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Neighborhood attributes have been shown to influence health, but advances in neighborhood research has been constrained by the lack of neighborhood data for many geographical areas and few neighborhood studies examine features of nonmetropolitan locations. We leveraged a massive source of Google Street View (GSV) images and computer vision to automatically characterize national neighborhood built environments. Using road network data and Google Street View API, from December 15, 2017-May 14, 2018 we retrieved over 16 million GSV images of street intersections across the United States. Computer vision was applied to label each image. We implemented regression models to estimate associations between built environments and county  health outcomes , controlling for county-level demographics, economics, and  population density . At the county level, greater presence of highways was related to lower chronic diseases and  premature mortality . Areas characterized by street view images as ‘rural’ (having limited infrastructure) had higher obesity,  diabetes , fair/poor self-rated health, premature mortality, physical distress, physical inactivity and teen birth rates but lower rates of excessive drinking. Analyses at the  census  tract level for 500 cities revealed similar adverse associations as was seen at the county level for neighborhood indicators of less urban development. Possible mechanisms include the greater abundance of services and facilities found in more developed areas with roads, enabling access to places and resources for promoting health. GSV images represents an underutilized resource for building national data on neighborhoods and examining the influence of built environments on community health outcomes across the United States.

The past three decades have seen the advent of major transformations in the Indian economy. The economy has achieved average growth rates of 5–9%, education has risen sharply for both men and women, fertility rates have declined, and infrastructure facilities, particularly access to electricity, cooking gas and piped water, have improved. All these factors are expected to reduce the demand for women’s time spent in domestic chores and increase their opportunities for paid work. Paradoxically, however, the National Sample Surveys document a substantial decline in women’s work participation rates (WPRs), particularly for rural women. Optimistic interpretation of these trends suggests that increasing prosperity accounts for women’s labour force withdrawal. For young women, rising school and college enrolment is incompatible with demands of the workforce. For both young and older women, rising prosperity allows for withdrawal from economic activities to focus on domestic duties. Pessimistic interpretations of these trends suggest that it is absence of suitable jobs rather than women’s withdrawal from the labour force that accounts for declining female work participation. A third explanation focuses on increasing measurement errors in work participation data from the National Sample Surveys. This paper examines these diverse explanations using data from National Sample Surveys and India Human Development Surveys for 2004–2005 and 2011–2012 and finds that: (1) Decline in rural women’s work participation recorded by National Sample Surveys may be overstated; (2) supply factors explain a relatively small proportion of the decline in women’s work participation rates; (3) public policies such as improvement and transportation facilities and MGNREGS that enhance work opportunities for women are associated with increased participation by women in the work force.

Predictive modeling methods from the field of machine learning have become a popular tool across various disciplines for exploring and analyzing diverse data. These methods often do not require specific prior knowledge about the functional form of the relationship under study and are able to adapt to complex non-linear and non-additive interrelations between the outcome and its predictors while focusing specifically on prediction performance. This modeling perspective is beginning to be adopted by survey researchers in order to adjust or improve various aspects of data collection and/or survey management. To facilitate this strand of research, this paper (1) provides an introduction to prominent tree-based machine learning methods, (2) reviews and discusses previous and (potential) prospective applications of tree-based supervised learning in survey research, and (3) exemplifies the usage of these techniques in the context of modeling and predicting nonresponse in panel surveys.

Sexual and gender minority (SGM) youth experience high rates of victimization leading to health disparities. Community size and community climate are associated with health outcomes among SGM youth; however, we lack studies that include them as covariates alongside victimization to understand their collective impact on health. This study utilized minority stress theory to understand how community context shapes experiences of victimization and health among SGM youth. SGM youth in one Midwestern U.S. state completed an online survey ( n = 201) with measures of physical health, mental health, community context, and victimization. Data were analyzed via multiple regression using a path analysis framework. Results indicate that perceived climate was associated with mental, but not physical, health; Community size was unrelated to health outcomes. Victimization mediated the association between community climate and mental health. Findings are discussed in light of current literature and implications for research and practice are shared.

Sexual and gender minority (SGM) youth experience high rates of victimization leading to health disparities. Community size and community climate are associated with health outcomes among SGM youth; however, we lack studies that include them as covariates alongside victimization to understand their collective impact on health. This study utilized minority stress theory to understand how community context shapes experiences of victimization and health among SGM youth. SGM youth in one Midwestern U.S. state completed an online survey ( n  = 201) with measures of physical health, mental health, community context, and victimization. Data were analyzed via multiple regression using a path analysis framework. Results indicate that perceived climate was associated with mental, but not physical, health; Community size was unrelated to health outcomes. Victimization mediated the association between community climate and mental health. Findings are discussed in light of current literature and implications for research and practice are shared.

Abstract Sexual and gender minority (SGM) youth face marginalization and oppression on the basis of their SGM identity, and they often lack traditional support systems to deal with these minority stressors. SGM community resources may alleviate the impact of the stressors that SGM youth face, but these have not been studied in relation to the size and climate of SGM youth's communities. This mixed-methods study examined the relationship between community size and the climate toward SGM individuals and the availability and utilization of SGM community resources. Survey findings indicate that nonmetropolitan communities were associated with less availability and utilization; hostile communities were associated with lower availability only. Interview findings reveal nuanced complexities on the relationship between community and resource provision. Implications for future research and community practice are discussed.

Black sexual and gender minorities (SGM) are at greater risk for HIV compared to their White, cisgender, heterosexual counterparts. Linkage to culturally sensitive health care is, therefore, pivotal for HIV prevention and treatment of Black SGM. Unfortunately, social and structural challenges undermine Black SGM individuals' abilities to obtain adequate health care services, indicating a need to understand Black SGM perceptions of health care. To address this gap, we interviewed Black men who have sex with men and transwomen about their experiences with health care providers. Participants discussed needs and concerns, including provider SGM identity diversity and education; assumptions, judgment, stigma, and discrimination; and ability to establish a personal bond, trust, and familiarity. Black SGM indicated that providers often did not meet their needs in different ways regarding their SGM identities. Findings suggest a need for provider cultural sensitivity education programs that address the needs of Black SGM in health care.

This article analyzes U.S. divorce trends over the past decade and considers their implications for future divorce rates. Modeling women’s odds of divorce from 2008 to 2017 using marital events data from the American Community Survey, I find falling divorce rates with or without adjustment for demographic covariates. Age-specific divorce rates show that the trend is driven by younger women, which is consistent with longer term trends showing uniquely high divorce rates among people born in the Baby Boom period. Finally, I analyze the characteristics of newly married women and estimate the trend in their likelihood of divorcing based on the divorce models. The results show falling divorce risks for more recent marriages. The accumulated evidence thus points toward continued decline in divorce rates. The United States is progressing toward a system in which marriage is rarer and more stable than it was in the past.

Although the decline in marriage has been cited as a possible contributor to the “despair” afflicting marginalized White communities, these studies have not directly considered mortality by marital status. This paper uses complete death certificate data from the Mortality Multiple Cause Files with American Community Survey data to examine age-specific mortality rates for married and non-married people from 2007 to 2017. The overall rise in White mortality is limited almost exclusively to those who are not married, for men and women. By comparison, mortality for Blacks and Hispanics has fallen or remained flat regardless of marital status (except for young, single Hispanic men). Analysis by education level shows death rates have risen most for Whites with the lowest education, but have also increased for those with high school or some college. Because mortality has risen faster for unmarried Whites at all but the lowest education levels, there has been an increase in the marriage mortality ratio. Mortality differentials are an increasingly important component of the social hierarchy associated with marital status.

Utilization of cost-effective essential preventive health services increased after the implementation of the Affordable Care Act’s (ACA) provision that non-grandfathered private insurers provide cost-effective preventive services without cost sharing in 2010. Little is known, however, whether this change is also observed among Asians in the US. We examined patterns of preventive services utilization among Asian subgroups relative to non-Latino whites (whites) after the implementation of the ACA’s preventive services provisions. Using 2013–2016 Medical Expenditure Panel Survey data, we examined utilization trends in preventive services among Asian Indians, Chinese, Filipinos, and other Asians relative to whites. We also ran logistic regression models to estimate the likelihood of having received each of the seven essential preventive services (routine checkups, flu vaccinations, cholesterol screenings, blood pressure checkups, Papanicolaou “pap” tests, mammograms, and colorectal cancer screenings). Compared to whites, Asians had higher rates of utilization of routine checkups, cholesterol screenings, and flu vaccinations, but they had lower utilization rates of blood pressure checkups, pap tests, and mammograms. The patterns of preventive services utilization differed across the Asian subgroups. All Asian subgroups, except for Filipinos, were less likely to have pap tests or mammograms than whites. Moreover, we observed a decreasing trend in having pap tests, mammograms, or colorectal cancer screenings among all Asian subgroups between 2013 and 2016. Our findings suggest that there are low cancer screening rates across Asian subgroups. This indicates the need for programs tailored to specific Asian subgroups to improve cancer screening.