and were readily available from state and federal agencies. The data source for each variab le is listed in Table 9.

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56 Variables which describe demographics and population characteristics include age, gender, and race. These percentages were calculated from population tables from the 2000 Census. The socioeconomic condition in each zip code affects the specific need for primary care services and the demand on the delivery system. The selected variables include percent unemployment and percent of the population below poverty level. The data were collected from the U.S. Census Bureaus American Fact Finder Quick Reports. Table 8 Access Model Variables Variable Effect on Primary Care A Demographics and population characteristics 1. Percent population over 65 years of age Negative 2. Percent population under 15 years of age Negative 3. Per cent blacks in the population Negative 4. Percent females in population Negative B Socio economics 1. Percent unemployment Negative 2. Percent of population below poverty level Negative C Primary Care Resources 1. Family practice physician / population Positive 2. General practice physician / population Positive 3. Obstetrics gynecology physician / population Positive 4. Internal medicine physician / population Positive 5. Pediatric physician / population Positive 6. Number of hospi tals with emergency departments Positive 7. Number of community care centers Positive The availability of health care resources directly affects primary care access. Intuitively, the more available resources, the better the capability to provide basic primary care services. The physician variables were collected from the American Medical Associations database of all the licensed physicians in the United States. The hospital variable was collected from the American Hospital Associa tions database. On ly

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57 hospital s that offered emergency room services were included in this study. The final primary care resource variable, community care centers, was collected from the Florida Association of Community Care Centers and the Bureau of Primary Health Care. T able 9 Data Sources for Access Variables Variable Data Sources Demographics and population characteristics U.S. Census Bureau 1. Percent population over 65 years of age Census 2000 2. Percent population under 15 years of age Summary File 1 and 3. Percent blacks in the population Summary File 3 4. Percent females in population Socio economics U.S. Census Bureau 1. Percent unemployment American Fact Finder 2. Percent of population below poverty level Quick Reports Primary Care Resources 1 Family practice physician / population AMA Physician Select 2. General practice physician / population (American Medical 3. Obstetrics gynecology physician / population Association) 4. Internal medicine physician / population 5. Pediatric physici an / population 6. Number of hospitals with emergency departments AHA Find a Hospital 7. Number of community care centers FACCC/ BPHC (HRSA) After the data were gathered from each source, variable specific primary access scores were calculated for eac h variable. The variable specific score was determined as follows: Variable specific = zip code percentage x 10 Score total zip code average Ratios of zip code and total zip code values were multiplied by 10 to avoid very small numbers. This process was repeated for every variable in each zip code. Subsequently, model weights were assigned to each of the access variables. These weights appear in a study by Fos and Zuniga and were calcu lated using numerical estimation techniques. In numerical estimation, variables are rated according to their

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58 importance in relation to the other variables on the outcome measure. In the Fos and Zuniga study, the importance of primary care access was esti mated by a panel of experts. The experts used in the estimation were part of a primary care advisory council composed of forty one physicians, health care planners, public health personnel, and citizen advocates. The assigned variable weights are listed in Table 10. Table 10 Access Model Variable Weights Variable Weights Percent unemployment 0.065251 Percent of population below poverty level 0.061036 Family practice physician / population 0.042514 Number of community care centers 0.040816 Obstetrics gynecology physician / population 0.032610 Pediatric physician / population 0.023333 General practice physician / population 0.019917 Internal medicine physician / population 0.014757 Percent blacks in the population 0.012931 Percent population under 15 years of age 0.009275 Number of hospitals with emergency departments 0.009106 Percent population over 65 years of age 0.007263 Percent females in population 0.007218 After the variable specific scores were determined, the primary care access scor e for each zip code was calculated. The model for calculating the access score is an additive model and is represented as follows: Primary care access score = S n w i v i i=1 where w i = each individual variable weight and v i = each variable specific score. The model in its extended form app ears as follows: Primary care access score = w 1 v 1 + w 2 v 2 + w 3 v 3 + . + w 13 v 13 The individual weighted scores are added together to determine the primary care access score for each zip code. The weight of variables which have a negative effect on

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59 primary care access decrease the capability to provide services while the variables that have a beneficial effect on primary care access increases the capability to provide services. Once primary care access scores were determined for each zip code, the se scores were used in the access analysis. The analysi s was conducted using a regression analysis to determine if there is a difference in access scores among the privatized counties versus the non privatized counties. The model for this analysis was: Acc ess Score = b (Privatization Status ) The in dependent variable, privatization status, was coded as 0 for non privatized counties and 1 for privatized counties. Again, the generalized estimating equations (GEE) procedure is used because the zip codes are clustered by c ounty. It is assumed that there will be some correlation between zip codes within the same county. The analysis tests the following hypothesis: In counties where primary care programs have been privatized, potential access to primary care services will b e significantly greater than in non privatized areas. Health Outcomes Data Sources and Variables The final analysis is a retrospective, longitudinal population based study to examine the health status of populations in areas with contracted primary care versus the health status of those in areas where primary care programs are provided by the coun ty health departments. Data were obtained from the United Census and the Florida Department of Health. The analysis included three years of data from 2001 thr ough 2003. The demographic and population variables: the percentage of Blacks and Hispanics in the population. The percentages for years 2001 through 2003 were calculated from the

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60 population estimates from the American Community Survey. The American Commun ity Survey is a part of the Census reengineering plan to produce more accurate data by surveying 3 million U.S. households each year to calculate demographic changes for the year. The percent of the population under 18 and the unemployment rate were extra cted from the American Fact Finder Quick Reports. Table 11 Variables and Data Sources for Outcomes Analysis Variable Data Sources Demographics and population characteristics U.S. Census Bureau 1. Percent Blacks in the population American Community 2. Percent Hispanics in the population Survey (2001 2003) 3. Percent population under 18 years of age American Fact Finder Quick Reports Socio economics American Fact Finder 1. Percent unemployment Quick Reports Primary Care Specific Popul ation Indicators Florida CHARTS* 1. Infant mortality rate 2. Neonatal mortality rate 3. Post neonatal mortality rate 4. Non white infant mortality rate 5. Births to mothers 15 19 yrs 6. Low birth weight births 7. No prenatal care in the fi rst trimester Note: CHARTS is the abbreviation for Community Health Assessment Resource Tool Set The primary care specific indicators were selected from the Florida Department of Healths Strategic Plan. An earlier section of the dissertation listed t he health status indicators that are specific for primary care. These indicators are widely available from state and federal sources; however, the health outcomes in this study were limited to the indicators used by the Department of Health to evaluate th eir performance. Primarily indicators linked to the primary care programs used in the cost analysis were included. These indicators are listed in Table 11. The data were extracted from Florida Community

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61 Health Assessment Resource Tool Set (CHARTS). CHARTS is a public health data website that contains a wide variety of health statistics for Florida such as live births, d eaths, hospitalizations, in addition to population statistics and community health status information. The analysis of the data was cond uc ted using a mixed model with repeated measures. As stated in a previous analysis, a mixed model adjusts for correlations found with clustered data or repeated measures. The following hypothesis was analyzed: In counties where primary care programs have be en privatized, health outcomes on primary care sensitive indicators (i.e. post neonatal mortality) will be significantly better when compared to non privatized areas. The unit of analysi s was county level. The dependent variables are primary care sensitiv e indicators used by the Florida Department of Health as performance measures. Equations were developed using the health status indicators as independent variables. The in dependent variables were percent Black and Hispanic in the population, percent below poverty level, under 18 years of age, and year. Privatization status (coded as 0 and 1) was also a n in dependent variable. A test of effects is used to determine whether outcome measures differ significantly based on their privatization status. Tab le 12 Health Status Models Using Primary Care Specific Indicators Outcome Measure Random Effects Fixed Effect Infant Mortality Rate %Black, %Hispanic % Below Poverty, Under 18, Year Privatization Status (Non privatized = 0) (Privatized = 1) Non white Infant Mortality %Black, %Hispanic % Below Poverty, Under 18, Year Privatization Status (Non privatized = 0) (Privatized = 1)

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62 Table 12, C ontinued Health Status Models Using Primary Care Specific Indicators Outcome Measure Random Effects Fixed Eff ect Neonatal Mortality %Black, %Hispanic % Below Poverty, Under 18, Year Privatization Status (Non privatized = 0) (Privatized = 1) Post neonatal Mortality %Black, %Hispanic % Below Poverty, Under 18, Year Privatization Status (Non privatized = 0) (Privatized = 1) Births to Mothers 15 19 years %Black, %Hispanic % Below Poverty, Under 18, Year Privatization Status (Non privatized = 0) (Privatized = 1) Low Birth Weight %Black, %Hispanic % Below Poverty, Under 18, Year Privatization Status (No n privatized = 0) (Privatized = 1) No Prenatal Care in the First Trimester %Black, %Hispanic % Below Poverty, Under 18, Year Privatization Status (Non privatized = 0) (Privatized = 1) Summary This chapter provided an explanation of the concepts and applications of the research methodology. For clarity each dimension (cost, access, and health outcomes) was presented separately. The definitions and treatment of study variables was explained as well as the selection of specific analytic techniques u sed to address the research questions. The next chapter entitled The Results presents the findings of this study.

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63 THE RESULTS Overview This chapter summarizes the results of the data analysis conducted for this study. The analysis was conducted in phases to answer the three research questions. In the first phase, the descriptive statistics and analysis was conducted to answer the research question addressing the cost of privatization. In the second phase, primary care access scores were compiled a nd the analysis was conducted to answer the second research question. In the third phase, the descriptive statistics and analysis was conducted to answer the third research question. To maintain consistency and for ease of review, the analysis is present ed in three segments. Results of Cost Analyses This study was a retrospective, longitudinal population based analysis to examine the cost of privatized primary care programs versus the cost of primary care programs provided by the county health departme nts. Table 13 gives the descriptive statistics for the number of clients, services, and expenditures in the nine counties for the fiscal years 2001 through 2004. Seven primary care programs were analyzed for each county for the four years. The total num ber of observations (programs) was 236. Total number of clients reported through the Contract Management Variance System was 944, 214. The actual number of clients may be slightly elevated. Each client was counted only once per year; however, this analy sis combined four years of data. The total number of services

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64 reported was 7,866,762 and the total expenditures reported was $ 378,473,155. There was no missing data. Table 13 Descriptive Statistics for Primary Care Cost Analysis Variables Total for A ll Counties Mean Average Cost Clients 944,214 4001 $400.83 Services 7,866,762 33,334 $48.11 Expenditures (Reported) 378,473,155 1,603,700 Note: n = 236 for all variables. Pearson correlations were used to measure the rel ationship between variables. The bivariate analysis was conducted with PROC CORR using SAS 9.0 version 9. There was a moderate correlation (.68) between expenditures and services and also a moderate correlation (.56) between expenditures and clients. How ever, there was a low correlation (.38) between clients and services. This analysis is important in that a moderate to strong correlation between clients and services may cause confounding in a regression analysis. Table 14 reports the results of the mix ed model for repeated measures analysis using cost as the dependent variable. The analysis was conducted using PROC GENMOD in SAS 9.0. The covariance matrix was specified as autoregressive order one to account for correlation between programs. This anal ysis determined if there was a difference in cost between privatized programs and non privatized programs. The results of the analysis shows a signific ant effect for clients (p < .0001) and services (p < .0001 ) on the cost of services. This result is expe cted. The findings also show that privatize and non privatized programs did not have a significant e ffect on the cost of services (p = 0. 3 8). Privatization status was coded as a categorical variable

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65 (Privatized = 1, Non privatized = 0), thus the model sh ows results for each level. Year, coded as a continuous variable in the table shown, was not significant. Year was originally coded as a categorical variable. None of the years were significant. In the final analysis, year was coded as a continuous var iable because consecutive years were used in the analysis. Also, coding the variable as continuous reduced the standard errors of other variables in the model. Table 14 Cost Analysis Parameter Estimates for Mixed Model with Repeated Measures Effect B S E T p Clients 42.90 9.03 4.75 < .0001 Services 23.48 1.93 12.17 < .0001 Privatized Programs 5.59E7 6.29E7 0.89 0.37 7 Non privatized Programs 5.60E7 6.29E7 0.89 0.37 8 Note: Model R 2 = 0.57 A second analysis was conduc ted using generalized estimating equations (GEE) to establish if there is a difference among privatized counties versus non privatized counties with regard to clients, services, or expenditures. The analysis was conducted using the PROC GENMOD procedure i n SAS version 9.0. Although the previous analysis examined the effect of each variable on cost, the purpose of this analysis is to determine if the effect shown for clients and services is based on privatization status. Table 15 shows the chi square resu lts reported in the logistic regression and the z scores as reported by the GEE analysis. As stated in the previous section, in PROC GENMOD, GEE estimation is used with a repeated statement because the data is clustered by counties. In addition, the data is longitudinal and correlated. The covariance structure selected for this analysis is autoregressive one because there is

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66 greater correlation between programs within counties and also a greater correlation between years that are closer in time. Table 15 Chi Square and Z Statis tics Comparison for GEE Analysis Standard Estimates GEE Estimat es Variable c 2 p value z p value Clients 0.01 0.91 0.33 0.07 Services 0.42 0.52 1.76 0.88 Expenditures 0.23 0.63 0.15 0.24 Year 2001 0.00 0.95 1.18 0.55 Year 2002 0.00 0.96 0.60 0.32 Year 2003 0.00 0.98 0.99 0.29 Year 2004 Note: N=236 Table 15 shows the results of the analysis. The first column shows the results of a standard logistic analysis of the data using privatization as the dependent variable with a binomial distribution and a logit link function. There were 236 observations used in the analysis and there was no missing data. The analysis shows that the effect shown for clients ( c 2 = 0.01, p=.91) is not significant in this analysis. This shows that effects of clients and services on cost are not a result of the privatization status of the program. There is however, an alternative explanation for the finding. The previous findi ng may occurred from bias that arising from correlations between individual and cluster level variables. The table also reveals that year is not significant. As in the first analysis year was coded as a continuous and then as a categorical variable. Th e variable was not significant in either case however, in Table 15, year was retained as a categorical variable because it reduced the standard errors of other variables in the model. Table 15 also reports the results of the GEE analysis. As in the stand ard logit analysis, none of the

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67 variables are significant, although the z statistic is larger and the p value is smaller when the correlation between variables is included. Results of Access Analyses The analysis to determine the potential access (availa bility) of primary care services was based on a primary care access model developed by Fos and Zuniga. The calculation of primary care access scores is discussed in detail in the previous section of this dissertation. As expected, the distribution of the primary care access scores varied across the nine counties. Because of the amount of data generated by this process, the primary care access scores are presented as Appendix A in this document. The variable specific scores used measure each zip code by a single indicator and also to compile primary care access scores are presented as Appendix B. Descriptive statistics were tabulated for each zip code. Information is available on demographics, socioeconomics, and physician resources. This information is presented in Appendix C. Once primary care access scores were determined for each zip code, these scores were used in the access analysis. The analysis used a simple logistic regression to determine if there is a difference in access scores between the p rivatized counties and the non pr ivatized counties. The data were evaluated using the PROC GENMOD procedure in SAS 9.0. Table 16 provides the results of this analysis.

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68 Table 16 GEE Analysis for Primary Care Access Scores Initial Parameter Estimates GEE Parameter Estimates Parameter B SE c 2 p B SE z p Intercept 0.840 0.123 46.37 .0001 0.840 0.134 6 .3 .0001 Privatization Status 0.336 0.153 4.84 0.028 0.336 0. 1 98 1.7 0.090 Note: N = 420 Access score was evaluated as the de pen dent variable with a normal distribution and an identity link function. There were 420 observations used in the analysis and there was no missing data. The GENMOD Procedu re reports the standard analysis of parameter estimates first and then the GEE param eter estimates The standard analysis assumes that all observations are independent and the effect shown for primary care access score ( c 2 = 4.84 p=.03) is significant. However, the zip codes are clustered by county therefore the observations are not independent. The GEE analysis accounts for this cor relation. Using an autoregressive one correlation structure, the effect for privatizat ion is not significant ( z = 1.70 p=.09). The result suggests that there is no difference in the primary care access scores between privatized counties and non privatized counties. Results of Health Outcomes Analyses This analysis used retrospective, lon gitudinal population based data to examine the health status of populations in areas with contracted primary care versus the health status of those in areas where primary care programs are provided by the county health departments. The analysis was condu cted using a series of mixed models to compare

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69 rates for the selected outcome measures. In each model the health status indicator (i.e. post neonatal mortality) was treated as the dependent variable. Table 17 presents the results of the mixed model anal ysis. The table represents a side by side comparison of privatization status as a fixed effect. An effect is fixed if the levels in the study represent all possible levels of the factor, or at least all the levels about which inference is made. Table 17 Health Outcomes Mixed Model with Repeated Measures Solution of Fixed Effects Non privatized Counties Privatized Counties Health Status Indicator T p value T p value Infant Mortality 0.73 0.47 0.64 0.53 Neonatal Mortality 0.06 0.95 0.17 0.87 P ost neonatal Mortality 0.63 0.53 0.74 0.47 Non white Infant Mortality 1.86 0.08 1.85 0.08 Births to Mothers 15 19 years 0.43 0.67 0.67 0.50 Low Birth Weight 1.53 0.14 1.87 0.08 No Pre natal Care during First Trimester 1.65 0.11 1 .90 0.07 The other effects in the mixed model were random effects which represent a random sample of a larger set of potential levels that affects the outcome measure; however, they were not of particular interest in this analysis so the results will n ot be discussed here. Table 17 shows that privatization status did not have a significant effect on any of the health status indicators selected for this study. The p value was greater than .05 for all indicators for both privatized and non privatized cou nties. As mentioned in the previous section, these indicators were selected for their sensitivity in measuring good primary care. Also the majority of these indicators are used by the Department of Health as

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70 performance measures. These results show that privatization status had no effect on health status. Summary This chapter presented the results of this study in three segments. In the first segment, a descriptive analysis, a mixed model and GEE analysis was conducted to test the hypothesis that prim ary care services costs less in privatized counties. In the second segment, primary care access scores were compiled and a GEE analysis was conducted to test the hypothesis that potential access (availability) is greater in privatized counties. The third segment provided the results of a series of mixed models which were used to test whether the health outcomes were better in the populations with privatized primary care programs than in counties with non privatized programs. The final chapter presents a d iscussion of these results as well as an overall summary of the conclusions and recommendations of this study.

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71 THE CONCLUSIONS AND RECOMMENDATIONS Discussion and Conclusions The results of this study provides empirical evidence that privatization of primary care programs does not result in cost savings or offer significant differences in health outcomes when compared to non privatized care. The analysis also revealed that potential access to care did not differ significantly in privatized and non privatized counties. These findings do not support economic theories that privatization results in cost savings and improved services. The explanations for these findings are complex. A few reasons will be proposed here however, they are not all inclusi ve. Again, for clarity, the discussion will examine each dimension separately. Impact of Privatization on Cost The desire to save money is frequently mentioned by government officials as the rationale for privatizing government services (Keene, et al, 2 002). However, the results of this study indicate that privatization of health care services does not automatically result in a reduction in cost. In a 2001 study, Keane, Marx and Ricci stated that one in ten health department directors reported increas ed costs, decreased revenue, or a loss of efficiency due to privatization. In previous research, increases in cost were the result of increased levels of administration associated with contracting out services. Although in a few cases, private providers actua lly did cost more. Public health physicians and nurses are likely to be paid less than similar employees in the private sector. Higher wages

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72 among private sector doctors and nurses may create the incentive for private contractors to use less skille d employees to reduce costs. In addition to cost, this study also examined the number of services and the number of clients as part of the analysis. The significant findings on clients and services in the mixed model analysis can be attributed to the di fferences in the number provided over the four year period. While cost savings are a prominent reason for privatization, public health officials should also focus on quality of services and health outcomes. Impact of Privatization on Access Adequate acce ss to primary care services is achieved when all community residents are able to use health care services according to their specific needs. Access is not possible if primary care providers, facilities, and supporting health care infrastructure are not in place. Even when essential primary care services are available, they might not be accessible. Barriers to access include language and culture, geography, weather, and the lack of affordable public transportation and medical transportation services. Thi s dimension of the analysis examined the variation of primary care access scores within each zip code across the nine counties. Although the logistic regression analysis revealed that privatization did not have a significant effect on primary care access scores between privatized and non privatized areas, there were large variations within counties. Examination of zip code level access scores within each county is also important. The greatest variation of scores ( 3.96 to 5.19) occurred within Duval coun ty. Pinellas county has the least amount of variation within the county ( 2.54 to 1.53), although they also had lower primary care access scores than other counties. Examining

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73 potential access across areas is important in order to establish where improve ment is needed. During the past few years, the health care system has experienced significant changes in service delivery methods. These changes have caused increasing levels of stress on the system especially with regard to primary care. Rural and inn er city areas are at greatest risk for negative impacts. Population characteristics, including age, gender, race, and socio economic status were ca lculated for each zip code area (See Appendix C ) These population characteristics along with primary care resources were used to develop variable specific scores. The variable specific score can be used to compare areas on individual items such as poverty or unemployment (See Appendix B). In summary, the scores found in Appendix B and C can be used for many purposes. The information derived can be used to (1) identify areas that are at greatest risk (2) establish which areas should be targeted for improvement, and (3) evaluate the allocation of primary care resources. Impact of Privatization on Health Outc omes The third analysis examined health status of the populations within privatized and non privatized counties. The indicators for this analysis were selected based on the performance measures used by the Florida Department of Health. P rivatization st atus did not have a significant effect on the outcomes measures selected for this stud y. As previously mentioned, only the indicators which were related to primary care were selected for analysis. The se indicators centere d heavily on maternal and child h ealth. This area is one in which the D epartment has focused attention for the last decade As a

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74 result, infant mortality has been significantly reduced Publicly provided maternal and child health programs have been available since the 1930s. With reg ard to t hese types of services, there may be an advantage for providing in house services. Implications and Recommendations Privatization proponents speak of the advantages of using the competitive forces of the marketplace to control costs. This is con sistent with economic theories such as market theory and public choice theory. However, several previous studies suggest that privatization in social services, i.e. primary care programs, take place for political reasons (Van Slyke, 2003; Savas, 1987; Ha lverson, et al, 1998). Politically, privatization symbolizes smaller government, more efficiency, and cooperation with private markets. However, shrinking government may compromise public health managers ability to provide sufficient oversight to prevent fraud, waste, and abuse. Competition, capacity, po litics, and defined outcomes have a significant effect on the quality of services and also how contracts are managed. It affects the level of funding and staffing allocated to public health programs. Th ese resources are critical to providing services and ensuring service quality and accountability when contracting. If a smaller, more results oriented government is what citizens and elected officials desire, then simply contracting out services without rigorous requirements will not meet anyones expectations. Evaluation of all dimensions of privatization are necessary if there is to be movement toward equitable allocation of finite resources and support from citizen s and government officials

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75 Summary This chapter brings this dissertation to a close by discussing the findings and summarizing the conclusions and recommendations from this study. Research questions on the effects of privatization on the cost of primary care services, access to primary ca re, and primary care specific health outcomes were answered. The answer to the first question was answered by the finding that there was no difference in cost between privately provided primary care when compared to care provided by the county health dep artment. The second research question was answered by the finding that there was no difference in potential access to primary care services in privatized and non privatized counties. And the final question on privatization was answered by the finding pri vatization status did not have a significant effect on health outcomes in privatized or non privatized counties. The findings of this dissertation have relevance to government officials, particularly those in public health, as well as the citizens of Flor ida in determining the direction of publicly provided primary care in the state.

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76 References Aiken, L. H., Lewis, C. E., Craig, J., Mendenhall, R. C., Blendon, R. J., and Rogers, D. E. (1979). The contribution of specialist to the delivery of pr imary care: a new perspective. The New England Journal of Medicine 300, 1363. Allison, P. (1999). Logistic Regression Using the SAS System: Theory and Application. Care, NC: SAS Institute, Inc. Andrulis, D. (1997). The public sector in health care: evolution or dissolution? Three scenarios for a changing public sector health care system. Health Affairs 16(4) July/August 1997, 131 140. Beauchamp, D. (1997). Public health, privatization, and market populism: a time for reflection. Quality Management in Health Care 5(2), 73 79. Bee cher, J. (1995). Regulatory Implications of Water and Wastewater Utility Privatization. National Regu latory Research Institute. Bluestein, F. (1996). Privatization of Local Government Functions or Services: Legal and Phi losophical Issues Chapel Hill, NC: Institute of Government, University of North Carolina at Chapel Hill Brammer, D. (1997) Privatization of programs and services: An increasingly popular option for state and local g overnment s. Public Administration Sur vey Oxford, MS: Public Policy Research Center, University of Mississippi 44, 1 4. Buchanan, J. (1978). Why d oes g overnment grow?. Budgets and Bureaucrats O ctober/November pp 13 14. Butler S. (1991) Privatization for Public P urposes, in Privatiza tion and Its Alternatives ed. Gormley, W.T. Madison, WI: Uni versity of Wisconsin Press. Chi K. (2003). Private Practices: A Review of Privatization in State Governments Lexington, KY : Council of State Governments. Chi K. and Jasper, C. (1998). Priv ate Practices: A Review of Privatization in State Governments Lexington, KY: Council of State Governments.

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77 Clancy, C. and Cooper, J. (1998). Approaches to primary care: current realities and future visions. The American Journal of Medicine 104, 215 2 18. Colman, W. (1989). State and Local Government and Public Private Partnerships New York: Greenwood Press Core functions of public health (1991) U. S. Centers for Disease Control and Prevention (Atlanta,GA: CDC) Crockett, M., Rainhart, E. (2000). The privatization of public health services in Florida. Health Evaluation, Assessment, and Research Studies Office Orange County Health Department. Department of Health and Human Services, Healthy People 2010 http://www.healthypeople.gov/Document/HT ML/Volume1/01Access.htm Dodenhoff, D. (1998). Privatizing Welfare in Wisconsin: Ending Administrative Entitlements -W 2's Untold Story WPRI Report, Wisconsin Policy Research Institute : 11(1) Eilbert, K.W., (1996). Measuring Expenditures for Essenti al Public Health Services Washington: Public Health Foundation. Essential public health services: the case for reinvestment a work group report. (1995) College of Public Health University of South Florida Florida Department of Transportation, Asse t Management Progra m, November 2003, available at: h ttp://www.dot.state.fl.us/statemaintenanceoffice/Asset%20Management%20Program%2 0November%2024,%202003.pdf Finl ey, L.K., (1989). Alternative service delivery, privatization, and c ompetition, in Public S ector Privatization ed. Finley, L.K. New York, Westport, CN, London: Quorum Books Franks, P., Clancy, C., and Nutting, P. (1997). Defining primary care: empirical analysis of the national ambulatory medical care survey, Medical Care 35 (7), 655 668. Fr anks, P., Nutting, P.A., Clancy, C. (1993). Health care reform, primary care, and the need for research, Journal of the American Medical Association 270 (12), 1449 1453. Friedman, M (1962). Capitalism and f ederalism Chicago: University of Chicago Pr ess The future of public health (1988). Institute of Medicine. (Washington, DC: National Academy Press).

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78 Greene, J., (2002). Cities and Privatization: Prospects for the New Century Upper Saddle River NJ: Prentice Hall Hadley, C.L., Feldman, L., Toomey, K.E., (2004) Local public health cost study in g eorgia, Journal of Public Health Management and Practice 10(5), September/October 2004, 400 405. Hardy, A.V. and Pynchon, M. (1964). Millstones and Milestones: Floridas Public Health from 1889. Jac ksonville, FL: Florida State Board of Health Hatry, H., (1983). A review of private approaches for deliv ery of public services. Washington, D.C: The Urban Institute Hazel, P. (1997). Privatize the Oregon DMV Portland: Cascade Policy Institute. h ttp:// www.cascadepolicy.org/bgc/dmv.htm. International Road Federation. (2003). Symposium on road maintenance c ontracting, Orla ndo, FL: International Road Federation James J. (2005). Gov. Bush Admits to Miscue in Outsourcing, St. Petersburg Times January 19, 2005. Available at http: //www.sptimes.com/2005/01/19/Business/Gov_Bush_admits_to_mi.shtml Judd, D., and Swanstrom, T. (1994). City Politics: Private Power and Public Policy New York : Harper Collins. Keane C. (2005). The effects of manag erial beliefs on service: privatization and discontinuation in local health departments. Health Care Management Review 30(1) 52 61. Keane C, Marx J, Ricci E. (2003). Managerial and professional beliefs influencing public health privatization: results o f a national survey of local health department directors. Journal for Health and Social Behavior 44(1) 97 110. Keane C, Marx J, and Ricci E. (2002). Services privatized in local health departments: a national survey of practices and perspectives. Amer ican Journal of Public Health 92(8) 1250 125 4 Keane C, Marx J, and Ricci E. (2002). Public health privatization: proponents, registers, and decision makers. Journal of Public Health Policy 23(2) 133 1 52. Keane C, Marx J, Ricci E, and Barron G. (2002 ). The perceived impact of privatization on local health departments. American Journal of Public Health 92(7):1178 80.

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79 Keane C, Marx J, Ricci E. (2001). Privatization and the scope of public health: a national survey of local health department direct ors. American Journal of Public Health 91(4), 611 61 7. Keane C, Marx J, Ricci E. (2001). Perceived outcomes of public health privatization: a national survey of local health department directors. Milbank Quarterly 79(1) 115 1 37 Lipson, D. J. and Naierman, N. (1996). Effects of health system changes on safety net providers. Health Affairs 15(2) 33 48 L oux, S. (1996). Prison Privatization in Pennsylvania Harrisburg, PA : Commonwealth Foundation January 1996. Mahtesian, C., (1994) The p ri vatizing Daley, Governing: The Magazine of States and Localities (April 1994), 26 33 Meier, B. (1999) "Experiment of privatized mental hospital shows b enefits," New York Times, December 28, 1999. Mendenhall, R. C., Lloyd, J. S., Repicky, P. A., Monso n, J. R., Girard, R. A., Abrahamson, S. (1978) A national study of medical and surgical specialties II: Description of the survey instrument. Journal of the American Medical Association 241, 2180. Mongan, J. J. and Lee, T. H. (2005) Do we really want bro ad access to health care?, The New England Journal of Medicine 352 (12), 1260 1263. Moy, E., Bartman, B. A., Clancy, C. M., and Cornelius, L. J. (1998) Changes in the usual sources of medical care between 1987 and 1992 Journal of Health Care for the P oor and Underserved 9 (2), 126 139. National Academy of Public Administration (1989). Privatization: The Challenge to Public Management Washing ton, D.C.; NAPA Napier, M. J., Street, P., Wright, R., Kouba, J. M., Ciereck, C., Dillon, M. J., Dollar R. C., Parizek, W. A., Stapp, C. P., Dickinson, R., (2004) The f l orida department of health and the f lorida association of county health department business administrators: A model of successful collaboration to sustain operational e xcellence Journal o f Public Health Management and Practice 10(5), 413 420. Office of Management and Budget, A Guide to Best Practices for Performance Based Service Contracting (Washington, D.C.: Office of Federal Procurement Policy, Office of Management and Budget, 1996).

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80 Office of Program Policy Analysis and Government Accountability (OPPAGA), Private Prison Review: South Bay Correctional Facility Provides Savings and Success; Room for Improvement, Report No 99 39 (Tallahassee, Florida: March 2000). Office of Program Po licy Analysis and Government Accountability, State of Florida Department of Transportation Expedites Privatization, But Savings Uncertain; May be Feasib le to Eliminate More Positions, April 2003, p. 2. Office of Program Policy Analysis and Government Ac countability, State of Florida, Department of Transportation Can Expedite Its Proposed Staffing Reduction Plan, March 2001, p. 3. Office of Program Policy Analysis and Government Accountability, State of Florida, Special Report: Medicaid Field Offices Can Improve Efficiency and Effectiveness; State Could Outsource Some Activities, May 2004. Prager, J. (1994). Contracting Out Government Services: Lessons from the Private Sector, Public Administration Review 54( 2 ), Primary care: americas heal th in a new era (1996). Institute of Medicine (U.S.). Division of Health Care Services. Committee on the Future of Primary Care. Washington, DC: National Academy Press Privatization and public health: a report of initiative and early lessons learned. (19 99) Washington, DC: Public Health Foundation Reinhardt, W. (2002). th Annual Outsourcing Survey, Public Works Financing March 2002, 1 Rosen, G. (1957). A history of public health New York, NY: MD Publications Rosenblatt, R. A., Hart, L.G., Gamlel, S., Goldstein, B., McClendon, B. J. (1995) Identifying primary care discipline by analyzing the diagnostic content of ambulatory care. Journal of the American Board of Family Practice 8:54. Rubin, I., (1981). Running in the r ed Albany, NY: Sta te Un iversity of New York Press Rutstein, D., Berenberg, W., Chalmers T., Child, C., Fishman, A., Perrin, E. (1976) Measuring the quality of medical care: a clinical method. New England Journal of Medicine ; 296: 582 588. Savas, E. S., Privatization and p risons Vanderbilt Law Review 40:868 899 Segal, G. F., (2005) Making Florida s government c ompetitive, Backgrounder April 2005, 44, 1 23

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81 Spiegel, J. S., Rubenstein, L. V., Scott, B., Brook R. H. (1983). Who is the primary physician? The New England Journal of Medicine 308: 1208. Starfield, B. (1998) Primary care an d its relationship to health. i n Primary Care: Balancing Health Needs, Services, and Technology New York, NY: Oxford University Press, Inc. Starr, P. The meaning of privatization in Privatization and the Welfare State ed. Kammerman, S. and Kahn, A. Princeton, NJ: Princeton University Press, 1988 The direction of public health privatization, (1997) Health Policy Monitor Fall 1997 2 (3), 1 2. United Health Foundations America s State Health Rankings 2004. Available at: http://www.unitedhealthfoundation.org/shr2004/components/healthexpend.html Van Slyke, D. (2003). The mythology of pr ivatization in contracting for social services. Public Administration Review 63 (3) 296 315. Wall, S. (1998) Transformations in public health systems. Health Affairs 17(3) 64 80. Wolf C., Markets or Governments: Choosing between imperfect alternativ es Cambr idge, MA: MIT press, 1988

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82 Bibliography Allison, P. (1999). Logistic Regression Using SAS System: Theory and Application. Cary, NC: SAS Institute Inc. Agresti, A., (1996). An Introduction to Categorical Data Analysis New York, NY: Jo hn Wiley & Sons, Inc. Cody, R. & Smith, G., (1995). Applied Statistics and the SAS Programming Language. (Fourth Edition). Upper Saddle River, NJ: Prentice Hall. Hatcher, L. & Stepanski, E., (1994) A Step by Step Approach to Using the SAS System for Uni variate and Multivariate Statistics. Cary, NC: SAS Institute Littell, R., Milliken, G., Stroup, W., and Wolfinger, R., (1996). SAS System for Mixed Models. Cary, NC: SAS Institute, Inc.

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83 Appendices

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84 Appendix A Primary Care Acces s Scores Case County Zip Code PC_Score Privatized 1 Brevard 32754 1.48 0 1 Brevard 32759 1.26 0 1 Brevard 32775 0.24 0 1 Brevard 32780 0.79 0 1 Brevard 32796 0.32 0 1 Brevard 32901 1.08 0 1 Brevard 32903 1.34 0 1 Brevard 32904 0.32 0 1 Bre vard 32905 0.53 0 1 Brevard 32907 1.10 0 1 Brevard 32908 1.17 0 1 Brevard 32909 0.98 0 1 Brevard 32920 1.11 0 1 Brevard 32922 3.20 0 1 Brevard 32925 1.11 0 1 Brevard 32926 1.41 0 1 Brevard 32927 1.08 0 1 Brevard 32931 0.84 0 1 Brevard 32 934 0.29 0 1 Brevard 32935 0.72 0 1 Brevard 32937 0.23 0 1 Brevard 32940 0.96 0 1 Brevard 32948 1.52 0 1 Brevard 32949 0.13 0 1 Brevard 32950 0.29 0 1 Brevard 32951 0.14 0 1 Brevard 32952 0.21 0 1 Brevard 32953 0.94 0 1 Brevard 32955 0.14 0 1 Brevard 32976 0.89 0 2 Broward 33004 0.05 1 2 Broward 33009 1.37 1 2 Broward 33019 0.52 1 2 Broward 33020 2.04 1 2 Broward 33021 1.07 1

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85 Appendix A, C ontinued Primary Care Access Scores Case County Zip Code PC_Score Privatized 2 Broward 33023 2.18 1 2 Broward 33024 0.40 1 2 Broward 33025 1.50 1 2 Broward 33026 0.22 1 2 Broward 33027 0.52 1 2 Broward 33028 0.25 1 2 Broward 33029 0.40 1 2 Broward 33060 2.03 1 2 Broward 33062 0.17 1 2 Broward 33063 0.56 1 2 Broward 33064 0.9 9 1 2 Broward 33065 0.68 1 2 Broward 33066 0.07 1 2 Broward 33067 1.07 1 2 Broward 33068 1.91 1 2 Broward 33069 0.55 1 2 Broward 33071 0.04 1 2 Broward 33073 0.25 1 2 Broward 33076 0.92 1 2 Broward 33301 0.17 1 2 Broward 33304 1.75 1 2 Bro ward 33305 0.37 1 2 Broward 33306 1.46 1 2 Broward 33308 2.12 1 2 Broward 33309 1.86 1 2 Broward 33311 3.66 1 2 Broward 33312 1.42 1 2 Broward 33313 2.52 1 2 Broward 33314 1.48 1 2 Broward 33315 1.13 1 2 Broward 33316 3.15 1 2 Broward 3331 7 0.25 1 2 Broward 33319 1.52 1 2 Broward 33321 0.47 1 2 Broward 33322 0.70 1

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86 Appendix A, C ontinued Primary Care Access Scores Case County Zip Code PC_Score Privatized 2 Broward 33323 0.39 1 2 Broward 33324 0.88 1 2 Broward 33325 0.59 1 2 Broward 33326 0.47 1 2 Broward 33327 1.77 1 2 Broward 33328 0.34 1 2 Broward 33330 0.47 1 2 Broward 33331 0.82 1 2 Broward 33332 4.93 1 2 Broward 33334 1.85 1 2 Broward 33351 1.19 1 2 Broward 33388 0.00 1 2 Broward 33394 0.00 1 2 Broward 33441 1.47 1 2 Broward 33442 0.65 1 3 Dade 33010 2.30 1 3 Dade 33012 0.69 1 3 Dade 33013 0.82 1 3 Dade 33014 0.73 1 3 Dade 33015 1.41 1 3 Dade 33016 0.49 1 3 Dade 33018 1.45 1 3 Dade 33030 2.33 1 3 Dade 33031 2.40 1 3 Dade 33032 3.13 1 3 Dade 33033 3.14 1 3 Dade 33034 4.55 1 3 Dade 33035 1.41 1 3 Dade 33054 4.22 1 3 Dade 3305 5 2.37 1 3 Dade 33056 3.29 1 3 Dade 33109 0.24 1 3 Dade 33122 0.00 1 3 Dade 33125 2.01 1 3 Dade 33126 1.49 1 3 Dade 33127 4.29 1

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87 Appendix A, C ontinued Primary Care Access Scores Case County Zip Code PC_Score Privatized 3 Dade 33128 3.45 1 3 Dade 33129 0.72 1 3 Dade 33130 2.49 1 3 Dade 33131 1.81 1 3 Dade 33132 1.07 1 3 Dade 33133 1.65 1 3 Dade 33134 2.11 1 3 Dade 33135 1.70 1 3 Dade 33136 0.71 1 3 Dade 33137 2.50 1 3 Dade 33138 2.10 1 3 Dade 33139 0.45 1 3 Dade 33140 3.19 1 3 Dade 33141 1.93 1 3 Dade 33142 3.87 1 3 Dade 33143 2.10 1 3 Dade 33144 0.13 1 3 Dade 33145 0.34 1 3 Dade 33146 2.96 1 3 Dade 33147 4.53 1 3 Dade 33149 1.10 1 3 Dade 33150 3.51 1 3 Dade 33154 1.10 1 3 Dade 33155 0.79 1 3 Dade 33156 1.64 1 3 Dade 33157 1.15 1 3 Dade 33158 1.32 1 3 Dade 33160 0.36 1 3 Dade 33161 3.32 1 3 Dade 33162 2.40 1 3 Dade 33165 0.18 1 3 Dade 33166 0.35 1 3 Dade 33167 3.74 1 3 Dade 33168 3.69 1 3 Dade 33169 2.78 1 3 Dade 33170 3.58 1

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88 Appendix A, C ontinued Primary Care Access Scores Case County Zip Code PC_Score Privatized 3 Dade 33172 1.42 1 3 Dade 33173 1.49 1 3 Dade 33174 0.46 1 3 Dade 33175 0.15 1 3 Dade 33176 1.33 1 3 Dade 33177 1.25 1 3 Dade 33178 0.77 1 3 Dade 33179 1.16 1 3 Dade 33180 2.77 1 3 Dade 33181 1.21 1 3 Dade 33182 0.89 1 3 Dade 33183 1.13 1 3 Dade 33184 0.67 1 3 Dade 33185 0.53 1 3 Dade 33186 0.43 1 3 Dade 33187 0.54 1 3 Dade 33189 1.80 1 3 Dade 33190 2.07 1 3 Dade 33193 1.81 1 3 Dade 33194 0.00 1 3 Dade 33196 0.82 1 4 Duval 32009 1.57 0 4 Duval 32073 0.08 0 4 Duval 32202 1.16 0 4 Duval 32204 5.19 0 4 Duval 32205 1.13 0 4 Duval 32206 3.96 0 4 Duval 32207 0.49 0 4 D uval 32208 2.58 0 4 Duval 32209 2.39 0 4 Duval 32210 1.17 0 4 Duval 32211 1.92 0 4 Duval 32212 1.73 0 4 Duval 32215 1.21 0 4 Duval 32216 1.18 0 4 Duval 32217 0.54 0

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89 Appendix A, C ontinued Primary Care Access Scores Case County Zip Code PC_Score Privatized 4 Duval 32218 1.69 0 4 Duval 32219 1.98 0 4 Duval 32220 1.13 0 4 Duval 32221 0.89 0 4 Duval 32222 1.36 0 4 Duval 32223 0.42 0 4 Duval 32224 0.25 0 4 Duval 32225 0.08 0 4 Duval 32226 0.37 0 4 Duval 32227 0.95 0 4 Duval 32233 1.02 0 4 Duval 32234 1.40 0 4 Duval 32244 1.36 0 4 Duval 32246 0.93 0 4 Duval 3225 0 0.51 0 4 Duval 32254 2.70 0 4 Duval 32256 0.71 0 4 Duval 32257 0.17 0 4 Duval 32258 0.50 0 4 Duval 32259 0.23 0 4 Duval 32266 0.06 0 4 Duval 32277 1.06 0 5 Hillsbo 33510 0.64 0 5 Hillsbo 33511 0.85 0 5 Hillsbo 33527 2.08 0 5 Hillsbo 33534 2.22 0 5 Hillsbo 33540 1.05 0 5 Hillsbo 33547 0.42 0 5 Hillsbo 33549 0.90 0 5 Hillsbo 33556 0.77 0 5 Hillsbo 33565 1.05 0 5 Hillsbo 33566 1.32 0 5 Hillsbo 33567 1.48 0 5 Hillsbo 33569 0.65 0 5 Hillsbo 33570 1.36 0 5 Hillsbo 33572 0.05 0

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90 Appendix A, C ontinued Primary Care Access Scores Case County Zip Code PC_Score Privatized 5 Hillsbo 33573 0.07 0 5 Hillsbo 33584 1.23 0 5 Hillsbo 33592 1.56 0 5 Hillsbo 33594 0.23 0 5 Hillsbo 33598 2.71 0 5 Hillsbo 33602 2.15 0 5 Hillsbo 33603 2.44 0 5 Hillsbo 33604 2.22 0 5 Hillsbo 33605 3.31 0 5 Hillsbo 33606 2.55 0 5 Hil lsbo 33607 1.84 0 5 Hillsbo 33609 1.55 0 5 Hillsbo 33610 2.85 0 5 Hillsbo 33611 0.34 0 5 Hillsbo 33612 1.84 0 5 Hillsbo 33613 0.10 0 5 Hillsbo 33614 0.58 0 5 Hillsbo 33615 0.78 0 5 Hillsbo 33616 1.93 0 5 Hillsbo 33617 1.45 0 5 Hillsbo 3361 8 0.83 0 5 Hillsbo 33619 2.01 0 5 Hillsbo 33620 17.33 0 5 Hillsbo 33621 1.16 0 5 Hillsbo 33624 0.44 0 5 Hillsbo 33625 0.67 0 5 Hillsbo 33626 2.72 0 5 Hillsbo 33629 1.04 0 5 Hillsbo 33634 0.35 0 5 Hillsbo 33635 0.44 0 5 Hillsbo 33637 1.14 0 5 Hillsbo 33647 1.93 0 5 Hillsbo 33834 2.00 0 5 Hillsbo 34221 1.14 0 6 Orange 32703 1.19 1 6 Orange 32709 1.55 1

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91 Appendix A, C ontinued Primary Care Access Scores Case County Zip Code PC_Score Privatized 6 Orange 32712 0.38 1 6 Orange 32751 0.61 1 6 Orange 32757 0.73 1 6 Orange 32776 0.60 1 6 Orange 32789 0.24 1 6 Orange 3279 2 0.00 1 6 Orange 32798 0.70 1 6 Orange 32801 1.53 1 6 Orange 32803 1.96 1 6 Orange 32804 1.78 1 6 Orange 32805 3.50 1 6 Orange 32806 3.65 1 6 Orange 32807 0.87 1 6 Orange 32808 2.59 1 6 Orange 32809 1.16 1 6 Orange 32810 1.48 1 6 Orange 32811 2.50 1 6 Orange 32812 0.44 1 6 Orange 32817 0.65 1 6 Orange 32818 1.65 1 6 Orange 32819 1.07 1 6 Orange 32820 1.77 1 6 Orange 32821 0.31 1 6 Orange 32822 1.28 1 6 Orange 32824 0.82 1 6 Orange 32825 0.86 1 6 Orange 32826 1.37 1 6 Orange 32827 0.92 1 6 Orange 32828 0.21 1 6 Orange 32829 0.89 1 6 Orange 32831 0.19 1 6 Orange 32832 0.68 1 6 Orange 32833 1.71 1 6 Orange 32835 0.07 1 6 Orange 32836 1.95 1 6 Orange 32837 0.24 1

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92 Appendix A, C ontinued Primary Care Access Scores Case County Zip Code PC_Score Privatized 6 Orange 32839 2.17 1 6 Orange 34734 0.16 1 6 Orange 34747 3.96 1 6 Orange 34760 1.40 1 6 Orange 34761 0.10 1 6 Orange 34786 3.59 1 6 Orange 34787 1.01 1 7 PalmBe 33401 0.33 1 7 PalmBe 33403 0.50 1 7 PalmBe 33404 2.58 1 7 PalmBe 33405 0.79 1 7 PalmBe 33406 0.37 1 7 PalmBe 33407 1. 44 1 7 PalmBe 33408 0.04 1 7 PalmBe 33409 1.22 1 7 PalmBe 33410 0.72 1 7 PalmBe 33411 0.24 1 7 PalmBe 33412 1.56 1 7 PalmBe 33413 0.83 1 7 PalmBe 33414 1.94 1 7 PalmBe 33415 1.59 1 7 PalmBe 33417 1.51 1 7 PalmBe 33418 1.12 1 7 PalmBe 33426 0.17 1 7 PalmBe 33428 0.16 1 7 PalmBe 33430 3.68 1 7 PalmBe 33431 0.57 1 7 PalmBe 33432 0.79 1 7 PalmBe 33433 0.36 1 7 PalmBe 33434 1.04 1 7 PalmBe 33435 1.00 1 7 PalmBe 33436 0.16 1 7 PalmBe 33437 0.34 1 7 PalmBe 33438 3.61 1 7 PalmBe 33 440 2.11 1 7 PalmBe 33444 2.26 1

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93 Appendix A, C ontinued Primary Care Access Scores Case County Zip Code PC_Score Privatized 7 PalmBe 33445 0.59 1 7 PalmBe 33446 1.05 1 7 PalmBe 33458 0.49 1 7 PalmBe 33460 1.61 1 7 PalmBe 33461 1.46 1 7 PalmBe 33462 0.69 1 7 PalmBe 33463 1.07 1 7 PalmBe 33467 0.08 1 7 PalmBe 33469 0.28 1 7 PalmB e 33470 0.17 1 7 PalmBe 33476 4.84 1 7 PalmBe 33477 0.28 1 7 PalmBe 33478 0.49 1 7 PalmBe 33480 0.60 1 7 PalmBe 33483 0.28 1 7 PalmBe 33484 0.17 1 7 PalmBe 33486 2.12 1 7 PalmBe 33487 1.12 1 7 PalmBe 33493 3.98 1 7 PalmBe 33496 2.38 1 7 Palm Be 33498 0.71 1 8 Pinellas 33701 1.53 1 8 Pinellas 33702 0.25 1 8 Pinellas 33703 0.02 1 8 Pinellas 33704 0.46 1 8 Pinellas 33705 1.86 1 8 Pinellas 33706 0.17 1 8 Pinellas 33707 0.07 1 8 Pinellas 33708 0.20 1 8 Pinellas 33709 0.81 1 8 Pinell as 33710 0.64 1 8 Pinellas 33711 2.54 1 8 Pinellas 33712 2.47 1 8 Pinellas 33713 0.91 1 8 Pinellas 33714 1.39 1 8 Pinellas 33715 0.14 1

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94 Appendix A, C ontinued Primary Care Access Scores Case County Zip Code PC_Score Privatized 8 Pinellas 33716 0.02 1 8 Pinellas 33755 1.44 1 8 Pinellas 33756 1.50 1 8 Pinellas 33759 1.31 1 8 Pinel las 33760 1.23 1 8 Pinellas 33761 0.48 1 8 Pinellas 33762 0.79 1 8 Pinellas 33763 0.26 1 8 Pinellas 33764 0.37 1 8 Pinellas 33765 0.59 1 8 Pinellas 33767 0.59 1 8 Pinellas 33770 0.12 1 8 Pinellas 33771 0.31 1 8 Pinellas 33772 0.46 1 8 Pinel las 33773 0.45 1 8 Pinellas 33774 0.65 1 8 Pinellas 33776 0.10 1 8 Pinellas 33777 0.34 1 8 Pinellas 33778 0.11 1 8 Pinellas 33781 1.07 1 8 Pinellas 33782 0.39 1 8 Pinellas 33785 0.04 1 8 Pinellas 33786 1.40 1 8 Pinellas 34677 0.54 1 8 Pin ellas 34681 0.38 1 8 Pinellas 34683 0.15 1 8 Pinellas 34684 0.34 1 8 Pinellas 34685 0.43 1 8 Pinellas 34689 0.34 1 8 Pinellas 34695 0.10 1 8 Pinellas 34698 0.27 1 9 Polk 33547 0.66 0 9 Polk 33801 1.75 0 9 Polk 33803 0.55 0 9 Polk 33805 0.60 0 9 Polk 33809 1.01 0

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95 Appendix A, C ontinued Primary Care Access Scores Case County Zip Code PC_Score Privatized 9 Polk 33810 1.01 0 9 Polk 33811 0.98 0 9 Polk 33813 0.88 0 9 Polk 33815 2.30 0 9 Polk 33823 1.27 0 9 Polk 33825 1.24 0 9 Polk 33827 2.59 0 9 Polk 33830 1.26 0 9 Polk 33835 0.26 0 9 Polk 33837 0.70 0 9 Polk 33838 1.80 0 9 Polk 33839 1.39 0 9 Polk 33841 2.07 0 9 Polk 33843 1.86 0 9 Polk 33844 1.77 0 9 Polk 33847 2.61 0 9 Polk 33849 1.97 0 9 Polk 33850 1.63 0 9 Polk 33851 1.32 0 9 Polk 33853 1.43 0 9 Polk 33860 1.57 0 9 Polk 33868 1.28 0 9 Po lk 33877 7.02 0 9 Polk 33880 0.46 0 9 Polk 33881 1.34 0 9 Polk 33884 0.28 0 9 Polk 34759 0.67 0 *Note: Privatized Coding (0 = Non privatized, 1 = Privatized)

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96 Appendix B Variable Specific Score by Zip Code Case County Zip Code Female Black Und er 15 Over 65 Unempl Poverty 1 Brevard 32754 9.97 7.01 10.46 11.37 8.31 9.81 1 Brevard 32759 9.72 7.18 8.12 15.94 5.82 8.65 1 Brevard 32775 9.89 0.67 10.82 7.56 0.00 0.00 1 Brevard 32780 10.36 6.80 9.02 15.39 7.76 8.02 1 Brevard 32796 10.27 7.26 10.52 12.82 9.97 9.28 1 Brevard 32901 10.26 13.63 7.49 18.94 13.30 14.45 1 Brevard 32903 10.02 0.46 8.23 15.59 4.71 4.32 1 Brevard 32904 10.51 0.84 7.36 19.64 8.31 4.43 1 Brevard 32905 10.32 8.30 8.88 15.05 8.03 12.87 1 Brevard 32907 10.16 7.45 11.97 9.53 8.03 5.80 1 Brevard 32908 10.13 5.96 13.81 6.13 8.86 4.96 1 Brevard 32909 10.05 7.18 12.48 6.58 10.80 5.38 1 Brevard 32920 9.50 0.94 4.75 16.23 8.59 9.49 1 Brevard 32922 10.38 24.26 11.91 9.66 14.96 27.53 1 Brevard 32925 9.18 13.18 18.15 0.29 9.42 4.0 1 1 Brevard 32926 10.08 8.95 10.52 9.74 7.76 9.18 1 Brevard 32927 9.22 5.50 11.44 6.33 7.76 5.70 1 Brevard 32931 9.86 0.39 5.04 22.53 6.65 3.69 1 Brevard 32934 10.19 2.12 10.50 11.42 5.54 4.75 1 Brevard 32935 10.20 2.93 9.00 11.60 6.37 7.38 1 Brevard 32937 10.22 1.26 8.83 15.09 6.09 3.06 1 Brevard 32940 10.40 1.64 8.72 19.06 5.26 2.95 1 Brevard 32948 8.47 4.12 13.55 4.67 13.30 19.51 1 Brevard 32949 10.16 0.64 6.06 13.62 0.00 17.72 1 Brevard 32950 9.77 3.10 9.44 9.79 3.05 5.80 1 Brevard 32951 9.99 0.14 6.64 19.95 3.88 1.69 1 Brevard 32952 10.04 0.57 9.14 13.64 6.93 4.96 1 Brevard 32953 10.23 5.61 9.56 12.55 9.42 8.65 1 Brevard 32955 10.35 8.31 9.70 12.06 5.82 4.22 1 Brevard 32976 10.58 0.20 2.64 39.32 4.43 5.06 2 Broward 33004 10.12 20.01 8.53 12.27 11.36 16.67 2 Broward 33009 10.60 10.97 6.18 23.01 9.70 15.40 2 Broward 33019 10.37 0.89 4.22 23.18 6.09 4.43 2 Broward 33020 9.94 15.41 9.60 9.19 12.74 18.99 2 Broward 33021 10.50 4.67 8.30 15.58 8.03 8.02

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97 Appendix B, C ontinued Variable Spec ific Score by Zip Code Case County Zip Code Female Black Under 15 Over 65 Unempl Poverty 2 Broward 33023 10.37 30.28 13.35 6.12 13.85 11.39 2 Broward 33024 10.31 6.59 11.78 7.48 8.31 8.12 2 Broward 33025 10.61 26.98 11.98 6.46 12.19 7.49 2 Broward 330 26 10.71 4.68 10.86 9.56 7.48 2.85 2 Broward 33027 11.19 7.14 7.97 25.82 5.54 4.22 2 Broward 33028 10.25 8.90 14.25 2.83 7.20 3.27 2 Broward 33029 10.16 8.94 14.94 3.21 6.37 2.95 2 Broward 33060 9.95 24.70 11.11 9.31 12.19 18.35 2 Broward 33062 10.37 0.47 3.30 25.71 6.37 6.96 2 Broward 33063 10.60 5.84 8.54 15.55 6.09 5.91 2 Broward 33064 9.93 12.77 10.12 10.55 9.42 11.92 2 Broward 33065 10.30 8.94 12.67 6.21 9.97 10.76 2 Broward 33066 11.50 2.08 4.37 36.89 3.32 3.38 2 Broward 33067 10.10 3.14 14. 01 3.00 7.20 3.59 2 Broward 33068 10.14 20.56 12.80 5.48 11.91 11.60 2 Broward 33069 10.10 20.65 6.28 19.32 8.31 12.45 2 Broward 33071 10.15 3.74 12.30 3.40 10.53 3.59 2 Broward 33073 9.95 5.01 11.34 5.83 7.48 5.91 2 Broward 33076 10.09 3.71 15.33 2.1 2 6.65 2.32 2 Broward 33301 7.85 8.67 4.41 8.42 6.65 6.65 2 Broward 33304 8.91 13.14 6.37 10.23 14.13 16.77 2 Broward 33305 8.65 3.62 6.06 10.21 7.20 8.76 2 Broward 33306 9.45 0.72 5.70 14.15 4.43 1.90 2 Broward 33308 10.26 0.58 4.48 22.07 5.26 4.43 2 Broward 33309 9.69 23.02 10.25 7.47 13.57 11.18 2 Broward 33311 10.38 54.53 13.86 7.03 17.45 29.01 2 Broward 33312 9.67 23.09 10.34 7.35 10.25 11.18 2 Broward 33313 10.71 45.57 13.02 8.07 15.24 19.51 2 Broward 33314 10.01 3.62 10.31 6.57 11.91 11.08 2 Broward 33315 9.52 4.73 7.33 8.58 7.48 9.92 2 Broward 33316 9.38 2.72 4.61 16.01 6.65 6.01 2 Broward 33317 10.17 13.47 10.90 9.03 9.42 5.91 2 Broward 33319 11.00 22.63 7.86 20.51 9.14 10.02 2 Broward 33321 10.98 4.95 5.94 26.56 6.37 5.06 2 Broward 33322 10.87 7.03 7.49 22.47 6.09 6.01

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98 Appendix B, C ontinued Variable Spec ific Score by Zip Code Case County Zip Code Female Black Under 15 Over 65 Unempl Poverty 2 Broward 33323 10.01 7.32 12.98 3.85 7.48 3.48 2 Broward 33324 10.55 4.50 8.85 10.76 6.37 4.11 2 Broward 33325 10.13 2.55 12.50 4.83 7.76 5.06 2 Broward 33326 10.32 2.16 13.09 6.15 6.93 5.91 2 Broward 33327 10.08 2.63 16.45 2.61 6.65 1.90 2 Broward 33328 10.23 1.41 11.28 6.52 6.09 4.01 2 Broward 33330 10.15 2.38 13.51 4.78 2.49 2.22 2 Broward 33331 10.11 3.09 14.73 3.41 4.43 2.00 2 Broward 33332 11.43 6.48 10.80 3.64 9.42 0.95 2 Broward 33334 9.42 8.27 9.78 7 .77 11.63 18.88 2 Broward 33351 10.46 12.27 11.84 6.58 11.36 8.12 2 Broward 33388 0.00 0.00 0.00 0.00 0.00 0.00 2 Broward 33394 0.00 0.00 0.00 0.00 0.00 0.00 2 Broward 33441 10.19 18.24 8.84 12.38 9.70 13.29 2 Broward 33442 11.18 2.25 4.87 29.59 5.26 5.59 3 Dade 33010 9.98 1.30 9.02 13.54 14.40 23.42 3 Dade 33012 10.52 0.34 8.88 13.89 13.30 15.19 3 Dade 33013 10.13 0.21 8.20 13.75 11.63 13.82 3 Dade 33014 10.32 1.19 10.66 8.48 13.85 14.98 3 Dade 33015 10.39 11.12 12.36 4.64 13.30 9.49 3 Dade 3301 6 10.46 0.95 12.07 6.83 16.34 13.61 3 Dade 33018 10.23 0.55 12.68 5.34 12.47 9.18 3 Dade 33030 9.33 13.42 14.56 4.83 16.07 27.95 3 Dade 33031 9.80 0.85 10.26 6.85 8.31 5.06 3 Dade 33032 10.01 22.43 16.00 4.02 19.67 23.31 3 Dade 33033 10.00 11.89 15.53 5.05 20.50 24.05 3 Dade 33034 9.46 24.96 13.85 4.74 27.42 36.39 3 Dade 33035 10.67 6.53 10.80 9.45 11.36 5.49 3 Dade 33054 10.67 50.35 13.45 7.42 26.04 28.06 3 Dade 33055 10.24 25.47 12.38 6.36 16.90 13.82 3 Dade 33056 10.60 58.08 14.32 4.32 20.22 16 .56 3 Dade 33109 9.87 2.15 7.32 10.50 0.00 0.00 3 Dade 33122 0.00 0.00 0.00 0.00 0.00 0.00 3 Dade 33125 10.09 1.29 9.33 13.54 14.96 22.36 3 Dade 33126 10.54 0.43 9.29 11.90 13.30 17.19 3 Dade 33127 10.05 41.62 13.21 7.71 25.48 36.18

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99 Appendix B, C ontinued Variable Spec ific Score by Zip Code Case County Zip Code Female Black Under 15 Over 65 Unempl Poverty 3 Dade 33128 8.84 3.67 9.07 13.32 23.55 35.34 3 Dade 33129 10.53 0.64 5.72 12.82 9.42 6.96 3 Dade 33130 9.78 1.23 9.16 13.31 15.79 34.81 3 Dade 33131 9.74 0.75 3.89 7.63 6.65 7.91 3 Dade 33132 6.52 7.04 2.76 8.12 9.70 21.62 3 Dade 33133 10.19 10.54 7.78 10.95 9.42 12. 55 3 Dade 33134 10.73 0.34 6.99 14.56 9.42 8.97 3 Dade 33135 10.25 0.28 8.30 15.30 17.45 24.79 3 Dade 33136 10.13 39.59 12.59 6.30 21.05 43.46 3 Dade 33137 9.52 23.45 9.40 8.14 24.10 29.01 3 Dade 33138 9.67 26.07 10.52 7.62 14.68 24.05 3 Dade 33139 9 .05 1.75 3.28 14.08 11.63 17.09 3 Dade 33140 10.21 0.80 7.13 15.75 6.93 8.65 3 Dade 33141 10.12 2.80 7.95 10.22 14.68 21.73 3 Dade 33142 9.63 33.63 11.86 8.72 19.11 34.49 3 Dade 33143 10.51 7.74 9.43 9.35 9.70 7.49 3 Dade 33144 10.57 0.10 7.20 17.19 1 1.63 11.29 3 Dade 33145 10.45 0.37 7.75 15.77 11.08 12.87 3 Dade 33146 10.42 3.16 6.90 8.77 17.17 1.90 3 Dade 33147 10.43 43.74 13.93 7.02 22.44 37.03 3 Dade 33149 10.55 0.18 11.05 10.75 4.99 6.01 3 Dade 33150 10.35 46.42 13.46 6.68 21.88 30.91 3 Dad e 33154 10.95 0.84 7.06 19.16 8.03 7.38 3 Dade 33155 10.51 0.35 8.54 13.59 9.14 6.65 3 Dade 33156 10.25 1.28 12.19 7.67 5.82 3.80 3 Dade 33157 10.37 20.60 12.77 7.04 12.19 12.76 3 Dade 33158 10.15 1.09 12.64 7.59 3.60 2.11 3 Dade 33160 10.77 3.18 5.59 20.47 7.76 12.03 3 Dade 33161 10.39 36.55 12.56 6.79 22.44 24.05 3 Dade 33162 10.40 31.49 13.03 6.64 18.28 19.20 3 Dade 33165 10.58 0.84 7.94 13.62 12.19 9.39 3 Dade 33166 9.90 3.00 9.56 8.27 9.14 9.39 3 Dade 33167 10.38 47.28 13.34 5.83 23.55 22.47 3 Dade 33168 10.21 42.96 13.15 5.59 23.55 22.47 3 Dade 33169 10.60 52.17 13.33 6.15 15.24 14.56 3 Dade 33170 10.35 39.21 14.72 6.42 15.79 29.22

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100 Appendix B, C ontinued Variable Spec ific Score by Zip Code Case County Zip Code Female Black Under 15 Over 65 Unempl Poverty 3 Dade 33172 10.54 0.75 9.81 7.93 13.02 13.40 3 Dade 33173 10.64 1.16 9.47 9.80 9.70 6.01 3 Dade 33174 10. 64 0.23 8.80 12.05 12.19 12.76 3 Dade 33175 10.39 0.37 10.19 9.35 9.42 7.28 3 Dade 33176 10.48 11.68 10.71 7.36 9.70 7.28 3 Dade 33177 9.86 11.73 13.54 4.79 13.57 8.54 3 Dade 33178 10.15 1.66 11.71 3.69 7.48 9.07 3 Dade 33179 10.90 20.17 10.04 11.63 1 2.47 9.18 3 Dade 33180 10.68 1.62 6.25 20.18 6.37 6.01 3 Dade 33181 10.20 21.02 9.30 8.29 14.96 17.09 3 Dade 33182 8.32 6.84 9.85 4.41 6.93 7.59 3 Dade 33183 10.53 1.50 11.12 7.09 10.80 9.28 3 Dade 33184 10.44 0.25 10.05 9.33 10.53 8.65 3 Dade 33185 10.40 0.90 12.46 5.42 8.31 6.54 3 Dade 33186 10.52 4.32 11.46 5.27 10.25 6.43 3 Dade 33187 9.96 4.36 13.45 4.87 11.08 4.64 3 Dade 33189 10.39 14.72 13.67 6.43 12.47 14.35 3 Dade 33190 10.28 16.91 14.93 3.09 12.74 12.97 3 Dade 33193 10.05 3.86 12.16 4. 79 14.13 13.50 3 Dade 33194 0.00 0.00 0.00 0.00 0.00 0.00 3 Dade 33196 10.40 4.55 13.07 3.93 11.63 6.33 4 Duval 32009 9.77 0.71 12.48 5.51 14.96 5.80 4 Duval 32073 10.18 6.06 11.57 7.23 7.76 3.90 4 Duval 32202 6.65 43.86 3.39 14.57 8.59 23.10 4 Duval 32204 10.71 33.82 9.64 13.86 10.25 28.16 4 Duval 32205 10.40 13.74 10.44 9.10 8.59 13.29 4 Duval 32206 10.32 55.29 12.89 7.59 16.62 36.81 4 Duval 32207 10.47 14.05 10.26 10.04 8.31 10.34 4 Duval 32208 10.74 50.59 11.81 8.91 13.02 16.03 4 Duval 32209 11.04 65.72 13.05 11.52 16.34 27.32 4 Duval 32210 10.35 16.77 12.10 8.06 8.03 10.34 4 Duval 32211 10.23 19.60 11.50 7.57 13.57 11.50 4 Duval 32212 7.26 18.60 14.42 0.11 4.99 19.30 4 Duval 32215 10.03 23.71 25.26 0.68 0.00 9.70 4 Duval 32216 10.34 13.1 4 11.06 9.11 8.03 8.86 4 Duval 32217 10.54 9.22 9.92 10.97 9.70 7.81

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101 Appendix B, C ontinued Variable Spec ific Score by Zip Code Case County Zip Code Female Black Under 15 Over 65 Unempl Poverty 4 Duval 32218 10.29 26.59 12.14 6.24 10.53 8.33 4 Duval 32219 10.03 28.55 11.08 7.50 11.08 10.86 4 Duval 32220 9.82 2.77 11.82 5.91 6.09 8.02 4 Duval 32221 10.18 6.75 11.69 6.53 8.31 6.12 4 Duval 32222 9.99 7.34 12.41 4.78 9.97 6.43 4 Duval 32223 10.20 2.84 10.99 5.83 6.09 1.79 4 Duval 32224 10.14 5.38 10.53 4.88 8.59 3.69 4 Duval 32225 10.16 10.50 12.42 4.89 6.37 3.27 4 Duval 32226 9.96 1.07 9.99 7.29 4.71 6.75 4 Duval 32227 5. 95 17.07 10.40 0.08 1.94 7.59 4 Duval 32233 9.95 11.30 11.99 6.71 6.93 7.28 4 Duval 32234 10.20 5.81 12.48 5.83 9.70 7.49 4 Duval 32244 10.20 14.98 13.10 5.00 7.20 9.39 4 Duval 32246 10.11 10.71 12.46 4.38 7.20 7.81 4 Duval 32250 9.89 2.97 7.74 8.93 1 0.53 4.01 4 Duval 32254 10.37 35.84 13.80 6.76 14.40 20.57 4 Duval 32256 10.05 8.08 8.46 5.87 7.76 3.48 4 Duval 32257 10.36 5.80 11.04 6.42 5.54 4.01 4 Duval 32258 10.12 5.25 12.61 4.52 6.09 1.05 4 Duval 32259 9.92 1.37 13.93 5.18 5.26 1.48 4 Duval 3 2266 9.74 0.49 8.00 8.41 8.03 2.00 4 Duval 32277 10.38 20.97 11.82 6.66 6.93 6.22 5 Hillsbo 33510 10.27 6.03 11.56 6.64 6.65 4.22 5 Hillsbo 33511 10.22 6.36 11.46 5.90 7.76 4.22 5 Hillsbo 33527 9.55 0.57 12.71 6.02 16.07 13.40 5 Hillsbo 33534 9.59 0.9 3 13.64 5.72 13.85 17.41 5 Hillsbo 33540 10.55 1.77 8.75 20.83 6.93 7.70 5 Hillsbo 33547 9.96 0.87 12.72 5.48 7.20 7.38 5 Hillsbo 33549 10.01 2.74 10.96 6.11 8.03 4.11 5 Hillsbo 33556 9.83 2.07 11.48 5.78 2.22 2.00 5 Hillsbo 33565 10.03 1.03 10.93 11. 78 6.09 6.96 5 Hillsbo 33566 10.36 13.81 12.55 8.55 9.42 13.61 5 Hillsbo 33567 9.88 4.61 12.83 7.17 8.59 10.13 5 Hillsbo 33569 9.94 5.21 12.05 6.90 5.82 6.96 5 Hillsbo 33570 9.80 0.67 10.36 15.23 8.31 10.02 5 Hillsbo 33572 9.97 0.57 7.56 12.87 8.03 2. 53

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102 Appendix B, C ontinued Variable Spec ific Score by Zip Code Case County Zip Code Female Black Under 15 Over 65 Unempl Poverty 5 Hillsbo 33573 11.40 0.09 0.18 57.35 1.66 2.32 5 Hillsbo 33584 9.98 4.75 11.96 6.33 9.14 5.70 5 Hillsbo 33592 9.88 7.82 11.62 8.49 9.14 10.13 5 Hillsbo 33594 10.11 4.01 12.44 6.23 5.54 3.16 5 Hillsbo 33598 9.33 2.89 16.15 4.36 14.40 24.37 5 Hills bo 33602 9.99 31.04 10.80 9.15 22.16 29.43 5 Hillsbo 33603 10.25 18.84 11.68 9.02 16.62 18.67 5 Hillsbo 33604 10.17 16.63 12.21 7.55 11.08 20.68 5 Hillsbo 33605 10.08 41.42 12.72 9.98 16.90 29.85 5 Hillsbo 33606 9.93 7.65 7.07 7.47 30.75 4.01 5 Hillsb o 33607 10.46 25.81 10.68 13.18 11.36 19.94 5 Hillsbo 33609 10.13 4.72 8.54 11.65 6.93 6.43 5 Hillsbo 33610 10.54 38.01 12.78 8.75 14.13 19.83 5 Hillsbo 33611 10.18 4.15 8.40 10.61 6.37 6.22 5 Hillsbo 33612 10.28 19.43 11.77 8.44 14.13 18.25 5 Hillsbo 33613 10.05 12.80 8.90 8.17 14.13 15.30 5 Hillsbo 33614 10.02 5.99 9.91 7.43 9.14 14.14 5 Hillsbo 33615 10.15 5.59 10.08 7.39 8.59 6.33 5 Hillsbo 33616 9.88 11.93 11.13 5.73 13.85 14.35 5 Hillsbo 33617 10.36 18.36 10.93 6.14 10.25 10.76 5 Hillsbo 336 18 10.27 3.02 10.07 7.51 6.09 4.64 5 Hillsbo 33619 9.53 26.90 12.15 6.09 10.53 16.77 5 Hillsbo 33620 0.00 21.19 0.00 0.00 263.43 0.00 5 Hillsbo 33621 9.32 16.44 18.68 0.15 7.76 4.32 5 Hillsbo 33624 10.39 4.31 10.75 5.76 5.82 3.59 5 Hillsbo 33625 10.13 4.90 12.47 4.90 10.80 5.06 5 Hillsbo 33626 10.12 3.40 12.81 3.78 4.16 1.79 5 Hillsbo 33629 10.38 0.79 9.13 11.67 5.54 2.43 5 Hillsbo 33634 10.28 5.41 10.84 6.26 7.76 6.86 5 Hillsbo 33635 10.10 3.91 11.15 7.04 5.82 6.65 5 Hillsbo 33637 10.28 11.35 11. 30 4.83 4.71 11.39 5 Hillsbo 33647 10.05 3.93 12.47 3.41 5.82 4.11 5 Hillsbo 33834 7.85 9.88 10.29 9.80 8.03 18.46 5 Hillsbo 34221 9.82 11.51 10.21 15.08 5.54 9.60 6 Orange 32703 10.07 12.25 12.39 6.73 8.86 10.02 6 Orange 32709 9.55 0.30 10.91 7.78 8. 31 12.76

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103 Appendix B, C ontinued Variable Spec ific Score by Zip Code Case County Zip Code Female Black Under 15 Over 65 Unempl Poverty 6 Orange 32712 10.07 6.02 11.83 7.56 6.37 5.70 6 Orange 32751 10.46 7.01 10.66 11.81 6.37 3.90 6 Orange 32757 10.43 9.66 9.00 16.79 4.71 9.28 6 Orange 32776 10.02 2.16 11.25 8.09 9.42 2.95 6 Orange 32789 10.47 8.53 8.63 11.70 14.96 5.38 6 Or ange 32792 10.10 4.08 8.49 9.67 8.03 6.01 6 Orange 32798 11.34 0.25 1.56 49.07 2.49 1.27 6 Orange 32801 10.62 8.84 4.46 21.01 12.47 16.77 6 Orange 32803 9.94 3.39 6.08 12.97 7.20 5.06 6 Orange 32804 10.12 1.66 7.95 10.42 5.26 3.59 6 Orange 32805 10.13 52.83 13.19 7.65 16.62 31.22 6 Orange 32806 10.01 3.57 8.67 10.79 7.48 6.65 6 Orange 32807 9.89 4.62 10.79 7.81 8.86 9.92 6 Orange 32808 10.44 35.56 14.41 5.72 15.24 19.51 6 Orange 32809 9.89 8.81 11.25 7.47 10.80 12.03 6 Orange 32810 10.05 17.88 12. 74 5.56 9.97 10.13 6 Orange 32811 10.25 36.20 11.35 4.36 12.74 17.62 6 Orange 32812 10.25 4.38 10.83 7.46 6.09 7.38 6 Orange 32817 9.85 3.36 9.71 4.44 9.42 6.12 6 Orange 32818 10.30 28.93 12.71 5.78 9.14 9.18 6 Orange 32819 10.03 8.67 10.98 5.65 8.31 5.06 6 Orange 32820 9.62 0.60 11.66 6.20 15.79 11.39 6 Orange 32821 9.90 3.16 6.00 11.93 4.43 2.85 6 Orange 32822 10.15 5.79 10.51 7.08 10.25 9.81 6 Orange 32824 10.11 7.90 13.04 4.90 12.47 6.75 6 Orange 32825 9.56 5.92 11.27 4.74 8.59 6.75 6 Orange 32826 9.74 5.22 7.45 5.36 13.30 8.33 6 Orange 32827 10.07 4.51 12.60 3.79 5.26 7.81 6 Orange 32828 10.03 4.73 13.33 3.07 8.31 3.69 6 Orange 32829 10.37 4.35 12.13 4.47 8.59 2.00 6 Orange 32831 9.41 9.41 0.00 0.00 0.00 0.00 6 Orange 32832 9.54 0.54 10. 33 5.64 4.43 0.00 6 Orange 32833 9.67 2.70 10.97 6.37 10.25 14.14 6 Orange 32835 9.91 8.01 10.56 3.48 7.76 6.96 6 Orange 32836 10.02 2.13 12.77 5.27 5.26 5.70 6 Orange 32837 10.12 5.45 12.23 4.74 6.65 5.27

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104 Appendix B, C ontinued Variable Spec ific Score by Zip Code Case County Zip Code Female Black Under 15 Over 65 Unempl Poverty 6 Orange 32839 9.17 23.82 10.69 4.28 12.47 17 .72 6 Orange 34734 10.34 6.60 13.57 4.71 2.22 2.95 6 Orange 34747 9.92 1.99 11.59 5.04 8.03 5.27 6 Orange 34760 10.43 28.61 12.38 6.90 4.16 8.44 6 Orange 34761 10.06 4.41 12.62 5.05 6.93 5.70 6 Orange 34786 9.97 1.83 13.31 5.57 2.22 1.48 6 Orange 347 87 10.21 9.26 11.31 9.30 7.76 8.97 7 PalmBe 33401 10.57 26.66 8.11 15.65 8.03 16.77 7 PalmBe 33403 10.06 22.06 10.42 12.41 11.91 9.60 7 PalmBe 33404 10.42 45.47 12.44 15.20 12.74 20.25 7 PalmBe 33405 9.64 3.24 9.49 12.06 7.48 12.34 7 PalmBe 33406 9.50 4.83 9.85 10.61 8.03 7.17 7 PalmBe 33407 10.08 40.67 12.72 12.39 11.91 21.10 7 PalmBe 33408 10.24 0.49 5.93 16.06 3.60 2.11 7 PalmBe 33409 9.97 14.82 9.21 10.87 10.80 12.45 7 PalmBe 33410 10.31 2.56 8.35 12.39 6.93 6.22 7 PalmBe 33411 10.25 8.60 11.0 8 13.44 7.20 4.64 7 PalmBe 33412 9.74 4.57 12.92 10.33 6.65 1.79 7 PalmBe 33413 10.05 6.92 10.25 11.53 6.65 7.07 7 PalmBe 33414 10.17 3.61 13.03 11.74 5.26 3.06 7 PalmBe 33415 10.46 7.89 10.88 13.81 9.42 12.55 7 PalmBe 33417 10.88 10.41 7.32 20.09 8.8 6 10.34 7 PalmBe 33418 10.32 1.10 8.61 12.83 38.78 1.79 7 PalmBe 33426 10.92 5.18 6.50 18.92 3.05 3.38 7 PalmBe 33428 10.24 2.35 11.51 13.62 7.48 4.32 7 PalmBe 33430 9.31 36.03 14.87 12.30 18.84 33.12 7 PalmBe 33431 10.22 3.83 7.11 11.77 21.88 2.85 7 PalmBe 33432 10.18 3.42 6.19 14.43 2.77 7.07 7 PalmBe 33433 10.79 0.90 6.84 16.62 6.09 2.00 7 PalmBe 33434 11.02 0.81 6.16 22.65 2.49 3.38 7 PalmBe 33435 10.41 23.85 9.82 15.76 10.53 12.03 7 PalmBe 33436 10.59 5.67 7.42 17.06 4.99 4.22 7 PalmBe 33437 10.38 2.29 5.37 20.07 3.88 3.06 7 PalmBe 33438 10.21 17.80 13.88 13.64 30.75 17.62 7 PalmBe 33440 9.60 14.81 14.17 12.42 16.34 16.98 7 PalmBe 33444 9.91 32.62 11.38 11.65 11.91 16.03

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105 Appendix B, C ontinued Variable Spec ific Score by Zip Code Case County Zip Code Female Black Under 15 Over 65 Unempl Poverty 7 PalmBe 33445 10.75 11.60 6.84 18.73 7.20 4.96 7 PalmBe 33446 11.1 6 0.35 1.49 30.10 3.05 3.48 7 PalmBe 33458 9.91 2.39 11.06 11.55 6.37 3.48 7 PalmBe 33460 9.42 13.38 10.36 11.73 12.47 17.51 7 PalmBe 33461 9.86 6.41 10.70 13.21 12.19 11.60 7 PalmBe 33462 10.11 7.12 9.19 13.99 7.48 6.96 7 PalmBe 33463 10.22 6.21 11.6 1 13.82 8.03 6.75 7 PalmBe 33467 10.32 2.09 9.66 17.31 4.43 2.95 7 PalmBe 33469 10.36 0.28 7.46 16.43 3.32 2.53 7 PalmBe 33470 9.80 3.95 14.30 10.99 5.82 3.80 7 PalmBe 33476 10.19 43.59 17.01 14.64 24.93 37.87 7 PalmBe 33477 10.47 0.14 3.49 16.13 3.60 2.74 7 PalmBe 33478 9.83 0.66 12.11 10.08 4.71 1.90 7 PalmBe 33480 11.09 1.61 4.02 23.00 2.77 3.27 7 PalmBe 33483 10.33 4.63 4.63 15.15 7.20 5.91 7 PalmBe 33484 11.23 1.54 2.06 29.19 2.77 3.06 7 PalmBe 33486 10.16 1.56 9.36 11.59 9.70 3.90 7 PalmBe 33487 10.37 1.77 5.52 16.44 4.99 2.74 7 PalmBe 33493 7.32 44.48 11.55 9.77 23.55 30.59 7 PalmBe 33496 10.49 0.86 7.92 15.48 4.43 3.06 7 PalmBe 33498 10.09 1.17 11.27 13.59 4.16 3.27 8 Pinellas 33701 9.81 14.07 6.34 9.39 15.51 14.24 8 Pinellas 33702 10 .26 1.89 7.82 7.10 5.82 5.49 8 Pinellas 33703 10.39 0.76 9.23 6.61 4.99 3.69 8 Pinellas 33704 10.33 1.22 8.75 5.47 4.43 5.49 8 Pinellas 33705 10.70 37.41 11.40 6.09 11.36 17.83 8 Pinellas 33706 10.05 0.34 4.11 11.03 6.37 4.01 8 Pinellas 33707 10.80 4. 09 5.99 12.07 6.93 8.65 8 Pinellas 33708 10.23 0.20 4.11 12.16 4.71 4.22 8 Pinellas 33709 10.53 1.92 7.86 10.10 6.65 9.92 8 Pinellas 33710 10.50 1.05 8.83 6.75 4.99 4.85 8 Pinellas 33711 10.82 39.66 11.40 5.30 14.13 18.04 8 Pinellas 33712 10.77 47.31 11.79 4.48 14.40 15.08 8 Pinellas 33713 9.99 6.31 10.08 4.77 8.59 10.65 8 Pinellas 33714 9.94 2.01 9.19 6.66 8.86 12.13 8 Pinellas 33715 10.22 0.82 3.50 12.44 6.37 1.69

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106 Appendix B, C ontinued Variable Spec ific Score by Zip Code Case County Zip Code Female Black Under 15 Over 65 Unempl Poverty 8 Pinellas 33716 9.82 3.74 4.87 3.86 8.86 6.96 8 Pinellas 33755 10.06 16.64 10.17 4.19 9.97 13.08 8 Pinellas 33756 10.47 4.35 8.44 8.80 5.82 8.44 8 Pinellas 33759 10.47 6.46 9.90 7.53 6.09 13.29 8 Pinellas 33760 8.86 10.43 8.65 3.30 6.37 14.03 8 Pinellas 33761 10.81 0.55 7.09 9.51 4.99 4.64 8 Pinellas 33762 10.22 1.44 5.33 7.33 6.3 7 2.74 8 Pinellas 33763 11.06 1.12 4.83 14.88 4.99 3.59 8 Pinellas 33764 10.43 1.63 7.60 9.67 6.37 5.59 8 Pinellas 33765 10.23 2.87 7.52 6.46 3.60 7.91 8 Pinellas 33767 10.08 0.14 2.85 13.41 7.20 3.69 8 Pinellas 33770 10.36 1.76 7.21 9.37 8.31 6.54 8 Pinellas 33771 10.69 1.79 6.41 11.27 3.88 7.07 8 Pinellas 33772 10.75 0.37 7.49 10.33 5.26 3.69 8 Pinellas 33773 10.27 1.42 9.16 6.26 6.37 4.22 8 Pinellas 33774 10.63 3.79 8.19 9.45 5.54 5.91 8 Pinellas 33776 10.42 0.31 9.13 6.93 4.71 2.11 8 Pinellas 33777 10.54 0.91 9.67 6.80 6.37 6.12 8 Pinellas 33778 10.53 8.66 8.92 8.05 6.37 4.85 8 Pinellas 33781 10.20 1.64 10.67 4.71 9.97 8.54 8 Pinellas 33782 10.62 0.82 8.26 8.94 6.09 5.49 8 Pinellas 33785 9.88 0.21 4.09 7.09 4.71 2.95 8 Pinellas 33786 9.95 0.17 5.34 9.66 3.60 3.59 8 Pinellas 34677 10.27 1.65 10.51 5.48 6.93 2.95 8 Pinellas 34681 9.81 0.22 11.41 4.12 1.66 5.27 8 Pinellas 34683 10.28 0.62 10.05 5.37 7.20 4.11 8 Pinellas 34684 10.83 0.74 7.50 12.26 3.88 4.85 8 Pinellas 34685 10.17 0.83 11 .09 5.37 7.20 4.01 8 Pinellas 34689 10.30 3.34 8.37 8.45 5.54 7.07 8 Pinellas 34695 10.40 2.83 9.10 6.73 5.26 3.69 8 Pinellas 34698 10.84 1.36 6.57 10.99 5.26 5.27 9 Polk 33547 9.96 0.87 12.72 5.48 7.20 7.38 9 Polk 33801 10.20 8.37 9.96 11.42 12.19 14 .35 9 Polk 33803 10.61 3.35 8.40 15.11 14.96 6.75 9 Polk 33805 10.57 32.60 12.52 11.79 11.63 19.51 9 Polk 33809 10.34 2.65 9.49 15.54 6.65 6.22

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107 Appendix B, C ontinued Variable Spec ific Score by Zip Code Case County Zip Code Female Black Under 15 Over 65 Unempl Poverty 9 Polk 33810 10.09 4.81 11.57 10.14 6.09 8.44 9 Polk 33811 10.04 4.65 11.25 7.66 6.65 4.85 9 Polk 33813 10 .22 2.87 11.54 8.61 6.37 2.53 9 Polk 33815 10.26 18.51 12.14 13.18 12.19 19.09 9 Polk 33823 10.11 5.58 11.46 10.03 9.14 11.60 9 Polk 33825 9.58 11.75 9.02 18.01 6.93 13.08 9 Polk 33827 9.91 6.10 10.78 9.76 23.55 11.92 9 Polk 33830 10.01 15.75 11.48 9. 45 11.36 12.24 9 Polk 33835 11.13 2.68 10.36 6.91 0.00 0.00 9 Polk 33837 9.99 2.93 8.79 14.44 4.99 5.91 9 Polk 33838 10.34 15.26 11.53 15.28 11.36 12.45 9 Polk 33839 10.37 3.84 12.11 8.94 6.09 12.97 9 Polk 33841 10.00 10.58 12.13 10.94 11.91 14.98 9 Polk 33843 8.95 6.55 9.67 14.65 9.70 14.87 9 Polk 33844 10.02 13.71 10.57 15.61 10.53 13.50 9 Polk 33847 7.09 10.66 11.35 8.79 0.00 36.81 9 Polk 33849 9.17 0.64 13.13 5.62 17.45 9.70 9 Polk 33850 10.52 10.28 11.35 11.66 6.09 13.71 9 Polk 33851 10.30 2 .44 10.16 9.82 11.36 4.96 9 Polk 33853 10.23 10.84 9.50 17.40 9.14 11.39 9 Polk 33860 9.85 7.95 12.11 8.70 11.63 7.49 9 Polk 33868 8.31 9.64 9.87 7.74 5.82 9.49 9 Polk 33877 10.01 51.34 15.63 8.41 43.77 52.74 9 Polk 33880 10.14 6.38 11.59 10.02 9.42 1 1.39 9 Polk 33881 10.49 18.05 9.04 19.87 8.86 11.60 9 Polk 33884 10.57 1.49 8.51 18.76 5.26 2.32 9 Polk 34759 10.25 9.42 12.22 7.25 4.71 5.59

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108 Appendix B C ontinued Variable Specific Scores by Zip Code Case County Zip Code Family General Ob Gyn Int ernal Pediatric 1 Brevard 32754 0.00 0.00 0.00 0.00 0.00 1 Brevard 32759 0.00 0.00 0.00 0.00 0.00 1 Brevard 32775 0.00 0.00 0.00 0.00 0.00 1 Brevard 32780 0.49 15.20 2.18 6.78 3.09 1 Brevard 32796 2.06 5.74 19.79 16.66 7.01 1 Brevard 32901 1.19 0.00 20.98 19.79 4.25 1 Brevard 32903 3.00 9.20 26.41 38.98 18.70 1 Brevard 32904 1.50 0.00 15.11 2.94 8.03 1 Brevard 32905 0.88 21.66 9.33 8.45 11.01 1 Brevard 32907 0.56 3.45 0.00 2.31 0.00 1 Brevard 32908 0.71 0.00 0.00 0.00 0.00 1 Brevard 32909 0.21 6 .46 3.71 4.33 2.63 1 Brevard 32920 0.42 13.02 0.00 0.00 0.00 1 Brevard 32922 0.72 0.00 0.00 0.00 0.00 1 Brevard 32925 3.59 0.00 0.00 0.00 0.00 1 Brevard 32926 0.40 0.00 0.00 0.00 0.00 1 Brevard 32927 0.57 0.00 0.00 0.00 1.77 1 Brevard 32931 1.82 0.00 41.25 19.58 3.25 1 Brevard 32934 1.64 8.40 0.00 11.25 10.25 1 Brevard 32935 0.38 0.00 8.30 5.80 2.35 1 Brevard 32937 2.14 4.38 7.54 8.79 3.56 1 Brevard 32940 4.42 6.17 21.24 34.38 12.54 1 Brevard 32948 6.28 0.00 13.84 5.38 0.00 1 Brevard 32949 15.64 0.00 0.00 35.71 0.00 1 Brevard 32950 0.84 0.00 0.00 0.00 21.06 1 Brevard 32951 0.73 0.00 6.41 14.94 13.61 1 Brevard 32952 1.30 0.00 13.06 7.61 9.25 1 Brevard 32953 0.88 0.00 9.34 2.42 6.62 1 Brevard 32955 0.88 9.04 10.38 16.13 14.70 1 Brevard 32976 0.39 0.00 0.00 5.35 0.00 2 Broward 33004 0.25 93.13 4.46 3.46 6.31 2 Broward 33009 0.67 10.23 7.83 10.65 0.00 2 Broward 33019 0.66 6.75 23.26 16.56 13.72 2 Broward 33020 0.38 2.91 1.67 8.43 4.73 2 Broward 33021 1.83 12.74 24.87 24.44 38.34

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109 Appendix B, C ontinued Variable Specific Scores by Zip Code Case County Zip Code Family General Ob Gyn Internal Pediatric 2 Broward 33023 0.31 1.93 0.00 0.43 0.00 2 Broward 33024 0.78 25.97 3.44 10.25 6.50 2 Broward 33025 0.33 5.07 1.46 2.26 6.19 2 Broward 330 26 0.39 3.98 11.42 13.31 22.64 2 Broward 33027 3.01 4.62 10.61 23.70 28.18 2 Broward 33028 1.38 5.32 15.26 20.16 12.97 2 Broward 33029 1.74 19.98 5.74 16.35 18.96 2 Broward 33060 0.34 7.05 6.07 3.14 1.43 2 Broward 33062 2.70 13.83 10.59 15.43 15.00 2 Broward 33063 1.20 4.61 6.63 7.72 2.81 2 Broward 33064 0.58 20.02 3.83 6.95 4.52 2 Broward 33065 1.11 6.84 11.78 5.59 13.90 2 Broward 33066 0.68 35.05 0.00 3.13 0.00 2 Broward 33067 1.33 35.64 8.77 13.63 33.13 2 Broward 33068 0.08 2.47 0.00 1.65 0.00 2 Broward 33069 0.31 57.56 0.00 2.14 3.90 2 Broward 33071 0.52 3.19 20.17 9.26 10.39 2 Broward 33073 0.19 40.99 10.09 7.84 4.76 2 Broward 33076 1.17 11.94 10.28 27.96 31.56 2 Broward 33301 1.28 9.81 16.90 26.26 3.99 2 Broward 33304 1.03 12.59 0.00 1 4.05 2.56 2 Broward 33305 0.64 9.79 5.62 13.11 11.95 2 Broward 33306 7.07 31.00 17.80 20.74 12.60 2 Broward 33308 2.42 24.72 40.23 43.20 20.11 2 Broward 33309 0.46 3.53 0.00 4.72 2.87 2 Broward 33311 0.12 3.59 1.03 3.61 1.46 2 Broward 33312 0.51 2.61 6.00 5.24 4.25 2 Broward 33313 0.20 6.21 5.94 6.00 4.21 2 Broward 33314 0.00 0.00 5.66 0.00 2.01 2 Broward 33315 0.59 0.00 5.24 2.03 0.00 2 Broward 33316 2.87 33.09 63.34 41.83 31.40 2 Broward 33317 1.12 10.27 27.53 12.99 12.53 2 Broward 33319 0.27 0.00 3.14 6.10 3.34 2 Broward 33321 0.29 2.98 12.00 8.65 2.43 2 Broward 33322 0.47 2.89 4.98 3.87 7.06

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110 Appendix B, C ontinued Variable Specific Scores by Zip Code Case County Zip Code Family General Ob Gyn Internal Pediatric 2 Broward 33323 1.08 0.00 19.00 11.81 24.21 2 Broward 33324 2.21 5.43 21.82 17.56 28.69 2 Broward 33325 1.11 4.27 2.45 7.62 6.95 2 Broward 33326 1. 92 7.86 18.04 11.39 23.96 2 Broward 33327 1.16 8.93 41.04 29.89 25.43 2 Broward 33328 1.53 29.36 6.74 5.24 11.94 2 Broward 33330 2.06 10.53 6.04 16.44 12.84 2 Broward 33331 1.64 5.61 16.11 21.27 20.53 2 Broward 33332 2.03 31.14 125.19 34.74 25.33 2 B roward 33334 0.99 3.81 0.00 12.76 3.10 2 Broward 33351 0.83 7.25 2.08 8.08 2.95 2 Broward 33388 0.00 0.00 0.00 0.00 0.00 2 Broward 33394 0.00 0.00 0.00 0.00 0.00 2 Broward 33441 0.57 13.09 0.00 2.92 5.32 2 Broward 33442 0.80 4.10 4.71 6.41 1.67 3 Dad e 33010 0.00 12.97 0.00 2.89 1.05 3 Dade 33012 0.51 43.96 5.41 5.95 8.94 3 Dade 33013 0.34 21.16 8.10 11.01 7.17 3 Dade 33014 1.19 12.17 13.98 10.86 18.56 3 Dade 33015 0.54 7.16 1.37 2.66 6.80 3 Dade 33016 0.53 24.43 17.15 10.29 18.76 3 Dade 33018 0. 41 0.00 0.00 3.48 3.80 3 Dade 33030 0.42 4.31 12.37 3.85 10.51 3 Dade 33031 0.69 42.68 73.52 0.00 8.68 3 Dade 33032 0.18 5.68 0.00 0.00 0.00 3 Dade 33033 0.12 0.00 2.15 0.00 0.00 3 Dade 33034 0.00 0.00 0.00 0.00 0.00 3 Dade 33035 0.00 0.00 0.00 0.00 0.00 3 Dade 33054 0.00 4.18 0.00 0.93 0.00 3 Dade 33055 0.25 2.61 0.00 0.00 3.18 3 Dade 33056 0.12 0.00 0.00 0.79 0.00 3 Dade 33109 0.00 0.00 0.00 0.00 0.00 3 Dade 33122 0.00 0.00 0.00 0.00 0.00 3 Dade 33125 0.87 14.53 1.39 9.72 3.94 3 Dade 33126 0. 35 18.80 3.08 4.19 5.46 3 Dade 33127 0.41 12.70 0.00 0.94 3.44

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111 Appendix B, C ontinued Variable Specific Scores by Zip Code Case County Zip Code Family General Ob Gyn Internal Pediatric 3 Dade 33128 0.00 0.00 9.65 3.75 6.83 3 Dade 33129 1.73 31.80 12.17 33.10 17.24 3 Dade 33130 0.00 28.64 6.58 2.56 4.66 3 Dade 33131 2.43 24.91 28.61 44.46 30.39 3 Dade 33132 0.72 22.11 0. 00 14.80 17.98 3 Dade 33133 1.02 15.73 63.21 32.45 20.78 3 Dade 33134 2.93 79.49 19.85 30.07 30.92 3 Dade 33135 0.54 36.24 3.78 8.82 9.38 3 Dade 33136 2.63 0.00 66.95 56.02 40.12 3 Dade 33137 1.52 20.01 7.66 8.93 21.70 3 Dade 33138 1.17 7.97 13.73 8. 00 4.86 3 Dade 33139 1.59 27.55 12.30 15.02 13.69 3 Dade 33140 1.89 29.07 40.06 77.81 56.74 3 Dade 33141 1.05 9.66 1.85 8.62 7.86 3 Dade 33142 0.22 2.20 2.53 0.98 0.00 3 Dade 33143 2.32 19.75 58.98 31.72 27.31 3 Dade 33144 1.81 41.80 8.00 13.47 9.44 3 Dade 33145 1.32 40.68 4.67 12.71 9.93 3 Dade 33146 1.45 35.63 40.92 49.67 68.82 3 Dade 33147 0.15 0.00 0.00 0.00 0.00 3 Dade 33149 1.09 11.19 12.85 34.95 36.41 3 Dade 33150 0.00 8.93 10.25 1.00 5.45 3 Dade 33154 1.15 35.23 25.29 25.54 17.91 3 Dade 33155 1.22 39.98 6.12 9.51 36.85 3 Dade 33156 2.19 3.74 27.93 34.22 39.56 3 Dade 33157 0.69 11.52 7.72 7.28 13.27 3 Dade 33158 0.59 0.00 10.46 36.58 44.46 3 Dade 33160 1.59 20.87 9.99 13.19 8.49 3 Dade 33161 0.36 4.42 2.54 3.45 3.59 3 Dade 33162 0.6 8 13.01 4.48 4.06 5.29 3 Dade 33165 1.01 55.66 3.55 7.82 18.44 3 Dade 33166 0.34 26.07 2.99 6.98 14.84 3 Dade 33167 0.42 0.00 0.00 0.00 0.00 3 Dade 33168 0.15 0.00 0.00 0.00 0.00 3 Dade 33169 0.42 0.00 0.00 0.00 0.00 3 Dade 33170 0.00 0.00 0.00 0.00 0.00

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112 Appendix B, C ontinued Variable Specific Scores by Zip Code Case County Zip Code Family General Ob Gyn Internal Pediatric 3 Dade 33172 0.10 15.27 1.75 0.00 4.97 3 Dade 33173 1.14 17.49 26.11 22.63 49.78 3 Dade 33174 0.51 54.40 2.23 2.60 6.32 3 Dade 33175 0.95 42.52 6.43 6.99 10.92 3 Dade 33176 1.69 18.07 42.81 21.17 27.56 3 Dade 33177 0.08 15.52 4.46 0.58 3.16 3 D ade 33178 1.76 0.00 17.70 24.06 43.86 3 Dade 33179 1.02 6.30 3.62 9.83 12.80 3 Dade 33180 2.03 50.91 48.73 31.55 29.91 3 Dade 33181 1.52 26.60 3.82 22.25 10.82 3 Dade 33182 0.45 20.90 40.01 9.33 8.50 3 Dade 33183 0.32 6.64 1.91 3.70 5.40 3 Dade 33184 0.19 23.70 0.00 5.29 9.64 3 Dade 33185 0.78 59.62 0.00 10.64 14.55 3 Dade 33186 0.83 11.78 6.76 6.57 13.57 3 Dade 33187 0.00 16.79 4.82 1.87 10.24 3 Dade 33189 0.76 0.00 0.00 9.06 7.08 3 Dade 33190 0.00 0.00 0.00 0.00 0.00 3 Dade 33193 0.27 2.77 0.0 0 2.47 4.51 3 Dade 33194 0.00 0.00 0.00 0.00 0.00 3 Dade 33196 0.11 13.58 0.00 7.57 9.66 4 Duval 32009 0.00 0.00 0.00 0.00 0.00 4 Duval 32073 2.29 4.68 17.47 10.44 9.52 4 Duval 32202 3.79 0.00 0.00 5.19 56.72 4 Duval 32204 5.42 30.26 121.63 60.75 110 .74 4 Duval 32205 2.04 3.91 6.74 6.11 7.96 4 Duval 32206 0.36 0.00 0.00 3.72 11.31 4 Duval 32207 0.79 3.49 36.03 21.00 21.26 4 Duval 32208 1.25 3.49 0.00 0.00 1.42 4 Duval 32209 1.64 2.97 22.15 9.27 20.51 4 Duval 32210 0.85 4.04 5.80 3.15 4.10 4 Duv al 32211 1.22 0.00 1.96 0.76 1.39 4 Duval 32212 4.63 0.00 0.00 0.00 0.00 4 Duval 32215 0.00 0.00 0.00 0.00 0.00 4 Duval 32216 2.99 19.95 48.13 12.46 16.23 4 Duval 32217 1.14 5.82 3.34 7.79 23.66

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113 Appendix B, C ontinued Variable Specific Scores by Zip Code Case County Zip Code Family General Ob Gyn Internal Pediatric 4 Duval 32218 0.61 0.00 0.00 2.08 1.27 4 Duval 32219 0. 00 0.00 0.00 0.00 0.00 4 Duval 32220 0.36 0.00 0.00 0.00 0.00 4 Duval 32221 0.43 13.13 0.00 0.00 2.67 4 Duval 32222 0.00 0.00 0.00 0.00 0.00 4 Duval 32223 2.56 9.26 13.29 4.13 16.94 4 Duval 32224 3.88 0.00 10.36 38.62 10.27 4 Duval 32225 1.34 0.00 10 .35 7.47 16.75 4 Duval 32226 0.47 14.40 0.00 0.00 11.71 4 Duval 32227 0.00 0.00 0.00 0.00 0.00 4 Duval 32233 0.75 4.63 0.00 3.10 3.77 4 Duval 32234 0.00 0.00 0.00 0.00 0.00 4 Duval 32244 1.07 0.00 0.00 1.13 2.05 4 Duval 32246 0.85 0.00 0.00 10.18 7.9 5 4 Duval 32250 2.73 9.85 28.27 12.08 12.01 4 Duval 32254 0.00 7.86 0.00 3.51 0.00 4 Duval 32256 3.81 16.15 0.00 35.13 31.20 4 Duval 32257 1.37 9.71 9.29 10.10 15.79 4 Duval 32258 1.82 9.34 26.81 2.08 3.80 4 Duval 32259 2.12 0.00 7.48 14.53 15.89 4 Duval 32266 1.59 0.00 0.00 18.14 19.84 4 Duval 32277 1.39 0.00 0.00 3.80 6.93 5 Hillsbo 33510 0.17 5.26 6.04 3.52 0.00 5 Hillsbo 33511 0.77 10.48 27.07 16.94 23.43 5 Hillsbo 33527 0.34 0.00 0.00 0.00 0.00 5 Hillsbo 33534 0.00 0.00 0.00 0.00 0.00 5 Hi llsbo 33540 0.20 0.00 3.59 5.57 0.00 5 Hillsbo 33547 1.35 0.00 23.77 15.39 22.44 5 Hillsbo 33549 0.09 0.00 0.00 3.53 3.21 5 Hillsbo 33556 2.19 8.41 9.66 20.63 17.09 5 Hillsbo 33565 0.00 0.00 0.00 3.12 0.00 5 Hillsbo 33566 0.53 5.46 3.14 10.96 6.66 5 Hillsbo 33567 0.00 0.00 0.00 0.00 0.00 5 Hillsbo 33569 0.54 3.30 3.79 8.09 5.36 5 Hillsbo 33570 0.89 0.00 0.00 2.04 0.00 5 Hillsbo 33572 2.57 15.77 9.06 10.55 0.00

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114 Appendix B, C ontinued Variable Specific Scores by Zip Code Case County Zip Code Family General Ob Gyn Internal Pediatric 5 Hillsbo 33573 1.17 14.42 4.14 9.65 2.93 5 Hillsbo 33584 0.00 0.00 0.00 0.00 0.00 5 H illsbo 33592 0.00 0.00 0.00 0.00 0.00 5 Hillsbo 33594 0.96 2.47 5.66 9.90 8.02 5 Hillsbo 33598 0.00 0.00 0.00 0.00 0.00 5 Hillsbo 33602 1.28 0.00 30.18 11.72 21.37 5 Hillsbo 33603 0.55 0.00 0.00 6.27 6.85 5 Hillsbo 33604 0.10 3.20 0.00 4.28 3.90 5 Hi llsbo 33605 0.22 13.78 3.96 0.00 0.00 5 Hillsbo 33606 1.02 15.73 76.78 35.09 73.56 5 Hillsbo 33607 1.01 5.16 94.83 12.66 41.97 5 Hillsbo 33609 2.37 14.54 37.58 30.82 26.61 5 Hillsbo 33610 0.47 0.00 0.00 0.00 0.00 5 Hillsbo 33611 1.03 7.89 9.06 6.16 6. 41 5 Hillsbo 33612 0.98 8.22 3.15 15.27 6.68 5 Hillsbo 33613 1.56 12.00 41.33 17.84 17.89 5 Hillsbo 33614 0.35 16.12 13.88 13.78 7.65 5 Hillsbo 33615 0.46 5.69 1.63 10.79 4.63 5 Hillsbo 33616 0.00 0.00 5.62 2.18 0.00 5 Hillsbo 33617 0.63 2.78 0.00 8. 07 5.66 5 Hillsbo 33618 2.26 17.34 6.64 27.07 30.55 5 Hillsbo 33619 0.40 4.13 0.00 3.69 5.04 5 Hillsbo 33620 3.03 0.00 0.00 0.00 0.00 5 Hillsbo 33621 1.42 0.00 0.00 0.00 0.00 5 Hillsbo 33624 0.43 5.22 6.00 5.82 1.06 5 Hillsbo 33625 0.92 0.00 6.50 3.7 9 13.81 5 Hillsbo 33626 5.86 10.58 24.31 61.39 51.65 5 Hillsbo 33629 1.34 5.15 17.74 19.52 33.49 5 Hillsbo 33634 1.39 12.22 10.53 2.73 7.45 5 Hillsbo 33635 0.92 9.46 0.00 8.44 11.54 5 Hillsbo 33637 0.92 0.00 0.00 6.28 3.82 5 Hillsbo 33647 2.48 17.90 17.99 59.90 38.22 5 Hillsbo 33834 0.00 0.00 0.00 0.00 0.00 5 Hillsbo 34221 0.85 0.00 2.14 1.66 4.54 6 Orange 32703 1.06 5.44 4.69 4.25 1.11 6 Orange 32709 0.00 0.00 0.00 0.00 0.00

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115 Appendix B, C ontinued Variable Specific Scores by Zip Code Case County Zip Code Family General Ob Gyn Internal Pediatric 6 Orange 32712 1.70 8.05 9.25 3.59 4.91 6 Orange 32751 3.17 0.00 18.65 17.39 26.41 6 Orange 32757 1.22 0.00 7.19 9.78 5.09 6 Orange 32776 1.16 17.74 0.00 3.96 0.00 6 Orange 32789 1.45 4.95 42.65 19.88 2.01 6 Orange 32792 1.81 6.95 22.62 8.27 3.77 6 Orange 32798 0.00 0.00 0.00 0.00 0.00 6 Orange 32801 1.92 0.00 8.47 13. 16 6.00 6 Orange 32803 3.06 11.06 34.93 20.97 51.71 6 Orange 32804 4.45 0.00 56.05 21.77 10.58 6 Orange 32805 0.31 14.45 0.00 2.15 3.92 6 Orange 32806 2.73 13.23 81.03 28.53 59.18 6 Orange 32807 0.53 8.07 6.95 2.70 6.56 6 Orange 32808 0.31 4.81 2.76 1.07 3.91 6 Orange 32809 0.34 15.57 5.96 3.47 2.11 6 Orange 32810 0.35 0.00 2.07 2.41 5.87 6 Orange 32811 0.00 0.00 0.00 1.57 2.87 6 Orange 32812 1.07 9.82 5.64 5.84 7.99 6 Orange 32817 1.10 8.43 0.00 2.82 13.71 6 Orange 32818 0.64 0.00 0.00 1.47 2.6 8 6 Orange 32819 3.85 14.76 22.60 27.44 28.01 6 Orange 32820 1.27 0.00 0.00 8.73 0.00 6 Orange 32821 0.28 0.00 9.70 0.00 3.43 6 Orange 32822 0.88 4.51 0.00 5.03 3.67 6 Orange 32824 3.77 12.18 0.00 19.01 2.48 6 Orange 32825 1.05 2.69 3.09 4.81 5.48 6 Orange 32826 0.00 4.85 0.00 1.08 5.92 6 Orange 32827 0.00 0.00 0.00 12.01 0.00 6 Orange 32828 2.06 5.28 9.09 3.53 12.87 6 Orange 32829 0.00 66.01 0.00 14.72 13.42 6 Orange 32831 0.00 0.00 0.00 0.00 0.00 6 Orange 32832 0.00 0.00 36.33 0.00 0.00 6 Ora nge 32833 0.00 0.00 0.00 5.15 0.00 6 Orange 32835 1.71 7.50 10.76 13.38 16.77 6 Orange 32836 2.53 0.00 27.90 58.52 43.46 6 Orange 32837 1.87 10.13 5.82 7.53 9.61

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116 Appendix B, C ontinued Variable Specific Scores by Zip Code Case County Zip Code Family General Ob Gyn Internal Pediatric 6 Orange 32839 0.19 0.00 3.34 1.95 3.55 6 Orange 34734 4.38 0.00 0.00 20.02 0.00 6 Orang e 34747 4.20 0.00 111.19 28.80 34.99 6 Orange 34760 0.00 0.00 0.00 0.00 0.00 6 Orange 34761 1.52 4.23 17.00 13.21 12.04 6 Orange 34786 4.08 27.85 39.99 49.70 56.63 6 Orange 34787 0.34 0.00 5.93 6.91 4.20 7 PalmBe 33401 0.93 0.00 59.30 26.87 4.67 7 Pa lmBe 33403 0.32 0.00 72.52 2.17 0.00 7 PalmBe 33404 0.51 7.85 4.51 3.50 0.00 7 PalmBe 33405 0.77 5.93 6.81 9.26 9.65 7 PalmBe 33406 0.45 9.30 10.69 5.19 11.35 7 PalmBe 33407 0.67 8.21 9.43 22.89 25.03 7 PalmBe 33408 0.90 6.89 3.95 10.75 8.40 7 PalmBe 33409 0.35 0.00 12.19 10.66 4.32 7 PalmBe 33410 1.83 8.66 14.92 34.77 24.65 7 PalmBe 33411 0.92 2.83 11.36 11.35 11.49 7 PalmBe 33412 0.86 26.54 15.24 41.44 32.37 7 PalmBe 33413 0.40 0.00 0.00 8.30 10.09 7 PalmBe 33414 1.86 19.06 34.65 28.34 33.58 7 PalmBe 33415 0.10 5.94 0.00 1.33 1.21 7 PalmBe 33417 0.14 0.00 0.00 5.67 1.72 7 PalmBe 33418 1.54 4.30 24.67 25.87 19.21 7 PalmBe 33426 0.50 0.00 8.78 13.64 3.11 7 PalmBe 33428 1.42 6.24 19.72 12.54 8.89 7 PalmBe 33430 0.54 0.00 3.18 2.47 6.76 7 Pal mBe 33431 0.66 6.77 23.32 25.66 0.00 7 PalmBe 33432 1.71 26.28 15.09 19.05 13.36 7 PalmBe 33433 0.75 5.77 19.87 7.72 10.55 7 PalmBe 33434 0.92 11.35 29.34 15.19 11.54 7 PalmBe 33435 0.75 3.85 19.87 9.44 4.69 7 PalmBe 33436 0.75 6.59 11.36 7.36 5.36 7 PalmBe 33437 1.17 9.00 10.34 15.40 12.20 7 PalmBe 33438 0.00 0.00 0.00 0.00 0.00 7 PalmBe 33440 0.63 6.46 0.00 4.32 10.50 7 PalmBe 33444 0.90 0.00 0.00 6.16 2.25

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117 Appendix B, C ontinued Variable Specific Scores by Zip Code Case County Zip Code Family General Ob Gyn Internal Pediatric 7 PalmBe 33445 0.96 8.39 4.82 11.22 3.41 7 PalmBe 33446 1.77 7.77 26.77 20.79 15.80 7 P almBe 33458 0.92 7.08 24.41 17.39 7.20 7 PalmBe 33460 0.48 7.35 0.00 2.46 20.93 7 PalmBe 33461 0.32 6.58 3.78 6.61 1.34 7 PalmBe 33462 0.58 4.49 2.58 17.02 5.47 7 PalmBe 33463 0.84 0.00 1.65 3.84 3.50 7 PalmBe 33467 0.75 2.88 8.26 17.96 10.53 7 PalmB e 33469 1.86 16.34 9.38 9.11 3.32 7 PalmBe 33470 1.00 0.00 21.22 9.62 10.02 7 PalmBe 33476 0.00 0.00 0.00 0.00 0.00 7 PalmBe 33477 1.61 0.00 17.03 6.62 8.04 7 PalmBe 33478 0.00 0.00 0.00 6.96 4.23 7 PalmBe 33480 1.37 0.00 30.16 16.40 0.00 7 PalmBe 33 483 1.50 46.22 5.31 4.12 7.52 7 PalmBe 33484 0.16 4.82 0.00 19.37 5.89 7 PalmBe 33486 1.92 58.92 33.83 33.46 17.43 7 PalmBe 33487 1.65 50.82 16.68 8.10 5.90 7 PalmBe 33493 2.95 0.00 0.00 6.74 0.00 7 PalmBe 33496 2.23 68.35 19.62 36.85 20.85 7 PalmBe 33498 0.79 24.34 9.32 18.10 16.50 8 Pinellas 33701 6.48 7.65 48.34 22.19 62.24 8 Pinellas 33702 0.76 7.83 8.99 6.11 4.78 8 Pinellas 33703 2.29 9.39 5.39 7.33 7.64 8 Pinellas 33704 1.38 14.08 8.09 10.99 22.90 8 Pinellas 33705 1.36 8.38 2.41 11.22 8.52 8 Pinellas 33706 1.54 20.31 7.78 7.55 8.26 8 Pinellas 33707 1.44 22.16 7.64 17.80 7.21 8 Pinellas 33708 0.89 13.68 3.93 4.58 2.78 8 Pinellas 33709 0.29 4.52 5.19 8.06 3.68 8 Pinellas 33710 0.92 17.71 18.31 10.27 14.41 8 Pinellas 33711 0.77 0.00 3.39 1.32 2.40 8 Pinellas 33712 0.29 0.00 2.58 0.00 5.47 8 Pinellas 33713 0.98 7.52 4.32 5.04 7.65 8 Pinellas 33714 0.43 0.00 3.81 1.48 0.00 8 Pinellas 33715 5.18 0.00 18.25 3.55 0.00

PAGE 130

118 Appendix B, C ontinued Variable Specific Scores by Zip Code Case County Zip Code Family General Ob Gyn Internal Pediatric 8 Pinellas 33716 2.21 0.00 6.49 15.13 27.58 8 Pinellas 33755 2.35 4.51 5.19 4.03 0.00 8 Pinellas 33756 1.32 20.23 41.82 31.59 16.45 8 Pinellas 33759 0.76 0.00 3.37 3.92 0.00 8 Pinellas 33760 0.68 6.94 0.00 3.10 5.64 8 Pinellas 33761 1.37 6.00 10.35 22.77 19.54 8 Pinellas 33762 1.69 17.26 9.91 23.10 21.05 8 Pinellas 3376 3 0.43 13.05 0.00 8.73 5.31 8 Pinellas 33764 1.94 4.97 8.56 7.76 2.02 8 Pinellas 33765 1.14 0.00 5.04 3.92 3.57 8 Pinellas 33767 1.57 12.05 27.68 18.81 0.00 8 Pinellas 33770 1.26 14.47 11.08 15.06 15.69 8 Pinellas 33771 0.92 16.10 2.31 1.80 6.55 8 Pi nellas 33772 0.99 0.00 2.91 6.78 4.12 8 Pinellas 33773 0.00 14.38 0.00 6.41 2.92 8 Pinellas 33774 3.12 6.38 0.00 5.70 0.00 8 Pinellas 33776 1.43 17.58 0.00 11.76 7.15 8 Pinellas 33777 2.21 0.00 7.80 13.63 5.52 8 Pinellas 33778 1.97 25.88 4.95 5.77 3.5 1 8 Pinellas 33781 0.91 4.65 0.00 3.11 5.68 8 Pinellas 33782 1.57 6.03 3.46 10.75 4.90 8 Pinellas 33785 1.93 19.78 0.00 8.82 0.00 8 Pinellas 33786 2.39 73.49 0.00 32.79 0.00 8 Pinellas 34677 2.15 0.00 0.00 4.01 7.31 8 Pinellas 34681 9.28 0.00 0.00 42 .37 0.00 8 Pinellas 34683 0.90 6.92 7.94 6.17 11.25 8 Pinellas 34684 0.98 8.58 12.32 16.27 12.21 8 Pinellas 34685 1.75 13.40 7.70 22.42 19.07 8 Pinellas 34689 1.20 8.18 7.05 7.30 6.66 8 Pinellas 34695 0.63 6.48 11.16 14.46 7.91 8 Pinellas 34698 1.45 17.18 11.84 16.87 5.59 9 Polk 33547 1.35 13.80 23.77 21.55 16.83 9 Polk 33801 0.00 3.72 2.14 4.15 3.03 9 Polk 33803 0.57 4.36 7.51 28.20 15.95 9 Polk 33805 1.88 0.00 26.46 26.98 30.45 9 Polk 33809 0.40 4.08 0.00 0.91 0.00

PAGE 131

119 Appendix B, C ontinued Variable Specific Scores by Zip Code Case County Zip Code Family General Ob Gyn Internal Pediatric 9 Polk 33810 0.00 0.00 2.37 4.59 3.35 9 Polk 33811 0.47 0.00 0.00 1.62 0.00 9 Polk 33813 1.08 19.94 20.99 21.49 12.16 9 Polk 33815 0.28 8.64 0.00 0.00 0.00 9 Polk 33823 0.29 13.33 2.55 0.00 0.00 9 Polk 33825 0.33 5.06 2.91 5.64 6.17 9 Polk 33827 0.00 0.00 0.00 0.00 0.00 9 Polk 33830 0.74 9.15 5.25 5.10 9.30 9 Polk 33835 0.00 0.00 0.00 0.00 0.00 9 Polk 33837 0.00 0.00 3.17 1.23 6.73 9 Polk 33838 1.35 0.00 0.00 9.23 0.00 9 Polk 33839 2.41 0.00 0.00 0.00 0.00 9 Polk 33841 0.49 0.00 0.00 0.00 0.00 9 Polk 33843 0.36 0.00 0.00 0.00 0.00 9 Polk 33844 0.43 0.00 2.54 3.95 1.80 9 Polk 33847 0.00 0.00 0.00 0.00 0.00 9 Polk 33849 0.00 0.00 0.00 0.00 0.00 9 Polk 33850 0.00 0.00 0.00 0.00 0.00 9 Polk 33851 0.00 0.00 0.00 0.00 0.00 9 Polk 33853 0.45 3.42 3.92 3.05 1.39 9 Polk 3386 0 0.00 0.00 0.00 0.00 0.00 9 Polk 33868 0.35 0.00 0.00 0.00 0.00 9 Polk 33877 0.00 0.00 0.00 0.00 0.00 9 Polk 33880 1.13 6.97 22.00 7.77 7.08 9 Polk 33881 0.95 0.00 4.79 8.37 6.78 9 Polk 33884 0.96 11.76 13.50 9.18 9.56 9 Polk 34759 0.00 0.00 8.95 3. 48 0.00

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120 Appendix C Descriptive Statistics for Population by Zip Code Case County Zip Code Population Female Male White Black Hispanic 1 Brevard 32754 8,972 50.18 49.82 87.33 10.45 1.29 1 Brevard 32759 2,448 48.94 51.06 86.93 10.70 0.98 1 Brevar d 32775 603 49.75 50.25 96.02 1.00 1.00 1 Brevard 32780 30,966 52.13 47.87 86.13 10.14 3.62 1 Brevard 32796 20,485 51.67 48.33 85.82 10.82 2.86 1 Brevard 32901 22,542 51.61 48.39 73.62 20.32 5.51 1 Brevard 32903 12,792 50.45 49.55 96.36 0.68 2.94 1 Br evard 32904 17,884 52.91 47.09 94.86 1.26 3.34 1 Brevard 32905 21,731 51.93 48.07 79.26 12.37 9.28 1 Brevard 32907 34,113 51.13 48.87 82.39 11.11 8.57 1 Brevard 32908 5,422 50.98 49.02 83.68 8.89 7.51 1 Brevard 32909 18,203 50.57 49.43 82.77 10.70 8.20 1 Brevard 32920 9,036 47.80 52.20 94.71 1.39 3.52 1 Brevard 32922 15,968 52.24 47.76 58.35 36.17 5.30 1 Brevard 32925 2,137 46.19 53.81 66.45 19.65 12.21 1 Brevard 32926 19,234 50.72 49.28 83.34 13.34 2.52 1 Brevard 32927 27,018 46.39 53.61 87.07 8.2 1 3.89 1 Brevard 32931 14,742 49.63 50.37 96.54 0.58 2.46 1 Brevard 32934 14,001 51.28 48.72 92.09 3.16 4.84 1 Brevard 32935 40,693 51.32 48.68 89.47 4.37 5.38 1 Brevard 32937 26,867 51.42 48.58 93.54 1.88 3.72 1 Brevard 32940 19,083 52.32 47.68 93.02 2.45 4.25 1 Brevard 32948 4,881 42.63 57.37 64.37 6.15 61.75 1 Brevard 32949 735 51.16 48.84 96.46 0.95 2.18 1 Brevard 32950 4,543 49.20 50.80 90.78 4.62 3.32 1 Brevard 32951 10,543 50.26 49.74 97.92 0.21 2.12 1 Brevard 32952 20,696 50.53 49.47 94.98 0.85 3.58 1 Brevard 32953 21,695 51.50 48.50 86.77 8.37 4.11 1 Brevard 32955 26,036 52.09 47.91 83.30 12.39 3.55 1 Brevard 32976 9,817 53.24 46.76 98.77 0.31 1.26 2 Broward 33004 15,161 50.95 49.05 65.12 29.83 10.51 2 Broward 33009 34,504 53.34 46.66 76.60 16.36 19.07 2 Broward 33019 17,432 52.21 47.79 95.47 1.33 13.31 2 Broward 33020 40,466 50.03 49.97 66.31 22.97 21.68 2 Broward 33021 46,177 52.86 47.14 85.46 6.96 17.81

PAGE 133

121 Appendix C C ontinued Descriptive Statistics for Population by Zip Code C ase County Zip Code Population Female Male White Black Hispanic 2 Broward 33023 60,897 52.20 47.80 42.67 45.14 24.10 2 Broward 33024 58,895 51.87 48.13 76.88 9.83 30.82 2 Broward 33025 46,392 53.41 46.59 46.14 40.22 24.13 2 Broward 33026 29,582 53.8 8 46.12 84.57 6.98 19.58 2 Broward 33027 25,471 56.31 43.69 80.97 10.65 30.28 2 Broward 33028 22,132 51.59 48.41 71.35 13.27 38.13 2 Broward 33029 35,326 51.15 48.85 74.84 13.32 37.17 2 Broward 33060 33,389 50.09 49.91 53.03 36.83 11.93 2 Broward 3306 2 25,514 52.18 47.82 96.91 0.70 5.05 2 Broward 33063 50,993 53.34 46.66 82.55 8.71 14.10 2 Broward 33064 52,892 49.99 50.01 69.03 19.04 14.17 2 Broward 33065 51,620 51.82 48.18 75.06 13.33 18.86 2 Broward 33066 16,785 57.90 42.10 93.57 3.10 5.65 2 Bro ward 33067 23,107 50.83 49.17 88.12 4.68 10.08 2 Broward 33068 47,696 51.01 48.99 54.51 30.66 22.55 2 Broward 33069 24,530 50.82 49.18 64.00 30.79 10.63 2 Broward 33071 36,841 51.07 48.93 87.52 5.58 13.32 2 Broward 33073 20,091 50.08 49.92 82.29 7.47 1 5.73 2 Broward 33076 19,710 50.77 49.23 86.97 5.54 11.86 2 Broward 33301 11,996 39.50 60.50 83.65 12.93 7.07 2 Broward 33304 18,684 44.86 55.14 70.33 19.59 9.02 2 Broward 33305 12,014 43.56 56.44 88.85 5.40 7.95 2 Broward 33306 3,796 47.55 52.45 95.60 1.08 4.98 2 Broward 33308 28,554 51.62 48.38 96.17 0.87 6.43 2 Broward 33309 33,342 48.78 51.22 56.53 34.33 13.04 2 Broward 33311 65,469 52.24 47.76 11.75 81.30 3.45 2 Broward 33312 45,055 48.66 51.34 57.65 34.43 14.93 2 Broward 33313 56,847 53.91 46 .09 23.69 67.94 7.87 2 Broward 33314 23,859 50.37 49.63 84.72 5.39 22.18 2 Broward 33315 12,905 47.91 52.09 85.85 7.06 16.09 2 Broward 33316 10,668 47.20 52.80 90.98 4.05 9.95 2 Broward 33317 34,359 51.18 48.82 70.94 20.08 17.45 2 Broward 33319 43,015 55.36 44.64 57.98 33.74 11.20 2 Broward 33321 39,427 55.27 44.73 85.58 7.37 14.37 2 Broward 33322 40,691 54.70 45.30 82.67 10.49 11.81

PAGE 134

122 Appendix C C ontinued Descriptive Statistics for Population by Zip Code C ase County Zip Code Population Female Male White Black Hispanic 2 Broward 33323 17,784 50.40 49.60 79.83 10.91 18.21 2 Broward 33324 43,355 53.11 46.89 85.80 6.71 14.22 2 Broward 33325 27,552 51.00 49.00 88.75 3.80 17.40 2 Broward 33326 29,956 51.93 48.07 88.69 3.22 29.21 2 Broward 33327 13,171 50.75 49.25 88.00 3.93 29.22 2 Broward 33328 20,035 51.47 48.53 91.39 2.10 12.87 2 Broward 33330 11,178 51.10 48.90 89.48 3.55 18.37 2 Broward 33331 20,975 50.87 49.13 85.79 4.60 26.57 2 Broward 33332 3,778 57.54 42.46 85.04 9.66 17.81 2 Broward 33334 30,847 47.39 52.61 76.45 12.34 21.51 2 Broward 33351 32,464 52.66 47.34 69.30 18.29 19.80 2 Broward 33388 0 0.00 0.00 0.00 0.00 0.00 2 Broward 33394 0 0.00 0.00 0.00 0.00 0.00 2 Broward 33441 26,973 51.31 48.69 65.00 27.20 8.93 2 Broward 33442 28,666 56.29 43.71 91.67 3.36 7.47 3 Dade 33010 45,353 50.24 49.76 6.32 1.94 91.37 3 Dade 33012 74,948 52.96 47.04 8.77 0.51 90.03 3 Dade 33 013 33,365 50.96 49.04 8.94 0.31 90.32 3 Dade 33014 38,667 51.94 48.06 18.16 1.78 78.51 3 Dade 33015 49,279 52.30 47.70 17.37 16.58 62.04 3 Dade 33016 43,347 52.64 47.36 9.67 1.42 87.58 3 Dade 33018 37,725 51.49 48.51 9.22 0.82 88.59 3 Dade 33030 27,3 04 46.97 53.03 26.71 20.01 49.38 3 Dade 33031 5,514 49.33 50.67 66.72 1.27 29.18 3 Dade 33032 20,716 50.41 49.59 19.59 33.44 43.19 3 Dade 33033 31,394 50.33 49.67 20.27 17.73 59.32 3 Dade 33034 15,402 47.62 52.38 19.59 37.22 39.65 3 Dade 33035 2,762 5 3.69 46.31 58.73 9.74 27.01 3 Dade 33054 28,177 53.72 46.28 2.97 75.07 20.37 3 Dade 33055 45,105 51.55 48.45 7.74 37.97 52.60 3 Dade 33056 33,223 53.34 46.66 1.82 86.60 9.33 3 Dade 33109 467 49.68 50.32 77.94 3.21 14.78 3 Dade 33122 0 0.00 0.00 0.00 0 .00 0.00 3 Dade 33125 48,598 50.77 49.23 7.32 1.92 89.84 3 Dade 33126 43,814 53.06 46.94 7.30 0.64 91.03 3 Dade 33127 27,796 50.58 49.42 2.57 62.05 32.01

PAGE 135

123 Appendix C C ontinued Descriptive Statistics for Population by Zip Code C ase County Zip Code Population Female Male White Black Hispanic 3 Dade 33128 7,002 44.50 55.50 5.24 5.47 88.70 3 Dade 33129 11,100 53.00 47.00 34.30 0.95 62.79 3 Dade 33130 20,541 49.25 50.75 6.83 1.83 90.54 3 Dade 33131 4,723 49.04 50.96 40.67 1.12 54.65 3 Dade 33132 5,322 32.83 67.17 32.39 10.50 54.06 3 Dade 33133 29,929 51.29 48.71 37.50 15.71 44.93 3 Dade 33134 34,045 53.99 46.01 28.96 0.51 69.08 3 Dade 33135 35,712 51.58 48.42 6.03 0.41 92.88 3 Dade 33136 13,119 50.99 49.01 8.68 59.03 28.23 3 Dade 33137 17,638 47.94 52.06 21.32 34.96 37.63 3 Dade 33138 29,522 48.67 51.33 29.27 38.87 22.34 3 Dade 33139 38,441 45.56 54.44 43.38 2.61 50.68 3 Dade 33140 20,240 51.37 48.63 56.60 1.20 40.03 3 Dade 33141 36,545 50.96 49.04 29.82 4.18 62.68 3 Dade 33142 53,398 48.45 51.55 3.44 50.15 45.39 3 Dade 33143 29,788 52.89 47.11 41.99 11.54 42.85 3 Dade 33144 25,332 53.19 46.81 10.47 0.15 88.63 3 Dade 33145 28,9 21 52.62 47.38 13.24 0.55 84.86 3 Dade 33146 13,210 52.44 47.56 54.20 4.71 36.92 3 Dade 33147 50,500 52.47 47.53 2.69 65.22 30.68 3 Dade 33149 10,513 53.08 46.92 48.12 0.27 49.81 3 Dade 33150 26,355 52.10 47.90 4.17 69.22 18.63 3 Dade 33154 13,359 55. 11 44.89 60.84 1.26 35.19 3 Dade 33155 44,142 52.91 47.09 22.40 0.53 75.84 3 Dade 33156 31,450 51.61 48.39 57.86 1.91 34.78 3 Dade 33157 61,288 52.20 47.80 33.95 30.72 30.48 3 Dade 33158 6,457 51.08 48.92 69.83 1.63 24.27 3 Dade 33160 33,833 54.21 45. 79 61.09 4.74 30.74 3 Dade 33161 53,248 52.30 47.70 17.18 54.50 21.29 3 Dade 33162 45,224 52.36 47.64 19.12 46.95 25.62 3 Dade 33165 57,079 53.27 46.73 16.11 1.25 81.29 3 Dade 33166 22,563 49.83 50.17 30.36 4.47 62.11 3 Dade 33167 18,203 52.23 47.77 4 .88 70.49 20.54 3 Dade 33168 25,151 51.37 48.63 8.52 64.06 21.98 3 Dade 33169 36,115 53.37 46.63 6.96 77.78 10.78 3 Dade 33170 8,460 52.09 47.91 18.45 58.46 21.10

PAGE 136

124 Appendix C C ontinued Descriptive Statistics for Population by Zip Code C ase County Zip Code Population Female Male White Black Hispanic 3 Dade 33172 38,515 53.03 46.97 10.59 1.12 85.46 3 Dade 33173 33,640 53.54 46.46 30.41 1 .72 64.63 3 Dade 33174 30,281 53.53 46.47 8.41 0.35 90.13 3 Dade 33175 52,581 52.28 47.72 13.75 0.55 84.25 3 Dade 33176 52,081 52.76 47.24 38.17 17.42 40.14 3 Dade 33177 45,482 49.63 50.37 14.99 17.49 63.78 3 Dade 33178 15,272 51.10 48.90 26.06 2.48 6 5.04 3 Dade 33179 37,380 54.84 45.16 38.57 30.07 25.42 3 Dade 33180 20,799 53.73 46.27 72.28 2.42 22.15 3 Dade 33181 17,694 51.34 48.66 36.86 31.34 26.39 3 Dade 33182 16,887 41.90 58.10 11.74 10.20 76.47 3 Dade 33183 35,422 53.00 47.00 20.78 2.23 73.9 4 3 Dade 33184 19,855 52.52 47.48 10.83 0.38 88.11 3 Dade 33185 9,868 52.34 47.66 18.00 1.35 78.35 3 Dade 33186 59,935 52.95 47.05 29.94 6.45 58.01 3 Dade 33187 14,014 50.12 49.88 29.57 6.49 59.91 3 Dade 33189 20,280 52.27 47.73 30.24 21.95 42.82 3 D ade 33190 4,820 51.72 48.28 26.99 25.21 43.86 3 Dade 33193 42,469 50.58 49.42 14.72 5.75 76.77 3 Dade 33194 0 0.00 0.00 0.00 0.00 0.00 3 Dade 33196 34,661 52.34 47.66 22.58 6.78 65.38 4 Duval 32009 2,730 49.16 50.84 96.96 1.06 0.88 4 Duval 32073 50,28 2 51.24 48.76 82.98 9.03 5.38 4 Duval 32202 5,061 33.49 66.51 31.99 65.40 2.21 4 Duval 32204 7,777 53.93 46.07 45.71 50.43 2.26 4 Duval 32205 30,067 52.37 47.63 73.83 20.48 2.95 4 Duval 32206 21,153 51.96 48.04 15.06 82.44 1.85 4 Duval 32207 33,753 52 .70 47.30 72.79 20.94 4.70 4 Duval 32208 33,667 54.08 45.92 23.01 75.44 0.96 4 Duval 32209 39,653 55.58 44.42 0.98 97.99 0.71 4 Duval 32210 58,283 52.11 47.89 67.34 25.01 4.34 4 Duval 32211 34,475 51.47 48.53 64.48 29.22 5.25 4 Duval 32212 2,485 36.54 63.46 60.36 27.73 12.23 4 Duval 32215 812 50.49 49.51 49.75 35.34 8.99 4 Duval 32216 29,483 52.06 47.94 73.83 19.59 5.50 4 Duval 32217 20,224 53.04 46.96 77.59 13.75 6.12

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125 Appendix C C ontinued Descriptive Statistics for Population by Zip Code C ase County Zip Code Population Female Male White Black Hispanic 4 Duval 32218 37,790 51.77 48.23 57.84 39.65 1.65 4 Duval 32219 9,448 50.47 49. 53 55.73 42.57 1.04 4 Duval 32220 10,615 49.45 50.55 93.16 4.13 1.84 4 Duval 32221 17,927 51.25 48.75 84.74 10.06 2.84 4 Duval 32222 4,423 50.26 49.74 83.68 10.94 4.11 4 Duval 32223 25,424 51.35 48.65 91.81 4.23 3.49 4 Duval 32224 32,625 51.03 48.97 8 3.27 8.02 5.66 4 Duval 32225 45,702 51.13 48.87 74.67 15.65 5.55 4 Duval 32226 8,173 50.13 49.87 96.67 1.59 1.82 4 Duval 32227 5,250 29.94 70.06 61.01 25.45 11.87 4 Duval 32233 25,398 50.08 49.92 74.79 16.85 5.97 4 Duval 32234 6,307 51.32 48.68 89.82 8.66 1.33 4 Duval 32244 46,584 51.32 48.68 68.09 22.34 5.44 4 Duval 32246 36,100 50.86 49.14 71.76 15.96 6.43 4 Duval 32250 23,900 49.80 50.20 91.49 4.42 2.93 4 Duval 32254 14,969 52.17 47.83 43.72 53.43 1.64 4 Duval 32256 29,141 50.60 49.40 75.79 12. 05 6.30 4 Duval 32257 36,364 52.13 47.87 84.19 8.64 5.13 4 Duval 32258 12,603 50.94 49.06 85.42 7.82 3.78 4 Duval 32259 18,063 49.91 50.09 94.82 2.04 2.39 4 Duval 32266 7,235 49.01 50.99 96.06 0.73 2.10 4 Duval 32277 27,622 52.26 47.74 62.48 31.26 4.5 0 5 Hillsbo 33510 22,374 51.68 48.32 82.80 9.00 11.40 5 Hillsbo 33511 44,927 51.44 48.56 81.27 9.49 12.80 5 Hillsbo 33527 11,431 48.04 51.96 82.93 0.86 30.29 5 Hillsbo 33534 7,496 48.28 51.72 87.65 1.39 20.13 5 Hillsbo 33540 18,837 53.11 46.89 92.71 2 .64 4.70 5 Hillsbo 33547 8,527 50.13 49.87 93.77 1.29 6.52 5 Hillsbo 33549 44,672 50.39 49.61 89.88 4.09 10.08 5 Hillsbo 33556 13,995 49.48 50.52 92.44 3.08 7.02 5 Hillsbo 33565 16,814 50.46 49.54 92.11 1.54 8.53 5 Hillsbo 33566 21,552 52.15 47.85 68. 65 20.59 16.39 5 Hillsbo 33567 25,920 49.74 50.26 78.26 6.88 22.16 5 Hillsbo 33569 35,689 50.05 49.95 85.19 7.77 10.70 5 Hillsbo 33570 12,857 49.32 50.68 84.30 1.00 32.04 5 Hillsbo 33572 7,461 50.17 49.83 93.70 0.84 7.59

PAGE 138

126 Appendix C C ontinued Descriptive Statistics for Population by Zip Code C ase County Zip Code Population Female Male White Black Hispanic 5 Hillsbo 33573 16,321 57.36 4 2.64 98.96 0.13 1.19 5 Hillsbo 33584 20,490 50.23 49.77 86.80 7.08 8.71 5 Hillsbo 33592 9,970 49.73 50.27 82.99 11.66 6.29 5 Hillsbo 33594 47,721 50.89 49.11 88.00 5.98 9.52 5 Hillsbo 33598 8,019 46.95 53.05 59.26 4.31 61.55 5 Hillsbo 33602 8,955 50.2 6 49.74 45.92 46.29 17.62 5 Hillsbo 33603 20,947 51.58 48.42 61.08 28.09 28.51 5 Hillsbo 33604 36,785 51.19 48.81 64.55 24.80 20.97 5 Hillsbo 33605 17,081 50.72 49.28 28.10 61.76 26.48 5 Hillsbo 33606 14,960 49.98 50.02 83.66 11.40 7.77 5 Hillsbo 3360 7 22,801 52.62 47.38 50.20 38.48 41.37 5 Hillsbo 33609 16,180 51.01 48.99 84.53 7.03 21.42 5 Hillsbo 33610 32,397 53.03 46.97 37.65 56.68 9.08 5 Hillsbo 33611 29,837 51.25 48.75 85.15 6.19 10.50 5 Hillsbo 33612 42,961 51.74 48.26 60.01 28.97 17.85 5 H illsbo 33613 29,424 50.58 49.42 68.17 19.09 17.11 5 Hillsbo 33614 43,803 50.44 49.56 74.51 8.92 47.20 5 Hillsbo 33615 41,349 51.10 48.90 77.69 8.34 28.63 5 Hillsbo 33616 12,014 49.71 50.29 67.36 17.79 13.38 5 Hillsbo 33617 42,281 52.13 47.87 62.30 27.3 8 13.92 5 Hillsbo 33618 20,358 51.69 48.31 87.37 4.50 15.59 5 Hillsbo 33619 28,459 47.95 52.05 50.62 40.11 18.10 5 Hillsbo 33620 2,532 0.00 0.00 60.51 31.60 8.14 5 Hillsbo 33621 2,689 46.89 53.11 61.81 24.51 12.01 5 Hillsbo 33624 45,065 52.29 47.71 83 .60 6.42 17.77 5 Hillsbo 33625 20,781 50.99 49.01 82.12 7.30 20.12 5 Hillsbo 33626 11,116 50.94 49.06 86.91 5.06 11.89 5 Hillsbo 33629 22,858 52.22 47.78 95.24 1.18 8.45 5 Hillsbo 33634 19,255 51.72 48.28 77.17 8.07 37.44 5 Hillsbo 33635 12,439 50.83 49.17 83.63 5.84 16.22 5 Hillsbo 33637 12,534 51.76 48.24 73.74 16.93 12.72 5 Hillsbo 33647 26,290 50.57 49.43 82.72 5.86 9.25 5 Hillsbo 33834 7,274 39.50 60.50 66.15 14.74 31.36 5 Hillsbo 34221 31,646 49.40 50.60 74.11 17.16 18.60 6 Orange 32703 43,2 63 50.69 49.31 71.44 18.27 16.35 6 Orange 32709 2,211 48.08 51.92 95.93 0.45 3.17

PAGE 139

127 Appendix C C ontinued Descriptive Statistics for Population by Zip Code C ase County Zip Code Population Female Male White Black Hispanic 6 Orange 32712 29,230 50.67 49.33 83.68 8.97 12.37 6 Orange 32751 18,114 52.64 47.36 85.39 10.46 5.59 6 Orange 32757 18,785 52.48 47.52 81.70 14.41 7.59 6 Orange 32776 6 ,634 50.41 49.59 91.78 3.23 6.38 6 Orange 32789 23,764 52.72 47.28 83.11 12.71 4.99 6 Orange 32792 50,783 50.85 49.15 82.73 6.09 15.84 6 Orange 32798 1,627 57.10 42.90 97.11 0.37 3.81 6 Orange 32801 7,979 53.44 46.56 79.77 13.18 12.61 6 Orange 32803 2 1,280 50.02 49.98 88.52 5.05 8.36 6 Orange 32804 18,083 50.92 49.08 92.78 2.48 4.58 6 Orange 32805 24,432 50.96 49.04 14.84 78.77 5.01 6 Orange 32806 26,682 50.39 49.61 88.11 5.32 9.73 6 Orange 32807 29,167 49.77 50.23 72.79 6.89 38.62 6 Orange 32808 48,886 52.54 47.46 33.76 53.03 12.19 6 Orange 32809 22,676 49.79 50.21 67.36 13.14 35.56 6 Orange 32810 32,623 50.56 49.44 61.97 26.66 14.30 6 Orange 32811 33,391 51.59 48.41 33.74 53.98 12.98 6 Orange 32812 35,952 51.56 48.44 82.14 6.53 21.08 6 Orang e 32817 27,923 49.58 50.42 80.45 5.01 20.33 6 Orange 32818 35,679 51.81 48.19 43.27 43.13 13.78 6 Orange 32819 23,913 50.46 49.54 74.06 12.93 9.50 6 Orange 32820 3,007 48.42 51.58 93.71 0.90 8.88 6 Orange 32821 13,930 49.82 50.18 83.50 4.72 13.14 6 Or ange 32822 52,182 51.07 48.93 71.33 8.63 37.55 6 Orange 32824 19,327 50.88 49.12 67.17 11.78 43.96 6 Orange 32825 43,682 48.11 51.89 72.39 8.83 31.85 6 Orange 32826 24,253 49.02 50.98 78.25 7.78 16.71 6 Orange 32827 2,186 50.69 49.31 74.61 6.72 48.49 6 Orange 32828 22,301 50.50 49.50 79.31 7.05 19.08 6 Orange 32829 3,565 52.17 47.83 78.06 6.48 30.41 6 Orange 32831 57 47.37 52.63 84.21 14.04 12.28 6 Orange 32832 1,860 48.01 51.99 95.86 0.81 4.30 6 Orange 32833 5,092 48.68 51.32 87.04 4.03 8.72 6 Or ange 32835 31,387 49.87 50.13 72.92 11.94 12.66 6 Orange 32836 12,109 50.42 49.58 80.19 3.18 12.10 6 Orange 32837 34,855 50.94 49.06 69.62 8.13 28.35

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128 Appendix C C ontinued Descriptive Statistics for Population by Zip Code C ase County Zip Code Population Female Male White Black Hispanic 6 Orange 32839 40,457 46.16 53.84 44.95 35.51 26.61 6 Orange 34734 2,622 52.02 47.98 80.51 9.84 13.65 6 Orange 34747 5,469 49.94 50.06 88.99 2.96 10.70 6 Orange 34760 661 52.50 47.50 54.46 42.66 2.72 6 Orange 34761 27,815 50.61 49.39 82.03 6.57 14.57 6 Orange 34786 8,449 50.18 49.82 91.18 2.72 4.54 6 Orange 34787 22,779 51.39 48.61 77.41 13.81 14.33 7 PalmBe 33401 20,510 53.21 46.79 51.97 39.76 10.17 7 PalmBe 33403 12,112 50.63 49.37 58.07 32.89 5.69 7 PalmBe 33404 29,975 52.46 47.54 28.70 67.80 3.47 7 PalmBe 33405 19,840 48.51 51.49 81.91 4.82 46.28 7 PalmBe 33406 25,292 47.79 52.21 81.65 7.20 29 .09 7 PalmBe 33407 28,672 50.75 49.25 29.90 60.63 9.32 7 PalmBe 33408 17,086 51.56 48.44 96.83 0.73 3.65 7 PalmBe 33409 22,164 50.18 49.82 68.00 22.09 17.05 7 PalmBe 33410 27,174 51.91 48.09 91.50 3.81 6.68 7 PalmBe 33411 41,637 51.58 48.42 80.07 12.8 2 11.40 7 PalmBe 33412 8,868 49.00 51.00 87.51 6.81 9.34 7 PalmBe 33413 9,484 50.59 49.41 79.74 10.31 19.51 7 PalmBe 33414 37,047 51.18 48.82 88.81 5.39 11.47 7 PalmBe 33415 39,584 52.64 47.36 76.40 11.77 27.22 7 PalmBe 33417 27,755 54.78 45.22 77.38 15.53 11.19 7 PalmBe 33418 27,391 51.96 48.04 94.13 1.65 5.26 7 PalmBe 33426 15,391 54.96 45.04 87.58 7.72 7.01 7 PalmBe 33428 37,682 51.56 48.44 89.12 3.50 12.14 7 PalmBe 33430 21,244 46.86 53.14 29.21 53.72 26.94 7 PalmBe 33431 17,386 51.44 48.56 88 .84 5.72 7.86 7 PalmBe 33432 17,910 51.22 48.78 88.61 5.10 10.37 7 PalmBe 33433 40,807 54.32 45.68 94.44 1.35 7.74 7 PalmBe 33434 20,728 55.47 44.53 95.59 1.21 5.15 7 PalmBe 33435 30,597 52.37 47.63 57.06 35.57 10.12 7 PalmBe 33436 35,683 53.29 46.71 86.24 8.45 7.69 7 PalmBe 33437 39,212 52.24 47.76 92.95 3.41 5.60 7 PalmBe 33438 780 51.41 48.59 53.59 26.54 30.26 7 PalmBe 33440 18,227 48.30 51.70 63.49 22.08 35.60 7 PalmBe 33444 21,293 49.85 50.15 41.49 48.63 9.42

PAGE 141

129 Appendix C C ontinued Descriptive Statistics for Population by Zip Code C ase County Zip Code Population Female Male White Black Hispanic 7 PalmBe 33445 28,061 54.10 45.90 77.61 17.30 5.76 7 PalmBe 33446 15,146 56.19 43.81 98.34 0.51 2.96 7 PalmBe 33458 33,214 49.86 50.14 91.80 3.56 8.88 7 PalmBe 33460 32,007 47.40 52.60 63.20 19.94 31.51 7 PalmBe 33461 35,754 49.61 50.39 75.40 9.56 29.57 7 PalmBe 33462 26,221 50.90 49 .10 81.07 10.61 14.76 7 PalmBe 33463 41,043 51.44 48.56 78.27 9.26 22.24 7 PalmBe 33467 40,914 51.95 48.05 92.58 3.11 8.41 7 PalmBe 33469 14,400 52.15 47.85 98.14 0.42 2.27 7 PalmBe 33470 19,103 49.32 50.68 88.54 5.88 10.98 7 PalmBe 33476 8,456 51.30 48.70 19.60 65.00 23.86 7 PalmBe 33477 11,903 52.68 47.32 98.31 0.21 1.79 7 PalmBe 33478 11,315 49.49 50.51 96.45 0.98 3.78 7 PalmBe 33480 11,200 55.83 44.17 96.20 2.40 2.54 7 PalmBe 33483 12,729 51.98 48.02 87.85 6.91 5.21 7 PalmBe 33484 24,390 56.51 43.49 95.78 2.30 3.02 7 PalmBe 33486 21,967 51.14 48.86 91.41 2.33 9.23 7 PalmBe 33487 16,206 52.19 47.81 93.97 2.63 6.53 7 PalmBe 33493 3,895 36.82 63.18 24.67 66.32 19.79 7 PalmBe 33496 20,658 52.80 47.20 95.05 1.28 6.20 7 PalmBe 33498 14,501 50.78 49.22 92.40 1.74 8.75 8 Pinellas 33701 15,374 49.36 50.64 73.41 20.98 3.80 8 Pinellas 33702 30,058 51.62 48.38 89.48 2.82 4.95 8 Pinellas 33703 25,063 52.28 47.72 93.80 1.13 3.90 8 Pinellas 33704 16,714 51.97 48.03 93.62 1.82 4.00 8 Pinellas 33705 28 ,083 53.85 46.15 39.21 55.78 3.18 8 Pinellas 33706 17,376 50.56 49.44 97.65 0.50 2.39 8 Pinellas 33707 26,542 54.34 45.66 90.97 6.09 2.82 8 Pinellas 33708 17,199 51.47 48.53 97.55 0.30 2.38 8 Pinellas 33709 26,039 53.02 46.98 90.33 2.87 4.77 8 Pinella s 33710 33,213 52.86 47.14 92.84 1.56 4.42 8 Pinellas 33711 19,915 54.46 45.54 37.21 59.14 2.45 8 Pinellas 33712 26,222 54.19 45.81 25.17 70.54 2.76 8 Pinellas 33713 31,273 50.29 49.71 78.80 9.40 6.13 8 Pinellas 33714 17,753 50.01 49.99 88.13 3.00 4.19 8 Pinellas 33715 7,403 51.44 48.56 96.61 1.22 2.89

PAGE 142

130 Appendix C C ontinued Descriptive Statistics for Population by Zip Code C ase County Zip Code Population Female Male White Black Hispanic 8 Pinellas 33716 10,409 49.44 50.56 85.18 5.58 8.23 8 Pinellas 33755 26,061 50.62 49.38 67.08 24.81 11.33 8 Pinellas 33756 29,081 52.70 47.30 88.28 6.49 8.25 8 Pinellas 33759 20,071 52.70 47.30 82.69 9.64 8.89 8 Pinellas 33760 16,958 44.59 55.41 76.47 15.54 7.68 8 Pinellas 33761 19,594 54.39 45.61 96.15 0.82 3.83 8 Pinellas 33762 6,818 51.45 48.55 92.05 2.14 3.56 8 Pinellas 33763 18,029 55.65 44.35 94.36 1.67 6.40 8 Pinellas 33764 23,673 52.48 47 .52 93.52 2.44 4.73 8 Pinellas 33765 13,403 51.50 48.50 88.08 4.28 10.60 8 Pinellas 33767 9,765 50.75 49.25 97.84 0.20 2.54 8 Pinellas 33770 24,394 52.14 47.86 92.58 2.63 4.52 8 Pinellas 33771 29,225 53.81 46.19 92.19 2.67 4.12 8 Pinellas 33772 23,232 54.08 45.92 95.94 0.56 2.96 8 Pinellas 33773 16,369 51.68 48.32 91.83 2.12 4.11 8 Pinellas 33774 18,431 53.52 46.48 91.32 5.65 2.91 8 Pinellas 33776 13,388 52.46 47.54 96.89 0.46 2.32 8 Pinellas 33777 17,328 53.06 46.94 93.37 1.36 3.78 8 Pinellas 337 78 13,639 53.00 47.00 83.64 12.91 3.40 8 Pinellas 33781 25,287 51.34 48.66 88.86 2.44 7.77 8 Pinellas 33782 19,527 53.43 46.57 90.48 1.22 4.12 8 Pinellas 33785 5,949 49.72 50.28 97.44 0.32 3.41 8 Pinellas 33786 1,601 50.09 49.91 96.75 0.25 2.94 8 Pine llas 34677 19,628 51.69 48.31 91.81 2.47 6.00 8 Pinellas 34681 1,239 49.39 50.61 98.22 0.32 2.02 8 Pinellas 34683 34,025 51.72 48.28 96.10 0.93 3.54 8 Pinellas 34684 27,429 54.52 45.48 95.28 1.10 3.50 8 Pinellas 34685 17,559 51.19 48.81 94.57 1.24 3.76 8 Pinellas 34689 28,752 51.82 48.18 91.16 4.97 3.98 8 Pinellas 34695 18,156 52.35 47.65 92.18 4.22 3.51 8 Pinellas 34698 34,235 54.53 45.47 94.97 2.02 3.18 9 Polk 33547 8,527 50.13 49.87 93.77 1.29 6.52 9 Polk 33801 31,593 51.33 48.67 81.15 12.48 7.4 7 9 Polk 33803 26,994 53.39 46.61 90.02 4.99 7.03 9 Polk 33805 20,426 53.20 46.80 45.89 48.60 7.33 9 Polk 33809 28,855 52.06 47.94 91.29 3.95 4.75

PAGE 143

131 Appendix C C ontinued Descriptive Statistics for Population by Zip Code C ase County Zip Code Population Female Male White Black Hispanic 9 Polk 33810 28,563 50.78 49.22 88.91 7.17 5.02 9 Polk 33811 16,176 50.54 49.46 88.41 6.93 6.21 9 Polk 33813 35,411 51.45 48.55 90.61 4.28 5.14 9 Polk 33815 13,620 51.64 48.36 63.38 27.59 12.14 9 Polk 33823 26,485 50.89 49.11 85.43 8.32 7.62 9 Polk 33825 23,257 48.23 51.77 72.27 17.52 15.11 9 Polk 33827 2,527 49.90 50.10 86.66 9.10 5.78 9 Polk 33830 25 ,723 50.40 49.60 69.40 23.48 9.98 9 Polk 33835 50 56.00 44.00 84.00 4.00 2.00 9 Polk 33837 21,315 50.27 49.73 88.07 4.37 14.29 9 Polk 33838 2,843 52.02 47.98 69.15 22.76 11.40 9 Polk 33839 1,591 52.17 47.83 88.37 5.72 7.79 9 Polk 33841 7,881 50.34 49. 66 69.83 15.77 22.48 9 Polk 33843 10,668 45.05 54.95 71.98 9.77 22.33 9 Polk 33844 26,600 50.42 49.58 66.87 20.44 22.13 9 Polk 33847 283 35.69 64.31 76.68 15.90 8.48 9 Polk 33849 418 46.17 53.83 92.82 0.96 4.78 9 Polk 33850 4,039 52.93 47.07 80.12 15. 33 6.36 9 Polk 33851 907 51.82 48.18 92.28 3.64 6.06 9 Polk 33853 34,439 51.49 48.51 78.45 16.16 7.74 9 Polk 33860 17,015 49.56 50.44 81.15 11.86 12.20 9 Polk 33868 10,885 41.85 58.15 81.26 14.38 6.85 9 Polk 33877 550 50.36 49.64 15.45 76.55 7.09 9 P olk 33880 33,778 51.02 48.98 79.67 9.51 15.45 9 Polk 33881 28,225 52.78 47.22 68.07 26.92 4.21 9 Polk 33884 20,016 53.20 46.80 93.86 2.23 3.50 9 Polk 34759 7,553 51.57 48.43 69.27 14.05 37.22

PAGE 144

132 Appendix C C ontinued Descriptive Statistics for Population by Zip Code Case County Zip Code Under_15 Over_65 Unemp Poverty Phy_Rate 1 Brevard 32754 20.20 16.46 3 9.3 0.00 1 Brevard 32759 15.69 23.08 2.1 8.2 0.00 1 Brevard 32775 20.90 10.95 0 0 0.00 1 Brevard 32780 17.42 22.28 2.8 7.6 3.07 1 Brevard 32796 20.31 18.56 3.6 8.8 8.30 1 Brevard 32901 14.46 27.43 4.8 13.7 7.32 1 Brevard 32903 15.89 22.57 1.7 4.1 15.63 1 Brevard 32904 14.21 28.43 3 4.2 4.47 1 Brevard 32905 17.14 21.79 2.9 12.2 5.52 1 Brevard 32907 23.11 13.79 2.9 5.5 1.32 1 Bre vard 32908 26.67 8.87 3.2 4.7 0.92 1 Brevard 32909 24.11 9.53 3.9 5.1 1.92 1 Brevard 32920 9.17 23.51 3.1 9 1.11 1 Brevard 32922 23.00 13.98 5.4 26.1 0.94 1 Brevard 32925 35.05 0.42 3.4 3.8 4.68 1 Brevard 32926 20.32 14.11 2.8 8.7 0.52 1 Brevard 3292 7 22.10 9.16 2.8 5.4 0.93 1 Brevard 32931 9.74 32.62 2.4 3.5 9.50 1 Brevard 32934 20.28 16.54 2 4.5 5.71 1 Brevard 32935 17.38 16.79 2.3 7 2.46 1 Brevard 32937 17.05 21.85 2.2 2.9 5.58 1 Brevard 32940 16.84 27.61 1.9 2.8 15.46 1 Brevard 32948 26.16 6 .76 4.8 18.5 10.24 1 Brevard 32949 11.70 19.73 0 16.8 27.21 1 Brevard 32950 18.23 14.18 1.1 5.5 3.30 1 Brevard 32951 12.82 28.89 1.4 1.6 5.69 1 Brevard 32952 17.65 19.75 2.5 4.7 5.07 1 Brevard 32953 18.47 18.17 3.4 8.2 3.00 1 Brevard 32955 18.72 17.4 6 2.1 4 6.91 1 Brevard 32976 5.09 56.94 1.6 4.8 1.53 2 Broward 33004 16.47 17.77 4.1 15.8 5.94 2 Broward 33009 11.93 33.31 3.5 14.6 3.91 2 Broward 33019 8.16 33.56 2.2 4.2 7.46 2 Broward 33020 18.54 13.30 4.6 18 2.84 2 Broward 33021 16.02 22.57 2.9 7 .6 13.43

PAGE 145

133 Appendix C, C ontinued Descriptive Statistics for Population by Zip Code Case County Zip Code Under_15 Over_65 Unemp Poverty Phy_Rate 2 Broward 33023 25.77 8.87 5 10.8 0.57 2 Broward 33024 22.75 10.83 3 7.7 5.01 2 Broward 33025 23.14 9.36 4 .4 7.1 1.83 2 Broward 33026 20.96 13.85 2.7 2.7 6.42 2 Broward 33027 15.38 37.39 2 4 12.37 2 Broward 33028 27.51 4.10 2.6 3.1 8.36 2 Broward 33029 28.86 4.65 2 2.8 8.63 2 Broward 33060 21.44 13.48 4.4 17.4 1.95 2 Broward 33062 6.37 37.22 2.3 6.6 9.41 2 Broward 33063 16.48 22.52 2.2 5.6 4.02 2 Broward 33064 19.55 15.28 3.4 11.3 3.69 2 Broward 33065 24.47 8.99 3.6 10.2 5.13 2 Broward 33066 8.45 53.42 1.2 3.2 2.98 2 Broward 33067 27.05 4.35 2.6 3.4 9.95 2 Broward 33068 24.71 7.94 4.3 11 0.52 2 Bro ward 33069 12.13 27.98 3 11.8 3.67 2 Broward 33071 23.75 4.92 3.8 3.4 5.16 2 Broward 33073 21.90 8.44 2.7 5.6 4.73 2 Broward 33076 29.59 3.07 2.4 2.2 11.42 2 Broward 33301 8.52 12.19 2.4 6.3 8.75 2 Broward 33304 12.29 14.81 5.1 15.9 4.82 2 Broward 33 305 11.71 14.78 2.6 8.3 5.41 2 Broward 33306 11.01 20.50 1.6 1.8 17.12 2 Broward 33308 8.65 31.96 1.9 4.2 17.51 2 Broward 33309 19.79 10.82 4.9 10.6 1.95 2 Broward 33311 26.76 10.17 6.3 27.5 1.22 2 Broward 33312 19.97 10.64 3.7 10.6 2.66 2 Broward 33 313 25.15 11.69 5.5 18.5 2.55 2 Broward 33314 19.90 9.52 4.3 10.5 0.63 2 Broward 33315 14.15 12.42 2.7 9.4 1.55 2 Broward 33316 8.90 23.18 2.4 5.7 21.09 2 Broward 33317 21.05 13.08 3.4 5.6 7.71 2 Broward 33319 15.18 29.69 3.3 9.5 2.09 2 Broward 33321 11.47 38.46 2.3 4.8 3.30 2 Broward 33322 14.46 32.54 2.2 5.7 2.58

PAGE 146

134 Appendix C, C ontinued Descriptive Statistics for Population by Zip Code Case County Zip Code Under_15 Over_65 Unemp Poverty Phy_Rate 2 Broward 33323 25.07 5.57 2.7 3.3 7.59 2 Broward 33324 17.10 15.58 2.3 3.9 11.07 2 Broward 33325 24.13 6.99 2.8 4.8 3.99 2 Broward 33326 25.28 8.91 2.5 5.6 8.85 2 Broward 33327 31.76 3.77 2.4 1.8 13.29 2 Broward 33328 21.79 9.44 2.2 3.8 5.99 2 Broward 33330 26.10 6.92 0.9 2.1 8.05 2 Broward 33331 28.44 4.93 1.6 1.9 9.77 2 Broward 33332 20.86 5.27 3.4 0.9 22.50 2 Broward 33334 18.89 11.25 4.2 17.9 4.21 2 Broward 33351 22.87 9.52 4.1 7.7 3.39 2 Broward 33388 0.00 0.00 0.00 0.00 0.00 2 Broward 33394 0.00 0.00 0.00 0.00 0.00 2 Broward 33441 17.08 17.93 3.5 12.6 2.41 2 Broward 33442 9.40 42.84 1.9 5.3 2.97 3 Dade 33010 17.42 19.60 5.2 22.2 1.21 3 Dade 33012 17.15 20.11 4.8 14.4 5.00 3 Dade 33013 15.84 19.90 4.2 13.1 4.80 3 Dade 33014 20.59 12.28 5 14.2 7.11 3 Dade 33015 23.86 6.72 4.8 9 2.33 3 Dade 33016 23.30 9.90 5.9 12.9 6.92 3 Dade 33018 24.48 7.73 4.5 8.7 1.59 3 Dade 33030 28.12 7.00 5.8 26.5 3.48 3 Dade 33031 19.80 9 .92 3 4.8 9.07 3 Dade 33032 30.90 5.82 7.1 22.1 0.48 3 Dade 33033 29.98 7.31 7.4 22.8 0.32 3 Dade 33034 26.75 6.87 9.9 34.5 0.00 3 Dade 33035 20.85 13.69 4.1 5.2 0.00 3 Dade 33054 25.98 10.74 9.4 26.6 0.35 3 Dade 33055 23.91 9.21 6.1 13.1 0.78 3 Dad e 33056 27.65 6.25 7.3 15.7 0.30 3 Dade 33109 14.13 15.20 0 0 0.00 3 Dade 33122 0.00 0.00 0 0 0.00 3 Dade 33125 18.02 19.61 5.4 21.2 4.12 3 Dade 33126 17.94 17.23 4.8 16.3 2.85 3 Dade 33127 25.52 11.17 9.2 34.3 1.62

PAGE 147

135 Appendix C, C ontinued Descriptive Statistics for Population by Zip Code Case County Zip Code Under_15 Over_65 Unemp Poverty Phy_Rate 3 Dade 33128 17.51 19.28 8.5 33.5 2.14 3 Dade 33129 11.05 18.56 3.4 6.6 12.61 3 Dade 33130 17.70 19.28 5.7 33 2.68 3 Dade 33131 7.52 11.05 2.4 7.5 18.00 3 Dade 33132 5.34 11.76 3.5 20.5 6.58 3 Dade 33133 15.02 15.86 3.4 11.9 15.04 3 Dade 33134 13.50 21.08 3.4 8.5 17.62 3 Dade 33135 16.03 22.15 6.3 23.5 5.18 3 Dade 33136 24.32 9.12 7.6 41.2 23.25 3 Dade 33137 18.15 11.78 8.7 27.5 7.37 3 Dade 33138 20.31 11.03 5.3 22.8 4.91 3 Dade 33139 6.33 20.39 4.2 16.2 8.45 3 Dade 33140 13.77 22.81 2.5 8.2 27.42 3 Dade 33141 15.34 14.79 5.3 2 0.6 4.38 3 Dade 33142 22.91 12.62 6.9 32.7 0.75 3 Dade 33143 18.21 13.54 3.5 7.1 17.12 3 Dade 33144 13.90 24.89 4.2 10.7 8.29 3 Dade 33145 14.96 22.84 4 12.2 7.26 3 Dade 33146 13.33 12.69 6.2 1.8 23.09 3 Dade 33147 26.89 10.16 8.1 35.1 0.20 3 Dade 3 3149 21.35 15.57 1.8 5.7 13.32 3 Dade 33150 25.99 9.67 7.9 29.3 1.90 3 Dade 33154 13.63 27.74 2.9 7 11.60 3 Dade 33155 16.49 19.68 3.3 6.3 9.40 3 Dade 33156 23.55 11.10 2.1 3.6 15.74 3 Dade 33157 24.66 10.19 4.4 12.1 4.73 3 Dade 33158 24.41 11.00 1.3 2 13.16 3 Dade 33160 10.80 29.64 2.8 11.4 7.09 3 Dade 33161 24.25 9.83 8.1 22.8 1.88 3 Dade 33162 25.16 9.62 6.6 18.2 3.10 3 Dade 33165 15.34 19.72 4.4 8.9 7.36 3 Dade 33166 18.45 11.98 3.3 8.9 4.65 3 Dade 33167 25.75 8.44 8.5 21.3 0.55 3 Dade 3316 8 25.39 8.09 8.5 21.3 0.20 3 Dade 33169 25.73 8.91 5.5 13.8 0.55 3 Dade 33170 28.43 9.30 5.7 27.7 0.00

PAGE 148

136 Appendix C, C ontinued Descriptive Statistics for Population by Zip Code Case County Zip Code Under_15 Over_65 Unemp Poverty Phy_Rate 3 Dade 33172 18.95 11.48 4.7 12.7 1.43 3 Dade 33173 18.28 14.19 3.5 5.7 13.67 3 Dade 33174 16.99 17.45 4.4 12.1 4.29 3 Dade 33175 19.69 13.54 3.4 6 .9 5.99 3 Dade 33176 20.67 10.66 3.5 6.9 13.06 3 Dade 33177 26.14 6.93 4.9 8.1 1.54 3 Dade 33178 22.62 5.35 2.7 8.6 12.77 3 Dade 33179 19.39 16.85 4.5 8.7 5.08 3 Dade 33180 12.07 29.22 2.3 5.7 17.55 3 Dade 33181 17.97 12.00 5.4 16.2 8.76 3 Dade 3318 2 19.01 6.38 2.5 7.2 7.11 3 Dade 33183 21.48 10.26 3.9 8.8 2.12 3 Dade 33184 19.42 13.51 3.8 8.2 3.27 3 Dade 33185 24.07 7.85 3 6.2 7.09 3 Dade 33186 22.12 7.63 3.7 6.1 4.76 3 Dade 33187 25.98 7.05 4 4.4 2.50 3 Dade 33189 26.40 9.31 4.5 13.6 3.45 3 Dade 33190 28.84 4.48 4.6 12.3 0.00 3 Dade 33193 23.49 6.94 5.1 12.8 1.41 3 Dade 33194 0.00 0.00 0 0 0.00 3 Dade 33196 25.25 5.69 4.2 6 3.17 4 Duval 32009 24.10 7.99 5.4 5.5 0.00 4 Duval 32073 22.35 10.47 2.8 3.7 7.46 4 Duval 32202 6.54 21.10 3.1 21. 9 11.86 4 Duval 32204 18.61 20.07 3.7 26.7 40.50 4 Duval 32205 20.17 13.18 3.1 12.6 5.32 4 Duval 32206 24.89 10.99 6 34.9 2.36 4 Duval 32207 19.81 14.54 3 9.8 10.07 4 Duval 32208 22.80 12.90 4.7 15.2 1.93 4 Duval 32209 25.19 16.67 5.9 25.9 7.82 4 Du val 32210 23.36 11.67 2.9 9.8 2.75 4 Duval 32211 22.20 10.97 4.9 10.9 2.03 4 Duval 32212 27.85 0.16 1.8 18.3 6.04 4 Duval 32215 48.77 0.99 0 9.2 0.00 4 Duval 32216 21.35 13.19 2.9 8.4 12.38 4 Duval 32217 19.16 15.89 3.5 7.4 5.93

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137 Appendix C, C ontinued Descriptive Statistics for Population by Zip Code Case County Zip Code Under_15 Over_65 Unemp Poverty Phy_Rate 4 Duval 32218 23.44 9 .04 3.8 7.9 1.32 4 Duval 32219 21.40 10.86 4 10.3 0.00 4 Duval 32220 22.83 8.56 2.2 7.6 0.47 4 Duval 32221 22.58 9.46 3 5.8 1.39 4 Duval 32222 23.97 6.92 3.6 6.1 0.00 4 Duval 32223 21.22 8.44 2.2 1.7 7.28 4 Duval 32224 20.33 7.07 3.1 3.5 14.25 4 Duv al 32225 23.98 7.08 2.3 3.1 5.69 4 Duval 32226 19.28 10.56 1.7 6.4 2.45 4 Duval 32227 20.08 0.11 0.7 7.2 0.00 4 Duval 32233 23.16 9.71 2.5 6.9 2.17 4 Duval 32234 24.10 8.44 3.5 7.1 0.00 4 Duval 32244 25.29 7.23 2.6 8.9 1.82 4 Duval 32246 24.05 6.34 2 .6 7.4 3.88 4 Duval 32250 14.94 12.93 3.8 3.8 9.62 4 Duval 32254 26.66 9.79 5.2 19.5 1.00 4 Duval 32256 16.33 8.50 2.8 3.3 15.61 4 Duval 32257 21.32 9.29 2 3.8 6.46 4 Duval 32258 24.34 6.54 2.2 1 5.55 4 Duval 32259 26.89 7.50 1.9 1.4 7.75 4 Duval 32 266 15.44 12.18 2.9 1.9 7.60 4 Duval 32277 22.82 9.64 2.5 5.9 3.26 5 Hillsbo 33510 22.32 9.61 2.4 4 1.56 5 Hillsbo 33511 22.12 8.55 2.8 4 9.13 5 Hillsbo 33527 24.54 8.71 5.8 12.7 0.44 5 Hillsbo 33534 26.35 8.28 5 16.5 0.00 5 Hillsbo 33540 16.89 30.16 2.5 7.3 1.59 5 Hillsbo 33547 24.57 7.94 2.6 7 8.80 5 Hillsbo 33549 21.15 8.85 2.9 3.9 1.12 5 Hillsbo 33556 22.17 8.37 0.8 1.9 9.65 5 Hillsbo 33565 21.11 17.06 2.2 6.6 0.59 5 Hillsbo 33566 24.23 12.38 3.4 12.9 3.94 5 Hillsbo 33567 24.78 10.38 3.1 9.6 0.00 5 Hillsbo 33569 23.26 9.99 2.1 6.6 3.22 5 Hillsbo 33570 20.01 22.06 3 9.5 1.56 5 Hillsbo 33572 14.60 18.63 2.9 2.4 6.70

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138 Appendix C, C ontinued Descriptive Statistics for Population by Zip Code Case County Zip Code Under_15 Over_65 Unemp Poverty Phy_Rate 5 Hillsbo 33573 0.34 83.05 0.6 2.2 4.60 5 Hillsbo 33584 23.09 9.16 3.3 5.4 0.00 5 Hillsbo 33592 22.45 12.30 3.3 9.6 0.00 5 Hillsbo 33594 24.03 9.02 2 3 4.51 5 Hillsbo 33598 31.19 6.31 5.2 23.1 0.00 5 Hillsbo 33602 20.85 13.26 8 27.9 8.38 5 Hillsbo 33603 22.56 13.07 6 17.7 2.63 5 Hillsbo 33604 23.58 10.94 4 19.6 1.50 5 Hillsbo 33605 24.55 14.45 6.1 28.3 1.17 5 Hillsbo 336 06 13.66 10.82 11.1 3.8 22.06 5 Hillsbo 33607 20.62 19.08 4.1 18.9 15.35 5 Hillsbo 33609 16.49 16.87 2.5 6.1 15.14 5 Hillsbo 33610 24.69 12.68 5.1 18.8 0.62 5 Hillsbo 33611 16.22 15.37 2.3 5.9 4.19 5 Hillsbo 33612 22.73 12.22 5.1 17.3 5.47 5 Hillsbo 33613 17.19 11.82 5.1 14.5 10.88 5 Hillsbo 33614 19.13 10.76 3.3 13.4 5.59 5 Hillsbo 33615 19.47 10.70 3.1 6 3.51 5 Hillsbo 33616 21.49 8.29 5 13.6 0.83 5 Hillsbo 33617 21.10 8.89 3.7 10.2 3.07 5 Hillsbo 33618 19.45 10.87 2.2 4.4 12.53 5 Hillsbo 3361 9 23.47 8.82 3.8 15.9 1.93 5 Hillsbo 33620 0.00 0.00 95.1 0 3.95 5 Hillsbo 33621 36.07 0.22 2.8 4.1 1.86 5 Hillsbo 33624 20.75 8.35 2.1 3.4 2.44 5 Hillsbo 33625 24.07 7.10 3.9 4.8 3.85 5 Hillsbo 33626 24.73 5.47 1.5 1.7 26.99 5 Hillsbo 33629 17.64 16 .89 2 2.3 10.50 5 Hillsbo 33634 20.93 9.07 2.8 6.5 4.41 5 Hillsbo 33635 21.53 10.19 2.1 6.3 4.42 5 Hillsbo 33637 21.83 7.00 1.7 10.8 2.79 5 Hillsbo 33647 24.07 4.94 2.1 3.9 20.73 5 Hillsbo 33834 19.88 14.19 2.9 17.5 0.00 5 Hillsbo 34221 19.71 21.84 2 9.1 2.05 6 Orange 32703 23.93 9.74 3.2 9.5 2.89 6 Orange 32709 21.08 11.26 3 12.1 0.00

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139 Appendix C, C ontinued Descriptive Statistics for Population by Zip Code Case County Zip Code Under_15 Over_65 Unemp Poverty Phy_Rate 6 Orange 32712 22.85 10.94 2.3 5.4 4.45 6 Orange 32751 20.58 17.10 2.3 3.7 11.59 6 Orange 32757 17.38 24.31 1.7 8.8 4.52 6 Orange 32776 21.72 11.71 3.4 2.8 3.01 6 Orange 32789 16.66 16.94 5.4 5.1 9.26 6 Orange 32792 16.39 14.01 2.9 5.7 6.30 6 Orange 32798 3.01 71.05 0.9 1.2 0.00 6 Orange 32801 8.61 30.42 4.5 15.9 6.27 6 Orange 32803 11.74 18.78 2.6 4.8 16.45 6 Orange 32804 15.36 15.09 1.9 3.4 15.21 6 Orange 32805 25.47 11.08 6 29.6 1.84 6 Orange 32806 16.74 15.62 2.7 6.3 21.74 6 Orange 32807 20.84 11.31 3.2 9.4 2.74 6 Orange 32808 27.82 8.28 5.5 18.5 1.43 6 Orange 32809 21.72 10.81 3.9 11.4 2.43 6 Orange 32810 24.59 8.05 3.6 9.6 1.69 6 Orange 32811 21.9 1 6.31 4.6 16.7 0.60 6 Orange 32812 20.91 10.79 2.2 7 4.17 6 Orange 32817 18.76 6.42 3.4 5.8 3.76 6 Orange 32818 24.55 8.37 3.3 8.7 1.40 6 Orange 32819 21.20 8.18 3 4.8 15.47 6 Orange 32820 22.51 8.98 5.7 10.8 3.33 6 Orange 32821 11.58 17.27 1.6 2.7 1.44 6 Orange 32822 20.30 10.26 3.7 9.3 2.68 6 Orange 32824 25.19 7.09 4.5 6.4 9.31 6 Orange 32825 21.76 6.86 3.1 6.4 3.20 6 Orange 32826 14.39 7.76 4.8 7.9 1.03 6 Orange 32827 24.34 5.49 1.9 7.4 2.29 6 Orange 32828 25.74 4.44 3 3.5 5.61 6 Orange 32 829 23.42 6.48 3.1 1.9 7.01 6 Orange 32831 0.00 0.00 0 0 0.00 6 Orange 32832 19.95 8.17 1.6 0 2.69 6 Orange 32833 21.19 9.23 3.7 13.4 0.98 6 Orange 32835 20.40 5.03 2.8 6.6 7.65 6 Orange 32836 24.67 7.63 1.9 5.4 21.06 6 Orange 32837 23.62 6.86 2.4 5 5.74

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140 Appendix C, C ontinued Descriptive Statistics for Population by Zip Code Case County Zip Code Under_15 Over_65 Unemp Poverty Phy_Rate 6 Orange 32839 20.64 6.19 4.5 16.8 1.24 6 Orange 34734 26.20 6.83 0.8 2.8 9.53 6 Orange 34747 22.38 7.30 2.9 5 22.86 6 Orange 34760 23.90 9.98 1.5 8 0.00 6 Orange 34761 24.37 7.31 2.5 5.4 7.19 6 Orange 34786 25.71 8.06 0.8 1.4 24.86 6 Orange 34 787 21.85 13.46 2.8 8.5 2.63 7 PalmBe 33401 15.65 22.66 2.9 15.9 11.21 7 PalmBe 33403 20.11 17.97 4.3 9.1 6.19 7 PalmBe 33404 24.03 22.01 4.6 19.2 2.00 7 PalmBe 33405 18.32 17.46 2.7 11.7 4.54 7 PalmBe 33406 19.03 15.36 2.9 6.8 3.95 7 PalmBe 33407 24 .56 17.94 4.3 20 8.89 7 PalmBe 33408 11.45 23.25 1.3 2 4.68 7 PalmBe 33409 17.78 15.74 3.9 11.8 3.84 7 PalmBe 33410 16.11 17.94 2.5 5.9 13.06 7 PalmBe 33411 21.39 19.47 2.6 4.4 5.52 7 PalmBe 33412 24.94 14.96 2.4 1.7 14.66 7 PalmBe 33413 19.79 16.70 2.4 6.7 3.16 7 PalmBe 33414 25.17 17.00 1.9 2.9 14.71 7 PalmBe 33415 21.02 20.00 3.4 11.9 0.76 7 PalmBe 33417 14.14 29.09 3.2 9.8 1.44 7 PalmBe 33418 16.63 18.58 14 1.7 10.95 7 PalmBe 33426 12.55 27.40 1.1 3.2 4.22 7 PalmBe 33428 22.23 19.73 2.7 4.1 6.90 7 PalmBe 33430 28.70 17.82 6.8 31.4 2.12 7 PalmBe 33431 13.73 17.05 7.9 2.7 7.76 7 PalmBe 33432 11.95 20.89 1 6.7 9.49 7 PalmBe 33433 13.21 24.06 2.2 1.9 5.27 7 PalmBe 33434 11.89 32.79 0.9 3.2 7.96 7 PalmBe 33435 18.97 22.82 3.8 11.4 4.90 7 Pa lmBe 33436 14.33 24.70 1.8 4 4.06 7 PalmBe 33437 10.37 29.06 1.4 2.9 6.89 7 PalmBe 33438 26.79 19.74 11.1 16.7 0.00 7 PalmBe 33440 27.36 17.98 5.9 16.1 3.02 7 PalmBe 33444 21.98 16.87 4.3 15.2 2.58

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141 Appendix C, C ontinued Descriptive Statistics for Population by Zip Code Case County Zip Code Under_15 Over_65 Unemp Poverty Phy_Rate 7 PalmBe 33445 13.21 27.13 2.6 4.7 4.45 7 PalmBe 334 46 2.89 43.58 1.1 3.3 10.23 7 PalmBe 33458 21.35 16.73 2.3 3.3 7.38 7 PalmBe 33460 20.01 16.98 4.5 16.6 3.59 7 PalmBe 33461 20.65 19.13 4.4 11 2.38 7 PalmBe 33462 17.75 20.26 2.7 6.6 4.96 7 PalmBe 33463 22.42 20.01 2.9 6.4 2.31 7 PalmBe 33467 18.66 2 5.06 1.6 2.8 6.23 7 PalmBe 33469 14.41 23.78 1.2 2.4 5.90 7 PalmBe 33470 27.62 15.92 2.1 3.6 5.76 7 PalmBe 33476 32.84 21.20 9 35.9 0.00 7 PalmBe 33477 6.74 23.36 1.3 2.6 5.46 7 PalmBe 33478 23.38 14.59 1.7 1.8 1.77 7 PalmBe 33480 7.77 33.30 1 3.1 7. 14 7 PalmBe 33483 8.95 21.94 2.6 5.6 5.89 7 PalmBe 33484 3.99 42.27 1 2.9 4.72 7 PalmBe 33486 18.08 16.78 3.5 3.7 15.71 7 PalmBe 33487 10.66 23.81 1.8 2.6 7.71 7 PalmBe 33493 22.31 14.15 8.5 29 5.13 7 PalmBe 33496 15.30 22.41 1.6 2.9 16.46 7 PalmBe 33498 21.76 19.68 1.5 3.1 7.93 8 Pinellas 33701 12.23 13.59 5.6 13.5 23.09 8 Pinellas 33702 15.09 10.28 2.1 5.2 3.66 8 Pinellas 33703 17.83 9.57 1.8 3.5 5.98 8 Pinellas 33704 16.89 7.92 1.6 5.2 7.48 8 Pinellas 33705 22.01 8.82 4.1 16.9 5.34 8 Pinella s 33706 7.94 15.96 2.3 3.8 5.76 8 Pinellas 33707 11.56 17.47 2.5 8.2 7.54 8 Pinellas 33708 7.94 17.61 1.7 4 3.20 8 Pinellas 33709 15.17 14.62 2.4 9.4 2.88 8 Pinellas 33710 17.06 9.77 1.8 4.6 6.77 8 Pinellas 33711 22.02 7.68 5.1 17.1 1.76 8 Pinellas 3 3712 22.76 6.49 5.2 14.3 1.14 8 Pinellas 33713 19.46 6.91 3.1 10.1 3.68 8 Pinellas 33714 17.75 9.64 3.2 11.5 1.13 8 Pinellas 33715 6.77 18.01 2.3 1.6 8.78

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142 Appendix C, C ontinued Descriptive Statistics for Population by Zip Code Case County Zip Code Under_15 Over_65 Unemp Poverty Phy_Rate 8 Pinellas 33716 9.40 5.59 3.2 6.6 9.13 8 Pinellas 33755 19.65 6.07 3.6 12.4 4.41 8 Pinellas 33 756 16.30 12.75 2.1 8 13.41 8 Pinellas 33759 19.11 10.91 2.2 12.6 1.99 8 Pinellas 33760 16.70 4.78 2.3 13.3 2.36 8 Pinellas 33761 13.69 13.76 1.8 4.4 9.19 8 Pinellas 33762 10.30 10.62 2.3 2.6 10.27 8 Pinellas 33763 9.33 21.54 1.8 3.4 3.33 8 Pinellas 33764 14.68 14.00 2.3 5.3 5.07 8 Pinellas 33765 14.52 9.36 1.3 7.5 2.98 8 Pinellas 33767 5.50 19.42 2.6 3.5 8.19 8 Pinellas 33770 13.93 13.56 3 6.2 7.58 8 Pinellas 33771 12.39 16.32 1.4 6.7 3.08 8 Pinellas 33772 14.46 14.96 1.9 3.5 3.23 8 Pinellas 33 773 17.68 9.07 2.3 4 2.14 8 Pinellas 33774 15.82 13.68 2 5.6 5.43 8 Pinellas 33776 17.63 10.04 1.7 2 5.60 8 Pinellas 33777 18.67 9.84 2.3 5.8 6.64 8 Pinellas 33778 17.22 11.65 2.3 4.6 5.50 8 Pinellas 33781 20.60 6.81 3.6 8.1 2.57 8 Pinellas 33782 15. 96 12.95 2.2 5.2 5.12 8 Pinellas 33785 7.90 10.27 1.7 2.8 5.04 8 Pinellas 33786 10.31 13.99 1.3 3.4 12.49 8 Pinellas 34677 20.29 7.94 2.5 2.8 4.33 8 Pinellas 34681 22.03 5.97 0.6 5 20.18 8 Pinellas 34683 19.41 7.77 2.6 3.9 4.41 8 Pinellas 34684 14.49 17.75 1.4 4.6 6.93 8 Pinellas 34685 21.41 7.77 2.6 3.8 9.68 8 Pinellas 34689 16.16 12.23 2 6.7 4.52 8 Pinellas 34695 17.58 9.74 1.9 3.5 5.51 8 Pinellas 34698 12.69 15.92 1.9 5 7.30 9 Polk 33547 24.57 7.94 2.6 7 9.97 9 Polk 33801 19.23 16.54 4.4 13.6 1.42 9 Polk 33803 16.23 21.88 5.4 6.4 8.52 9 Polk 33805 24.18 17.07 4.2 18.5 12.73 9 Polk 33809 18.33 22.51 2.4 5.9 0.87

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143 Appendix C, C ontinued Descriptive Statistics for Population by Zip Code Case County Zip Code Under_15 Over_65 Unemp Poverty Phy_Rate 9 Polk 33810 22.34 14.68 2.2 8 1.40 9 Polk 33811 21.72 11.09 2.4 4.6 0.93 9 Polk 33813 22.28 12.46 2.3 2.4 9.18 9 Polk 338 15 23.44 19.08 4.4 18.1 0.73 9 Polk 33823 22.13 14.52 3.3 11 1.13 9 Polk 33825 17.42 26.08 2.5 12.4 2.58 9 Polk 33827 20.82 14.13 8.5 11.3 0.00 9 Polk 33830 22.16 13.68 4.1 11.6 3.69 9 Polk 33835 20.00 10.00 0 0 0.00 9 Polk 33837 16.97 20.91 1.8 5.6 1.17 9 Polk 33838 22.27 22.12 4.1 11.8 3.52 9 Polk 33839 23.38 12.95 2.2 12.3 3.14 9 Polk 33841 23.42 15.85 4.3 14.2 0.63 9 Polk 33843 18.67 21.21 3.5 14.1 0.47 9 Polk 33844 20.41 22.61 3.8 12.8 1.69 9 Polk 33847 21.91 12.72 0 34.9 0.00 9 Polk 33849 25.36 8.13 6.3 9.2 0.00 9 Polk 33850 21.91 16.89 2.2 13 0.00 9 Polk 33851 19.63 14.22 4.1 4.7 0.00 9 Polk 33853 18.35 25.19 3.3 10.8 1.74 9 Polk 33860 23.39 12.59 4.2 7.1 0.00 9 Polk 33868 19.05 11.21 2.1 9 0.46 9 Polk 33877 30.18 12.18 15.8 50 0.00 9 Polk 33880 22.38 14.51 3.4 10.8 5.62 9 Polk 33881 17.46 28.77 3.2 11 3.90 9 Polk 33884 16.43 27.16 1.9 2.2 5.50 9 Polk 34759 23.59 10.50 1.7 5.3 1.32

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About the Author Arlesia Brock was admitted to the Department of Health Policy and Ma nagement in Fall 2002 on a fully funded fellowship from the Florida Education Fund. Subsequently, she was awarded a pre doctoral fellowship from the Demographic and Behavioral Sciences Center for Population Research at the National Institute of Child Hea lth and Human Development for her research on the effects of privatization in the provision of public health services. Ms. Brock earned Bachelor of Science degrees in Microbiology and Psychology from the Louisiana State University and a Master of Arts in Industrial/Organization al Psychology from the University of West Florida. She also completed a certification in Public Management from Florida State University Center for Professional Development. She holds numerous professional and academic awards includ ing the prestigious McK night Doctoral Fellowship and the Ruth L. Kirschstein Nati onal Research Service Award