I. Background

An earlier version of proposed rules for a consolidated, revised MUP/HPSA designation methodology and implementation process was published on September 1, 1998 [63 FR 46538-55]. Those proposed rules generated nearly 800 public comments, principally concerning the perceived high impact in terms the safety-net programs which would have lost their existing designations if the rule were finalized. Comments were also received on several other important issues related to the methodology, types of primary care clinicians included, and data collection burden. On June 3, 1999, a Federal Register document was published [64 FR 29831] which extended the comment period based on the large volume of comments received and the level of concern expressed. In light of the volume of comments, it was determined that the impact of the proposal as published would be more carefully tested, possible revisions and alternative approaches developed as necessary, and a new notice of proposed rulemaking (NPRM) would be published.

A. Explanation of Provisions

This proposed rule describes a revised methodology which combines indicators of diminished access to health care services, shortages of health professionals, and reduced health status. Developed by a research team at the University of North Carolina's Cecil G. Sheps Center in consultation with staff from the Health Resources and Services Administration (HRSA) and a group of State partners in the designation process, this approach was also tested with a comprehensive impact analysis (see section VI).

This proposed rule will replace the existing Part 5 with regulations governing both MUP and HPSA designations, and will make conforming changes to Part 51c. Together, these changes meet the legislative requirements for both MUP designation and HPSA designation, while consolidating the two processes to the greatest extent possible given the differences in the two authorities. This combined metric, which we propose to call ``the Index of Primary Care Underservice,'' will replace the existing MUP and HPSA criteria and procedures, while maintaining the two separate designations in order to meet the legislative requirements of the relevant statutes. Note that the abbreviation MUP used here includes not only population group designations but also the populations of designated geographic areas, also known as medically underserved areas or MUAs. Similarly, the abbreviation HPSA includes not only geographic area designations, but also population group and facility designations. Pursuant to Section 302(b) of the Health Care Safety Net Amendments of 2002, a copy of this NPRM will be submitted to the Committee on Energy and Commerce of the House of Representatives and to the Committee on Health, Education, Labor and Pensions of the Senate upon or before the date of its publication, in fulfillment of the statutory requirement for a report to those committees describing any regulation that revises the definition of a health professional shortage area. HRSA has also asked a panel of outside experts to review the proposed methodology and provide an assessment of its appropriateness, validity, and general approach.
These regulations will not be finalized until the public comment period referenced above is over, and any comments received during that time from the public, the panel of outside experts, and from the referenced House and Senate Committees have been taken into consideration. Moreover, this rule will not be finalized until 180 days after delivery of the report to the Congressional committees identified above, in accordance with statute.

B. Current Uses of Designations

The MUP and HPSA designations are currently used in a number of Departmental programs. The major use of MUP designations is as a basis for eligibility for grant funding of health centers under sections 330(c) and (e) of the Act, which require that these health centers serve medically underserved populations. The major use of HPSA designations is by the National Health Service Corps (NHSC); health professionals placed through the NHSC can be assigned only to designated HPSAs.

Other health centers not funded by section 330 grants but otherwise meeting the definition of a health center in section 330(a)--including those which provide services to a MUP--may be certified by the Centers for Medicare and Medicaid Services (CMS) upon recommendation by HRSA as federally qualified health center (FQHC) look-alikes. FQHC look-alikes, like all health centers funded under Section 330, are eligible for special Medicare and Medicaid reimbursement methods. Clinics in rural areas designated either as an MUA or as a geographic or population group HPSA, and whose staff include nurse practitioners and/or physician assistants, may be certified by CMS as Rural Health Clinics (RHCs). These RHCs are also eligible for special methods for determining Medicaid and Medicare reimbursement. Physicians delivering services in an area designated as a geographic HPSA are eligible for the Medicare Incentive Payments (MIP) of an additional 10 percent above the Medicare reimbursement they would otherwise receive. The Medicare Modernization Act of 2003 included beneficial changes to this incentive program. Payments to providers are now automated based on the zip codes of the providers, and the information on eligibility is now available on the CMS Web site. The MIP, also known as the HPSA Bonus Payment, is distinct from the Physician Scarcity Area Program, which does not use HRSA designations in determining eligibility.

Interested Federal Government Agencies and State Health Departments can also recommend waiver of the return-home requirements for an International Medical Graduate physician who came to the United States on a J-1 visa, in return for three years of service by that physician in a particular HPSA or MUA.

In addition, a number of health professions programs funded under Title VII of the Public Health Service Act give preference to applicants with a high rate of training health professionals in medically underserved communities and/or for placing graduates in medically underserved communities, defined (in Section 799B of the Act) to include both HPSAs and MUPs.

For most of the programs that use these designations, designation of the area or population to be served is a necessary but not sufficient condition for allocation of program resources, in that other eligibility requirements must also be met and/or there is competition among eligible applicants for available resources.

II. Revising the Methodology and Designation Mechanisms

A. Relevant Statutes

Authorizing Statutes
The current HPSA criteria date back to 1978, when they were issued under Section 332 of the Public Heath Service (PHS) Act, as amended in 1976; their predecessor, the ``Critical Health Manpower Shortage Area'' or CHMSA criteria, dates back to the 1971 legislation creating the NHSC. Section 332(b) of the Public Health Service Act states that the Secretary shall take into consideration the following when establishing criteria for the designation of areas, groups, or facilities as HPSAs: (1) The ratio of available health manpower to the number of individuals in an area or population group, and (2) Indicators of a need for health services, notwithstanding the supply of health manpower.

The current MUA/P criteria date back to 1975, when they were issued to implement legislation enacted in 1973 and 1974 creating grants for Health Maintenance Organizations (HMOs) and Community Health Centers (CHCs), respectively. Section 330(b)(3) of the Public Health Service Act defines ``medically underserved population'' as the population of an urban or rural area designated by the Secretary of Health and Human Services as an area with a shortage of personal health services, or a population group designated by the Secretary as having a shortage of such services. No specific criteria were included in the statute. Health Care Safety Net Amendments of 2002.

The Health Care Safety Net Amendments of 2002, Public Law 107-251, as amended by Public Law 108-163, included modification of Section 332 to require the ``automatic'' designation as HPSAs of all FQHCs and RHCs meeting the requirements of Section 334 (concerning the provision of services without regard to ability-to-pay) for at least six years. After six years, such entities must demonstrate that they meet the designation criteria for HPSAs, as then in force. This legislative provision appears to have had two major goals:

1. To avoid requiring FQHCs or RHCs from going through two separate designation processes. Given that most FQHCs must demonstrate service to an MUP in order to be funded (or to be certified as an FQHC look- alike), it was deemed unnecessary to also require these entities to obtain a HPSA designation in order to apply for placement of NHSC clinicians. Similarly, every RHC must obtain one of several types of designation in order to achieve RHC status (either a HPSA, MUA, or Governor Designated and Secretary Certified Shortage Area designation); arguably, those for whom this was not a HPSA designation should not be required to obtain a second type of designation to apply for NHSC. (It is worth noting that this goal will be met once the regulations herein are in force, since areas and population groups designated or updated under the criteria herein would be both HPSAs and MUPs, eligible for the FQHC, RHC and NHSC programs).

2. To allow a long transition period for phasing in the new designation criteria as they might affect existing projects. Existing FQHCs and RHCs will have plenty of time to show that the areas where they are located, the populations they serve, or the facilities involved in fact meet the new criteria, so that their services will not be disrupted due to the criteria change. Although an extensive impact analysis of the proposed new criteria has been conducted to demonstrate that such disruption is unlikely in all but a few cases, this legislatively required smooth transition should ease concerns about the changes and allow plenty of time to adapt to the new designation criteria.

B. Purpose of Revising the Methodology and Designation Process

As previously stated, the current HPSA and MUA/P criteria date back to the 1970s. The original CHMSA criteria required that a simple population-to-primary care physician ratio threshold be exceeded to demonstrate shortage. The HPSA criteria went further and allowed a lower threshold ratio for areas with high needs as indicated by high poverty, infant mortality or fertility rates, and for population groups with access barriers. The original MUA/P criteria, still in effect, employ a four-variable Index of Medical Underservice, including percent of the population with incomes below poverty, population-to-primary care physician ratio, infant mortality rate and percent elderly. Since the time these designation criteria were first developed, there has been an evolution both in the types of requests for designation received and the application of the HPSA criteria. Instead of relatively simple geographic area requests, such as whole counties and rural subcounty areas, more requests have been made for urban neighborhood and population group designations. The availability of census data on poverty, race, and ethnicity at the census tract level has enabled the delineation of urban service areas based on their economic and race/ethnicity characteristics. Areas with concentrations of poor, minority and/or linguistically isolated populations have achieved area or population group HPSA designations based on their limited access to physicians serving other parts of their metropolitan areas. As a result, the differences between HPSA and MUA/P designations have become less distinct.

The methodology for identifying underserved areas, as well as the process by which interested State and community parties can obtain designation as underserved areas, are being revised to accomplish several goals and alleviate problems associated with the existing methods of designation.

In revising the underlying methodology for identifying underserved areas, our goals were to create a new system that:
(a) Is simple to understand for those who seek designation;
(b) is intuitive and has face validity;
(c) incorporates better measures or correlates of health status and access;
(d) is based on scientifically recognized methods and is replicable;
(e) minimize unnecessary disruption; and
(f) constitutes an improvement over current methods in fairly and consistently identifying places and people who are in need of primary health care and who encounter barriers to meeting those needs.

In revising the designation process, our goals were to:
(a) Consolidate the two existing procedures, sets of criteria, and lists of designations;
(b) make the system more proactive and better able to identify new, currently undesignated areas of need and areas no longer in need;
(c) automate the scoring process as much as possible, making maximum use of national data and reducing the effort at State and
community levels associated with information gathering for designation and updating;
(d) expand the State role in the designation process, with special attention to the State role in definition of rational service areas;
(e) reduce the need for time-consuming population group designations, by specifically including indicators representing access
barriers experienced by these groups in the criteria applied to area data.


These goals are explained more fully below. We believe the proposed methodology and designation process address all of these goals and therefore offers a significant improvement in the identification of communities experiencing limited access to primary care services. In turn, we believe these revisions will assist the Department in targeting key resources more effectively to areas of greater relative need for assistance.

1. Methodological Goals
Simplicity
The new underservice measure must be understandable and usable by those who seek designation. In this vein, we decided the new methodology should continue to use the population-to-provider ratio as the fundamental metric of underservice because such ratios are well- recognized and understood by the program participants and would provide some continuity between a new proposal and the older methods that included the ratios very prominently in the calculations. Discussions with the federal agencies and stakeholder groups during the development of the revised approach also revealed a preference for using that metric as the basis for a revised method.

Face Validity
The new underservice measure must be intuitive and have face validity. For example, factors that reflect progressively worse access should result in proportionately increasing scores. Incorporate Better Measures or Correlates of Health Status and Access While both designation statutes speak of the inclusion of health status indicators, the only specific measure of health status historically mentioned in either statute or included in the existing designation criteria is infant mortality rate. Low birthweight rate is a more robust indicator of health status because there are more events per unit population. Because both infant mortality and low birthweight rate are nationally available for all counties and for a limited number of sub-county areas (generally, for places of population 10,000 or more), these measures were incorporated in the proposed methodology. In addition, a new measure of actual/ expected death rate (standardized mortality ratio) is incorporated. As described in more detail in section IV, this methodology further incorporates other correlates of health status and access, such as ethnic minority status and unemployment, based on ready national availability of data and the health inequalities literature.

Science-Based
The new underservice measure must be based on scientifically recognized methods and be replicable. For example, the current Index of Medical Underservice comprises four variables, each of which contributes approximately a quarter to the maximum score. In other words, each of the four variables are weighted equally. However, there is no empirical justification for why the income variable should have a weight equal to the infant mortality rate variable. Rather, in designing the new methodology, we believed the contribution of each variable to an overall measure should be based on some verifiable statistical relationship. As discussed further in section IV, the new methodology used an overall conceptual framework to describe access and used analytical techniques such as regression and factor analysis to arrive at the weighting/scoring system proposed herein.

Minimize Unnecessary Disruption
Partly due to the Health Care Safety net Amendments of 2002, as described earlier, we have attempted to achieve a reasonable transition to this new methodology for underserved areas. Though the revised designation method will not (and should not) generate the exact same designations as the previous method, we have attempted to minimize unnecessary disruption where applicable. The new measure will allow us to better focus the designations to more needy areas and populations.

Acceptable Performance
The new system must perform better than the current designation criteria using updated data, and it should be seen as an improvement by the multiple key stakeholder groups who rely on these designations. We used many different evaluating criteria for this guiding principle, but the fundamental criterion we used is whether the method fairly and consistently identifies places and people who were in need of primary health care and who had barriers to meeting those needs.

2. Designation Process Goals
Consolidation and Simplification
The separate statutes authorizing MUP and HPSA designations address the same fundamental policy concern: That is, the identification of those areas and populations with unmet health care needs for the purpose of determining eligibility for certain Federal health care resources. The existence of two similar but quite distinct procedures and sets of criteria has been confusing to many and has often led to contradictory or inconsistent results.

The legislative requirements for the two designations are similar in many respects, but the designation processes have, until now, been largely separate. A major reason for the disparity in the designation process is that regular updating of HPSAs is required by statute, though such updating is not statutorily required for the MUA/Ps and has not regularly been done.

The rules proposed below attempt to establish uniform procedures and criteria, not only to simplify the designation process for the agencies, communities, entities, and individuals involved, but also to increase the efficient and effective use of Departmental resources. To do so, all the legislatively mandated elements of both statutes are included in the proposed procedures. The revised criteria for geographic HPSAs and MUAs are identical, as are those for most types of MUPs and corresponding population group HPSAs, wherever permitted by statutory requirements. Since facility designations are only authorized for HPSAs, this is one domain for which the two could not be the same.

Proactivity
The proposed methodology can be applied using national data obtained by HRSA and made available to State partners in the designation process, thereby enabling more universal application of the designation criteria. Applicant familiarity with the designation process should also become less of a factor in obtaining designation, and the need for independent data collection by applicants will be less of a barrier and burden.

The national databases include updated versions of the data used in the development of this methodology: Provider data from appropriate professional associations, such as the American Medical Association (AMA) physician data; socio-demographic data from the U.S. Census Bureau or a vendor which produces intercensal estimates; unemployment data from the Department of Labor; and health status data from the National Center for Health Statistics. At the same time, States and communities will continue to have the opportunity to substitute State and local data for the national data if the State and local data are more reliable and/or more current. Data from recognized sources such as State Data Centers, economic forecasting agencies such as J.D. Powers, and similar entities, and that are used for other state purposes may be submitted. Provider data may be secured from a variety of sources: State licensing boards, state or local professional societies, professional directories, etc. Data sources, methodologies, and dates must be specified.

Automation
The proposed methodology will enable a more automated process for designation, through the use of a tabular method for scoring areas and updating these scores. The new method makes considerable use of census variables for which data are available not only at the county level but also at subcounty levels (e.g., for census tracts and census divisions), so that a wide variety of State- and community-defined service areas can be evaluated for possible designation. Also, an interactive system for processing designation requests and updates will permit State partners in the designation process to work together with the federal designation staff using the same databases. The intent is to minimize the effort required by States, communities, and other entities to designate an area or update its designation.

Increased State Role
The proposed approach seeks to foster an increased partnership between the various levels of government involved in designation, including a significantly larger State and local role in defining service areas, underserved population groups and unusual local conditions. The new criteria are less prescriptive in terms of travel time and mileage standards for defining service areas. Each State will be encouraged to define, with community input and in collaboration with the Secretary, a complete set of rational service areas (RSA) covering its territory. Once developed, these service areas will be used in underservice/shortage area designations unless and until new census data or health system changes require further area boundary changes. Currently the agency allows States to provide their own provider data through a new interactive system. States with more reliable data can substitute them for national data, which will reduce the time required for case-by-case review.

Reduce the Need for Population Group Designations
Designation of population groups is typically more resource- intensive than designation of geographic areas, both from the standpoint of data collection (since obtaining data for a particular population is often more difficult than for the area as a whole) and in terms of review. As discussed below, specific indicators included in the proposed approach represent the access barriers of poverty/low income, unemployment, racial minority or Hispanic ethnicity, population density and population over 65 years. This approach specifically adjusts an area's base population-to-primary care clinician ratio for the effects of these variables. Therefore, it is hoped that this method will reduce the need for specific population group designations by increasing the probability of designation of geographic areas with concentrations of these groups.

III. Development of Methodology To Achieve Goals

By adjusting for these bi-directional effects of thwarted demand, this methodology effectively allows us to ask, ``What level of care would these individuals utilize if they were well-served and barrier free?'' This adjusted utilization rate becomes the proxy in our revised model for the ``effective need'' in an underserved population. For example, an underserved area that contains 100 people may nevertheless ``effectively need'' the same level of services an area of 1,000 people needs. In this underserved area, the ``actual'' population may be 100 but the ``effective'' population can be thought of as 1,000.

We then compare this ``effective need'' in an underserved population to the available supply of primary care providers in that area to create a population-to-provider ratio. The underlying logic is that meeting community needs could be expressed in ratios of appropriate use to optimal service productivity. The use rate would be expressed in population counts and the service productivity in practitioner counts. The goal was to reflect the level of a population's need for office-based primary care visits in terms of an adjusted population count that took into consideration characteristics that would affect use of services.

We considered various other proxies for need besides the population-to-provider ratio. We ultimately decided to use an adjusted population-to-provider ratio for several reasons. First, the prominence of population-to-practitioner ratios in the two existing measurements of underservice was recognized. Discussions with the federal agencies and stakeholder groups during the development of the revised approach also revealed a preference for using that metric as the basis for a revised method. Furthermore, practical reasons for the use of this ratio as a starting point for the construction of an index included the fact that such ratios are well-recognized and understood by the program participants and would provide some continuity between a new proposal and the older methods that included the ratios in the calculations. Such a metric is also sensitive to the two different sources of unmet need--provider shortages and barriers to care--that programs which rely on the HPSA and MUA/P designations attempt to address. In HPSAs, by definition, access is restricted because there are few or no primary care health professionals who will take care of certain patients. The remedy for this is to supplement the professional supply with practitioners who will see all patients, in order to bring the numbers of professionals more into line with a level of supply generally considered adequate. For MUA/Ps, the primary reasons for designation relate to barriers to accessing existing primary care services (e.g., financial) or the combination of higher needs and lower availability. The central task in combining these two systems was to find a common metric that was sensitive to both of these characteristics of underservice, which the adjusted population-to- provider ratio is.

B. Methodology

The model can be thought of as compromising six basic steps. Step 1: Calculate the numerator for the population-to-provider ratio: The ``effective barrier free population.'' The first step is to estimate the effects that differences in the structure of the population would have on service utilization based on age and gender by assigning weights according to the national use rates for people without barriers to care. Accordingly, we call this the ``effective barrier free population'' because it allows us to estimate what the utilization rate would be, after adjusting for age and gender, if the population of a community were able to use primary care services at the same rate as a population with no constraints due to factors like poverty, race, or ethnicity. This step is necessary because research shows that age and gender affect utilization rates independent of barriers to care. The elderly, for example, use services at higher rates than the non-elderly even when barriers to care are controlled for.

To calculate the ``effective barrier free population,'' we adjust the area's base population to reflect differential requirements by age and gender for primary care services, using utilization rates for populations who are effectively ``barrier-free.'' This adjustment uses the latest available Medical Expenditure Panel Survey (MEPS) utilization data to determine what the expected number of primary care office visits for the area's population would be (based on its age/ gender make-up) if usage were at the national average for persons who are non-minority, not poor, and employed. This total expected number of primary care visits is then divided by the corresponding current national mean number of primary care visits per person to obtain the ``effective barrier free population.'' The effect of this adjustment is that a community with more older people or more women of child-bearing age than the average national age-gender distribution will appear to be a larger population than if the age-gender mix were like the nation's as a whole.

The utilization rates used in developing and testing the methodology proposed herein are shown in Table IV-1. These will be updated when this regulation is finalized and periodically thereafter by notice in the Federal Register that updated data will be posted on the HRSA Web site.

Table IV-1.--Barrier Free Population Use Rate, Adjusted for Age and Gender, Expressed as Primary Care Visits Per Person Per Year

The calculations for Wichita County, Kansas are shown as an illustration of how this step of the model works. The chart below provides the population breakout by age and gender, the visit rates for each category, and the adjusted population that results from dividing by the average visit rate. The steps are detailed below the chart.
The basic formula is: Barrier-free use rate = 4.046 * ( # of females aged 0-4) + 2.256 * ( # of females aged 5-17) +5.007* ( # of females aged 18-44) + 5.480 * ( # of females aged 45-64) + 6.710 * ( # of females aged 65-74) + 8.160 * ( # of females aged 75+) + 5.164 * ( # of males aged 0-4) + 2.499 * ( of males aged 5-17) + 2.867 * ( # of males aged 18-44) + 4.410 * ( # of males aged 45-64) + 6.052 * ( # of males aged 65- 74) + 8.056 * ( # of males aged 75+)

Table IV-1A.--Applying Table IV-1 Using Wichita, Kansas as an Example

Using 1996 MEPS data, individuals who were barrier free had, on average, 3.741 visits to their primary care providers. If we then divide the barrier-free use rate by this average number of visits, we can obtain the ``effective barrier-free population'' estimate. In Wichita, the calculation would be: Effective barrier-free population = 11068.659 / 3.741 = 2958.74338. This ``effective barrier-free population'' becomes the numerator-- the ``population'' value--in the population-to-provider ratio. For example, the actual population of Wichita, Kansas was 2,436. By going through these calculations, however, we see in Table IV-2 that the effective barrier-free population is 2,959.

Table IV-2

Step 2: Calculate the denominator in the population-to-provider ratio: The supply of primary care providers. The second step is to calculate the actual number of FTE primary care clinicians in the target area, including primary care physicians (allopathic and osteopathic), NPs, PAs, and CNMs in primary care settings. Each active physician in the primary care specialties (i.e., General Practice, Family Practice, General Internal Medicine, General Pediatrics, Ob/Gyn) is included as 1.0 FTE unless there is evidence of less than full-time practice, in which case their actual FTE in the area is used based on guidance set by the Secretary on the calculation of FTEs. As before, physicians in residency training in these specialties are counted as 0.1 FTE. In this proposed rule, NP/PA/CNMs are also included, but they are counted either as 0.5 FTE or, at the applicant's option, 0.8 times a State-specific practice scope factor running from 0.5 to 1.0 (in recognition that not all NP/PA/CNM practices operate at the same level due to state policies). We discuss this issue in further detail in section V.G below. Data sources are: American Medical Association Masterfile-Dec. 1998, American Osteopathic Association-May 1999, American College of Nurse Midwives-1999, American Association of Nurse Practitioners-1999, and American Association of Physician Assistants-July 1999. For example, there are 2.5 FTE primary care providers in Wichita, Kansas, according to our national data. Step 3: Calculate the base population-to-provider ratio. The population-to-provider ratio is then calculated using the ``effective barrier-free population'' (from step 1) as the numerator and the number of FTE primary care clinicians (from step 2) as the denominator. Using Wichita, Kansas as an example, the base population- to-provider ratio is 1,183 (table IV-3, column E).

Table IV-3

Step 4: Adjust for increases in need for primary care services based on community characteristics. Because the programs that rely on HPSA and MUA/P designations aim to improve access and thereby improve health, this consideration drove the design of the analysis to develop weights for need for services in areas and for populations. The fourth step of this methodology thus computes the effects of community factors that have been demonstrated to indicate an even greater need for services but also a lower utilization of services than the average well-insured and healthy population due to barriers to care.

The general approach was to take population-level variables that correlate with barriers to care and then determine the relationship of those variables to the adjusted population-to-practitioner ratio described above, using regression analysis. From this analysis, the relative influence of those variables on the ratio would be derived and, from those parameters, scores could be estimated to adjust or ``weight'' the overall index.

Because step 4 can be quite technical, we present only an overview here. For a more detailed discussion of step 4 and its place in the overall methodology, please refer to Appendix B (please note that what we refer to in this rule as ``step 4'' is referred to as ``steps 4-5'' and ``step 7'' in Appendix B). The methodology is also described in a journal article recently published in the Journal of Health Care for the Poor and Underserved entitled ``Designating Places and Populations as Medically Underserved: A Proposal for a New Approach'' (Ricketts et al., 2007).

In developing step 4, we followed the conceptual framework of access proposed by Andersen and colleagues, who posit that there are predisposing and enabling characteristics that can represent need (Andersen et al., 1973; Andersen 1995; Aday and Andersen 1975). There is no consensus set of community-level indicators that reflect need within their framework. Because the programs that rely on HPSA and MUA/ P designations largely address unmet need by placing primary care practitioners in areas designated as underserved, we chose to use the effective barrier-free population-to-practitioner ratio (calculated in steps 1, 2, and 3) as a proxy indicator of relevant need for this step in the methodology.

We then ran regression analyses to examine how the ratio varied with socio-demographic indicators that research has shown to correlate with low access and/or poor health status (Mansfield et al., 1999; CDC, 2000; Krieger et al., 2003; Andersen and Newman 1973; Aday and Andersen 1975; Robert 1999; Robert and House, 2000; Kawachi and Berkman, 2003). We also included factors in the regression model that closely parallel the statutory elements of the current HPSA and MUA designation processes (health status, ability to pay for services and their accessibility), and also directly relate to the programs they initially were designed to support: the NHSC and the CHC Programs.

Three categories of high need indicators were ultimately used, for a total of nine indicators, as described in Table IV-4. These factors were used because they were shown by the regression to have independent effects on access to care as measured by the population-provider ratio.

Table IV-4.--Variables Used in Creating Proposed Method

* Population density is a measure of the market potential for an area as well as an indicator of the rural or urban character of a place. As places become more densely populated, they tend to attract employment and services. Density is also associated with rural and urban settings and the behavioral characteristics of populations vary along that continuum (Amato and Zuo, 1992). A number of other need indicators were considered in the development of the methodology. Table IV-5 provides a brief listing and an explanation why they were not chosen. In many cases, these elements are highly correlated with the ones listed above, so their impact on access is already captured by the variables that are included.

Table IV-5.--Variables Considered for Inclusion But Not Chosen

¹ Infant mortality remains a relatively rare phenomenon and published rates are often compiled from multi-year data. Comparing rates for small areas would compound the instability of those rates. The same problems are encountered with data that describe the character of prenatal care in small and rural areas, although these Indices are based on assessments of all births, the degree to which prenatal care meets standards of adequacy in smaller and less populated areas may vary from year to year due to isolated events or poor care for a limited number of newborns due to factors that do not reflect the character of the health care in the area (e.g. weather, relocation).

² These data are reported by the Census Bureau and are ``imputed'' from other variables (reported ethnicity and the likelihood of being a refugee or immigrant). The data are not collected directly.

³ The mortality rate varies widely according to the age structure of a place. A much higher proportion of elderly is often associated with a much higher mortality rate. Adjusting for the age structure allows for a better comparison of the mortality burden of the community relative to its risk. To calculate the adjustment factors or ``weights,'' the actual value of each high need indicator was converted to a percentile relative to the national county distribution, using a conversion table (see Table IV-6). For all variables except population density, the theoretically worst actual value corresponded to the 99th percentile (e.g., the higher the unemployment rate in an area, the higher the percentile.) In Wichita, Kansas for example, 3.59% of the population were unemployed.

Table IV-6 is used to translate this percentage into a percentile: In this case, Wichita falls in the 24th percentile.

Table IV-6.--High Need Indicators--Breakpoints for Conversion From Community Values to National Percentiles *

Data Sources: Census Estimates from Claritas 1998; Bureau of Labor Statistics 1998, National Center for Health Statistics 1998. The resulting percentile rankings for each of the high need indicators in the area are then converted to a score, using a second table (see Table IV-7), which expresses the results of the regression analysis in terms of partial scores or weights for each indicator. Using Table IV-7 and using Wichita as an example, we see that a percentile ranking of 24 for unemployment translates into a score of 32.21.

Table IV-7.--Scores for High Need Indicators, Given Their National Percentiles

This same conversion of percentages to percentiles to scores is then done for each of the nine high need indicators. An example is included in Table IV-8 to illustrate this step, again using Wichita as an example.

Table IV-8

* The infant mortality rate was not used for Wichita County since it was unstable (too few events-births and death in low population county). The alternative low birth weight rate was used.

Because the same metric (i.e. population-to-provider ratio) was used to calculate both the effective barrier-free population and the scores, the scores can simply be added to the effective barrier-free population-to- primary care provider ratio to derive the final adjusted population-to- primary care provider ratio. This adjusted ratio reflects the combination of the ``effective barrier free population'' (age-adjusted) and the effect of community needs and use factors. These ratios can then be used to reflect the relative need of the areas, with the highest ratios indicating the areas of greatest need. An example is included in Table IV-9, again using Wichita as an example and Burlington, New Jersey for comparison. Column G reflects the new measure of underservice proposed in these rules and is intended to resemble the current MUA/P method in that it creates a score or index of underservice.

Table IV-9

Even though there are far fewer people in Wichita than in Burlington and the actual population-to-provider ratios are roughly equivalent (column D), this methodology shows that the true need in Wichita (i.e., the level of care the Wichita population would demand if they did not have any barriers to care) is actually much greater than in Burlington (column G).

Though this underlying methodology is conceptually and computationally complex, one advantage of this new method is that the actual calculations involved have been automated through the use of the conversion tables. The new method is, therefore, relatively simple to implement by State and local applicants. The system has also been developed in a way that allows an applicant to enter their area- specific or population-specific data into an Internet-based query system and have their score returned in real time. This would allow applicants to compare their level of underservice with those of other designated and undesignated areas and populations in an accessible system. Moreover, the use of a tabular method for scoring allows for future changes in the scaling of the scores when there are changes in the distribution of values. It also allows HRSA to update these values without having to change the overall approach to developing scores.

Step 5: Comparing the final adjusted effective barrier-free population-to-provider ratio against a threshold of underservice.

The fifth step in this method involves comparing the final adjusted ratios for various areas against a threshold of underservice. A county or other RSA will be designated as undeserved if its final adjusted ratio equals or exceeds this threshold. The threshold level proposed is 3,000 persons for every FTE primary care clinician. A population of 3,000, distributed according to the national average age-sex distribution, is about twice the normal load for a busy primary care physician, which is approximately 1500:1. Accordingly, when the threshold level of 3000:1 is reached, an area is already one primary care clinician short for each primary care clinician it has. The impact analysis in Section VI below deals with the effect of this choice on the number and population of designated areas. While there is no one figure that is a universally accepted standard, the 3000:1 threshold is based on an adequacy ratio of 1500:1 as noted above and is similar to the target ratio used in a number of organizations and identified in a variety of studies:

• A study of the Canadian system and its process for measuring medical underservice, for example, identified 1500:1 or greater as a level of underservice appropriate for a recruitment incentive program (Goldsmith 2000).
• A Veterans Administration study recommended a target for a primary care panel between 1,000-1,400 patients (Perlin and Miller, 2003).
• According to the Bureau of Primary Health Care (unpublished data), Community Health Centers averaged 1,439 medical users per medical FTE in 1999, and this number is very consistent with the 1997 and 1998 figures. In addition, the NHSC reports an average of 1,527 patients per provider.
• A George Washington University (GWU) report on Standards for Managed Care related to the Balanced Budget Act of 1997 found that State Medicaid programs most frequently required that Medicaid HMOs have a panel size of 1500:1
• An article published in the Journal of the American Medical Association suggested benchmark ratios to compare relative supply that were slightly above and below 1500:1 (Goodman et al, 1996).
• Using data from the National Ambulatory Medical Care Survey (NAMCS), which estimates visits per person per year to physicians, the national mean ratio of primary care physicians per population of 1498:1, very close to 1500:1.
  
  The 3000:1 threshold is a very conservative estimate of the level of need and identifies the worst quartile of the areas analyzed, which is a similar standard to that used when the original thresholds were set in the existing designation methods. Moreover, this threshold is consistent with the level used for HPSA designation of high-need areas and population groups in the past.
  
  Step 6: Determining tiers of shortage. An important issue in the preparation of these regulations is whether federally-sponsored primary care providers who are present in currently-designated areas should be included in computations when updating the designations. On the one hand, including these providers in the provider count could result in ``yo-yo'' effects, in which an area is designated as underserved; a CHC or NHSC intervention occurs as a result of the designation; those practitioners are then counted, resulting in a loss of the designation; the intervention is removed; the area again becomes eligible for designation; and the cycle repeats itself. On the other hand, there are concerns about areas remaining on the list of designations whose needs have already been met through a federally supported program or provider. This has led to situations in which additional resources are allocated to an area where providers or clinics have previously been placed to help meet the needs of the area.
  
  To deal with both sides of this issue, we propose to publish a two- tiered list of designations. Each designated area or population group will be identified as having either a first or second tier of shortage. Tier 1 designations will be those areas which continue to exceed the threshold even when all federal resources placed in the area are counted. Tier 2 designations will be those areas exceed the threshold only when certain federal resources placed in those areas are excluded.
  
  Thus, one final set of calculations is undertaken to identify those ``Tier 2'' areas which fall below the threshold when certain federally- sponsored clinicians are counted but would exceed the threshold if they were withdrawn. The federally-sponsored clinicians considered here are NHSC affiliated clinicians, clinicians obligated under the State Loan Repayment Program (SLRP) (a loan repayment program involving joint Federal and State funding), physicians with J-1 visa return-home waivers, and other clinicians providing services at health centers funded under Section 330.
  
  When determining Tier 2 designations, these federally-sponsored clinicians are not counted in the denominator of the area's ratio. Finally, steps 3 and 4 are repeated to recalculate the final adjusted ratio using this lower clinician count and to compare it with the designation threshold. The areas exceeding the threshold when this procedure is followed are identified as ``Tier 2'' designations.
  
  Both types of designations would be eligible for federal programs authorized to place resources in MUPs or HPSAs. However, Tier 2 areas would typically be eligible only to maintain the approximate levels of federal resources already deployed, while Tier 1 areas could apply for additional resources.

C. Example Calculations

Table IV-10 shows calculations for actual population-to-provider ratios, the effective barrier-free population-to-provider ratios, the scores based on high need indicator percentiles for the area, and the resulting population to primary care clinician ratios.

Table IV-10.--Example of calculation of Adjusted Population-to-Primary Care Clinician Ratio