Tulsa Metropolitan Area BASELINE RESEARCH in SUPPORT of a REGIONAL STRATEGIC PLAN

A HEALTH POLICY ANALYSIS OF THE Tulsa Metropolitan Area BASELINE RESEARCH IN SUPPORT OF A REGIONAL STRATEGIC PLAN

OUTCOMES

UTILIZATION

INCOMES

DEMOGRAPHICS

Michael Lapolla, Co-Director and Bina P. Patel, MPH, Research Assistant, Center for Health Policy Research and Development, University of Oklahoma College of Public Health EXECUTIVE SUMMARY

SECTION 1-1: Purpose ...... 4

SECTION 1-2: Policy Analysis ...... 5 Safety Nets in U.S. Metropolitan Areas ...... 7 Sources of Health Insurance...... 9 Causes of Death...... 11 Hospital Utilization ...... 12

SECTION 1-3: Policy Options...... 14

Summary Tables ...... 15 Zip Code Tabulation Area Maps for Tulsa Metropolitan Area

THE TULSA METROPOLITAN AREA

SECTION 2: Safety Nets in U.S. Metropolitan Areas ...... 19

SECTION 3: Sources of Health Insurance...... 37

SECTION 4: Causes of Death...... 51

SECTION 5: Hospital Utilization ...... 68

END NOTES

DATABASES AND OTHER RESOURCES

Primary Database for Tulsa MSA This database is a worksheet with 138 columns representing each of the 127 ZCTAs in the Tulsa MSA and summarized by each of the seven counties. It contains data elements for demographic, economic, employment, health insurance status, hospital utilization, causes of death, education and poverty data for each ZCTA. The database was obtained from various national, regional and local sources. The database is available upon request as an electronic file.

Age-Adjusted Death Rates for Tulsa MSA This database contains over 30,000 records of the deceased in the Tulsa region from 2000-2003, including demographic and specific cause information for each death. This database is a worksheet with 138 columns representing each of the 127 ZCTAs in the Tulsa MSA and summarized by each of the seven counties. Per a confidentiality agreement signed with the State Department of Health, this database will remain restricted, and not available upon request. Essential masked data was processed through statistical analysis software and summarized. The summary appears in the Tulsa MSA database listed above.

Hospital Utilization: Tulsa County Hospitals This database accounts for the ZCTA origin and pay sources of all emergency room visits and hospital admissions in 2004 to major Tulsa hospitals (Saint Francis Hospital, St. John Medical Center, Hillcrest Medical Center, Tulsa Regional Medical Center and SouthCrest Hospital). This database is not available to others. Specific hospital information will not be released without the expressed permission of the respective hospital CEOs. The data was aggregated and included in the database for the Tulsa MSA.

Kaiser Family Foundation Health Insurance Coverage in America: 2003 Data Update. Kaiser Commission on Medicaid and the Uninsured, The Henry J. Kaiser Family Foundation. Publication Number: 7153. Publish Date: 2004-12-08. It is a 55 page document available online (www.kff.org/uninsured/loader.cfm?url=/commonspot/security/getfile.cfm&PageID=49550).

Employee Benefits Research Institute Sources of Health Insurance and Characteristics of the Uninsured: Analysis of the March 2004 Current Population Survey, December 2004, #276, Employee Benefit Research Institute, © 2004. This is a 31 page document available online. Link: http://www.ebri.org/ibpdfs/1204ib.pdf © 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 2 SECTION 1 EXECUTIVE SUMMARY

SECTION 1-1 STUDY PURPOSE

This analysis was initiated in order to provide baseline health policy and planning research for a Greater Tulsa Strategic Master Plan, and to provide the Oklahoma Secretary of Health a replicable template for regional analysis elsewhere in the state.

All of the county and MSA analyses use a geographic measure developed by Bureau of the Census - the Zip Code Tabulation Areas (ZCTA), which generally correspond to areas served by a given Zip code. This new tool makes it possible to combine the familiar geography of the U.S. Postal Service (USPS) Zip Codes with the wealth of data available for the Census Tracts.

In most cases, analyses will be of the seven-county Tulsa MSA. In cases relating to hospital utilization, the analysis will be limited to Tulsa County to avoid difficulties in measuring the utilization of smaller hospitals in surrounding counties.

This research product was designed to:

. Measure and examine:

o Tulsa’s safety net health care services in the context of other metro areas in the nation. o The health insurance status of Oklahomans and Tulsans by county and ZCTA. o The age-adjusted death rates of the major causes of death in the seven county area o The basic ER and inpatient utilization of hospitals by Tulsa MSA residents.

. Observe the correlations of incomes, insurance, geographic location, access and causes of death.

. Suggest policy approaches to be explored by any community-wide strategic planning process aimed at organizing infrastructure and delivering effective services, with particular focus on the health care safety net.

A primary purpose of this research is to provide basic information to assist in developing a Tulsa Strategic Health Plan. Such a plan will be required to make choices about which strategic direction to pursue with regard to the provision of health care services for the medically underserved. While there are a host of needs and ideas to match, all options cannot be pursued concurrently—some are mutually exclusive, while others simply cannot be addressed at the local level. Nor is it intended to be an encyclopedia of health research, but rather a starting point to establish common ground.

It is envisioned that this product will grow in both breadth and depth as others add to the body of knowledge in Tulsa.

Tulsa Health “We are the only state where our age-adjusted death rates 1,050 became worse through the 1990s and into this century.” 1 Age-Adusted Death Rates The most fundamental health status measurement is the age- 1,000 adjusted death rate (AADR). This single statistic will perfectly measure the complete lack of health – that is premature death. Tulsa Tulsa County County trends have shadowed state trends.

950 From 1980-1992, Tulsans and Oklahomans enjoyed a declining AADR that paralleled that of the nation. In 1992, the Tulsa County (and Oklahoma) AADR dramatically separated from the national trend and began to increase while the national average decreased. 900 Since 1980, the national rate declined 20% while Tulsa fell only 5%. Since 1990, the national average declined 11% while the Tulsa rate increased 3%. (see chart at right). 2 This is not a random event. There are reasons. They are complex. 850 United States The State Health Department is concerned. All Oklahomans should be. The conclusions and recommendations of the State Health Department are in the State of the State’s Health 2005 follow: 800 1990 1985 1995 1980 2000 State of the State Health 2005 3 “The evidence is overwhelming. Placing a priority on prevention will improve the health status of our citizens, is cost effective, and in a variety of ways will enhance the economy of our state. Previous reports have outlined more specific recommendations, and indeed, some progress has been made in Oklahoma. Most recently and notably, our collective efforts to diminish the impact of nicotine addiction are having an impact, and with the recent increase on tobacco taxes, we will see a significant decline in tobacco use, especially in our youth. The Board of Health, again, issues a Call to Action and in broad terms recommends the following:

1. The training of all health professionals must include clinical prevention.

2. All third party payers, public and private, should appropriately reimburse health providers for clinical prevention.

3. Our health care system, public and private, must give a greater priority to our public health infrastructure and effective public health measures.

4. Our citizens must experience greater incentives for adopting healthy lifestyles.

5. Our business community, faith organizations, educational institutions, community organizations, and public health structures must rethink their roles in improving our state of health and become more involved in this process.

“A rock-solid investment with guaranteed returns for our prevention efforts is a strong public health system. Assuring that this system is well funded and has an adequate, well-trained workforce is critical … with more Oklahomans falling into the category of the uninsured, a meltdown of our health care system is © 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 5 likely in the future, unless thoughtful investments into prevention and our public health system are made soon … if this meltdown occurs, not only will the health of our citizens be put in jeopardy, but Oklahoma businesses also will suffer tremendously as the cost of health care further erodes their bottom line.” 4

Global Findings What is the essential message of this analysis? The findings will not surprise those experienced with health issues in Tulsa and the region; and it would be disappointing if less experienced people were shocked. What the analysis does do is validate suspicions and better explains “conventional wisdom”. It will provide a sound statistical and geographic basis for doing the right thing – in the right place – in the right way – for the right reasons.

Abraham Lincoln’s Gettysburg Address was 266 words – and explained the entire context of the nation’s most important conflict. The summary text below is also 266 words – and captures the essential strategic messages of this analysis concerning Tulsa health care. Consider …

Health care problems in Tulsa are rooted in the community being ill organized to appropriately provide necessary health care services for “medically marginalized” residents; and too many of these residents are unable or unwilling to properly use existing services. While almost every metropolitan area in the nation employs at least one – and up to five – public strategies to provide safety net services, Tulsa employs none. In the contemporary health care environment, these failures will adversely impact every citizen and provider - one way or another - sooner or later. It is has become an inescapable law of contemporary social physics.

The option of doing nothing is seductive and destructive. The option of doing everything is neither possible nor optimal. Many efforts are “ready-fire-aim”. This analysis suggests, “ready-aim-fire”.

This analysis explains, and localizes, the following cycle. Lower income citizens have poorer health status – requiring more services that are not readily available – that are sought in an episodic manner resulting in the wasteful utilization of expensive services. As these services are episodic, the heath status does not change and the “system” is disrupted. As the health status does not improve – a host of social dynamics are unleashed insuring poor health and stunted incomes in the future.

This analysis suggests a more unified community approach to the provision of inpatient services; and more energetic approach in distributing outpatient services; and strategies to build necessary infrastructure through optimizing public reimbursements. In the end, the most perfect “system” will not eliminate illness and death. It will give Tulsans a better chance to adopt healthier behaviors and optimize their quality of life.

This analysis focuses on four topics. The Executive Summary immediately follows, and the complete discussion is at a supplemental section in this report. The sections are: (2) Safety Nets in U.S. Metropolitan Areas (3) Health Insurance Status (4) Age-Adjusted Death Rates By Cause and (5) Hospital ER and Inpatient Utilization

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 6 POLICY ANALYSIS SAFETY NETS IN U.S. METROPOLITAN AREAS This analysis examined the general health care infrastructure of America’s 80 largest metropolitan areas. It does so by evaluating the presence of the five traditional methods employed to provide public dollar support to serve the medically indigent. These methods are (1) a public and/or university hospital(s), (2) a comprehensive medical school, (3) Medicaid Disproportionate Share Hospital (DSH) payments, (4) the presence of hospitals focused upon this mission, and (5) state support for Federally Qualified Health Centers (FQHC).

For generations, a Tulsa slogan has been, “We are the largest city in the country without a public hospital.” For decades it was said with civic pride. The changing health care culture and landscape has shifted the tone to one of denied entitlement and frustration. At one time the slogan was technically true, but irrelevant. Today it does not apply at all as there are many forms of supporting indigent care services beyond simply having a public hospital.

Public hospitals were rapidly developed after WWII to provide services for those who could not afford care. They were traditionally called the “county hospital” or the “city hospital.” The financial support was provided by governments. The services were traditionally minimal in the sense that these hospitals did not have private rooms, sophisticated equipment and the like. Over time, most public hospitals have re- invented themselves through alliances with comprehensive medical schools and/or multi-hospital systems. In many cases, the ownership and governance of these hospitals has changed to “private, non- profit” institutions with public revenue being provided through a variety of streams.

Tulsa (and Wichita) are the only two metro areas lacking all traditional infrastructure, financing mechanisms and organizational tools for providing coordinated and focused safety net services.

SELECTED METROPOLITAN AREAS IN THE U.S. SEE APPENDIX FOR ALL 80 METROPOLITAN AREAS

Public Comprehensive Medicaid Focused State FQHC Hospital Medical School DSH Hospital(s) Emphasis

Fresno County No Yes Yes Yes Fresno

Birmingham State Yes Yes Yes Yes Birmingham

Honolulu Public Yes Alternate Yes Yes Honolulu

Albany University Yes Yes Yes Yes Albany

Tucson University Yes Yes Yes No Tucson

Tulsa No No No No No Tulsa

Syracuse State Yes Yes Yes Yes Syracuse

Omaha University Yes Yes Yes No Omaha

PUBLIC HOSPITAL is any hospital with governance listed in the American Hospital Association Guide as City, County, City-County, Hospital District or State - or a University operated hospital. COMPREHENSIVE MEDICAL SCHOOL is any accredited medical school that offers a four year curriculum and an array of specialty and sub-specialty education programs and services. This definition excludes community-based medical schools and osteopathic medical schools. MEDICAID DSH means that one or more hospitals in the community receive significant amounts of Medicaid Disproportionate Share Hospital monies. FOCUSED HOSPITAL (S) indicate that one or more hospitals serve the medically marginalized in significant amounts by charter or mission. STATE FQHC EMPHASIS means the community is in a state with at least three times the FQHCs per population of Oklahoma. Aside from charity and some philanthropy, Tulsa lacks almost every tool used in other communities to serve the medically marginalized citizens. It lacks public facilities and dedicated private ones. It lacks a comprehensive medical school and broad-based graduate medical education training, but does have a significant medical education presence provided by two schools. It lacks an adequate network of Federally Qualified Health Centers. It is in a state that receives exceptionally low federal supplemental

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 7 (Disproportionate Share Hospital) payments for its Medicaid program. The federal Medicaid program allocated almost $13 billion to “disproportionate share hospitals” in FY 2004.5 Hospitals in Oklahoma received .18% of that amount ($27 million) and virtually all of it was directed to public facilities in central Oklahoma.

Publicly funded health care infrastructures are intended to complement private organizations. In the absence of publicly funded services for the medically marginalized, private non-profit health systems operate at a long-term disadvantage by definition. The disadvantage is not always obvious or measurable – but it creates ongoing problems and will erode health systems over time. Those systems will be required to divert patient care revenues rather than improve service. In the longer term, every potential patient will have fewer available resources and limited access to services.

Because Tulsa is so lacking in safety net health care infrastructure relative to other metropolitan areas, Tulsa has many options from which to choose. The proper choice is beyond the scope of this analysis. But the generic options are many. For example, Tulsa could:

• build/lease and operate a publicly owned facility (Denver, Miami or Portland); • use state and/or local tax dollars to contract with private facilities (Cincinnati); • create a multi-county hospital district with taxing authority and governing board (Austin); • significantly expand medical schools to provide a specialist workforce (Milwaukee or Las Vegas); • politically obtain significant Medicaid DSH payments (New Orleans, Columbus or Louisville) • obtain public and private matching funds to significantly improve/expand FQHCs and/or • pursue Medicaid Upper Payment Limit improvements.

What if the Tulsa County created a hospital district, and assessed a levy similar to the average of all major urban areas in Texas? (See table in the appendix this section) That levy would assess every county resident approximately $105 per year or less than $9 per month, raising almost $59 million in Tulsa County alone and a total of over $90 million in the MSA. Consider that in 2002 the entire cost of uncompensated hospital and ER care in the five largest Tulsa County hospitals was $56 million. 6

Oklahoma ranks 49th nationally in the number of FQHCs per population. Even with the April 2005 approval of four additional FQHC sites in the state, the overall ranking has not changed. Tulsa is underserved by Federally Qualified Health Centers (FQHCs). FQHCs are neither easy to start or manage. Moreover, since FQHCs principally provide primary care services, formal linkages with special services and hospitals are needed to have a comprehensive system. Notably, those communities that made that investment years ago are reaping significant rewards. Patients will tend to have a medical home and financing is shared between federal, state and local sources.

Tulsa has only one fully operational FQHC, the Morton Comprehensive Health Center. The Morton CHC is located at the epicenter of the areas having both the highest death rates and highest ER utilization rates. A second FQHC, Community Health Connections, has been approved with federal funding not due before December 2005. Recent federal action has frozen approvals of new or expanded operations. One could certainly conclude that the Tulsa needs are significantly beyond the scope of that single FQHC. Any efforts to expand the current FQHC – and to establish effective new operations – should deserve Tulsa’s tangible and intangible support. Private foundations in Tulsa have recently established a PriCare Review Panel. This Panel gathers and grants monies to any organization that is creating a new – or expanding an existing – Federally Qualified Health Center. Additionally, community health leaders and private foundations recognize that Tulsa will need special attention from the federal government in order to create any rational system of FQHC development. That process is being explored.

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 8 POLICY ANALYSIS HEALTH INSURANCE STATUS This analysis estimates the health insurance status for the residents of each of the region’s 127 ZCTAs. This is done by creating a Master Regional Database; adding the known Medicare and Medicaid enrollments; then creating an algorithm to estimate the number of those lacking health insurance – and inversely, those possessing private insurance. The same methodology was applied to each county in the state.

Since the inception of health insurance in the 1930s until the mid 1960s, people without health insurance were not a major ongoing public policy issue. The enactment of Medicare and Medicaid granted benefits to specific populations, but didn’t immediately establish the “uninsured” as a political constituency. “Cost shifting” allowed providers to use insurance over-billings to pay for those without the means to pay.

The mid-1980s saw three significant events that changed this dynamic permanently. These were: (1) in 1984, Medicare changed from cost-plus reimbursement to prospective payment and private insurance transformed into managed care with a variety of cost-cutting payment schemes; (2) in 1986, the EMTALA (Emergency Medical Treatment and Labor Act) created a legal entitlement to emergency room services; and (3) in 1987, the federal government began to count the “uninsured” in the March Supplement to the annual Current Population Survey.

However, there has been almost no information available about the sources of health insurance at the sub- state level. Therefore, this analysis will suggest the sources of health insurance for Tulsa MSA residents at the ZCTA level. The same methodology will also be used to estimate the uninsured at the county level statewide.

The essential finding is that the number (or percentage) of uninsured is quantifiable, but that the highest proportions of the uninsured are not necessarily in the poorest areas. For policy purposes, this analysis combines the uninsured and Medicaid populations (creating a cohort called the “medically marginalized”) to analyze access problems caused by an inability to pay or the unwillingness of providers to accept Medicaid patients. The ZCTAs in the Tulsa region having the highest percentage of the “medically marginalized” were, unsurprisingly, in the poorest ZCTA regions.

The expansion of private insurance by local policy initiative is not possible. With enactment of the Tobacco Tax initiative, Oklahoma is poised to expand Medicaid coverage somewhat, but by no means is capable of assuring universal coverage. In looking for ways to assist the medically marginalized, it is helpful to understand the numbers and locations of the uninsured, but there are no immediately available policy options to change that situation. Therefore, the shorter term policy approach would be to provide lower cost facilities and accessible services for the uninsured and Medicaid recipients.

Recent initiatives by the Oklahoma Health Care Authority include creating a partially subsidized set of insurance products to be marketed to smaller business and individuals. The subsidy is a product of the Oklahoma Tobacco Tax. The key to the introduction and acceptance of these products will be communications and marketing. The products will be sold by the insurance industry, but if Tulsa civic organizations (i.e.: Tulsa Chamber of Commerce) and health care organizations (hospital systems and medical schools) reinforced this marketing, the community and health care industry would benefit.

The complete methodology can be found in the Appendix of this section. Additionally, it is known that the Bureau of the Census is creating a “Small Area Health Insurance Estimates (SAHIE) program. That program is not yet public and prohibits citation at this time. But it can be observed that the 7 county

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 9 estimates here compare favorably to the SAHIE findings. This analysis is for 2003. It estimated 20,000 more uninsured than the SAHIE program did for the year 2000. This seems more than reasonable given the SAHIE program is for 2000 and the changing demographic and economic conditions in Tulsa.

Estimated Health Insurance Status for 2003 TULSA METROPOLITAN AREA BY INSURANCE STATUS AND COUNTY

Medicare Medicaid Medicaid+ Insured Uninsured Creek 7,495 10,591 885 33,861 12,292

Osage 5,212 10,348 1,161 17,471 7,714 Okmulgee 6,102 9,764 910 24,249 9,496 Pawnee 2,277 3,338 348 8,935 3,386 Rogers 6,628 17,434 1,509 32,405 13,428 Tulsa 60,011 83,701 5,838 299,749 109,374

Wagoner 4,890 9,116 765 30,055 10,616

MSA 92,614 144,292 11,416 446,727 166,306

Estimated Health Insurance Status TULSA METROPOLITAN AREA BY INSURANCE STATUS AND COUNTY

Medicare Medicaid Medicaid+ Insured Uninsured Creek 11.5% 16.3% 1.4% 52.0% 18.9%

Okmulgee 12.4% 24.7% 2.8% 41.7% 18.4% Osage 12.1% 19.3% 1.8% 48.0% 18.8% Pawnee 12.5% 18.3% 1.9% 48.9% 18.5% Rogers 9.3% 24.4% 2.1% 45.4% 18.8% Tulsa 10.7% 15.0% 1.0% 53.7% 19.6%

Wagoner 8.8% 16.4% 1.4% 54.2% 19.1%

MSA 10.8% 16.8% 1.3% 51.9% 19.3%

Tulsa County Tulsa County has a population of 558,672. This represents two-thirds (65%) of the seven-county MSA population. It is estimated that there are over 109,000 people without health insurance and another almost 90,000 with Medicaid benefits. This total of 200,000 people represents over one-third of the county population (35.6%). When the estimated 60,000 Medicare beneficiaries are considered, then over 46% of all Tulsans are covered by public insurance or are uninsured and rely upon the public to pay for their health care services.

Suburban Counties There are six suburban counties in the Tulsa MSA—Creek, Okmulgee, Osage, Pawnee, Rogers and Wagoner. Pawnee and Okmulgee were newly added counties to the MSA in 2000. The major concentrations of the medically marginalized—the uninsured and Medicaid beneficiaries, are in north Tulsa County and parts of Okmulgee and Rogers counties. These counties together have a population of 558,672. This represents over one-third (35%) of the seven-county MSA population. It is estimated that there are 57,000 people without health insurance, and another almost 66,000 with Medicaid benefits. This total of 123,000 people represents almost 41% of the population of these counties. When the estimated 33,000 Medicare beneficiaries are considered, then over 51% of all suburban Tulsans are covered by public insurance or are uninsured and rely upon the public to pay for their health care services.

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 10 POLICY ANALYSIS CAUSES OF DEATH The State Health Department provided a database of over 30,000 regional death records for the period of 2000-2003. This data was blended with the Master Regional Database to allow for the computation of age-adjusted death rates (AADR) for each cause of death for each ZCTA.

The AADR normalized the causes of death for age. Age-adjusted death rates were expressed as rates per 100,000 people. The AADRs were measured for heart disease, cancer, stroke, diabetes, unintentional injury, respiratory disease, and suicide.

There is a significant difference in age-adjusted death rates at the sub-county level. Findings showed a direct correlation between death rates and incomes. While this is not new information, the location of these areas is important for targeting outreach efforts.

The Tulsa region clearly has areas where residents have much higher than average death rates – after the rates are adjusted for age. There is an observed correlation between those without effective insurance (Medicaid and uninsured) and family incomes. Presently, there is no identifiable or effective outreach into these communities at the present time. No specific agency is currently responsible for these outcomes, and it is likely that Tulsa needs a strategy that will fix responsibility before any meaningful interventions can be made. It is also noteworthy that the higher age-adjusted death rates are in the counties surrounding Tulsa. These counties are more “rural” and lack health care services. They are perfect candidates for FQHC operations that are affiliated with larger systems in Tulsa.

The Tulsa MSA age-adjusted death rate from all causes was 935.5. This is higher than the national rate for 2002 of 847.3, but slightly lower than the state rate of 950.1 for 1999-2002. The Tulsa MSA was higher (worse) than the state and national averages for respiratory disease. The Tulsa MSA was lower (better) than the state and national averages for accidents. The Tulsa MSA was lower (better) than the state - and higher (worse) than the national averages in all other causes of death.

AGE-ADJUSTED DEATH RATES FOR TULSA MSA, STATE AND U.S. (deaths per 100,000 population)

CAUSE TULSA MSA COUNTIES COMPARISONS

Creek Okmulgee Osage Pawnee Rogers Tulsa Wagoner MSA OK US All Causes 1,039.4 943.6 892.5 1,066.3 991.8 918.0 1,005.2 935.5 950.1 847.3 Heart 366.6 375.6 259.3 334.3 289.3 313.3 253.1 292.7 316.1 241.7 Cancer 217.1 220.6 208.5 241.6 205.8 217.2 199.7 203.5 210.0 193.2 Respiratory 88.3 64.8 66.1 75.1 84.8 70.7 78.9 72.9 57.4 43.3 Stroke 72.7 62.4 56.7 78.8 77.0 65.9 58.1 62.1 69.6 56.4 Diabetes 35.3 43.6 24.8 22.5 29.0 26.8 29.1 26.6 28.7 25.4 Accidents 44.3 53.5 37.5 ** 39.5 37.9 35.1 26.8 44.0 37.0 Suicide 16.2 12.9 14.4 13.2 9.3 14.0 10.1 14.0 13.8 -

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 11 POLICY ANALYSIS HOSPITAL UTILIZATION The five general hospitals in Tulsa County furnished annual figures for both emergency room (ER) visits and hospital admissions by originating ZCTA and pay source (Medicaid, Medicare, Insured and Uninsured). This utilization data was blended with both the Master Regional Database and the causes of death analysis.

Health policy analyses often examine utilization through four major categories: Medicare, Medicaid, Insured, and Uninsured. For the purposes of this discussion, we consider the following:

• Medicare to be the elderly - everyone over 65 years of age • The Medicaid patients using acute health care services (as opposed to nursing homes or other services) will be predominantly children in lower income families • The Insured likely to be the majority of adults (age 18-64) and their dependents • The Uninsured to be younger workers and/or the working poor and their families.

Each cohort will have very different utilization patterns, thus rigid comparisons of the groups may not always be advisable. The utilization patterns are a better reflection of the needs of each particular cohort.

ALL TULSA HOSPITALS PERCENTAGE OF PATIENTS BY INSURANCE STATUS

EMERGENCY ROOM VISITS INPATIENT ADMISSIONS MCE MCD INS NONE MCE MCD INS NONE High 20% 40% 45% 36% 36% 43% 54% 9% Average 15% 25% 34% 26% 30% 23% 40% 7% Low 9% 15% 14% 21% 26% 12% 16% 5%

MCE: Medicare; MCD: Medicaid; INS: Private health insurance; NONE: Lacking any health insurance

The above table represents the aggregate, and high/low data for the five reporting hospitals within Tulsa County. For example, on average 15% of all ER visits are by Medicaid (MCD) patients. Of the five hospitals, the highest proportion of Medicaid patients was 40% and the lowest was 15%.

Inpatient Admissions Hospital admissions in the Tulsa area are well within the national norms. The mix of pay sources shows that only an estimated 7% of all admissions to Tulsa hospitals are “uninsured.” Some may contend that this is because uninsured patients cannot easily obtain hospital admission. There may likely be an element of truth to that, particularly when it comes to elective procedures. However, it is also likely that uninsured patients may be enrolled in Medicaid upon inpatient admission for purposes of the hospitalization, or conversely that many uninsured have a less intensive need for inpatient hospital services.

• An estimated 30% of all inpatient hospital admissions are by the “medically marginalized” patients (uninsured plus Medicaid). For one Tulsa hospital, that percentage is 52%.

• Of all admissions of Medicaid patients, almost two out of three (65%) will be admitted to either Hillcrest or Tulsa Regional Medical Center; and 31% admitted to St. John Medical Center/Saint Francis Hospital. On the other hand, St. John/Saint Francis will serve 59% of all uninsured admissions in Tulsa while Hillcrest/Tulsa Regional Medical Center will serve only 38%.

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 12 Oklahoma-specific studies have indicated that Medicaid will pay less than 70% of inpatient cost. 7 The uninsured will certainly pay much less.

Given these utilization data, and the mix of services at each hospital, it is not unreasonable to conclude that community poles of Hillcrest/TRMC and St. John/Saint Francis are each bearing different community loads of services to the medically marginalized. One group will treat larger numbers of Medicaid patients yielding a partial reimbursement of cost; the other will provide significant expensive services for the uninsured who pay very little.

Emergency Room Visits ER visits in the Tulsa area are within the national norms.

. The mix of pay sources shows that only an estimated 26% of all visits to Tulsa hospitals are “uninsured” and another 25% are Medicaid recipients; thus an estimated 51% of all ER visits are by the “medically marginalized” patients (uninsured plus Medicaid). For one Tulsa hospital, that percentage was 72%.

. Of all ER visits of Medicaid patients, more than half (54%) will be treated at either Hillcrest or Tulsa Regional Medical Center; and 34% at St. John Medical Center/Saint Francis Hospital. Also, St. John/Saint Francis will provide 46% of all uninsured visits while Hillcrest/Tulsa Regional Medical Center will provide 44% of Tulsa’s uninsured visits.

At question is the overall rate of ER utilization. There have been two recent studies of ER utilization in Tulsa hospitals. Both yielded similar findings.

The first was performed by the Community HealthNet using 2000 data. 8 The study categorized ER visits by the clinical acuity (Level 0, 1, 2 or >2) and payer (Medicare, Medicaid, Insured and Self-Pay). Levels 0-1 indicate an “unnecessary” visit; Level 2 indicates that the “urgent” problem could be handled in another outpatient setting. Overall, 77% of visits were for Level 0, 1, or 2 visits. Among children, 88% were Levels 0-2 with 90% of Medicaid children being at Level 2 or below.

The University of Oklahoma College of Public Health conducted a second study of Tulsa hospital emergency rooms in 2003. 9 It concluded that “of the survey population treated in the ER, 30% were for non-emergency conditions; however, an additional 43% could have received medical treatment in a non- emergency treatment facility if they would have been able to receive treatment within the next 48 hours”. The study also ascertained that “at least half of the participants indicated they used the ER more than 2 times ER UTILIZATION RATES BY INSURANCE STATUS (annual ER visits per 1,000 population) during the past 12 months; therefore they appear to be using it as a source of primary care.” 600.0

500.0 482.6 416.3 390.8 It is concluded that three-quarters of all the ER visits 400.0 were tantamount to extended physician office visits. This 296.8 coupled with the fact that the Medicaid population has, 300.0 183.1 by far, the highest utilization rate of any other group 200.0 suggests that policies to redirect the number of 100.0 unnecessary ER visits must target Medicaid patients at 0.0 least as much, if not more, than the uninsured. All Medicare Medicaid Insured Uninsured

Theoretically there are many policy options and approaches that may be pursued for the Tulsa region. This section states several options that are practical, potentially effective and achievable from a variety of perspectives. Historically, there have been policy decisions and inactions that have adversely affected Tulsa. It is not likely there is the political will or available resources to rectify them. Nor is it likely they could be achieved in any realistic time frame. Examples are:

• It is unlikely that Tulsa and Oklahoma will be better served by the Medicaid DSH payment system. Any likelihood of rectifying disparities seem to be extraordinarily low for reasons that go well beyond financing.

• It is unlikely that Oklahoma should - will – or can afford to –replicate the health care infrastructure of the OU Medical Center in Tulsa.

• It is unlikely that local governments will become proactive in these issues without significant pressures from community leadership.

Most recently, there has been public knowledge of incomplete proposals for the state to lease a local private hospital. The details of the proposal are unknown at this time, but all public indications are that the proposal’s purpose is to solidify the present medical education situation—not to provide a community-wide approach to caring for the medically marginalized in Tulsa. Given the findings of this analysis, the following are some of the more practical options presented for discussion and consideration. They are mutually supportive and could create the foundation for a more unified, stable and equitable health care system in Tulsa. Options include:

• Provide a stable stream of public revenue to support hospitals and systems that care for the medically marginalized. Aside from a direct appeal to the state legislature, an unexplored method of generating these funds is to directly ask the people to create a multi-county health and hospital district that has governing, taxing, and borrowing authority. This district may be organized and governed in such a manner as to optimize private health and hospital services and conduct operations in the public view. In this manner, Tulsans take the lead in solving local problems, then request that the state and federal government follow suit.

• Aggressively promoting and supporting the growth and development of a series of networked and collaborative FQHCs to serve the uninsured and medically marginalized citizens in the primary care outpatient setting. This mechanism provides the maximum leverage of private and local dollars with state and federal matching funds.

• Aggressively promote state legislation seeking a maximum increase in the Medicaid Upper Payment Limit. This mechanism also provides the maximum leverage of private and local dollars with state and federal funds. This method would significantly offset the disadvantages suffered from an unresponsive federal Medicaid DSH system.

• Expand the scope and membership of the Community Hospitals Authority to create a Tulsa Health and Hospitals Authority; and to seek and obtain appropriate powers, mission and financial support to serve as a focal point for Tulsa health care coordination.

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 14 METROPOLITAN AREA SUMMARY DEMOGRAPHICS – INCOMES – HEALTH INSURANCE – HOSPITAL UTILIZATION – AGE-ADJUSTED DEATH RATES

County Creek Okmulgee Osage Pawnee Rogers Tulsa Wagoner MSA Total Population 65,124 41,907 50,521 18,284 71,405 558,672 55,442 861,355 Under 5 yrs 4,429 2,865 3,374 1,174 5,188 40,709 3,926 61,665 5 to 17 yrs 13,329 8,384 10,400 3,715 15,302 105,637 11,624 168,390 18 to 64 yrs 38,986 24,285 29,736 10,771 42,777 346,478 34,237 527,270 65 yrs and over 8,380 6,373 7,012 2,625 8,137 65,849 5,655 104,030

Percent in each age group Under 5 yrs 6.8% 6.8% 6.7% 6.4% 7.3% 7.3% 7.1% 7.2% 5 to 17 yrs 20.5% 20.0% 20.6% 20.3% 21.4% 18.9% 21.0% 19.5% 18 to 64 yrs 59.9% 57.9% 58.9% 58.9% 59.9% 62.0% 61.8% 61.2% 65 yrs and over 12.9% 15.2% 13.9% 14.4% 11.4% 11.8% 10.2% 12.1%

Median Family Income (1999) $38,470 $33,987 $40,784 $37,274 $50,707 $47,489 $47,062

Number of Workers Earning (1999) 33,110 19,338 24,925 9,038 37,796 311,167 29,905 465,280 Less than $10,000 8,189 5,548 6,606 2,345 7,940 68,371 6,662 105,662 $10,000 - $19,999 7,432 4,982 6,031 2,194 7,537 66,724 6,269 101,167 $20,000 - $29,999 7,279 3,775 5,194 1,825 7,600 62,455 5,745 93,872 $30,000 - $39,000 4,805 2,293 3,101 1,203 5,987 41,140 4,520 63,049 $40,000 - $49,999 2,094 1,108 1,639 646 3,092 23,709 2,548 34,835 $50,000 or more 3,312 1,632 2,354 825 5,641 48,769 4,161 66,694

Estimated Sources of Health Insurance (exclusive categories) 65,124 41,907 50,521 18,284 71,405 558,672 55,442 861,355 Medicare 7,495 5,212 6,102 2,277 6,628 60,011 4,890 92,614 Medicaid 10,591 10,348 9,764 3,338 17,434 83,701 9,116 144,292 Medicare/Medicaid 885 1,161 910 348 1,509 5,838 765 11,416 Nonelderly Insured 33,861 17,471 24,249 8,935 32,405 299,749 30,055 446,727 Nonelderly Uninsured 12,292 7,714 9,496 3,386 13,428 109,374 10,616 166,306

Pct Sources of Health Insurance (non-exclusive categories) Medicare 11.5% 12.4% 12.1% 12.5% 9.3% 10.7% 8.8% 10.8% Medicaid 16.3% 24.7% 19.3% 18.3% 24.4% 15.0% 16.4% 16.8% Medicare/Medicaid * 1.4% 2.8% 1.8% 1.9% 2.1% 1.0% 1.4% 1.3% Nonelderly Insured 52.0% 41.7% 48.0% 48.9% 45.4% 53.7% 54.2% 51.9% Nonelderly Uninsured 18.9% 18.4% 18.8% 18.5% 18.8% 19.6% 19.1% 19.3% Medicaid + Uninsured 36.5% 45.9% 39.9% 38.7% 45.3% 35.6% 37.0% 37.4% Medicare + Medicaid + Uninsured 48.0% 58.3% 52.0% 51.1% 54.6% 46.3% 45.8% 48.1%

ER Visits 10,356 3,262 8,345 1,831 5,569 165,806 6,134 201,303 Hospital Admissions 7,379 3,252 3,876 1,632 4,343 70,264 5,763 96,509

Age-Adjusted Death Rates per 100,000 Population 1,039.4 943.6 892.5 1,066.3 991.8 918.0 1,005.2 935.5 Heart 366.6 375.6 259.3 334.3 289.3 313.3 253.1 292.7 Cancer 217.1 220.6 208.5 241.6 205.8 217.2 199.7 203.5 Diabetes 35.3 43.6 24.8 22.5 29.0 26.8 29.1 26.6 Stroke 72.7 62.4 56.7 78.8 77.0 65.9 58.1 62.1 Accidents 44.3 53.5 37.5 ** 39.5 37.9 35.1 26.8 Suicide 16.2 12.9 14.4 13.2 9.3 14.0 10.1 14.0

* Those dually eligible for Medicare and Medicaid have been categorized as Medicaid. ** Pawnee County did not have a calculated age-adjusted death rate for accidents.

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 15 RANK ORDERS FOR TULSA COUNTY INCOME, INSURANCE, HOSPITAL UTILIZATION AND DEATH RATES 1 is BEST– 36 is WORST

COMMUNITY RESOURCES HOSPITAL AGE-ADJUSTED DEATH RATES

RANK ZCTA POP INC INS ER ADM HRT CAN RES ALL ORD

1 74137 22,960 1 1 3 3 4 6 2 5 3.1

2 74114 16,913 3 3 4 8 3 5 6 4 4.5

3 74133 37,778 2 7 5 5 11 15 4 8 7.1

4 74145 18,020 15 12 13 11 2 4 1 2 7.5

5 74136 32,712 11 17 15 6 5 10 3 3 8.8

6 74105 28,455 12 9 14 12 6 7 12 7 9.9

7 74008 15,351 10 10 8 7 9 18 7 15 10.5

8 74011 23,031 4 5 1 1 24 24 19 20 12.3

9 74135 21,320 13 11 22 24 7 9 8 6 12.5

10 74012 47,249 8 8 2 2 30 21 15 26 14.0

11 74055 19,197 5 21 11 26 19 3 17 13 14.4

12 74134 12,998 14 2 7 4 21 20 34 23 15.6

13 74146 14,380 24 29 19 10 12 16 9 10 16.1

14 74129 18,542 20 25 17 17 14 14 13 11 16.4

15 74132 4,616 7 4 20 25 17 29 11 18 16.4

16 74021 9,595 16 26 12 30 10 13 25 9 17.6

17 74037 9,428 6 6 6 18 32 19 24 31 17.8

18 74104 14,050 25 19 23 13 13 12 21 17 17.9

19 74119 3,790 9 13 29 29 20 11 23 12 18.3

20 74112 21,222 22 20 18 19 8 25 22 16 18.8

21 74103 2,173 36 23 36 35 1 1 20 1 19.1

22 74063 21,250 18 14 27 31 18 22 16 19 20.6

23 74120 5,496 26 22 28 15 23 2 30 21 20.9

24 74127 10,901 29 30 16 14 22 28 10 24 21.6

25 74070 3,180 19 16 10 21 31 27 27 28 22.4

26 74128 12,430 23 24 21 22 28 17 14 30 22.4

27 74108 6,638 27 33 24 16 16 23 29 14 22.8

28 74033 8,475 17 18 9 9 35 31 35 35 23.6

29 74073 3,360 21 15 26 27 15 32 31 29 24.5

30 74107 20,284 28 27 31 28 29 8 26 22 24.9

31 74115 23,687 31 31 30 23 25 26 28 27 27.6

32 74106 17,164 35 34 33 33 27 33 5 32 29.0

33 74116 2,270 32 36 35 36 26 34 18 25 30.3

34 74130 2,624 30 28 25 20 36 36 36 36 30.9

35 74110 15,267 34 32 32 32 34 30 32 33 32.4

36 74126 9,047 33 35 34 34 33 35 33 34 33.9

Reading this table: ZCTA 74137 (south Tulsa) has 22,960 residents. The median family income ranks it BEST in the county; the combined privately insured and Medicare beneficiaries also ranks it BEST in the county. The ER visits and hospital admissions per 100,000 are both the 3rd BEST; and the death rates from heart disease, cancer and respiratory disease rank as the 4th, 6th, and 2nd BEST respectively. The death rate from all causes was the 5th best in the county. The average ordinal rank was 3.1, ranking 74137 the “BEST” in Tulsa County.

SEVEN-COUNTY METROPOLITAN AREA TULSA COUNTY (RED IS HIGHEST – GREEN IS LOWEST) (RED IS HIGHEST – GREEN IS LOWEST – YELLOW IN BETWEEN). SOME THERE ARE 127 ZCTAS IN THE METRO AREA. THOSE DEPICTED COLORS WILL SLIGHTLY DIFFER FROM ACCOMPANYING MAP REPRESENT THE 27 BEST AND 27 WORST ZCTAS. BECAUSE OF SEPARATE GEOGRAPHICAL COMPARISONS

Selected Correlations These charts are displayed for visual correlations of selected variables. Traditional statistical analysis techniques would (1) consider actual values as opposed to rank orders and (2) perform extensive culling of outlier data caused by small area analysis.

The visual depictions below simply array the rank orders for opposing variables for Tulsa County ZCTAs only. The inclusion of all 127 MSA ZCTAs would be to risk unfair comparisons due to data limitations within outlier ZCTA. The two charts below indicate the relationships between both incomes and insurance with age-adjusted death rates. Each chart arrays 36 ZCTAs in Tulsa County. The Income/Mortality chart is read as follows. The marker to the far left depicts the ZCTA with the highest income and 5th best (lowest) age-adjusted death rate. The Insurance/Mortality chart is read as follows. The marker to the far left depicts the ZCTA with the highest percentage of Medicare/Private Insurance and 5th best (lowest) age-adjusted death rate.

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 17 TULSA COUNTY ZCTA RANK ORDER CORRELATIONS INCOMES, INSURANCE AND AGE-ADJUSTED DEATH RATES

36 36

30 30

24 24 MORTALITY RANK MORTALITY RANK

18 18

12 12

6 6

INCOME RANK INSURANCE RANK 0 0 0 6 12 18 24 30 36 0 6 12 18 24 30 36

The two charts below indicate the relationships between both Heart Disease/Insurance and Hospital Admissions/ER Visits. Each chart arrays 36 ZCTAs in Tulsa County. The Heart Disease/Insurance chart is read as follows. The marker to the far left depicts the ZCTA with the best Medicare/Private insurance and 5th best (lowest) heart disease age-adjusted death rate. The Hospital Admissions/ER Visits chart is read as follows. The marker to the far left depicts the ZCTA with the highest percentage of ER Utilization Rate and highest Hospital Admission Rate.

TULSA COUNTY ZCTA RANK ORDER CORRELATIONS HEART DISEASE, INSURANCE, ER VISIT AND HOSPITAL ADMISSIONS

36 36

30 30

24 HEART DISEASE 24

18 18 HOSPITAL ADMIT RANK

12 12

6 6

INSURANCE RANK ER VISIT RANK

0 0 0 6 12 18 24 30 36 0 6 12 18 24 30 36

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 19 THE 80 LARGEST METROPOLITAN AREAS IN THE U.S. ARRAYED IN DESCENDING ORDER OF POPULATION

Public Comprehensive Medicaid Focused State FQHC Hospital Medical School DSH Hospital(s) Emphasis

New York City City Yes Yes Yes Yes New York City

Los Angeles City/County Yes Yes Yes Yes Los Angeles

Washington-Baltimore University Yes Yes Yes No Washington-Baltimore

San Francisco City/County Yes Yes Yes Yes San Francisco

Philadelphia University Yes Yes Yes Yes Philadelphia

Boston District Yes Yes Yes Yes Boston

Detroit University Yes Yes Yes No Detroit

Dallas-Fort Worth District Yes Yes Yes No Dallas-Fort Worth

Houston-Galveston District Yes Yes Yes No Houston-Galveston

Atlanta District Yes Yes Yes No Atlanta

Miami District Yes Yes Yes No Miami

Seattle-Tacoma County Yes Yes Yes Yes Seattle-Tacoma

Phoenix District Yes Yes Yes No Phoenix

Minneapolis-St. Paul County Yes Yes Yes No Minneapolis-St. Paul

Cleveland County Yes Yes Yes No Cleveland

San Diego State Yes Yes Yes Yes San Diego

St. Louis University Yes Yes Yes Yes St. Louis

Denver District Yes Yes Yes Yes Denver

San Juan Public Yes Yes Yes N/A San Juan

Tampa University Yes Yes Yes No Tampa

Pittsburgh University Yes Unknown Yes Yes Pittsburgh

Portland District Yes Yes Yes Yes Portland

Cincinnati University Yes Yes Yes No Cincinnati

Sacramento State Yes Yes Yes Yes Sacramento

Kansas City District Yes Yes Yes Yes Kansas City

Milwaukee University Yes Yes Yes No Milwaukee

Orlando No No Yes Yes No Orlando

Indianapolis City/County Yes Yes Yes No Indianapolis

San Antonio District Yes Yes Yes No San Antonio

Norfolk No Yes Yes Yes Yes Norfolk

Las Vegas County Yes Yes Yes No Las Vegas

Columbus State Yes Yes Yes No Columbus

Charlotte District No Yes Yes No Charlotte

New Orleans State Yes Yes Yes No New Orleans

Salt Lake City State Yes Yes Yes Yes Salt Lake City

Greensboro University Yes Yes Yes No Greensboro

Austin District No Yes Yes No Austin

Nashville City/County Yes Alternate Yes Yes Nashville

Providence No Yes Yes Yes Yes Providence

Raleigh State Yes Yes Yes No Raleigh

Hartford University Yes Yes Yes Yes Hartford

Buffalo County Yes Yes Yes Yes Buffalo

Memphis University Yes Alternate Yes Yes Memphis © 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 20 THE 80 LARGEST METROPOLITAN AREAS IN THE U.S. ARRAYED IN DESCENDING ORDER OF POPULATION

Public Comprehensive Medicaid Focused State FQHC Hospital Medical School DSH Hospital(s) Emphasis

West Palm Beach 45 miles No Yes Yes No West Palm Beach

Jacksonville University Yes Yes Yes No Jacksonville

Rochester University Yes Yes Yes Yes Rochester

Grand Rapids No No Probable Yes No Grand Rapids

Oklahoma City University Yes Yes Yes No Oklahoma City

Louisville University Yes Yes Yes Yes Louisville

Richmond District Yes Yes Yes Yes Richmond

Greenville District Yes Yes Yes No Greenville

Dayton No Yes Yes Yes No Dayton

Fresno County No Yes Yes Yes Fresno

Birmingham State Yes Yes Yes Yes Birmingham

Honolulu Public Yes Alternate Yes Yes Honolulu

Albany University Yes Yes Yes Yes Albany

Tucson University Yes Yes Yes No Tucson

Tulsa No No No No No Tulsa

Syracuse State Yes Yes Yes Yes Syracuse

Omaha University Yes Yes Yes No Omaha

Albuquerque State Yes Yes Yes Yes Albuquerque

Knoxville University Yes Alternate Yes Yes Knoxville

El Paso District Yes Yes Yes No El Paso

Bakersfield County No Yes Yes Yes Bakersfield

Allentown No No Probable Yes Yes Allentown

Harrisburg University Yes Probable Yes Yes Harrisburg

Scranton No No Probable Yes Yes Scranton

Toledo State Yes Yes Yes No Toledo

Baton Rouge State No Yes Yes No Baton Rouge

Youngstown No No Yes Yes No Youngstown

Springfield (MA) University Yes Yes Yes Yes Springfield (MA)

Sarasota District No Probable Yes No Sarasota

Little Rock State Yes Yes Yes Yes Little Rock

McAllen No No Yes Yes No McAllen

Stockton County No Yes Yes Yes Stockton

Charleston, SC State Yes Yes Yes Yes Charleston, SC

Wichita No No No No No Wichita

Mobile State Yes Yes Yes Yes Mobile

Columbia, SC No Yes Yes Yes Yes Columbia, SC

LEGEND

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 21 Major Metro Areas There are 23 metropolitan areas with populations greater than 2 million, ranging from New York (21 million) to Portland (2.3 million).

Each MSA was supported by either a public hospital(s) and/or university hospital(s). One or more comprehensive medical schools supported every city. Every community had one or more clearly identified safety net hospital(s), and these hospitals in every community received significant Medicaid DSH payments. About half (eleven) of the cities were in states that had a significant FQHC infrastructure.

Metro Areas Below 2 Million There are 27 metropolitan areas with populations between 1 to 2 million. These communities ranged from Cincinnati (just below 2 million) to Louisville (just over 1 million).

Twenty-three MSAs were supported by either a public hospital(s) and/or university hospital(s). The only four that were not were Orlando, Norfolk, Providence and Grand Rapids. One or more comprehensive medical schools supported 22 of the 27 cities. The only five that were not supported were Orlando, Charlotte, Austin, West Palm Beach and Grand Rapids, but a GME program served each of the five.

The only two communities lacking both a public hospital and comprehensive medical school were Orlando and Grand Rapids. Safety-net hospitals in both these communities received significant Medicaid DSH payments.

Every community had one or more clearly identified safety net hospital(s), and these hospitals in every community received significant Medicaid DSH payments. Eleven of the 27 MSAs were in states that had a significant infrastructure of FQHCs.

Metro Areas Below 1 Million There are 30 metropolitan areas with a population between 500,000 and 1 million. Twenty-two of the 30 communities were served by a public hospital, while 19 of the 30 cities had a comprehensive medical school. In summary, there were only 6 communities that had neither a comprehensive medical school nor a public hospital. These MSAs were Allentown, Scranton, Youngstown, McAllen, Wichita and Tulsa. Every community except for Tulsa received Medicaid DSH payments, although DSH payments allotted to Wichita were meager. Of the 30 communities, 18 were in states that had a significant FQHC infrastructure.

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 22 SECTION 2 U.S. METROPOLITAN AREAS AN EXAMINATION OF AMERICA’S 80 LARGEST METROPOLITAN AREAS

Introduction Contrary to some prevailing thought, our nation does not have a health care system that simply disenfranchises the uninsured, the poor, and underserved. Our nation does have systems and services targeted at the uninsured, but they require significant state and local initiative to take advantage of them. Tulsa and Oklahoma have not optimized these opportunities. Consider the following:

• Federal Government The federal government sponsors three programs: (1) Medicaid, (2) FQHCs, and (3) National Health Service Corps (NHSC) of physicians serving scholarship obligations. Tulsa has neither a designated hospital nor a network of FQHC clinics. It is highly unlikely that the federal government will provide additional programmatic opportunities.

• State Government The state government could help in the following variety of ways: (1) Medicaid, (2) medical education infrastructure, and (3) FQHC promotion. The state government has: (1) defaulted on FQHC development, thereby suppressing the means to maximize Medicaid; (2) provided no public hospital-like apparatus, thus eliminating another means to maximize Medicaid; and (3) provided medical school infrastructure, but it is limited to primary care levels. The OCHA has recently provided significant monies through Medicaid-related medical education programs. It is highly unlikely that the state government will provide additional opportunities.

• Local Government The state constitution seemingly requires that counties provide for the poor. Thus, Tulsa has defaulted all health care responsibility to the Tulsa City-County Health Department. The TCCHD has limited its services to non- patient care related services that are the core of public health, but provide little in the way of clinical health care services. It is highly unlikely that the local government will provide additional opportunities or leadership.

• Community Hospitals Community hospitals contribute to care of the underserved by subsidizing medical school teaching clinics and absorbing losses created by medical school inpatient services. Hospitals are reimbursed for these services in a variety of ways. Local hospitals have already leveraged their community involvement through medical schools. It is highly unlikely that community hospitals will provide additional resources from operating funds in the competitive environment of 2004.

• Medical Schools Medical schools in Tulsa were perceived as the solution for providing care for the medically marginalized. As demand for services has increased and medical school services are capped for academic reasons, medical schools can now only be part of a greater community-wide solution.

The health care services in Tulsa are nationally unique, or anachronistic. This poses difficult challenges to local and state policymakers. This multi-section analysis provides fundamental information required for any serious policy deliberations.

The Master Database was created using numerous variables obtained from the 2000 Census. These variables included social, economic, and workplace characteristics. Data was gathered for all the ZCTAs within the Tulsa MSA. For practical reasons and purposes of this study, ZCTAs were used in lieu of census tracts.

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 23 Tulsa For generations, a Tulsa slogan has been, “We are the largest city in the country without a public hospital.” For decades it was said with civic pride. The changing health care culture and landscape has shifted the tone to one of denied entitlement and frustration. At one time the slogan was technically true, but irrelevant. Today it does not apply at all as there are many forms of supporting indigent care services beyond simply having a public hospital.

Some health care services may easily be delivered in rural communities, city neighborhoods and small suburban areas. However, more complex services—both clinical and diagnostic, are delivered in larger hospitals located in urban settings and people requiring these services will travel great distances to receive them.

Pluralistic Strategies The U.S. health care system is characteristically pluralistic. Communities are allowed to organize themselves and employ strategies that work for their community. In contemporary America, there are five major strategies employed to serve the medically marginalized:

• Government-owned and -operated public hospitals • Medicaid DSH payments allotted to hospitals for significant indigent care services rendered • Mature and comprehensive 4-year medical schools that are capable of providing hospitals with the full range of service specialties and sub-specialties • Hospital(s) specifically organized and tasked to provide significant indigent care • Statewide emphasis on fostering the development of FQHC clinics

Some metro areas employ all five strategies. Other metro areas do not have public hospitals, but do use medical schools, DSH payments and/or public subsidies to support local providers.

Public Hospitals 10 Public hospitals were rapidly developed after WWII to serve as service centers for those who could not afford hospital care. They were traditionally called the “county hospital” or the “city hospital.” Their financial support was provided by governments and was traditionally minimal in the sense that these hospitals did not have private rooms, sophisticated equipment or the like. Over time, most public hospitals have re-invented themselves through alliances with comprehensive medical schools. In some cases, the ownership and governance of these hospitals has changed to “private, non-profit” institutions with public revenue being provided through a variety of streams.

For example, in Cincinnati, the University of Cincinnati Medical Center is a private, non-profit. However, the Medical Center is the sole recipient of a county levy called the Health and Hospitalization Levy supported through a Hamilton County Property Tax. The levy is used as the payer of last resort for inpatient and outpatient services for medically indigent Hamilton County residents at University Hospital and Children’s Hospital Medical Center.

Hospital Districts and Authorities The appendix to this section includes thumbnail summaries of local districts and/or tax levies used in the provision of care for the medically marginalized. The communities are Austin, Cincinnati, Phoenix, and Westchester County (NY). These four areas have recently undertaken political measures to raise public revenue to support health care operations.

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 24 There are many more metropolitan areas using specific hospital districts. A preliminary count indicated that at least 15 major metropolitan areas were served specifically by local hospital districts. An additional 53 communities of the largest 80 in the nation provided combinations of municipal, county or state public revenue to hospital and health care operations. The appendix also includes a detailed description of major urban hospital districts in Texas.

An in-depth focus on public authorities and districts is beyond the scope of this analysis. Any desire to plan and pursue such an approach would require significant legal research and political organization. With that said, here are some basic facts about these approaches in Oklahoma.

• The Hospital District Act of 1967 (Senate Bill No. 183 of the First Session of the Thirty-first Oklahoma Legislature; Chapter 83, Oklahoma Session Laws 1967; 19 O.S. Supp. 1967, §§ 1051 through 1077). Ostensibly this act authorizes some legal vehicle to operate/govern public hospitals.

• There are at least 32 hospitals in Oklahoma that benefit from some measure of local public revenue. 11 The amounts and purposes of this financing vary. It is presumed that the amounts are small to inconsequential, and are more likely to be used for specific capital projects as opposed to operations. There are additional hospital “authorities” organized in Oklahoma, but the extent to which they influence health care is not known.

In summary, while Oklahoma has some level of organized governmental involvement in hospital and health care operations, it is well below the scope and sophistication of traditional hospital districts or authorities observed nationwide.

Medicaid DSH Payments 13 The federal Medicaid program allocated almost $13 billion to “disproportionate share hospitals” in FY 2004.14 Hospitals in Oklahoma received .18% of that amount ($27 million) and virtually all of it was directed to public facilities in central Oklahoma.

Since 1993, the intent of the Medicaid DSH payment system since 1992 was to allot 12% of all Medicaid expenditures would be paid to qualifying hospitals that provided a “disproportionate share” of hospital care to the medically needy. Over time, the DSH payment system became corrupted to the point that the federal government locked the system in place for allocating funds among the states. In 2004, only 5% of all Medicaid expenses went to this program. Thus prevails an embarrassing and distorted payment system that results in payment disparities per capita as reflected in the chart below. Nevertheless, many major metropolitan areas receive significant allotments to provide services for their medically needy, while some areas receive minimal to no payments. Oklahoma receives the national minimum (1% of Medicaid expense), all of which is directed to central Oklahoma.

Four states do not participate in the DSH program. These states provide public hospital dollars using different methods. The states are Tennessee (Memphis, Nashville and Knoxville), Hawaii, Maine and Wyoming.

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 25 $200 2004 Medicaid $180 DSH Payments Per Capita U.S. average is $45.01 per capita; Oklahoma is $6.67

$160

$140

$120

$100

$80

$60

$40

$20

$0 IL IA ID RI IN HI MI FL WI LA NJ AL VT TX AZ PA UT VA TN KY AK KS SD NV SC AR NE DE NY CA ND NH OK MT CN DC NC GA OH OR MS ME MA CO NM MN MD WA WV WY MO

2003 OK MEDICAID DISPROPORTIONATE SHARE HOSPITAL PAYMENTS

ORGANIZATION TYPE GOV UNCOMP CARE DSH PAYMENT

ADAIR COUNTY HEALTHCARE INC Acute Private $135,819 $ 581 ARKANSAS CHILD HOSP Acute Teaching Children's State $511,424 $ 511,424 GEORGE NIGH REHAB INST VA Rehabilitative State $124,698 $ 55,512 GRIFFIN MEMORIAL HOSPITAL Psychiatric State $13,364,991 $ 2,883,251 HENRYETTA MED CNTR Acute NSGO $6,163 $ 91 INTEGRIS BASS MEM BAP Acute Private $2,438,454 $ 17,255 J D MCCARTY C P CTR Children's Rehabilitative State $1,288,351 $ 858,108 MEDICAL CENTER HOSPITALS Acute Teaching State $37,433,005 $ 21,927,750 OKLAHOMA YOUTH CENTER Children's Psychiatric State $867,598 $ 107,528 PARKSIDE INC Psychiatric Private $286,330 $185,403 ST ANTHONY HSP Acute Teaching Private $2,977,395 $ 250,030 VISTA HEALTH Psychiatric Private $201,594 $ 97,066

TOTAL $59,635,822 $26,894,000

Comprehensive Medical Schools 15 Many communities leverage their public hospital and/or DSH payments with the presence of a comprehensive medical school. In this regard, a comprehensive medical school offers a range of faculty and GME programs that provide the core staff to a full-service tertiary care hospital. Such a designation would preclude branch medical schools, osteopathic schools, geographically disparate GME programs and the like.

There are some communities that are not home to a medical school, but benefit from a significant metropolitan presence of medical school programs. © 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 26 Both Oklahoma City and Tulsa benefit from additional non-patient care public funds to be used for serving the medically needy. These are Medicaid funds provided as “medical education” dollars. The data below is directly quoted from the OCHA 2004 Annual Report. 16 There are four types of revenue streams as listed below. (See appendix for definitions and detail.)

• GME - Graduate Medical Education • IME - Indirect Medical Education • DSH - Disproportionate Share Hospital • DME - Direct Medical Education

In SFY 2004, almost $159 million was distributed. Tulsa organizations received over half ($87 million) of these funds.

SFY 2004 MEDICAID MEDICAL EDUCATION FUNDING

GME IME DSH DME TOTAL TULSA $33,202,265 $11,393,839 $137.33 $16,098,895 $60,695,136 OKLAHOMA CITY $24,571,832 $11,393,839 $26,204,466 $35,129,845 $97,299,982 OTHER $625,704 $184,592 $810,296 TOTAL $57,774,097 $22,787,678 $26,830,307 $51,413,332 $158,805,414

Focused Hospital(s) 17 Almost every metropolitan area is served by a hospital that is organized to serve the medically underserved as a part of their institutional mission. These hospitals are active receivers of such patients and do not have policies in place to exclude or limit services. They form the logical “center of gravity” for indigent care hospital services. In most cases, these hospitals are supported with public appropriations, served by a comprehensive medical school and/or receive significant DSH payment. In many cases, all three apply.

State FQHC Emphasis 18 An additional strategy would be the presence of an organized and mature system of FQHC clinics. This can be important because Oklahoma, and by extension Tulsa, ranks last among the states in the number of FQHC clinics per population—with the exception of Wyoming. This ranking is notwithstanding the fact that four new Oklahoma sites were approved in April 2005—Tulsa, Fairfax, Battiest, and Clayton. The benefits of an FQHC designation include:

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 27 • Annual Federal Operating Subsidy Grantees receive an annual federal operating subsidy of $650,000. Larger supplements of several million dollars are available to larger and more mature FQHCs.

• Enhanced Reimbursement FQHCs essentially receive Medicaid reimbursements at twice the cost of delivering care. Medicare deductibles are waived and reimbursed. The excess payments are intended to cover the uninsured.

• Physician Workforce FQHCs have access to NHSC provider placements. These physicians will serve in FQHCs as a scholarship obligation.

• Malpractice Insurance Waivers FQHC providers have access to free medical malpractice coverage under the Federal Tort Claims Act (FTCA).

• Special Funds FQHCs have access to (1) federal grants to support the costs of planning and developing a health care network or plan, (2) grant support and loan guarantees for capital improvements, (3) Vaccines for Children (VFC) program, and (4) Public Health Service Drug Pricing Discounts.

FQHCs Per Million People U.S. average is 11.5 per million population; Oklahoma is 4.6 40

0 IL IA RI IN HI ID MI FL AL LA WI NJ VT AZ TX AK CT KS KY VA TN UT PA CA AR NY SC DE NE NV SD NC OK GA ND MT NH OH OR MS ME MA CO MD MN NM MO WA WV

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 28 Summary Tulsa and Wichita are the only metropolitan areas in the United States that do not employ any of the five available strategies to finance health care services for the underserved or medically marginalized population.

Ironically, one of the two general hospitals in Wichita is in the same hospital group as a major Tulsa hospital, and both communities sponsor community-based, primary care-oriented medical schools established concurrently in the 1970s. A major and notable difference is in the size of their respective service areas—Tulsa is almost 50% larger than Wichita, with a population of 803,000 and 545,000, respectively. Tulsa also has five general hospitals compared to only two in Wichita.

• None of the Tulsa hospitals is governed as a public or university hospital. Efforts are underway to secure public subsidies for one hospital, yet it is not clear if the intent is for a stabilizing operating subsidy or to significantly change the scope and mission of the institution. While Tulsa does benefit from $60 million of Medicaid funds related to education, it is presumed that other communities nationwide have access to the same funds through their comprehensive medical schools and state Medicaid programs.

• A GME program of medical residents and faculty serves every metropolitan area. Tulsa and Wichita do have a significant presence of GME programs, but neither sponsors specialist and sub-specialist programs to any significant degree. Primary care medical education programs are plentiful, but not as comprehensive and varied as traditional academic health centers.

• No Tulsa hospital has ever received Medicaid DSH payments to help serve medically marginalized patients. The fundamental reason is that community services have been so evenly distributed that there is not a single institution that can meet the volume concentration required to qualify for such payments.

• While every Tulsa hospital provides significant amounts of services to the medically marginalized, each does so as part of its normal non-profit mission, and in proportions that insure that each does its “fair share.” At present, there is no Tulsa hospital that could be deemed a traditional “safety net” hospital for the community. Those that look like a “safety net” hospital do so by circumstance not by design.

• FQHCs are one of many vehicles to serve the medically marginalized. The revenue streams include local, state and federal dollars. Operating policies provide significant advantages. The establishment of such centers requires promotion, work, collaboration and time. Oklahoma ranks next to last in the country in this effort. Only a single FQHC serves the Tulsa region.

The following text, analyses, tables and appendices categorize, explain and chronicle the health services utilization and health status of Tulsans in 2005. They serve as a policy guide and reference for any thoughtful deliberation in “moving Tulsa forward.”

Cincinnati, OH 19 Issue 1 is the Health and Hospitalization Levy. It is supported through a Hamilton County Property Tax. The levy is used as the payer of last resort for inpatient and outpatient services for medically indigent Hamilton County residents at University Hospital and Children’s Hospital Medical Center. The levy needs to be renewed every five years. For 35 years, the voters of Hamilton County have supported this levy, which helps provide health care to more than 50,000 people in the county each year. This ongoing commitment and support for medical care for all, regardless of their ability to pay, is rare among major cities in the United States.

The Health and Hospitalization Levy is designed to help medically indigent patients living in Hamilton County who receive care at University or Children’s Hospital Medical Center. These patients have limited or no resources for medical care, and they do not qualify for government assistance programs, such as Medicare or Medicaid. The levy is the payer of last resort. Financial counselors make sure that a patient is not covered by any third-party payers, and a full collection effort is made before levy funds are used to cover medical costs. Without the levy, University Hospital and Children’s Hospital Medical Center would have to severely restrict care and services for the medically indigent in Hamilton County or reduce funds for other necessary programs to cover the cost. This could seriously limit essential medical services for everyone in the county.

Westchester County, NY 20 The Board of Directors of the Westchester Medical Center today approved a plan to place the 1,000-bed, regional medical center on sound financial footing. The plan, Operation Cure Westchester Medical Center, offers two different scenarios. The Board said today that the two-year effort to restructure the medical center and make extensive operational improvements has reached a point where the future needs of the medical center have come into sharper focus. Westchester Medical Center is the only public hospital in New York State that does not currently receive an annual public subsidy. Chairman of the Board of Directors Richard A. Berman said, "Westchester Medical Center is essential to the region as an advanced care center, where the most severely injured and ill patients come for the most advanced treatment, regardless of the cost of that treatment or of their ability to pay. This mission is right and today we have vowed to continue that mission and have put in place a plan to continue that mission.

The first alternative is the 5-Party Contributory Plan, involving sharing costs among the county, state and federal governments, the medical center and the medical center's unions. The second alternative is the No New Tax Plan for Recovery, which calls for the extension of the _ % state sales tax set to expire on May 31. The funds, $650 million to $700 million annually, would be set aside for all public hospitals and public nursing homes across the state.

Phoenix, AZ 21 The Arizona State Legislature passed House Bill 2530 during the year ended June 30, 2003, to allow Maricopa County to ask its voters to decide in the November 2003 election whether to create a special healthcare taxing district to operate the Maricopa Integrated Health Systems (MIHS), which includes the Medical Center. Subsequent to the publication of the Medical Center’s audited financial statements, voters approved the creation of the healthcare district. Once the district is created, it will have the authority to levy taxes, and an independently elected governing board will control its operations. Since the Medical Center is part of MIHS, the new healthcare district could significantly affect future Medical Center operations.

Austin, TX 22 Hospital district issue on May ballot, Updated: 3/2/2004 10:00 PM. By: Jitin Hingorani

Whether or not Austin will have a hospital district is now up to Travis County voters. On Tuesday, the county commissioners voted unanimously to put the controversial issue on the May 15 ballot. Austin is the largest city in Texas that doesn't have a hospital district. Close to 25 percent of Central Texans are uninsured. So, who foots the majority of the bill when an indigent patient from outside the city of Austin is transported to Brackenridge?—Austin taxpayers.

"We have a city asset, that being Brackenridge Hospital, that is a regional asset. So, it's disproportionately being paid for by city of Austin residents. One of the intentions of a health district would cure that," Travis County Commissioner Karen Sonleitner said. The county commissioners approved the addition to the ballot because enough registered voters signed a petition asking them to. Last spring, Texas legislators passed a bill enabling the creation of a hospital district. No district tax rate has been approved yet. City of Austin taxpayers currently pay more than seven cents per $100 valuation for health care. While Travis County residents outside of the city limits pay just more than one cent.

Travis County is already the highest-taxed county in the state and opponents said a district will not save taxpayers money. "It doesn't guarantee that our health insurance rates are going to go down, it doesn't guarantee that we're going to get better care and it does nothing about the surrounding counties sending in patients who get medical care on the Travis taxpayer's ticket," Don Zimmerman, with Save Our Taxpayers, said. Supporters of a hospital district said the need is overwhelming. Voters can decide on May 15 whether the city can create a hospital district.

"We have overcrowded emergency rooms that threaten our ability to deliver good, quality, trauma and emergency care to people who need it and we have problems with access as a result of the high, uninsured rate that we have in Central Texas," Clarke Heidrick said. The creation of a hospital district will not change the way indigent patients are treated, but it will change the way health care is funded. Other counties could join the hospital district only if voters approve it in separate elections.

The preceding discussion mentions Austin, Texas. Austin was the largest city in Texas without a hospital district until the voters approved the Travis County Hospital District last May. In providing the research and analysis for this proposal, research was done on the magnitude of the other large districts in the state. That summary is below and offers per capita multipliers and tax rates for Tulsans to consider.

MAJOR HOSPITAL DISTRICTS IN TEXAS Morningside Research and Consulting, Inc. 23

COUNTY BEXAR DALLAS EL PASO HARRIS TARRANT OVERALL CITY SAN ANTONIO DALLAS EL PASO HOUSTON FORT WORTH Population 1,391,770 2,220,787 680,691 3,397,566 1,443,752 9,134,567 Uninsured 349,176 499,810 231,668 812,674 325,658 2,218,987 In Poverty 255,594 230,837 199,886 386,034 157,500 1,229,852

2001 Tax Rate 0.244 0.254 0.185 0.203 0.234 2002 Tax Rate 0.244 0.254 0.185 0.190 0.234

Tax Levy $124,078,000 $310,236,000 $36,346,435 $315,600,000 $170,557,000 $956,817,435 Net Patient Revenue $199,372,000 $245,766,000 $122,904,345 $196,500,000 $96,427,000 $860,969,345 Other Operating Revenue $24,178,000 $138,640,000 $2,383,000 $11,700,000 $15,668,000 $192,569,000 Tobacco Settlement $3,167,000 $11,268,000 $165,252 $13,800,000 $5,016,000 $33,416,252 Medicaid DSH $20,954,000 $37,618,000 $14,500,000 $50,500,000 $22,000,000 $145,572,000 Total $371,749,000 $743,528,000 $176,299,032 $588,100,000 $309,668,000 $2,189,344,032

Tax Levy/Total Revenue 33% 42% 21% 54% 55% 46% Patient Revenue/Total 54% 33% 70% 33% 31% 42%

Total Expense $393,871,000 $626,261,873 $176,299,032 $563,996,000 $303,188,000 $2,063,615,905

Tax Levy Per Capita $89 $140 $53 $93 $67 $105 Tax Levey Per Uninsured $355 $621 $157 $388 $296 $431 Tax Levy Per Person in Poverty $486 $1,344 $182 $818 $612 $778

Expenditures Per Capita $283 $282 $259 $166 $210 $226 Expenditures Per Uninsured $1,128 $1,253 $761 $694 $931 $930 Expenditures Per Person in Poverty $1,541 $2,713 $882 $1,461 $1,925 $1,678

What is a Public Hospital District (PHD)? Simply put, hospital districts are community supported governmental entities charged with delivering health care to their communities. They fulfill a vital role in Washington State's health care system because without them, many people would be unable to receive health care in their own communities. The Washington State legislature granted local communities the ability to create their own hospital districts in 1945. Nearly half of Washington's 90 hospitals are part of a public hospital district. Hospital districts are authorized not only to operate a hospital, but to deliver any service to help people stay healthy-physically, socially and mentally. Because they're owned and governed by local citizens, hospital districts tailor their services to meet the unique needs of their individual communities. It is this community-based mission that defines and distinguishes hospital districts from other health care entities.

Overview and Purpose of Public Hospital Districts Public hospital districts belong to the family of special purpose districts and municipal corporations. Thus, they are governmental entities created by statute and operating under all applicable statutory, constitutional and regulatory provisions of the State of Washington and the United States.

Sources of Public Hospital Districts' Power Public hospital districts are organized and exist as a result of chapter 70.44 of the Revised Code of Washington (RCW). It is this statute that created public hospital districts and fundamentally defines their purpose, operations, powers and limitations.

Comparisons and Differences From Non-Profit Hospitals Public hospital districts operate approximately 40% of the entities licensed as acute care hospitals in Washington. The vast majority of the other hospitals are operated as not-for-profit corporations, with a handful owned and operated by private corporations on a for-profit or proprietary basis.

In many cases, public hospital district hospitals and not-for-profit hospitals may appear very similar. Both may be focused on community service with "profits" being applied not to rates of return for investors but to enhanced community services, facility upgrading, or subsidized care for persons unable to pay the full costs of service. The major difference is that Washington state law reinforces the need for public hospital districts to maintain this focus in a very precise way, for example through the election of board members (not-for-profit members are normally appointed) and strict legal restrictions on the use of funds for certain purposes. Also, as a municipal corporation, many of the documents and proceedings of a public hospital district are open to close public scrutiny through Open Meetings and Public Records laws, while not-for-profit activities are monitored in a much more general fashion through state and federal review activities. As a last example, public hospital district commissioners, as public officials, are prohibited from various conflicts of interests. RCW 42.23.030 prohibits a municipal officer from being "beneficially interested, directly or indirectly, in any contract which may be made, through or under the supervision of such officer, in whole or in part, or which may be made for the benefit of his office..."

In exchange for meeting these procedural barriers and requirements, which may be viewed as the tools for assuring that a community truly desires a public hospital district, hospital districts are able to access the benefits associated with being such an entity, such as access to tax revenues, low cost bonds, exclusions from payment of certain taxes, and the like.

What Is a Public Hospital District and Why Should You Care? 25 Whidbey General is a Public Hospital District. This means it was created, and is supported, by the community specifically to ensure the availability of quality health care services, close to home. As a community-driven institution, we are directly accountable to the people who live here, not to stockholders or some out-of-area corporation. Our elected board of trustees is your neighbors, who share your hopes, dreams, and concerns about the future of this community.

Because we were created as a public hospital district, instead of just a hospital, we have a broad mandate to deliver any health care service necessary to help people stay healthy. Here at Whidbey General Hospital, we have developed specific programs in direct response to the health care needs of our community. We have expanded throughout the years to include services such as Home Health Care & Hospice, Lifeline, Paramedics and Emergency Medical Services, Medical Ambulatory Care, our North Whidbey and South Whidbey Community Clinics, our Dental Clinic in Oak Harbor, our Community Health Education program, and Childbirth Education classes, just to name a few.

Our health care offerings are the most obvious benefits of having a community owned hospital. But we believe we also have a special obligation to ensure that anyone in need of health care services receives quality care-regardless of ability to pay.

ZIP Code® Tabulation Areas (ZCTATM) (Adapted from the US Census Bureau26) The ZIP Code Tabulation Areas (ZCTATM) is a new statistical unit created by the U.S. Census Bureau for charting Census 2000 statistics at a level equivalent to the ZIP Code®. ZCTAs are generally representative of U.S. Postal Service (USPS) ZIP Code service areas. Each ZCTA is an aggregate of the Census 2000 blocks, whose addresses use a given ZIP Code, and thus each ZCTA is assigned the prevailing ZIP Code as its ZCTA code. A ZCTA code represents the majority USPS five-digit ZIP Code found in a given area.27

Tulsa Metropolitan Statistical Area (MSA) (Adapted from the Office of Management and Budget28) The Office of Management and Budget (OMB) published the Standards for Defining Metropolitan Statistical Areas in a Federal Register Notice (65 FR 82228 - 82238)29 on December 27, 2000. OMB’s 2000 standards provide for the identification of Metropolitan Statistical Areas (MSAs) in the US. The OMB appendix includes information on the statistical areas that are recognized under the 2000 standards using data from Census 2000 and Census Bureau population estimates for 2002 and 2003.30

MSAs have at least one urbanized area of a population of 50,000 or more and adjacent territory that has a high degree of social and economic integration with the core as measured by commuting ties. MSAs are defined in terms of whole counties. About 83 percent of the US population resides in MSAs. Of 3,141 counties in the US, 1,090 will be in the 361 MSAs in the US. OMB’s standards provide for the identification of one or more principal cities within each MSA. The more significant places in each MSA are identified in terms of population and employment, and the principal cities are used in titling each MSA. According to the OMB’s definition, Tulsa MSA includes the following seven counties: Creek, Okmulgee, Osage, Pawnee, Rogers, Tulsa, and Wagoner. The principal city in Tulsa MSA is Tulsa. Tulsa MSA Counties and ZIP Codes: Tulsa MSA includes seven counties—Creek, Okmulgee, Osage, Pawnee, Rogers, Tulsa, and Wagoner counties. Information on the proportion of ZIP code addresses that fall within a given county was obtained through the Melissa DATA website.31 As of March 2, 2005, the information was as follows:

ZIP Codes Apportioned to Counties

CREEK COUNTY 74084 Wynona 100.0% 74055 Owasso 72.3% 74010 Bristow 99.6% 74106 Tulsa 5.2% 74063 Sand Springs 74.8% 74028 Depew 99.2% 74126 Tulsa 25.1% 74066 Sapulpa 1.8% 74030 Drumright 99.0% 74127 Tulsa 39.8% 74070 Skiatook 29.7% 74033 Glenpool 7.2% 74604 Ponca City 29.6% 74073 Sperry 66.2% 74038 Jennings 36.4% 74633 Burbank 100.0% 74103 Tulsa 100.0% 74039 Kellyville 100.0% 74637 Fairfax 100.0% 74104 Tulsa 100.0% 74041 Kiefer 100.0% 74650 Ralston 34.0% 74105 Tulsa 100.0% 74044 Mannford 68.6% 74652 Shidler 97.0% 74106 Tulsa 94.8% 74047 Mounds 29.4% 74107 Tulsa 100.0% 74052 Oilton 100.0% 74108 Tulsa 87.4% 74063 Sand Springs 5.1% PAWNEE COUNTY 74110 Tulsa 100.0% 74066 Sapulpa 98.2% 74020 Cleveland 100.0% 74112 Tulsa 100.0% 74068 Shamrock 100.0% 74032 Glencoe 11.3% 74114 Tulsa 100.0% 74071 Slick 100.0% 74034 Hallett 100.0% 74115 Tulsa 100.0% 74079 Stroud 3.0% 74038 Jennings 63.6% 74116 Tulsa 61.9% 74081 Terlton 0.6% 74044 Mannford 31.4% 74117 Tulsa 100.0% 74085 Yale 0.2% 74045 Maramec 99.2% 74119 Tulsa 100.0% 74131 Tulsa 100.0% 74058 Pawnee 99.9% 74120 Tulsa 100.0% 74132 Tulsa 36.5% 74081 Terlton 99.4% 74126 Tulsa 74.9% 74085 Yale 0.4% 74127 Tulsa 60.2% OKMULGEE COUNTY 74650 Ralston 66.0% 74128 Tulsa 100.0% 74047 Mounds 50.8% 74651 Red Rock 3.6% 74129 Tulsa 100.0% 74421 Beggs 100.0% 74130 Tulsa 100.0% 74422 Boynton 55.4% ROGERS COUNTY 74132 Tulsa 63.5% 74431 Dewar 100.0% 74015 Catoosa 72.4% 74133 Tulsa 100.0% 74436 Haskell 30.8% 74016 Chelsea 93.5% 74134 Tulsa 100.0% 74437 Henryetta 98.9% 74017 Claremore 100.0% 74135 Tulsa 100.0% 74445 Morris 100.0% 74021 Collinsville 18.1% 74136 Tulsa 100.0% 74447 Okmulgee 100.0% 74036 Inola 96.9% 74137 Tulsa 100.0% 74456 Preston 100.0% 74053 Oologah 100.0% 74145 Tulsa 100.0% 74460 Schulter 100.0% 74055 Owasso 27.7% 74146 Tulsa 100.0% 74880 Weleetka 2.4% 74080 Talala 82.2% 74116 Tulsa 38.1% WAGONER COUNTY OSAGE COUNTY 74332 Big Cabin 12.6% 74008 Bixby 0.8% 74001 Avant 100.0% 74014 Broken Arrow 99.1% 74002 Barnsdall 100.0% TULSA COUNTY 74015 Catoosa 25.8% 74003 Bartlesville 14.3% 74008 Bixby 99.2% 74108 Tulsa 12.6% 74022 Copan 3.8% 74011 Broken Arrow 100.0% 74337 Chouteau 5.2% 74035 Hominy 100.0% 74012 Broken Arrow 100.0% 74352 Locust Grove 5.1% 74051 Ochelata 1.6% 74014 Broken Arrow 0.9% 74403 Muskogee 0.3% 74054 Osage 100.0% 74015 Catoosa 1.7% 74429 Coweta 100.0% 74056 Pawhuska 100.0% 74021 Collinsville 78.4% 74434 Fort Gibson 1.8% 74060 Prue 100.0% 74033 Glenpool 92.8% 74436 Haskell 9.9% 74063 Sand Springs 20.1% 74037 Jenks 100.0% 74446 Okay 100.0% 74070 Skiatook 61.0% 74047 Mounds 19.8% 74454 Porter 100.0% 74073 Sperry 33.8% 74050 Oakhurst 100.0% 74467 Wagoner 100.0%

2000 Decennial Census of Population and Housing: The 2000 Decennial Census of Population and Housing, also known as the Census 2000, provides information on demographic, economic, housing and social characteristics of the population collected in 2000. This data was retrieved using the Census Bureau’s data extraction tool, American FactFinder, which provides access to specific data sets and detailed tables.

Census 2000 Data Sets: Summary File 1 (SF 1) 100-Percent Data Summary File 1 (SF 1) contains basic information on the US population based on answers from the Census 2000 Short-Form questionnaire. SF 1 presents counts and basic cross tabulations of information collected from all people and housing units. It includes 100 percent data on people's age, sex, and race, their family and household groups, and whether their home is owned or rented. There are a total of 286 Detailed Tables available in the SF 1 data product.32

Summary File 3 (SF 3) - Sample Data Summary File 3 (SF 3) contains some of the most complete statistical data available on US residents. SF 3 is based on the Long-Form questionnaire that presents in-depth housing and population data collected on a sample basis (1 out of 6 units) as part of Census 2000. Data is presented on such topics as income, ancestry, citizenship status, home values, commute time to work, occupation, education, veteran status, language ability, migration, place of birth, and many others. No other U.S. survey matches this level of data collection from this many people. There are 813 Detailed Tables available in the SF 3 data product.33

Data Elements: The following data elements were extracted from the Census Bureau’s website using American FactFinder for the analysis of the uninsured:

SF1 Data Set P1. Total Population P7. Race P8. Hispanic or Latino by Race P11. Hispanic or Latino P12. Sex by Age

SF3 Data Set P19. Age by Language Spoken at Home by Ability to Speak English for the Population 5 Years and Over P43. Sex by Employment Status for the Population 16 Years and Over P49. Sex by Industry for the Employed Civilian Population 16 Years and Over P50. Sex by Occupation for the Employed Civilian Population 16 Years and Over P51. Sex by Industry by Class of Worker for the Employed Civilian Population 16 Years and Over P53. Median Household Income in 1999 (Dollars) P77. Median Family Income in 1999 (Dollars) P82. Per Capita Income in 1999 (Dollars) P84. Sex by Earnings in 1999 for the Population 16 Years and Over with Earnings P87. Poverty Status in 1999 by Age

Industry and Occupation Classification (Adapted from the US Census Bureau)

Industry and occupation categories used in the uninsured analysis were classified using standard classification systems as defined by the North American Industrial Classification System (NAICS) and 2000 Standard Occupational Classification (SOC) system, respectively.

The NAICS, prepared by the Office of Management and Budget (OMB) and published in the NAICS Manual, is the standard for industrial classification systems in the U.S. Government. The Census 2000 Industrial Classification System was developed using the structure of the NAICS. In the NAICS, establishments are grouped into industries based on the activities in which they are primarily engaged. The Census Bureau coding system consists of 265 categories arranged into 20 sectors. The sectors are exactly the same as those found in the 1997 NAICS.34

2002 NAICS Codes and Titles35 11- Agriculture, Forestry, Fishing and Hunting 21-Mining 22-Utilities 23-Construction 31-33 Manufacturing 42-Wholesale Trade 44-45 Retail Trade 48-49 Transportation and Warehousing 51-Information

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 34 52-Finance and Insurance 53-Real Estate and Rental and Leasing 54- Professional, Scientific, and Technical Services 55- Management of Companies and Enterprises 56- Administrative and Support and Waste Management and Remediation Services 61- Educational Services 62- Health Care and Social Assistance 71- Arts, Entertainment, and Recreation 72- Accommodation and Food Services 81- Other Services (except Public Administration) 92- Public Administration

The 2000 Standard Occupational Classification (SOC) system is used by Federal statistical agencies to classify workers into occupational categories for the purpose of collecting, calculating, or disseminating data. All workers are classified into one of over 820 occupations according to their occupational definition. To facilitate classification, occupations are combined to form 23 major groups, 96 minor groups, and 449 broad occupations. Each broad occupation includes detailed occupation(s) requiring similar job duties, skills, education, or experience.36

SOC Major Groups37 Each occupation in the SOC is placed within one of these 23 major groups: 11-Management Occupations 13-Business and Financial Operations Occupations 15-Computer and Mathematical Occupations 17-Architecture and Engineering Occupations 19-Life, Physical, and Social Science Occupations 21-Community and Social Services Occupations 23-Legal Occupations 25-Education, Training, and Library Occupations 27-Arts, Design, Entertainment, Sports, and Media Occupations 29-Healthcare Practitioners and Technical Occupations 31-Healthcare Support Occupations 33-Protective Service Occupations 35-Food Preparation and Serving Related Occupations 37-Building and Grounds Cleaning and Maintenance Occupations 39-Personal Care and Service Occupations 41-Sales and Related Occupations 43-Office and Administrative Support Occupations 45-Farming, Fishing, and Forestry Occupations 47-Construction and Extraction Occupations 49-Installation, Maintenance, and Repair Occupations 51-Production Occupations 53-Transportation and Material Moving Occupations 55-Military Specific Occupations*

*This category was not included as part of the major SOC groups for the uninsured analysis, as data was not available.

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 35 Appendix – U.S. Metropolitan Areas Medicaid Graduate Medical Education Dollars

Both Oklahoma City and Tulsa benefit from additional public funds to be used for serving the medically needy. They are Medicaid funds masked as “medical education” dollars. The data below is directly quoted from the 2004 Annual Report of the Oklahoma Health Care Authority. 38

Graduate Medical Education (GME) Graduate medical education refers to the residency training that doctors receive after completing medical school. Most residency programs are set up in teaching hospitals across the United States. GME derives funding from a variety of sources. Funding sources include patient care dollars and university funding, but the bulk of the money for GME comes from public, tax supported sources, such as Medicare, Medicaid, the Department of Defense and Veterans’ Affairs. Medicaid payments are made to the major colleges of medicine based on the number of managed care beneficiaries where Primary Care Physicians (PCP) are qualified participants. The state matching funds are transferred to OHCA from the University Hospital Authority.

Indirect Medical Education (IME) Acute care hospitals that qualify as major teaching hospitals receive an indirect medical education (IME) payment adjustment that covers the increased operating or patient care costs associated with approved intern or resident programs. Currently, the only qualifying hospitals are the OU Medical Center in Oklahoma City and the Hillcrest health system hospitals in Tulsa. In order to qualify as a teaching hospital and be deemed eligible for IME supplemental incentive payment adjustments, the hospital must: be licensed in the state of Oklahoma; have 150 or more full-time equivalent residents enrolled in approved teaching programs using the 1996 annual cost report; and belong to the Council of Teaching Hospitals or show proof of affiliation with an approved Medical Education Program.

Disproportionate Share Hospital (DSH) Payments Hospitals provide health care to the poor and uninsured in the form of uncompensated care, defined as the sum of charity care and bad debt charges. Uncompensated care has always been unevenly distributed – urban safety net hospitals have had to assume a larger burden of care for the under- and un-insured. The Medicaid DSH payment adjustment was born in a clause in the Omnibus Budget Reconciliation Act of 1981 (OBRA ’81) that required state Medicaid agencies to make allowances when determining reimbursement rates for hospitals that served a disproportionate number of Medicaid or low-income patients. The federal disproportionate share payments are made to each state annually. The eligible hospitals are identified and the total funds are allocated on a “weighted” basis. The weighting is based on each hospital’s share of Medicaid plus charity care revenues.

Direct Medical Education (DME) In-state hospitals that qualify as teaching hospitals receive a supplemental payment adjustment for direct medical education (DME) expenses based on resident-months. These payments are made in order to encourage training in rural hospital and primary care settings and to recognize the loss of support for GME due to the advent of managed care capitation programs. In order to qualify as a teaching hospital and be deemed eligible for DME supplemental incentive payment adjustments, the hospital must: be licensed in the state of Oklahoma; have a medical residency program; apply for certification by the OHCA prior to receiving payments for any quarter; have a contract with OHCA to provide Medicaid services; and belong to the Council of Teaching Hospitals or show proof of affiliation with an approved Medical Education Program. These payments are made by allocating a pool of funds by the share of residents per month to total residents per month in all qualifying hospitals. The state matching funds are transferred to OHCA from the University Hospital Authority.

Disproportionate Share Hospitals DME Qualified Hospitals University Hospitals ...... $21,996,524 Bone and Joint Hospital – OKC ...... $6,347 OK Youth Center ...... $80,876 Comanche County Memorial Hospital ...... $62,092 George Nigh Rehab (Okmulgee)...... $55,124 Deaconess Hospital ...... $66,381 Griffin Memorial ...... $2,944,321 Hillcrest Medical Center – Tulsa ...... $6,626,590 Jim Taliaferro ...... $31,503 Bass Baptist Health Care Center ...... $14,760 Choctaw Memorial ...... $6,104 Southwest Medical Center ...... $265,907 Cimarron Memorial Hospital ...... $2,348 Baptist Medical Center ...... $3,310,477 J.D. McCarty Center for Children ...... $926,965 Jackson County Memorial ...... $2,897 Henryetta Medical Center ...... $ 670 Jane Phillips Hospital ...... $19,295 Hillcrest/St. Michael's ...... $255,780 Laureate Psych Hospital ...... $3,564 Logan County ...... $1,424 Medical Center of SE Oklahoma ...... $85,548 Mission Hill ...... $6,255 Saint Francis – Tulsa ...... $2,442,876 Parkside/Tulsa Psychiatric ...... $137,332 Shadow Mt/Brown Schools Hospital ...... $47,504 Share Medical Center ...... $161 St. Anthony ...... $2,017,541 Willow View ...... $1,975 St. John – Tulsa ...... $2,276,201 Arkansas Children's Hospital ...... $520,140 Tulsa Regional Medical ...... $4,702,160 TOTAL $24,029,082 University Health Partners ...... $29,463,192 TOTAL $51,413,332

SFY2004 Specific Information Estimated total payments to be made to GME qualified colleges of medicine:

OUCOM – OKC ...... $ 24,571,832 OUCOM – Tulsa ...... $ 21,594,451 OSU COM – Tulsa ...... $ 11,607,814

Payments made to IME qualified hospitals:

Oklahoma Medical Center – OKC ...... $ 11,393,839 Hillcrest Health System – Tulsa...... $ 11,393,839

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 37 TULSA MSA COUNTIES RANK ORDER OF SELECTED ZCTAS HEALTH INSURANCE STATUS

COMMUNITY ESTIMATED PERCENTAGES ESTIMATED NUMBER

ZCTA COUNTY CITY POP MCE MCD MCD+ INS NONE MCE MCD MCD+ INS NONE

74002 Osage Barnsdall 2,300 14.4% 15.6% 1.7% 50.3% 18.0% 332 359 40 1,156 413 74003 Osage Bartlesville 2,125 12.4% 21.6% 1.2% 45.5% 19.3% 264 458 26 966 411 74008 Tulsa Bixby 15,351 9.1% 9.9% 0.8% 61.0% 19.3% 1,395 1,520 117 9,358 2,961 74010 Creek Bristow 10,129 13.4% 16.4% 2.0% 49.5% 18.7% 1,361 1,658 199 5,015 1,897 74011 Tulsa Broken Arrow 23,031 7.8% 8.1% 0.7% 64.5% 19.0% 1,789 1,858 155 14,860 4,369 74012 Tulsa BA 47,249 7.0% 9.1% 0.7% 63.7% 19.5% 3,291 4,309 332 30,081 9,236 74014 Wagoner BA 23,231 6.4% 9.0% 0.6% 64.6% 19.4% 1,491 2,087 139 15,011 4,503 74015 Rogers Catoosa 5,597 7.9% 31.1% 1.9% 39.8% 19.2% 441 1,741 109 2,229 1,077 74015 Wagoner Catoosa 1,995 7.9% 31.1% 2.0% 39.8% 19.2% 157 621 39 794 384 74016 Rogers Chelsea 5,184 8.3% 68.1% 4.3% 0.9% 18.4% 428 3,531 223 48 954 74017 Rogers Claremore 38,719 10.6% 17.7% 1.9% 51.1% 18.7% 4,104 6,866 738 19,778 7,233 74020 Pawnee Cleveland 7,604 13.4% 16.3% 1.6% 50.1% 18.6% 1,016 1,239 125 3,810 1,414 74021 Rogers Collinsville 2,215 9.5% 20.0% 2.1% 49.3% 19.1% 210 444 46 1,092 423 74021 Tulsa Collinsville 9,595 9.5% 20.1% 2.1% 49.3% 19.1% 909 1,927 198 4,728 1,833 74028 Creek Depew 1,785 11.1% 16.3% 1.3% 52.1% 19.2% 198 290 24 930 342 74030 Creek Drumright 3,792 17.8% 21.2% 2.2% 41.1% 17.6% 676 805 83 1,559 669 74033 Tulsa Glenpool 8,475 5.2% 12.9% 0.8% 60.4% 20.8% 437 1,092 71 5,116 1,759 74035 Osage Hominy 4,860 9.9% 16.7% 1.9% 51.3% 20.1% 480 814 94 2,495 977 74036 Rogers Inola 5,955 7.2% 23.6% 2.7% 47.5% 19.1% 426 1,403 159 2,831 1,135 74037 Tulsa Jenks 9,428 8.1% 7.8% 1.2% 64.1% 18.8% 762 737 114 6,041 1,774 74038 Pawnee Jennings 1,291 10.8% 16.2% 1.3% 53.1% 18.6% 139 209 17 686 241 74039 Creek Kellyville 3,105 7.4% 23.7% 1.4% 48.0% 19.5% 229 737 42 1,492 605 74044 Pawnee Mannford 2,166 10.7% 16.3% 1.3% 53.0% 18.7% 231 354 29 1,147 405 74044 Creek Mannford 4,731 10.7% 16.4% 1.3% 52.9% 18.7% 504 775 63 2,504 885 74047 Okmulgee Mounds 3,293 6.6% 15.5% 1.2% 56.8% 20.0% 216 510 40 1,869 658 74047 Creek Mounds 1,906 6.6% 15.5% 1.2% 56.7% 20.0% 125 296 23 1,081 381 74047 Tulsa Mounds 1,284 6.5% 15.5% 1.2% 56.7% 20.0% 84 199 16 728 256 74052 Creek Oilton 1,350 13.6% 28.4% 2.4% 37.2% 18.4% 183 383 33 502 249 74053 Rogers Oologah 2,628 6.3% 34.3% 3.1% 37.1% 19.1% 166 902 82 976 502 74055 Rogers Owasso 7,355 7.7% 15.3% 1.4% 56.9% 18.8% 566 1,122 104 4,184 1,379 74055 Tulsa Owasso 19,197 7.7% 15.3% 1.4% 56.9% 18.8% 1,476 2,928 272 10,920 3,600 74056 Osage Pawhuska 5,835 15.2% 24.3% 2.7% 39.6% 18.1% 886 1,418 160 2,312 1,059 74058 Pawnee Pawnee 4,152 13.9% 21.7% 2.8% 43.4% 18.2% 576 902 115 1,802 756 74063 Tulsa S. Springs 21,250 10.5% 12.0% 1.1% 57.3% 19.0% 2,242 2,552 244 12,175 4,037 74063 Osage S. Springs 5,710 10.5% 12.0% 1.2% 57.3% 19.0% 602 686 66 3,272 1,085 74063 Creek S. Springs 1,449 10.5% 12.0% 1.2% 57.3% 19.0% 152 174 17 830 275 74066 Creek Sapulpa 29,270 11.9% 14.3% 1.1% 53.9% 18.8% 3,479 4,181 323 15,782 5,506 74070 Tulsa Skiatook 3,180 9.1% 13.4% 1.6% 56.6% 19.3% 290 427 50 1,799 614 74070 Osage Skiatook 6,531 9.1% 13.4% 1.6% 56.6% 19.3% 596 878 102 3,694 1,261 74073 Tulsa Sperry 3,360 8.8% 12.3% 1.1% 58.9% 18.9% 297 413 36 1,978 635 74073 Osage Sperry 1,715 8.9% 12.4% 1.0% 58.8% 18.9% 152 212 18 1,009 324 74080 Rogers Talala 2,113 8.2% 18.8% 0.9% 53.6% 18.5% 173 398 18 1,133 392 74081 Pawnee Terlton 1,790 8.0% 16.5% 1.6% 54.9% 19.0% 144 295 29 983 340 74103 Tulsa Tulsa 2,173 -0.5% 14.3% 1.4% 62.3% 22.4% -10 311 30 1,354 488 74104 Tulsa Tulsa 14,050 8.2% 11.1% 0.7% 57.1% 23.0% 1,149 1,553 99 8,020 3,229 74105 Tulsa Tulsa 28,455 15.7% 10.1% 0.6% 54.7% 18.8% 4,468 2,875 184 15,574 5,354 74106 Tulsa Tulsa 17,164 11.4% 36.5% 3.8% 29.9% 18.4% 1,948 6,266 650 5,135 3,165

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 38 TULSA MSA COUNTIES RANK ORDER OF SELECTED ZCTAS HEALTH INSURANCE STATUS

COMMUNITY ESTIMATED PERCENTAGES ESTIMATED NUMBER

ZCTA COUNTY CITY POP MCE MCD MCD+ INS NONE MCE MCD MCD+ INS NONE

74107 Tulsa Tulsa 20,284 11.1% 23.3% 1.3% 44.9% 19.4% 2,259 4,724 259 9,107 3,935 74108 Tulsa Tulsa 6,638 7.5% 36.9% 0.9% 34.4% 20.2% 500 2,449 63 2,286 1,340 74110 Tulsa Tulsa 15,267 9.1% 32.5% 1.8% 35.7% 20.9% 1,385 4,965 280 5,451 3,186 74112 Tulsa Tulsa 21,222 15.7% 14.4% 1.0% 49.4% 19.5% 3,324 3,060 215 10,486 4,137 74114 Tulsa Tulsa 16,913 17.5% 5.0% 0.6% 59.9% 17.0% 2,957 843 98 10,132 2,883 74115 Tulsa Tulsa 23,687 10.5% 27.7% 1.2% 40.6% 20.0% 2,486 6,563 285 9,611 4,742 74116 Tulsa Tulsa 2,270 6.6% 66.9% 1.7% 4.2% 20.7% 150 1,518 38 96 469 74116 Rogers Tulsa 1,398 6.5% 66.8% 1.7% 4.3% 20.7% 91 934 24 59 289 74119 Tulsa Tulsa 3,790 16.5% 10.0% 2.0% 51.7% 19.8% 624 380 75 1,960 751 74120 Tulsa Tulsa 5,496 6.6% 13.6% 0.8% 56.5% 22.4% 365 750 46 3,106 1,229 74126 Osage Tulsa 3,032 6.2% 38.8% 2.4% 32.6% 20.0% 187 1,176 73 989 607 74126 Tulsa Tulsa 9,047 6.1% 38.8% 2.4% 32.6% 20.0% 555 3,510 219 2,950 1,813 74127 Osage Tulsa 7,207 11.2% 27.9% 1.7% 40.1% 19.1% 804 2,010 121 2,892 1,379 74127 Tulsa Tulsa 10,901 11.2% 27.9% 1.7% 40.1% 19.1% 1,217 3,041 183 4,374 2,086 74128 Tulsa Tulsa 12,430 15.2% 18.1% 0.8% 46.6% 19.3% 1,890 2,247 99 5,794 2,400 74129 Tulsa Tulsa 18,542 13.8% 19.7% 1.2% 45.8% 19.4% 2,565 3,656 231 8,490 3,600 74130 Tulsa Tulsa 2,624 9.2% 23.1% 1.7% 46.3% 19.7% 242 605 45 1,216 516 74131 Creek Tulsa 2,538 7.3% 22.7% 1.2% 49.0% 19.8% 185 575 31 1,245 502 74132 Creek Tulsa 2,653 7.5% 7.3% 0.5% 65.9% 18.8% 199 193 14 1,748 500 74132 Tulsa Tulsa 4,616 7.5% 7.3% 0.5% 65.9% 18.8% 345 336 25 3,040 870 74133 Tulsa Tulsa 37,778 9.2% 7.5% 0.7% 62.8% 19.8% 3,467 2,827 277 23,728 7,479 74134 Tulsa Tulsa 12,998 4.0% 0.0% 0.0% 73.8% 22.2% 522 0 0 9,591 2,885 74135 Tulsa Tulsa 21,320 21.8% 11.6% 0.8% 47.8% 18.0% 4,648 2,479 181 10,183 3,829 74136 Tulsa Tulsa 32,712 10.6% 11.9% 0.6% 55.1% 21.8% 3,457 3,909 201 18,010 7,135 74137 Tulsa Tulsa 22,960 10.0% 4.5% 0.4% 68.1% 17.1% 2,300 1,034 82 15,626 3,918 74145 Tulsa Tulsa 18,020 18.3% 10.9% 0.8% 51.3% 18.8% 3,289 1,967 138 9,238 3,388 74146 Tulsa Tulsa 14,380 5.6% 23.3% 1.2% 47.9% 22.1% 800 3,344 177 6,883 3,176 74421 Okmulgee Beggs 4,203 10.5% 19.5% 1.7% 49.7% 18.5% 443 820 71 2,090 779 74429 Wagoner Coweta 10,445 7.1% 18.4% 1.8% 53.0% 19.7% 745 1,924 185 5,532 2,059 74431 Okmulgee Dewar 1,029 9.4% 44.8% 3.8% 22.8% 19.2% 97 461 39 234 198 74436 Okmulgee Haskell 1,380 10.5% 21.6% 2.8% 45.9% 19.1% 145 298 39 634 264 74437 Okmulgee Henryetta 10,277 14.7% 23.1% 3.1% 41.3% 17.8% 1,515 2,371 320 4,240 1,831 74445 Okmulgee Morris 2,600 9.8% 34.2% 2.4% 34.9% 18.7% 256 888 63 908 485 74447 Okmulgee Okmulgee 17,997 13.4% 25.7% 3.1% 39.4% 18.3% 2,419 4,632 552 7,098 3,296 74454 Wagoner Porter 2,906 10.2% 16.7% 2.2% 52.0% 18.8% 297 486 64 1,511 548 74467 Wagoner Wagoner 13,780 13.8% 22.3% 2.0% 43.6% 18.4% 1,906 3,071 271 6,003 2,529 74604 Osage Ponca City 3,935 18.3% 7.0% 1.0% 57.1% 16.7% 719 276 38 2,246 656 74637 Osage Fairfax 2,063 16.6% 19.0% 3.2% 43.8% 17.5% 342 391 66 903 361 74652 Osage Shidler 1,044 18.3% 17.5% 2.6% 43.5% 18.0% 191 183 27 454 188

LEGEND: The percentage of Insured and Medicare have been summed, hen sorted in descending order. The top third has been designated as GREEN, the bottom third as RED, and the middle third as YELLOW. Then the table was sorted and displayed in ZCTA number order to provide a geographical perspective. MCE: Medicare; MCD: Medicaid; INS: Private insurance; and NONE: Lacking health care insurance or benefits.

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 39 TULSA MSA COUNTIES RANK ORDER OF SELECTED ZCTAs HEALTH INSURANCE STATUS

COMMUNITY ESTIMATED PERCENTAGES ESTIMATED NUMBER

ZCTA COUNTY CITY POP MCE MCD MCD+ INS NONE MCE MCD MCD+ INS NONE

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 40 TULSA MSA COUNTIES RANK ORDER OF SELECTED ZCTAs HEALTH INSURANCE STATUS

COMMUNITY ESTIMATED PERCENTAGES ESTIMATED NUMBER

ZCTA COUNTY CITY POP MCE MCD MCD+ INS NONE MCE MCD MCD+ INS NONE

LEGEND: MCE: Medicare; MCD: Medicaid; INS: Private insurance; and NONE: Lacking health care insurance or benefits.

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 41 SECTION 3 HEALTH INSURANCE STATUS ESTIMATING SOURCES OF HEALTH INSURANCE AT THE ZCTA LEVEL

Introduction Since the inception of health insurance in the 1930s until the mid 1960s, people without health insurance were not a major ongoing public policy issue. The enactment of Medicare and Medicaid granted benefits to specific populations, but didn’t immediately establish the “uninsured” as a political constituency. “Cost shifting” allowed providers to use insurance over-billings to pay for those without the means to pay.

Since then, the “uninsured” have been extensively enumerated and categorized.

It is known that a number of demographic and socioeconomic characteristics—such as income, employment status, employment industry and occupation, and attitudes and behaviors, influence one’s health insurance status and inevitably one’s health. One of the major determinants of health is whether one has adequate health insurance coverage. Employer-sponsored health insurance (ESI) is the most common link to health insurance coverage for most nonelderly Americans today. However, many small employers don’t provide this benefit to their employees.

General types of health insurance coverage can be categorized as either private or public health insurance. Private sources of health insurance can either be direct-purchase “individual” plans or employment-based “group” plans—provided through an employer or union. Public or government-sponsored health insurance comprises Medicare, Medicaid (SCHIP), military health care (TRICARE or CHAMPUS, CHAMPVA, or VA plans), state-sponsored plans, and Indian Health Service. Without any of these forms of health insurance coverage either directly or through family or a spouse, with the exception of Indian Health Service coverage, one is presumed to be without health insurance or uninsured.

Problem This analysis aimed to produce indirect estimates of the nonelderly uninsured at the ZIP code level for each county in the Tulsa MSA. Since data is not readily available for direct estimation of the uninsured at this level of geography, indirect or “synthetic” estimates were generated using data already available from various sources of information.

Data on the number of Medicaid enrollees per county within the Tulsa MSA was used to obtain a direct estimate of the Medicaid population for each ZCTA. It was presumed that the population 65 years and over is universally covered by Medicare. The number of people that are uninsured in each ZCTA of the MSA was derived using an indirect method of estimation incorporating rates of lack of insurance by age © 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 42 group, gender and total earnings categories. The final indirect estimate that was derived was that of the insured population in each ZCTA and the Tulsa MSA, which was the residual of the total population that was not insured or receiving coverage through any public insurance program.

The U.S. Census Bureau was the main source for population-based data that was employed in this analysis of the uninsured. The 2000 Census was the source for specific demographic and socioeconomic data that provided the baseline inputs necessary for the actual uninsured estimation. Two key data sets were utilized for information at the Zip Code Tabulation Area level of data. They were Summary File 1 (SF 1), which is 100 percent data, and Summary File 3 (SF 3), which is sample data. For total population, race/ethnicity, age, and gender characteristics of the population, SF 1 was employed for data extraction. For information on poverty status, the labor force (employment status, occupation, industry), and all other characteristics, SF 3 was utilized.

Data To accurately estimate the population without health insurance in the Tulsa MSA region, a thorough analysis was conducted of the uninsured population using data collected at a sub-county level. Demographic and socioeconomic data was obtained from the U.S. Census Bureau39—the official national source for housing and population data. From the Census Bureau’s website, data was extracted from SF 1 and SF 3 of the 2000 Decennial Census for Population and Housing using American FactFinder as the data extraction tool.40

For each county within the Tulsa MSA, data was extracted at a level of geography similar to the U.S. Postal Service (USPS) ZIP code. The Census Bureau’s equivalent to the standard USPS ZIP code is what’s known as the ZIP Code Tabulation Area (ZCTA).41 ZCTAs provide not only specificity of data for a geographical location, but provide a larger cohort for purposes of study and analysis. For this analysis, the use of the ZCTA level of geography also allowed for the ability to later match mortality data that was available at the ZIP code level with its corresponding ZCTA.

There are some inherent drawbacks to using the ZCTA level of geography for this analysis. The ZCTA is a product of the U.S. Census Bureau. Thus, there are limits to the extent to which ZCTA data can be cross-referenced with data from sources outside of the U.S. Census Bureau. Also, ZCTAs may cross county boundaries. This imposes another limitation in that the Census Bureau does not present data proportional to the county that the ZCTA falls in, regardless of what proportion of a ZCTA falls in any given county. Therefore, in order to achieve a more accurate measure of data for each ZCTA, it is necessary to apply a multiplier that accounts for the proportion that each ZCTA falls in its respective county within the MSA.

The source for this information, in particular the proportion of addresses per ZIP code within a given county, was obtained from the Melissa website.42 A corresponding percentage was given for any counties where ZIP codes were duplicated. These specific multipliers were applied to the data extracted from the Census Bureau so that data was more precise to the percentage that a ZIP code fell within a county. The challenge with using this method is that ZIP codes and ZIP code boundaries change often. Thus, data used in this analysis reflects ZIP code proportions that were obtained from the Melissa DATA website as of March 2, 2005. The data are best approximations for ZIP codes that fall proportionally within each given county.

These percent multipliers were applied to all data received at the ZIP code level without any corresponding county source data linked to it. Such was the case with Medicaid enrollee data obtained through the OCHA and the mortality data obtained through the Oklahoma State Department of Health.

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 43 Information on Medicaid enrollees per ZIP code, including dual enrollees in Medicaid and Medicare, for each county in the Tulsa MSA for year 2004 was obtained from the Oklahoma Health Care Authority (OHCA). Data was sorted by age group (under 18, 18-64, and 65 and over) and categorized by aged/blind/disabled and children/parents per ZCTA. The total number of Medicaid enrollees per ZCTA including persons enrolled in both Medicare and Medicaid was used in calculating the proportion of the population within each ZCTA that was enrolled in Medicaid in 2004.

Methods The complete methodology can be found in the Appendix of this section. Additionally, it is known that the Bureau of the Census is creating a “Small Area Health Insurance Estimates (SAHIE) program. That program is not yet public and prohibits citation at this time. But it can be observed that the 7 county estimates here compare favorably to the SAHIE findings. This analysis is for 2003. It estimated 20,000 more uninsured than the SAHIE program did for the year 2000. This seems more than reasonable given the SAHIE program is for 2000 and the changing demographic and economic conditions in Tulsa.

Findings The overall findings for Tulsa County are displayed below. The highest rates of the uninsured are in red; the lowest in green. The preceding pages contain comprehensive tables to allow for a myriad of analyses.

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 44 Estimated Health Insurance Status TULSA METROPOLITAN AREA BY INSURANCE STATUS AND COUNTY

Medicare Medicaid Medicaid+ Insured Uninsured Creek 7,495 10,591 885 33,861 12,292

Osage 5,212 10,348 1,161 17,471 7,714 Okmulgee 6,102 9,764 910 24,249 9,496 Pawnee 2,277 3,338 348 8,935 3,386 Rogers 6,628 17,434 1,509 32,405 13,428 Tulsa 60,011 83,701 5,838 299,749 109,374

Wagoner 4,890 9,116 765 30,055 10,616

MSA 92,614 144,292 11,416 446,727 166,306

Estimated Health Insurance Status TULSA METROPOLITAN AREA BY INSURANCE STATUS AND COUNTY

Medicare Medicaid Medicaid+ Insured Uninsured Creek 11.5% 16.3% 1.4% 52.0% 18.9%

Okmulgee 12.4% 24.7% 2.8% 41.7% 18.4% Osage 12.1% 19.3% 1.8% 48.0% 18.8% Pawnee 12.5% 18.3% 1.9% 48.9% 18.5% Rogers 9.3% 24.4% 2.1% 45.4% 18.8% Tulsa 10.7% 15.0% 1.0% 53.7% 19.6%

Wagoner 8.8% 16.4% 1.4% 54.2% 19.1%

MSA 10.8% 16.8% 1.3% 51.9% 19.3%

Suburban Counties There are six suburban counties in the Tulsa MSA—Creek, Okmulgee, Osage, Pawnee, Rogers and Wagoner. Pawnee and Okmulgee were newly added counties to the MSA in 2000.

The major concentrations of the medically marginalized—the uninsured and Medicaid beneficiaries, are in north Tulsa County and parts of Okmulgee and Rogers counties. These counties together have a population of 558,672. This represents over one-third (35%) of the seven-county MSA population. It is estimated that there are 57,000 people without health insurance, and another almost 66,000 with Medicaid benefits.

This total of 123,000 people represents almost 41% of the population of these counties. When the estimated 33,000 Medicare beneficiaries are considered, then over 51% of all suburban Tulsans are covered by public insurance or are uninsured and rely upon the public to pay for their health care services.

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 45 Appendix – Health Insurance Status Methodology for Estimating Health Insurance Status

Synthetic Model for Estimation Using a simple model approach, a synthetic estimate of the uninsured was derived for each ZCTA within Tulsa MSA. Established rates of uninsurance per age group by gender and total earnings categories for the nonelderly population ages 18-64 were obtained from Employee Benefit Research Institute (EBRI).43 Census data of the same level of specificity was already obtained at the ZCTA level. Then selected rates of uninsurance were applied to the data for each ZCTA. The complete methodology included some major components that were necessary in the computation of the uninsured estimate.

I. The first major component of the model required estimation of the uninsured nonelderly adults in each ZCTA. This required estimating the uninsured based on uninsurance rates for certain demographic characteristics applied to known data.

Uninsured nonelderly adults in OK and the US: Uninsurance rates for nonelderly adults in Oklahoma (21.7%) and the US (17.2%) were first established and then later applied to the calculation of the uninsured CNE adult.44

Uninsured per age group by gender: Basic demographic information from SF1 of the 2000 Census was used for the baseline inputs in this estimation. The total population for each ZCTA was obtained using data element P1 (Total Population) and the population breakdown by age and gender were derived using data element P12 (Sex by Age). Figures for the civilian nonelderly adult (CNE) population between ages 18-64 specifically were derived using data element P12.45 Smaller age groupings within this cohort were created per gender category based on established EBRI age groupings for which uninsurance rates were available for. Then, the number of uninsured CNE adults was calculated using EBRI rates of uninsurance for each age grouping by gender.46 Uninsurance rates were applied to each given age group for the total number of uninsured CNE adults in each ZCTA.47

Uninsured by total earnings: Information on the population’s total earnings was obtained from SF3, data element P84.48 The “Earnings in 1999”49 database category was created to match total earnings groupings that EBRI uninsurance rates were available for. Thus, using rates of uninsurance for each different earnings group, the number of uninsured nonelderly adult by total earnings was derived for each ZCTA.50

Estimate of uninsured nonelderly adults (ages 18-64):51 Average P(18-64){UIAge+ UIEarnings} = Average UP(18-64) Average UP(18-64) * [OK UI Rate/US UI Rate] = Total UP(18-64)

II. The second major component of the methodology was estimating the number of children, ages 0-17, without health insurance.

Uninsured children: ZCTA level data on the population, ages 0-17, was derived from data element P12 of SF1. According to the Kaiser Family Foundation, the rate of uninsured children between the ages of 0-17 years is 15.3%. This rate was then applied to the number of children in each ZIP code area for the total number of uninsured children, ages 0-17.52

Estimate of uninsured children (age 0-17): 53 Total P(0-17) * 15.3% = Total UP(0-17)

III. The third component of the methodology included calculating the estimated number of uninsured nonelderly, ages 0-64.

Uninsured nonelderly: The sum total of the number of uninsured CNE and uninsured children produced an estimate of the total nonelderly uninsured.

Estimate of total uninsured nonelderly: Total UP(18-64) + Total UP(0-17) = PUI

IV. The fourth and final component of the uninsured estimation methodology was the indirect estimation of the nonelderly in each ZCTA with health insurance.

Knowing the population age 65 and over receives Medicare, having estimated the number of uninsured nonelderly adults in the population, and given the data for the population covered by Medicaid for each ZCTA, the number of insured nonelderly in the population was now indirectly derived. The number of insured was the residual of the total population, less Medicaid enrollees, less the population on Medicare, less dual enrollees, and less the estimated number of uninsured in the population.

Estimate of Insured: PTotal – PMedicare – PMedicaid – PDual – PUI = Total Nonelderly Insured

The final outcome of this methodology was direct and indirect estimates of the sources of health insurance for each ZCTA in the Tulsa MSA geography.54 © 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 46 Appendix – Health Insurance Status 55 Proposal to Offer Affordable Health Coverage to Low-Income Families Oklahoma 1115a/HIFA Waiver Amendment

The focus of the proposal is Oklahoma’s uninsured population, particularly low income workers employed by small firms of 25 or fewer employees, and the premium assistance program that would offer affordable health insurance coverage to this population.

I. Introduction

A. Oklahoma’s uninsured • In 2003, 20.4% were uninsured in OK (15.2% nationally) • Fourth highest uninsurance rate in the country • Rural state dominated by small employers who pay low wages and are unable to fund health insurance coverage

Rural state • 63 of 77 counties “entirely rural” • 34.7% of Oklahomans live in rural areas (21% national average) • Higher incidence of uninsurance in rural areas reflective of lower incomes in these areas

Small employers • 72% of private businesses have fewer than 25 workers (61% nationally) • 22.8% of all employees work in firms with fewer than 25 workers (20.7% nationally) • In 2002, 33.1% of employers with 9 or less employees offered health coverage (36.8% nationally), and 58.1% of employers with 10-24 employees offered coverage (67.8% nationally)

Low income • Relatively low-income state • In 2002, ranked 44th in median household income ($35,500 in OK vs. $43,052 nationally) • In 2003, about 660,000 adults, ages 18-64, were living in households with incomes below 185% FPL, of which 44% (290,000 persons) were uninsured • In 2003, about 185,000 with household incomes below 185% FPL either employed by small businesses or seeking work; uninsurance rate of 57% (106,000 persons) • 106,000 persons represent primary group in need of health insurance coverage • 78,000 who already have coverage represent a secondary group who are at risk of losing coverage due to rising premium costs

B. Building blocks for reform With SCHIP, OK raised Medicaid eligibility standard for children and adolescents to 185% FPL. Childless adults, including low-income workers, have no categorical link to coverage through Medicaid.

Legislation to expand access to coverage • Senate Bill 1546 – outlines state’s objectives and provides authorization for the state premium assistance program—Oklahoma Health Care Recovery Act (OKHRA), an initiative geared to the private business sector • House Bill 2660 – identified the revenue source (levy of 55-cent tobacco tax) for the state portion of the premium assistance program; expected to yield $50 million per year to fund OKHRA

Major OKHRA components • Establish an insurance subsidy for low-income adults employed with small businesses (including spouses) • Develop a public safety net product for individuals who qualify for a subsidy but don’t have employer-sponsored insurance (ESI) coverage

II. Premium Assistance Program (PAP)

A. Eligibility criteria for employees • Adults with household incomes at or below 185% FPL, employed with an eligible small employer or self-employed • Full-time, part-time, or unemployed • Spouses

B. Participation criteria for employers • “Small” business designation – 25 or less employees regardless of whether ESI is offered • Premium assistance restricted to OK residents employed at a site within state boundaries • Must cover 25% of cost of health care premiums for both PT and FT workers • Must offer a health plan that covers at least hospital and physician services and includes a drug benefit • Must offer a health plan with a deductible no greater than what is allowed for state employees • Must allow enrollment of uninsured spouses; government subsidizes their premium and regular payments

C. Employer participation process • Employers file application form with total number of employees, number eligible for premium subsidy, selected health insurer, covered benefits, premiums and employer contribution amounts • Eligible employees have 31 days to file a separate enrollment form documenting their household income and number of enrollees • State processes application within 30 days of receipt • One-time opportunity for enrollment in public safety net product if employee does not enroll within specified timeframe; safety net coverage effective until employer’s next annual open enrollment period, at which time employee is required to enroll in PAP

D. Premium subsidy • Oklahoma provides direct state/federal contribution to participating employers

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 47 • For employees, subsidy will equal what is not covered by employer and employee shares; for spouses, subsidy will equal the employer share • Premium subsidy payments will be made monthly to employers, by direct deposit in advance of coverage month, based on a roster of eligible, enrolled employees in employer’s health benefit plan

E. Enrollee share of cost • Based on income-based limits, employees will be responsible for up to 15% of the premium cost and any deductibles, coinsurance, or co-payments included with their coverage • Enrollee’s premium liability will be capped at 3% of gross annual household income; state will make up the difference for those whose premium costs exceed the 3% cap; enrollees with total annual liability greater than 5% will also be eligible for further assistance—program reimbursement

F. Safety Net coverage (Public Product) • For the unemployed, self-employed, and employees whose employers are unable or unwilling to participate in PAP, the state offers safety net coverage (public product) • An application is filed directly with OHCA attesting nonparticipation in PAP; upon approval, enrollees make premium payments prior to the month the coverage applies • Premiums are about 3% of monthly household income for the bottom of each income range for single or double coverage • Enrollees choose a PCP who will be capitated for routine office visits; all other care on a FFS basis

G. Crowd out • With reliance on commercial insurance market and state subsidies to employers who offer coverage, private sector “crowd out” is largely avoided • Six-month waiting period imposed on workers whose coverage is dropped or firm fails to meet PAP requirements; employees can apply for a state waiver of waiting period in extenuating circumstances

H. Ticket to work population • “Qualified” working disabled adults, ages 18-64, ineligible for Medicaid, eligible for SSI benefits, with income at or below 250% FPL, will be eligible for PAP or safety net coverage, regardless of their employer’s firm size

I. Seriously mentally ill persons • Non-Medicaid, low-income SMI persons served by DMHSAS will be eligible for coverage as determined by DMHSAS and OHCA

J. Future program restructuring • Raise maximum employer size for OKHRA to 26-50 employees • Raise income maximum to 200% FPL; thus, raise SCHIP income limit from 185% to 200% FPL; option to enroll children into this “notch group”

K. Coverage goals • Tobacco tax revenues of $50 million per year; per approval, a federal match at the standard Medicaid FMAP for OK (67.91% for FY2006), for an additional $105 million, and a grand total of $155 million • Funding will allow many low-income residents to obtain or retain affordable health insurance

III. Waiver implementation and administration

A. Administering agency OHCA; may contract with a private vendor for third party administration of PAP and safety net programs

B. Financing Tobacco tax revenues and, upon approval, federal matching funds per Medicaid FMAP

C. Outreach • Newspaper and television advertisements, and statewide “town meetings” • Local chambers of commerce acting as advocates and “recruiters” for the program

D. Implementation • December1, 2004 – introduction of tobacco tax • January 13, 2005 – submission of 1115a/HIFA waiver to CMS • April 30, 2005 – waiver approval • June 1, 2005 – completion of information system changes, training of new eligibility workers, and outreach/education • July 1, 2005 – begin enrollment

IV. Waivers requested • Per CMS approval, health insurance will be afforded to adults ineligible for coverage under state Medicaid and SCHIP programs • Premium assistance will be provided through small employers for the purchase of health insurance for the uninsured • A public safety net option will also be available for those not enrolled in ESI

V. OKHRA monthly per capita expenditures • Average monthly premium was estimated based on recent capitation rates for adults developed for the SoonerCare Plus program; premiums were inflated using an 8.0% trend factor for Oklahoma • Average household income for an enrollee in the PAP was set at 150% FPL and in the Safety Net program was set at 125% • Household income is assumed to rise 3% each year for both groups

VI. Program evaluation and monitoring

A. Performance measures OHCA will monitor how PAP affects the level of uninsurance, other factors impacting the uninsurance rate, impact on small employers, and access to care

B. Progress reports Six months after the end of each demonstration year, the state will summarize results of its monitoring and evaluation efforts; interim, quarterly reports and annual progress reports will detail enrollment and demographic data on enrollees and participating employers. © 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 48 Appendix – Health Insurance Status 2003 County Estimates for Oklahoma

DEMOGRAPHICS HEALTH INSURANCE STATUS PERCENTAGES

COUNTY POP MCE MCD INS NONE MCE MCD INS NONE

Adair 21,038 2,535 7,236 7,168 4,099 12.0% 34.4% 34.1% 19.5%

Alfalfa 6,105 1,243 584 3,134 1,144 20.4% 9.6% 51.3% 18.7%

Atoka 13,879 2,050 3,486 5,725 2,618 14.8% 25.1% 41.2% 18.9%

Beaver 5,857 992 727 3,065 1,073 16.9% 12.4% 52.3% 18.3%

Beckham 19,799 3,059 4,272 8,640 3,828 15.5% 21.6% 43.6% 19.3%

Blaine 11,976 2,015 2,305 5,401 2,255 16.8% 19.2% 45.1% 18.8%

Bryan 36,534 5,638 8,765 15,071 7,060 15.4% 24.0% 41.3% 19.3%

Caddo 30,150 4,499 7,680 12,385 5,586 14.9% 25.5% 41.1% 18.5%

Canadian 87,697 8,364 10,320 51,929 17,084 9.5% 11.8% 59.2% 19.5%

Carter 45,621 7,293 10,982 18,902 8,444 16.0% 24.1% 41.4% 18.5%

Cherokee 42,521 5,097 10,279 18,505 8,640 12.0% 24.2% 43.5% 20.3%

Choctaw 15,342 2,664 5,519 4,391 2,768 17.4% 36.0% 28.6% 18.0%

Cimarron 3,148 585 462 1,516 585 18.6% 14.7% 48.2% 18.6%

Cleveland 208,016 17,537 24,443 122,375 43,661 8.4% 11.8% 58.8% 21.0%

Coal 6,031 1,078 1,818 2,031 1,104 17.9% 30.1% 33.7% 18.3%

Comanche 114,996 11,220 19,773 60,101 23,902 9.8% 17.2% 52.3% 20.8%

Cotton 6,614 1,174 1,224 3,018 1,198 17.8% 18.5% 45.6% 18.1%

Craig 14,950 2,418 3,926 5,784 2,822 16.2% 26.3% 38.7% 18.9%

Creek 67,367 8,650 13,007 33,022 12,688 12.8% 19.3% 49.0% 18.8%

Custer 26,142 3,593 5,144 11,853 5,552 13.7% 19.7% 45.3% 21.2%

Delaware 37,077 6,501 8,872 15,038 6,666 17.5% 23.9% 40.6% 18.0%

Dewey 4,743 995 621 2,270 857 21.0% 13.1% 47.9% 18.1%

Ellis 4,075 895 542 1,909 729 22.0% 13.3% 46.8% 17.9%

Garfield 57,813 9,262 11,064 26,319 11,168 16.0% 19.1% 45.5% 19.3%

Garvin 27,210 4,883 6,459 10,863 5,005 17.9% 23.7% 39.9% 18.4%

Grady 45,516 5,958 8,230 22,579 8,749 13.1% 18.1% 49.6% 19.2%

Grant 5,144 1,103 664 2,444 933 21.4% 12.9% 47.5% 18.1%

Greer 6,061 1,215 1,254 2,481 1,111 20.0% 20.7% 40.9% 18.3%

Harmon 3,283 691 868 1,136 588 21.0% 26.4% 34.6% 17.9%

Harper 3,562 773 505 1,643 641 21.7% 14.2% 46.1% 18.0%

Haskell 11,792 2,024 3,479 4,155 2,134 17.2% 29.5% 35.2% 18.1%

Hughes 14,154 2,626 3,918 5,055 2,555 18.6% 27.7% 35.7% 18.0%

Jackson 28,439 3,388 5,655 13,722 5,674 11.9% 19.9% 48.3% 20.0%

Jefferson 6,818 1,372 1,796 2,435 1,215 20.1% 26.3% 35.7% 17.8%

Johnston 10,513 1,621 2,592 4,356 1,944 15.4% 24.7% 41.4% 18.5%

Kay 48,080 8,154 10,344 20,644 8,938 17.0% 21.5% 42.9% 18.6%

Kingfisher 13,926 2,139 1,832 7,318 2,637 15.4% 13.2% 52.6% 18.9%

Kiowa 10,227 2,079 2,215 4,086 1,847 20.3% 21.7% 40.0% 18.1%

Latimer 10,692 1,718 2,767 4,274 1,933 16.1% 25.9% 40.0% 18.1% © 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 49 Appendix – Health Insurance Status 2003 County Estimates for Oklahoma

DEMOGRAPHICS HEALTH INSURANCE STATUS PERCENTAGES

COUNTY POP MCE MCD INS NONE MCE MCD INS NONE

Le Flore 48,109 6,615 14,008 18,319 9,167 13.8% 29.1% 38.1% 19.1%

Lincoln 32,080 4,463 5,684 15,895 6,038 13.9% 17.7% 49.5% 18.8%

Logan 33,924 4,188 5,808 17,192 6,736 12.3% 17.1% 50.7% 19.9%

Love 8,831 1,428 2,049 3,724 1,630 16.2% 23.2% 42.2% 18.5%

Major 7,545 1,465 1,059 3,635 1,386 19.4% 14.0% 48.2% 18.4%

Marshall 13,184 2,576 3,039 5,241 2,328 19.5% 23.1% 39.8% 17.7%

Mayes 38,369 5,703 8,727 16,808 7,131 14.9% 22.7% 43.8% 18.6%

McClain 27,740 3,321 4,074 14,997 5,348 12.0% 14.7% 54.1% 19.3%

McCurtain 34,402 4,811 11,422 11,797 6,372 14.0% 33.2% 34.3% 18.5%

McIntosh 19,456 4,238 4,738 7,235 3,245 21.8% 24.4% 37.2% 16.7%

Murray 12,623 2,331 2,759 5,203 2,330 18.5% 21.9% 41.2% 18.5%

Muskogee 69,451 10,624 17,437 28,486 12,904 15.3% 25.1% 41.0% 18.6%

Noble 11,411 1,737 2,101 5,417 2,156 15.2% 18.4% 47.5% 18.9%

Nowata 10,569 1,829 2,250 4,546 1,944 17.3% 21.3% 43.0% 18.4%

Okfuskee 11,814 1,925 3,127 4,616 2,146 16.3% 26.5% 39.1% 18.2%

Oklahoma 660,448 80,716 130,283 318,642 130,807 12.2% 19.7% 48.2% 19.8%

Okmulgee 39,685 6,003 10,943 15,436 7,303 15.1% 27.6% 38.9% 18.4%

Osage 44,437 5,807 6,573 23,719 8,338 13.1% 14.8% 53.4% 18.8%

Ottawa 33,194 5,601 8,744 12,548 6,301 16.9% 26.3% 37.8% 19.0%

Pawnee 16,612 2,453 3,367 7,718 3,074 14.8% 20.3% 46.5% 18.5%

Payne 68,190 7,349 9,523 35,616 15,702 10.8% 14.0% 52.2% 23.0%

Pittsburg 43,953 7,536 10,096 18,351 7,970 17.1% 23.0% 41.8% 18.1%

Pontotoc 35,143 5,260 7,996 14,857 7,030 15.0% 22.8% 42.3% 20.0%

Pottawatomie 65,521 9,014 14,697 29,198 12,612 13.8% 22.4% 44.6% 19.2%

Pushmataha 11,667 2,131 3,303 4,206 2,027 18.3% 28.3% 36.1% 17.4%

Roger Mills 3,436 644 352 1,785 655 18.7% 10.2% 51.9% 19.1%

Rogers 70,641 7,961 9,993 39,537 13,150 11.3% 14.1% 56.0% 18.6%

Seminole 24,894 4,169 8,165 7,955 4,605 16.7% 32.8% 32.0% 18.5%

Sequoyah 38,972 5,256 11,199 15,211 7,306 13.5% 28.7% 39.0% 18.7%

Stephens 43,182 7,982 8,394 19,102 7,704 18.5% 19.4% 44.2% 17.8%

Texas 20,107 2,056 3,363 10,453 4,235 10.2% 16.7% 52.0% 21.1%

Tillman 9,287 1,795 2,290 3,563 1,639 19.3% 24.7% 38.4% 17.7%

Tulsa 563,299 66,735 92,946 293,180 110,438 11.8% 16.5% 52.0% 19.6%

Wagoner 57,491 5,838 8,009 32,629 11,015 10.2% 13.9% 56.8% 19.2%

Washington 48,996 8,700 7,818 23,756 8,722 17.8% 16.0% 48.5% 17.8%

Washita 11,508 2,160 2,273 4,904 2,171 18.8% 19.8% 42.6% 18.9%

Woods 9,089 1,808 1,239 4,195 1,847 19.9% 13.6% 46.2% 20.3%

Woodward 18,486 2,621 3,349 8,887 3,629 14.2% 18.1% 48.1% 19.6%

Statewide 3,450,654 455,950 668,826 1,655,299 670,579 13.2% 19.4% 48.0% 19.4%

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 51 WEIGHTED RANKINGS FOR AGE-ADJUSTED DEATH RATES FOR THE TULSA MSA COUNTIES OF TULSA – WAGONER – ROGERS – CREEK – PAWNEE – OSAGE - OKMULGEE

ZCTA DESCRIPTORS AGE-ADJUSTED DEATH RATE WEIGHTED RANK

RANK CITY COUNTY ZCTA POP NO. HRT CAN DIA STR RES ACC SUI OTH ALL ORD

1 Tulsa Tulsa 74103 2,173 32 1 1 29 1 44 31 5 1 361.5 6.07

2 Morris Okmulgee 74445 2,600 77 5 6 66 6 1 4 1 5 639.2 6.59

3 Tulsa Tulsa 74145 18,020 649 4 7 20 7 2 37 63 11 706.8 8.76

4 Tulsa Tulsa 74136 32,712 980 10 14 28 23 10 17 19 10 773.2 12.57

5 Tulsa Tulsa 74114 16,913 731 7 8 4 16 14 47 44 18 754.9 12.77

6 Tulsa Tulsa 74137 22,960 623 9 9 24 50 7 5 54 19 789.0 15.06

7 Tulsa Creek 74131 2,538 75 21 5 1 14 64 1 60 3 741.7 15.30

8 Porter Wagoner 74454 2,906 90 3 27 68 13 5 65 32 13 775.3 15.63

9 Tulsa Tulsa 74135 21,320 1,201 13 12 11 19 16 41 57 22 813.9 17.11

10 Tulsa Tulsa 74105 28,455 1,185 12 10 10 28 27 29 81 20 828.1 17.29

11 Tulsa Tulsa 74133 37,778 1,014 18 21 12 21 11 11 15 27 824.6 20.17

12 Collinsville Rogers 74021 2,215 73 16 18 21 39 56 34 12 14 870.2 21.17

13 Oilton Creek 74052 1,350 57 25 34 83 2 4 74 9 4 864.7 21.30

14 Collinsville Tulsa 74021 9,595 318 17 19 22 40 57 35 13 15 870.2 22.17

15 Tulsa Tulsa 74146 14,380 312 19 25 43 8 18 54 31 33 878.9 24.91

16 Shidler Osage 74652 1,044 47 54 15 2 68 9 6 2 2 827.1 26.13

17 Owasso Rogers 74055 7,355 196 39 3 41 48 39 22 20 25 882.6 27.48

18 Tulsa Tulsa 74129 18,542 736 22 20 7 25 28 25 22 45 897.3 27.72

19 Tulsa Tulsa 74119 3,790 192 41 16 13 20 48 82 58 12 936.5 28.03

20 Owasso Tulsa 74055 19,197 513 40 4 42 49 40 23 21 26 882.6 28.48

21 Tulsa Tulsa 74108 6,638 173 28 47 35 18 63 58 33 6 900.1 29.79

22 Bixby Tulsa 74008 15,351 455 15 36 37 17 15 27 66 52 923.0 30.80

23 Tulsa Tulsa 74112 21,222 964 14 49 23 29 46 16 35 37 921.2 31.56

24 Tulsa Tulsa 74104 14,050 441 20 17 25 24 45 62 64 53 947.9 31.93

25 Claremore Rogers 74017 38,719 1,307 33 22 46 59 47 26 14 32 959.1 33.03

26 Tulsa Creek 74132 2,653 60 29 59 6 4 23 48 7 30 901.6 33.25

27 Tulsa Tulsa 74132 4,616 104 30 58 5 3 22 49 8 31 901.6 33.47

28 Henryetta Okmulgee 74437 10,277 545 24 26 72 22 30 45 56 50 977.2 33.78

29 Barnsdall Osage 74002 2,300 116 2 70 51 45 26 14 3 47 898.4 34.65

30 Bristow Creek 74010 10,129 485 23 32 50 41 55 44 10 49 976.0 36.39

31 Okmulgee Okmulgee 74447 17,997 891 64 24 63 26 33 12 47 21 988.2 37.68

32 Hominy Osage 74035 4,860 183 50 23 48 5 53 15 49 42 969.5 38.27

33 Sand Springs Creek 74063 1,449 49 36 44 31 35 35 38 37 40 975.8 38.55

34 Pawhuska Osage 74056 5,835 298 49 52 56 42 25 21 74 16 995.9 38.73

35 Sand Springs Osage 74063 5,710 192 37 45 30 34 36 39 36 39 975.8 38.82

36 Wagoner Wagoner 74467 13,780 636 35 57 57 44 49 53 27 24 1,012.3 39.72

37 Sand Springs Tulsa 74063 21,250 713 38 46 32 36 37 40 38 41 975.8 40.18

38 Broken Arrow Tulsa 74011 23,031 500 51 48 19 56 43 2 48 23 984.3 40.61

39 Tulsa Tulsa 74120 5,496 164 46 2 8 12 65 80 83 69 1,041.8 41.94

40 Sapulpa Creek 74066 29,270 1,256 58 43 36 52 51 55 61 17 1,034.6 42.76

41 Depew Creek 74028 1,785 69 11 78 71 57 6 46 6 61 1,062.7 43.52

42 Mounds Creek 74047 1,906 50 79 29 53 53 68 7 70 8 1,170.9 44.61

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 52 WEIGHTED RANKINGS FOR AGE-ADJUSTED DEATH RATES FOR THE TULSA MSA COUNTIES OF TULSA – WAGONER – ROGERS – CREEK – PAWNEE – OSAGE - OKMULGEE

ZCTA DESCRIPTORS AGE-ADJUSTED DEATH RATE WEIGHTED RANK

RANK CITY COUNTY ZCTA POP NO. HRT CAN DIA STR RES ACC SUI OTH ALL ORD

43 Mounds Okmulgee 74047 3,293 86 80 30 54 54 69 8 71 9 1,170.9 45.61

44 Mounds Tulsa 74047 1,284 33 81 31 55 55 70 9 72 7 1,170.9 45.85

45 Cleveland Pawnee 74020 7,604 347 47 50 52 27 24 71 23 51 1,044.9 46.03

46 Mannford Creek 74044 4,731 173 31 40 39 63 67 57 46 59 1,065.3 46.17

47 Tulsa Tulsa 74107 20,284 794 60 11 47 37 58 51 59 57 1,053.3 46.25

48 Mannford Pawnee 74044 2,166 79 32 41 38 62 66 56 45 60 1,065.3 46.74

49 Broken Arrow Wagoner 74014 23,231 458 76 13 17 79 31 30 26 43 1,153.4 46.87

50 Drumright Creek 74030 3,792 229 61 33 44 61 17 64 50 46 1,055.3 47.12

51 Tulsa Osage 74127 7,207 307 44 55 69 30 20 67 40 55 1,071.9 47.69

52 Oologah Rogers 74053 2,628 82 8 77 61 72 74 3 30 63 1,122.9 48.00

53 Pawnee Pawnee 74058 4,152 229 43 64 58 80 32 63 55 34 1,129.4 48.07

54 Tulsa Tulsa 74134 12,998 208 42 39 67 15 80 81 51 58 1,107.7 48.54

55 Tulsa Tulsa 74127 10,901 464 45 56 70 31 21 68 41 56 1,071.9 48.69

56 Tulsa Tulsa 74116 2,270 66 55 72 33 10 41 77 16 36 1,072.4 49.27

57 Fairfax Osage 74637 2,063 132 6 81 78 66 61 66 24 65 1,173.4 49.55

58 Tulsa Rogers 74116 1,398 41 56 73 34 11 42 78 17 35 1,072.4 49.77

59 Broken Arrow Tulsa 74012 47,249 1,301 62 42 40 58 34 43 39 48 1,062.5 50.14

60 Tulsa Tulsa 74115 23,687 913 53 51 49 46 62 36 82 44 1,092.0 50.36

61 Skiatook Tulsa 74070 3,180 113 65 53 64 32 59 59 67 29 1,094.6 50.46

62 Skiatook Osage 74070 6,531 231 66 54 65 33 60 60 68 28 1,094.6 50.95

63 Beggs Okmulgee 74421 4,203 163 48 60 59 43 3 73 42 62 1,084.9 51.26

64 Sperry Osage 74073 1,715 56 26 65 15 70 71 32 79 66 1,152.1 51.70

65 Chelsea Rogers 74016 5,184 222 52 28 18 67 50 42 73 72 1,132.9 51.74

66 Sperry Tulsa 74073 3,360 109 27 66 16 71 72 33 78 67 1,152.1 52.67

67 Tulsa Tulsa 74128 12,430 531 59 35 62 38 29 61 65 70 1,122.7 53.06

68 Talala Rogers 74080 2,113 42 63 74 3 51 8 18 62 54 1,103.3 55.04

69 Jennings Pawnee 74038 1,291 62 34 71 9 73 81 10 76 64 1,195.6 55.13

70 Terlton Pawnee 74081 1,790 60 75 38 60 9 19 83 69 68 1,232.5 56.16

71 Inola Rogers 74036 5,955 202 74 61 76 69 38 52 18 38 1,216.5 57.50

72 Jenks Tulsa 74037 9,428 316 67 37 77 64 54 24 11 71 1,192.7 58.45

73 Tulsa Tulsa 74106 17,164 1,023 57 67 81 65 12 69 34 79 1,318.7 62.44

74 Dewar Okmulgee 74431 1,029 49 71 75 45 47 13 13 77 81 1,379.5 65.97

75 Tulsa Tulsa 74110 15,267 669 70 62 74 60 73 70 80 73 1,320.2 68.85

76 Catoosa Rogers 74015 5,597 202 72 68 27 78 76 19 28 75 1,333.7 69.14

77 Catoosa Wagoner 74015 1,995 72 73 69 26 77 77 20 29 74 1,333.7 69.44

78 Tulsa Osage 74126 3,032 127 68 79 79 75 78 75 52 77 1,453.8 74.19

79 Coweta Wagoner 74429 10,445 360 77 76 82 74 52 72 25 80 1,593.8 74.61

80 Tulsa Tulsa 74126 9,047 380 69 80 80 76 79 76 53 76 1,453.8 74.68

81 Kellyville Creek 74039 3,105 118 78 82 14 82 75 50 75 78 1,725.7 76.25

82 Glenpool Tulsa 74033 8,475 174 82 63 73 81 82 79 43 82 1,904.9 76.87

83 Tulsa Tulsa 74130 2,624 109 83 83 75 83 83 28 4 83 3,502.8 80.00

LEGEND ORD: Is the weighted average of the ordinal rankings in each of the eight cause of death categories. ALL: Is the total age-adjusted death rate number. GREEN: The lowest (best) death rates. RED: The highest (worst) death rates. YELLOW: The middle third between the lowest and highest rates. Data is for the four year period of 2000-2003. HRT: Heart; CAN: Cancer; DIA: Diabetes; STR: Stroke; RES: Respiratory Disease; ACC: Unintentional Injuries (Accidents); SUI: Suicide; and OTH: All other causes.

Introduction This analysis used age-adjusted death rates (AADR) to identify sub-areas of the greater Tulsa region that have significant mortality compared to the average. The areas examined were ZCTAs. The AADR normalized the causes of death for age. Age-adjusted death rates were expressed as rates per 100,000 people. The AADRs were measured for heart disease, cancer, stroke, diabetes, unintentional injury, respiratory disease, and suicide.

Problem On average the Tulsa region has an overall AADR higher than the national average, but lower than the state average. However, the AADR is not uniform throughout the seven-county (127 ZCTA) region. This analysis allows for the sub-region identification of problem areas by both geography and cause of death.

Data The Center for Health Information of the Oklahoma State Department of Health (DOH) was the source for mortality data specific to Tulsa MSA. A Data Use Agreement for Confidential Vital Records was completed by the OU Center for Health Policy prior to receipt of any mortality data from the state. Mortality information that was received from the agency included records of the deceased from 2000 – 2003 for the Tulsa MSA geography at the ZIP code level. For purposes of confidentiality, coded records were received as a password-protected zip file from the DOH. This coded data was then imported into an Excel spreadsheet and decoded using various standard coding classifications before any significant data analysis could be conducted of the causes of death for Tulsa MSA.56 Each death record included the following information on the deceased: date of birth, date of death, age of decedent, gender, race, county of residence, mailing address ZIP code, and underlying cause of death (ICD-10 code).

Two major sources were consulted for guidance with definitions and classification of the coded mortality data. The 1999 Oklahoma Resident Deaths Coding Manual, provided by the Oklahoma State Department of Health (DOH), was one source for code definitions and data classification for mortality data and the National Center for Health Statistics(NCHS) website57 was another source. The DOH provided specific definitions for state, county, city/place, and hospital/institution codes used in the mortality data, whereas the NCHS website provided a useful resource for guidance and classification of underlying cause of death.

Methods Mortality data from 2000-2003 was used to conduct an analysis of the age-adjusted death rates for each ZCTA and county in the Tulsa MSA. An analysis of the underlying causes of death58 for varying geography levels was conducted using Excel and StatView software.

AADRs were calculated for each ZCTA and county in the Tulsa MSA for the total causes of death and for the following separate causes of death: heart disease, cancer, diabetes, stroke, respiratory disease, unintentional accidents, and suicide. The following method59 was employed in the AADR calculation for each age group for each cause of death at the ZCTA level:

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 54 • The total population per age group was determined using Census 2000 data, from SF3 data element P8 (Sex by Age). Age groups were delineated based on the National Center for Health Statistics (NCHS) 2000 US standard population age breakdown.

• The total population estimate for years 2000-2003 that is required in the AADR calculation was derived using the total population for each ZCTA and multiplying that number by four to reflect the span of years that cause of death data was available.

• The NCHS was the source for the US Standard Population 2000 per age group.

Table: U.S. Standard Population for Year 2000 60

Number Weight All ages...... 1,000,000...... 1.000000

Under 1 year...... 13,818...... 0.013818 1-4 years...... 55,317...... 0.055317 5-14 years...... 145,565...... 0.145565 15-24 years...... 138,646...... 0.138646 25-34 years...... 135,573...... 0.135573 35-44 years...... 162,313...... 0.162313 45-54 years...... 134,834...... 0.134834 55-64 years...... 87,247...... 0.087247 65-74 years...... 66,037...... 0.066037 75-84 years...... 44,842...... 0.044842 85 years and over...... 15,508...... 0.015508

• The total number of deaths for each cause of death was determined using a count of deaths based on the underlying cause of death classification standard, the ICD-10. This was achieved using StatView software to extract data for each ZIP code for the various causes of death per age group.

• Age-specific death rates per 100,000 were calculated for each cause of death using the following method:

[Total deaths per age group / Total population estimate per ZCTA]*100,000

• Expected deaths per cause of death were calculated as follows:

[Age-specific death rates per age group/ 100,000]* US standard population per age group

• Age-adjusted death rates for each cause of death for each ZCTA were achieved through this final step in the methodology:

[Sum total of expected deaths of all age groups/ 10] .

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 55 Comparison Rates The quintessential health status measurement is likely the age-adjusted death rate (AADR). This single statistic will perfectly measure the complete lack of health – that is premature death. Tulsa County trends have shadowed state trends. From 1980-1992, Tulsans and Oklahomans enjoyed a declining AADR that paralleled that of the nation. In 1992, the Tulsa County (and Oklahoma) AADR dramatically separated from the national trend and began to increase while the national average decreased. Since 1980, the national rate declined 20% while Tulsa fell only 5%. Since 1990, the national average declined 11% while the Tulsa rate increased 3%. See below.

AGE-ADJUSTED DEATH RATES 1980-2003 TULSA COUNTY – STATE OF OKLAHOMA – UNITED STATES 61

Yr Tulsa State National 1,050

1980 1,037.8 1,018.1 1,039.1

1981 1,036.4 1,010.8 1,007.1 County

1982 1,005.2 1,006.8 985.0 1,000 1983 986.9 993.1 990.0 State 1984 991.1 958.5 982.5

1985 1,012.7 986.4 988.1

1986 1,007.0 977.6 978.6 950 1987 952.6 951.8 970.0

1988 1,002.8 968.7 975.7

1989 981.3 947.2 950.5

1990 956.3 958.3 938.7 900

1991 953.2 944.4 922.3

1992 919.4 933.9 905.6 1993 959.9 977.7 926.1 National 1994 963.7 960.4 913.5 850

1995 958.9 958.8 909.8

1996 950.0 958.4 894.1 Age-Adjusted Death Rates

1997 933.6 973.2 878.1 800 1998 938.5 962.6 870.6 1980 1985 1990 1995 2000 1999 951.9 963.3 875.6

2000 964.7 969.4 869.0

2001 972.6 956.0 854.5

2002 963.2 970.2 845.3

2003 984.4 971.2 831.2

Regional Findings The Tulsa region is comprised of dense urban areas, sprawling suburban tracts, and near-rural exurban areas. The highest mortalities were found in the lower income urban areas and the sparsely populated exurban counties. While a certain concentration of pathologies can be observed on maps, problems areas are many and scattered. The following section provides a single page summary of the AADR by ZCTA and pathology. The Appendix (upon request) has an exhaustive listing of all AADRs in all ZCTA areas. The data is comprehensive and allows for an infinite amount of epidemiological investigation. This analysis simply focuses on areas: (1) of more than 1,000 population, (2) with more than 25 deaths, and (3) that are more than one standard deviation from the MSA average.

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 56 AGE-ADJUSTED DEATH RATES FOR TULSA METROPOLITAN AREA BY ZCTA (RED IS HIGHEST – GREEN IS LOWEST)

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 57 AGE-ADJUSTED DEATH RATES FOR TULSA COUNTY BY ZCTA (RED IS HIGHEST – GREEN IS LOWEST – YELLOW IN BETWEEN) SOME COLORS WILL SLIGHTLY DIFFER FROM PRECEDING MAP BECAUSE OF SEPARATE GEOGRAPHICAL COMPARISONS

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 58 ALL CAUSES The Tulsa MSA age-adjusted death rate from all causes was 935.5. This is higher than the national rate for 2002 of 847.3, but lower than the state rate of 950.1 for 1999-2002.

Table The standard deviation was 352.6. Therefore, any ZCTA above 1,288.1 was at least one standard deviation from the mean and was considered a “high risk” ZCTA. There were 11 ZCTA regions with a rate above 1,288.1. Only 4 ZCTAs are within Tulsa County proper. The ZCTA with the highest rate was 74130. The remaining 7 ZCTAs were located in outlying counties with rural characteristics. See table below. The highest ZCTAs (74130, 74033, 74039, and 74429) are profiled on subsequent pages.

All Causes Age-Adjusted Death Rates for the Tulsa Metropolitan Area

All County City ZCTA Pop Deaths Causes Tulsa Tulsa 74130 2,624 109 3,502.8 Tulsa Glenpool 74033 8,475 174 1,904.9 Creek Kellyville 74039 3,105 118 1,725.7 Wagoner Coweta 74429 10,445 360 1,593.8 Tulsa Tulsa 74126 9,047 380 1,453.8 Osage Tulsa 74126 3,032 127 1,453.8 Okmulgee Dewar 74431 1,029 49 1,379.5 Rogers Catoosa 74015 5,597 202 1,333.7 Wagoner Catoosa 74015 1,995 72 1,333.7 Tulsa Tulsa 74110 15,267 669 1,320.2 Tulsa Tulsa 74106 17,164 1,023 1,318.7

Map The map below depicts the 27 ZCTAs with the lowest age-adjusted death rates (green) and the 27 ZCTA’s with the highest (red).

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 59 DISEASES OF THE HEART The Tulsa MSA death rate for diseases of the heart was 292.7. This is higher than the national rate for 2002 of 241.7, but lower than the state rate of 316.1 for 1999-2002. The standard deviation was 135.2. Therefore, any ZCTA above 427.9 was at least one standard deviation from the mean and was considered a “high risk” ZCTA. There were 12 ZCTA regions with a rate above 427.9. The only ZCTA within Tulsa County proper was 74130. The remaining 11 ZCTAs were located in outlying counties with rural characteristics. See table below.

ZCTA 74130 (North Tulsa) This ZCTA is located in North Tulsa and has a population of 2,624. The racial composition is 43% white, 40% black, 8% Indian, 3% Hispanic and 6% other. The per capita income in 1999 was $13,480 and the median family income was $35,365. During the period of 2000-2003, this small area recorded 109 total deaths, 39 of which were from heart disease. The age-adjusted death rate for heart disease was 1,233.2—almost double the rate of the next highest area at 712.2.

ZCTA 74033 (Glenpool) This ZCTA is located in the Glenpool area of far south Tulsa County with a small portion in Creek County. It has a population of 8,475. The racial composition is 76% white, 2% black, 12% Indian, 3% Hispanic and 7% other. The per capita income was $16,457 and the median family income was $46,817. During the period of 2000-2003, this area recorded 174 total deaths, 48 of which were from heart disease. The age-adjusted death rate from heart disease was 712.2—almost 200 index points higher than the third highest ZCTA rate of 523.7.

ZCTA 74047 (Mounds) This ZCTA is located in Okmulgee, Creek and Tulsa Counties—half of it in Okmulgee, 30% in Creek, and 20% in Tulsa. Its population of 6,483 people is comprised of 79% white, 1% black, 12% Indian, 3% Hispanic, and 6% other. The per capita income for this area in 1999 was $15,937 and the median family income was $44,977. From 2000 – 2003, there were a total of 169 recorded deaths for this area, 56 of which were as a result of heart disease. This area’s age-adjusted death rate for heart disease was 523.7—more than twice that of the national rate of 241.7.

Diseases of the Heart Age-Adjusted Death Rates for the Tulsa Metropolitan Area

County City ZCTA Pop Heart All Causes

Tulsa Tulsa 74130 2,624 1,233.2 3,502.8

Tulsa Glenpool 74033 8,475 712.2 1,904.9

Okmulgee Mounds 74047 3,293 523.7 1,170.9

Creek Mounds 74047 1,906 523.7 1,170.9

Tulsa Mounds 74047 1,284 523.7 1,170.9

Creek Kellyville 74039 3,105 516.5 1,725.7

Wagoner Coweta 74429 10,445 511.4 1,593.8

Wagoner Broken Arrow 74014 23,231 472.2 1,153.4

Pawnee Terlton 74081 1,790 462.3 1,232.5

Rogers Inola 74036 5,955 445.6 1,216.5

Wagoner Catoosa 74015 1,995 439.0 1,333.7

Rogers Catoosa 74015 5,597 439.0 1,333.7

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 60 CANCER The Tulsa MSA death rate from cancer was 203.5. This is higher than the national rate for 2002 of 193.2, but lower than the state rate of 210.0 for 1999-2002. The standard deviation was 56.4. Therefore, any ZCTA above 259.9 was at least one standard deviation from the mean and was considered a “high risk” ZCTA. There were 21 ZCTA regions with a rate above 259.9. There were only 6 ZCTAs within Tulsa County proper. The remaining 15 ZCTAs were located in outlying counties with rural characteristics.

ZCTA 74130 (North Tulsa) This ZCTA is located in North Tulsa and has a population of 2,624. The racial composition is 43% white, 40% black, 8% Indian, 3% Hispanic and 6% other. The per capita income was $13,480 and the median family income was $35,365. During the period of 2000-2003, this small area recorded 109 total deaths, 21 of which were from cancer. The age-adjusted death rate for cancer was 433.8—almost double that of the regional rate.

ZCTA 74039 (Kellyville) This ZCTA is located in the Kellyville area of Creek County. It has a population of 3,105. The racial composition is 79% white, 9% Indian, and 12% other. The per capita income was $14,232 and the median family income was $39,797. During the period of 2000-2003, this area recorded 118 total deaths, 30 of which were from cancer. The age-adjusted death rate from cancer was 376.8—almost twice the national rate.

ZCTA 74637 (Fairfax) This ZCTA, located in Osage County, has a population of 2,063 people with a racial composition of 66% White, 1% Black, 25% Indian, 2% Hispanic, and 6% other. The per capita income in 1999 was $17,796 and the median family income was $29,702. From 2000 – 2003, there were a total of 132 deaths recorded for this area, 38 of which were due to cancer. This area’s age-adjusted death rate for cancer was 353.7—less than twice that of the national rate of 193.2.

Cancer Age-Adjusted Death Rates for the Tulsa Metropolitan Area County City ZCTA Pop Cancer All Causes Tulsa Tulsa 74130 2,624 433.8 3,502.8 Creek Kellyville 74039 3,105 376.8 1,725.7 Osage Fairfax 74637 2,063 353.7 1,173.4 Tulsa Tulsa 74126 9,047 332.3 1,453.8 Osage Tulsa 74126 3,032 332.3 1,453.8 Creek Depew 74028 1,785 324.4 1,062.7 Rogers Oologah 74053 2,628 321.6 1,122.9 Wagoner Coweta 74429 10,445 300.7 1,593.8 Okmulgee Dewar 74431 1,029 297.0 1,379.5 Rogers Talala 74080 2,113 285.8 1,103.3 Rogers Tulsa 74116 1,398 285.4 1,072.4 Tulsa Tulsa 74116 2,270 285.4 1,072.4 Pawnee Jennings 74038 1,291 277.7 1,195.6 Osage Barnsdall 74002 2,300 273.0 898.4 Wagoner Catoosa 74015 1,995 267.8 1,333.7 Rogers Catoosa 74015 5,597 267.8 1,333.7 Tulsa Tulsa 74106 17,164 264.1 1,318.7 Tulsa Sperry 74073 3,360 264.0 1,152.1 Osage Sperry 74073 1,715 264.0 1,152.1 Pawnee Pawnee 74058 4,152 263.7 1,129.4 Tulsa Glenpool 74033 8,475 262.4 1,904.9 Tulsa Tulsa 74110 15,267 258.0 1,320.2

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 61 STROKE The Tulsa MSA death rate from stroke was 62.1. This is higher than the national rate for 2002 of 56.4, but lower than the state rate of 69.6 for 1999-2002. The standard deviation was 62.9. Therefore, any ZCTA above 125.0 was at least one standard deviation from the mean and was considered a “high risk” ZCTA. There were 5 ZCTA regions with a rate above 125.0. There were 2 ZCTAs within Tulsa County proper. The remaining 3 ZCTAs were located in outlying counties with rural characteristics. See table below.

ZCTA 74130 (North Tulsa) This ZCTA is located in North Tulsa and has a population of 2,624. The racial composition is 43% white, 40% black, 8% Indian, 3% Hispanic and 6% other. The per capita income was $13,480 and the median family income was $35,365. During the period of 2000-2003, this small area recorded 109 total deaths, 7 of which were from stroke. The age-adjusted death rate for stroke was 548.2—almost 10 times that of the regional rate.

ZCTA 74039 (Kellyville) This ZCTA is located in the Kellyville area of Creek County. It has a population of 3,105 of which the racial composition is 79% white, 9% Indian, and 12% other. The per capita income was $14,232 and the median family income was $39,797. During the period of 2000-2003, this area recorded 118 total deaths, 13 of which were from cancer. The age-adjusted death rate for stroke was 260.0—almost four times the regional rate.

ZCTA 74033 (Glenpool) This ZCTA is mostly located in Tulsa County—about 93% of it, with a population of 8,475 people and a racial composition of 76% White, 2% Black, 12% Indian, 3% Hispanic, and 6% other. The per capita income in 1999 was $16,457 and the median family income was $46,817. From 2000 – 2003, there were a total of 174 recorded deaths for this area, of which 15 were as a result of stroke. This area’s age- adjusted death rate for stroke was 147.6—more than twice that of the state rate of 69.6.

Stroke Age-Adjusted Death Rates for the Tulsa Metropolitan Area

County City ZCTA Pop Stroke All Causes

Tulsa Tulsa 74130 2,624 548.2 3,502.8

Creek Kellyville 74039 3,105 260.0 1,725.7

Tulsa Glenpool 74033 8,475 147.6 1,904.9

Pawnee Pawnee 74058 4,152 135.7 1,129.4

Wagoner Broken Arrow 74014 23,231 127.3 1,153.4

Rogers Catoosa 74015 5,597 114.5 1,333.7

Wagoner Catoosa 74015 1,995 114.5 1,333.7

Tulsa Tulsa 74126 9,047 110.4 1,453.8

Osage Tulsa 74126 3,032 110.4 1,453.8

Wagoner Coweta 74429 10,445 103.3 1,593.8

Pawnee Jennings 74038 1,291 103.1 1,195.6

Rogers Oologah 74053 2,628 101.3 1,122.9

Tulsa Sperry 74073 3,360 100.5 1,152.1

Osage Sperry 74073 1,715 100.5 1,152.1

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 62 DIABETES The Tulsa MSA death rate from diabetes was 26.6. This is higher than the national rate for 2002 of 25.4, but lower than the state rate of 28.7 for 1999-2002. The standard deviation was 17.6. Therefore, any ZCTA above 44.2 was at least one standard deviation from the mean and was considered a “high risk” ZCTA. There were 16 ZCTA regions with a rate above 44.2. There were 7 ZCTAs within Tulsa County proper. The remaining 9 ZCTAs were located in outlying counties with rural characteristics.

ZCTA 74052 (Oilton) This ZCTA, located in Creek County, has a population of 1,350 with a racial composition of 86% white, less than 1% black, 3% Hispanic, 7% Indian, and 4% other. The per capita income was $13,078 and the median family income was $33,088. From 2000-2003, there were a total of 57 recorded deaths for this area, of which 7 were related to diabetes. The age-adjusted death rate for diabetes for this area was 104.5—about four times that of the regional rate.

ZCTA 74429 (Coweta) This ZCTA is located in the Coweta area of Wagoner County. It has a population of 10,445 with a racial makeup of 77% white, 3% black, 3% Hispanic, 11% Indian, and 6% other. The per capita income was $16,072 and the median family income was $34,250. During the period of 2000-2003, this area recorded 360 total deaths, 17 of which were from diabetes. The age-adjusted death rate for diabetes was 81.0—over three times the regional rate.

ZCTA 74106 (Tulsa) This ZCTA is located mainly in Tulsa County with a small portion in Osage County. It has a population of 18,106 of which the racial composition is 10% white, 80% black, 5% Hispanic, and 5% other. The per capita income was $11,725 and the median family income was $23,451. During the period of 2000-2003, this area recorded 1,023 total deaths, 54 of which were from diabetes. The age-adjusted death rate for diabetes was 69.6—almost three times the regional rate.

Diabetes Age-Adjusted Death Rates for the Tulsa Metropolitan Area

County City ZCTA Pop Diabetes All Causes

Creek Oilton 74052 1,350 104.5 864.7

Wagoner Coweta 74429 10,445 81.0 1,593.8

Tulsa Tulsa 74106 17,164 69.6 1,318.7

Tulsa Tulsa 74126 9,047 59.9 1,453.8

Osage Tulsa 74126 3,032 59.9 1,453.8

Osage Fairfax 74637 2,063 57.4 1,173.4

Tulsa Jenks 74037 9,428 56.5 1,192.7

Rogers Inola 74036 5,955 55.3 1,216.5

Tulsa Tulsa 74130 2,624 53.3 3,502.8

Tulsa Tulsa 74110 15,267 52.6 1,320.2

Tulsa Glenpool 74033 8,475 52.4 1,904.9

Okmulgee Henryetta 74437 10,277 50.4 977.2

Creek Depew 74028 1,785 49.2 1,062.7

Tulsa Tulsa 74127 10,901 46.6 1,071.9

Osage Tulsa 74127 7,207 46.6 1,071.9

Wagoner Porter 74454 2,906 44.5 775.3

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 63 RESPIRATORY DISEASE The Tulsa MSA death rate for respiratory illness was 72.9. This is higher than the national rate for 2002 of 43.3, and higher than the state rate of 57.2 for 2002. The standard deviation was 39.2. Therefore, any ZCTA above 112.1 was at least one standard deviation from the mean and was considered a “high risk” ZCTA. There were 11 ZCTA regions with a rate above 112.1. There were 4 ZCTAs within Tulsa County proper. The remaining 7 ZCTAs were located in outlying counties with rural characteristics. See table below.

ZCTA 74130 (North Tulsa) This ZCTA is located in North Tulsa and has a population of 2,624. The racial composition is 43% white, 40% black, 8% Indian, 3% Hispanic and 6% other. The per capita income was $13,480 and the median family income was $35,365. During the period of 2000-2003, this small area recorded 109 total deaths, 10 of which were from respiratory illness. The age-adjusted death rate from respiratory illness was 347.5—almost 5 times that of the regional rate.

ZCTA 74033 (Glenpool) This ZCTA is located in the Glenpool area of far south Tulsa County with a small portion in Creek County. It has a population of 8,475. The racial composition is 76% white, 2% black, 12% Indian, 3% Hispanic and 7% other. The per capita income was $16,457 and the median family income was $46,817. During the period of 2000-2003, this area recorded 174 total deaths, 15 of which were from respiratory problems. The age-adjusted death rate for respiratory illness was 153.8—over twice the regional rate.

ZCTA 74038 (Jennings) This ZCTA, about two-thirds of it located in Pawnee County, has a population of 1,291 with a racial composition of 85% white, less than 1% black and Hispanic, 9% Indian, and 4% other. The per capita income in 1999 for this area was $13,847 and the median family income was $37,972. From 2000-2003, there were a total of 62 recorded deaths for this area, with 8 deaths being attributed to respiratory problems. The age-adjusted death rate for respiratory disease was 152.1—over three times the national rate 43.3.

Respiratory Disease Age-Adjusted Death Rates for the Tulsa Metropolitan Area

County City ZCTA Pop Resp All Causes

Tulsa Tulsa 74130 2,624 347.5 3,502.8

Tulsa Glenpool 74033 8,475 153.8 1,904.9

Pawnee Jennings 74038 1,291 152.1 1,195.6

Tulsa Tulsa 74134 12,998 134.0 1,107.7

Tulsa Tulsa 74126 9,047 128.3 1,453.8

Osage Tulsa 74126 3,032 128.3 1,453.8

Wagoner Catoosa 74015 1,995 124.8 1,333.7

Rogers Catoosa 74015 5,597 124.8 1,333.7

Creek Kellyville 74039 3,105 120.5 1,725.7

Rogers Oologah 74053 2,628 117.9 1,122.9

Tulsa Tulsa 74110 15,267 117.3 1,320.2

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 64 UNINTENTIONAL INJURY (ACCIDENTS) The Tulsa MSA death rate from unintentional injury (not including motor vehicle accidents) was 26.8. This is lower than the national rate for 2002 of 37.0, and lower than the state rate of 44.0 for 2002. The standard deviation was 13.9. Therefore, any ZCTA above 40.7 was at least one standard deviation from the mean and was considered a “high risk” ZCTA. There were 17 ZCTA regions with a rate above 40.7. There were 7 ZCTAs within Tulsa County proper. The remaining 10 ZCTAs were located in outlying counties with rural characteristics. See table below.

ZCTA 74081 (Terlton) This ZCTA, located almost entirely in Pawnee County, has a population of 1,790 people of which 80% are white, less than 1% are black, 12% are Indian, almost 2% are Hispanic, and almost 7% are of another race(s). The per capita income in 1999 was $13,909 and the median family income was $35,000. During 2000-2003, there were a total of 60 deaths recorded for this area, of which 5 deaths were related to unintentional injuries. The age-adjusted death rate for unintentional injuries for this area was 74.1—twice the national rate at 37.0.

ZCTA 74119 (Downtown Tulsa) This ZCTA is located in the downtown Tulsa. It has a population of 3,790 and the racial composition is 78% white, 9% black, 5% Indian, 3% Hispanic and 5% other. The per capita income was $25,908 and the median family income was $56,538. During the period of 2000-2003, this area recorded 192 total deaths, 13 of which were from unintentional injury. The age-adjusted death rate from unintentional injury was 73.3—almost three times that of the regional rate at 26.8.

ZCTA 74134 (Tulsa) This ZCTA is located in Tulsa County, with a population of almost 13,000 of which 68% are White, 9% Black, 9% Hispanic, about 5% Asian, and about 5 of another race(s). The per capita income in 1999 was $18,817 and the median family income was $50,952. From 2000-2003, 208 total deaths were recorded for this area, of which 13 deaths were attributed to unintentional injuries. The age-adjusted death rate for unintentional injury for this area was 59.6—over twice that of the regional rate.

Unintentional Injury (Accidents) Age-Adjusted Death Rates for the Tulsa Metropolitan Area

County City ZCTA Pop Acc All Causes

Pawnee Terlton 74081 1,790 74.1 1,232.5

Tulsa Tulsa 74119 3,790 73.3 936.5

Tulsa Tulsa 74134 12,998 59.6 1,107.7

Tulsa Tulsa 74120 5,496 57.5 1,041.8

Tulsa Glenpool 74033 8,475 53.1 1,904.9

Rogers Tulsa 74116 1,398 52.3 1,072.4

Tulsa Tulsa 74116 2,270 52.3 1,072.4

Tulsa Tulsa 74126 9,047 51.2 1,453.8

Osage Tulsa 74126 3,032 51.2 1,453.8

Creek Oilton 74052 1,350 48.8 864.7

Okmulgee Beggs 74421 4,203 46.0 1,084.9

Wagoner Coweta 74429 10,445 45.9 1,593.8

Pawnee Cleveland 74020 7,604 45.0 1,044.9

Tulsa Tulsa 74110 15,267 44.0 1,320.2

Tulsa Tulsa 74106 17,164 43.1 1,318.7

Tulsa Tulsa 74127 10,901 42.3 1,071.9

Osage Tulsa 74127 7,207 42.3 1,071.9

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 65 SUICIDE The Tulsa MSA death rate from suicide was 14.0. This is at the state rate of 13.8 for 2002. The standard deviation was 8.3. Therefore, any ZCTA above 22.3 was at least one standard deviation from the mean and was considered a “high risk” ZCTA. There were 14 ZCTA regions with a rate above 22.3. There were 4 ZCTAs within Tulsa County proper that had the highest four rates. The remaining 10 ZCTAs were located in outlying counties with rural characteristics. See table below.

ZCTA 74120 (Downtown Tulsa) This ZCTA is located just east of downtown Tulsa and has a population of 5,496. The racial composition is 67% white, 8% black, 7% Indian, 11% Hispanic and 7% other. The per capita income was $22,650 and the median family income was $38,551. During the period of 2000-2003, this small area recorded 164 total deaths, 7 of which were from suicide. The age-adjusted death rate from suicide was 38.3—about 3 times that of the state rate at 13.8.

ZCTA 74115 (Tulsa) This ZCTA is located east of Harvard and just north of I-244 in Tulsa. It has a population of 23,687. The racial composition is 62% white, 15% black, 8% Indian, 9% Hispanic and 6% other. The per capita household income was $13,123 and the median family income was $32,077. During the period of 2000- 2003, this area recorded 913 total deaths, 25 of which were a result of suicide. The age-adjusted death rate for suicide in this area was 29.1—over twice the regional rate of 14.0.

ZCTA 74105 (Tulsa) This ZCTA, located in Tulsa County as well, has a population of 28,455 composed of 79% White, about 8% Black, 5% Hispanic, 4% Indian, and 4% of another race(s). The per capita income in 1999 was $28,412 and the median family income was $53,174. From 2000-2003, the total number of deaths recorded for this area was 1,185, of which 33 were attributed to suicide. The age-adjusted death rate for suicide for this area of Tulsa was 28.3—over twice that of the regional and the state rates.

Suicide Age-Adjusted Death Rates for the Tulsa Metropolitan Area

County City ZCTA Pop Suicide All Causes

Tulsa Tulsa 74120 5,496 38.3 1,041.8

Tulsa Tulsa 74115 23,687 29.1 1,092.0

Tulsa Tulsa 74105 28,455 28.3 828.1

Tulsa Tulsa 74110 15,267 27.8 1,320.2

Osage Sperry 74073 1,715 26.4 1,152.1

Tulsa Sperry 74073 3,360 26.4 1,152.1

Okmulgee Dewar 74431 1,029 26.1 1,379.5

Pawnee Jennings 74038 1,291 25.6 1,195.6

Creek Kellyville 74039 3,105 24.1 1,725.7

Osage Pawhuska 74056 5,835 23.9 995.9

Rogers Chelsea 74016 5,184 23.6 1,132.9

Tulsa Mounds 74047 1,284 23.2 1,170.9

Okmulgee Mounds 74047 3,293 23.2 1,170.9

Creek Mounds 74047 1,906 23.2 1,170.9

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 66 Appendix Age-Adjusted Death Rates (Source: Texas Department of State Health Services) 62

The crude death rate gives a general estimate of mortality in a population. However, it does not provide an accurate depiction of mortality in a population since the rate does not account for differences in a population’s composition. For example, two populations, with widely divergent crude mortality rates, may in fact have very similar patterns of mortality. A developing population with a low crude mortality rate may simply have a very young population, while an industrialized population with a higher crude mortality rate may be composed of more older individuals and thus has a greater number of dying adults.

The need for age adjustment becomes particularly important when cause-specific mortality is of interest. Unadjusted rates for chronic diseases (cardiovascular diseases, cancers, or COPD) may appear to be higher for older populations when compared to a younger population. With age-adjustment those differences may be reduced or even reversed. A mechanism for adjusting for differences in the age structure of a population is necessary to determine if there really are differences in mortality between two populations.

The method typically used to adjust for differences between populations is direct standardization. Direct standardization divides a population into smaller age groups, estimates mortality rates for each group, and applies these rates to a standard population. Typically, age groups no more than 10 years in length adequately provide accurate age-specific mortality rates. The age-specific rates observed in the population under study are multiplied by the number of people in the specified age group in the standard population. From the sum of those estimates, a single comparable measure of mortality is obtained.

By applying age-specific mortality rates to a standard population (for this purpose, the 2000 US standard population), direct standardization controls for differences in population composition. Mortality trends can be more accurately compared along geographic, temporal or race/ethnicity lines, etc. In short, standardization shows what the death rate would be in one population if that population had the same age structure as the standard population.

Methodology for Calculating Age-Adjusted Death Rates (Refer to the table below): 1. Calculate Age-Specific Death Rate: [Total Number of Deaths/ Population Estimate] * 100,000 2. Calculate Expected Number of Deaths: [Age-Specific Death Rate/ 100,000] * US Standard Population 3. Sum Expected Number of Deaths 4. Calculate Age-Adjusted Death Rate: [Sum of Expected Deaths/ 10]

Table: Age-Adjusted Death Rate Calculation (Based on 1989 data for Texas)

(A) (B) (C) (D) (E) (F) =(B/C)*100,000 =(D/100,000)*E Age Group Total Number Population Age-Specific Death US Standard Expected Deaths of Deaths Estimate Rates per 100,000 Population (2000) 1989 Less than 5 3,415 1,473,057 231.8 80,061 186 5-14 718 2,801,342 25.6 170,355 44 … … … … … … 75+ 57,537 734,147 7,837.3 20,073 1,573 Total 124,521 17,470,679 … 1,000,000 5,319

Age-Adjusted Death Rate = (F)/10 or (F/1,000,000)*100,000 = 531.9 per 100,000 population. (Divide by 10 because the standard population has 1,000,000 people and it’s best to present the rate per 100,000 people.)

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 68 TULSA COUNTY RESIDENTS EMERGENCY ROOM AND INPATIENT HOSPITAL UTILIZATION

COMMUNITY EMERGENCY ROOM HOSPITAL

COUNTY CITY ZCTA POP VISITS RATE ADMITS RATE

Tulsa Bixby 74008 15,351 3,591 233.9 1,661 108.2

Tulsa Broken Arrow 74011 23,031 2,407 104.5 1,794 77.9

Tulsa Broken Arrow 74012 47,249 5,988 126.7 3,885 82.2

Tulsa Collinsville 74021 9,595 2,387 248.8 1,623 169.2

Tulsa Glenpool 74033 8,475 2,030 239.5 943 111.3

Tulsa Jenks 74037 9,428 2,065 219 1,257 133.3

Tulsa Oakhurst 74050 538 652

Tulsa Owasso 74055 19,197 4,710 245.3 2,865 149.2

Tulsa Sand Springs 74063 21,250 7,574 356.4 3,664 172.4

Tulsa Skiatook 74070 3,180 778 244.7 450 141.5

Tulsa Sperry 74073 3,360 1,182 351.8 521 155.1

Tulsa Tulsa 74104 14,050 4,352 309.8 1,667 118.6

Tulsa Tulsa 74105 28,455 7,428 261 3,346 117.6

Tulsa Tulsa 74106 17,164 9,627 560.9 3,190 185.8

Tulsa Tulsa 74107 20,284 8,959 441.7 3,180 156.8

Tulsa Tulsa 74108 6,638 2,250 339 853 128.5

Tulsa Tulsa 74110 15,267 7,920 518.8 2,658 174.1

Tulsa Tulsa 74112 21,222 6,266 295.3 2,890 136.2

Tulsa Tulsa 74114 16,913 3,080 182.1 1,858 109.9

Tulsa Tulsa 74115 23,687 10,156 428.8 3,464 146.2

Tulsa Tulsa 74116 2,270 1,833 807.3 674 296.9

Tulsa Tulsa 74119 3,790 1,549 408.7 640 168.9

Tulsa Tulsa 74120 5,496 2,082 378.8 701 127.5

Tulsa Tulsa 74126 9,047 6,469 715 1,957 216.3

Tulsa Tulsa 74127 10,901 3,088 283.3 1,384 127

Tulsa Tulsa 74128 12,430 3,810 306.5 1,782 143.4

Tulsa Tulsa 74129 18,542 5,419 292.3 2,456 132.5

Tulsa Tulsa 74130 2,624 914 348.3 359 136.8

Tulsa Tulsa 74132 4,616 1,399 303.1 682 147.8

Tulsa Tulsa 74133 37,778 8,027 212.5 3,799 100.6

Tulsa Tulsa 74134 12,998 3,009 231.5 1,206 92.8

Tulsa Tulsa 74135 21,320 6,568 308.1 3,130 146.8

Tulsa Tulsa 74136 32,712 8,984 274.6 3,291 100.6

Tulsa Tulsa 74137 22,960 3,556 154.9 1,990 86.7

Tulsa Tulsa 74145 18,020 4,498 249.6 2,070 114.9

Tulsa Tulsa 74146 14,380 4,328 301 1,628 113.2

ER and admissions utilization rate is per 1,000 residents. The overall utilization rate determines the corresponding colors. The rates within the cells are provided for reference. RED: highest 12 areas in Tulsa County. YELLOW: mid-12 areas in Tulsa County. GREEN: lowest 12 areas in Tulsa County. © 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 69 TULSA COUNTY RESIDENTS ONLY EMERGENCY ROOM & HOSPITAL UTILIZATION RATES BY INSURANCE STATUS (PER 1,000 POPULATION)

COMMUNITY VISITS/ADMITS EMERGENCY ROOM VISITS INPATIENT ADMISSIONS

ZCTA CITY POP ER HOSP ALL MCE MCD INS NONE ALL MCE MCD INS NONE

74008 Bixby 15,351 3,625 1,661 236.1 358.5 408.7 188.6 233.4 108.2 296.8 143.6 99.2 28.4

74011 Broken Arrow 23,031 2,452 1,794 106.5 162.1 231.5 88.6 87.0 77.9 226.4 113.3 74.1 13.7

74012 Broken Arrow 47,249 6,101 3,885 129.1 244.3 194.6 110.8 114.8 82.2 273.5 115.9 76.1 17.1

74021 Collinsville 9,595 2,524 1,623 263.1 480.7 191.5 266.5 229.2 169.2 479.6 88.5 198.6 32.7

74033 Glenpool 8,475 2,052 943 242.1 524.5 368.9 191.5 235.3 111.3 426.0 169.4 99.3 29.6

74037 Jenks 9,428 2,092 1,257 221.9 492.1 305.5 180.6 206.3 133.3 476.4 135.1 122.5 22.0

74055 Owasso 19,197 4,865 2,865 253.4 498.6 256.6 235.2 205.3 149.2 521.6 107.2 152.1 25.3

74063 Sand Springs 21,250 7,886 3,664 371.1 537.6 627.7 265.9 418.1 172.4 507.7 191.7 143.5 60.2

74070 Skiatook 3,180 819 450 257.5 451.6 247.4 242.9 216.6 141.5 482.7 62.9 141.7 40.7

74073 Sperry 3,360 1,225 521 364.6 542.1 797.3 245.2 347.8 155.1 498.3 184.9 136.0 33.0

74103 Tulsa 2,173 3,375 472

74104 Tulsa 14,050 4,448 1,667 316.6 552.7 747.6 149.9 426.1 118.6 366.4 278.5 73.9 59.8

74105 Tulsa 28,455 7,626 3,346 268.0 321.6 565.5 154.2 384.4 117.6 272.2 215.1 79.0 45.0

74106 Tulsa 17,164 9,775 3,190 569.5 814.5 511.0 351.5 900.1 185.8 542.5 158.2 142.9 96.4

74107 Tulsa 20,284 9,121 3,180 449.7 515.3 672.9 240.2 614.0 156.8 378.0 199.5 117.5 66.6

74108 Tulsa 6,638 2,285 853 344.2 482.1 318.9 307.5 403.0 128.5 386.1 101.9 150.9 44.0

74110 Tulsa 15,267 8,050 2,658 527.3 711.9 581.7 264.7 806.7 174.1 486.6 211.8 113.6 79.7

74112 Tulsa 21,222 6,470 2,890 304.9 325.2 436.6 206.6 433.4 136.2 283.7 158.5 114.9 53.9

74114 Tulsa 16,913 3,227 1,858 190.8 253.3 446.3 145.0 204.3 109.9 239.1 142.4 91.0 33.0

74115 Tulsa 23,687 10,309 3,464 435.2 469.0 525.7 246.2 670.0 146.2 368.1 170.1 110.4 68.1

74116 Tulsa 2,270 1,852 674 815.7 1,230.3 478.8 296.9 922.7 179.9 113.0

74119 Tulsa 3,790 1,585 640 418.2 658.7 833.0 191.8 558.2 168.9 450.3 294.5 88.3 69.3

74120 Tulsa 5,496 2,117 701 385.2 800.0 727.4 153.6 625.8 127.5 515.1 250.0 69.5 79.7

74126 Tulsa 9,047 6,546 1,957 723.5 1,424.0 661.3 422.4 1,127.1 216.3 896.5 198.7 170.8 118.1

74127 Tulsa 10,901 3,239 1,384 297.1 414.3 186.1 305.0 384.0 127.0 327.1 59.9 157.5 49.9

74128 Tulsa 12,430 3,873 1,782 311.6 341.8 504.3 179.8 417.5 143.4 297.4 202.0 109.1 47.5

74129 Tulsa 18,542 5,527 2,456 298.1 351.3 404.2 185.8 410.5 132.5 298.2 177.8 100.1 41.7

74130 Tulsa 2,624 936 359 356.7 504.1 452.3 209.0 515.1 136.8 322.3 192.3 102.0 62.0

74132 Tulsa 4,616 1,425 682 308.7 715.5 756.2 202.0 334.6 147.8 611.2 277.0 108.9 46.0

74133 Tulsa 37,778 8,106 3,799 214.6 382.2 442.0 162.2 208.7 100.6 373.2 171.7 76.4 21.3

74134 Tulsa 12,998 3,045 1,206 234.3 465.5 119.9 295.6 92.8 337.2 58.9 30.8

74135 Tulsa 21,320 6,724 3,130 315.4 348.3 542.9 194.1 439.8 146.8 295.8 200.4 103.4 44.1

74136 Tulsa 32,712 9,103 3,291 278.3 424.6 572.0 156.2 346.2 100.6 296.2 199.8 66.8 34.1

74137 Tulsa 22,960 3,620 1,990 157.7 270.0 473.1 123.0 140.1 86.7 298.3 168.5 66.7 18.6

74145 Tulsa 18,020 4,586 2,070 254.5 256.9 450.8 184.4 321.4 114.9 213.4 192.4 91.4 35.1

74146 Tulsa 14,380 4,361 1,628 303.3 612.5 402.2 180.4 381.9 113.2 397.5 179.8 83.5 32.1

ER and Admissions utilization rate is per 1,000 residents. The overall utilization rate determines the corresponding colors. The rates within the cells are provided for reference. RED: highest 12 areas in Tulsa County. YELLOW: mid-12 areas in Tulsa County. GREEN: lowest 12 areas in Tulsa County.

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 70 SUBURBAN COUNTIES USING TULSA HOSPITALS INPATIENT ADMISSIONS & EMERGENCY ROOM LOW POPULATION/UTILIZATION AREAS OMITTED FOR CLARITY

COMMUNITY EMERGENCY ROOM INPATIENT

COUNTY CITY ZCTA POP VISITS RATE ADMITS RATE

Osage Sand Springs 74063 5,710 90 15.8 53 9.3

Osage Sperry 74073 1,715 60 35.0 37 21.6

Creek Sand Springs 74063 1,449 58 40.0 33 22.8

Osage Pawhuska 74056 5,835 121 20.7 183 31.4

Osage Barnsdall 74002 2,300 114 49.6 83 36.1

Pawnee Pawnee 74058 4,152 105 25.3 201 48.4

Rogers Claremore 74017 38,719 1,972 50.9 1,959 50.6

Wagoner Porter 74454 2,906 195 67.1 173 59.5

Osage Fairfax 74637 2,063 53 25.7 131 63.5

Okmulgee Henryetta 74437 10,277 497 48.4 672 65.4

Creek Drumright 74030 3,792 163 43.0 265 69.9

Rogers Chelsea 74016 5,184 377 72.7 371 71.6

Wagoner Wagoner 74467 13,780 655 47.5 1,034 75.0

Okmulgee Morris 74445 2,600 178 68.5 196 75.4

Rogers Talala 74080 2,113 230 108.8 160 75.7

Creek Oilton 74052 1,350 113 83.7 106 78.5

Okmulgee Okmulgee 74447 17,997 1,280 71.1 1,427 79.3

Creek Depew 74028 1,785 113 63.3 146 81.8

Osage Hominy 74035 4,860 380 78.2 412 84.8

Wagoner Broken Arrow 74014 23,231 2,784 119.8 2,044 88.0

Creek Bristow 74010 10,129 886 87.5 943 93.1

Wagoner Coweta 74429 10,445 1,379 132.0 991 94.9

Pawnee Terlton 74081 1,790 326 182.1 171 95.5

Okmulgee Beggs 74421 4,203 618 147.0 406 96.6

Rogers Inola 74036 5,955 859 144.3 623 104.6

Okmulgee Dewar 74431 1,029 53 51.5 112 108.8

Rogers Oologah 74053 2,628 396 150.7 291 110.7

Creek Sapulpa 74066 29,270 4,584 156.6 3,313 113.2

Osage Bartlesville 74003 2,125 268 126.1 241 113.4

Pawnee Cleveland 74020 7,604 1,045 137.4 873 114.8

Creek Tulsa 74131 2,538 588 231.7 308 121.4

Pawnee Jennings 74038 1,291 203 157.2 173 134.0

Creek Kellyville 74039 3,105 623 200.6 418 134.6

Osage Skiatook 74070 6,531 1,790 274.1 899 137.6

Rogers Catoosa 74015 5,597 1,591 284.2 775 138.5

Okmulgee Haskell 74436 1,380 437 316.6 222 160.9

Osage Tulsa 74127 7,207 4,962 688.5 1,423 197.4

Creek Mannford 74044 4,731 1,606 339.4 945 199.7

Creek Mounds 74047 1,906 1,212 635.9 614 322.1

TOTALS 261,283 32,964 23,427

ER and Admissions utilization rate is per 1,000 residents. The overall utilization rate determines the corresponding colors. The rates within the cells are provided for reference. RED: highest 12 rates using Tulsa County. YELLOW: mid-12 rates using Tulsa County. GREEN: lowest 12 rates using Tulsa County. The numbers/rates within cells provided for reference.

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 71 SECTION 5 HOSPITAL UTILIZATION EMERGENCY ROOM AND HOSPITAL ADMISSIONS BY ZCTA AND COUNTY

Introduction The advent of prospective, fixed, and discounted payments to hospitals gained traction 20 years ago and is now a fundamental feature of hospital financing. In 1986, Congress enacted the Emergency Medical Treatment and Labor Act (EMTALA) as a condition of participation for hospitals in Medicare. Under EMTALA, anyone arriving in an ER must be evaluated to see if they have an emergent condition or are in labor; if so, they must be stabilized medically before they can be transferred.

The practical effect of this has been that many patients are not merely screened, they are diagnosed and offered treatment regardless of the urgency of their situation. With the continued growth of the uninsured population and reluctance of private physicians to accept patients with Medicaid coverage, the combination of factors now has essentially converted many hospital ERs into after-hour care centers and substitutes for primary care physicians for those unable to pay.

Since EMTALA provides no funding for any of the resulting care, and because hospitals have increasing constraints on their ability to cross-subsidize indigent care, this is an area of great concern for many hospitals. Therefore, an overall analysis of ER utilization and hospital admissions is crucial.

Problem On average, the Tulsa region has an overall hospital utilization rate lower than the national or state averages. However, the rates are not uniform throughout the seven-county (127 ZCTA) region. This analysis allowed for the sub-region identification of high use areas by geography.

Data The ZCTA sources of ER visits and hospital admissions were provided by the five full-service hospitals in Tulsa County - Saint Francis Hospital, St. John Medical Center, Hillcrest Medical Center, SouthCrest Hospital, and Tulsa Regional Medical Center (TRMC). The data are aggregately displayed so as to maintain the anonymity of the individual hospitals.

Methods This analysis used utilization rates (UR) as visits per 1,000 population to identify sub-areas of the greater Tulsa region that have significant ER visits or hospital admissions compared to the average. The areas examined are ZCTAs. The UR adjusts for the population sizes of different ZCTAs. Data was only arrayed for Tulsa County residents. To include residents of the suburban counties would require tracking ER use in a host of smaller hospitals - an undertaking beyond the intended scope of this analysis.

General Findings The five general hospitals in Tulsa County furnished annual figures for both emergency room (ER) visits and hospital admissions by originating ZCTA and pay source (Medicaid, Medicare, Insured and Uninsured). This utilization data was merged with the Master Regional Database and the causes of death analysis.

It was shown that the greatest ER utilization was by Medicaid patients followed by Medicare patients. Although the ER utilization rate by those without health insurance was about a third higher than the overall county average, it was the Medicaid clientele that were likely over-utilizing the Tulsa hospital emergency rooms. © 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 72 • An estimated 30% of all admissions were typically the “medically marginalized” patient—uninsured or Medicaid recipient. For one Tulsa hospital, that population accounted for 52% of admissions.

• Of all admissions of Medicaid patients, 64% were admitted to either Hillcrest Medical Center (HMC) or Tulsa Regional Medical Center (TRMC), while 31% were admitted to St. John/Saint Francis. On the other hand, St. John/Saint Francis served 58% of all uninsured admissions in Tulsa while Hillcrest/TRMC served 38%.

With regard to inpatient admissions, Medicaid paid an estimated 70% of costs, while the uninsured paid very little. Presuming that the severity of illness was higher at St. John and Saint Francis, it is not unreasonable to conclude that, given these utilization findings, HMC/TRMC and St. John/Saint Francis would provide approximately equal amounts of inpatient services to the medically marginalized population. The same calculations and conclusions were applicable for ER visits in Tulsa County.

TULSA COUNTY RESIDENTS ONLY HOSPITAL UTILIZATION RATES BY INSURANCE STATUS ER VISITS/INPATIENT ADMISSIONS PER 1,000 POPULATION

EMERGENCY ROOM VISITS INPATIENT ADMISSIONS

600.0 400.0 345.2 350.0 500.0 482.6

416.3 300.0 390.8 400.0 250.0 296.8 300.0 200.0 173.5

150.0 125.8 200.0 183.1 97.4 100.0

100.0 44.1 50.0

0.0 0.0 All Medicare Medicaid Insured Uninsured All Medicare Medicaid Insured Uninsured

ALL TULSA HOSPITALS PERCENTAGE OF PATIENTS BY INSURANCE STATUS

EMERGENCY ROOM VISITS INPATIENT ADMISSIONS MCE MCD INS NONE MCE MCD INS NONE High 18% 40% 45% 36% 36% 43% 54% 9% Average 15% 26% 34% 26% 30% 23% 40% 7% Low 9% 16% 14% 23% 26% 12% 16% 5%

MCE: Medicare; MCD: Medicaid; INS: Private health insurance; NONE: Lacking any health insurance

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 73 Emergency Rooms There were over 212,000 visits to Tulsa hospital ERs in 2004 for an average of almost 600 visits per day. The calculated rate for Tulsa County residents was 297 ER visits per 1,000 population. This is less than the state, regional and national averages of 355, 390, and 372, respectively. Of these visits, 95% were from the seven counties in the Tulsa metropolitan area. On average, 26% of these visits were by people without health insurance coverage and an additional 25% were by Medicaid recipients. Therefore, over half of all ER visits were by the “medically marginalized” cohort.

Much is said about the uninsured using Tulsa hospital ERs as a primary source for health care services. On the contrary, little is said about Medicaid patients doing the same. It should be more disconcerting that patients who theoretically “have a doctor” have a higher utilization rate than those who do not. The data irrefutably suggests several findings:

• Medicaid patients using the ER at much greater rates than the uninsured.

• The four largest hospitals share the burden of the medically marginalized services in different ways. Hillcrest will provide for 34% of all community Medicaid ER visits; Tulsa Regional Medical Center will provide 36% of all ER visits by the Tulsa’s uninsured; Saint John will provide 32% of all Medicare inpatient services and a significant amount of services for the uninsured in the ER; and Saint Francis will serve the greatest volume of ER care for the uninsured and serve 25% of all uninsured ER visits in Tulsa.

• Uninsured patients do not use one hospital ER significantly more than another, with the exception of TRMC, where 36% of the ER visits were by the uninsured.

2004 Emergency Room Visits Origins of Admissions to Tulsa County Hospitals by Pay Source

MCE MCD INS NONE ALL Creek 1,597 2,172 4,392 2,215 10,376 Okmulgee 762 608 1,207 685 3,262 Osage 1,331 2,649 1,932 2,433 8,345 Pawnee 376 330 702 465 1,873 Rogers 865 945 2,647 1,074 5,531 Tulsa 24,973 43,210 54,864 42,739 165,786 Wagoner 917 1,161 2,981 1,077 6,136 MSA Total 30,821 51,075 68,725 50,688 201,309 Other 2,047 1,854 3,740 3,536 11,177 GRAND TOTAL 32,868 52,929 72,465 54,224 212,486

MCE MCD INS NONE ALL Creek 15% 21% 42% 21% 100% Okmulgee 23% 19% 37% 21% 100% Osage 16% 32% 23% 29% 100% Pawnee 20% 18% 37% 25% 100% Rogers 16% 17% 48% 19% 100% Tulsa 15% 26% 33% 26% 100% Wagoner 15% 19% 49% 18% 100% MSA Total 15% 25% 34% 25% 100% Other 18% 17% 33% 32% 100% GRAND TOTAL 15% 25% 34% 26% 100%

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 74 Hospital Admissions There were over 109,000 admissions to Tulsa’s general hospitals in 2004. This is an average of almost 300 admissions per day. Of these admissions, 88% were from counties within the Tulsa metropolitan area; he range for the hospitals was between 80-93%. The calculated rate for Tulsa County residents was 126 inpatient admissions per 1,000 population. Much is written about the financial impacts of the “uninsured” on hospitals. These data suggest that, on average, only 7% of inpatient admissions are people without health insurance. Conversely and theoretically, 93% of all hospitalized patients have health care coverage, but there are several things to consider when analyzing these data. It is however beyond the scope of this analysis to definitively assess all impacts.

• An estimated 30% of all admissions are by the “medically marginalized” patient (an uninsured person or Medicaid recipient). For one Tulsa hospital, that percentage was 52%. Given that Medicaid will pay less than 70% of cost, and the uninsured certainly much less than that, having a patient volume of almost 30% of medically marginalized patients is a significant service.

• Some would interpret that a 7% hospital admissions rate by the uninsured is an indicator that this population cannot get admitted to a hospital without health insurance. The likely reasons may be more complex. First, the uninsured tend to be younger adults with minimal inpatient needs. Services foregone by the uninsured may be non-life threatening or elective procedures. Finally, some uninsured, particularly children, may be enrolled in Medicaid upon admission.

• Inspection of the charts and tables in this section indicate that any observed “over-utilization” likely occurred in ERs rather than with inpatient services. It appears that hospitals ERs really are a substitute for available and responsive outpatient services.

• Of all admissions of MEDICAID patients, two-thirds (64%) were admitted to either HMC or TRMC, and 31% were admitted to St. John and Saint Francis. On the other hand, St. John and Saint Francis served 58% of all UNINSURED admissions in Tulsa while HMC and TRMC served 38%.

• When combined, 58% of the “medically marginalized” inpatient admissions were to either HMC or TRMC, and 37% were admitted to either St. John or Saint Francis. These percentages measure admissions and visits only and do not account for severity of illness of intensity of services provided.

2004 Hospital Admissions Origins of Admissions to Tulsa County Hospitals by Pay Source

MCE MCD INS NONE ALL Creek 2,031 1,499 3,394 465 7,389 Okmulgee 1,330 610 1,056 256 3,252 Osage 1,360 1,157 1,084 275 3,876 Pawnee 681 286 640 107 1,714 Rogers 1,149 630 2,248 243 4,270 Tulsa 20,710 15,531 29,191 4,822 70,254 Wagoner 1,551 1,096 2,839 277 5,763 MSA Total 28,812 20,809 40,452 6,445 96,518 Areas Outside MSA 4,512 3,465 3,916 860 12,753 GRAND TOTAL 33,324 24,274 44,368 7,305 109,271

MCE MCD INS NONE ALL Creek 27% 20% 46% 6% 100% Okmulgee 41% 19% 32% 8% 100% Osage 35% 30% 28% 7% 100% Pawnee 40% 17% 37% 6% 100% Rogers 27% 15% 53% 6% 100% Tulsa 29% 22% 42% 7% 100% Wagoner 27% 19% 49% 5% 100% MSA Total 30% 22% 42% 7% 100% Areas Outside MSA 35% 27% 31% 7% 100% OVERALL 30% 22% 41% 7% 100% © 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 75 ZCTA Profiles and Utilization Rates There are four Tulsa ZCTAs whose residents had the highest utilization rates for both ER and hospital admissions. All are north of Admiral Boulevard and three (74126, 74106 and 74110) are contiguous. The other (74116) is a few miles to the east. These four ZCTAs rank second through fifth as the poorest in Tulsa County. ZCTA 74103 (downtown Tulsa) is the poorest in the county with $18,021 (38% of the county average). This analysis does not include the anomalous ZCTAs of Oakhurst (74050) and downtown Tulsa (74103). They are in order:

1. ZCTA 74116 There are 3,700 residents in this ZCTA. This area is north of Admiral Boulevard (Interstate 244), east of the Mingo Valley Expressway, and extends north for several miles. The racial composition is 61% white, 12% black, 11% Hispanic, 8% Indian and 8% other. The median family income was $26,117 - one of the lowest in Tulsa County. It is reported that two-thirds (69%) of the residents were Medicaid recipients and another 21% were uninsured. Therefore, 89% were dependent upon social and public programs for care. The county-wide ER utilization rate was 296.8 visits per 1,000 population. This ZCTA had a rate of 807.3 - the highest in Tulsa County. The county-wide hospital admission rate was 125.8 admissions per 1,000 population. This ZCTA had a rate of 296.9—also the highest in Tulsa County. People in this ZCTA are admitted to the hospital at the same rate that the average Tulsan uses the ER! The median family income in this ZCTA was 55% of the county average, ranking the ZCTA as the third poorest in Tulsa County.

2. ZCTA 74126 There are 8,150 residents in this ZCTA. This area is in NW Tulsa County south of Sperry and west of Turley and extends into Osage county. The racial composition is 18% white, 74% black, 2% Hispanic, 3% Indian and 3% other. The median family income was $26,109—almost identical to ZCTA 74116. It was reported that 41% of the residents were Medicaid recipients and another 20% were uninsured. Therefore, 61% were dependent upon social and public programs for care. The county-wide ER utilization rate was 296.8 visits per 1,000 population. This ZCTA had a rate of 723.5 - the second highest in Tulsa county. The county-wide hospital admission rate was 125.8 admissions per 1,000 population. This ZCTA had a rate of 216.3—also the second highest in Tulsa County. The median family income in this ZCTA was 55% of the county average, ranking this ZCTA as the fifth poorest in Tulsa County.

3. ZCTA 74106 There are 18,100 residents in this ZCTA. This area is immediately north of downtown Tulsa. It is east of Peoria Avenue and west of the Cherokee Expressway and to the south of Turley. The racial composition is 10% white, 80% black, 5% Hispanic, and 5% other. The median family income was $23,451—one of the lowest in Tulsa county. It was reported that over 40% of the residents were Medicaid recipients and another 19% were uninsured. Therefore, 59% were dependent upon social and public programs for care. The county-wide ER utilization rate was 296.8 visits per 1,000 population. This ZCTA had a rate of 569.5 - the third highest in Tulsa County. The county-wide hospital admission rate was 125.8 admissions per 1,000 population. This ZCTA had a rate of 185.8—also the third highest in Tulsa County. The median family income in this ZCTA was 49% of the county average, ranking this ZCTA as the second poorest in Tulsa County.

4. ZCTA 74110 There are 15,267 residents in this ZCTA. This area is north of Admiral Boulevard (Interstate 244) immediately to the east of ZCTA 74106. Cherokee Expressway bisects this ZCTA from the NE to SW. The racial composition is 44% white, 26% black, 16% Hispanic, 8% Indian and 6% other. The median family income was $25,853—one of the lowest in Tulsa County. It was reported that 34% of the residents were Medicaid recipients and another 21% were uninsured. Therefore, 55% were dependent upon social and public programs for care. The county-wide ER utilization rate was 296.8 visits per 1,000 population. This ZCTA had a rate of 527.3—the fourth highest in Tulsa County. The county-wide hospital admission rate was 125.8 admissions per 1,000 population. This ZCTA had a rate of 174.1—also the highest in Tulsa County. The median family income in this ZCTA was 54% of the county average, ranking the ZCTA as the fourth poorest in Tulsa County.

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 76 UTILIZATION OF TULSA HOSPITALS BY ORIGINATING ZCTA (NOTE: SOME ZCTA’S ARE IN MULTIPLE COUNTIES AS SHOWN)

EMERGENCY ROOM VISITS INPATIENT ADMISSIONS

MCE MCD INS NONE ALL MCE MCD INS NONE ALL Creek County 74010 138 170 385 193 886 269 223 382 69 943 74028 15 20 51 27 113 49 32 49 16 146 74030 46 38 46 33 163 117 51 82 15 265 74033 - Tulsa 0 0 0 0 0 0 0 0 0 0 74038 - Pawnee 1 0 3 2 6 1 0 5 1 7 74039 67 131 293 132 623 60 100 224 34 418 74041 35 52 134 64 285 42 38 74 15 169 74044 263 332 694 317 1,606 266 162 457 60 945 74047 134 262 584 232 1,212 128 153 301 32 614 74050 - Tulsa 9 0 8 3 20 6 1 3 0 10 74052 21 34 34 24 113 32 28 39 7 106 74063 - Tulsa 17 3 32 6 58 12 0 20 1 33 74066 735 976 1,871 1,002 4,584 932 642 1,557 182 3,313 74068 0 6 3 12 21 4 4 0 3 11 74071 2 1 6 5 14 1 2 3 1 7 74079 18 6 21 10 55 32 8 25 4 69 74081 - Pawnee 0 0 1 1 2 0 0 2 0 2 74085 - Pawnee 0 0 0 0 0 0 0 0 0 0 74131 88 138 212 150 588 72 55 157 24 308 74132 - Tulsa 8 3 14 2 27 8 0 14 1 23 74421 - Okmulgee 0 0 0 0 0 0 0 0 0 0 Creek County 1,597 2,172 4,392 2,215 10,376 2,031 1,499 3,394 465 7,389

Okmulgee County 74047 - Creek 7 0 9 3 19 11 0 6 2 19 74421 97 108 288 125 618 128 65 179 34 406 74422 9 8 17 6 40 13 5 12 3 33 74431 13 14 19 7 53 43 25 34 10 112 74436 68 95 153 121 437 80 33 92 17 222 74437 133 91 171 102 497 292 150 182 48 672 74445 42 30 76 30 178 64 32 83 17 196 74447 364 235 410 271 1,280 645 267 400 115 1,427 74456 6 5 28 7 46 9 4 18 2 33 74460 8 13 17 7 45 17 12 22 3 54 74880 15 9 19 6 49 28 17 28 5 78 Okmulgee County 762 608 1,207 685 3,262 1,330 610 1,056 256 3,252

Osage County 74001 19 26 25 31 101 19 7 11 2 39 74002 12 10 63 29 114 21 10 46 6 83 74003 48 42 97 81 268 82 52 87 20 241 74020 - Pawnee 0 0 0 0 0 0 0 0 0 0 74022 2 6 8 5 21 8 4 9 2 23 74035 100 79 144 57 380 149 96 145 22 412 74051 11 2 30 8 51 27 2 27 1 57 74054 10 25 34 26 95 21 26 37 8 92 74056 26 23 50 22 121 64 46 64 9 183 74060 41 39 55 37 172 43 17 31 5 96 74063 - Tulsa 10 7 60 13 90 15 0 32 6 53 74070 - Tulsa 374 404 651 361 1,790 339 184 330 46 899

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 77 UTILIZATION OF TULSA HOSPITALS BY ORIGINATING ZCTA (NOTE: SOME ZCTA’S ARE IN MULTIPLE COUNTIES AS SHOWN)

EMERGENCY ROOM VISITS INPATIENT ADMISSIONS

MCE MCD INS NONE ALL MCE MCD INS NONE ALL 74073 - Tulsa 20 4 27 9 60 20 1 15 1 37 74084 3 6 11 10 30 5 4 9 1 19 74126 - Tulsa 0 0 7 4 11 0 0 3 0 3 74127 - Tulsa 633 1,957 646 1,726 4,962 417 689 185 132 1,423 74604 3 2 8 4 17 27 2 17 1 47 74633 0 1 1 0 2 7 2 3 1 13 74637 14 16 14 9 53 77 14 30 10 131 74650 - Pawnee 0 0 0 0 0 0 0 0 0 0 74652 5 0 1 1 7 19 1 3 2 25 Osage County 1,331 2,649 1,932 2,433 8,345 1,360 1,157 1,084 275 3,876

Pawnee County 73061 1 2 1 0 4 5 2 2 0 9 74015 - Rogers 0 0 0 0 0 0 0 0 0 0 74016 - Rogers 0 0 0 0 0 0 0 0 0 0 74017 - Rogers 0 0 0 0 0 0 0 0 0 0 74020 187 187 389 282 1,045 355 162 300 56 873 74021 - Tulsa 0 0 0 0 0 0 0 0 0 0 74032 2 2 1 3 8 13 3 9 2 27 74034 9 8 13 11 41 11 10 15 2 38 74036 - Rogers 0 0 0 0 0 0 0 0 0 0 74038 34 28 80 61 203 59 30 70 14 173 74044 - Creek 4 1 21 1 27 4 0 24 0 28 74045 3 4 1 3 11 8 1 4 1 14 74053 - Rogers 0 0 0 0 0 0 0 0 0 0 74055 - Tulsa 0 0 0 0 0 0 0 0 0 0 74058 33 25 27 20 105 97 36 53 15 201 74080 - Rogers 0 0 0 0 0 0 0 0 0 0 74081 66 58 132 70 326 68 20 77 6 171 74085 18 4 16 10 48 19 8 26 3 56 74116 - Tulsa 0 0 0 0 0 0 0 0 0 0 74332 13 9 15 1 38 23 7 36 7 73 74650 6 1 6 3 16 19 6 22 1 48 74651 0 1 0 0 1 0 1 2 0 3 Pawnee County 376 330 702 465 1,873 681 286 640 107 1,714

Tulsa County 74008 500 669 1,765 691 3,625 414 235 928 84 1,661 74011 290 466 1,316 380 2,452 405 228 1,101 60 1,794 74012 804 903 3,334 1,060 6,101 900 538 2,289 158 3,885 74015 - Rogers 2 0 3 1 6 2 0 0 1 3 74021 437 407 1,260 420 2,524 436 188 939 60 1,623 74033 229 429 980 414 2,052 186 197 508 52 943 74037 375 260 1,091 366 2,092 363 115 740 39 1,257 74047 - Creek 8 4 14 2 28 13 4 15 1 33 74050 102 174 149 239 664 84 61 54 19 218 74055 736 821 2,569 739 4,865 770 343 1,661 91 2,865 74063 1,205 1,755 3,238 1,688 7,886 1,138 536 1,747 243 3,664 74066 - Creek 2 10 6 2 20 2 0 0 0 2 74070 131 118 437 133 819 140 30 255 25 450 74073 161 358 485 221 1,225 148 83 269 21 521 74103 235 678 530 1,932 3,375 32 154 85 201 472

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 78 UTILIZATION OF TULSA HOSPITALS BY ORIGINATING ZCTA (NOTE: SOME ZCTA’S ARE IN MULTIPLE COUNTIES AS SHOWN)

EMERGENCY ROOM VISITS INPATIENT ADMISSIONS

MCE MCD INS NONE ALL MCE MCD INS NONE ALL 74104 635 1,235 1,202 1,376 4,448 421 460 593 193 1,667 74105 1,437 1,730 2,401 2,058 7,626 1,216 658 1,231 241 3,346 74106 1,587 3,534 1,805 2,849 9,775 1,057 1,094 734 305 3,190 74107 1,164 3,353 2,188 2,416 9,121 854 994 1,070 262 3,180 74108 241 801 703 540 2,285 193 256 345 59 853 74110 986 3,051 1,443 2,570 8,050 674 1,111 619 254 2,658 74112 1,081 1,430 2,166 1,793 6,470 943 519 1,205 223 2,890 74114 749 420 1,469 589 3,227 707 134 922 95 1,858 74115 1,166 3,600 2,366 3,177 10,309 915 1,165 1,061 323 3,464 74116 184 745 357 566 1,852 138 280 203 53 674 74117 1 55 11 29 96 3 2 2 1 8 74119 411 379 376 419 1,585 281 134 173 52 640 74120 292 579 477 769 2,117 188 199 216 98 701 74126 791 2,466 1,246 2,043 6,546 498 741 504 214 1,957 74127 504 600 1,334 801 3,239 398 193 689 104 1,384 74128 646 1,183 1,042 1,002 3,873 562 474 632 114 1,782 74129 901 1,571 1,577 1,478 5,527 765 691 850 150 2,456 74130 122 294 254 266 936 78 125 124 32 359 74132 247 273 614 291 1,425 211 100 331 40 682 74133 1,325 1,372 3,848 1,561 8,106 1,294 533 1,813 159 3,799 74134 243 799 1,150 853 3,045 176 376 565 89 1,206 74135 1,619 1,444 1,977 1,684 6,724 1,375 533 1,053 169 3,130 74136 1,468 2,351 2,814 2,470 9,103 1,024 821 1,203 243 3,291 74137 621 528 1,922 549 3,620 686 188 1,043 73 1,990 74145 845 949 1,703 1,089 4,586 702 405 844 119 2,070 74146 490 1,416 1,242 1,213 4,361 318 633 575 102 1,628 Tulsa County 24,973 43,210 54,864 42,739 165,786 20,710 15,531 29,191 4,822 70,254

Wagoner County 74008 - Tulsa 0 0 4 0 4 2 0 6 0 8 74014 309 486 1,524 465 2,784 386 305 1,267 86 2,044 74015 - Rogers 0 5 13 5 23 0 0 9 1 10 74036 - Rogers 0 0 1 1 2 0 0 0 0 0 74108 - Tulsa 5 2 15 4 26 7 4 18 0 29 74337 53 45 91 45 234 88 37 91 13 229 74352 84 41 122 39 286 124 55 120 15 314 74403 84 40 131 69 324 275 116 222 41 654 74429 175 319 613 272 1,379 210 227 503 51 991 74434 38 11 53 16 118 66 20 93 14 193 74436 - Okmulgee 10 11 35 12 68 8 9 16 3 36 74446 4 15 12 7 38 17 14 11 6 48 74454 25 56 81 33 195 40 44 78 11 173 74467 130 130 286 109 655 328 265 405 36 1,034 Wagoner County 917 1,161 2,981 1,077 6,136 1,551 1,096 2,839 277 5,763

Rogers County 74015 209 337 700 345 1,591 193 143 376 63 775 74016 85 69 135 88 377 121 87 143 20 371 74017 316 337 926 393 1,972 544 282 1,037 96 1,959 74021 - Pawnee 8 4 21 1 34 6 2 18 0 26 74036 159 113 451 136 859 165 70 348 40 623 74053 47 61 230 58 396 70 28 179 14 291

© 2005 Center for Health Policy, University of Oklahoma College of Public Health, Schusterman Campus. Released April 29, 2005. 79 UTILIZATION OF TULSA HOSPITALS BY ORIGINATING ZCTA (NOTE: SOME ZCTA’S ARE IN MULTIPLE COUNTIES AS SHOWN)

EMERGENCY ROOM VISITS INPATIENT ADMISSIONS

MCE MCD INS NONE ALL MCE MCD INS NONE ALL 74055 - Tulsa 10 0 34 3 47 10 1 31 1 43 74080 29 21 135 45 230 37 17 98 8 160 74116 - Tulsa 0 0 0 0 0 0 0 0 0 0 74332 - Pawnee 2 3 15 5 25 3 0 18 1 22 Rogers County 865 945 2,647 1,074 5,531 1,149 630 2,248 243 4,270

Total MSA 30,821 51,075 68,725 50,688 201,309 28,812 20,809 40,452 6,445 96,518 Other Areas 2,047 1,854 3,740 3,536 11,177 4,512 3,465 3,916 860 12,753 GRAND TOTAL 32,868 52,929 72,465 54,224 212,486 33,324 24,274 44,368 7,305 109,271

Total MSA 94% 96% 95% 93% 95% 86% 86% 91% 88% 88% Other Areas 6% 4% 5% 7% 5% 14% 14% 9% 12% 12% GRAND TOTAL 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Creek 1,597 2,172 4,392 2,215 10,376 2,031 1,499 3,394 465 7,389 Okmulgee 762 608 1,207 685 3,262 1,330 610 1,056 256 3,252 Osage 1,331 2,649 1,932 2,433 8,345 1,360 1,157 1,084 275 3,876 Pawnee 376 330 702 465 1,873 681 286 640 107 1,714 Rogers 865 945 2,647 1,074 5,531 1,149 630 2,248 243 4,270 Tulsa 24,973 43,210 54,864 42,739 165,786 20,710 15,531 29,191 4,822 70,254 Wagoner 917 1,161 2,981 1,077 6,136 1,551 1,096 2,839 277 5,763 MSA Total 30,821 51,075 68,725 50,688 201,309 28,812 20,809 40,452 6,445 96,518 Other 2,047 1,854 3,740 3,536 11,177 4,512 3,465 3,916 860 12,753 GRAND TOTAL 32,868 52,929 72,465 54,224 212,486 33,324 24,274 44,368 7,305 109,271

Creek 5% 4% 6% 4% 5% 6% 6% 8% 6% 7% Okmulgee 2% 1% 2% 1% 2% 4% 3% 2% 4% 3% Osage 4% 5% 3% 4% 4% 4% 5% 2% 4% 4% Pawnee 1% 1% 1% 1% 1% 2% 1% 1% 1% 2% Rogers 3% 2% 4% 2% 3% 3% 3% 5% 3% 4% Tulsa 76% 82% 76% 79% 78% 62% 64% 66% 66% 64% Wagoner 3% 2% 4% 2% 3% 5% 5% 6% 4% 5% MSA Total 94% 96% 95% 93% 95% 86% 86% 91% 88% 88% Other 6% 4% 5% 7% 5% 14% 14% 9% 12% 12% GRAND TOTAL 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

Creek 15% 21% 42% 21% 100% 27% 20% 46% 6% 100% Okmulgee 23% 19% 37% 21% 100% 41% 19% 32% 8% 100% Osage 16% 32% 23% 29% 100% 35% 30% 28% 7% 100% Pawnee 20% 18% 37% 25% 100% 40% 17% 37% 6% 100% Rogers 16% 17% 48% 19% 100% 27% 15% 53% 6% 100% Tulsa 15% 26% 33% 26% 100% 29% 22% 42% 7% 100% Wagoner 15% 19% 49% 18% 100% 27% 19% 49% 5% 100% MSA Total 15% 25% 34% 25% 100% 30% 22% 42% 7% 100% Other 18% 17% 33% 32% 100% 35% 27% 31% 7% 100% GRAND TOTAL 15% 25% 34% 26% 100% 30% 22% 41% 7% 100%

1 State of the State Health, 2005. Oklahoma State Department of Health

2 CDC, National Center for Health Statistics.

3 State of the State Health, 2005. Oklahoma State Department of Health

4 State of the State Health, 2005. Oklahoma State Department of Health

5 Medicaid DSH discussion from the Green Book prepared by the Committee on Ways and Means, U.S. House of Representatives: “Disproportionate share hospital payments States must provide for additional payments to hospitals serving a disproportionate share of low-income patients. Unlike comparable Medicare payments, Medicaid disproportionate share hospital (DSH) payments must follow a formula that considers a hospital's charity patients as well as its Medicaid caseload. Beginning in fiscal year 1992, State DSH payments were limited as part of an effort to rein in fast growth. DSH payments were limited to 12 percent of total Medicaid spending. The 12 percent figure was phased in through the use of State-specific DSH allotments (caps on Federal matching payments) for each Federal fiscal year. BBA 97 lowered the DSH allotments by imposing a freeze and making graduated proportional reductions for 1998 - 2002. Thereafter, annual DSH allotments for a State equal the allotment for the preceding fiscal year increased by the percentage change in the medical care component of the Consumer Price Index for All Urban Consumers. BBA 97 also imposed a new cap on DSH payments to institutions for mental disease and other mental health facilities. The Medicare, Medicaid and SCHIP Benefits Improvement and Protection Act of 2000 (BIPA 2000, P.L. 106-554) established a 175 percent (of uncompensated care costs) cap for all public hospitals in the nation for a two-year period beginning in State fiscal year 2003.“

6 Hospital Indigent Care: A Statewide Analysis, Oklahoma Hospital Association, May 2003.

7 Hospital Indigent Care: A Statewide Analysis, Oklahoma Hospital Association, May 2003.

8 Analysis of Tulsa Emergency Room Utilization Data, 2000, prepared for Community HealthNet, October 1 2001, David Blatt, Community Action Project.

9 Ehrlich, Natalie, et al; Pilot Study of ER Utilization at Tulsa Hospitals, Journal of the Oklahoma State Medical Association, February 2004.

10 Public Hospital: The AHA Guide has listed the “control” of the institution to be a 12-16 (state, county, city-county, municipal or hospital district).

11 Unpublished data, Oklahoma Hospital Association

12 Hospital Indigent Care: A Statewide Analysis, Oklahoma Hospital Association, May 2003.

13 Medicaid DSH Payments: If a community contains hospitals receiving significant Medicaid DSH payments according to records published by the federal CMS.

14 Medicaid DSH discussion from the Green Book prepared by the Committee on Ways and Means, U.S. House of Representatives: “Disproportionate share hospital payments States must provide for additional payments to hospitals serving a disproportionate share of low-income patients. Unlike comparable Medicare payments, Medicaid disproportionate share hospital (DSH) payments must follow a formula that considers a hospital's charity patients as well as its Medicaid caseload. Beginning in fiscal year 1992, State DSH payments were limited as part of an effort to rein in fast growth. DSH payments were limited to 12 percent of total Medicaid spending. The 12 percent figure was phased in through the use of State-specific DSH allotments (caps on Federal matching payments) for each Federal fiscal year. BBA 97 lowered the DSH allotments by imposing a freeze and making graduated proportional reductions for 1998 - 2002. Thereafter, annual DSH allotments for a State equal the allotment for the preceding fiscal year increased by the percentage change in the medical care component of the Consumer Price Index for All Urban Consumers. BBA 97 also imposed a new cap on DSH payments to institutions for mental disease and other mental health facilities. The Medicare, Medicaid and SCHIP Benefits Improvement and Protection Act of 2000 (BIPA 2000, P.L. 106-554)

established a 175 percent (of uncompensated care costs) cap for all public hospitals in the nation for a two-year period beginning in State fiscal year 2003.“

15 Comprehensive Medical School: Based upon data provided by the Association of American Medical Colleges, and community specific research

16 Oklahoma Health Care Authority, 2004 Annual Report

17 Focused Hospital(s): The starting point was a metropolitan analysis conducted at Northwestern University. This analysis assigned nominal values to hospitals measuring the “market burden” for hospital serving the poor. These values were examined to determine if there were one or more hospitals “focused” upon indigent care services. In some cases these data were supplemented by individual community research.

18 FQHC State: Does the state have FQHC clinics (adjusted for population) that are at or greater than three times Oklahoma?

19 www.health-alliance.com/newsflash_taxlevy.html. March 29, 2005

20 www.wcmc.com/home/press/pressrelease.html?id=116. March 29, 2005

21 mihs.org/newsevents/mshcdpress012605.html. March 29,2005

22 www.news8austin.com/content/top_stories/default.asp?ArID=99646. March 29, 2005

23 Comparison of Texas Hospital District Costs, Report to the Technical Advisory Committee, August 29, 2002. Morningside Research and Consulting, Inc, Austin Texas. www.morningsideresearch.com

24 Association of Washington (State) Hospital Districts (www.awphd.org/about_whatare.asp)

25 www.whidbeygen.com/Public%20Hospital%20Disrict.htm

26 Source: U.S. Census Bureau; http://www.census.gov

27 Source: U.S. Census Bureau, http://www.census.gov. For more information, refer to the Census Bureau’s page on ZCTAs at http://www.census.gov/geo/ZCTA/zcta.html

28 Source: The Office of Management and Budget, The Executive Office of the President, website URL: http://www.whitehouse.gov/omb/inforeg/statpolicy.html

29 Source: OMD, http://www.whitehouse.gov/omb/fedreg/metroareas122700.pdf

30 Source: OMB Bulletin No. 05-02 Appendix, November 2004 (Statistical and Science Policy Branch, Office of Information and Regulatory Affairs, Office of Management and Budget); http://www.whitehouse.gov/omb/bulletins/fy05/b05-02_appendix.pdf

31 Source: Melissa DATA website, http://www.melissadata.com, March 2005

32 Source: U.S. Census Bureau. For more information on data set SF1, refer to Summary File 1, 2000 Census of Population and Housing, Technical Documentation, http://www.census.gov/prod/cen2000/doc/sf1.pdf

33 Source: U.S. Census Bureau. For more information on data set SF3, refer to Summary File 3, 2000 Census of Population and Housing, Technical Documentation, http://www.census.gov/prod/cen2000/doc/sf3.pdf

34 Source: NAICS, U.S. Census Bureau. For more information on NAICS, refer to: http://www.census.gov/epcd/www/naics.html

35 Source: NAICS, U.S. Census Bureau, http://www.census.gov/epcd/naics02/naicod02.htm

36 Source: U.S. Department of Labor, Bureau of Labor Statistics, Standard Occupational Classification; http://www.bls.gov/soc/

37 Source: U.S. Department of Labor, Bureau of Labor Statistics, Standard Occupational Classification; http://www.bls.gov/soc/soc_majo.htm

38 Oklahoma Health Care Authority, 2004 Annual Report

39 U.S. Census Bureau, http://www.census.gov

40 U.S. Census Bureau, http://www.census.gov; Refer to Appendix B for more information on the 2000 Decennial Census, Summary File 1 and Summary File 3.

41 Source: U.S. Census Bureau, ZIP Code Tabulation Areas (ZCTAsTM) are a new statistical entity developed by the U.S. Census Bureau for tabulating summary statistics from Census 2000. The ZCTA was developed to overcome the difficulties in precisely defining the land area covered by each ZIP Code®. Defining the extent of an area is necessary in order to accurately tabulate census data for that area. ZCTAs are generalized area representations of U.S. Postal Service (USPS) ZIP Code service areas. Each ZCTA is built by aggregating the Census 2000 blocks, whose addresses use a given ZIP Code, into a ZCTA which gets that ZIP Code assigned as its ZCTA code. They represent the majority USPS five-digit ZIP Code found in a given area. For those areas where it is difficult to determine the prevailing five-digit ZIP Code, the higher-level three-digit ZIP Code is used for the ZCTA code.

42 Melissa DATA, http://www.melissadata.com

43 Source: Employee Benefit Research Institute, http://www.ebri.org; Issue Brief No. 276, Sources of Health Insurance and Characteristics of the Uninsured: Analysis of the March 2004 Current Population Survey, Dec 2004

44 See “Summary Analysis” of Master Database (lines 296 and 297)

45 See Master Database category “CNE Adult: Age Range by Sex” (lines 43-59)

46 See Master Database category “UI CNE Adult, Age Range by Sex” (lines 61-77)

47 See “Summary Analysis” of Master Database (line 299)

48 Census 2000, Data Set SF3, Data Element: P84. Sex by Earnings in 1999 for the Population 16 Years and Over with Earnings

49 See Master Database category “Earnings in 1999” (lines 150-156)

50 See Master Database category “Uninsured Nonelderly Adult, By Earnings” (lines 158-164), and “Summary Analysis” (line 300)

51 See “Summary Analysis” of Master Database, lines 303 and 305

52 See Master Database category “Estimated UI Children, (15.3%)” and “Summary Analysis” (line 79 and 306)

53 Source: Kaiser Family Foundation, www.kff.org. The rate of uninsured children age 0-17 years in the state of Oklahoma is 15.3%.

54 See “Summary Analysis” of Master Document, category “Estimated Sources of Health Insurance” (lines 323-338)

55 Oklahoma Health Care Authority

56 1999 Oklahoma Resident Deaths Coding Manual (DOH); International Classification of Diseases, 10th Revision (ICD-10), National Center for Health Statistics (NCHS), CDC, DHHS

57 NCHS, http://www.cdc.gov/nchs/about/major/dvs/mortdata.htm

58 (Source: NCHS, CDC) For the purpose of national mortality statistics, every death is attributed to one underlying condition, based on information reported on the death certificate and using the international rules for selecting the underlying cause of death from the conditions stated on the death certificate. The underlying cause is defined by the World Health Organization (WHO) as the disease or injury that initiated the train of events leading directly to death, or the circumstances of the accident or violence, which produced the fatal injury. Cause of death is coded according to the appropriate revision of the International Classification of Diseases (ICD).

59 Adapted from Pennsylvania Department of Health, Health Statistics, Technical Assistance; http://www.dsf.health.state.pa.us/health/cwp/view.asp?a=175&q=201904

60 Source: Age Standardization of Death Rates: Implementation of the Year 2000 Standard, Volume 47, Number 3, October 7, 1998, National Vital Statistics Reports, NCHS, CDC; http://www.cdc.gov/nchs/data/nvsr/nvsr47/nvs47_03.pdf

61 National Center for Health Statistics, Centers for Disease Control; Oklahoma State Department of Health

62 Source: Texas Department of State Health Services; http://www.tdh.state.tx.us/chs/vstat/latest/ageadj.htm