Pay-for-performance models could widen disparities in the quality of care if they systematically penalize physicians who care for disadvantaged populations.
14 Adjustment for the sociodemographic characteristics of a physician's patients could ameliorate this concern, but may have the unintended consequence of rewarding substandard care if sociodemographic factors are not consistently associated with physician performance.
15 We examined rates of delivery of preventive services using common quality metrics for a large population of primary care physicians and found that associations between patient sociodemographics and performance were both inconsistent across different measures and unstable between time periods.
Specifically, of the four sociodemographic characteristics we evaluated, two (patient residential area-level household income and panel Medicaid eligibility) were more likely to be significantly associated with provider performance, but even these associations did not appear consistent across measures or between time periods. Patient-panel Medicaid eligibility is associated in our sample with the most disparities in care, and these disparities were most likely to persist. The racial make-up of a physician’s panel was associated with only two of eight preventive care measures in Round 1, and one in Round 2. A general pattern appeared to be present, with many of the associations seen in the first round of measurement no longer present in the second round of measurement. Our results mirror those of other research demonstrating inconsistent associations between sociodemographics and patient ratings of care.
Influenza vaccine is a notable exception to our findings. Unlike the other preventive measures examined, the delivery of influenza vaccine was associated with significant disparities in care across all sociodemographic measures and in both study periods. This is particularly remarkable given that, unlike some of the other preventive measures we studied, influenza vaccine is indicated for all Medicare beneficiaries. Previous studies have identified potential sources of disparities in the delivery of influenza vaccine, including access to care, patient preferences or concerns about vaccines, and patient initiation of a visit specifically for vaccination.
16 Interestingly, delivery of pneumococcal vaccine, which is also indicated for all Medicare beneficiaries and presumably is influenced by similar barriers, was associated with only two associations in the first study period and none in the second study period, although the overall rate of service delivery was much lower. This further illustrates the challenge of predicting the relationship between sociodemographic factors and quality of care.
Among sociodemographic factors, the proportion of a provider’s patient panel that is Medicaid-eligible was most consistently associated with disparities in the provision of recommended preventive services. This may reflect that Medicaid-eligible patients have been found to have lower quality of care in other studies; they are also more likely to report poor health, which may increase the number of problems providers must address at each appointment and make it less likely that routine preventive services can be administered.
17,18 However, it may also be related to some other aspect of the way Medicaid services are administered or reimbursed. In anticipation of possible expansions of Medicaid programs nationwide, the delivery of preventive care to Medicaid recipients may merit further study.
A number of providers in our sample were exposed to a variety of payment incentives for performance during the study period. Some of these incentives were linked to physician behaviors that would likely have influenced our results (for example, incentives for meeting quality targets) while others reflected physician behaviors that would be less likely to do so (for example, incentives for maintaining high levels of patient satisfaction). Adjusting for exposure to these incentives did not significantly affect the presence or absence of associations between providers’ patient panel sociodemographics and their delivery of recommended preventive measures. It is still possible that the providers who are currently unexposed to incentives would, in fact, change their behavior significantly under a Medicare pay for performance program in ways related to their patients’ sociodemographic status. However, studies in the United Kingdom evaluating the impact of national pay-for-performance programs have shown a limited or inconsistent effect on disparities.
19,20Limitations
Our results should be interpreted within the context of our study’s limitations. We considered only Medicare beneficiaries, whose health disparities may be attenuated compared with other age groups since they are all eligible for some level of health insurance coverage. We considered only measures of preventive care; sociodemographic disparities in the delivery of more resource-intensive services, such as recommended diagnostic testing or treatment may exhibit more consistency and stability. Although our analytic sample was drawn from a nationally representative survey respondent pool, only physicians who treated the required minimum number of eligible patients and participated in both rounds of the CTS survey were examined. Thus, our results may not generalize to the broader population of physicians. An optimal analysis of health disparities related to race and ethnicity would include multiple categories for race and ethnicity, reflecting the true diversity of the US population. Unfortunately, the ways in which race and ethnicity have historically been collected and categorized for Medicare claims data have led to researcher concerns that these data are not accurate for categories other than white and black.
21 An analysis comparing race and ethnicity assignment in 1998–2001 Medicare Current Beneficiary Survey data with beneficiary self-report showed that Current Beneficiary Survey assignments were 96.5% sensitive for identifying White respondents and 95.6% sensitive for identifying Black respondents. However, sensitivities fell off rapidly for respondents identified as members of other racial and ethnic groups (54% for respondents identified as Asian and 35.7% for respondents identified as Hispanic, for example).
22 Because of concerns that claims data would not accurately capture other categories, race and ethnicity are combined in our analysis and categorized as White, Black and Other. Some markers of patient sociodemographic status were drawn from outside sources such as the US Census and measured at the level of the area rather than the individual. However, previous research suggests that area-level sociodemographic variables are valid predictors of individual health status.
23,24 Some services, such as influenza vaccination, may be delivered outside of physicians’ offices (e.g., by free vaccination vans), which may bias our results toward stronger associations between sociodemographics and service delivery as disadvantaged populations may be more reliant on these alternative sites of care. Lastly, a minority (19%) of the physicians in our sample were exposed to performance incentives during our initial study period, which makes it difficult to determine what would have happened in the absence of such incentives. However, we adjusted for exposure to incentives, and our findings were robust when we repeated our analyses excluding these physicians (data not shown).
We chose not to adjust for patients’ underlying health status, except to ensure that delivery of preventive services was measured only for the appropriate patients. For example, in measuring providers’ measurement of Hemoglobin A1c, we only considered patients who were known to have diabetes, but did not distinguish among diabetics who do and do not have comorbidities such as hypertension. While adjusting for patients’ comorbid illness is standard in many studies, we did not feel it was relevant to our research questions since none of these comorbidities would affect providers’ obligation to perform recommended services.
Despite these limitations, our study provides important insights into the potentially complex interplay between performance-based incentive programs and health disparities.
Our study raises concerns about the strategy of adjusting performance based on patient panel sociodemographics to mitigate the potential risk that pay-for-performance would worsen disparities. This could occur if adjustment for these factors had the unintended consequence of legitimizing differences in quality of care that many low-performing physicians can (and should) change, and thus lowering the average quality of care received by poor and minority patients. Unnecessary adjustment could let those who care for underserved populations perform at a lower level and still receive the same performance bonuses as other physicians who treat less disadvantaged patients but have higher levels of performance before adjustment. Our study also highlights the complexity of specifying an adjustment model. In our data, adjusters selected based on year 2000 data would not be relevant a few years later as the predictors of performance and the measures they are associated with shifted considerably.
Given the inconsistency of the relationships between patient panel sociodemographics and the quality of care delivered by individual physicians, adjusting performance scores for patient panel sociodemographics may not be routinely useful in a Medicare pay-for-performance program.
1 Eric Schneider, MD, MSc,2 Hoangmai H. Pham, MD, MPH,3 and Peter B. Bach, MD, MAPP4
