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PurposeThis study aims to characterize the June 2020 COVID-19 outbreak at San Quentin California State Prison and to describe what made San Quentin so vulnerable to uncontrolled transmission.Design/methodology/approachSince its onset, the COVID-19 pandemic has exposed and exacerbated the profound health harms of carceral settings, such that nearly half of state prisons reported COVID-19 infection rates that were four or more times (and up to 15 times) the rate found in the state's general population. Thus, addressing the public health crises and inequities of carceral settings during a respiratory pandemic requires analyzing the myriad factors shaping them. In this study, we reported observations and findings from environmental risk assessments during visits to San Quentin California State Prison. We complemented our assessments with analyses of administrative data.FindingsFor future respiratory pathogens that cannot be prevented with effective vaccines, this study argues that outbreaks will no doubt occur again without robust implementation of additional levels of preparedness - improved ventilation, air filtration, decarceration with emergency evacuation planning - alongside addressing the vulnerabilities of carceral settings themselves.Originality/valueThis study addresses two critical aspects that are insufficiently covered in the literature: how to prepare processes to safely implement emergency epidemic measures when needed, such as potential evacuation, and how to address unique challenges throughout an evolving pandemic for each carceral setting.

A greater risk of cardiovascular disease is associated with low educational attainment and high adiposity. Despite the correlation between low educational attainment and high adiposity, whether educational attainment modifies the risk of CVD caused by high adiposity remains poorly understood. We investigated the effect of adiposity (body mass index [BMI] and waist-to-hip ratio adjusted for BMI [WHRadjBMI]) on incident CVD among individuals with varying education levels, using associational and one-sample Mendelian randomization (MR) survival analyses. Data were collected from 2006 to 2021, and sample sizes were 254,281 (27,511 CVD cases) for BMI and 253,968 (27,458 CVD cases) for WHRadjBMI. In the associational model, a standard deviation (SD) higher BMI was associated with 19.81 (95% CI: 18.55-21.06) additional cases of incident CVD per 10,000 person-years for individuals with a secondary education, versus 32.96 (95% CI: 28.75-37.17) for those without. When university degree served as the education variable, education group differences attenuated, with 18.26 (95% CI: 16.37-20.15) cases from a one SD higher BMI for those with a university degree versus 23.18 [95% CI: 21.56-24.72] for those without. For the MR model, an SD higher BMI resulted in 11.75 (95% CI: -0.84-24.38) and 29.79 (95% CI: 17.20-42.44) additional cases of incident CVD per 10,000 person-years for individuals with versus without a university degree. WHRadjBMI exhibited no effect differences by education. While the associational model showed evidence of educational attainment modifying the relationship between adiposity and incident CVD, it does not modify the association between adiposity and incident CVD in the MR models. This suggests either less education does not cause greater risk of incident CVD from high adiposity, or MR models cannot detect the effect difference. The associational point estimates exist within the MR models' confidence intervals in all BMI analyses, so we cannot rule out the effect sizes in the associational models.

Previous research demonstrates the joint association of self-reported physical activity and genotype with coronary artery disease. However, an existing research gap is whether accelerometer-measured overall physical activity or physical activity intensity can offset genetic predisposition to coronary artery disease. This study explores the independent and joint associations of accelerometer-measured physical activity and genetic predisposition with incident coronary artery disease. Incident coronary artery disease based on hospital inpatient records and death register data serves as the outcome of this study. Polygenic risk score and overall physical activity, measured as Euclidean Norm Minus One, and intensity, measured as minutes per day of moderate-to-vigorous intensity physical activity (MVPA), are examined both linearly and by decile. The UK Biobank population-based cohort recruited over 500,000 individuals aged 40 to 69 between 2006 and 2010, with 103,712 volunteers participating in a weeklong wrist-worn accelerometer study from 2013 to 2015. Individuals of White British ancestry (n = 65,079) meeting the genotyping and accelerometer-based inclusion criteria and with no missing covariates were included in the analytic sample. In the sample of 65,079 individuals, the mean (SD) age was 62.51 (7.76) and 61% were female. During a median follow-up of 6.8 years, 1,382 cases of coronary artery disease developed. At the same genetic risk, physical activity intensity had a hazard ratio (HR) of 0.41 (95% CI: 0.29–0.60) at the 90th compared to 10th percentile, equivalent to 31.68 and 120.96 minutes of moderate-to-vigorous physical activity per day, respectively, versus an HR of 0.61 (95% CI: 0.52–0.72) for overall physical activity. The combination of high genetic risk and low physical activity intensity showed the greatest risk, with an individual at the 10th percentile of genetic risk and 90th percentile of intensity facing an HR of 0.14 (95% CI: 0.09–0.21) compared to an individual at the 90th percentile of genetic risk and 10th percentile of intensity. Physical activity, especially physical activity intensity, is associated with an attenuation of some of the risk of coronary artery disease but this pattern does not vary by genetic risk. This accelerometer-based study provides the clearest evidence to date regarding the joint influence of genetics, overall physical activity, and physical activity intensity on coronary artery disease. [ABSTRACT FROM AUTHOR]

Background and Aims: In light of the accelerating drug overdose epidemic in North America, new strategies are needed to identify communities most at risk to prioritize geographically the existing public health resources (e.g. street outreach, naloxone distribution efforts). We aimed to develop PROVIDENT (Preventing Overdose using Information and Data from the Environment), a machine learning‐based forecasting tool to predict future overdose deaths at the census block group (i.e. neighbourhood) level. Design Randomized, population‐based, community intervention trial. Setting: Rhode Island, USA. Participants: All people who reside in Rhode Island during the study period may contribute data to either the model or the trial outcomes. Intervention: Each of the state's 39 municipalities will be randomized to the intervention (PROVIDENT) or comparator condition. An interactive, web‐based tool will be developed to visualize the PROVIDENT model predictions. Municipalities assigned to the treatment arm will receive neighbourhood risk predictions from the PROVIDENT model, and state agencies and community‐based organizations will direct resources to neighbourhoods identified as high risk. Municipalities assigned to the control arm will continue to receive surveillance information and overdose prevention resources, but they will not receive neighbourhood risk predictions. Measurements The primary outcome is the municipal‐level rate of fatal and non‐fatal drug overdoses. Fatal overdoses will be defined as unintentional drug‐related death; non‐fatal overdoses will be defined as an emergency department visit for a suspected overdose reported through the state's syndromic surveillance system. Intervention efficacy will be assessed using Poisson or negative binomial regression to estimate incidence rate ratios comparing fatal and non‐fatal overdose rates in treatment vs. control municipalities. Comments The findings will inform the utility of predictive modelling as a tool to improve public health decision‐making and inform resource allocation to communities that should be prioritized for prevention, treatment, recovery and overdose rescue services. [ABSTRACT FROM AUTHOR]

Predictors of opioid overdose death in neighborhoods are important to identify, both to understand characteristics of high-risk areas and to prioritize limited prevention and intervention resources. Machine learning methods could serve as a valuable tool for identifying neighborhood-level predictors. We examined statewide data on opioid overdose death from Rhode Island (log-transformed rates for 2016–2019) and 203 covariates from the American Community Survey for 742 US Census block groups. The analysis included a least absolute shrinkage and selection operator (LASSO) algorithm followed by variable importance rankings from a random forest algorithm. We employed double cross-validation, with 10 folds in the inner loop to train the model and 4 outer folds to assess predictive performance. The ranked variables included a range of dimensions of socioeconomic status, including education, income and wealth, residential stability, race/ethnicity, social isolation, and occupational status. The R 2 value of the model on testing data was 0.17. While many predictors of overdose death were in established domains (education, income, occupation), we also identified novel domains (residential stability, racial/ethnic distribution, and social isolation). Predictive modeling with machine learning can identify new neighborhood-level predictors of overdose in the continually evolving opioid epidemic and anticipate the neighborhoods at high risk of overdose mortality. [ABSTRACT FROM AUTHOR]