Epidemiology最新文献

{"title":"Mixture Models for Social Epidemiology: Opportunities and Cautions.","authors":"Alina Schnake-Mahl, Ghassan Badri Hamra","doi":"10.1097/EDE.0000000000001778","DOIUrl":"10.1097/EDE.0000000000001778","url":null,"abstract":"","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"748-752"},"PeriodicalIF":4.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141859350","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":"A Counterfactual Analysis of Impact of Cesarean Birth in a First Birth on Severe Maternal Morbidity in the Subsequent Birth.","authors":"Shalmali Bane, Jonathan M Snowden, Julia F Simard, Michelle Odden, Peiyi Kan, Elliott K Main, Suzan L Carmichael","doi":"10.1097/EDE.0000000000001775","DOIUrl":"10.1097/EDE.0000000000001775","url":null,"abstract":"<p><strong>Background: </strong>It is known that cesarean birth affects maternal outcomes in subsequent pregnancies, but specific effect estimates are lacking. We sought to quantify the effect of cesarean birth reduction among nulliparous, term, singleton, vertex (NTSV) births (i.e., preventable cesarean births) on severe maternal morbidity (SMM) in the second birth.</p><p><strong>Methods: </strong>We examined birth certificates linked with maternal hospitalization data (2007-2019) from California for NTSV births with a second birth (N = 779,382). The exposure was cesarean delivery in the first birth and the outcome was SMM in the second birth. We used adjusted Poisson regression models to calculate risk ratios and population attributable fraction for SMM in the second birth and conducted a counterfactual impact analysis to estimate how lowering NTSV cesarean births could reduce SMM in the second birth.</p><p><strong>Results: </strong>The adjusted risk ratio for SMM in the second birth given a prior cesarean birth was 1.7 (95% confidence interval: 1.5, 1.9); 15.5% (95% confidence interval: 15.3%, 15.7%) of this SMM may be attributable to prior cesarean birth. In a counterfactual analysis where 12% of the California population was least likely to get a cesarean birth instead delivered vaginally, we observed 174 fewer SMM events in a population of individuals with a low-risk first birth and subsequent birth.</p><p><strong>Conclusion: </strong>In our counterfactual analysis, lowering primary cesarean birth among an NTSV population was associated with fewer downstream SMM events in subsequent births and overall. Additionally, our findings reflect the importance of considering the cumulative accrual of risks across the reproductive life course.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"853-863"},"PeriodicalIF":4.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11560597/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141765749","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":"The Causal Roadmap and Simulations to Improve the Rigor and Reproducibility of Real-data Applications.","authors":"Nerissa Nance, Maya L Petersen, Mark van der Laan, Laura B Balzer","doi":"10.1097/EDE.0000000000001773","DOIUrl":"10.1097/EDE.0000000000001773","url":null,"abstract":"<p><p>The Causal Roadmap outlines a systematic approach to asking and answering questions of cause and effect: define the quantity of interest, evaluate needed assumptions, conduct statistical estimation, and carefully interpret results. To protect research integrity, it is essential that the algorithm for statistical estimation and inference be prespecified prior to conducting any effectiveness analyses. However, it is often unclear which algorithm will perform optimally for the real-data application. Instead, there is a temptation to simply implement one's favorite algorithm, recycling prior code or relying on the default settings of a computing package. Here, we call for the use of simulations that realistically reflect the application, including key characteristics such as strong confounding and dependent or missing outcomes, to objectively compare candidate estimators and facilitate full specification of the statistical analysis plan. Such simulations are informed by the Causal Roadmap and conducted after data collection but prior to effect estimation. We illustrate with two worked examples. First, in an observational longitudinal study, we use outcome-blind simulations to inform nuisance parameter estimation and variance estimation for longitudinal targeted minimum loss-based estimation. Second, in a cluster randomized trial with missing outcomes, we use treatment-blind simulations to examine type-I error control in two-stage targeted minimum loss-based estimation. In both examples, realistic simulations empower us to prespecify an estimation approach with strong expected finite sample performance, and also produce quality-controlled computing code for the actual analysis. Together, this process helps to improve the rigor and reproducibility of our research.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"791-800"},"PeriodicalIF":4.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11444352/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141859351","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":"Associations Between Gestational Residential Radon Exposure and Term Low Birthweight in Connecticut, USA.","authors":"Seulkee Heo, Longxiang Li, Ji-Young Son, Petros Koutrakis, Michelle L Bell","doi":"10.1097/EDE.0000000000001771","DOIUrl":"10.1097/EDE.0000000000001771","url":null,"abstract":"<p><strong>Background: </strong>Studies suggest biologic mechanisms for gestational exposure to radiation and impaired fetal development. We explored associations between gestational radon exposure and term low birthweight, for which evidence is limited.</p><p><strong>Methods: </strong>We examined data for 68,159 singleton full-term births in Connecticut, United States, 2016-2018. Using a radon spatiotemporal model, we estimated ZIP code-level basement and ground-level exposures during pregnancy and trimesters for each participant's address at birth or delivery. We used logistic regression models, including confounders, to estimate odds ratios (ORs) for term low birth weight in four exposure quartiles (Q1-Q4) with the lowest exposure group (Q1) as the reference.</p><p><strong>Results: </strong>Exposure levels to basement radon throughout pregnancy (0.27-3.02 pCi/L) were below the guideline level set by the US Environmental Protection Agency (4 pCi/L). The ORs for term low birth weight in the second-highest (Q3; 1.01-1.33 pCi/L) exposure group compared with the reference (<0.79 pCi/L) group for basement radon during the first trimester was 1.22 (95% confidence interval [CI] = 1.02, 1.45). The OR in the highest (Q4; 1.34-4.43 pCi/L) quartile group compared with the reference group during the first trimester was 1.26 (95% CI = 1.05, 1.50). Risks from basement radon were higher for participants with lower income, lower maternal education levels, or living in urban regions.</p><p><strong>Conclusion: </strong>This study found increased term low birth weight risks for increases in basement radon. Results have implications for infants' health for exposure to radon at levels below the current national guideline for indoor radon concentrations and building remediations.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"834-843"},"PeriodicalIF":4.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11560713/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141751373","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":"Pseudo-random Number Generator Influences on Average Treatment Effect Estimates Obtained with Machine Learning.","authors":"Ashley I Naimi, Ya-Hui Yu, Lisa M Bodnar","doi":"10.1097/EDE.0000000000001785","DOIUrl":"10.1097/EDE.0000000000001785","url":null,"abstract":"<p><strong>Background: </strong>The use of machine learning to estimate exposure effects introduces a dependence between the results of an empirical study and the value of the seed used to fix the pseudo-random number generator.</p><p><strong>Methods: </strong>We used data from 10,038 pregnant women and a 10% subsample (N = 1004) to examine the extent to which the risk difference for the relation between fruit and vegetable consumption and preeclampsia risk changes under different seed values. We fit an augmented inverse probability weighted estimator with two Super Learner algorithms: a simple algorithm including random forests and single-layer neural networks and a more complex algorithm with a mix of tree-based, regression-based, penalized, and simple algorithms. We evaluated the distributions of risk differences, standard errors, and P values that result from 5000 different seed value selections.</p><p><strong>Results: </strong>Our findings suggest important variability in the risk difference estimates, as well as an important effect of the stacking algorithm used. The interquartile range width of the risk differences in the full sample with the simple algorithm was 13 per 1000. However, all other interquartile ranges were roughly an order of magnitude lower. The medians of the distributions of risk differences differed according to the sample size and the algorithm used.</p><p><strong>Conclusions: </strong>Our findings add another dimension of concern regarding the potential for \"p-hacking,\" and further warrant the need to move away from simplistic evidentiary thresholds in empirical research. When empirical results depend on pseudo-random number generator seed values, caution is warranted in interpreting these results.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"779-786"},"PeriodicalIF":4.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11560583/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141992227","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":"Don't Let Your Analysis Go to Seed: On the Impact of Random Seed on Machine Learning-based Causal Inference.","authors":"Lindsey Schader, Weishan Song, Russell Kempker, David Benkeser","doi":"10.1097/EDE.0000000000001782","DOIUrl":"10.1097/EDE.0000000000001782","url":null,"abstract":"<p><p>Machine learning techniques for causal effect estimation can enhance the reliability of epidemiologic analyses, reducing their dependence on correct model specifications. However, the stochastic nature of many machine learning algorithms implies that the results derived from such approaches may be influenced by the random seed that is set before model fitting. In this work, we highlight the substantial influence of random seeds on a popular approach for machine learning-based causal effect estimation, namely doubly robust estimators. We illustrate that varying seeds can yield divergent scientific interpretations of doubly robust estimates produced from the same dataset. We propose techniques for stabilizing results across random seeds and, through an extensive simulation study, demonstrate that these techniques effectively neutralize seed-related variability without compromising the statistical efficiency of the estimators. Based on these findings, we offer practical guidelines to minimize the influence of random seeds in real-world applications, and we encourage researchers to explore the variability due to random seeds when implementing any method that involves random steps.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"764-778"},"PeriodicalIF":4.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141992225","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":"Re: Bias in Calculation of Attributable Fractions Using Relative Risks from Nonsmokers Only.","authors":"Etsuji Suzuki, Eiji Yamamoto","doi":"10.1097/EDE.0000000000001786","DOIUrl":"10.1097/EDE.0000000000001786","url":null,"abstract":"","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":"e21-e22"},"PeriodicalIF":4.7,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142003946","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":"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":" ","pages":"874-884"},"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":" ","pages":"864-873"},"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":" ","pages":"805-812"},"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}