A Proposal for a Method to Designate Communities as Underserved
Technical Report on the Derivation of Weights

Prepared for the
Division of Shortage Designation
Bureau of Primary Health Care
Health Resources and Services Administration
Department of Health and Human Services
Under a Cooperative Agreement with the Office of Rural Health Policy (HRSA) (1 UIC RH 0027-01)
Prepared by the Cecil G. Sheps Center for Health Services Research
The University of North Carolina at Chapel Hill

Printer-friendly Technical Report (168 KB)

Purpose:

The proposal to create a revised approach to the designation of underserved areas is summarized in a separate document entitled, “Proposal for a Method to Designate Communities as Underserved.”  That document outlines the proposed methods and illustrates how it would be used in practice.  This document is intended to provide the technical background to how the proposed method was developed.  The principal authors of this document are, alphabetically:  Laurie Goldsmith, Mark Holmes, Jan Ostermann, and Tom Ricketts.

We begin with five guiding principles shaping the analysis plan.  These principles guided the application of many of the technical approaches to creating and adjusting the method:

1. Simplicity: The new system must be simple to understand.
2. Science-based: The new system must be based on scientifically recognized methods and be replicable.
3. Face Validity: The new system must be intuitive and have face validity.  For example, scores that were applied to communities should give heavier weight to conditions that are generally accepted to indicate need for services and which reduce access; those scores should be cumulative, and the scoring should readily identify areas, populations and communities recognized as underserved.
4. Retaining designations for places with safety net practitioners:  Federally-supported safety net resources which are currently serving uninsured, low-income people or persons without reasonable access to primary care have demonstrated that, as facilities, their service populations qualify as underserved.  The new system should not dramatically affect the overall number of designations for places with safety net practitioners—in particular, places with Community Health Centers (CHCs) or other Federal Qualified Health Centers (FQHCs), Rural Health Centers (RHCs), and National Health Service Corps personnel (NHSC).
5. Acceptable performance: The use of more contemporary data with the proposed rule published September 1, 1998 would have resulted in the loss of designation of a very large proportion of areas and populations.  The new proposal should recognize that, over time there will be changes in the factors that predict underservice and allow for future adjustment of the indicators.  We used many different evaluating criteria for this guiding principle, including the model’s ability to predict current HPSA and MUA status, but the fundamental criterion was whether the method fairly and consistently identified places and people who were in need of primary health care and who had barriers to meeting those needs.

The General Approach

The overall approach for deriving an empirical, data driven system to identify underserved areas and populations is to estimate the effect of demographic factors on the population-to-practitioner ratio, using a sample of counties as proxies for a health care market.  These effects are then translated to a score which is added to an adjusted ratio for a total “need” measure.  Thus, the implementation is similar to the current IPCS or MUA method in that it creates a “score” or “index” of underservice, however, the proposed system’s score is based on an adjusted ratio that is meant to represent an “effective” or “apparent” population and its primary health care needs.

There are eight steps to the project, which we divide for expository purposes into two distinct “Tasks”.

Task One: Calculate The Factors Affecting Ratios (“Analysis”)

This is the analytical portion of the project in which we explore the degree to which observable demographic characteristics tend to be associated with population to provider ratios.  The specific steps in this task include:

1. Create an age-sex adjusted population.
2. Calculate the base population-provider ratio for regression to determine weights for need variables.
3. Select study sample primary care service area proxies.
4.  Create factor scores to control for interactions of variables.
5.  Run regression models to create weights for community variables.

Task Two: Calculate The Scores Based On These Factors (“Computation”)

This is the portion of the process in which scores are assigned to geographic areas based on the weights calculated in Task One. 

6.  Calculate the base population-practitioner ratio for designation determination  
7. Calculate the scores for each area based on the values for each variables for each area and add to the ratio.
8. Step 8: Compare the ratio to a designation threshold ratio.

We describe each of these steps in detail in the following sections.

Task 1: Analysis

Step 1: Create an age-sex adjusted population

Using estimated visit rates from individual-level surveys, we weight the population to create a “base population.”  In this manner, populations can be compared across areas.  The use of these data for this adjustment are discussed in detail in reports and background papers for the proposal including the report

that estimates the national impact of the NPRM-2 proposal, “National Impact Analysis of a Proposed Method to Designate Communities as Underserved” dated September 7, 2001; the background paper, “Designating Underserved Populations. A Proposal For An Integrated System Of Identifying Communities With Multiple Access Challenges,” which is in draft form; and the “Executive Summary” of the “Designating …” paper which has been circulated in draft form to the Bureau of Primary Health Care.

The weights are summarized in Table 1.

Table 1: Visit weights for age-sex adjustment

The weighted sum of these populations is calculated as 4.046 * (# Females 0-4) + 2.256 * (# Females 5-17) +…+ 8.056 *( # Males 75 and over) and equals an age-sex adjusted number of visits for a particular population.  Dividing this number of visits by the mean visit rate (3.741) creates a “base population”.  Areas with equal base populations (and equal demographics) have an equal need for primary care visits per year.  This adjustment allows us to compare, say, the population-based visit differentials between an area with a high concentration of elderly (with a higher need for visits) and an area with a high population of middle aged individuals (with a lower need for visits).  The visit rates were obtained from the Medical Expenditure Panel Survey (1996) and were calculated for non-poor, white, non-Hispanic individuals.  Employment status, which was included in the MEPS survey and was a significant correlate of use of service, was also intercorrelated with the other variables and was not included in the final visit calculation.

Step 2: Calculate the base population-provider ratio for regression to determine weights for need variables

With the base population in hand, we calculate the population-provider ratio to use in the regression to determine factor weights.  The number of practitioners is calculated as

Providers = physicians-(J1--physicians + MHSC--physicians + SLRP-- physicians) + .5* [midlevels-(NHSC--midlevels + SLRP--midlevels)] + .1* [residents-(NHSC--residents + SLRP--residents)]

where all practitioners are measured in FTE units and the practitioner total includes NPs, PAs and CNMs weighted for relative productivity and scope of practice. 

The number of practitioners calculated for this step is different than the number of practitioners calculated for determining designation.  The number of practitioners used in the regression to determine weights for the need variables only represent those practitioners that are considered to be the private supply.  That is, the practitioners who would choose to practice in the community without federal support or incentives to practice in state- or federally-operated facilities.  As such, government practitioners (whether federal or state) are not counted here.  Community Health Center practitioners who are not federal employees, however, are counted since many of these are not “placed” into communities but are practitioners already located in the area that are “reclassified” as CHC practitioners for later subtraction from the practitioner supply at a later step.  For the estimation of the formula, an area with no practitioners is dropped from use in the regression analysis to determine weights for the need variables as a ratio is undefined (not calculable).

Step 3: Select study sample

A sample of counties and county equivalents that serve as proxies for a health care market are then selected for analysis to derive formula weights.  This step was done to identify places which functioned as primary care service areas and which reported stable, reliable, usable data.  Many U.S. counties meet these general qualifications and the process selected a range of counties that met certain criteria, including:

1. populations below 125,000
2. area below 900 square miles
3. base population to provider ratio below 4250

The third criterion effectively eliminated very small counties and counties with unusual distributions of health practitioners.  The goal was to determine the relationship of area characteristics to practitioner supply under “normal” conditions in order to create stable estimates of those relationships in order to apply them to all appropriate populations and areas.

These sample selection criteria were varied; we tested over 2000 combinations in the estimation process described in the next step to test for robustness and sensitivity.  The variations included testing within the following ranges: population 80,000-150,000; area 700-1200 sq. miles; ratio 3000-4250.  Overall, the estimations derived from the models were not substantially different among the different samples   The study sample contained 1643 counties.  Counties were chosen because they are well-defined and are not endogenous to the current system. 

Using currently designated areas would lead to biased conclusions due to the fact the subcounty areas are carefully and deliberately constructed for purposes of designation.  Furthermore, dividing a county into a subcounty-designated and subcounty-undesignated would generate an extremely large number of possible observations in the analysis since the county could be divided in many different ways and into many subsets of county parts.  Finally, since some data are calculated and available primarily on a county level, measurement error is minimized by using counties.  Using other units of analysis requires interpolating values for subcounty and multicounty areas based on the constituent geographic units.

Step 4: Create factors

The proposed designation process, in keeping with the original MUA/MUP and HPSA approaches, identified commonly available statistics that correlated with a small number of primary care practitioners-to-population ratio.  The selection of the measures was based on reviews of the scientific literature on access to care and preliminary work on the development of an alternative measures of underservice conducted by Donald H. Taylor, Jr. (Taylor & Ricketts, 1994) .  Candidate statistics were also suggested by a working group of State Primary Care Associations (PCAs) and Primary Care Offices (PCOs) convened by the Division of Shortage Designation (DSD) to gather state-level input into the process of revising the method.  The staff and leadership of the DSD also provided extensive input into the design. More than 20 specific variables were suggested during this process.  Some candidate variables could not be used, despite being highly correlated with low access and poor health outcomes, due to lack of availability of data for small areas (e.g. lack of health insurance).  Ultimately, the high intercorrelations among candidate variables restricted the calculation to 7-9 individual indicators (the actual number to be tested depended upon the specific combination of variables).  The final choice of variables and the priority for inclusion in the analysis was based on the degree to which the variables best reflected underlying components of access as qualitatively assessed by the UNC-CH team, the PCA/PCO group, and staff of Bureau of Primary Health Care (BPHC).  The final measures consist of demographic, economic and health status indicators (presented in Table 2).

Demographic: Population characteristics, especially racial and ethnic characteristics, have been consistently shown to affect access to primary care (Berk, Bernstein, & Taylor, 1983; Berk, Schur, & Cantor, 1995; Schur & Franco, 1999) .  Measures of the percent of population that is non-White and percent of population that is Hispanic were used to further adjust the ratio.  The inclusion of the percentage of population older than 65 years was also included because communities with higher percentages of elderly have different community characteristics not captured in the initial population adjustment.  This is likely due to the relative lack of younger people to provide supportive care and the fact that communities with declining economies, especially rural communities, have older age profiles that combine with other factors to create overall lower access.

Economic: Income and employment are very strong indicators of ability to access primary health care and to afford health insurance (Mansfield, Wilson, Kobrinski, & Mitchell, 1999; Prevention, 2000; Robert, 1999) .  The unemployment rate and the percent of population below 200 percent of the poverty level were used to further adjust the ratio.

Health Status: Certain populations and communities have higher than average need for health care services based primarily on their health status independent of other factors.  Therefore, health status measures used to adjust the ratio include the standardized mortality ratio (General Accounting Office, 1996) and either the infant mortality rate or the low birthweight rate (Matteson, Burr, & Marshall, 1998; O'Campo, Xue, Wang, & Caughy, 1997) .  These special epidemiological conditions that increase need are not fully represented in the age-gender adjustment.

Table 2. Variables Used in Creating Proposed Method

These measures are highly intercorrelated.  Table 3, below shows the Pearson-product moment correlations.  The first column shows that poverty and unemployment are positively correlated (+0.64), meaning, in counties with high proportions of the population living in poverty there is usually a higher unemployment rate.  Poverty and density are negatively correlated (–0.55), meaning that where there is higher density there are lower percentages of the population living in poverty.  The correlation matrix is population-weighted.

Table 3: Percentile Correlation Matrix

Variable definitions

Variables were assigned a percentile based on the distribution of values of all US counties to all U.S. counties.  This allows for continuity in the use of the proposed scores if variables are defined differently in the future (e.g. the poverty measure is changed to 100 percent below poverty instead of 200 percent).  It also allows policymakers a choice of how often (or whether) to update the percentile values without having to change the weights.  If poverty conditions improve markedly across the nation, scores will tend to fall unless the percentile tables are updated.  For all variables except DENSITY the theoretically worst value corresponded to the 99^th percentile.  At first glance, it might appear that places with very low population density would be worse off with regard to primary care access and health service needs.  Places with extremely high density may also have problems caused by overcrowding and the population density may reflect problems that are commonly encountered in inner-cities.  For this variable there is no apparent “right” direction for the weights.  We arbitrarily specified the functional form such that lower population density corresponds to a worse off (higher percentile score) community.  Accounting for the negative effects of very high density is described below.

We combined low birth weight and infant mortality into one measure (called HEALTH), defined as the maximum percentile of low birth weight and the infant mortality rate for a given area.   This is due to a medium level of correlation between the two and the fact that not all areas report both measures.  Finally, the use of the infant mortality rate in measures of underservice is required by existing law and there is precedent for using these measures as rough substitutes.  The original Index of Primary Care Shortage described in NPRM-1 of September 1, 1998 used them interchangeably.

We defined nonwhite as the maximum of zero or the percentile minus 40, so that only the top (most nonwhite) 60 percent of areas get “points” for the nonwhite variable.  In other words,  all areas less than the 40^th percentile are treated equally.   There were two main reasons for this.  The first is that many of the areas have low nonwhite percentages (the 40^th percentile is about 2.6 percent nonwhite). By not making this adjustment, we are differentiating areas that have little difference in the underlying measure.  The second reason is that without this adjustment, the scores were not stable; small differences in the definition of this variable resulted in wide swings in the magnitude of the nonwhite variable when testing multiple randomly chosen samples.  We experimented with a multitude of cutoff points (0-50 in 10 unit increments).  In the final specification, small changes in the definition of NONWHITE had little substantive effect. 

With the corresponding percentiles in hand, the associated scores were transformed to a logarithmic scale so that the highest derivative corresponded to the theoretically worst end of the scale.  For example, the independent variable corresponding to poverty (lnpcpov) was defined as Inpcpov = In(100 – pcpov) so that the fastest acceleration in the poverty score occurs at high levels of poverty rather than at low levels.  In other words, we specified the model to allow a greater score to accrue to areas “moving” from the 95^th percentile to the 96^th percentile than to areas “moving” from the 5^th percentile to the 6^th percentile.  All variables were assumed to have this shape (so that the theoretically worst values have the largest derivative). 

Basing the Scores on the Population-Practitioner Ratio

Although this approach specifies the shape of the function as logarithmic and this constrains the rate of change in the scoring as variables differ from one percentile to another, it does not constrain the sign nor the absolute magnitude of the parameters that create the weights.  That is, the regression models are indifferent to whether a parameter comes out positive or negative or how large or small it is when the statistical model is run to create the weights.  The magnitude is the most important parameter of the three and will be used for estimating the scores but the potential effects of the size and sign of the weights must fit into our logic of additivity of factors.  The magnitude of the weights are expressed as a synthetic unit which cannot be compared to any other unit—the weight for UNEMPLOYMENT, for example, when transformed to the log-normal form and constrained to a positive value in the course of the estimation, is not a “percent of workforce not working but seeking work” but an abstract number that describes the relative contribution of that factor to a total access score at that percentile of unemployment given all the value of all the other variables and the population structure.  The final model creates an estimate for the weight for each set of variables using this abstract number but that number has to be brought back into a logical relationship with the key unit of access we are using—the population portion of a practitioner-to-population ratio.  The final combined sum of these abstract values has to be adjusted back to an interpretable relationship with the practitioner-population ratio.  This requires that some form of restraint on the parameter (weight) values be imposed or the solution set may produce a “best result” that causes one or two variables to dominate the weighting and others to vary from positive indicators of barriers to access to negative in various combinations. 

In an unconstrained solution of the regression models this is, indeed, the case.  There are possible solution sets that include mixes of positive and negative values; in statistical parlance the functions are “two-sided.”  The logic of the scoring system anticipated this when we stipulated that factors which restrain use of services by creating barriers to access, also create subsequent higher levels of need likely to be met by higher levels of use, use of services that was preventable but now necessary.  In the real community, both things are happening, an access program is promoting appropriate utilization by overcoming access barriers and all practitioners are involved in caring for people who are using the system because emergent conditions were not treated appropriately.  The amount of the increase in use brought about by delayed care  must be added into the reduction in use to produce a sum of the access “problem” in a community.  To account for the “mirror” effects of these variables, the final value, the sum of the weights are doubled, to produce a population estimate that is scaled to represent the overall effect on the population need.

Factor analysis

Because many of these measures are highly correlated, we perform factor analysis in order to compute factors for the independent variables defined above.  Essentially, factor analysis provides a method to translate highly correlated variables into orthogonal measures to obtain more precise estimates and minimize the impact of multicollinearity in the variables of interest.  Often used as an end product statistical tool, we use it here to improve the precision of the estimates.

Our procedure here was to decompose the independent variables into factors and then create scores based on these factors. The factor scores follow in Table 3.  The bold elements are the largest weight in the row, or on which factor the variable weighs most heavily (except for SMR, which has two maximum weights of almost equal magnitude).  Four factors might be interpreted as structuring the data:

1. High health risk, nonwhite
2. Geo-demographics
3. Economic conditions
4. Hispanic

Table 2: Factor Scores

Step 5: Run Regressions

We regress the base population-to-private supply practitioner ratio on the scores obtained from the factor analysis (Ratio = Factor I + Factor II … + error).  By combining the scores from the factor analysis with the estimated coefficients from the regression, we obtain the effect of our underlying variables on the ratio.

As an example, the factor analysis might yield a result such as:

Which we could translate into a matrix

Suppose regressing the ratio onto these two scores yields estimates of

Variable   beta

factor1      1

factor2    -.4

which would translate to a vector

 By multiplying these two matrices, we can obtain the total effect of one variable on the ratio:

(1) 

Thus, (in this simple example) the overall effect of Poverty on the ratio is calculated as .04 and the overall effect of Unemployment is .34.  We use the rightmost matrix for computing the scores (see the next section) except for one correction (see below).

Weights/Heteroskedasticity

Because the dependent variable is a ratio with population in the denominator, we are concerned about possible heteroskedasticity in the dependent variable.  This is the property that the sampling variability in the dependent variable is not constant across the sample.  Specifically, we expect the ratio to be estimated more precisely as the population grows.  See Figure 1 below for support of this hypothesis—the ratio tends to become less variable as the population increases (population category 1 is the lowest population category and population category 10 is the highest population category).  (The upper and lower bands are the values for the 25^th and 75^th percentiles).  The consequence of this violation is that the standard errors from the regression are biased and a more efficient estimator may exist.  As such, we weight the regressions by the total population of the county.

Percentile for variables, 1-99

There is a question of whether we are even dealing with a “sample” in the conventional statistical sense.  If our analysis is composed of the population of interest, then classical statistical inference is a bit artificial; there is no uncertainty if we have data on all the units of interest.  We argue that this is a sample in the conventional sense, for reasons including but not limited to the following:

1. Measurement error occurs more often then we expect.  County population values are estimated in 1997 and the accuracy of provider supply is not 100 percent.  As the nation observed in the presidential vote count in Florida, even simple computations are not immune from error.  Thus, because the data used here are affected by measurement error we have a sample drawn from the possible data for the population of counties.
2. The units used here are a sample of a much bigger population of interest.  Not only are we interested in counties other than those included in the analysis due to sample criteria, ultimately we are using counties as approximations for “health care markets” or rational primary care service areas, whether they follow the boundaries of a county or not.  These methods are designed to be applied to data for future years and the construction of the areas may vary from one based on geography to ZIP code boundaries. 

Other considerations, such as errors in model specification or the discrete “lumpiness” associated with using a dependent variable like this one provide support for the use of factor scores.

Sampling error in the regression

We wish to reduce the error in predicting the designation of communities.  As such, we seek to incorporate the precision with which the regression parameters are estimated into the scoring procedure.  As an example, it is entirely possible, given two factors, to have one coefficient be estimated as 100 with a standard error of 1 and the other coefficient to be estimated as 400 with a standard error of 1000.  If asked which factor is more important, most people would probably admit that although the 400 is a larger point estimate, the 100 is probably more important given its statistical significance.  As such, the regression estimates are adjusted for the statistical significance by the algorithm defined below.^[1]

1. Obtain the variance-covariance matrix V of the parameter estimates from the regression.
2. Compute the weighting matrix W defined as the inverse of the Cholesky transformation of a zero matrix except for the diagonal, which consists of the diagonal of V.  (This is identical to a zero matrix with diagonal elements equal to the reciprocal of the standard errors of the parameter estimates).
3. Transform the vector of parameter estimates (omitting the constant) b by b* = b *W * number of factors/trace(W).  The trace() portion of the expression ensures the weights sum to the number of factors.
4. Compute F = S b* as above.

As an example, return to the hypothetical results for poverty and unemployment above.  Suppose the (estimated) variance-covariance matrix from the regression was

then

so

(2) 

The estimated scores in equation (2) differ from those obtained in equation (1) (page 17) due to the weight.  Because the regression estimate for the first factor is estimated with roughly three times the precision as the estimate for the second factor (5/1.42 »3), the estimate for the first factor (1) is weighted more heavily than the estimate for the second factor (-.4).  In this case, this has the end result of increasing the scores from .04 to .24 for poverty and .34 to .4844 for unemployment.  Vector F is the scoring vector used in the next step.

Although the process for obtaining matrix F is complex and multi-stage the process was completed for all possible values of the variables.  Having done this, data describing a service area can be translated readily into percentile scores using a look-up table, a simple spreadsheet, or a web-based application. This parallels the existing MUA scoring process.  Applicants do not need to perform Cholesky transformations or any other mathematical calculations.

Task 2: Computation

Step 6: Calculate the base population-provider ratio for designation determination

Using the same age-sex adjusted population from Step 1, we calculate the population-practitioner ratio.  All primary care practitioner FTEs in the area are counted to initially determine designation, this is termed the “Tier 1 designation.”  For applicants not meeting the threshold criterion, the FTEs for practitioners who are supported by safety net programs (e.g., NHSC providers, J-1 visa practitioners, CHC providers) are subtracted from the supply total and the applicant ratio is compared to the threshold.  That step is termed “ Tier 2 designations.” 

Step 7: Calculate Scores

With row vector F in hand, we then turn to computing scores for geographical units. We compute the ratio of population to providers using the algorithm outlined above.  We use the percentile scores as computed above for the counties.  See the document “Completing the NPRM2 Application” for these percentiles.

We then calculate the score for the communities and add this score, upweighted by 2 to account for the 2-sided properties of the regression estimates so the total score for the community equals

ADJUSTED RATIO (or “INDEX”) = RATIO + 2 * SCORE

This is the total score for the community and determines its designation status.  The applicants never see the regression multiplier; it is embedded in the tables. 

Because the use of the multiplier for the score is applied at this stage of the process, it may be seen as an ad-hoc adjustment.  The statistical logic for this has been described above, the policy logic for applying this adjustment is supported by these points:

1. The multiplier is used to account for the fact that the existing measures and processes including: the HPSA formula, the IPCS/MUA formulae, and the practical application of the CHC/RHC clinic placement process—all recognize the importance of the basic population-to-practitioner ratio in determining need.  Indeed, some simple models run on the study

sample provide evidence that the multiplier should be closer to 10 rather than 2 if the goal were to include every area containing a CHC under the proposed designation process (this assumes that the presence of a CHC is an indicator of need in and of itself as opposed to the result of the calculation of pre-existing unmet need).  The IPCS mechanism provided for a maximum score from the population-practitioner ratio of 35 points.  The maximum score available from other factors (poverty 35 points, IMR/LBW 5 points, minority 5 points, Hispanic 5 points, LI 5 points, density 10 points = 65 points) are, collectively, almost twice that in terms of potential contribution.  Thus, the weighted contribution of the factors besides the ratio is roughly twice that of the ratio itself.  Multiplying the ratio denominator by two intensifies the relative effect of the underlying, basic population to practitioner ratio in the designation process providing continuity with prior policy.

2. The multiplier functions as a scale /weighting factor. The score has a much smaller variance than the ratio.  This is not just an annoyance—it is used to generate a prediction, and thus will have smaller variance than the dependent variable. The dependent variable and the score used here have some sort of meaning, a person per provider, although the various adjustments make this unit of measurement not as meaningful as we might think.  One alternative we considered is rescaling the ratio and the score into z-scores and using these standardized measures rather than the unscaled measures.  This rescaling would involve multiplying the score by a larger factor than the ratio.
3. The multiplier helps control for the (observed) low ratios in, (eg, metro) areas with high scores.  The following example illustrates this:

4. The multiplier fills a statistical role.  The score is (likely) more stable across years; e.g., if one physician moves out of a rural area, the ratio varies dramatically.  The score is not going to change drastically across years.  Thus, it should be given more weight.
5. The multiplier creates a standard which designates roughly the same number of people as the IPCS and the current HPSA designations. 
6. It performs better than without the doubling.  Although this particular argument has little theoretical basis, it is still compelling

Why is a portion of the density score function negative?

The astute reader will note that the constant from the regression was dropped and never used.  The reason for this is that the constant has no clear meaning in this context.  We decided to norm the scores so that the minimum score—that is, the best area in the country—was zero.  Thus, although in theory an area could receive a negative score if it had very favorable demographics and had a high population density, in practice no area had a negative score (by definition).

Step 8: Compare to Threshold

Areas are designated if and only if the “adjusted ratio” (or ratio+score) is greater than 3000.  This threshold was adopted for its reflection of the clear need for a single full-time equivalent primary care physicians, its consistency with prior threshold values, and its familiarity to stakeholders. 

Areas with No Practitioners

The problem of how to treat areas with zero providers emerged early in the process of ranking areas as medically underserved.  There is an informative treatment of the phenomenon in Black and Chui (1981).*  For areas with zero providers, we have not made any firm recommendations and have treated them in one of three ways for various parts of the analysis

1. Every area with zero providers automatically gets an adjusted ratio of 3000 (which guarantees them designation), to which a score for community need indicators are added.  This results in all areas having a NPRM2 score, including areas with zero providers.  This method was used in early tabulations and compilations.
2. Automatically designate areas with zero providers without assigning an adjusted ratio or a score for community need indicators.  Therefore, areas with zero providers will not have a NPRM2 total score.  This has occurred when calculations and tabulations of the database using the NPRM2 scoring system was applied.  The places with no score were dropped.  This method was used in the final impact analysis.
3. Assigning an arbitrarily small FTE to the area, such as 0.1 to create a score that is primarily dependent upon the denominator population.  This was used only in selected tests of the scoring system as an alternative.

Addition to Technical Document:

EXAMPLE OF HOW TO CALCULATE SCORES FOR HIGH NEED INDICATORS

The chart below shows for how the scores for each individual factor for one county are determined, using the tables below; look up the percentile for each actual value (i.e. 49.8% @ <200% poverty is in the 79^th percentile on Table IV-2; the 79^th percentile for poverty from Table IV-3 shows 466 additional need factor should be added-very high poverty is correlated with greater need and less access to care).  The same process is followed for each of the nine need factors to get total score to be added to the effective barrier free ratio calculated above

[1] An alternative treatment would be to discard any statistically insignificant estimates.  We have strong conceptual biases against employing such stepwise procedures.

* Black, R. A., and Chui, K.-F. (1981). Comparing schemes to rank areas according to degree of health manpower shortage. Inquiry, 18(3), 274-280.

[U1]Is this level set high enough to capture the working poor, who are disproportionately represented among the uninsured?

[U2] Since HRSA is already adjusting for age and gender in “step 1,” why is HRSA adjusting for age again here in “step 2”?

[U3] Please explain why low population density is a good proxy for higher healthcare need and/or utilization and/or demand for primary care.