Recreational cannabis and opioid distribution.

Twenty-one U.S. states have passed recreational cannabis laws as of November 2022. Cannabis may be a substitute for prescription opioids in the treatment of chronic pain. Previous studies have assessed recreational cannabis laws' effects on opioid prescriptions financed by specific private or public payers or dispensed to a unique endpoint. Our study adds to the literature in three important ways: by (1) examining these laws' impacts on prescription opioid dispensing across all payers and endpoints, (2) adjusting for important opioid-related policies such as opioid prescribing limits, and (3) modeling opioids separately by type. We implement two-way fixed-effects regressions and leverage variation from eleven U.S. states that adopted a recreational cannabis law (RCL) between 2010 and 2019. We find that RCLs lead to a reduction in codeine dispensed at retail pharmacies. Among prescription opioids, codeine is particularly likely to be used non-medically. Thus, the finding that RCLs appear to reduce codeine dispensing is potentially promising from a public health perspective.

COVID-19 booster uptake among US adults: Assessing the impact of vaccine attributes, incentives, and context in a choice-based experiment.

OBJECTIVE: Evidence shows that booster shots offer strong protection against the Omicron variant of COVID-19. However, we know little about why individuals would receive a booster compared to the initial decision to vaccinate. We investigate and assess the factors that affect individuals' reported willingness to receive the COVID-19 vaccine booster. This information can aid in tailoring public health messaging to communicate attributes that are associated with individuals' attitudes toward the COVID-19 booster. RATIONALE: Existing research provides little insight into whether the same factors that affect Americans' likelihood of accepting initial vaccination against COVID-19 also affect booster uptake. Our experiment also examines the influence of contextual information about a novel variant on willingness to receive a booster. METHODS: We administered a conjoint experiment (N = 2740 trials) in a survey of fully vaccinated US adults that had not yet received a COVID-19 booster (N = 548) to assess the impact of varied vaccine attributes on willingness to receive a booster. RESULTS: The most important factors associated with higher willingness to receive a booster were efficacy, manufacturer, and the size of a financial incentive. Protection duration and protection against future variants vs. only current variants had modest influence. A contextual prime reporting that some public health experts believe the Omicron variant is more contagious, but less lethal than those previously seen, significantly increased favorability toward boosters. This provides potential motivation and guidance for vaccination campaigns to emphasize these variant-specific traits. CONCLUSION: With several vaccines with varying degrees of efficacy available to consumers, emphasizing boosters with a high efficacy would likely improve attitudes toward boosters. Financial incentives and predispositions toward manufacturers also matter. Concerns about more contagious variants may spur uptake, even if such variants are less lethal.

Recreational cannabis legalizations associated with reductions in prescription drug utilization among Medicaid enrollees.

The potential substitution of cannabis for prescription medication has attracted a substantial amount of attention within the context of medical cannabis laws (MCLs). However, much less is known about the association between recreational cannabis laws (RCLs) and prescription drug use. With recent evidence supporting substitution of cannabis for prescription drugs following MCLs, it is reasonable to ask what effect RCLs may have on those outcomes. We use quarterly data for all Medicaid prescriptions from 2011 to 2019 to investigate the effect of state-level RCLs on prescription drug utilization. We estimate this effect with a series of two-way fixed effects event study models. We find significant reductions in the volume of prescriptions within the drug classes that align with the medical indications for pain, depression, anxiety, sleep, psychosis, and seizures. Our results suggest substitution away from prescription drugs and potential cost savings for state Medicaid programs.

Modeling elevated blood lead level risk across the United States.

Lead exposure adversely affects child health and continues to be a major public health concern in the United States (US). Lead exposure risk has been linked with older housing and households in poverty, but more studies of neighborhood socioeconomic status (SES) and lead exposure risk over large and diverse geographic areas are needed. In this paper, we combined lead test result data over many states for a majority of the US ZIP Codes in order to estimate its association with many SES variables and predict lead exposure risk in all populated ZIP Codes in the US. The methods used for estimation and prediction of lead risk included the Vox lead exposure risk score, random forest, weighted quantile sum (WQS) regression, and a Bayesian SES index model. The results showed that the Bayesian index model had the best overall performance for modeling elevated blood lead level (EBLL) risk and therefore was used to create a lead exposure risk score for US ZIP Codes. There was a statistically significant association between EBLL risk and the SES index and the most important SES variables for explaining EBLL risk were percentage of houses built before 1940 and median home value. When mapping the lead exposure risk scores, there was a clear pattern of elevated risk in the Northeast and Midwest, but areas in the South and Southwest regions of the US also had high risk. In summary, the Bayesian index model was an effective method for modeling EBLL risk associated with neighborhood deprivation while accounting for additional heterogeneity in risk using lead test result data covering a majority of the US. The resulting lead exposure risk score can be used for targeting public health intervention efforts.

Bayesian deprivation index models for explaining variation in elevated blood lead levels among children in Maryland.

Lead exposure adversely affects children's health. Exposure in the United States is highest among socioeconomically disadvantaged individuals who disproportionately live in substandard housing. We used Bayesian binomial regression models to estimate a neighborhood deprivation index and its association with elevated blood lead level (EBLL) risk using blood lead level testing data in Maryland census tracts. Our results show the probability of EBLL was spatially structured with high values in Baltimore city and low values in the District of Columbia suburbs and Baltimore suburbs. The association between the neighborhood deprivation index and EBLL risk was statistically significant after accounting for spatial dependence in probability of EBLL. The percent of houses built before 1940, African Americans, and renter occupied housing were the most important variables in the index. Bayesian models provide a flexible one-step approach to modeling risk associated with neighborhood deprivation while accounting for spatially structured and unstructured heterogeneity in risk.