Epidemiologic Reviews最新文献_第5页

The Revolution Will Be Hard to Evaluate: How Co-Occurring Policy Changes Affect Research on the Health Effects of Social Policies. 革命难以评估：同时发生的政策变化如何影响社会政策对健康影响的研究》（The Revolution Will Hard Evaluate: How Co-Occurcurring Policy Changes Affect Research on the Health Effects of Social Policies）。

IF 5.2 2区医学

Epidemiologic Reviews Pub Date : 2022-01-14 DOI: 10.1093/epirev/mxab009

Ellicott C Matthay, Erin Hagan, Spruha Joshi, May Lynn Tan, David Vlahov, Nancy Adler, M Maria Glymour

{"title":"The Revolution Will Be Hard to Evaluate: How Co-Occurring Policy Changes Affect Research on the Health Effects of Social Policies.","authors":"Ellicott C Matthay, Erin Hagan, Spruha Joshi, May Lynn Tan, David Vlahov, Nancy Adler, M Maria Glymour","doi":"10.1093/epirev/mxab009","DOIUrl":"10.1093/epirev/mxab009","url":null,"abstract":"Extensive empirical health research leverages variation in the timing and location of policy changes as quasi-experiments. Multiple social policies may be adopted simultaneously in the same locations, creating co-occurrence that must be addressed analytically for valid inferences. The pervasiveness and consequences of co-occurring policies have received limited attention. We analyzed a systematic sample of 13 social policy databases covering diverse domains including poverty, paid family leave, and tobacco use. We quantified policy co-occurrence in each database as the fraction of variation in each policy measure across different jurisdictions and times that could be explained by covariation with other policies. We used simulations to estimate the ratio of the variance of effect estimates under the observed policy co-occurrence to variance if policies were independent. Policy co-occurrence ranged from very high for state-level cannabis policies to low for country-level sexual minority-rights policies. For 65% of policies, greater than 90% of the place-time variation was explained by other policies. Policy co-occurrence increased the variance of effect estimates by a median of 57-fold. Co-occurring policies are common and pose a major methodological challenge to rigorously evaluating health effects of individual social policies. When uncontrolled, co-occurring policies confound one another, and when controlled, resulting positivity violations may substantially inflate the variance of estimated effects. Tools to enhance validity and precision for evaluating co-occurring policies are needed.","PeriodicalId":50510,"journal":{"name":"Epidemiologic Reviews","volume":"43 1","pages":"19-32"},"PeriodicalIF":5.2,"publicationDate":"2022-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/c7/e9/mxab009.PMC8763115.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9293536","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}

引用次数: 0

What to Do When Everything Happens at Once: Analytic Approaches to Estimate the Health Effects of Co-Occurring Social Policies. 当所有事情同时发生时该怎么办：估算同时发生的社会政策对健康影响的分析方法》（What to Do When Everything Happens at Once: Analytic Approaches to Estimate the Health Effects of Co-Occurring Social Policies.

IF 5.2 2区医学

Epidemiologic Reviews Pub Date : 2022-01-14 DOI: 10.1093/epirev/mxab005

Ellicott C Matthay, Laura M Gottlieb, David Rehkopf, May Lynn Tan, David Vlahov, M Maria Glymour

{"title":"What to Do When Everything Happens at Once: Analytic Approaches to Estimate the Health Effects of Co-Occurring Social Policies.","authors":"Ellicott C Matthay, Laura M Gottlieb, David Rehkopf, May Lynn Tan, David Vlahov, M Maria Glymour","doi":"10.1093/epirev/mxab005","DOIUrl":"10.1093/epirev/mxab005","url":null,"abstract":"Social policies have great potential to improve population health and reduce health disparities. Increasingly, those doing empirical research have sought to quantify the health effects of social policies by exploiting variation in the timing of policy changes across places. Multiple social policies are often adopted simultaneously or in close succession in the same locations, creating co-occurrence that must be handled analytically for valid inferences. Although this is a substantial methodological challenge for researchers aiming to isolate social policy effects, only in a limited number of studies have researchers systematically considered analytic solutions within a causal framework or assessed whether these solutions are being adopted. We designated 7 analytic solutions to policy co-occurrence, including efforts to disentangle individual policy effects and efforts to estimate the combined effects of co-occurring policies. We used an existing systematic review of social policies and health to evaluate how often policy co-occurrence is identified as a threat to validity and how often each analytic solution is applied in practice. Of the 55 studies, only in 17 (31%) did authors report checking for any co-occurring policies, although in 36 studies (67%), at least 1 approach was used that helps address policy co-occurrence. The most common approaches were adjusting for measures of co-occurring policies; defining the outcome on subpopulations likely to be affected by the policy of interest (but not other co-occurring policies); and selecting a less-correlated measure of policy exposure. As health research increasingly focuses on policy changes, we must systematically assess policy co-occurrence and apply analytic solutions to strengthen studies on the health effects of social policies.","PeriodicalId":50510,"journal":{"name":"Epidemiologic Reviews","volume":"43 1","pages":"33-47"},"PeriodicalIF":5.2,"publicationDate":"2022-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/72/16/mxab005.PMC8763089.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10421339","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}

引用次数: 0

Matching Methods for Confounder Adjustment: An Addition to the Epidemiologist's Toolbox. 混杂因素调整的匹配方法:流行病学家工具箱的补充。

IF 5.5 2区医学

Epidemiologic Reviews Pub Date : 2022-01-14 DOI: 10.1093/epirev/mxab003

Noah Greifer, Elizabeth A Stuart

引用次数: 18

Assessment of Physical Activity in Adults Using Wrist Accelerometers. 使用腕部加速度计评估成人的身体活动。

IF 5.5 2区医学

Epidemiologic Reviews Pub Date : 2022-01-14 DOI: 10.1093/epirev/mxab004

Fangyu Liu, Amal A Wanigatunga, Jennifer A Schrack

{"title":"Assessment of Physical Activity in Adults Using Wrist Accelerometers.","authors":"Fangyu Liu, Amal A Wanigatunga, Jennifer A Schrack","doi":"10.1093/epirev/mxab004","DOIUrl":"https://doi.org/10.1093/epirev/mxab004","url":null,"abstract":"The health benefits of physical activity (PA) have been widely recognized, yet traditional measures of PA, including questionnaires and category-based assessments of volume and intensity, provide only broad estimates of daily activities. Accelerometers have advanced epidemiologic research on PA by providing objective and continuous measurement of PA in free-living conditions. Wrist-worn accelerometers have become especially popular because of low participant burden. However, the validity and reliability of wrist-worn devices for adults have yet to be summarized. Moreover, accelerometer data provide rich information on how PA is accumulated throughout the day, but only a small portion of these rich data have been used by researchers. Last, new methodological developments are emerging that aim to overcome some of the limitations of accelerometers. In this review, we provide an overview of accelerometry research, with a special focus on wrist-worn accelerometers. We describe briefly how accelerometers work; summarize the validity and reliability of wrist-worn accelerometers; discuss the benefits of accelerometers, including measuring light-intensity PA; and discuss pattern metrics of daily PA recently introduced in the literature. A summary of large-scale cohort studies and randomized trials that implemented wrist-worn accelerometry is provided. We conclude the review by discussing new developments and directions of research using accelerometers, with a focus on wrist-worn accelerometers.","PeriodicalId":50510,"journal":{"name":"Epidemiologic Reviews","volume":" ","pages":"65-93"},"PeriodicalIF":5.5,"publicationDate":"2022-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8900289/pdf/mxab004.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39064740","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}

引用次数: 15

Evolution and Reproducibility of Simulation Modeling in Epidemiology and Health Policy Over Half a Century. 半个世纪以来流行病学和卫生政策模拟模型的演变和可重复性。

IF 5.5 2区医学

Epidemiologic Reviews Pub Date : 2022-01-14 DOI: 10.1093/epirev/mxab006

Mohammad S Jalali, Catherine DiGennaro, Abby Guitar, Karen Lew, Hazhir Rahmandad

{"title":"Evolution and Reproducibility of Simulation Modeling in Epidemiology and Health Policy Over Half a Century.","authors":"Mohammad S Jalali, Catherine DiGennaro, Abby Guitar, Karen Lew, Hazhir Rahmandad","doi":"10.1093/epirev/mxab006","DOIUrl":"https://doi.org/10.1093/epirev/mxab006","url":null,"abstract":"Simulation models are increasingly being used to inform epidemiologic studies and health policy, yet there is great variation in their transparency and reproducibility. In this review, we provide an overview of applications of simulation models in health policy and epidemiology, analyze the use of best reporting practices, and assess the reproducibility of the models using predefined, categorical criteria. We identified and analyzed 1,613 applicable articles and found exponential growth in the number of studies over the past half century, with the highest growth in dynamic modeling approaches. The largest subset of studies focused on disease policy models (70%), within which pathological conditions, viral diseases, neoplasms, and cardiovascular diseases account for one-third of the articles. Model details were not reported in almost half of the studies. We also provide in-depth analysis of modeling best practices, reporting quality and reproducibility of models for a subset of 100 articles (50 highly cited and 50 randomly selected from the remaining articles). Only 7 of 26 in-depth evaluation criteria were satisfied by more than 80% of samples. We identify areas for increased application of simulation modeling and opportunities to enhance the rigor and documentation in the conduct and reporting of simulation modeling in epidemiology and health policy.","PeriodicalId":50510,"journal":{"name":"Epidemiologic Reviews","volume":"43 1","pages":"166-175"},"PeriodicalIF":5.5,"publicationDate":"2022-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/07/ed/mxab006.PMC8763126.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10770253","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}

引用次数: 11

Longitudinal Methods for Modeling Exposures in Pharmacoepidemiologic Studies in Pregnancy. 妊娠药物流行病学研究中暴露建模的纵向方法。

IF 5.5 2区医学

Epidemiologic Reviews Pub Date : 2022-01-14 DOI: 10.1093/epirev/mxab002

Mollie E Wood, Angela Lupattelli, Kristin Palmsten, Gretchen Bandoli, Caroline Hurault-Delarue, Christine Damase-Michel, Christina D Chambers, Hedvig M E Nordeng, Marleen M H J van Gelder

{"title":"Longitudinal Methods for Modeling Exposures in Pharmacoepidemiologic Studies in Pregnancy.","authors":"Mollie E Wood, Angela Lupattelli, Kristin Palmsten, Gretchen Bandoli, Caroline Hurault-Delarue, Christine Damase-Michel, Christina D Chambers, Hedvig M E Nordeng, Marleen M H J van Gelder","doi":"10.1093/epirev/mxab002","DOIUrl":"https://doi.org/10.1093/epirev/mxab002","url":null,"abstract":"In many perinatal pharmacoepidemiologic studies, exposure to a medication is classified as \"ever exposed\" versus \"never exposed\" within each trimester or even over the entire pregnancy. This approach is often far from real-world exposure patterns, may lead to exposure misclassification, and does not to incorporate important aspects such as dosage, timing of exposure, and treatment duration. Alternative exposure modeling methods can better summarize complex, individual-level medication use trajectories or time-varying exposures from information on medication dosage, gestational timing of use, and frequency of use. We provide an overview of commonly used methods for more refined definitions of real-world exposure to medication use during pregnancy, focusing on the major strengths and limitations of the techniques, including the potential for method-specific biases. Unsupervised clustering methods, including k-means clustering, group-based trajectory models, and hierarchical cluster analysis, are of interest because they enable visual examination of medication use trajectories over time in pregnancy and complex individual-level exposures, as well as providing insight into comedication and drug-switching patterns. Analytical techniques for time-varying exposure methods, such as extended Cox models and Robins' generalized methods, are useful tools when medication exposure is not static during pregnancy. We propose that where appropriate, combining unsupervised clustering techniques with causal modeling approaches may be a powerful approach to understanding medication safety in pregnancy, and this framework can also be applied in other areas of epidemiology.","PeriodicalId":50510,"journal":{"name":"Epidemiologic Reviews","volume":"43 1","pages":"130-146"},"PeriodicalIF":5.5,"publicationDate":"2022-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/47/62/mxab002.PMC8763114.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9487891","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}

引用次数: 15

Epidemiologic Methods: Seeing the Forest and the Trees. 流行病学方法:看到森林和树木。

IF 5.5 2区医学

Epidemiologic Reviews Pub Date : 2022-01-14 DOI: 10.1093/epirev/mxab010

Kara E Rudolph, Bryan Lau

引用次数: 0

Is the Way Forward to Step Back? Documenting the Frequency With Which Study Goals Are Misaligned With Study Methods and Interpretations in the Epidemiologic Literature. 后退是前进的道路吗?记录研究目标与流行病学文献中的研究方法和解释不一致的频率。

IF 5.5 2区医学

Epidemiologic Reviews Pub Date : 2022-01-14 DOI: 10.1093/epirev/mxab008

Katrina L Kezios

{"title":"Is the Way Forward to Step Back? Documenting the Frequency With Which Study Goals Are Misaligned With Study Methods and Interpretations in the Epidemiologic Literature.","authors":"Katrina L Kezios","doi":"10.1093/epirev/mxab008","DOIUrl":"https://doi.org/10.1093/epirev/mxab008","url":null,"abstract":"In any research study, there is an underlying process that should begin with a clear articulation of the study's goal. The study's goal drives this process; it determines many study features, including the estimand of interest, the analytic approaches that can be used to estimate it, and which coefficients, if any, should be interpreted. Misalignment can occur in this process when analytic approaches and/or interpretations do not match the study's goal; misalignment is potentially more likely to arise when study goals are ambiguously framed. In this study, misalignment in the observational epidemiologic literature was documented and how the framing of study goals contributes to misalignment was explored. The following 2 misalignments were examined: use of an inappropriate variable selection approach for the goal (a \"goal-methods\" misalignment) and interpretation of coefficients of variables for which causal considerations were not made (e.g., Table 2 Fallacy, a \"goal-interpretation\" misalignment). A random sample of 100 articles published 2014-2018 in the top 5 general epidemiology journals were reviewed. Most reviewed studies were causal, with either explicitly stated (n = 13; 13%) or associational-framed (n = 71; 69%) aims. Full alignment of goal-methods-interpretations was infrequent (n = 9; 9%), although clearly causal studies (n = 5 of 13; 38%) were more often fully aligned than were seemingly causal ones (n = 3 of 71; 4%). Goal-methods misalignments were common (n = 34 of 103; 33%), but most frequently, methods were insufficiently reported to draw conclusions (n = 47; 46%). Goal-interpretations misalignments occurred in 31% (n = 32) of the studies and occurred less often when the methods were aligned (n = 2; 2%) compared with when the methods were misaligned (n = 13; 13%).","PeriodicalId":50510,"journal":{"name":"Epidemiologic Reviews","volume":" ","pages":"4-18"},"PeriodicalIF":5.5,"publicationDate":"2022-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9005115/pdf/mxab008.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39426753","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}

引用次数: 1

Burden of Antimicrobial Resistance: Compared to What? 抗菌素耐药性负担：与什么相比？

IF 5.2 2区医学

Epidemiologic Reviews Pub Date : 2022-01-14 DOI: 10.1093/epirev/mxab001

Marlieke E A de Kraker, Marc Lipsitch

{"title":"Burden of Antimicrobial Resistance: Compared to What?","authors":"Marlieke E A de Kraker, Marc Lipsitch","doi":"10.1093/epirev/mxab001","DOIUrl":"10.1093/epirev/mxab001","url":null,"abstract":"The increased focus on the public health burden of antimicrobial resistance (AMR) raises conceptual challenges, such as determining how much harm multidrug-resistant organisms do compared to what, or how to establish the burden. Here, we present a counterfactual framework and provide guidance to harmonize methodologies and optimize study quality. In AMR-burden studies, 2 counterfactual approaches have been applied: the harm of drug-resistant infections relative to the harm of the same drug-susceptible infections (the susceptible-infection counterfactual); and the total harm of drug-resistant infections relative to a situation where such infections were prevented (the no-infection counterfactual). We propose to use an intervention-based causal approach to determine the most appropriate counterfactual. We show that intervention scenarios, species of interest, and types of infections influence the choice of counterfactual. We recommend using purpose-designed cohort studies to apply this counterfactual framework, whereby the selection of cohorts (patients with drug-resistant, drug-susceptible infections, and those with no infection) should be based on matching on time to infection through exposure density sampling to avoid biased estimates. Application of survival methods is preferred, considering competing events. We conclude by advocating estimation of the burden of AMR by using the no-infection and susceptible-infection counterfactuals. The resulting numbers will provide policy-relevant information about the upper and lower bound of future interventions designed to control AMR. The counterfactuals should be applied in cohort studies, whereby selection of the unexposed cohorts should be based on exposure density sampling, applying methods avoiding time-dependent bias and confounding.","PeriodicalId":50510,"journal":{"name":"Epidemiologic Reviews","volume":"43 1","pages":"53-64"},"PeriodicalIF":5.2,"publicationDate":"2022-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8763122/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10425629","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}

引用次数: 0

Monte Carlo Simulation Approaches for Quantitative Bias Analysis: A Tutorial. 定量偏差分析的蒙特卡罗模拟方法:教程。

IF 5.5 2区医学

Epidemiologic Reviews Pub Date : 2022-01-14 DOI: 10.1093/epirev/mxab012

Hailey R Banack, Eleanor Hayes-Larson, Elizabeth Rose Mayeda

{"title":"Monte Carlo Simulation Approaches for Quantitative Bias Analysis: A Tutorial.","authors":"Hailey R Banack, Eleanor Hayes-Larson, Elizabeth Rose Mayeda","doi":"10.1093/epirev/mxab012","DOIUrl":"https://doi.org/10.1093/epirev/mxab012","url":null,"abstract":"Quantitative bias analysis can be used to empirically assess how far study estimates are from the truth (i.e., an estimate that is free of bias). These methods can be used to explore the potential impact of confounding bias, selection bias (collider stratification bias), and information bias. Quantitative bias analysis includes methods that can be used to check the robustness of study findings to multiple types of bias and methods that use simulation studies to generate data and understand the hypothetical impact of specific types of bias in a simulated data set. In this article, we review 2 strategies for quantitative bias analysis: 1) traditional probabilistic quantitative bias analysis and 2) quantitative bias analysis with generated data. An important difference between the 2 strategies relates to the type of data (real vs. generated data) used in the analysis. Monte Carlo simulations are used in both approaches, but the simulation process is used for different purposes in each. For both approaches, we outline and describe the steps required to carry out the quantitative bias analysis and also present a bias-analysis tutorial demonstrating how both approaches can be applied in the context of an analysis for selection bias. Our goal is to highlight the utility of quantitative bias analysis for practicing epidemiologists and increase the use of these methods in the epidemiologic literature.","PeriodicalId":50510,"journal":{"name":"Epidemiologic Reviews","volume":" ","pages":"106-117"},"PeriodicalIF":5.5,"publicationDate":"2022-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9005059/pdf/mxab012.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39529829","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}

引用次数: 6