Background: Cannabis may be a substitute for opioids but previous studies have found conflicting results when using data from more recent years. Most studies have examined the relationship using state-level data, missing important sub-state variation in cannabis access. Objective: To examine cannabis legalization on opioid use at the county level, using Colorado as a case study. Colorado allowed recreational cannabis stores in January 2014. Local communities could decide whether to allow dispensaries, creating variation in the level of exposure to cannabis outlets. Design: Observational, quasi-experimental design exploiting county-level variation in allowance of recreational dispensaries. Subjects: Colorado residents Measures: We use licensing information from the Colorado Department of Revenue to measure county-level exposure to cannabis outlets. We use the state’s Prescription Drug Monitoring Program (2013–2018) to construct opioid-prescribing measures of number of 30-day fills and total morphine equivalents, both per county resident per quarter. We construct outcomes of opioid-related inpatient visits (2011–2018) and emergency department visits (2013–2018) with Colorado Hospital Association data. We use linear models in a differences-in-differences framework that accounts for the varying exposure to medical and recreational cannabis over time. There are 2048 county-quarter observations used in the analysis. Results: We find mixed evidence of cannabis exposure on opioid-related outcomes at the county level. We find increasing exposure to recreational cannabis is associated with a statistically significant decrease in number of 30-day fills (coefficient: −117.6, p-value<0.01) and inpatient visits (coefficient: −0.8, p-value: 0.03), but not total MME nor ED visits. Counties with no medical exposure prior to recreational legalization experience greater reductions in the number of 30-day fills and MME than counties with prior medical exposure (p=0.02 for both). Conclusions: Our mixed findings suggest that further increases in cannabis beyond medical access may not always reduce opioid prescribing or opioid-related hospital visits at a population level.

Tools for assessing multiple exposures across several domains (e.g., physical, chemical, and social) are of growing importance in social and environmental epidemiology because of their value in uncovering disparities and their impact on health outcomes. Here we describe work done within the Environmental influences on Child Health Outcomes (ECHO)-wide Cohort Study to build a combined exposure index. Our index considered both environmental hazards and social stressors simultaneously with national coverage for a 10-year period. Our goal was to build this index and demonstrate its utility for assessing differences in exposure for pregnancies enrolled in the ECHO-wide Cohort Study. Our unitless combined exposure index, which collapses census-tract level data into a single relative measure of exposure ranging from 0–1 (where higher values indicate higher exposure to hazards), includes indicators for major air pollutants and air toxics, features of the built environment, traffic exposures, and social determinants of health (e.g., lower educational attainment) drawn from existing data sources. We observed temporal and geographic variations in index values, with exposures being highest among participants living in the West and Northeast regions. Pregnant people who identified as Black or Hispanic (of any race) were at higher risk of living in a “high” exposure census tract (defined as an index value above 0.5) relative to those who identified as White or non-Hispanic. Index values were also higher for pregnant people with lower educational attainment. Several recommendations follow from our work, including that environmental and social stressor datasets with higher spatial and temporal resolutions are needed to ensure index-based tools fully capture the total environmental context.

Robust evidence on relationships between the human microbiome and health are critical for understanding and improving the human condition. However, there is little information about methodological approaches to combine and analyze multiple microbiome data sets. To address this gap, we conducted a scoping review of studies that combine sequencing data from multiple data sets to understand the study objectives, data sources and selection, feature -table assembly methods, and analyses. References were identified through a systematic search of literature published between 2011 and 2022. Our final review included 60 articles. Despite the wide-spread use of the word “meta-analysis,” we found that only 24 studies used a systematic process to select their data sets, suggesting multiple meanings of the term within the field. While more than two thirds of studies used at least one publicly available data source, 19 had to request data from the original authors. Most studies (60%) combined data sets from multiple disjoint hypervariable regions. The number of hypervariable regions combined was not associated with the table construction method, but feature table construction method and the number of hypervariable regions influenced analytical resolution. Our results suggest the microbiome community needs to examine the use of terminology and analytic approaches for combining data sets; that additional work is needed to explore the impact of data source on bias in combined studies; and invite an independent evaluation of the methods used for feature table construction across disjoint regions.