Designation of Medically Underserved Populations and Health Professional Shortage Areas, 11232-11281 [E8-3643]

Agencies

• DEPARTMENT OF HEALTH AND HUMAN SERVICES

[Federal Register Volume 73, Number 41 (Friday, February 29, 2008)]
[Proposed Rules]
[Pages 11232-11281]
From the Federal Register Online via the Government Printing Office [www.gpo.gov]
[FR Doc No: E8-3643]



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Part III





Department of Health and Human Services





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42 CFR Parts 5 and 51c



 Designation of Medically Underserved Populations and Health 
Professional Shortage Areas; Proposed Rule

Federal Register / Vol. 73, No. 41 / Friday, February 29, 2008 / 
Proposed Rules

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DEPARTMENT OF HEALTH AND HUMAN SERVICES

42 CFR Part 5 and 51c

RIN 0906-AA44


Designation of Medically Underserved Populations and Health 
Professional Shortage Areas

AGENCY: Department of Health and Human Services (DHHS).

ACTION: Notice of proposed rulemaking.

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SUMMARY: This proposed rule would revise and consolidate the criteria 
and processes for designating medically underserved populations (MUPs) 
and health professional shortage areas (HPSAs), designations that are 
used in a wide variety of Federal government programs. These revisions 
are intended to improve the way underserved areas and populations are 
designated, by incorporating up-to-date measures of health status and 
access barriers, eliminating inconsistencies and duplication of effort 
between the two existing processes. These revisions are intended to 
reduce the effort and data burden on States and communities by 
simplifying and automating the designation process as much as possible 
while maximizing the use of technology. No changes are proposed at this 
time with respect to the criteria for designating dental and mental 
health HPSAs. Podiatric, vision care, pharmacy, and veterinary care 
HPSAs, which are no longer in use, would be abolished under the rules 
proposed below.
    Additional background information will be available for review on 
the web site of the Health Resources and Services Administration: 
https://bhpr.hrsa.gov/shortage. 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).

DATES: Comments on this proposed rule are invited. In particular, 
comments are invited regarding the indicators of need and the weighted 
values of the health care practitioners used in the methodology. To be 
considered, comments must be submitted on or before April 29, 2008.

ADDRESSES: You may submit comments in one of four ways (no duplicates, 
please):
    1. Electronically. You may submit electronic comments on specific 
issues in this regulation to https://www.regulations.gov. Click on the 
link ``Submit electronic comments on HRSA regulations with an open 
comment period.'' (Attachments should be in Microsoft Word, 
WordPerfect, or Excel; however, we prefer Microsoft Word.)
    2. By regular mail. You may mail written comments (one original and 
two copies) to the following address only: Health Resources and Service 
Administration, Department of Health and Human Services, Attention: Ms. 
Andy Jordan, 8C-26 Parklawn Building, 5600 Fishers Lane, Rockville, MD 
20857.
    Please allow sufficient time for mailed comments to be received 
before the close of the comment period.
    3. By express or overnight mail. You may send written comments (one 
original and two copies) to the following address only: Health 
Resources and Service Administration, Department of Health and Human 
Services, Attention: Ms. Andy Jordan, 8C-26 Parklawn Building, 5600 
Fishers Lane, Rockville, MD 20857.
    4. By hand or courier. If you prefer, you may deliver (by hand or 
courier) your written comments (one original and two copies) before the 
close of the comment period to one of the following addresses. If you 
intend to deliver your comments to the Rockville address, please call 
telephone number (301) 594-0816 in advance to schedule your arrival 
with one of our staff members: Room 445-G, Hubert H. Humphrey Building, 
200 Independence Avenue, SW., Washington, DC 20201; or 8C-26 Parklawn 
Building, 5600 Fishers Lane, Rockville, MD 20857. (Because access to 
the interior of the HHH Building is not readily available to persons 
without Federal Government identification, commenters are encouraged to 
leave their comments in the HRSA drop slots located in the main lobby 
of the building. A stamp-in clock is available for persons wishing to 
retain a proof of filing by stamping in and retaining an extra copy of 
the comments being filed.).
    Comments mailed to the addresses indicated as appropriate for hand 
or courier delivery may be delayed and received after the comment 
period.
    Submission of comments on paperwork requirements. You may submit 
comments on this document's paperwork requirements by mailing your 
comments to the addresses provided at the end of the ``Collection of 
Information Requirements'' section in this document.

FOR FURTHER INFORMATION CONTACT: Andy Jordan, 301-594-0197.

SUPPLEMENTARY INFORMATION: The Secretary of Health and Human Services 
proposes below a consolidated, revised process for designation of 
Medically Underserved Populations (MUPs) pursuant to section 330(b)(3) 
of the Public Health Service Act (as amended by the Health Centers 
Consolidation Act of 1996, Public Law 104-299), 42 U.S.C. 254b, and for 
designation of Health Professional Shortage Areas (HPSAs) pursuant to 
section 332 of the Act (as amended by the Health Care Safety Net 
Amendments of 2002, Pub. L.107-251), 42 U.S.C. 254e. Currently, 
regulations at 42 CFR Part 5 govern the procedures and criteria for 
designation of HPSAs, while designation of MUPs has been carried out 
under the Grants for Community Health Services regulations at 42 CFR 
Part 51c.102(e), and implementing Federal Register notices.

Table of Contents

I. Background
    A. Explanation of Provisions
    B. Current Uses of Designations
II. Revising the methodology and designation mechanisms
    A. Relevant Statutes
    B. Purpose of revising the methodology and designation process
III. Development of Methodology to Achieve Goals
    A. 1998 NPRM and summary of comments received
    B. Development of method proposed in this NPRM
IV. Description of Conceptual Framework and Methodology and 
Alternatives Considered
    A. Conceptual Framework
    B. Methodology
    C. Example Calculations
    D. Alternative Approaches Considered
V. Description of Proposed Regulations
    A. Procedures (Subpart A)
    B. General Criteria for Designation of Geographic Areas as MUAs/
Primary Care HPSAs
    C. Rational Service Areas
    D. Applying the Designation Methodology
    E. Data definitions.
    F. Population and clinician counts.
    G. Non-physician primary care clinicians
    H. Contiguous Area Considerations.
    I. Population group designations
    J. ``Facility Designation Method'': Designation of facility 
primary care HPSAs
    K. Dental and mental health HPSAs
    L. Podiatry, vision care, pharmacy and veterinary care HPSAs
    M. Technical and conforming amendments
VI. Impact Analysis
    A. Impact on Number of HPSA Designations
    B. Impact on Number of MUA/P Designations
    C. Impact on number of unduplicated HPSA/MUP designations
    D. Impact on Population of all Designated HPSAs and/or MUPs
    E. Impact on Number of CHCs Covered by Designations

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    F. Impact on Number of NHSC Sites Covered by Designations
    G. Impact on Number of RHCs Covered by Designations
    H. Impact on Distribution of Designations by Metropolitan/Non-
Metropolitan and Frontier Status
    I. Impact on Distribution of Population of Underserved Area and 
Underserved Populations by Metropolitan/Non-Metropolitan and 
Frontier Status
    J. Impact of Practitioner ``Back-outs'' on Number of 
Designations and Safety-Net Providers
VII. Economic Impact
VIII. Information Collection Requirements under Paperwork Reduction 
Act of 1995
IX. Appendix A: References
X. Appendix B: A Proposal for a Method to Designate Communities as 
Underserved: Technical Report on the Derivation of Weights

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

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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.

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    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

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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

A. 1998 NPRM and Summary of Comments Received

    Following consultation with two panels of experts and in-house 
impact testing, an NPRM to revise the designation methodology was 
published on September 1, 1998. Those proposed rules (referred to 
hereinafter as ``NPRM1'') would have created one process for 
simultaneous designation of MUPs and HPSAs; set forth revised criteria 
for designation of MUPs using a new Index of Primary Care Services 
(IPCS); and defined HPSAs as a subset of the MUPs, consisting of those 
MUPs with a population-to-practitioner ratio exceeding a certain level. 
The use of RSAs would have been required for application of both the 
MUP and HPSA criteria.
    The IPCS score would have been calculated based on a weighted 
combination of seven variables: Population-to-primary care clinician 
ratio, percent population below 200% poverty, percent population racial 
minorities, percent population Hispanic, percent population 
linguistically isolated, infant mortality rate or percent low 
birthweight births, and low population density. The maximum possible 
IPCS score would have been 100, and RSAs whose IPCS score equaled or 
exceeded 35 would qualify for MUP designation.
    In counts of primary care clinicians, nurse practitioners (NP), 
physician assistants (PA), and certified nurse midwives (CNM) would 
have been included with a weight of 0.5 full time equivalents (FTE) 
relative to primary care physicians. There would have been two tiers of 
designations, with the first tier consisting of those areas which meet 
the criteria when all primary care clinicians practicing in the area 
are counted, and the second tier consisting of those additional areas 
which meet the criteria when certain categories of practitioners (NHSC 
assignees and those practicing in CHCs) are excluded from clinician 
counts.
    HPSA designation would have required a minimum population-to-
primary care physician ratio of 3,000:1, but this threshold could only 
be applied to those RSAs found to have an IPCS score which exceeded the 
MUP designation threshold of 35.
    The period for public comment on the 1998 proposed rule was 
extended to January 4, 1999. Over 800 comments were received, analyzed, 
and categorized. Major issues raised are summarized briefly below:
    1. Impact in Terms of Designations Lost--Many commenters estimated 
that unacceptably high numbers of HPSA designations would be lost in 
their State if the proposed methodology were adopted, particularly in 
rural and frontier areas, as well as significant numbers of MUPs. They 
believed that the impact stated in NPRM1's preamble, in terms of 
percentages of designations lost, was substantially underestimated.
    2. Inclusion of nonphysician primary care providers--A number of 
commenters objected to the inclusion of NPs/PAs/CNMs in primary care 
clinician counts, based on the additional burden on applicants of 
counting them, and cited the lack of adequate State or national 
databases for these clinicians. Others questioned the reasonableness of 
weighting them at 0.5 FTE relative to a primary care physician. 
Typically, responding NPs, PAs, CNMs, professional organizations 
representing them, and certain other health care advocates felt the 0.5 
should be adjusted upward; others felt it should be adjusted downward, 
particularly in States where the scope of practice of these clinicians 
is limited. There were also concerns that NPs, PAs and CNMs who were 
not in clinical, primary care practice would be inadvertently counted 
if available data were used, and that truly underserved areas would 
lose designation as a result.
    3. Threshold for HPSA Designation--The proposed 3,000:1 population-
to-primary care clinician threshold ratio for HPSA designation was 
considered too high by many commenters, especially if NPs/PAs/CNMs were 
to be counted as well as primary care physicians.
    4. Urban/Rural Balance--Many of the indicators selected for 
inclusion in the new IPCS (such as race, Hispanic ethnicity, linguistic 
isolation, and low birthweight births), were viewed as tending to bias 
the new index toward designation of urban areas (as compared with 
indicators like percent elderly,

[[Page 11237]]

which had been included in the previously-used Index of Medical 
Underservice and was seen as favoring rural areas).
    5. HPSAs required to be a subset of MUPs--the proposed requirement 
that an area could receive HPSA designation only if it first qualified 
as an MUP (by having an IPCS score which exceeded the 35 threshold) was 
seen as threatening many legitimate currently-designated HPSAs (i.e., 
HPSAs with population-to-practitioner ratios higher than 3000:1 but 
whose poverty rates and scores on other IPCS variables were not high 
enough to achieve the IPCS threshold).
    6. Two-tiered Designations--The idea of two-tiered designations was 
generally supported, but an issue arose as to which federally-supported 
primary care clinicians should be excluded from counts in tier 2. Most 
agreed that NHSC assignees and physicians in CHCs should be excluded 
(as the proposed rule did). Many felt that those physicians on J-1 
waivers should also be excluded from tier 2 counts, and some suggested 
that primary clinicians in other safety-net settings (such as RHCs or 
State-funded health centers) should also be excluded.
    On June 3, 1999, notice was given in the Federal Register that 
further analysis would be conducted, to include a thorough, updated 
analysis of the impact of the proposed approach as published, as well 
as the testing of alternatives based on analysis of the comments 
received. The Notice indicated that these impact analyses would be 
applied to the most current obtainable national data for all counties 
and currently-defined subcounty MUPs and HPSAs, and that one or more 
outside organizations would verify the impact testing. A new NPRM would 
then be published for public comment.

B. Development of Method Proposed in This NPRM

    During the remainder of 1999, HRSA acquired components of the 
national databases necessary for impact testing, such as practice 
addresses for primary care physicians, PAs, NPs, and CNMs. An extensive 
data cleaning and provider site geocoding process ensued. 
Simultaneously, HRSA began working with researchers at HRSA-funded 
Rural Health Research Centers and Health Professions Workforce Centers 
to develop specifics of the plan for further analysis and testing. 
Ultimately, the Cecil G. Sheps Center of the University of North 
Carolina (UNC) was funded to undertake national testing of the 
previously-proposed methodology in NPRM1 and alternative methodologies, 
and to coordinate efforts by other research groups who would do State 
or regional testing.
    In January 2000, a group of sixteen State Primary Care Office (PCO) 
representatives volunteered to assist by providing recommendations for 
a revised approach to designation from their standpoint, as the ones 
primarily responsible for providing data to HRSA in support of 
designation requests and updates for their States. This led to a series 
of conference calls, a two-day meeting, and eventual preparation of 
draft recommendations for consideration by the appropriate federal 
officials. Meanwhile, researchers at the Sheps Center were considering 
alternative methodologies for simultaneous consideration of various 
indicators of shortage and underservice. The two groups met on several 
occasions to coordinate efforts; the methodology finally developed by 
Sheps researchers and used as the basis for these proposed rules was 
consistent with the recommendations of the group of PCOs.
    Over time, the following specific steps took place:
    (a) A comprehensive database for impact testing was established. 
This entailed: ``cleaning'' and geocoding the various physician 
databases acquired (from professional associations and from federal and 
State agencies approving J-1 visa waivers), and matching them with each 
other and with HRSA's NHSC database; similar activity for data acquired 
on non-physician primary care clinicians (NP/PA/CNM); adding geocoded 
location data for HHS-sponsored safety-net provider sites, including 
CHCs, NHSC sites and RHCs; and the inclusion of appropriate Census data 
(or vendor-supplied intercensal estimates for Census variables) as well 
as data on other health status and access-related variables.
    (b) The group of sixteen PCOs developed their recommended approach 
to a new designation methodology and provided their recommendations to 
HRSA staff. Their original recommendation was essentially to expand the 
number of high need indicators which could be used to adjust the 
population-to-practitioner ratio threshold for designation, to allow 
several different threshold levels depending on the number of high need 
indicators present, and then to compare the area's actual ratio with 
the adjusted threshold appropriate for that area.
    (c) HRSA staff worked with the UNC-Sheps Center team to develop a 
conceptual framework and a methodology responsive to concerns raised in 
public comments and in the PCO recommendations. In response to the 
criticism of the earlier 1998 proposal as using appropriate indicators 
but an arbitrary weighting scheme, this methodology was developed based 
on a general conceptual framework of access and underservice and 
statistical methods. The overall goal was to identify areas and 
communities in need of services to increase access, relative to other 
communities across the country.
    The conceptual framework and methodology will be described further 
in sections IV.A and IV.B. A more technical description is also 
provided in Appendix B. The way the method is applied to determine 
designation status is described in Sections IV.C and V. below. Finally, 
further details are available on HRSA's Web site (https://bhpr.hrsa.gov/
shortage) and 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).
    (d) The impact of the proposed method on the number and population 
of geographic and low income designations at national and state levels 
was explored and compared with alternatives using updated national data 
allied to: (a) The criteria currently in place; (b) the criteria 
proposed in the September 1, 1998 rule, and (c) the new methodology 
proposed in this rule. In addition, impact analyses with State data 
were performed by Regional Centers for Health Workforce Studies and/or 
PCOs in four States. This analysis, discussed in detail in Section VI 
below, indicated that this proposed method would not have severe 
adverse effects on most safety net providers, and would--at the 
transition from the old method to the new--maintain a similar total 
underserved population.
    (e) However, there remained concerns that some safety net 
facilities--despite serving populations clearly underserved, such as 
the uninsured--might be located in areas that did not meet geographic 
or population group criteria. Consequently, with the help of the group 
of 16 PCOs, a separate method was developed (hereafter referred to as 
the ``facility designation method'') for facility designation of those 
safety-net facilities which could demonstrate high levels of service to 
the uninsured and/or Medicaid-eligibles. This was tested using the 
Uniform Data System for community health centers and found to support 
designation of most Section 330-funded health centers.
    (f) The new methodology's concepts and impact analysis approaches 
have been discussed in a preliminary fashion

[[Page 11238]]

at various meetings of national and State organizations whose members 
are affected by shortage/underservice designations.

IV. Description of Conceptual Framework and Methodology and 
Alternatives Considered

A. Conceptual Framework

    In our model, as in health services research more widely, we 
consider utilization of services an outcome of the demand and supply 
forces within the healthcare system. The conceptual framework for the 
model is based on the idea that barriers to care reduce appropriate 
use, which is reflected in delayed and therefore higher subsequent use 
rates. We call this concept ``thwarted demand.'' For example, 
individuals with diabetes living in remote, rural areas may put off 
seeing their doctors regularly-not because they do not recognize the 
need for regular treatment-but because of the distances involved or 
other potential barriers. These barriers initially reduce utilization. 
When these individuals eventually do seek treatment, it is often 
because their condition worsened to the point where they could no 
longer defer treatment. As the severity of their condition worsens and 
their need for care increases, so too does their utilization of 
services, in terms of treatment volume and/or intensity. They may 
require hospitalization, for instance, or present at an emergency room.
    To estimate the dimensions of both the (a) delayed--and thus 
initially reduced utilization rate--as well as the (b) subsequent 
higher use rates, we created a methodology that centers around the 
level of care experienced by a ``well-served population'' in order to 
establish an initial standard against which an ``under-served 
population'' can be defined. In a ``well-served population,'' where 
there are no barriers to care, healthcare utilization will be an 
expression of healthcare demand (i.e., demand is not thwarted). The 
assumption was made that, for groups without significant barriers to 
care, primary care utilization rates would cluster around the most 
appropriate level of care and, in turn, that their demand for care will 
also reflect their need for care. In an ``under-served population,'' by 
contrast, demand will be initially thwarted and healthcare utilization 
will therefore understate true demand.
    Moreover, healthcare needs tend to be greater in areas with 
disadvantaged populations. The health inequalities literature has 
shown, for example, that conditions like diabetes and cancer are more 
prevalent among minorities. In turn, we can expect that areas with a 
high proportion of minorities will--on average--have greater healthcare 
needs than areas with a lower proportion of minorities. To the extent 
that healthcare needs tend to be greater in underserved populations, 
the level of healthcare utilization observed in underserved populations 
would understate true demand even further. Thus, the model adjusts for 
this increased need and thwarted demand.
    As stated earlier, however, thwarted demand potentially creates a 
paradox since low access often results in subsequent illness that may 
require a higher level of health care use, in terms of either treatment 
volume or intensity. The entry of the patient into a structured care 
system may also induce subsequently higher rates of use of primary care 
services incident to hospitalizations or due to raised familiarity with 
the system. This paradox is likely to affect overall use rates in low-
access areas in such a way as to increase use rates.
    We accepted that these positive and negative factors would be 
simultaneously operating and sought ways to estimate their individual 
effects in terms of both initially reduced and subsequently increased 
visits. The net, overall need for services can be reflected in a 
combination of visits precluded with visits induced.
[GRAPHIC] [TIFF OMITTED] TP29FE08.006

    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

[[Page 11239]]

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
----------------------------------------------------------------------------------------------------------------
                                                  Average primary care visits ( per year) by age group category
                      Age                      -----------------------------------------------------------------
                                                   0-4        5-17      18-44      45-64      65-74       75+
----------------------------------------------------------------------------------------------------------------
Male..........................................      5.164      2.499      2.867      4.410      6.052      8.056
Standard Error................................       .488       .401       .372       .386       .469       .533
Female........................................      4.046      2.256      5.007      5.480      6.710      8.160
Standard Error................................       .491       .403       .373       .389       .456     .533*
----------------------------------------------------------------------------------------------------------------
The above table is from MEPS, 1996. These data are applied to the actual area age-gender total to derive the
  barrier free total utilization for a population with these age and gender characteristics. The corresponding
  national mean utilization rate is 3.471. *Imputed.

    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
----------------------------------------------------------------------------------------------------------------
                                Ages 0-4        5-17          18-44         45-64         65-74      75 and over
----------------------------------------------------------------------------------------------------------------
Females:                      ............  ............  ............  ............  ............  ............
    Population..............        65           207           363           281           106           113
    Multiplier (from Table           4.046         2.256         5.007         5.48          6.71          8.16
     IV-1)..................
    Visits..................       262.99        466.992      1817.541      1539.88        711.26        922.08
Males:                        ............  ............  ............  ............  ............  ............
    Population..............        93           234           386           108           321            94
    Multiplier (from Table           5.164         2.499         2.867         4.41          6.052         8.056
     IV-1)..................
    Visits..................       480.252       584.766      1106.662       476.28       1942.692       757.264
Female visits...............      5720.743
Male visits.................      5347.916
        Total visits........     11068.659
----------------------------------------------------------------------------------------------------------------

    For Wichita, the calculations are:

Barrier-free use rate
    = 4.046 * (65) + 2.256 * (207) + 5.007 * (363) + 5.480 * (281) + 
6.710 * (1060) + 8.160 * (113) + 5.164 * (93) + 2.499 * (234) + 2.867 * 
(386) + 4.410 * (108) + 6.052 * (321) + 8.056 * (94)
    = 262.99 + 466.992 + 1817.541 + 1539.88 + 711.26 + 922.08 + 480.252 
+ 584.766 + 1106.662 + 476.28 +1942.692 + 757.264

[[Page 11240]]

    = 11068.659 visits.

    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
------------------------------------------------------------------------
                                               A                B
------------------------------------------------------------------------
                                                            Effective
              County name                Total pop 1999    barrier-free
                                                            population
------------------------------------------------------------------------
Wichita, KS...........................           2,436             2959
------------------------------------------------------------------------

    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
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                               A                   B                   C                   D                   E
                                                     ---------------------------------------------------------------------------------------------------
                     County name                                                                                                      Effective  barrier-
                                                           Total pop      Effective barrier-    Tot FTE primary    Actual population     free  pop/FTE
                                                                            free population          care         to FTE ratio (A/C)      ratio (B/C)
--------------------------------------------------------------------------------------------------------------------------------------------------------
Wichita, KS.........................................               2436                2959                 2.5                 974                1183
--------------------------------------------------------------------------------------------------------------------------------------------------------

    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