Provider Retention in High Need Areas. Executive Summary

12/22/2014

Since 1972, the National Health Services Corps (NHSC) has provided scholarships and loan repayment incentives for a large number of primary care, mental health, and dental health clinicians to work in Health Professional Shortage Areas (HPSAs) across the country. The NHSC deploys annually almost 9,000 health care professionals to thousands of sites across the country. These clinicians include physicians, dentists, certified nurse practitioners, certified nurse-midwives, physician assistants, registered dental hygienists, health service psychologists, licensed clinical social workers, psychiatric nurse specialists, marriage and family therapists, and licensed professional counselors. The objective of this study was to examine short- and long-term retention in high-need areas of providers who participated in the NHSC’s Loan Repayment Program (LRP) and Scholarship Program (SP) and compare their retention with retention of non-participants. We also conducted multivariate regression analyses to provide additional insights into the individual-level factors and local area characteristics that are associated with retention of program participants in HPSAs.

For this study, we constructed two provider-level analytic datasets based on NHSC administrative datasets and other publicly available and proprietary datasets. The first analytic dataset was constructed using NHSC data, Provider360 data (a proprietary dataset from OptumInsight Corporation, including detailed information on virtually the entire population of medical providers) and Medicare provider data. Using a number of individual-level variables, we matched the NHSC administrative files with Provider360 data and then with a Medicare provider file, obtained by rolling up Medicare claims at the provider level. The resulting analytic file contains annual information on over 1 million non-NHSC providers as well as on 8,973 NHSC participants (out of the total of 22,703 participants from NHSC data). The resulting dataset allows us to track the annual location of participants and non-participants (at the zip code level) over the timeframe between 2005 and 2011. The main advantage of this analytic dataset stems from the fact that it allows us to track providers after they complete their NHSC service.

The second analytic dataset relied mainly on NHSC and Provider360 data and its main feature was that it provided information on the NHSC providers’ location in two points in time: the year of NHSC program termination and December 2013 (i.e., the time when Provider360 information was recorded). The number of NHSC participants in this dataset increases to about 18,500. Nonetheless, once we compared the HPSA retention rates of participants after program completion from the first analytic dataset with the retention rates obtained with the second analytic dataset, we found them to be virtually indistinguishable from each other for primary care and mental health providers.1 We concluded that the first dataset provided a sufficiently representative picture of the entire sample of NHSC participants, and in addition it provided us with the opportunity to track providers’ location yearly. As a result, most of our quantitative analyses were conducted using this dataset.

It is also important to note that we dropped from the retention analyses the NHSC participants who left service in 2013, the last year of our timeframe. Of the initial sample of 8,973 participants we identified in the first analytic dataset, we ended up using a number of 6,296 participants, while of 18,500 participants in the second analytic dataset, we ended up using a number of 11,210 participants.

To guide the interpretation of our empirical analyses, we constructed a conceptual framework in which we modeled the location choices of providers across HPSA and non-HPSA locations. This framework yielded a sharp prediction – retention of NHSC participants in HPSAs after the completion of their obligations can never be higher than the retention of providers who choose to locate in those areas without participating in the program. The most important ingredients in our theoretical model were the concepts of being ‘fit’ for the program and the individual provider’s ‘preference’ for a HPSA location (both depending on individual characteristics that are unobservable in the data available for this project). The correlation between these two variables has a direct implication for the retention of participants in HPSAs. In the limiting case where acceptance into the NHSC program is based solely on preferences for being in a HPSA location, the program selects individuals who would have served in high-need areas even in the absence of the program, and therefore retention differences between participants and non-participants are nil. As the correlation between location preferences and program fit weakens, the program tends to select - at least to some extent - individuals who would not have gone to high-need areas in the absence of the program. In this case, participant retention after program completion will tend to be lower than non-participant retention. Somewhat counterintuitively, a lower retention rate for participants is a signal of the program’s success (not failure) in attracting to high-need areas providers who would not have located there in the absence of the program. As we show in a model simulation in Chapter VIII, the number of provider-years in HPSAs the NHSC obtains from participants after program completion is highest when the correlation between fit for the program and HPSA preference is zero.

We defined four measures of retention at the provider level:

  • Serving in the same HPSA and in the same county (‘same HPSA – same county’). This variable takes the value of 1 if the NHSC provider remains in the same county as the one where he or she served in the NHSC, and 0 otherwise.

  • Serving in a HPSA in another county (‘HPSA – other county’). This variable takes the value of 1 if the NHSC provider remains in a HPSA that is located in a different county than the one in which he or she served while in NHSC service, and 0 otherwise.

  • Serving in a non-HPSA from the same county (‘non-HPSA – same county’). This variable takes the value of 1 if the NHSC provider moves to a non-HPSA area from the county where he or she served while in the NHSC, and 0 otherwise.

  • Serving in a non-HPSA in another county (‘non-HPSA – another county’). This variable takes the value of 1 if the NHSC provider moves to a non-HPSA area from another county than the county he or she served while in the NHSC, and 0 otherwise.

Combining the first two measures, we also constructed an ‘any HPSA’ measure, taking the value of 1 if the provider remains in any HPSA and 0 otherwise. To ensure comparability across all providers, we defined these measures for non-participants as well. While in the case of participants, the reference point in time was the end of the NHSC service, for non-participants we chose the first year (“start year”) that the non-participants appear in Medicare data as their reference point-in-time. Calculating these measures for each cohort, we were able to construct retention rates one year after separation from NHSC, two years after separation from the NHSC and so on. In the case of non-participants the annual retention rates were calculated as one year since start year, two years since start year and so on for each cohort.

Using the first analytic dataset, we found that about 49% of NHSC primary care participants were located in the same HPSA one year after obligation completion, and 82% were located in any HPSA location (Figure ES.1). By the 6th year after obligation completion, 35% of participants were located in the same HPSA where they served during NHSC service, and 72% of them were in any HPSA location. Consistent with the main prediction of our theoretical model, non-participant retention in HPSAs is higher, with the difference being much bigger for retention in the same HPSA than retention in any HPSA location. Another important finding was that much of the geographic mobility of participants was from one HPSA location to another HPSA location. Also, after an initial higher mobility, participants have better retention in HPSAs than non-participants.

Figure ES. 1: Retention Rates of NHSC Participants and Non-Participants—Primary Care

Retention Rates of NHSC Participants and Non-Participants—Primary Care

Figure ES. 2: Retention Rates of NHSC Participants and Non-Participants—Mental Health

Retention Rates of NHSC Participants and Non-Participants—Mental Health

Similar to primary care HPSAs, non-participants in mental health HPSAs were much more likely to stay in the same HPSA than participants (Figure ES.2). Their retention rate declined by 3-4 percentage points each year since the start year, while the retention rate in any HPSA declined at a lower rate, about 2-3 percentage points. Also, the retention rates in any mental health HPSA was very similar across participants and non-participants, especially in the further out years.

Next, we estimated multivariate regression models at the provider level in which we modeled the ‘same HPSA’ and ‘any HPSA’ outcomes as a function of participation in NHSC programs, a set of individual-level characteristics (age, gender, provider type), Census division indicator variables and local area characteristics at the zip code level (average family income, poverty rate, percent White, percent Black, fraction of the population over 25 years of age with a high school degree and percent of the population over the age of 65).

As shown in Figure ES.3, in the first year since separation/start year, NHSC participants are 37.0% less likely to remain in the same HPSA relative to non-participants. This difference was obtained by netting out the impact of other (observable) individual socio-demographic and local area characteristics. Given that the unadjusted difference in the retention rate in the same HPSA in the first separation/start year is 42.8 percentage points (=83.5-40.7, from Figure ES.1), it follows that 86.4% (=37/42.8) of the observed difference in primary care ‘same HPSA’ retention is explained by NHSC participation. A similar fraction, 85.6% (=13.7/16.0), in the observed retention difference was explained by NHSC participation in the case of primary care ‘any HPSA’ in the first separation/start year. The other ratios between adjusted and unadjusted retention differences in retention between participants and non-participants remained similar for the other further out separation/start years, for both primary care ‘same HPSA’ and ‘any HPSA’ measures.

Figure ES. 3: Differences in the Participants’ Retention Probability Relative to Non-Participants—Primary Care

Differences in the Participants’ Retention Probability Relative to Non-Participants—Primary Care

Figure ES.4 presents the regression-adjusted retention differentials by NHSC participation for mental health HPSAs. The retention differentials are lower across the board for the ‘same HPSA’ measure than in the case of primary care HPSAs. As shown in Figure ES.2, the unadjusted retention in ‘any HPSA’ was higher for non-participants in the first separation/start years than that of participants. Nonetheless, after accounting for individual-level and local area characteristics, there was no statistically significant difference between the retention of participants and non-participants in mental health HPSAs for any of the separation/start years.

Figure ES. 4: Differences in the Participants’ Retention Probability Relative to Non-Participants—Mental Health

Differences in the Participants’ Retention Probability Relative to Non-Participants—Mental Health

Other findings from the regression analyses include the following. First, HPSA retention rises with age and local characteristics, but differences by gender, discipline, and Census division are small. Second, as reflected by regression estimates showing that providers have higher retention in poorer and less educated communities, providers select into HPSAs based on their preferences for serving underserved populations.

1 This finding holds despite the fact that pediatricians are under-represented in Medicare data.

 

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