Epidemiology最新文献

Background: Cannabis use and alcohol use are associated with self-harm injuries, but little research has assessed links between recreational cannabis outlet openings on rates of self-harm within communities or the interactions of cannabis outlets with the density of alcohol outlets. We estimated the associations of recreational cannabis outlets, alcohol outlets, and their interaction on rates of fatal and nonfatal self-harm injuries in California, 2017-2019.

Methods: Using California statewide data on recreational cannabis outlets, alcohol outlets, and hospital discharges and deaths due to self-harm injuries, we conducted Bayesian spatiotemporal analyses of quarterly ZIP code-level data over 3 years, accounting for confounders and spatial autocorrelation. Using the model posteriors, we estimated parameters corresponding to hypothetical shifts in outlet densities.

Results: If recreational cannabis outlets had never opened, we estimated that nonfatal self-harm injuries would have been -0.35 per 100,000 lower (95% credible interval [CI]: -1.25, 0.51), while fatal self-harm injuries would have been -0.004 per 100,000 lower (95% CI: -0.26, 0.25). These associations did not depend on alcohol outlet density, but a hypothetical 20% reduction in alcohol outlet densities was associated with fewer self-harm injuries (risk difference per 100,000, nonfatal: -1.59; 95% CI: -2.60, -0.59; fatal: -0.10; 95% CI: -0.37, 0.16). Associations for nonfatal incidents were strongest for people aged 15-34 years, and White and Hispanic people.

Conclusion: We did not find evidence that the introduction of recreational cannabis outlets was associated with self-harm injuries or that cannabis and alcohol outlet densities interact, but alcohol outlet density had a strong association with nonfatal self-harm injuries.

Background: From 2019 to 2020, homicide showed its largest single-year increase in modern US history. While many have cited the COVID-19 pandemic or the police killing of George Floyd as initiating the rise, there has been limited systematic investigation of how the timing of the increase corresponded with these key events. We investigated trends in firearm and nonfirearm homicide across sociodemographic and geographic groups to clarify the timing and nature of the recent increase.

Methods: We conducted a descriptive epidemiologic study using the National Vital Statistics System weekly mortality data from January 2018 to December 2022 in the United States. We seasonally adjusted and smoothed weekly firearm and nonfirearm homicide data, quantifying changes in relation to key event dates for the COVID-19 pandemic, the killing of George Floyd, and the 2020 national election. We disaggregated trends by sociodemographic and geographic characteristics.

Results: Between January 2018 and December 2022, firearm homicide increased by 54% while nonfirearm homicide was stable. The increase in firearm homicide started in October 2019 and stabilized by November 2020; 28% of the eventual increase had already occurred by the time COVID-19 was declared a national emergency. All sociodemographic and geographic groups experienced large recent increases in firearm homicide.

Conclusions: The magnitude and timing of the recent increase in homicide have been previously understated and obscured by crude data and seasonal patterns. Existing theories, including the COVID-19 pandemic, fall short in explaining the historic surge, which is specific to firearm homicide, started in late 2019, and affected all persons and places across the United States.

Background: Cold temperatures are associated with increased risk for cardiovascular and respiratory disease mortality. Due to limited temperature regulation in prisons, incarcerated populations may be particularly vulnerable to cold-related mortality.

Methods: We analyzed mortality data in US prisons from 2001 to 2019. Using a case-crossover approach, we estimated the association of a 10 °F decrease in cold temperature and extreme cold (days below the 10th percentile) with the risk of total mortality and deaths from heart disease, respiratory disease, and suicide. We assessed effect modification by personal, facility, and regional characteristics.

Results: There were 18,578 deaths during cold months. The majority were male (96%) and housed in a state-operated prison (96%). We found a delayed association with mortality peaking 3 days after and remaining positive until 6 days after cold exposure. A 10 °F decrease in temperature averaged over 6 days was associated with a 5.1% (95% confidence interval [CI]: 2.1%, 8.0%) increase in total mortality. The 10-day cumulative effect of an extreme cold day was associated with an 11% (95% CI: 2.2%, 20%) increase in total mortality and a 55% (95% CI: 11%, 114%) increase in suicides. We found the greatest increase in total mortality for prisons built before 1980, located in the South or West, and operating as a dedicated medical facility.

Conclusions: Cold temperatures were associated with an increased risk of mortality in prisons, with marked increases in suicides. This study contributes to the growing evidence that the physical environment of prisons affects the health of the incarcerated population.

The analysis of perinatal studies is complicated by twins and other multiple births even when multiples are not the exposure, outcome, or a confounder of interest. In analyses of infant outcomes restricted to live births, common approaches to handling multiples include restriction to singletons, counting outcomes at the pregnancy level (i.e., by counting if at least one twin experienced a binary outcome), or infant-level analysis including all infants and accounting for clustering of outcomes, such as by using generalized estimating equations or mixed effects models. Several healthcare administration databases only support restriction to singletons or pregnancy-level approaches. For example, in MarketScan insurance claims data, diagnoses in twins are often assigned to a single infant identifier, thereby preventing ascertainment of infant-level outcomes among multiples. Different approaches correspond to different questions, produce different estimands, and often rely on different assumptions. We demonstrate the differences that can arise from these different approaches using Monte Carlo simulations, algebraic formulas, and an applied example.

Background: Electronic health record data are an underused source for lactation-related research. The validity of the International Classification of Diseases, 10th Revision Clinical Modification (ICD-10-CM)-coded lactational mastitis is unknown.

Methods: We assessed lactational mastitis diagnosis code validity by medical record review. We included patients from three health care systems with a live birth between December 2020 and September 2022 whose infant had ≥1 well visit and for whom there was electronic health record documentation of lactation in patient or infant records. We used ICD-10-CM diagnosis codes (N61.0 and O91.2) to identify patients with suspected lactational mastitis and assessed antibiotic dispensings. We performed medical record reviews on a random sample to determine whether suspected lactational mastitis cases met definitions for "probable" (breast symptoms with systemic symptoms) or "possible" (breast symptoms without systemic symptoms) lactational mastitis. We report positive predictive values (PPV) with 95% confidence intervals (CI).

Results: Among 19,660 eligible patients, 1,023 (5.2%) had either N61.0 or O91.2 diagnosis code and 768 (3.9%) had a diagnosis code and antibiotic dispensed. Chart reviews of 119 identified PPV of 76% (95% CI: 67.3, 82.9) for probable and 97% (95% CI: 91.6, 98.7) for probable or possible lactational mastitis. Restricting to those dispensed an antibiotic (n = 87), PPVs improved to 80% (95% CI: 69.6, 87.4) for probable and 100% (95% CI: 95.8, 100) for probable or possible lactational mastitis.

Conclusions: Diagnosis codes alone have good PPV for lactational mastitis. PPV for lactational mastitis improves when including antibiotic data, although case numbers decrease. Future research may consider the use of ICD-10 codes alone for the identification of lactational mastitis.

Age-period-cohort models have a long history in epidemiology, social science, and econometrics. An important feature of these models is that they suffer from an inherent identifiability problem, due to the deterministic linear relation between age, period, and cohort. A proposed solution to this problem is the mechanism-based approach, which uses sets of mediators to identify the causal age, period, and cohort effects. Although this approach is conceptually general, previous literature has been limited to special cases and parametric identification. We derive a general nonparametric identification result, which is valid under explicit assumptions about the underlying data-generating mechanism and the set of mediators used for identification. We show how this identification result lends itself naturally to parametric estimation of the causal age, period, and cohort effects similar to the parametric G-formula estimation in causal inference.

Background: To account for misclassification of dichotomous variables using probabilistic bias analysis, beta distributions are often assigned to bias parameters (e.g., positive and negative predictive values) based on data from an internal validation substudy. Due to the small sample size of validation substudies, zero-cell frequencies can occur. In these scenarios, it may be helpful to assign prior distributions or apply continuity corrections to the predictive value estimates.

Methods: We simulated cohort studies of varying sizes, with a binary exposure and outcome and a true risk ratio (RR) = 2.0, as well as internal validation substudies, to account for exposure misclassification. We conducted bias adjustment under five approaches assigning prior distributions to the positive and negative predictive value parameters: (1) conventional method (i.e., no prior), (2) uniform prior beta ( α = 1, β = 1), (3) Jeffreys prior beta ( α = 0.5, β = 0.5), (4) using Jeffreys prior as a continuity correction only when zero cells occurred, and (5) using the uniform prior as a continuity correction only when zero cells occurred. We evaluated performance by measuring coverage probability, bias, and mean squared error.

Results: For sparse validation data, methods (2)-(5) all had better coverage and lower mean squared error than the conventional method, with the uniform prior (2) yielding the best performance. However, little difference between methods was observed when the validation substudy did not contain zero cells.

Conclusion: If sparse data are expected in a validation substudy, using a uniform prior for the beta distribution of bias parameters can improve the validity of bias-adjusted measures.

Background: Medications for opioid use disorder are associated with a lower risk of drug overdoses at the individual level. However, little is known about whether these effects translate to population-level reductions. We investigated whether county-level efforts to increase access to medication for opioid use disorder in 2012-2014 were associated with opioid overdose deaths in New York State during the first years of the synthetic opioid crisis.

Methods: We performed an ecologic county-level study including data from 60 counties (2010-2018). We calculated rates of people receiving medication for opioid use disorder among the population misusing opioids in 2012-2014 and categorized counties into quartiles of this exposure. We modeled synthetic and nonsynthetic opioid overdose death rates using Bayesian hierarchical models.

Results: Counties with higher rates of receiving medications for opioid use disorder in 2012-2014 had lower synthetic opioid overdose deaths in 2016 (highest vs. lowest quartile: rate ratio [RR] = 0.33, 95% credible interval [CrI] = 0.12, 0.98; and second-highest vs. lowest: RR = 0.20, 95% CrI = 0.07, 0.59) and 2017 (quartile second-highest vs. lowest: RR = 0.22, 95% CrI = 0.06, 0.83), but not 2018. There were no differences in nonsynthetic opioid overdose death rates comparing higher quartiles versus lowest quartile of exposure.

Conclusions: A spatio-temporal modeling approach incorporating counts of the population misusing opioids provided information about trends and interventions in the target population. Higher rates of receiving medications for opioid use disorder in 2012-2014 were associated with lower rates of synthetic opioid overdose deaths early in the crisis.

Background: Individual traditional anthropometric measures such as body mass index and waist circumference may not fully capture the relation of adiposity to mortality. Investigating multitrait body shapes could overcome this limitation, deepening insights into adiposity and mortality.

Methods: Using UK Biobank data from 462,301 adults (40-69 years at baseline: 2006-2010), we derived four body shapes from principal component analysis on body mass index, height, weight, waist and hip circumference, and waist-to-hip ratio. We then used multivariable-adjusted Cox proportional hazard models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for associations between body shapes and mortality for principal component scores of +1 and -1.

Results: During 6,114,399 person-years of follow-up, 28,807 deaths occurred. A generally obese body shape exhibited a U-shaped mortality association. A tall and centrally obese body shape showed increased mortality risk in a dose-response manner (comparing a score of +1 and 0: HR = 1.16, 95% CI = 1.14, 1.18). Conversely, tall and lean or athletic body shapes displayed no increased mortality risks when comparing a score of +1 and 0, with positive relations for the comparison between a score of -1 and 0 in these shapes (short and stout shape: HR = 1.12, 95% CI = 1.10, 1.14; nonathletic shape: HR = 1.15, 95% CI = 1.13, 1.17).

Conclusion: Four distinct body shapes, reflecting heterogeneous expressions of obesity, were differentially associated with all-cause and cause-specific mortality. Multitrait body shapes may refine our insights into the associations between different adiposity subtypes and mortality.