Epidemiology最新文献

{"title":"Prenatal Exposure to Nonpersistent Chemicals and Fetal-to-childhood Growth Trajectories.","authors":"Paige A Bommarito, Sophia M Blaauwendraad, Danielle R Stevens, Michiel A van den Dries, Suzanne Spaan, Anjoeka Pronk, Henning Tiemeier, Romy Gaillard, Leonardo Trasande, Vincent V W Jaddoe, Kelly K Ferguson","doi":"10.1097/EDE.0000000000001772","DOIUrl":"10.1097/EDE.0000000000001772","url":null,"abstract":"<p><strong>Introduction: </strong>Prenatal exposure to nonpersistent chemicals, including organophosphate pesticides, phthalates, and bisphenols, is associated with altered fetal and childhood growth. Few studies have examined these associations using longitudinal growth trajectories or considering exposure to chemical mixtures.</p><p><strong>Methods: </strong>Among 777 participants from the Generation R Study, we used growth mixture models to identify weight and body mass index trajectories using weight and height measures collected from the prenatal period to age 13. We measured exposure biomarkers for organophosphate pesticides, phthalates, and bisphenols in maternal urine at three timepoints during pregnancy. Multinomial logistic regression was used to estimate associations between averaged exposure biomarker concentrations and growth trajectories. We used quantile g-computation to estimate joint associations with growth trajectories.</p><p><strong>Results: </strong>Phthalic acid (OR = 1.4; 95% CI = 1.01, 1.9) and bisphenol A (OR = 1.5; 95% CI = 1.0, 2.2) were associated with higher odds of a growth trajectory characterized by smaller prenatal and larger childhood weight relative to a referent trajectory of larger prenatal and average childhood weight. Biomarkers of organophosphate pesticides, individually and jointly, were associated with lower odds of a growth trajectory characterized by average prenatal and lower childhood weight.</p><p><strong>Conclusions: </strong>Exposure to phthalates and bisphenol A was positively associated with a weight trajectory characterized by lower prenatal and higher childhood weight, while exposure to organophosphate pesticides was negatively associated with a trajectory of average prenatal and lower childhood weight. This study is consistent with the hypothesis that nonpersistent chemical exposures disrupt growth trajectories from the prenatal period through childhood.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11444368/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141751374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exposure to Ambient Heat and Risk of Spontaneous Abortion: A Case-Crossover Study.","authors":"Amelia K Wesselink, Emma L Gause, Keith D Spangler, Perry Hystad, Kipruto Kirwa, Mary D Willis, Gregory A Wellenius, Lauren A Wise","doi":"10.1097/EDE.0000000000001774","DOIUrl":"10.1097/EDE.0000000000001774","url":null,"abstract":"<p><strong>Background: </strong>Few epidemiologic studies have examined the association of ambient heat with spontaneous abortion, a common and devastating pregnancy outcome.</p><p><strong>Methods: </strong>We conducted a case-crossover study nested within Pregnancy Study Online, a preconception cohort study (2013-2022). We included all participants reporting spontaneous abortion (N = 1,524). We defined the case window as the 7 days preceding the event and used time-stratified referent selection to select control windows matched on calendar month and day of week. Within each 7-day case and control window, we measured the mean, maximum, and minimum of daily maximum outdoor air temperatures. We fit splines to examine nonlinear relationships across the entire year and conditional logistic regression to estimate odds ratios (ORs) and 95% confidence interval (CI) of spontaneous abortion with increases in temperature during the warm season (May-September) and decreases during the cool season (November-March).</p><p><strong>Results: </strong>We found evidence of a U-shaped association between outdoor air temperature and spontaneous abortion risk based on year-round data. When restricting to warm season events (n = 657), the OR for a 10-percentile increase in the mean of lag 0-6 daily maximum temperatures was 1.1 (95% CI: 0.96, 1.2) and, for the maximum, 1.1 (95% CI: 0.99, 1.2). The OR associated with any extreme heat days (>95th county-specific percentile) in the preceding week was 1.2 (95% CI: 0.95, 1.5). Among cool season events (n = 615), there was no appreciable association between lower temperatures and spontaneous abortion risk.</p><p><strong>Conclusion: </strong>Our study provides evidence of an association between high outdoor temperatures and the incidence of spontaneous abortion.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141765750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating Binary Outcome Classifiers Estimated from Survey Data.","authors":"Adway S Wadekar, Jerome P Reiter","doi":"10.1097/EDE.0000000000001776","DOIUrl":"10.1097/EDE.0000000000001776","url":null,"abstract":"<p><p>Surveys are commonly used to facilitate research in epidemiology, health, and the social and behavioral sciences. Often, these surveys are not simple random samples, and respondents are given weights reflecting their probability of selection into the survey. We show that using survey weights can be beneficial for evaluating the quality of predictive models when splitting data into training and test sets. In particular, we characterize model assessment statistics, such as sensitivity and specificity, as finite population quantities and compute survey-weighted estimates of these quantities with test data comprising a random subset of the original data. Using simulations with data from the National Survey on Drug Use and Health and the National Comorbidity Survey, we show that unweighted metrics estimated with sample test data can misrepresent population performance, but weighted metrics appropriately adjust for the complex sampling design. We also show that this conclusion holds for models trained using upsampling for mitigating class imbalance. The results suggest that weighted metrics should be used when evaluating performance on test data derived from complex surveys.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141975420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Commentary: The Seedy Side of Causal Effect Estimation with Machine Learning.","authors":"Paul N Zivich","doi":"10.1097/EDE.0000000000001783","DOIUrl":"10.1097/EDE.0000000000001783","url":null,"abstract":"","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141992226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Author Responds.","authors":"Katherine M Flegal","doi":"10.1097/EDE.0000000000001787","DOIUrl":"10.1097/EDE.0000000000001787","url":null,"abstract":"","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142003947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generalizability of Heat-related Health Risk Associations Observed in a Large Healthcare Claims Database of Patients with Commercial Health Insurance.","authors":"Chad W Milando, Yuantong Sun, Yasmin Romitti, Amruta Nori-Sarma, Emma L Gause, Keith R Spangler, Ian Sue Wing, Gregory A Wellenius","doi":"10.1097/EDE.0000000000001781","DOIUrl":"10.1097/EDE.0000000000001781","url":null,"abstract":"<p><strong>Background: </strong>Extreme ambient heat is unambiguously associated with a higher risk of illness and death. The Optum Labs Data Warehouse (OLDW), a database of medical claims from US-based patients with commercial or Medicare Advantage health insurance, has been used to quantify heat-related health impacts. Whether results for the insured subpopulation are generalizable to the broader population has, to our knowledge, not been documented. We sought to address this question, for the US population in California from 2012 to 2019.</p><p><strong>Methods: </strong>We examined changes in daily rates of emergency department encounters and in-patient hospitalization encounters for all-causes, heat-related outcomes, renal disease, mental/behavioral disorders, cardiovascular disease, and respiratory disease. OLDW was the source of health data for insured individuals in California, and health data for the broader population were gathered from the California Department of Health Care Access and Information. We defined extreme heat exposure as any day in a group of 2 or more days with maximum temperatures exceeding the county-specific 97.5th percentile and used a space-time-stratified case-crossover design to assess and compare the impacts of heat on health.</p><p><strong>Results: </strong>Average incidence rates of medical encounters differed by dataset. However, rate ratios for emergency department encounters were similar across datasets for all causes [ratio of incidence rate ratios (rIRR) = 0.989; 95% confidence interval (CI) = 0.969, 1.009], heat-related causes (rIRR = 1.080; 95% CI = 0.999, 1.168), renal disease (rIRR = 0.963; 95% CI = 0.718, 1.292), and mental health disorders (rIRR = 1.098; 95% CI = 1.004, 1.201). Rate ratios for inpatient encounters were also similar.</p><p><strong>Conclusions: </strong>This work presents evidence that OLDW can continue to be a resource for estimating the health impacts of extreme heat.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7616519/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141909829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian Kernel Machine Regression for Social Epidemiologic Research.","authors":"Jemar R Bather, Taylor J Robinson, Melody S Goodman","doi":"10.1097/EDE.0000000000001777","DOIUrl":"10.1097/EDE.0000000000001777","url":null,"abstract":"<p><strong>Background: </strong>Little attention has been devoted to framing multiple continuous social variables as a \"mixture\" for social epidemiologic analysis. We propose using the Bayesian kernel machine regression analytic framework that yields univariate, bivariate, and overall exposure mixture effects.</p><p><strong>Methods: </strong>Using data from the 2023 Survey of Racism and Public Health, we conducted a Bayesian kernel machine regression analysis to study several individual, social, and structural factors as an exposure mixture and their relationships with psychological distress among individuals with at least one police arrest. Factors included racial and economic polarization, neighborhood deprivation, perceived discrimination, police perception, subjective social status, and substance use. We complemented this analysis with a series of unadjusted and adjusted models for each exposure mixture variable.</p><p><strong>Results: </strong>We found that more self-reported discrimination experiences in the past year (posterior inclusion probability = 1.00) and greater substance use (posterior inclusion probability = 1.00) correlated with higher psychological distress. These associations were consistent with the findings from the unadjusted and adjusted linear regression analyses: past year perceived discrimination (unadjusted b = 2.58, 95% confidence interval [CI]: 1.86, 3.30; adjusted b = 2.20, 95% CI: 1.45, 2.94) and substance use (unadjusted b = 2.92, 95% CI: 2.21, 3.62; adjusted b = 2.59, 95% CI: 1.87, 3.31).</p><p><strong>Conclusion: </strong>With the rise of big data and the expansion of variables in long-standing cohort and census studies, novel applications of methods from adjacent disciplines are a step forward in identifying exposure mixture associations in social epidemiology and addressing the health needs of socially vulnerable populations.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141859349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Maternal History of Childhood Maltreatment and Pregnancy Weight Outcomes.","authors":"Susan M Mason, Kriszta Farkas, Lisa M Bodnar, Jessica K Friedman, Sydney T Johnson, Rebecca L Emery Tavernier, Richard F MacLehose, Dianne Neumark-Sztainer","doi":"10.1097/EDE.0000000000001788","DOIUrl":"10.1097/EDE.0000000000001788","url":null,"abstract":"<p><strong>Background: </strong>Childhood maltreatment is associated with elevated adult weight. It is unclear whether this association extends to pregnancy, a critical window for the development of obesity.</p><p><strong>Methods: </strong>We examined associations of childhood maltreatment histories with prepregnancy body mass index (BMI) and gestational weight gain among women who had participated for >20 years in a longitudinal cohort. At age 26-35 years, participants reported childhood maltreatment (physical, sexual, and emotional abuse; emotional neglect) and, 5 years later, about prepregnancy weight and gestational weight gain for previous pregnancies (n = 656). Modified Poisson regression models were used to estimate associations of maltreatment history with prepregnancy BMI and gestational weight gain z -scores, adjusting for sociodemographics. We used multivariate imputation by chained equations to adjust outcome measures for misclassification using data from an internal validation study.</p><p><strong>Results: </strong>Before misclassification adjustment, results indicated a higher risk of prepregnancy BMI ≥30 kg/m 2 in women with certain types of maltreatment (e.g., emotional abuse risk ratio = 2.4; 95% confidence interval: 1.5, 3.7) compared with women without that maltreatment type. After misclassification adjustment, estimates were attenuated but still modestly elevated (e.g., emotional abuse risk ratio = 1.7; 95% confidence interval: 1.1, 2.7). Misclassification-adjusted estimates for maltreatment associations with gestational weight gain z -scores were close to the null and imprecise.</p><p><strong>Conclusions: </strong>Findings suggest an association of maltreatment with prepregnancy BMI ≥30 kg/m 2 but not with high gestational weight gain. Results suggest a potential need for equitable interventions that can support all women, including those with maltreatment histories, as they enter pregnancy.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142003945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Maternal Health During the COVID-19 Pandemic in the United States: An Interrupted Time-series Analysis.","authors":"Kaitlyn Jackson, Deborah Karasek, Alison Gemmill, Daniel F Collin, Rita Hamad","doi":"10.1097/EDE.0000000000001779","DOIUrl":"10.1097/EDE.0000000000001779","url":null,"abstract":"<p><strong>Background: </strong>The COVID-19 pandemic, and subsequent policy responses aimed at curbing disease spread and reducing economic fallout, had far-reaching consequences for maternal health. There has been little research to our knowledge on enduring disruptions to maternal health trends beyond the early pandemic and limited understanding of how these impacted pre-existing disparities in maternal health.</p><p><strong>Methods: </strong>We leveraged rigorous interrupted time-series methods and US National Center for Health Statistics Vital Statistics Birth Data Files of all live births for 2015-2021 (N = 24,653,848). We estimated whether changes in maternal health trends after the onset of the COVID-19 pandemic (March 2020) differed from predictions based on pre-existing temporal trends. Outcomes included gestational diabetes, hypertensive disorders of pregnancy, gestational weight gain, and adequacy of prenatal care.</p><p><strong>Results: </strong>We found an increased incidence of gestational diabetes (December 2020 peak: 1.7 percentage points (pp); 95% confidence interval [CI]: 1.3, 2.1), hypertensive disorders of pregnancy (January 2021 peak: 1.3 pp; 95% CI: 0.4, 2.1), and gestational weight gain (March 2021 peak: 0.1 standard deviation; 95% CI: 0.03, 0.1) and declines in inadequate prenatal care (January 2021 nadir: -0.4 pp; 95% CI: -0.7, -0.1). Key differences by subgroups included greater and more sustained increases in gestational diabetes among Black, Hispanic, and less educated individuals.</p><p><strong>Conclusion: </strong>These patterns in maternal health likely reflect not only effects of COVID-19 infection but also changes in healthcare access, health behaviors, remote work, economic security, and maternal stress. Further research about causal pathways and longer-term trends will inform public health and clinical interventions to address maternal disease burden and disparities.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142132180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Socioeconomic status, smoking, and lung cancer: mediation and bias analysis in the SYNERGY study.","authors":"Jan Hovanec, Benjamin Kendzia, Ann Olsson, Joachim Schüz, Hans Kromhout, Roel Vermeulen, Susan Peters, Per Gustavsson, Enrica Migliore, Loredana Radoi, Christine Barul, Dario Consonni, Neil E Caporaso, Maria Teresa Landi, John K Field, Stefan Karrasch, Heinz-Erich Wichmann, Jack Siemiatycki, Marie-Elise Parent, Lorenzo Richiardi, Lorenzo Simonato, Karl-Heinz Jöckel, Wolfgang Ahrens, Hermann Pohlabeln, Guillermo Fernández-Tardón, David Zaridze, John R McLaughlin, Paul A Demers, Beata Świątkowska, Jolanta Lissowska, Tamás Pándics, Eleonora Fabianova, Dana Mates, Miriam Schejbalova, Lenka Foretova, Vladimír Janout, Paolo Boffetta, Francesco Forastiere, Kurt Straif, Thomas Brüning, Thomas Behrens","doi":"10.1097/EDE.0000000000001807","DOIUrl":"10.1097/EDE.0000000000001807","url":null,"abstract":"<p><strong>Background: </strong>Increased lung-cancer risks for low socioeconomic status (SES) groups are only partially attributable to smoking habits. Little effort has been made to investigate the persistent risks related to low SES by quantification of potential biases.</p><p><strong>Methods: </strong>Based on 12 case-control studies, including 18 centers of the international SYNERGY project (16,550 cases, 20,147 controls), we estimated controlled direct effects (CDE) of SES on lung cancer via multiple logistic regression, adjusted for age, study center, and smoking habits, and stratified by sex. We conducted mediation analysis by inverse odds ratio weighting to estimate natural direct effects (NDE) and natural indirect effects via smoking habits. We considered misclassification of smoking status, selection bias, and unmeasured mediator-outcome confounding by genetic risk, both separately as well as by multiple quantitative bias analysis, using bootstrap to create 95% simulation intervals (SI).</p><p><strong>Results: </strong>Mediation analysis of lung-cancer risks for SES estimated mean proportions of 43% in men and 33% in women attributable to smoking. Bias analyses decreased direct effects of SES on lung cancer, with selection bias showing the strongest reduction in lung-cancer risk in the multiple bias analysis. Lung-cancer risks remained increased for lower SES groups, with higher risks in men [4th versus 1st (highest) SES quartile: CDE 1.50 (SI 1.32-1.69)] than women [CDE 1.20 (SI 1.01-1.45)]. NDE were similar to CDE, particularly in men.</p><p><strong>Conclusions: </strong>Bias adjustment lowered direct lung-cancer risk estimates of lower SES groups. However, risks for low SES remained elevated, likely attributable to occupational hazards or other environmental exposures.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142460886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}