Proposed Rule
Federal Register: February 29, 2008 (Volume 73, Number 41)
Department of Health and Human Services
42 CFR Part 5 and 51c
RIN 0906-AA44
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. 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 (comment
period extended to May 29, 2008 on April 21, 2008 (comment period extended
to June 30 on June 2, 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 http://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 on this Page
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
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
[[Page 11234]]
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.
[[Page 11235]]
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
[[Page 11236]]
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 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 underserved, we chose to use the
effective barrier-free population-to-practitioner ratio (calculated in
steps 1,
[[Page 11241]]
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
------------------------------------------------------------------------
Demographic Economic Health status
------------------------------------------------------------------------
Percent Non-white Percent population Actual/expected
``NONWHITE'', (src: 1998 <200% FPL death rate (adj)
Claritas estimates). ``POVERTY'', (src: ``SMR'', (src:
1998 Claritas National Center for
estimates). Health Statistics,
1998: for previous
5 year period).
Percent Hispanic Unemployment rate Low birth weight
``HISPANIC'', (src: 1998 ``UNEMPLOYMENT'', rate ``LBW'', (src:
Claritas estimates). (src: Bureau of National Center for
Labor Statistics, Health Statistics,
1998). 1998: for previous
5 year period).
Percent population >65 years .................... Infant mortality
``ELDERLY'', (src: 1998 rate ``IMR'', (src:
Claritas estimates). National Center for
Health Statistics,
1998: for previous
5 year period).
------------------------------------------------------------------------
Population density ``DENSITY'' * (src: 1998
Claritas estimates)
------------------------------------------------------------------------
* 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
------------------------------------------------------------------------
Suggested variables Reason for rejection
------------------------------------------------------------------------
Percent low income elderly............. Used elderly and low income.
Percent children <6.................... Used component in adjusted pop.
Percent children low income............ Used overall low income.
Percent children <4.................... Used component in adjusted pop.
Dependency ratio (%>65+%<18/total Used combination of factors
population). that capture this.
Racial disparity in low birth weight Not available for small areas.
rates.
Disparity in IMR rates................. Small numbers.\1\
Birth rate............................. Highly correlated with chosen
measures.
Teen birth rate........................ Not available in sub-county
areas.
Prenatal care (Kessner)................ Unstable in small areas.\1\
Prenatal care index (Kotelchuck)....... Unstable in small areas.\1\
Ambulatory care sensitive admissions Not available in all states.
(ACS rates).
Ambulatory care sensitive admissions Not available in all states.
for children.
ACS rates restricted to common disease Not available in all states.
(diabetes, hypertension, cellulitis.
ACS rates for Medicare population...... Not available in all states.
ACS Rates for common disease for Not available in all states.
Medicare population.
Ratio of 100-200% poverty to 100% High correlation with chosen
poverty. variables.
Uninsured population................... Not available in small areas.
Uninsured <18 years.................... Not available in small areas.
Population density threshold (LT 6 p sq Density used as a continuous
mile, 7 p sq mile). variable instead.
Linguistic isolation................... Not calculated on a regular
basis. Imputed data.\2\
Migrant impact......................... Not available.
Farmworker impact...................... Not available.
Seasonal worker impact................. Not available.
Percent refugees, immigrant............ Not calculated on a regular
basis. Imputed data.\2\
Medicaid eligible population........... Not readily available in small
areas.
Tuberculosis incidence................. Not available in small areas.
HIV incidence.......................... Not available in small areas.
STD incidence.......................... Not available in small areas.
Cancer incidence....................... Not available in small areas.
Cervical cancer incidence.............. Not available in small areas.
Breast cancer incidence................ Not available in small areas.
Hypertension rate...................... Not available in small areas.
COPD rates............................. Not available in small areas.
[[Page 11242]]
Diabetes rates......................... Not available in small areas.
Diabetes rates for children............ Not available in small areas.
Asthma rates........................... Not available in small areas.
Asthma rates for children.............. Not available in small areas.
Smoking rates.......................... Not available in small areas.
Smoking rates for children/adolescents. Not available in small areas.
Obesity................................ Not available in small areas.
Obesity among children................. Not available in small areas.
Alcohol use rates...................... Not available in small areas.
Alcohol use rates for adolescents...... Not available in small areas.
Binge drinking rates................... Not available in small areas.
Disparity measures (ratio of rates for Not available in small areas.
whites and minorities for disease
incidence various combinations).
Raw mortality rate..................... Prefer adjusted mortality
rate.\3\
Disparity in mortality rate............ Small numbers.
Cancer mortality....................... Small numbers.
Cardiovascular disease mortality....... Small numbers.
Infectious disease mortality........... Small numbers.
Suicide rate........................... Small numbers.
Teen suicide rate...................... Small numbers.
Percent rural population............... Density captures.
Percent urban population............... Density captures.
Perceptual measures (other Varied from state to state.
designations).
------------------------------------------------------------------------
\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 *
--------------------------------------------------------------------------------------------------------------------------------------------------------
Percentile Poverty Unemp Elderly Density Hispanic Non white Death rate LBW IMR
--------------------------------------------------------------------------------------------------------------------------------------------------------
1........................................... 13.31 1.70 6.32 0.66 0.13 0.23 0.674 3.23 0.00
2........................................... 16.15 1.90 7.55 1.01 0.19 0.30 0.729 3.66 0.00
3........................................... 18.29 2.10 8.18 1.49 0.23 0.36 0.766 3.94 0.00
4........................................... 19.74 2.20 8.79 1.79 0.26 0.40 0.788 4.13 0.00
5........................................... 21.15 2.30 9.34 2.16 0.29 0.45 0.805 4.32 3.09
6........................................... 22.27 2.40 9.70 2.54 0.30 0.48 0.816 4.44 3.49
7........................................... 23.25 2.40 9.97 3.01 0.33 0.53 0.826 4.60 3.89
8........................................... 24.24 2.50 10.23 3.38 0.34 0.58 0.837 4.69 4.13
9........................................... 25.01 2.60 10.50 3.80 0.36 0.61 0.846 4.80 4.43
10.......................................... 25.68 2.70 10.71 4.24 0.38 0.64 0.853 4.88 4.63
11.......................................... 26.25 2.70 10.90 4.73 0.40 0.67 0.861 4.95 4.76
12.......................................... 26.83 2.80 11.11 5.32 0.41 0.71 0.867 5.02 4.90
13.......................................... 27.36 2.90 11.26 6.23 0.42 0.76 0.873 5.10 4.99
14.......................................... 27.83 2.90 11.43 6.82 0.44 0.79 0.878 5.16 5.09
15.......................................... 28.42 3.00 11.61 7.82 0.46 0.83 0.883 5.22 5.22
16.......................................... 28.93 3.10 11.75 8.41 0.47 0.88 0.889 5.28 5.33
17.......................................... 29.39 3.10 11.92 9.36 0.49 0.93 0.894 5.34 5.43
18.......................................... 29.91 3.20 12.06 9.97 0.50 0.97 0.899 5.38 5.55
19.......................................... 30.29 3.20 12.17 10.98 0.51 1.01 0.903 5.42 5.63
20.......................................... 30.66 3.30 12.30 11.96 0.53 1.06 0.908 5.47 5.74
21.......................................... 31.12 3.30 12.46 13.02 0.55 1.11 0.913 5.52 5.86
22.......................................... 31.57 3.40 12.57 13.90 0.56 1.16 0.917 5.57 5.91
23.......................................... 31.90 3.40 12.72 14.60 0.58 1.20 0.920 5.60 6.00
24.......................................... 32.24 3.50 12.82 15.78 0.59 1.27 0.925 5.65 6.08
25.......................................... 32.62 3.60 12.94 16.66 0.60 1.33 0.928 5.71 6.17
[[Page 11243]]
26.......................................... 32.98 3.60 13.04 17.63 0.62 1.40 0.932 5.76 6.27
27.......................................... 33.43 3.70 13.14 18.40 0.64 1.49 0.937 5.80 6.32
28.......................................... 33.71 3.70 13.24 19.03 0.65 1.54 0.938 5.84 6.39
29.......................................... 34.07 3.80 13.33 19.94 0.67 1.63 0.941 5.88 6.45
30.......................................... 34.45 3.80 13.41 20.92 0.68 1.73 0.945 5.92 6.53
31.......................................... 34.83 3.90 13.51 22.15 0.70 1.79 0.948 5.96 6.62
32.......................................... 35.15 3.90 13.63 22.85 0.72 1.89 0.952 6.00 6.68
33.......................................... 35.57 4.00 13.73 23.76 0.74 1.99 0.956 6.03 6.74
34.......................................... 35.85 4.00 13.83 24.61 0.76 2.06 0.958 6.08 6.82
35.......................................... 36.22 4.10 13.90 25.83 0.78 2.12 0.961 6.12 6.88
36.......................................... 36.53 4.10 14.02 26.76 0.81 2.20 0.965 6.15 6.95
37.......................................... 36.82 4.20 14.12 27.67 0.83 2.29 0.968 6.20 7.05
38.......................................... 37.07 4.30 14.18 28.48 0.85 2.44 0.971 6.24 7.11
39.......................................... 37.34 4.30 14.26 29.56 0.87 2.57 0.974 6.28 7.18
40.......................................... 37.62 4.40 14.31 30.35 0.90 2.69 0.978 6.33 7.26
41.......................................... 37.83 4.40 14.39 31.51 0.93 2.82 0.981 6.36 7.35
42.......................................... 38.16 4.50 14.49 32.46 0.95 3.04 0.985 6.41 7.42
43.......................................... 38.35 4.50 14.57 33.33 0.98 3.18 0.989 6.45 7.48
44.......................................... 38.63 4.60 14.67 34.49 1.01 3.35 0.992 6.49 7.55
45.......................................... 38.85 4.60 14.76 35.63 1.04 3.49 0.996 6.54 7.61
46.......................................... 39.14 4.70 14.84 36.72 1.07 3.67 0.999 6.60 7.67
47.......................................... 39.44 4.80 14.94 37.69 1.11 3.87 1.002 6.63 7.74
48.......................................... 39.74 4.80 15.00 38.72 1.15 4.04 1.005 6.67 7.81
49.......................................... 40.06 4.90 15.12 39.88 1.20 4.22 1.009 6.70 7.86
50.......................................... 40.31 4.90 15.20 41.38 1.24 4.44 1.013 6.76 7.91
51.......................................... 40.61 5.00 15.31 42.64 1.27 4.65 1.018 6.78 7.98
52.......................................... 40.93 5.00 15.43 44.24 1.30 4.90 1.021 6.82 8.08
53.......................................... 41.21 5.10 15.52 45.78 1.35 5.17 1.024 6.86 8.14
54.......................................... 41.49 5.20 15.63 47.24 1.39 5.50 1.027 6.91 8.19
55.......................................... 41.72 5.20 15.71 48.65 1.44 5.81 1.030 6.96 8.27
56.......................................... 42.04 5.30 15.78 49.94 1.49 6.12 1.034 7.00 8.32
57.......................................... 42.35 5.30 15.91 51.61 1.54 6.37 1.039 7.06 8.43
58.......................................... 42.62 5.40 15.99 53.18 1.60 6.72 1.042 7.10 8.50
59.......................................... 42.98 5.50 16.09 54.53 1.65 7.03 1.045 7.14 8.58
60.......................................... 43.38 5.50 16.21 56.26 1.72 7.31 1.049 7.20 8.66
61.......................................... 43.67 5.60 16.30 58.03 1.80 7.74 1.052 7.25 8.76
62.......................................... 44.01 5.70 16.39 61.20 1.88 8.23 1.055 7.29 8.81
63.......................................... 44.25 5.80 16.52 63.54 1.98 8.69 1.060 7.33 8.87
64.......................................... 44.65 5.90 16.67 66.32 2.08 9.24 1.064 7.38 8.92
65.......................................... 44.90 5.90 16.76 68.59 2.16 9.60 1.067 7.44 9.02
66.......................................... 45.15 6.00 16.86 70.91 2.26 9.97 1.071 7.50 9.11
67.......................................... 45.38 6.10 16.96 73.19 2.37 10.40 1.074 7.55 9.18
68.......................................... 45.77 6.30 17.11 74.78 2.48 10.96 1.079 7.61 9.24
69.......................................... 46.13 6.40 17.24 79.13 2.60 11.54 1.083 7.65 9.35
70.......................................... 46.52 6.50 17.38 82.37 2.74 12.36 1.087 7.73 9.41
71.......................................... 46.90 6.60 17.49 85.72 2.89 13.18 1.093 7.78 9.54
72.......................................... 47.19 6.70 17.64 88.76 3.05 14.08 1.097 7.83 9.64
73.......................................... 47.48 6.80 17.76 92.97 3.17 14.81 1.102 7.90 9.76
74.......................................... 47.85 6.90 17.90 97.05 3.35 15.80 1.108 7.95 9.89
75.......................................... 48.14 7.00 17.99 101.55 3.58 16.60 1.112 8.01 10.00
76.......................................... 48.49 7.10 18.17 107.04 3.78 17.38 1.117 8.07 10.16
77.......................................... 48.83 7.30 18.33 113.07 4.03 18.18 1.122 8.14 10.27
78.......................................... 49.15 7.30 18.48 120.40 4.35 19.40 1.127 8.23 10.34
79.......................................... 49.66 7.50 18.64 129.38 4.61 20.67 1.132 8.30 10.50
80.......................................... 50.03 7.70 18.88 137.50 5.04 22.01 1.137 8.42 10.63
81.......................................... 50.39 7.80 19.10 147.51 5.62 23.26 1.143 8.48 10.75
82.......................................... 50.88 7.90 19.29 157.66 5.99 24.48 1.146 8.56 10.94
83.......................................... 51.22 8.00 19.53 168.72 6.64 25.73 1.153 8.69 11.11
84.......................................... 51.70 8.10 19.79 184.45 7.43 26.83 1.160 8.81 11.28
85.......................................... 52.21 8.20 20.09 198.45 8.05 28.24 1.167 8.93 11.53
86.......................................... 52.63 8.40 20.31 215.14 8.88 30.57 1.173 9.04 11.76
87.......................................... 53.05 8.60 20.62 236.02 9.74 31.78 1.181 9.16 11.98
88.......................................... 53.51 8.80 20.89 264.75 10.66 33.74 1.190 9.24 12.25
89.......................................... 54.01 9.00 21.25 291.58 12.34 35.30 1.200 9.36 12.50
90.......................................... 54.75 9.30 21.54 321.29 13.82 37.43 1.210 9.58 12.81
91.......................................... 55.46 9.50 21.92 357.86 15.88 39.16 1.218 9.77 13.15
92.......................................... 56.23 9.80 22.33 413.68 17.90 41.17 1.230 9.92 13.58
93.......................................... 57.26 10.10 22.67 488.71 21.81 43.77 1.238 10.17 13.87
94.......................................... 58.23 10.50 23.16 595.16 25.73 46.18 1.252 10.35 14.21
95.......................................... 59.13 10.80 23.53 755.53 28.66 48.01 1.268 10.55 14.79
96.......................................... 61.07 11.50 24.53 995.22 34.72 52.62 1.289 10.87 15.63
[[Page 11244]]
97.......................................... 62.59 12.20 25.06 1356.41 42.03 57.51 1.310 11.31 16.56
98.......................................... 65.07 13.20 26.22 1759.93 48.46 62.78 1.341 11.72 17.54
99.......................................... 68.05 15.20 27.75 3090.35 65.75 69.42 1.407 12.47 19.70
--------------------------------------------------------------------------------------------------------------------------------------------------------
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
--------------------------------------------------------------------------------------------------------------------------------------------------------
Percentile Poverty Unemp Elderly Density Hispanic Non white Death rate LBW/IMR
--------------------------------------------------------------------------------------------------------------------------------------------------------
0....................................................... 0.00 0.00 0.00 995.20 0.00 0.00 0.00 0.00
1....................................................... 3.01 1.18 0.54 831.13 0.81 0.00 0.82 0.72
2....................................................... 6.04 2.37 1.09 735.15 1.64 0.00 1.65 1.44
3....................................................... 9.11 3.58 1.65 667.05 2.47 0.00 2.49 2.17
4....................................................... 12.21 4.79 2.21 614.23 3.31 0.00 3.33 2.91
5....................................................... 15.34 6.02 2.77 571.07 4.15 0.00 4.19 3.65
6....................................................... 18.50 7.26 3.34 534.58 5.01 0.00 5.05 4.40
7....................................................... 21.70 8.52 3.92 502.98 5.88 0.00 5.93 5.17
8....................................................... 24.93 9.79 4.51 475.10 6.75 0.00 6.81 5.93
9....................................................... 28.20 11.07 5.10 450.16 7.64 0.00 7.70 6.71
10...................................................... 31.50 12.37 5.69 427.59 8.53 0.00 8.60 7.50
11...................................................... 34.84 13.68 6.30 407.00 9.44 0.00 9.52 8.29
12...................................................... 38.22 15.00 6.91 388.05 10.35 0.00 10.44 9.10
13...................................................... 41.64 16.35 7.53 370.51 11.28 0.00 11.37 9.91
14...................................................... 45.10 17.70 8.15 354.18 12.21 0.00 12.32 10.73
15...................................................... 48.59 19.08 8.78 338.90 13.16 0.00 13.27 11.57
16...................................................... 52.13 20.46 9.42 324.55 14.12 0.00 14.24 12.41
17...................................................... 55.71 21.87 10.07 311.02 15.09 0.00 15.22 13.26
18...................................................... 59.34 23.29 10.72 298.22 16.07 0.00 16.21 14.12
19...................................................... 63.00 24.73 11.39 286.08 17.07 0.00 17.21 15.00
20...................................................... 66.72 26.19 12.06 274.53 18.07 0.00 18.22 15.88
21...................................................... 70.48 27.67 12.74 263.52 19.09 0.00 19.25 16.78
22...................................................... 74.29 29.16 13.43 253.00 20.12 0.00 20.29 17.68
23...................................................... 78.15 30.68 14.12 242.92 21.17 0.00 21.34 18.60
24...................................................... 82.06 32.21 14.83 233.26 22.23 0.00 22.41 19.53
25...................................................... 86.02 33.77 15.55 223.98 23.30 0.00 23.49 20.48
26...................................................... 90.03 35.34 16.27 215.04 24.39 0.00 24.59 21.43
27...................................................... 94.10 36.94 17.01 206.43 25.49 0.00 25.70 22.40
28...................................................... 98.22 38.56 17.75 198.13 26.61 0.00 26.83 23.38
29...................................................... 102.40 40.20 18.51 190.10 27.74 0.00 27.97 24.38
30...................................................... 106.64 41.86 19.28 182.34 28.89 0.00 29.13 25.39
31...................................................... 110.95 43.55 20.05 174.83 30.05 0.00 30.30 26.41
32...................................................... 115.31 45.27 20.84 167.54 31.23 0.00 31.49 27.45
33...................................................... 119.74 47.01 21.64 160.47 32.43 0.00 32.70 28.50
34...................................................... 124.24 48.77 22.45 153.61 33.65 0.00 33.93 29.57
35...................................................... 128.80 50.56 23.28 146.94 34.89 0.00 35.18 30.66
36...................................................... 133.44 52.38 24.12 140.46 36.14 0.00 36.45 31.76
37...................................................... 138.15 54.23 24.97 134.15 37.42 0.00 37.73 32.88
38...................................................... 142.93 56.11 25.83 128.00 38.72 0.00 39.04 34.02
39...................................................... 147.79 58.02 26.71 122.00 40.03 0.00 40.37 35.18
40...................................................... 152.74 59.96 27.61 116.16 41.37 0.00 41.72 36.36
41...................................................... 157.76 61.93 28.51 110.46 42.73 1.39 43.09 37.55
42...................................................... 162.87 63.94 29.44 104.89 44.12 2.81 44.48 38.77
43...................................................... 168.07 65.98 30.38 99.44 45.53 4.25 45.90 40.01
44...................................................... 173.36 68.06 31.33 94.12 46.96 5.71 47.35 41.27
45...................................................... 178.75 70.17 32.31 88.92 48.42 7.20 48.82 42.55
46...................................................... 184.24 72.33 33.30 83.83 49.90 8.72 50.32 43.86
47...................................................... 189.83 74.52 34.31 78.85 51.42 10.27 51.85 45.19
48...................................................... 195.52 76.75 35.34 73.97 52.96 11.85 53.40 46.54
49...................................................... 201.33 79.03 36.39 69.18 54.53 13.46 54.99 47.92
50...................................................... 207.25 81.36 37.46 64.50 56.14 15.10 56.60 49.33
51...................................................... 213.29 83.73 38.55 59.90 57.77 16.77 58.25 50.77
52...................................................... 219.45 86.15 39.66 55.39 59.44 18.48 59.94 52.24
53...................................................... 225.75 88.62 40.80 50.97 61.15 20.22 61.66 53.74
54...................................................... 232.18 91.15 41.96 46.62 62.89 22.00 63.41 55.27
[[Page 11245]]
55...................................................... 238.75 93.73 43.15 42.36 64.67 23.82 65.21 56.83
56...................................................... 245.47 96.36 44.37 38.17 66.49 25.68 67.04 58.43
57...................................................... 252.34 99.06 45.61 34.05 68.35 27.58 68.92 60.07
58...................................................... 259.38 101.82 46.88 30.01 70.26 29.53 70.84 61.74
59...................................................... 266.59 104.65 48.18 26.03 72.21 31.53 72.81 63.46
60...................................................... 273.97 107.55 49.52 22.11 74.21 33.57 74.83 65.21
61...................................................... 281.54 110.52 50.89 18.27 76.26 35.67 76.89 67.02
62...................................................... 289.30 113.57 52.29 14.48 78.36 37.82 79.02 68.87
63...................................................... 297.28 116.70 53.73 10.75 80.52 40.03 81.19 70.76
64...................................................... 305.47 119.92 55.21 7.08 82.74 42.30 83.43 72.71
65...................................................... 313.89 123.22 56.73 3.47 85.02 44.63 85.73 74.72
66...................................................... 322.56 126.63 58.30 -0.09 87.37 47.03 88.10 76.78
67...................................................... 331.49 130.13 59.91 -3.60 89.79 49.50 90.54 78.91
68...................................................... 340.69 133.74 61.58 -7.06 92.28 52.05 93.05 81.10
69...................................................... 350.18 137.47 63.29 -10.46 94.85 54.68 95.64 83.36
70...................................................... 359.98 141.32 65.06 -13.82 97.51 57.39 98.32 85.69
71...................................................... 370.12 145.30 66.90 -17.13 100.25 60.20 101.09 88.10
72...................................................... 380.61 149.41 68.79 -20.40 103.10 63.11 103.95 90.60
73...................................................... 391.49 153.68 70.76 -23.62 106.04 66.12 106.92 93.19
74...................................................... 402.77 158.11 72.80 -26.79 109.10 69.24 110.01 95.87
75...................................................... 414.50 162.72 74.92 -29.93 112.27 72.49 113.21 98.67
76...................................................... 426.70 167.51 77.12 -33.02 115.58 75.87 116.54 101.57
77...................................................... 439.43 172.50 79.42 -36.08 119.03 79.39 120.02 104.60
78...................................................... 452.72 177.72 81.83 -39.09 122.63 83.07 123.65 107.76
79...................................................... 466.63 183.18 84.34 -42.07 126.39 86.93 127.45 111.08
80...................................................... 481.22 188.91 86.98 -45.01 130.35 90.97 131.43 114.55
81...................................................... 496.55 194.93 89.75 -47.92 134.50 95.21 135.62 118.20
82...................................................... 512.72 201.28 92.67 -50.78 138.88 99.69 140.04 122.05
83...................................................... 529.81 207.98 95.76 -53.62 143.51 104.42 144.70 126.11
84...................................................... 547.94 215.10 99.03 -56.42 148.42 109.44 149.65 130.43
85...................................................... 567.23 222.68 102.52 -59.19 153.65 114.79 154.92 135.02
86...................................................... 587.86 230.77 106.25 -61.93 159.23 120.50 160.56 139.93
87...................................................... 610.02 239.47 110.26 -64.63 165.23 126.64 166.61 145.21
88...................................................... 633.95 248.87 114.58 -67.31 171.72 133.26 173.15 150.90
89...................................................... 659.97 259.08 119.28 -69.95 178.76 140.47 180.25 157.10
90...................................................... 688.47 270.27 124.43 -72.57 186.48 148.36 188.04 163.88
91...................................................... 719.97 282.63 130.13 -75.15 195.02 157.08 196.64 171.38
92...................................................... 755.19 296.46 136.49 -77.71 204.56 166.84 206.26 179.76
93...................................................... 795.11 312.13 143.71 -80.24 215.37 177.89 217.16 189.27
94...................................................... 841.20 330.23 152.04 -82.75 227.85 190.66 229.75 200.24
95...................................................... 895.72 351.63 161.89 -85.23 242.62 205.75 244.64 213.21
96...................................................... 962.43 377.82 173.95 -87.68 260.69 224.23 262.86 229.10
97...................................................... 1048.45 411.58 189.50 -90.11 283.99 248.05 286.36 249.57
98...................................................... 1169.68 459.18 211.41 -92.51 316.83 281.62 319.47 278.43
99...................................................... 1376.93 540.53 248.87 -94.89 372.97 339.02 376.07 327.76
--------------------------------------------------------------------------------------------------------------------------------------------------------
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
------------------------------------------------------------------------
Wichita
High need indicators County, KS
------------------------------------------------------------------------
% < 200% Poverty.................... ...................... 49.8%
Percentile............ 79
Score................. 467
Unemployment Rate................... ...................... 3.59%
Percentile............ 24
Score................. 32
% 65+............................... ...................... 15.6%
Percentile............ 53
Score................. 41
Population/Sq Mile.................. ...................... 3.7%
Percentile............ 8
Score................. 475
% Hispanic.......................... ...................... 16.4%
Percentile............ 91
Score.... |
