Dana E. Goin

Optimizing matching and analysis combinations for estimating causal effects

Matching methods are common in studies across many disciplines. However, there is limited evidence on how to optimally combine matching with subsequent analysis approaches to minimize bias and maximize efficiency for the quantity of interest. We conducted simulations to compare the performance of a wide variety of matching methods and analysis approaches in terms of bias, variance, and mean squared error (MSE). We then compared these approaches in an applied example of an employment training program. The results indicate that combining full matching with double robust analysis performed best in both the simulations and the applied example, particularly when combined with machine learning estimation methods. To reduce bias, current guidelines advise researchers to select the technique with the best post-matching covariate balance, but this work finds that such an approach does not always minimize mean squared error (MSE). These findings have important implications for future research utilizing matching. To minimize MSE, investigators should consider additional diagnostics, and use of simulations tailored to the study of interest to identify the optimal matching and analysis combination.

Living in Violent Neighbourhoods is Associated with Gestational Weight Gain Outside the Recommended Range

BACKGROUND During pregnancy, most women do not meet gestational weight gain (GWG) guidelines, potentially resulting in adverse maternal and infant health consequences. Social environment determinants of GWG have been identified, but evidence on the relationship between neighbourhood violence and GWG is scant. Our study aims to examine the relationship between neighbourhood violence and GWG outside the recommended range. METHODS We used statewide vital statistics and health care utilization data from California for 2006-12 (n = 2 364 793) to examine the relationship of neighbourhood violence (quarters of zip-code rates of homicide and assault) in the first 37 weeks of pregnancy with GWG (categorized using the Institute of Medicines pregnancy weight gain guidelines). We estimated risk ratios (RR) and marginal risk differences, and analyses were stratified by maternal race/ethnicity and prepregnancy body mass index. RESULTS Residence in neighbourhoods with the highest quartile of violence was associated with more excessive GWG (adjusted RR 1.04, 95% confidence interval CI 1.03, 1.05), compared to the lowest quartile of violence; violence was not associated with inadequate GWG. On the difference scale, this association translates to 2.3% more women gaining weight excessively rather than adequately if all women were exposed to high violence compared to if all women were exposed to low violence. Additionally, associations between neighbourhood violence and excessive GWG were larger in non-white women than in white women. CONCLUSIONS These findings support the hypothesis that violence can affect weight gain during pregnancy, emphasizing the importance of neighbourhood violence as a public health issue.

Village community mobilization is associated with reduced HIV incidence in young South African women participating in the HPTN 068 study cohort

Adolescent girls and young women (AGYW) in South Africa bear a disproportionate burden of HIV. Community mobilization (CM), defined as community members taking collective action to achieve a common goal related to health, equity and rights, has been associated with increased HIV testing and condom use and has been called a ‘critical enabler’ for addressing the HIV epidemic. However, limited research has examined whether CM is associated with HIV incidence among AGYW.

Retirements of Coal and Oil Power Plants in California: Association With Reduced Preterm Birth Among Populations Nearby

Coal and oil power plant retirements reduce air pollution nearby, but few studies have leveraged these natural experiments for public health research. We used California Department of Public Health birth records and US Energy Information Administration data from 2001-2011 to evaluate the relationship between the retirements of 8 coal and oil power plants and nearby preterm (gestational age of <37 weeks) birth. We conducted a difference-in-differences analysis using adjusted linear mixed models that included 57,005 births-6.3% of which were preterm-to compare the probability of preterm birth before and after power plant retirement among mothers residing within 0-5 km and 5-10 km of the 8 power plants. We found that power plant retirements were associated with a decrease in the proportion of preterm birth within 5 km (-0.019, 95% CI: -0.031, -0.008) and 5-10 km (-0.015, 95% CI: -0.024, -0.007), controlling for secular trends with mothers living 10-20 km away. For the 0-5-km area, this corresponds to a reduction in preterm birth from 7.0% to 5.1%. Subgroup analyses indicated a potentially larger association among non-Hispanic black and Asian mothers than among non-Hispanic white and Hispanic mothers and no differences in educational attainment. Future coal and oil power plant retirements may reduce preterm birth among nearby populations.

Predictors of firearm violence in urban communities: A machine-learning approach

Abstract Interpersonal firearm violence is a leading cause of death and injuries in the United States. Identifying community characteristics associated with firearm violence is important to improve confounder selection and control in health research, to better understand community‐level factors that are associated with firearm violence, and to enhance community surveillance and control of firearm violence. The objective of this research was to use machine learning to identify an optimal set of predictors for urban interpersonal firearm violence rates using a broad set of community characteristics. The final list of 18 predictive covariates explain 77.8% of the variance in firearm violence rates, and are publicly available, facilitating their inclusion in analyses relating violence and health. This list includes the black isolation and segregation indices, rates of educational attainment, marital status, indicators of wealth and poverty, longitude, latitude, and temperature. HighlightsUsed machine‐learning to identify an optimal set of predictors of firearm violence.Considered over 300 community characteristics from publically available sources.Identified an optimal set of 18 variables that explained 77.8% of variation.Poverty, wealth, and marital status were part of the set of optimal predictors.Housing segregation, education, and temperature also predicted violence rates.

Impact of drought on crime in California: A synthetic control approach

Climate and weather have been linked to criminal activity. The connection between climatological conditions and crime is of growing importance as we seek to understand the societal implications of climate change. This study describes the mechanisms theorized to link annual variations in climate to crime in California and examines the effect of drought on statewide crime rates from 2011–2015. California has suffered severe drought since 2011, resulting in intensely dry winters and several of the hottest days on record. It is likely that the drought increased economic stress and shifted routine activities of the population, potentially increasing the likelihood of crime. We used a synthetic control method to estimate the impact of California’s drought on both property and violent crimes. We found a significant increase in property crimes during the drought, but no effect on violent crimes. This result was robust to several sensitivity analyses, including a negative control.