Epidemiology最新文献

Background: Investigations into long-term fine particulate matter (PM 2.5 ) exposure's impact on nonaccidental and cardiovascular (CVD) deaths primarily involve nonrepresentative adult populations at concentrations above the new Environmental Protection Agency annual PM 2.5 standard.

Methods: Using generalized linear models, we studied PM 2.5 exposure on rates of five mortality outcomes (all nonaccidental, CVD, myocardial infarction, stroke, and congestive heart failure) in 12 US states from 2000 to 2016. We aggregated predicted annual PM 2.5 exposures from a validated ensemble exposure model, ambient temperature from Daymet predictions, and mortality rates to all census tract-years within the states. We obtained covariates from the decennial Census and the American Community Surveys and assessed effect measure modification by race and education with stratification.

Results: For each 1-µg/m 3 increase in annual PM 2.5 , we found positive associations with all five mortality outcomes: all nonaccidental (1.08%; 95% confidence interval [CI]: 0.96%, 1.20%), all CVD (1.27%; 95% CI: 1.14%, 1.41%), myocardial infarction (1.89%; 95% CI: 1.67%, 2.11%), stroke (1.08%; 95% CI: 0.87%, 1.30%), and congestive heart failure (2.20%; 95% CI: 1.97%, 2.44%). Positive associations persisted at <8 µg/m 3 PM 2.5 levels and among populations with only under 65. In our study, race, but not education, modifies associations. High-educated Black had a 2.90% larger increased risk of CVD mortality (95% CI: 2.42%, 3.39%) compared with low-educated non-Black.

Conclusion: Long-term PM 2.5 exposure is associated with nonaccidental and CVD mortality in 12 states, below the new Environmental Protection Agency standard, for both low PM 2.5 regions and the general population. Vulnerability to CVD mortality persists among Black individuals regardless of education level.

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.