There exists controversy as to the impact gentrification of cities has on the well-being of minorities. Some accuse gentrification of causing health disparities for disadvantaged minority populations residing in neighborhoods that are changing as a result of these socioeconomic shifts. Past scholarship has suggested that fears of displacement and social isolation associated with gentrification lead to poorer minority health. However, there is a lack of research that directly links gentrification to minority health outcomes. We address this gap with individual data from the 2008 Philadelphia Health Management Corporation's Southeastern Pennsylvania Household Health Survey and census tract data from the 2000 Decennial Census and the 2006-2010 American Community Survey. We implement logistic multilevel models to determine whether and how a resident's self-rated health is affected by gentrification of their neighborhoods. We find that while gentrification does have a marginal effect improving self-rated health for neighborhood residents overall, it leads to worse health outcomes for Blacks. Accounting for racial change, while gentrification leading to increases in White population has no measurable effect on minority health, BBlack gentrificationl eads to marginally worse health outcomes for Black respondents. These results demonstrate the limitations that improvements of neighborhood socioeconomic character have in offsetting minority health disparities.

Despite recent declines, racial segregation remains a detriment to minority neighborhoods. However, existing research is inconclusive as to the effects racial segregation has on health. Some argue that racial segregation is related to poor health outcomes, whereas others suspect that racial segregation may actually lead to improved health for some minority communities. Even less is known about whether minority access to white neighborhoods improves health. We address these gaps with individual data from the 2010 Public Health Management Corporation's Southeastern Pennsylvania Household Health Survey and census tract data from the 2010 Decennial Census and the 2006-2010 American Community Survey. We implement logistic multilevel models to determine whether and how a resident's self-rated health is affected by the racial/ethnic segregation of their neighborhoods. Our key finding suggests that the effects of segregation on self-rated health depend on an individual's race/ethnicity, with blacks and Latino residents most likely to experience adverse effects. Particularly, minorities living in predominantly white communities have a significantly higher likelihood to report poor/fair health than they would in segregated minority neighborhoods. These findings make clear that access to white neighborhoods is not sufficient to improve minority health; fuller neighborhood integration is necessary to ensure all have health equity.

Gentrification has been argued to contribute to urban inequalities, including those of health disparities. Extant research has yet to conduct a systematic study of gentrification’s relation with neighborhood health outcomes nationally. This gap is addressed in the current study through the utilization of census-tract data from the Center for Disease Control’s 500 Cities project, the 2000 Census and the 2010–2014 American Community Survey to examine how gentrification relates to local self-rated physical health in select cities across the United States. We examine gentrification’s association with neighborhood rates of poor self-rated physical health. We contextualize this relationship by evaluating gentrification’s relation with city-level self-rated health inequalities. We find gentrification was significantly and positively related with self-rated physical neighborhood health outcomes. However, the presence and magnitude of gentrification within a city was not associated with health outcomes for cities overall. Based on these findings, we argue that gentrification’s health benefits for cities are limited at best, though gentrification does not appear to be associated with deepening city-level health inequalities, either.

There exists an active discussion as to the effects of racial/ethnic composition on community connection. Research has suggested that racial segregation is beneficial to one's community connection. To explore this dynamic, we investigate how an individual's community connection is determined by the racial/ethnic segregation of his or her neighborhood, among other independent variables. We implement multilevel models using individual data from the socioeconomic status of a neighborhood explains much of the variations in community connection, non-Hispanic Blacks living in predominantly White or mixed communities tend to have a weaker community connection than their counterparts in other types of neighborhood. This demonstrates that segregation and socioeconomic status explain community connection.

Several studies have recently applied sentiment-based lexicons to Twitter to gauge local sentiment to understand health behaviors and outcomes for local areas. While this research has demonstrated the vast potential of this approach, lingering questions remain regarding the validity of Twitter mining and surveillance in local health research. First, how well does this approach predict health outcomes at very local scales, such as neighborhoods? Second, how robust are the findings garnered from sentiment signals when accounting for spatial effects? To evaluate these questions, we link 2,076,025 tweets from 66,219 distinct users in the city of San Diego over the period of 2014-12-06 to 2017-05-24 to the 500 Cities Project data and 2010–2014 American Community Survey data. We determine how well sentiment predicts self-rated mental health, sleep quality, and heart disease at a census tract level, controlling for neighborhood characteristics and spatial autocorrelation. We find that sentiment is related to some outcomes on its own, but these relationships are not present when controlling for other neighborhood factors. Evaluating our encoding strategy more closely, we discuss the limitations of existing measures of neighborhood sentiment, calling for more attention to how race/ethnicity and socio-economic status play into inferences drawn from such measures.