Despite the recent flourishing of mediation analysis techniques, many modern approaches are difficult to implement or applicable to only a restricted range of regression models. This report provides practical guidance for implementing a new technique utilizing inverse odds ratio weighting (IORW) to estimate natural direct and indirect effects for mediation analyses. IORW takes advantage of the odds ratio's invariance property and condenses information on the odds ratio for the relationship between the exposure (treatment) and multiple mediators, conditional on covariates, by regressing exposure on mediators and covariates. The inverse of the covariate-adjusted exposure-mediator odds ratio association is used to weight the primary analytical regression of the outcome on treatment. The treatment coefficient in such a weighted regression estimates the natural direct effect of treatment on the outcome, and indirect effects are identified by subtracting direct effects from total effects. Weighting renders treatment and mediators independent, thereby deactivating indirect pathways of the mediators. This new mediation technique accommodates multiple discrete or continuous mediators. IORW is easily implemented and is appropriate for any standard regression model, including quantile regression and survival analysis. An empirical example is given using data from the Moving to Opportunity (1994-2002) experiment, testing whether neighborhood context mediated the effects of a housing voucher program on obesity. Relevant Stata code (StataCorp LP, College Station, Texas) is provided.

Introduction: The objective of this study was to investigate the association between state-level publicly expressed sentiment towards racial and ethnic minorities and birth outcomes for mothers who gave birth in that state. Methods: We utilized Twitter’s Streaming Application Programming Interface (API) to collect 1,249,653 tweets containing at least one relevant keyword pertaining to a racial or ethnic minority group. State-level derived sentiment towards racial and ethnic minorities were merged with data on all 2015 U.S. births (N=3.99 million singleton births). Results: Mothers living in states in the lowest tertile of positive sentiment towards racial/ethnic minorities had greater prevalences of low birth weight (+6%), very low birth weight (+9%), and preterm birth (+10%) compared to mothers living in states in the highest tertile of positive sentiment, controlling for individual-level maternal characteristics and state demographic characteristics. Sentiment towards specific racial/ethnic groups showed a similar pattern. Mothers living in states in the lowest tertile of positive sentiment towards blacks had an 8% greater prevalence of low birth weight and very low birth weight, and a 16% greater prevalence of preterm birth, compared to mothers living in states in the highest tertile. Lower state-level positive sentiment towards Middle Eastern groups was also associated with a 4–13% greater prevalence of adverse birth outcomes. Results from subgroup analyses restricted to racial/ethnic minority mothers did not differ substantially from those seen for the full population of mothers. Conclusions: More negative area-level sentiment towards blacks and Middle Eastern groups was related to worse individual birth outcomes, and this is true for the full population and minorities.

Objectives Using publicly available, geotagged Twitter data, we created neighborhood indicators for happiness, food and physical activity for three large counties: Salt Lake, San Francisco and New York. Methods We utilize 2.8 million tweets collected between February-August 2015 in our analysis. Geo-coordinates of where tweets were sent allow us to spatially join them to 2010 census tract locations. We implemented quality control checks and tested associations between Twitter-derived variables and sociodemographic characteristics. Results For a random subset of tweets, manually labeled tweets and algorithm labeled tweets had excellent levels of agreement: 73% for happiness; 83% for food, and 85% for physical activity. Happy tweets, healthy food references, and physical activity references were less frequent in census tracts with greater economic disadvantage and higher proportions of racial/ethnic minorities and youths. Conclusions Social media can be leveraged to provide greater understanding of the well-being and health behaviors of communities—information that has been previously difficult and expensive to obtain consistently across geographies. More open access neighborhood data can enable better design of programs and policies addressing social determinants of health.

Sentiments towards racial/ethnic racial/ethnic minorities may impact cardiovascular disease (CVD) through direct and indirect pathways. In this study, we assessed the association between Twitter-derived sentiments towards racial/ethnic minorities at state level and individual level CVDrelated outcomes from the 2017 Behavioral Risk Factor Surveillance System (BRFSS). Outcomes included hypertension, diabetes, obesity, stroke, myocardial infarction (MI), coronary heart disease (CHD), and any CVD from BRFSS 2017 (N=433,434 to 433,680 across outcomes). A total of 30 million race-related tweets were collected using Twitter Streaming Application Programming Interface (API) from 2015 to 2018. Prevalence of negative and positive sentiment towards racial/ ethnic minorities were constructed at state level and merged with CVD outcomes. Poisson regression was used, and all the models adjusted for individual level demographics as well as state level demographics. Individuals living in states with the highest level of negative sentiment towards racial/ethnic minorities had 11% higher prevalence of hypertension (

Social media represents a new type of real-time data that may enable public health officials to examine movement of norms, sentiment, and behaviors that may portend emerging issues or outbreaks-thus providing a way to intervene to prevent adverse health events and measure the impact of health interventions.