National polydrug use patterns among people who misuse prescription opioids and people who use heroin. Results from the National Household Survey on Drug Use and Health.

BACKGROUND: Polysubstance use among people who misuse opioids (PWMO) is highly prevalent, but understudied. We defined, estimated, and analyzed national polysubstance use patterns among PWMO using National Household Survey on Drug Use and Health data (2017-2019). METHODS: We obtained estimates of past-month patterns of polydrug use using cluster analysis and latent class/profile analysis. We considered misuse of prescription opioids and use of heroin, cocaine (including crack), marijuana, alcohol, and "other" substances. RESULTS: We identified a five-cluster solution for binary indicators of past-month use and a six-cluster solution for frequency of use. The largest binary cluster (37%) included misuse of prescription opioids and use of alcohol. The second-largest cluster (15%) included misuse of prescription opioids, alcohol, marijuana, and "other" substances. Among those who used heroin, 36% used methamphetamine. In terms of frequency of use, the largest cluster among people who misuse opioid who used multiple substances (almost 40%) misused prescription pain relievers, alcohol, and marijuana infrequently. The second-largest cluster (23%) used marijuana almost daily and misused prescription pain relievers an average of 6.6 days. PWMO in a cluster of almost daily heroin use indicated use of methamphetamine, marijuana, and prescription opioids. Those who used methamphetamine, were using it more than 15 days a month. CONCLUSIONS: We have developed reference measures of polydrug patterns among US household population and estimated their demographic characteristics. We identified clusters of high-risk polydrug use. These findings have implications for the development of prevention and treatment solutions in the United States.

CRITICAL WINDOW VARIABLE SELECTION FOR MIXTURES: ESTIMATING THE IMPACT OF MULTIPLE AIR POLLUTANTS ON STILLBIRTH.

Understanding the role of time-varying pollution mixtures on human health is critical as people are simultaneously exposed to multiple pollutants during their lives. For vulnerable subpopulations who have well-defined exposure periods (e.g., pregnant women), questions regarding critical windows of exposure to these mixtures are important for mitigating harm. We extend critical window variable selection (CWVS) to the multipollutant setting by introducing CWVS for mixtures (CWVSmix), a hierarchical Bayesian method that combines smoothed variable selection and temporally correlated weight parameters to: (i) identify critical windows of exposure to mixtures of time-varying pollutants, (ii) estimate the time-varying relative importance of each individual pollutant and their first order interactions within the mixture, and (iii) quantify the impact of the mixtures on health. Through simulation we show that CWVSmix offers the best balance of performance in each of these categories in comparison to competing methods. Using these approaches, we investigate the impact of exposure to multiple ambient air pollutants on the risk of stillbirth in New Jersey, 2005-2014. We find consistent elevated risk in gestational weeks 2, 16-17, and 20 for non-Hispanic Black mothers, with pollution mixtures dominated by ammonium (weeks 2, 17, 20), nitrate (weeks 2, 17), nitrogen oxides (weeks 2, 16), PM2.5 (week 2), and sulfate (week 20). The method is available in the R package CWVSmix.