Place and Health
Session C in Room 1.302 D
Moderator: Seth Schwartz, Professor, Health Behavior and Health Education, College of Education, UT Austin
Ying Huang, Assistant Professor, Demography, College for Health, Community and Policy (HCAP), UT at San Antonio
Changing Neighborhood Conditions and Despair in the Mid-adulthood
Recent evidence suggests that despair-related conditions are on the rise among middle-aged adults in the United States. Although emerging studies have explored various individual- and contextual determinants of despair, only a few studies have used cross-sectional or time-series data to examine how changing neighborhood conditions (e.g., improving or worsening neighborhood contexts) are related to the prevalence of despair. Virtually no study has considered the mediating role that social disengagement. In this study, I use Add Health data to address these knowledge gaps. Analysis of the mediating role of social disengagement will inform efforts to reverse the rising tide of despair.
Xi Pan, Associate Professor, Sociology, Texas State University
Drinking Water Quality, Dyslipidemia, and Cognitive Function in Older Adults
Given the high prevalence of dyslipidemia and dementia in China, it is critical to study how drinking water quality is associated with dyslipidemia and cognitive
function and whether dyslipidemia mediates the relationship between drinking water quality and cognitive function in older adults. The current study selected 4,951 respondents aged 60 and above from the China Health and Retirement Longitudinal Study (CHARLS, 2015). Mixed effects models were conducted to assess the associations between drinking water quality or dyslipidemia and cognitive function. The mediation effects of dyslipidemia were examined by path analyses. Exposure to high quality drinking water was significantly associated with higher scores in mental status, episodic memory, and global cognition (β = 0.34, p < 0.001 for mental status; β = 0.24, p < 0.05 for episodic memory; β = 0.58, p < 0.01 for global cognition). Respondents who reported dyslipidemia diagnosis had higher scores in the three composite measures of cognitive function (β = 0.39, p < 0.001 for mental status; β = 0.27 p < 0.05 for episodic memory; β = 0.66, p < 0.001 for global cognition). An elevated blood triglycerides was only associated with higher scores in mental status (β = 0.21, p < 0.05). Self-reported dyslipidemia diagnosis was a suppressor, which increased the magnitude of the direct effect of drinking water quality on mental status, episodic memory, and global cognition. Improving drinking water could be a potential public health effort to delay the onset of cognitive impairment and prevent the dementia pandemic in older people.
Su Yeong Kim, Professor, Department of Human Development and Family Sciences, UT Austin
Discrimination, Air Pollution Exposure, and Housing Mobility of Mexican Immigrant Families
People of color and lower socioeconomic status groups in the United States are exposed to higher concentrations of air pollution. A sample of 604 linguistically isolated Mexican-origin families in central Texas provided data on demographics and psychosocial experiences. Outdoor air pollution concentrations at participants’ home addresses were based on high-resolution estimates of fine particulate matter (PM2.5). Lower PM2.5 concentrations were associated with reports of more ethnic discriminatory experiences, higher socioeconomic status, and higher perceived neighborhood safety. Families with mothers reporting a greater sense of neighborhood safety/acculturation levels tended to move from one area low in air pollutants to another.
Junfeng Jiao, Director of Urban Information Lab, Director of Texas Smart Cities, Founding Member, Good Systems Grand Challenge, Associate Professor, Community and Regional Planning Program, School of Architecture, UT Austin
Using Machine Learning to Predict Neighborhood-Scale Health Outcomes
Estimating health outcomes at a neighborhood scale is important for promoting urban health, yet costly and time-consuming. Here we present a machine-learning-enabled approach to predicting the prevalence of six common non-communicable chronic diseases at the census tract level. We apply our approach to the City of Austin and show that our method can yield fairly accurate predictions. In searching for the best predictive models, we have experimented with eight different machine learning algorithms and 60 predictor variables that characterize the social environment, the physical environment, and the aspects and degrees of neighborhood disorder. Our analysis suggests that (a) the sociodemographic and socioeconomic variables are the strongest predictors for tract-level health outcomes and (b) the historical records of 311 service requests can be a useful complementary data source as the information distilled from the 311 data often helps improve the models’ performance. The machine learning models yielded from this study can help the public and city officials evaluate future scenarios and understand how changes in the neighborhood conditions can lead to changes in the health outcomes. By analyzing where the most significant discrepancies between the predicted and the actual values are, we will also be ready to identify areas of best practice and areas in need of greater investment or policy intervention.
Keryn E. Pasch, Associate Professor, Health Behavior and Health Education, College of Education, UT Austin
Marketing of Unhealthy Products Around Schools
Marketers of unhealthy products (e.g. food/beverage and tobacco) use marketing at retail outlets to increase sales, and expose and promote their products to consumers, particularly youth and young adults. Exposure to tobacco marketing is one of the strongest and most consistent modifiable risk factors associated with use of cigarettes and other tobacco products and exposure to food/beverage marketing is associated with risk for obesity. This presentation will focus on research examining retail outlet marketing around middle schools, high schools, and colleges in the Texas, with a specific focus on vulnerable populations such as youth, young adults, communities of color, and individuals with greater depressive symptoms.
Session D in Room 1.302 E
Moderator: Néstor Rodríguez, Professor, Department of Sociology, UT Austin
Fernando Riosmena, Professor, Demography, Sociology, UT at San Antonio
Health of Aging Mexicans on Both Sides of the Border
The experience of both elderly Mexican immigrants in the United States and that of older Mexico-based adults -connected to the US via their own past migration or that of their children – is greatly influenced by the accumulation of social and economic experiences over the life course, including contextual factors that shape immigrant inclusion. Using nationally representative panel data for 2006-2018 in the United States and 2001-2018 in Mexico, we examine 1) how contemporary changes in factors affecting immigrant social inclusion are associated with the mental and physical health of Mexican immigrants and Mexico-based elderly with children in the United States; and 2) the way in which disadvantages experienced by these individuals across the life course are associated with chronic physical and cognitive health and mortality.
René Zenteno, Professor, Demography, UT at San Antonio
The Silent Non-Migrant Population: Understanding Immobility in Mexico
In Mexico, migration has been a big topic during the last sixty years, from the early studies of the massive mobility of people from rural to urban areas to the most recent great Mexican migration to the United States. However, most Mexicans never leave the place where they were born. Why do so many Mexicans prefer not to move or face severe constraints to move? Who resists migrating? Very few studies have explored the factors associated with the immobility of the Mexican population. By examining these questions, we would like to understand the structural forces that constrain or resist migration and explore its implication for migration policies.
Abby Weitzman, Assistant Professor, Department of Sociology, UT Austin
Respondent Driven Sampling Among Forced Migrants in Costa Rica
Forced migrants represent a growing share of all migrants worldwide, yet efforts to understand their migration dynamics and population parameters are thwarted by nonexistent sampling frames. In this study, we report on our attempt at respondent driven sampling (RDS) to generate a plausibly representative sample of forced migrants in Costa Rica. Our findings suggest that RDS can yield long and diverse chains of recruitment and may be an invaluable method for sampling and measuring populations of forced migrants.
Ernesto Amaral, Associate Professor, Department of Sociology, Texas A&M University
Effects of the COVID-19 Pandemic on the Restaurant Industry: Comparisons Between Immigrants and US-Born Workers
The COVID-19 pandemic reduced employment in the U.S. Restaurants were especially affected and they are the fourth largest industry in the country, have the lowest wages of any occupation group, and have overrepresentation of immigrants. We analyzed monthly data from the Current Population Survey (2000–2022) and conducted a nationally-representative survey with restaurant owners and managers (Fall 2021). Foreign-born workers in the restaurant industry had higher chances of either losing their jobs or keeping only low-paying jobs, compared to US-born workers. We observed rising hourly wages, increased cross training, and increased use of delivery and technology, requiring fewer front-of-the-house workers..