Santaella-Tenorio J, Rivera-Aguirre A, Hepler SA
… +6 more, Kline DM, Cantor J, DeYoreo M, Martins SS, Krawczyk N, Cerda M
Epidemiology
· 2025 Mar · PMID 39774411
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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. W...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.
Schlüter DK, Keogh RH, Daniel RM
… +2 more, Agbla SC, Taylor-Robinson D
Epidemiology
· 2025 Mar · PMID 39760864
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BACKGROUND: Children with cystic fibrosis (CF) from socioeconomically deprived areas have poorer growth, worse lung function, and shorter life expectancy than their less-deprived peers. While early growth is associated w...BACKGROUND: Children with cystic fibrosis (CF) from socioeconomically deprived areas have poorer growth, worse lung function, and shorter life expectancy than their less-deprived peers. While early growth is associated with lung function around age 6, it is unclear whether improving early growth in the most deprived children reduces inequalities in lung function. METHODS: We used data from the UK CF Registry, tracking children born 2000-2010 up to 2016. We extended the interventional disparity effects approach to the setting of a longitudinally measured mediator. Applying this approach, we estimated the association between socioeconomic deprivation (children in the least vs. most deprived population quintile; exposure) and lung function at first measurement (ages 6-8, outcome), and the role of early weight trajectories (ages 0-6) as mediators of this relationship. We adjusted for baseline confounding by sex, birthyear, and genotype and time-varying intermediate confounding by lung infection. RESULTS: The study included 853 children, with 165 children from the least and 172 from the most deprived quintiles. The average lung function difference between the least and most deprived quintiles was 4.5% of predicted forced expiratory volume in 1 second (95% confidence interval: 1.1-7.9). If the distribution of early weight trajectories in the most deprived children matched that in the least deprived children, this difference would reduce to 4% (95% confidence interval: 0.57- 7.4). CONCLUSION: Socioeconomic deprivation has a strong negative association with lung function for children with CF. We estimate that improving early weight trajectories in the most deprived children would only marginally reduce these inequalities.
Liang W, Han A, Hou D
… +5 more, Li R, Hu Q, Shen H, Jin Y, Xiang H
Epidemiology
· 2025 Mar · PMID 39739426
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BACKGROUND: Anemia is common among HIV/AIDS patients, impacting prognosis. Particulate matter (PM) exposure is an understudied, potentially modifiable risk factor in this group. METHODS: We gathered 36,266 hemoglobin (Hb...BACKGROUND: Anemia is common among HIV/AIDS patients, impacting prognosis. Particulate matter (PM) exposure is an understudied, potentially modifiable risk factor in this group. METHODS: We gathered 36,266 hemoglobin (Hb) measurements from 6808 HIV/AIDS patients from the HIV/AIDS Comprehensive Response Information Management System from 1 January 2004 to 31 December 2021. We evaluated the relationship between Hb levels and short-term PM exposure using linear mixed-effects models. We used logistic regression to estimate the association of long-term PM exposure with baseline anemia prevalence and time-varying Cox models to estimate the association of long-term PM exposure with follow-up incidence of anemia. Mediation analysis explored the role of chronic kidney disease (CKD) in the association between PM exposure and anemia. RESULTS: For every 5 µg/m³ increase in 28-day average PM 1 , Hb levels decreased by 0.43 g/l. For a 10 µg/m³ increase in PM 2.5 , Hb decreased by 0.55 g/l; for the same increase in PM 10, Hb decreased by 0.35 g/l. A 5 µg/m³ increase in 1-year average PM 1 corresponded to a 7% higher prevalence of anemia at baseline, a 10 µg/m³ increase in PM 2.5 to 8% higher prevalence, and a 10 µg/m³ increase in PM 10 to 6% higher prevalence. These rises in average PM concentrations during follow-up were associated with increased incident anemia by 54% (PM 1 ), 72% (PM 2.5 ), and 51% (PM 10 ). CKD partially mediated the positive associations between PM exposure and the incidence of anemia. CONCLUSION: PM exposure was associated with lower Hb levels and higher incidence of anemia in HIV/AIDS patients and CKD with mediating estimated effects in PM-induced anemia.
Skarha J, Spangler K, Dosa D
… +3 more, Rich JD, Savitz DA, Zanobetti A
Epidemiology
· 2025 Mar · PMID 39739406
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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 vulner...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.
Lovett SM, Woo JMP, O'Brien KM
… +2 more, Parker SE, Sandler DP
Epidemiology
· 2025 Mar · PMID 39739403
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BACKGROUND: Early-life trauma (before age of 18 years) is hypothesized to increase the risk for adverse pregnancy outcomes through stress pathways, yet epidemiologic findings are mixed. METHODS: Sister Study participants...BACKGROUND: Early-life trauma (before age of 18 years) is hypothesized to increase the risk for adverse pregnancy outcomes through stress pathways, yet epidemiologic findings are mixed. METHODS: Sister Study participants (US women aged 35-74 years enrolled 2003-2009) completed an adapted Brief Betrayal Trauma Survey at the first follow-up visit. Lifetime history of gestational diabetes mellitus (GDM) or hypertensive disorders of pregnancy (HDP: pregnancy-related high blood pressure, pre-eclampsia/toxemia, or eclampsia) in pregnancies lasting ≥20 weeks was self-reported. We used log-binomial regression to estimate relative risks (RR) and 95% confidence intervals (CIs) for the association between early-life trauma (modeled using conventional measures [e.g., any experience, substantive domains, individual types] and latent classes of co-occurring traumas) and GDM or HDP among 34,879 parous women. RESULTS: Approximately, 4% of participants reported GDM and 11% reported HDP. Relative to no early-life trauma, the RRs for any were 1.1 (95% CI = 1.0, 1.3) for GDM and 1.2 (95% CI = 1.2, 1.3) for HDP. Women reporting physical trauma had the highest risk of GDM and HDP in comparison to other substantive domains. In analyses using latent classes of early-life trauma, high trauma was associated with an elevated risk of both GDM (RR = 1.9, 95% CI = 1.5, 2.6) and HDP (RR = 1.7, 95% CI = 1.4, 2.0) compared with low trauma. CONCLUSIONS: Women experiencing high levels of trauma in early life were at higher risk of GDM and HDP, adding to a growing evidence base for this association.
Epidemiology
· 2025 Mar · PMID 39679582
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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 intera...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.
DeSilva MB, Seburg EM, Ehresmann K
… +5 more, Vazquez-Benitez G, Daida YG, Vesco KK, Kharbanda EO, Palmsten K
Epidemiology
· 2025 Mar · PMID 39679575
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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 lactat...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.
Epidemiology
· 2025 Mar · PMID 39670644
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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 be...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.
Wilson-Barthes MG, Park JW, Mugavero MJ
… +8 more, Napravnik S, Carey MP, Fava JL, Dale SK, Earnshaw VA, Agil D, Howe CJ, Dulin AJ
Epidemiology
· 2025 Jan · PMID 39589016
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BACKGROUND: Attending clinic appointments supports HIV viral suppression, yet racial disparities are documented. We assessed whether multilevel resilience resources were associated with appointment attendance among Afric...BACKGROUND: Attending clinic appointments supports HIV viral suppression, yet racial disparities are documented. We assessed whether multilevel resilience resources were associated with appointment attendance among African American/Black (AA/B) adults living with HIV in the United States. METHODS: We ascertained data from 291 AA/B clinical cohort participants from 2018 to 2021. We assessed resilience using the Multilevel Resilience Resource Measure. Binary outcomes were a nonrepeated indicator of attending ≥87.5% of scheduled HIV appointments over 12 months (i.e., visit adherence) and a repeated measure of attending appointments during two sequential 6-month follow-up windows (i.e., clinic attendance). Modified Poisson models estimated adjusted risk ratios (aRRs). RESULTS: The aRR for clinic attendance among participants with greater versus lesser multilevel resilience resource endorsement was 0.95 (95% confidence interval: 0.88, 1.0). The aRR for visit adherence among participants with greater versus lesser multilevel resilience resource endorsement was 1.2 (0.95, 1.4). CONCLUSIONS: This analysis is one of the first to assess appointment attendance as a function of resilience. Findings should be confirmed in larger cohorts.
Didden C, Egger M, Folb N
… +7 more, Maartens G, Rohner E, Kassanjee R, Mesa-Vieira C, Kriel A, Seedat S, Haas AD
Epidemiology
· 2025 Jan · PMID 39589015
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BACKGROUND: The increased prevalence of physical diseases among individuals with mental illness contributes to their increased risk of mortality. However, the mediating role of specific diseases in the effect of mental i...BACKGROUND: The increased prevalence of physical diseases among individuals with mental illness contributes to their increased risk of mortality. However, the mediating role of specific diseases in the effect of mental illness on mortality is not well understood. METHOD: We conducted a longitudinal causal mediation analysis using data from beneficiaries of a South African medical insurance scheme from 2011 to 2020. We estimated the overall effect of major depressive disorder (MDD) on mortality and evaluated reductions in this overall effect through hypothetical interventions on the risks of mediating physical diseases using an interventional effects approach. Monte Carlo simulation-based g-computation was used for estimation. RESULTS: Among 981,540 individuals, 143,314 (14.6%) were diagnosed with MDD. Mortality risk after 8 years was 6.5% under MDD, and 5.3% under no MDD (risk ratio 1.23, 95% CI = 1.19, 1.26). Overall, 43.4% of this disparity could be attributed to higher rates of physical comorbidities due to MDD. Cardiovascular diseases accounted for 17.8%, followed by chronic respiratory diseases (8.6%), cancers (7.5%), diabetes and chronic kidney disease (5.8%), tuberculosis (4.3%), and HIV (2.7%). CONCLUSION: Within the privately insured population of South Africa, MDD is associated with increased mortality. We found that noncommunicable diseases, rather than infectious diseases, are important mediators of the effect of MDD on mortality.
Kilpatrick K, Cahill K, Chandran U
… +1 more, Riskin D
Epidemiology
· 2025 Jan · PMID 39589014
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BACKGROUND: Asthma is a phenotypically complex disease requiring nuanced data to generate clinically and scientifically robust real-world evidence. A quantitative measure of data quality is important for variables key to...BACKGROUND: Asthma is a phenotypically complex disease requiring nuanced data to generate clinically and scientifically robust real-world evidence. A quantitative measure of data quality is important for variables key to the research questions at hand. Using electronic health record (EHR) data, this study compared accuracy for asthma features between traditional real-world evidence approaches using structured data and advanced approaches applying artificial intelligence technologies to unstructured clinical data. METHODS: We extracted 18 protocol-defined features from 6037 healthcare encounters among 3481 patients. Features included asthma severity subtypes, comorbidities, symptoms, findings, and procedures. We created a manual reference standard through chart abstraction, with two annotators reviewing each record. We assessed interrater reliability using Cohen's kappa score and accuracy against the reference standard as an F1-score. RESULTS: In the traditional study arm, average recall was 40.8%, precision 72.5%, and F1-score across features was 52.2%. In the advanced study arm, average recall was 95.7%, precision 93.8%, and F1-score was 94.7%. There was an absolute increase of 42.5% and a relative increase of 81.4% in the F1-score between traditional and advanced approaches. Cohen's kappa score indicated 0.80 inter-rater reliability, reflecting a credible reference standard. CONCLUSIONS: Use of advanced approaches can enable high-quality real-world data sets in asthma, including granular clinical features such as disease subtypes and symptomatic outcomes. Data quality can be measured and, when high, can support generation of high-validity real-world evidence using routinely collected healthcare data.
Epidemiology
· 2025 Mar · PMID 39588976
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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 d...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.
Zheng X, Wang T, Hao H
… +5 more, D'Souza RR, Strickland MJ, Warren JL, Darrow LA, Chang HH
Epidemiology
· 2025 Mar · PMID 39588975
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BACKGROUND: Annual influenza epidemics lead to a substantial public health burden, and pregnant people are vulnerable to severe outcomes. Influenza during pregnancy is hypothesized to increase the risk of adverse birth o...BACKGROUND: Annual influenza epidemics lead to a substantial public health burden, and pregnant people are vulnerable to severe outcomes. Influenza during pregnancy is hypothesized to increase the risk of adverse birth outcomes, but population-based epidemiologic evidence remains limited and inconsistent. METHODS: We conducted a time-series analysis to estimate short-term associations between community-level seasonal influenza activity and daily counts of preterm births in Atlanta, United States from October 17, 2010 to July 10, 2017. We defined weekly influenza exposures four ways: (1) percent test-positive from virologic surveillance, (2) percent of patients with symptoms of influenza-like illness (ILI) in outpatient settings, (3) a composite measure of percent test-positive and ILI, and (4) influenza hospitalization rates. We used Poisson log-linear models to estimate associations, adjusting for time-varying confounders and ongoing at-risk pregnancies. We further examined associations by influenza type and exposure lags and effect modification by maternal characteristics. RESULTS: We studied a total of 316,253 births. We found consistent positive associations between influenza activity and preterm birth across different exposure measures and exposure lags. An interquartile range increase in a composite measure of ILI activity and percent test-positive was associated with a 1.014 (95% confidence interval: 1.001-1.027) increase in preterm birth during the same week. In stratified analyses, associations were more pronounced among married, non-Black, and Hispanic pregnant people. CONCLUSION: Periods of high influenza activity were associated with an increased risk of preterm birth.