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Author(s): Tran, Linda Diem | Advisor(s): Ponce, Ninez | Abstract: Gentrification is a dynamic process that changes the physical, economic, social, and cultural characteristics of historically underserved neighborhoods. This neighborhood transition process can improve the material and environmental circumstances of some residents and bring forth harmful consequences such as heightened financial stress and residential displacement for other community members. The subsequent impact of gentrification on population health is understudied, and little is known about how gentrification influences the mental wellness of residents.This dissertation advances the small but growing literature on the relationship between gentrification and adult mental health. Using multiple data sources, we identified Southern California neighborhoods that gentrified between 2010 and 2015 and investigated the impact of living in a gentrified neighborhood on mental health distress. Econometric techniques such as instrumental variables estimation and propensity score analyses were applied to reduce bias arising from residential selection and reverse causality.The first study compared three quantitative approaches for identifying gentrified neighborhoods and demonstrated that each approach generated a different set of results. Findings highlighted the importance of the strategy used for identifying gentrified neighborhoods, especially when assessing gentrification’s effects on health outcomes. The second study used five years of pooled data from the California Health Interview Survey to examine the causal relationship between gentrification and adult mental health. Relative to living in a low-income and not gentrified neighborhood, living in a gentrified neighborhood was associated with increased likelihood of serious psychological distress among longtime residents, renters, and people with low incomes. In the third study, we evaluated reasons for moving between residents who moved within gentrified and not gentrified neighborhoods and found evidence that people in gentrified neighborhoods were more likely to experience within-neighborhood displacement. Residents who experienced within-neighborhood displacement had greater likelihoods of having serious psychological distress.Taken together, findings suggest that gentrification imposes a mental health cost on longtime residents and the most financially vulnerable residents, which has important implications for population health. By elevating levels of mental health distress of population groups who are already disproportionately exposed to stressors, gentrification can exacerbate mental health inequities.

Objective To assess racial/ethnic differential impacts of the ACA's Medicaid expansion on low-income, nonelderly adults' access to primary care. Data sources Behavioral Risk Factor Surveillance System, State Physicians Workforce Data Book, and Bureau of Labor Statistics, in 2013 and 2015. Study design Quasi-experimental design with difference-in-differences analyses. Outcomes included health insurance coverage, having personal doctor(s), being unable to see doctors because of cost, and receiving a flu shot. We tested racial/ethnic differential impacts using the "Seemingly unrelated estimation" method. Multiple imputations and survey weights were used. Data collection/extraction methods Low-income, nonelderly adults were identified based on age, household income, and family size. Principal findings Among the low-income, nonelderly adults, Medicaid expansion was associated with statistically significant gains in health insurance coverage, having personal doctors, and affordability. Hispanics got the fewest benefits, which significantly widened racial/ethnic disparities for the Hispanic group. Racial/ethnic disparity in having personal doctors narrowed for non-Hispanic black and non-Hispanic others, although not statistically significant. Conclusion Medicaid expansion improved access to primary care, but it had differential effects among racial/ethnic groups resulting in mixed effects on disparities. Further research is necessary to develop tailored policy tools for racial/ethnic groups.

Racial and ethnic health disparities are well documented (Institute of Medicine 2003; Kim et al. 2011, 2012; Agency for Healthcare Research and Quality 2012). The Institute of Medicine recommends that to improve quality of care across racial/ethnic groups that valid and reliable data on race/ethnicity must be collected (Institute of Medicine 2009). Quality improvement efforts to reduce disparities in care across groups rely upon the existence of valid and reliable measurements of patient characteristics, including race and ethnicity (R/E). The National Quality Forum now recommends that future performance measures be stratified—or calculated separately—by sociodemographic factors, including income, race, and education (National Quality Forum 2014). The Affordable Care Act requires standardized collection of race/ethnicity across federal health care databases precisely for this reason (The Patient Protection and Affordable Care Act 2010). Hospital medical records data on patient sociodemographic characteristics serve as the foundation for identifying disparities in care and disease outcomes within and across medical systems. They are also the primary source of patient information for disease-based databases, such as cancer registries, which are the basis for identifying disparities in cancer occurrence and survival (Glaser et al. 2005). Despite their importance, hospital medical record data have proven to be problematic sources of demographic data. Several studies have shown that medical record data on R/E are subject to misclassification (Stewart et al. 1999; Kressin et al. 2003; Gomez and Glaser 2005; Gomez et al. 2005). Questions remain as to the consistency in collection of these data within and across hospitals (Stewart et al. 1999; Kressin et al. 2003; Gomez and Glaser 2005; Gomez et al. 2005). Efforts have been made to improve self-reported data, including periodic contact via postcard to elicit R/E (Arday et al. 2000) and introduction of the National Consumer Assessment of Health Plans (Morales et al. 2001). However, because the collection of valid and reliable self-reported data on R/E in health care continues to lag, analytic approaches to improve the accuracy of these measures have been attempted, including name-matching techniques to identify Hispanic and Asian-Pacific Islander enrollees (Morgan, Wei, and Virnig 2004; Wei et al. 2006; Eicheldinger and Bonito 2008) and Bayesian techniques (Elliott et al. 2008, 2009). Although great effort has been expended to develop processes to improve the validity and reliability of self-reported R/E in the health care setting, a major weakness has been a lack of metrics to track and feedback the accuracy and completeness of R/E data collected by hospitals. For example, in California, the Office of Statewide Health Planning and Development (OSHPD) examines hospital discharge data for completeness (low rates of “other” or “unknown” reported) and for internal consistency (patients admitted to the same hospital have the same R/E across encounters). Unfortunately, these checks are not equivalent to accuracy, which requires comparison to gold standard (self-reported) R/E information. These self-report patient-level data are harder to obtain. Nationwide, few, if any, organizations that manage the respective statewide hospital data are currently making this a part of their data improvement efforts. In order to improve data quality, we attempted to create a measure of overall accuracy of hospital reported R/E using the California inpatient data linked to the US Census, which could be used with all-payer hospital discharge datasets collected in California and other states. The hospital measure was validated through comparison to a gold standard–derived measure of accuracy. Finally, we attempted to use the metric to assess trends in data accuracy (new metric) and in data completeness (rate of missing/unknown) in California and six other demographically diverse states that submit data to the national Healthcare Cost and Utilization Project (HCUP). Our overarching goal was to create a validated measure that could be easily employed using existing data with the recognition that as more detailed information becomes available, more refined measures will create better estimates of hospital reporting.

Federally qualified health centers (FQHCs) are a critical source of primary care for disadvantaged populations with limited access to care, serving nearly one of four low-income Americans (National Association of Community Health Centers 2009). Over 90 percent of health center patients report incomes below 200 percent of the federal poverty level and 75 percent are either uninsured or covered by Medicaid; 35 percent are Hispanic/Latino, and 27 percent are African American (Taylor 2010). In largely urban counties, limited access to care has been attributed to not only the uneven geographic distribution of providers but also to the disinclination of providers to care for disadvantaged groups, rather than an overall deficit in physician supply (Fossett and Peterson 1989; Greene, Blustein, and Weitzman 2006). Thus, a community may develop an FQHC to serve its underserved population despite the relative proximity to other providers (Salinsky 2010).1 That some counties may actually demand FQHCs despite adequate supply suggests that within-county provider imbalances occur (Gaskin et al. 2012). We hypothesize that how segregated a county is by race/ethnicity and income may contribute to the difficulty in fairly distributing its primary care workforce, thus strongly contributing to the interest of counties in developing FQHCs. Understanding the influence of such community stratification factors could inform the policy discussions on allocation of federal resources for community health centers and broaden the policy debate to social reforms that might be requisite in building a more effective primary care safety net. In this study, we address the impact of one aspect of community social stratification: residential segregation on FQHC supply. Residential segregation has been defined as “the degree to which two or more groups live separately from one another in different parts of the urban environment” (Massey and Denton 1988). Prior research has shown that residential segregation is associated with a number of disparities in access and quality of care, including reduced physician visits, fewer ambulatory surgical facilities, lower supply of general surgeons and colorectal subspecialists, lower odds of receipt of appropriate breast cancer care, and delayed time to renal transplantation (Rodriguez et al. 2007; Haas et al. 2008; Hayanga et al. 2009a, b; Gaskin et al. 2011). Furthermore, studies of residents of integrated communities have shown either elimination or reversal of racial disparities in health and health outcomes (Gaskin et al. 2009; LaVeist et al. 2011). In the context of FQHCs, residential segregation may contribute to the need for safety net primary care services through multiple mechanisms: (1) geographic segregation of health services, with physicians physically distant from low-income and minority populations; (2) increased physician preferences to serve patients of similar race and socioeconomic background; and, as a consequence of (2), (3) lower rates of physician participation in Medicaid in segregated areas (Fossett and Peterson 1989; Greene, Blustein, and Weitzman 2006). Using national administrative data linked with social indicators, we studied the 8-year period of FQHC expansion from 2000 to 2007 to test our hypothesis that residential segregation by income and residential segregation by race/ethnicity is associated with the local supply of FQHCs.