Pub Date : 2024-09-04DOI: 10.1097/EDE.0000000000001779
Kaitlyn Jackson, Deborah Karasek, Alison Gemmill, Daniel F Collin, Rita Hamad
Background: 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.
Methods: 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) and 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.
Results: We found increased incidence of gestational diabetes (December 2020 peak:1.7 percentage points (pp); 95%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 (SD); 95%CI: 0.03, 0.1), and declines in inadequate prenatal care (January 2021 nadir: -0.4pp; 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.
Conclusion: 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.
{"title":"Maternal health during the COVID-19 pandemic in the U.S.: an interrupted time series analysis.","authors":"Kaitlyn Jackson, Deborah Karasek, Alison Gemmill, Daniel F Collin, Rita Hamad","doi":"10.1097/EDE.0000000000001779","DOIUrl":"https://doi.org/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) and 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 increased incidence of gestational diabetes (December 2020 peak:1.7 percentage points (pp); 95%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 (SD); 95%CI: 0.03, 0.1), and declines in inadequate prenatal care (January 2021 nadir: -0.4pp; 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":"2024-09-04","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}
Pub Date : 2024-09-01Epub Date: 2024-07-05DOI: 10.1097/EDE.0000000000001756
Norihiro Suzuki, Masataka Taguri
When conducting database studies, researchers sometimes use an algorithm known as "case definition," "outcome definition," or "computable phenotype" to identify the outcome of interest. Generally, algorithms are created by combining multiple variables and codes, and we need to select the most appropriate one to apply to the database study. Validation studies compare algorithms with the gold standard and calculate indicators such as sensitivity and specificity to assess their validities. As the indicators are calculated for each algorithm, selecting an algorithm is equivalent to choosing a pair of sensitivity and specificity. Therefore, receiver operating characteristic curves can be utilized, and two intuitive criteria are commonly used. However, neither was conceived to reduce the biases of effect measures (e.g., risk difference and risk ratio), which are important in database studies. In this study, we evaluated two existing criteria from perspectives of the biases and found that one of them, called the Youden index always minimizes the bias of the risk difference regardless of the true incidence proportions under nondifferential outcome misclassifications. However, both criteria may lead to inaccurate estimates of absolute risks, and such property is undesirable in decision-making. Therefore, we propose a new criterion based on minimizing the sum of the squared biases of absolute risks to estimate them more accurately. Subsequently, we apply all criteria to the data from the actual validation study on postsurgical infections and present the results of a sensitivity analysis to examine the robustness of the assumption our proposed criterion requires.
{"title":"A New Criterion for Determining a Cutoff Value Based on the Biases of Incidence Proportions in the Presence of Non-differential Outcome Misclassifications.","authors":"Norihiro Suzuki, Masataka Taguri","doi":"10.1097/EDE.0000000000001756","DOIUrl":"10.1097/EDE.0000000000001756","url":null,"abstract":"<p><p>When conducting database studies, researchers sometimes use an algorithm known as \"case definition,\" \"outcome definition,\" or \"computable phenotype\" to identify the outcome of interest. Generally, algorithms are created by combining multiple variables and codes, and we need to select the most appropriate one to apply to the database study. Validation studies compare algorithms with the gold standard and calculate indicators such as sensitivity and specificity to assess their validities. As the indicators are calculated for each algorithm, selecting an algorithm is equivalent to choosing a pair of sensitivity and specificity. Therefore, receiver operating characteristic curves can be utilized, and two intuitive criteria are commonly used. However, neither was conceived to reduce the biases of effect measures (e.g., risk difference and risk ratio), which are important in database studies. In this study, we evaluated two existing criteria from perspectives of the biases and found that one of them, called the Youden index always minimizes the bias of the risk difference regardless of the true incidence proportions under nondifferential outcome misclassifications. However, both criteria may lead to inaccurate estimates of absolute risks, and such property is undesirable in decision-making. Therefore, we propose a new criterion based on minimizing the sum of the squared biases of absolute risks to estimate them more accurately. Subsequently, we apply all criteria to the data from the actual validation study on postsurgical infections and present the results of a sensitivity analysis to examine the robustness of the assumption our proposed criterion requires.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11309335/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141537786","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}
Pub Date : 2024-09-01Epub Date: 2024-07-05DOI: 10.1097/EDE.0000000000001755
Guangyi Wang, Rita Hamad, Justin S White
Difference-in-differences (DiD) is a powerful, quasi-experimental research design widely used in longitudinal policy evaluations with health outcomes. However, DiD designs face several challenges to ensuring reliable causal inference, such as when policy settings are more complex. Recent economics literature has revealed that DiD estimators may exhibit bias when heterogeneous treatment effects, a common consequence of staggered policy implementation, are present. To deepen our understanding of these advancements in epidemiology, in this methodologic primer, we start by presenting an overview of DiD methods. We then summarize fundamental problems associated with DiD designs with heterogeneous treatment effects and provide guidance on recently proposed heterogeneity-robust DiD estimators, which are increasingly being implemented by epidemiologists. We also extend the discussion to violations of the parallel trends assumption, which has received less attention. Last, we present results from a simulation study that compares the performance of several DiD estimators under different scenarios to enhance understanding and application of these methods.
差分法(DiD)是一种功能强大的准实验研究设计,广泛应用于健康结果的纵向政策评估中。然而,DiD 设计在确保可靠的因果推论方面面临着一些挑战,比如当政策环境较为复杂时。最近的经济学文献显示,当出现异质性治疗效果(交错实施政策的常见后果)时,DiD 估计器可能会出现偏差。为了加深对这些流行病学进展的理解,在本方法论入门指南中,我们首先介绍了 DiD 方法的概述。然后,我们总结了与具有异质性治疗效果的 DiD 设计相关的基本问题,并为最近提出的异质性稳健 DiD 估计器提供了指导,流行病学家正在越来越多地使用这些估计器。我们还将讨论扩展到违反平行趋势假设的情况,这一点关注较少。最后,我们介绍了一项模拟研究的结果,该研究比较了几种 DiD 估计器在不同情况下的性能,以加深对这些方法的理解和应用。
{"title":"Advances in Difference-in-differences Methods for Policy Evaluation Research.","authors":"Guangyi Wang, Rita Hamad, Justin S White","doi":"10.1097/EDE.0000000000001755","DOIUrl":"10.1097/EDE.0000000000001755","url":null,"abstract":"<p><p>Difference-in-differences (DiD) is a powerful, quasi-experimental research design widely used in longitudinal policy evaluations with health outcomes. However, DiD designs face several challenges to ensuring reliable causal inference, such as when policy settings are more complex. Recent economics literature has revealed that DiD estimators may exhibit bias when heterogeneous treatment effects, a common consequence of staggered policy implementation, are present. To deepen our understanding of these advancements in epidemiology, in this methodologic primer, we start by presenting an overview of DiD methods. We then summarize fundamental problems associated with DiD designs with heterogeneous treatment effects and provide guidance on recently proposed heterogeneity-robust DiD estimators, which are increasingly being implemented by epidemiologists. We also extend the discussion to violations of the parallel trends assumption, which has received less attention. Last, we present results from a simulation study that compares the performance of several DiD estimators under different scenarios to enhance understanding and application of these methods.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11305929/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141537787","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}
Pub Date : 2024-09-01Epub Date: 2024-07-05DOI: 10.1097/EDE.0000000000001757
Edmond D Shenassa, Jessica L Gleason, Kathryn Hirabayashi
Background: Sibling studies of maternal smoking during pregnancy and subsequent risk of depression have produced mixed results. A recent study identified not considering the amount of maternal smoking and age of onset as potentially masking a true association. We examine these issues and also the amount of maternal smoking during pregnancy as a determinant of the severity of depressive symptoms.
Methods: We analyzed data from the community-based National Longitudinal Survey of Youth (US, 1994-2016). Mothers reported smoking during pregnancy (none, <1 pack/day, ≥1 pack/day). We assessed offspring's lifetime depression (i.e., ≥8 symptoms) and symptom counts with the Centers for Epidemiologic Studies Depression scale. We estimated the risk of these two outcomes in the full sample (n = 7172) and among siblings (n = 6145) using generalized linear mixed-effects models with random intercepts by family and family-averaged means for sibling analyses.
Results: Among siblings, we observed dose-dependent elevations for both risk of depression (smoking during pregnancy <1 pack/day adjusted risk ratio [aRR] = 1.18; 95% confidence interval [CI] = 1.07, 1.30; smoking ≥1 aRR = 1.36; 95% CI = 1.19, 1.56) and severity of depressive symptoms (smoking <1 pack/day aRR = 1.12; 95% CI = 1.08, 1.16); smoking ≥1 pack/day aRR = 1.25; 95% CI = 1.18, 1.31). Among both samples, the P for trend was <0.01. In analysis limited to offspring diagnosed before age 18, results for severity were attenuated.
Conclusions: This evidence supports the existence of an independent association between maternal smoking during pregnancy and both the risk of depression and the severity of depressive symptoms. The results highlight the utility of considering the amount of smoking, severity of symptoms, and age of onset.
{"title":"Fetal Exposure to Tobacco Metabolites and Depression During Adulthood: Beyond Binary Measures.","authors":"Edmond D Shenassa, Jessica L Gleason, Kathryn Hirabayashi","doi":"10.1097/EDE.0000000000001757","DOIUrl":"10.1097/EDE.0000000000001757","url":null,"abstract":"<p><strong>Background: </strong>Sibling studies of maternal smoking during pregnancy and subsequent risk of depression have produced mixed results. A recent study identified not considering the amount of maternal smoking and age of onset as potentially masking a true association. We examine these issues and also the amount of maternal smoking during pregnancy as a determinant of the severity of depressive symptoms.</p><p><strong>Methods: </strong>We analyzed data from the community-based National Longitudinal Survey of Youth (US, 1994-2016). Mothers reported smoking during pregnancy (none, <1 pack/day, ≥1 pack/day). We assessed offspring's lifetime depression (i.e., ≥8 symptoms) and symptom counts with the Centers for Epidemiologic Studies Depression scale. We estimated the risk of these two outcomes in the full sample (n = 7172) and among siblings (n = 6145) using generalized linear mixed-effects models with random intercepts by family and family-averaged means for sibling analyses.</p><p><strong>Results: </strong>Among siblings, we observed dose-dependent elevations for both risk of depression (smoking during pregnancy <1 pack/day adjusted risk ratio [aRR] = 1.18; 95% confidence interval [CI] = 1.07, 1.30; smoking ≥1 aRR = 1.36; 95% CI = 1.19, 1.56) and severity of depressive symptoms (smoking <1 pack/day aRR = 1.12; 95% CI = 1.08, 1.16); smoking ≥1 pack/day aRR = 1.25; 95% CI = 1.18, 1.31). Among both samples, the P for trend was <0.01. In analysis limited to offspring diagnosed before age 18, results for severity were attenuated.</p><p><strong>Conclusions: </strong>This evidence supports the existence of an independent association between maternal smoking during pregnancy and both the risk of depression and the severity of depressive symptoms. The results highlight the utility of considering the amount of smoking, severity of symptoms, and age of onset.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141537788","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}
Pub Date : 2024-09-01Epub Date: 2024-07-18DOI: 10.1097/EDE.0000000000001760
Amy E Kalkbrenner, Cheng Zheng, Justin Yu, Tara E Jenson, Thomas Kuhlwein, Christine Ladd-Acosta, Jakob Grove, Diana Schendel
Background: Testing etiologic heterogeneity, whether a disorder subtype is more or less impacted by a risk factor, is important for understanding causal pathways and optimizing statistical power. The study of mental health disorders especially benefits from strategic subcategorization because these disorders are heterogeneous and frequently co-occur. Existing methods to quantify etiologic heterogeneity are not appropriate for noncompeting events in an open cohort of variable-length follow-up. Thus, we developed a new method.
Methods: We estimated risks from urban residence, maternal smoking during pregnancy, and parental psychiatric history, with subtypes defined by the presence or absence of a codiagnosis: autism alone, attention deficit hyperactivity disorder (ADHD) alone, and joint diagnoses of autism + ADHD. To calculate the risk of a single diagnosis (e.g., autism alone), we subtracted the risk for autism + ADHD from the risk for autism overall. We tested the equivalency of average risk ratios over time, using a Wald-type test and bootstrapped standard errors.
Results: Urban residence was most strongly linked with autism + ADHD and least with ADHD only; maternal smoking was associated with ADHD only but not autism only; and parental psychiatric history exhibited similar associations with all subgroups.
Conclusion: Our method allowed the calculation of appropriate P values to test the strength of association, informing etiologic heterogeneity wherein two of these three risk factors exhibited different impacts across diagnostic subtypes. The method used all available data, avoided neurodevelopmental outcome misclassification, exhibited robust statistical precision, and is applicable to similar heterogeneous complex conditions using common diagnostic data with variable follow-up.
背景:检测病因异质性--一种失调症亚型受风险因素的影响是大还是小--对于了解因果途径和优化统计能力非常重要。心理健康疾病的研究尤其受益于战略性的亚分类,因为这些疾病是异质性的,而且经常并发。现有的量化病因异质性的方法并不适合随访时间长短不一的开放队列中的非竞争事件。因此,我们开发了一种新方法:我们估算了城市居住地、母亲孕期吸烟和父母精神病史的风险,并根据是否存在共同诊断定义了亚型:单独自闭症、单独注意缺陷多动障碍(ADHD)和自闭症+ADHD联合诊断。为了计算单一诊断(如单独自闭症)的风险,我们从自闭症总体风险中减去自闭症+多动症的风险。我们使用 Wald 类型检验和引导标准误差检验了不同时期平均风险比的等效性:结果:城市居民与自闭症+ADHD的关联度最高,而仅与ADHD的关联度最低;母亲吸烟仅与ADHD相关,而与自闭症无关;父母精神病史与所有亚组的关联度相似:我们的方法可以计算出适当的 p 值来检验关联强度,并告知病因异质性,即这三个风险因素中有两个在不同诊断亚型中表现出不同的影响。该方法使用了所有可用数据,避免了神经发育结果的误分类,显示了强大的统计精度,适用于使用常见诊断数据和不同随访的类似异质性复杂病症。
{"title":"Method for Testing Etiologic Heterogeneity Among Noncompeting Diagnoses, Applied to Impact of Perinatal Exposures on Autism and Attention Deficit Hyperactivity Disorder.","authors":"Amy E Kalkbrenner, Cheng Zheng, Justin Yu, Tara E Jenson, Thomas Kuhlwein, Christine Ladd-Acosta, Jakob Grove, Diana Schendel","doi":"10.1097/EDE.0000000000001760","DOIUrl":"10.1097/EDE.0000000000001760","url":null,"abstract":"<p><strong>Background: </strong>Testing etiologic heterogeneity, whether a disorder subtype is more or less impacted by a risk factor, is important for understanding causal pathways and optimizing statistical power. The study of mental health disorders especially benefits from strategic subcategorization because these disorders are heterogeneous and frequently co-occur. Existing methods to quantify etiologic heterogeneity are not appropriate for noncompeting events in an open cohort of variable-length follow-up. Thus, we developed a new method.</p><p><strong>Methods: </strong>We estimated risks from urban residence, maternal smoking during pregnancy, and parental psychiatric history, with subtypes defined by the presence or absence of a codiagnosis: autism alone, attention deficit hyperactivity disorder (ADHD) alone, and joint diagnoses of autism + ADHD. To calculate the risk of a single diagnosis (e.g., autism alone), we subtracted the risk for autism + ADHD from the risk for autism overall. We tested the equivalency of average risk ratios over time, using a Wald-type test and bootstrapped standard errors.</p><p><strong>Results: </strong>Urban residence was most strongly linked with autism + ADHD and least with ADHD only; maternal smoking was associated with ADHD only but not autism only; and parental psychiatric history exhibited similar associations with all subgroups.</p><p><strong>Conclusion: </strong>Our method allowed the calculation of appropriate P values to test the strength of association, informing etiologic heterogeneity wherein two of these three risk factors exhibited different impacts across diagnostic subtypes. The method used all available data, avoided neurodevelopmental outcome misclassification, exhibited robust statistical precision, and is applicable to similar heterogeneous complex conditions using common diagnostic data with variable follow-up.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11309336/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141723300","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}
Pub Date : 2024-09-01Epub Date: 2024-07-10DOI: 10.1097/EDE.0000000000001763
Ben Wilson, Matthew Wallace, Jan Saarela
Background: Children of immigrants often have excess mortality rates, in contrast to the low mortality typically exhibited by their parents' generation. However, prior research has studied children of immigrants who were selected for migration, thereby rendering it difficult to isolate the intergenerational impact of migration on adult mortality.
Methods: We use semiparametric survival analysis to carry out a total population cohort study estimating all-cause and cause-specific mortality among all adult men and women from age of 17 years among all men and women born in 1953-1972 and resident in Finland in 1970-2020. We compare children of forced migrants from ceded Karelia, an area of Finland that was ceded to Russia during the Second World War, with the children of parents born in present-day Finland.
Results: Children with two parents who were forced migrants have higher mortality than children with two parents born in Northern, Southern, and Western Finland, but similar or lower mortality than the subpopulation of children whose parents were born in the more comparable areas of Eastern Finland. For women and men, a mortality advantage is largest for external causes and persists after controlling for socioeconomic factors.
Conclusion: Our findings suggest that forced migration can have a beneficial impact on the mortality of later generations, at least in the case where forced migrants are able to move to contextually similar locations that offer opportunities for rapid integration and social mobility. The findings also highlight the importance of making appropriate comparisons when evaluating the impact of forced migration.
{"title":"Understanding the Intergenerational Impact of Migration: An Adult Mortality Advantage for the Children of Forced Migrants?","authors":"Ben Wilson, Matthew Wallace, Jan Saarela","doi":"10.1097/EDE.0000000000001763","DOIUrl":"10.1097/EDE.0000000000001763","url":null,"abstract":"<p><strong>Background: </strong>Children of immigrants often have excess mortality rates, in contrast to the low mortality typically exhibited by their parents' generation. However, prior research has studied children of immigrants who were selected for migration, thereby rendering it difficult to isolate the intergenerational impact of migration on adult mortality.</p><p><strong>Methods: </strong>We use semiparametric survival analysis to carry out a total population cohort study estimating all-cause and cause-specific mortality among all adult men and women from age of 17 years among all men and women born in 1953-1972 and resident in Finland in 1970-2020. We compare children of forced migrants from ceded Karelia, an area of Finland that was ceded to Russia during the Second World War, with the children of parents born in present-day Finland.</p><p><strong>Results: </strong>Children with two parents who were forced migrants have higher mortality than children with two parents born in Northern, Southern, and Western Finland, but similar or lower mortality than the subpopulation of children whose parents were born in the more comparable areas of Eastern Finland. For women and men, a mortality advantage is largest for external causes and persists after controlling for socioeconomic factors.</p><p><strong>Conclusion: </strong>Our findings suggest that forced migration can have a beneficial impact on the mortality of later generations, at least in the case where forced migrants are able to move to contextually similar locations that offer opportunities for rapid integration and social mobility. The findings also highlight the importance of making appropriate comparisons when evaluating the impact of forced migration.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11309332/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141579273","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}
Pub Date : 2024-09-01Epub Date: 2024-05-21DOI: 10.1097/EDE.0000000000001752
Richard Liang, Danielle M Panelli, David K Stevenson, David H Rehkopf, Gary M Shaw, Henrik Toft Sørensen, Lars Pedersen
Background: Gestational diabetes is associated with adverse outcomes such as preterm birth (<37 weeks). However, there is no international consensus on screening criteria or diagnostic levels for gestational diabetes, and it is unknown whether body mass index (BMI) or obesity modifies the relation between glucose level and preterm birth.
Methods: We studied a pregnancy cohort restricted to two Danish regions from the linked Danish Medical Birth Register to study associations between glucose measurements from the 2-hour postload 75-g oral glucose tolerance test (one-step approach) and preterm birth from 2004 to 2018. In Denmark, gestational diabetes screening is a targeted strategy for mothers with identified risk factors. We used Poisson regression to estimate rate ratios (RR) of preterm birth with z-standardized glucose measurements. We assessed effect measure modification by stratifying analyses and testing for heterogeneity.
Results: Among 11,337 pregnancies (6.2% delivered preterm), we observed an adjusted preterm birth RR of 1.2 (95% confidence interval [CI] = 1.1, 1.3) for a one-standard deviation glucose increase of 1.4 mmol/l from the mean of 6.7 mmol/l. There was evidence for effect measure modification by obesity, for example, adjusted RR for nonobese (BMI, <30): 1.2 (95% CI = 1.1, 1.3) versus obese (BMI, ≥30): 1.3 (95% CI = 1.2-1.5), P = 0.05 for heterogeneity.
Conclusion: Among mothers screened for gestational diabetes, increased glucose levels, even those below the diagnostic level for gestational diabetes in Denmark, were associated with increased preterm birth risk. Obesity (BMI, ≥30) may be an effect measure modifier, not just a confounder, of the relation between blood glucose and preterm birth risk.
{"title":"Outcome of Pregnancy Oral Glucose Tolerance Test and Preterm Birth.","authors":"Richard Liang, Danielle M Panelli, David K Stevenson, David H Rehkopf, Gary M Shaw, Henrik Toft Sørensen, Lars Pedersen","doi":"10.1097/EDE.0000000000001752","DOIUrl":"10.1097/EDE.0000000000001752","url":null,"abstract":"<p><strong>Background: </strong>Gestational diabetes is associated with adverse outcomes such as preterm birth (<37 weeks). However, there is no international consensus on screening criteria or diagnostic levels for gestational diabetes, and it is unknown whether body mass index (BMI) or obesity modifies the relation between glucose level and preterm birth.</p><p><strong>Methods: </strong>We studied a pregnancy cohort restricted to two Danish regions from the linked Danish Medical Birth Register to study associations between glucose measurements from the 2-hour postload 75-g oral glucose tolerance test (one-step approach) and preterm birth from 2004 to 2018. In Denmark, gestational diabetes screening is a targeted strategy for mothers with identified risk factors. We used Poisson regression to estimate rate ratios (RR) of preterm birth with z-standardized glucose measurements. We assessed effect measure modification by stratifying analyses and testing for heterogeneity.</p><p><strong>Results: </strong>Among 11,337 pregnancies (6.2% delivered preterm), we observed an adjusted preterm birth RR of 1.2 (95% confidence interval [CI] = 1.1, 1.3) for a one-standard deviation glucose increase of 1.4 mmol/l from the mean of 6.7 mmol/l. There was evidence for effect measure modification by obesity, for example, adjusted RR for nonobese (BMI, <30): 1.2 (95% CI = 1.1, 1.3) versus obese (BMI, ≥30): 1.3 (95% CI = 1.2-1.5), P = 0.05 for heterogeneity.</p><p><strong>Conclusion: </strong>Among mothers screened for gestational diabetes, increased glucose levels, even those below the diagnostic level for gestational diabetes in Denmark, were associated with increased preterm birth risk. Obesity (BMI, ≥30) may be an effect measure modifier, not just a confounder, of the relation between blood glucose and preterm birth risk.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11305920/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141075036","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}
Pub Date : 2024-09-01Epub Date: 2024-06-11DOI: 10.1097/EDE.0000000000001758
Julia Debertin, Javier A Jurado Vélez, Laura Corlin, Bertha Hidalgo, Eleanor J Murray
Background: Causal graphs are an important tool for covariate selection but there is limited applied research on how best to create them. Here, we used data from the Coronary Drug Project trial to assess a range of approaches to directed acyclic graph (DAG) creation. We focused on the effect of adherence on mortality in the placebo arm, since the true causal effect is believed with a high degree of certainty.
Methods: We created DAGs for the effect of placebo adherence on mortality using different approaches for identifying variables and links to include or exclude. For each DAG, we identified minimal adjustment sets of covariates for estimating our causal effect of interest and applied these to analyses of the Coronary Drug Project data.
Results: When we used only baseline covariate values to estimate the cumulative effect of placebo adherence on mortality, all adjustment sets performed similarly. The specific choice of covariates had minimal effect on these (biased) point estimates, but including nonconfounding prognostic factors resulted in smaller variance estimates. When we additionally adjusted for time-varying covariates of adherence using inverse probability weighting, covariates identified from the DAG created by focusing on prognostic factors performed best.
Conclusion: Theoretical advice on covariate selection suggests that including prognostic factors that are not exposure predictors can reduce variance without increasing bias. In contrast, for exposure predictors that are not prognostic factors, inclusion may result in less bias control. Our results empirically confirm this advice. We recommend that hand-creating DAGs begin with the identification of all potential outcome prognostic factors.
背景:因果图是选择协变量的重要工具,但关于如何最好地创建因果图的应用研究却很有限。在此,我们利用冠心病药物项目(CDP)试验的数据评估了一系列创建有向无环图(DAG)的方法。我们重点研究了坚持用药对安慰剂组死亡率的影响,因为我们相信真正的因果效应具有很高的确定性:我们使用不同的方法来确定变量和链接的包含或排除,从而创建了安慰剂依从性对死亡率影响的 DAG。对于每个 DAG,我们都确定了用于估计我们感兴趣的因果效应的协变量最小调整集,并将其应用于 CDP 数据的分析:结果:当我们仅使用基线协变量值来估算安慰剂依从性对死亡率的累积效应时,所有调整集的表现相似。协变量的具体选择对这些(有偏差的)点估算值的影响很小,但包括非混杂预后因素会导致方差估算值较小。当我们使用反概率加权法对随时间变化的依从性协变量进行额外调整时,通过关注预后因素创建的 DAG 所确定的协变量表现最佳:关于协变量选择的理论建议表明,纳入非暴露预测因素的预后因素可以减少方差,而不会增加偏差。相反,对于不属于预后因素的暴露预测因子,纳入后可能会减少偏差控制。我们的研究结果从经验上证实了这一建议。我们建议在手工创建 DAG 时首先识别所有潜在的结果预测因素。
{"title":"Synthesizing Subject-matter Expertise for Variable Selection in Causal Effect Estimation: A Case Study.","authors":"Julia Debertin, Javier A Jurado Vélez, Laura Corlin, Bertha Hidalgo, Eleanor J Murray","doi":"10.1097/EDE.0000000000001758","DOIUrl":"10.1097/EDE.0000000000001758","url":null,"abstract":"<p><strong>Background: </strong>Causal graphs are an important tool for covariate selection but there is limited applied research on how best to create them. Here, we used data from the Coronary Drug Project trial to assess a range of approaches to directed acyclic graph (DAG) creation. We focused on the effect of adherence on mortality in the placebo arm, since the true causal effect is believed with a high degree of certainty.</p><p><strong>Methods: </strong>We created DAGs for the effect of placebo adherence on mortality using different approaches for identifying variables and links to include or exclude. For each DAG, we identified minimal adjustment sets of covariates for estimating our causal effect of interest and applied these to analyses of the Coronary Drug Project data.</p><p><strong>Results: </strong>When we used only baseline covariate values to estimate the cumulative effect of placebo adherence on mortality, all adjustment sets performed similarly. The specific choice of covariates had minimal effect on these (biased) point estimates, but including nonconfounding prognostic factors resulted in smaller variance estimates. When we additionally adjusted for time-varying covariates of adherence using inverse probability weighting, covariates identified from the DAG created by focusing on prognostic factors performed best.</p><p><strong>Conclusion: </strong>Theoretical advice on covariate selection suggests that including prognostic factors that are not exposure predictors can reduce variance without increasing bias. In contrast, for exposure predictors that are not prognostic factors, inclusion may result in less bias control. Our results empirically confirm this advice. We recommend that hand-creating DAGs begin with the identification of all potential outcome prognostic factors.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11309331/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141300384","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}
Pub Date : 2024-09-01Epub Date: 2024-05-21DOI: 10.1097/EDE.0000000000001751
Elizabeth W Diemer
{"title":"Interpreting Violations of Falsification Tests in the Context of Multiple Proposed Instrumental Variables.","authors":"Elizabeth W Diemer","doi":"10.1097/EDE.0000000000001751","DOIUrl":"10.1097/EDE.0000000000001751","url":null,"abstract":"","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141075032","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}
Pub Date : 2024-09-01Epub Date: 2024-06-13DOI: 10.1097/EDE.0000000000001754
Yu Ni, Alexis Sullivan, Adam A Szpiro, James Peng, Christine T Loftus, Marnie F Hazlehurst, Allison Sherris, Erin R Wallace, Laura E Murphy, Ruby H N Nguyen, Shanna H Swan, Sheela Sathyanarayana, Emily S Barrett, W Alex Mason, Nicole R Bush, Catherine J Karr, Kaja Z LeWinn
Background: Executive function, which develops rapidly in childhood, enables problem-solving, focused attention, and planning. Animal models describe executive function decrements associated with ambient air pollution exposure, but epidemiologic studies are limited.
Methods: We examined associations between early childhood air pollution exposure and school-aged executive function in 1235 children from three US pregnancy cohorts in the ECHO-PATHWAYS Consortium. We derived point-based residential exposures to ambient particulate matter ≤2.5 µm in aerodynamic diameter (PM 2.5 ), nitrogen dioxide (NO 2 ), and ozone (O 3 ) at ages 0-4 years from spatiotemporal models with a 2-week resolution. We assessed executive function across three domains, cognitive flexibility, working memory, and inhibitory control, using performance-based measures and calculated a composite score quantifying overall performance. We fitted linear regressions to assess air pollution and child executive function associations, adjusting for sociodemographic characteristics, maternal mental health, and health behaviors, and examined modification by child sex, maternal education, and neighborhood educational opportunity.
Results: In the overall sample, we found hypothesized inverse associations in crude but not adjusted models. Modified associations between NO 2 exposure and working memory by neighborhood education opportunity were present ( Pinteraction = 0.05), with inverse associations more pronounced in the "high" and "very high" categories. Associations of interest did not differ by child sex or maternal education.
Conclusion: This work contributes to the evolving science regarding early-life environmental exposures and child development. There remains a need for continued exploration in future research endeavors, to elucidate the complex interplay between natural environment and social determinants influencing child neurodevelopment.
{"title":"Ambient Air Pollution Exposures and Child Executive Function: A US Multicohort Study.","authors":"Yu Ni, Alexis Sullivan, Adam A Szpiro, James Peng, Christine T Loftus, Marnie F Hazlehurst, Allison Sherris, Erin R Wallace, Laura E Murphy, Ruby H N Nguyen, Shanna H Swan, Sheela Sathyanarayana, Emily S Barrett, W Alex Mason, Nicole R Bush, Catherine J Karr, Kaja Z LeWinn","doi":"10.1097/EDE.0000000000001754","DOIUrl":"10.1097/EDE.0000000000001754","url":null,"abstract":"<p><strong>Background: </strong>Executive function, which develops rapidly in childhood, enables problem-solving, focused attention, and planning. Animal models describe executive function decrements associated with ambient air pollution exposure, but epidemiologic studies are limited.</p><p><strong>Methods: </strong>We examined associations between early childhood air pollution exposure and school-aged executive function in 1235 children from three US pregnancy cohorts in the ECHO-PATHWAYS Consortium. We derived point-based residential exposures to ambient particulate matter ≤2.5 µm in aerodynamic diameter (PM 2.5 ), nitrogen dioxide (NO 2 ), and ozone (O 3 ) at ages 0-4 years from spatiotemporal models with a 2-week resolution. We assessed executive function across three domains, cognitive flexibility, working memory, and inhibitory control, using performance-based measures and calculated a composite score quantifying overall performance. We fitted linear regressions to assess air pollution and child executive function associations, adjusting for sociodemographic characteristics, maternal mental health, and health behaviors, and examined modification by child sex, maternal education, and neighborhood educational opportunity.</p><p><strong>Results: </strong>In the overall sample, we found hypothesized inverse associations in crude but not adjusted models. Modified associations between NO 2 exposure and working memory by neighborhood education opportunity were present ( Pinteraction = 0.05), with inverse associations more pronounced in the \"high\" and \"very high\" categories. Associations of interest did not differ by child sex or maternal education.</p><p><strong>Conclusion: </strong>This work contributes to the evolving science regarding early-life environmental exposures and child development. There remains a need for continued exploration in future research endeavors, to elucidate the complex interplay between natural environment and social determinants influencing child neurodevelopment.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11305919/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141317182","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}