Objectives. To assess the relationship between adult cannabis use and time-varying local measures of retail cannabis market presence before and after legalization (2012) and market opening (2014) in Washington State. Methods. We used 2009 to 2016 data on 85 135 adults’ current (any) and frequent (20 or more days) past-month cannabis use from the Washington Behavioral Risk Factor Surveillance System linked to local retailer proximity and density. Multilevel models predicted use over time, accounting for nesting within communities. Results. Current and frequent cannabis use grew significantly between 2009 and 2016; use did not significantly change immediately after legalization but increased subsequently with greater access to cannabis retailers. Specifically, current use increased among adults living in areas within 18 miles of a retailer and, especially, within 0.8 miles (odds ratio [OR] = 1.45; 95% confidence interval [CI] = 1.24, 1.69). Frequent use increased among adults living within 0.8 miles of a retailer (OR = 1.43; 95% CI = 1.15, 1.77). Results related to geospatial retailer density were consistent. Conclusions. Increasing cannabis retail access was associated with increased current and frequent use. Public Health Implications. Policymakers might consider density limits as a strategy for preventing heavy cannabis use among adults.

What is already known about this topic? Research from tobacco and alcohol markets suggests advertising exposure is associated with perceptions of lower risk and increased use among young people. What is added by this report? We assessed marijuana advertising exposure among youths in Oregon. About three-quarters of youths reported exposure to marijuana advertising. Exposure was higher among girls than boys; lesbian, gay, or bisexual youths than straight youths; and youths in school districts with a closer average proximity to retail marijuana stores. What are the implications for public health practice? Groups with higher exposure to advertising may benefit from targeted prevention efforts or countermessaging to delay initiation of marijuana use.

Although monitoring childhood obesity prevalence is critical for state public health programs to assess trends and the effectiveness of interventions, few states have comprehensive body mass index measurement systems in place. In some states, however, assorted school districts collect measurements on student height and weight as part of annual health screenings. To estimate childhood obesity prevalence in Alaska, we created a logistic regression model using such annual measurements along with public data on demographics and socioeconomic status. Our mixed-effects model-generated prevalence estimates validated well against weighted estimates, with 95% confidence intervals overlapping between methodologies among 7 of 8 participating school districts. Our methodology accounts for variation in school-level and student-level demographic factors across the state, and the approach we describe can be applied by other states that have existing nonrandom student measurement data to estimate childhood obesity prevalence.