Carla Shoff

Untangling the associations among distrust, race, and neighborhood social environment: a social disorganization perspective.

Over the past decade, interest in exploring how health care system distrust is associated with individual health outcomes and behaviors has grown substantially, and the racial difference in distrust has been well documented, with African Americans demonstrating higher distrust than whites. However, relatively little is known about whether the individual-level determinants of distrust differ by various dimensions of distrust, and even less is understood regarding whether the race-distrust association could be moderated by the neighborhood social environment. This study used a dual-dimensional distrust scale (values and competence distrust), and applied social disorganization theory to address these gaps. We combined the 2008 Philadelphia Health Management Corporations household survey (N = 3746 adult respondents, 51% of which are of African American race) with neighborhood-level data (N = 45 neighborhoods) maintained by the 2000 U.S. Census and the Philadelphia Police Department. Using multilevel modeling, we found that first, after controlling for individual- and neighborhood-level covariates, African American residents have greater values distrust than whites, but no racial difference was found in competence distrust; second, competence distrust is more likely to be determined by personal health status and access to health care services than is values distrust; and third, ceteris paribus, the association between race and values distrust was weakened by the increasing level of neighborhood stability. These results not only indicate that different aspects of distrust may be determined via different mechanisms, but also suggest that establishing a stable neighborhood may ameliorate the level of distrust in the health care system among African Americans. As distrust has been identified as a barrier to medical research, the insight provided by this study can be applied to develop a health care system that is trusted, which will, in turn, improve population health.

Understanding maternal smoking during pregnancy: Does residential context matter?

The goal of this paper was to investigate whether or not the factors beyond individual characteristics were associated with maternal smoking during pregnancy. Social capital has been found to have both negative and positive implications for health behaviors, and this study attempted to understand its association with maternal smoking during pregnancy. Specifically, the association between county-level social capital and rurality and maternal smoking during pregnancy was investigated. In this study, Putmans definition of social capital was used (e.g., connections among individuals-social networks and the norms of reciprocity and trustworthiness that arise from them). The ecological dimension of rurality was used to define rurality, where rural areas are smaller in population size and are less densely populated when compared to non-rural areas. Using data for all women who gave birth during the year 2007 in the United States, we implemented a series of multilevel logistic regression models. The results showed that social capital was significantly associated with maternal smoking during pregnancy. Specifically, higher social capital in a county was associated with higher odds that women smoked during their pregnancy. However, in rural counties, higher social capital was associated with a decrease in the odds that a woman smoked during her pregnancy. A one unit increase in the social capital index was found to reduce the risk of smoking during pregnancy among those women living in rural counties by 11 percent. The results also showed that improvement of the socioeconomic status of the counties in which women live reduced the risk of maternal smoking during pregnancy. As this study found that factors beyond individual characteristics are important for reducing the risk that women smoked during pregnancy, county characteristics should be taken into account when developing policies focused on intervening maternal smoking during pregnancy.

Individual Health Care System Distrust and Neighborhood Social Environment: How Are They Jointly Associated with Self-rated Health?

Americans’ distrust in the health care system has increased in the past decades; however, little research has explored the impact of distrust on self-rated health and even less is known about whether neighborhood social environment plays a role in understanding the relationship between distrust and self-rated health. This study fills these gaps by investigating both the direct and moderating associations of neighborhood social environment with self-rated health. Our analysis is based on the 2008 Philadelphia Health Management Corporation’s household survey and neighborhood-level data. Findings from multilevel logistic regression show that after controlling for individual- and neighborhood-level covariates, distrust is directly and adversely related to self-rated health, and that neighborhood social affluence and stability are directly and negatively associated with the odds of reporting poor/fair health. Neighborhood disadvantage and crime rates are not directly related to self-rated health, but increase the odds of having poor/fair health via distrust. Overall, our results suggest that macro-level actions can alter individual’s perception of residential environment and lead to improved health. To improve the public health in an urban setting, rebuilding confidence in the health care system is integral, and the policies that help establish safe and cohesive neighborhoods may reduce the adverse effect of distrust on self-rated health.

Using quantile regression to examine the effects of inequality across the mortality distribution in the U.S. counties

The U.S. has experienced a resurgence of income inequality in the past decades. The evidence regarding the mortality implications of this phenomenon has been mixed. This study employs a rarely used method in mortality research, quantile regression (QR), to provide insight into the ongoing debate of whether income inequality is a determinant of mortality and to investigate the varying relationship between inequality and mortality throughout the mortality distribution. Analyzing a U.S. dataset where the five-year (1998-2002) average mortality rates were combined with other county-level covariates, we found that the association between inequality and mortality was not constant throughout the mortality distribution and the impact of inequality on mortality steadily increased until the 80th percentile. When accounting for all potential confounders, inequality was significantly and positively related to mortality; however, this inequality-mortality relationship did not hold across the mortality distribution. A series of Wald tests confirmed this varying inequality-mortality relationship, especially between the lower and upper tails. The large variation in the estimated coefficients of the Gini index suggested that inequality had the greatest influence on those counties with a mortality rate of roughly 9.95 deaths per 1000 population (80th percentile) compared to any other counties. Furthermore, our results suggest that the traditional analytic methods that focus on mean or median value of the dependent variable can be, at most, applied to a narrow 20 percent of observations. This study demonstrates the value of QR. Our findings provide some insight as to why the existing evidence for the inequality-mortality relationship is mixed and suggest that analytical issues may play a role in clarifying whether inequality is a robust determinant of population health.

Examining the spatially non-stationary associations between the second demographic transition and infant mortality: A Poisson GWR approach.

Based on ecological studies, second demographic transition (SDT) theorists concluded that some areas in the US were in vanguard of the SDT compared to others, implying spatial non-stationarity may be inherent in the SDT process. Linking the SDT to the infant mortality literature, we sought to answer two related questions: Are the main components of the SDT, specifically marriage postponement, cohabitation, and divorce, associated with infant mortality? If yes, do these associations vary across the US? We applied global Poisson and geographically weighted Poisson regression (GWPR) models, a place-specific analytic approach, to county-level data in the contiguous US. After accounting for the racial/ethnic and socioeconomic compositions of counties and prenatal care utilization, we found (1) marriage postponement was negatively related to infant mortality in the southwestern states, but positively associated with infant mortality in parts of Indiana, Kentucky, and Tennessee, (2) cohabitation rates were positively related to infant mortality, and this relationship was stronger in California, coastal Virginia, and the Carolinas than other areas, and (3) a positive association between divorce rates and infant mortality in southwestern and northeastern areas of the US. These spatial patterns suggested that the associations between the SDT and infant mortality were stronger in the areas in vanguard of the SDT than in others. The comparison between global Poisson and GWPR results indicated that a place-specific spatial analysis not only fit the data better, but also provided insights into understanding the non-stationarity of the associations between the SDT and infant mortality.

Spatial Variation in the Relationship between Hispanic Concentration and County Poverty: A Migration Perspective

Racial/ethnic minority concentration is generally positively related to county poverty. Yet, spatial variation in this relationship may call into question the meaning attached to racial/ethnic concentration. We argue that racial/ethnic concentration reflects more than just the concentration of individuals from a disadvantaged group. In addition, we extend previous work by taking a migration perspective to explain spatial non-stationarity in racial/ethnic concentration’s relationship with county poverty. Factors related to the migration process, including migrant selectivity and spatial differentiation in place characteristics, could alter the relationship between a minority group’s concentration and poverty. We employ spatially informed methods and 2006–2010 American Community Survey data to examine the relationship between Hispanic concentration and county poverty. The GWR results indicate significant spatial variation in the percent Hispanic-county poverty relationship. Hispanic migration regimes capture some of the observed relationship non-stationarity, suggesting migration-related processes partially drive Hispanic-county poverty relationship non-stationarity. However, we discuss other explanations that should be considered in future research. This work advances research on spatial inequality by examining the social implications of migration and by investigating the role of place in shaping the meaning of minority concentration.

Revisiting the Rural Paradox in US Counties with Spatial Durbin Modeling

The rural paradox refers to the phenomenon that the standardized mortality rates are lower in rural than in urban areas despite the relatively poor socioeconomic profiles among rural residents. Previous research on the geographic mortality differential has failed to recognize the complexity of the concept of rurality and the spatial structure underlying the ecological mortality data has not been fully utilized to advance our understanding of the rural paradox. Drawing from the drift and breeder hypotheses, this study first uses county-level data to measure “rural” with three distinct aspects, namely ecological dimension, economic integration, and natural resources dependency. Then, it employs the spatial Durbin approach to capture the exogenous relationships between the mortality of a county and the features of its neighbors. The key findings include that (1) the drift hypothesis (i.e., internal migration) did not appear to explain the rural paradox, but the breeder hypothesis (i.e., exposures to environments) partially accounts for the rural-urban mortality disparity, (2) the associations between the ecological dimension and economic integration with mortality were explained after accounting for the exogenous relationships, (3) the observed spatial feedback effects reflected the spatial dynamics across county boundaries, and (4) the spatial dynamic processes between mortality and its determinants were largely confined to the first- and second-order neighbors. The results of this study indicate that future ecological mortality research should further utilize the spatial structure to explain the variation of mortality across space.