Background: Although the indoor environment has been proposed to be associated with childhood sleep health, to our knowledge no study has investigated the association between home renovation and childhood sleep problems.
Methods: The study included 186,470 children aged 6-18 years from the National Chinese Children Health Study (2012-2018). We measured childhood sleeping problems via the Chinese version of the Sleep Disturbance Scale for Children (C-SDSC). Information on home renovation exposure within the recent 2 years was collected via parent report. We estimated associations between home renovation and various sleeping problems, defined using both continuous and categorized (binary) C-SDSC t-scores, using generalized mixed models. We fitted models with city as a random effect variable, and other covariates as fixed effects.
Results: Out of the overall participants, 89,732 (48%) were exposed to recent home renovations. Compared to the unexposed group, children exposed to home renovations had higher odds of total sleep disorder (odd ratios [OR] = 1.3; 95% confidence interval [CI] = 1.2, 1.4). Associations varied when we considered different types of home renovation materials. Children exposed to multiple types of home renovation had higher odds of sleeping problems. We observed similar findings when considering continuous C-SDSC t-scores. Additionally, sex and age of children modified the associations of home renovation exposure with some of the sleeping problem subtypes.
Conclusions: We found that home renovation was associated with higher odds of having sleeping problems and that they varied when considering the type of renovation, cumulative exposure, sex, and age differences.
背景：尽管室内环境被认为与儿童睡眠健康有关，但据我们所知，还没有研究调查过家庭装修与儿童睡眠问题之间的关系：研究纳入了《中国儿童健康状况全国调查（2012-2018年）》中186470名6至18岁的儿童。我们采用中文版儿童睡眠障碍量表（C-SDSC）测量儿童睡眠问题。我们还通过家长报告收集了最近两年内家庭装修暴露的信息。我们使用广义混合模型估算了家庭装修与各种睡眠问题之间的关系，这些睡眠问题使用连续和分类（二元）的 C-SDSC t 分数来定义。我们将城市作为随机效应变量，将其他协变量作为固定效应变量，对模型进行了拟合：在所有参与者中，有 89 732 人[48%]受到近期房屋装修的影响。与未受影响组相比，受房屋装修影响的儿童患总睡眠障碍的几率更高[奇数比（OR）=1.3，95% CI：1.2-1.4]。当我们考虑到不同类型的家庭装修材料时，两者之间的关系也有所不同。接触过多种类型装修材料的儿童出现睡眠问题的几率更高。在考虑连续的 C-SDSC t 分数时，我们也观察到了类似的结果。此外，儿童的性别和年龄也改变了家庭装修与某些睡眠问题亚型之间的关系：我们发现，房屋装修与较高的睡眠问题几率有关，而且在考虑到装修类型、累积接触、性别和年龄差异时，睡眠问题的几率也有所不同。
{"title":"Association Between Home Renovation and Sleeping Problems Among Children Aged 6-18 Years: A Nationwide Survey in China.","authors":"Dao-Sen Wang, Hong-Zhi Zhang, Si-Han Wu, Zheng-Min Qian, Stephen Edward McMillin, Elizabeth Bingheim, Wei-Hong Tan, Wen-Zhong Huang, Pei-En Zhou, Ru-Qing Liu, Li-Wen Hu, Gong-Bo Chen, Bo-Yi Yang, Xiao-Wen Zeng, Qian-Sheng Hu, Li-Zi Lin, Guang-Hui Dong","doi":"10.1097/EDE.0000000000001719","DOIUrl":"10.1097/EDE.0000000000001719","url":null,"abstract":"<p><strong>Background: </strong>Although the indoor environment has been proposed to be associated with childhood sleep health, to our knowledge no study has investigated the association between home renovation and childhood sleep problems.</p><p><strong>Methods: </strong>The study included 186,470 children aged 6-18 years from the National Chinese Children Health Study (2012-2018). We measured childhood sleeping problems via the Chinese version of the Sleep Disturbance Scale for Children (C-SDSC). Information on home renovation exposure within the recent 2 years was collected via parent report. We estimated associations between home renovation and various sleeping problems, defined using both continuous and categorized (binary) C-SDSC t-scores, using generalized mixed models. We fitted models with city as a random effect variable, and other covariates as fixed effects.</p><p><strong>Results: </strong>Out of the overall participants, 89,732 (48%) were exposed to recent home renovations. Compared to the unexposed group, children exposed to home renovations had higher odds of total sleep disorder (odd ratios [OR] = 1.3; 95% confidence interval [CI] = 1.2, 1.4). Associations varied when we considered different types of home renovation materials. Children exposed to multiple types of home renovation had higher odds of sleeping problems. We observed similar findings when considering continuous C-SDSC t-scores. Additionally, sex and age of children modified the associations of home renovation exposure with some of the sleeping problem subtypes.</p><p><strong>Conclusions: </strong>We found that home renovation was associated with higher odds of having sleeping problems and that they varied when considering the type of renovation, cumulative exposure, sex, and age differences.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139542147","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 : 2024-05-01Epub Date: 2024-02-01DOI: 10.1097/EDE.0000000000001726
Hailey R Banack, Samantha N Smith, Lisa M Bodnar
Background: We describe the use of Apisensr, a web-based application that can be used to implement quantitative bias analysis for misclassification, selection bias, and unmeasured confounding. We apply Apisensr using an example of exposure misclassification bias due to use of self-reported body mass index (BMI) to define obesity status in an analysis of the relationship between obesity and diabetes.
Methods: We used publicly available data from the National Health and Nutrition Examination Survey. The analysis consisted of: (1) estimating bias parameter values (sensitivity, specificity, negative predictive value, and positive predictive value) for self-reported obesity by sex, age, and race-ethnicity compared to obesity defined by measured BMI, and (2) using Apisensr to adjust for exposure misclassification.
Results: The discrepancy between self-reported and measured obesity varied by demographic group (sensitivity range: 75%-89%; specificity range: 91%-99%). Using Apisensr for quantitative bias analysis, there was a clear pattern in the results: the relationship between obesity and diabetes was underestimated using self-report in all age, sex, and race-ethnicity categories compared to measured obesity. For example, in non-Hispanic White men aged 40-59 years, prevalence odds ratios for diabetes were 3.06 (95% confidence inerval = 1.78, 5.30) using self-reported BMI and 4.11 (95% confidence interval = 2.56, 6.75) after bias analysis adjusting for misclassification.
Conclusion: Apisensr is an easy-to-use, web-based Shiny app designed to facilitate quantitative bias analysis. Our results also provide estimates of bias parameter values that can be used by other researchers interested in examining obesity defined by self-reported BMI.
{"title":"Application of a Web-based Tool for Quantitative Bias Analysis: The Example of Misclassification Due to Self-reported Body Mass Index.","authors":"Hailey R Banack, Samantha N Smith, Lisa M Bodnar","doi":"10.1097/EDE.0000000000001726","DOIUrl":"10.1097/EDE.0000000000001726","url":null,"abstract":"<p><strong>Background: </strong>We describe the use of Apisensr, a web-based application that can be used to implement quantitative bias analysis for misclassification, selection bias, and unmeasured confounding. We apply Apisensr using an example of exposure misclassification bias due to use of self-reported body mass index (BMI) to define obesity status in an analysis of the relationship between obesity and diabetes.</p><p><strong>Methods: </strong>We used publicly available data from the National Health and Nutrition Examination Survey. The analysis consisted of: (1) estimating bias parameter values (sensitivity, specificity, negative predictive value, and positive predictive value) for self-reported obesity by sex, age, and race-ethnicity compared to obesity defined by measured BMI, and (2) using Apisensr to adjust for exposure misclassification.</p><p><strong>Results: </strong>The discrepancy between self-reported and measured obesity varied by demographic group (sensitivity range: 75%-89%; specificity range: 91%-99%). Using Apisensr for quantitative bias analysis, there was a clear pattern in the results: the relationship between obesity and diabetes was underestimated using self-report in all age, sex, and race-ethnicity categories compared to measured obesity. For example, in non-Hispanic White men aged 40-59 years, prevalence odds ratios for diabetes were 3.06 (95% confidence inerval = 1.78, 5.30) using self-reported BMI and 4.11 (95% confidence interval = 2.56, 6.75) after bias analysis adjusting for misclassification.</p><p><strong>Conclusion: </strong>Apisensr is an easy-to-use, web-based Shiny app designed to facilitate quantitative bias analysis. Our results also provide estimates of bias parameter values that can be used by other researchers interested in examining obesity defined by self-reported BMI.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11022994/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139650504","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 : 2024-04-22DOI: 10.1097/ede.0000000000001735
{"title":"ERRATUM: Toward a clearer definition of selection bias when estimating causal effects.","authors":"","doi":"10.1097/ede.0000000000001735","DOIUrl":"https://doi.org/10.1097/ede.0000000000001735","url":null,"abstract":"","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140673043","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 : 2024-04-18DOI: 10.1097/ede.0000000000001714
Hanxi Zhang, Amy S Clark, Rebecca A Hubbard
Accurate outcome and exposure ascertainment in electronic health record (EHR) data, referred to as EHR phenotyping, relies on the completeness and accuracy of EHR data for each individual. However, some individuals, such as those with a greater comorbidity burden, visit the health care system more frequently and thus have more complete data, compared with others. Ignoring such dependence of exposure and outcome misclassification on visit frequency can bias estimates of associations in EHR analysis. We developed a framework for describing the structure of outcome and exposure misclassification due to informative visit processes in EHR data and assessed the utility of a quantitative bias analysis approach to adjusting for bias induced by informative visit patterns. Using simulations, we found that this method produced unbiased estimates across all informative visit structures, if the phenotype sensitivity and specificity were correctly specified. We applied this method in an example where the association between diabetes and progression-free survival in metastatic breast cancer patients may be subject to informative presence bias. The quantitative bias analysis approach allowed us to evaluate robustness of results to informative presence bias and indicated that findings were unlikely to change across a range of plausible values for phenotype sensitivity and specificity. Researchers using EHR data should carefully consider the informative visit structure reflected in their data and use appropriate approaches such as the quantitative bias analysis approach described here to evaluate robustness of study findings.
电子健康记录（EHR）数据中准确的结果和暴露确定，即 EHR 表型分析，依赖于每个人 EHR 数据的完整性和准确性。然而，与其他人相比，有些人，如合并症负担较重的人，会更频繁地访问医疗保健系统，因此拥有更完整的数据。如果忽略了暴露和结果误分类对就诊频率的这种依赖性，就会使电子病历分析中对相关性的估计出现偏差。我们建立了一个框架，用于描述电子病历数据中信息性就诊过程导致的结果和暴露误分类的结构，并评估了定量偏倚分析方法在调整信息性就诊模式导致的偏倚方面的实用性。通过模拟实验，我们发现如果表型敏感性和特异性指定正确，该方法可在所有信息性就诊结构中产生无偏估计值。我们在一个例子中应用了这种方法，在这个例子中，转移性乳腺癌患者的糖尿病与无进展生存期之间的关联可能会受到信息性存在偏差的影响。定量偏倚分析方法使我们能够评估结果对信息性存在偏倚的稳健性，并表明在表型敏感性和特异性的一系列可信值范围内，研究结果不太可能发生变化。使用电子病历数据的研究人员应仔细考虑其数据中反映的信息性就诊结构，并使用适当的方法（如本文所述的定量偏倚分析方法）来评估研究结果的稳健性。
{"title":"A Quantitative Bias Analysis Approach to Informative Presence Bias in Electronic Health Records.","authors":"Hanxi Zhang, Amy S Clark, Rebecca A Hubbard","doi":"10.1097/ede.0000000000001714","DOIUrl":"https://doi.org/10.1097/ede.0000000000001714","url":null,"abstract":"Accurate outcome and exposure ascertainment in electronic health record (EHR) data, referred to as EHR phenotyping, relies on the completeness and accuracy of EHR data for each individual. However, some individuals, such as those with a greater comorbidity burden, visit the health care system more frequently and thus have more complete data, compared with others. Ignoring such dependence of exposure and outcome misclassification on visit frequency can bias estimates of associations in EHR analysis. We developed a framework for describing the structure of outcome and exposure misclassification due to informative visit processes in EHR data and assessed the utility of a quantitative bias analysis approach to adjusting for bias induced by informative visit patterns. Using simulations, we found that this method produced unbiased estimates across all informative visit structures, if the phenotype sensitivity and specificity were correctly specified. We applied this method in an example where the association between diabetes and progression-free survival in metastatic breast cancer patients may be subject to informative presence bias. The quantitative bias analysis approach allowed us to evaluate robustness of results to informative presence bias and indicated that findings were unlikely to change across a range of plausible values for phenotype sensitivity and specificity. Researchers using EHR data should carefully consider the informative visit structure reflected in their data and use appropriate approaches such as the quantitative bias analysis approach described here to evaluate robustness of study findings.","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140627922","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 : 2024-04-18DOI: 10.1097/ede.0000000000001720
Bronner P Gonçalves, Piero L Olliaro, Peter Horby, Laura Merson, Benjamin J Cowling
This article discusses causal interpretations of epidemiologic studies of the effects of vaccination on sequelae after acute severe acute respiratory syndrome coronavirus 2 infection. To date, researchers have tried to answer several different research questions on this topic. While some studies assessed the impact of postinfection vaccination on the presence of or recovery from post-acute coronavirus disease 2019 syndrome, others quantified the association between preinfection vaccination and postacute sequelae conditional on becoming infected. However, the latter analysis does not have a causal interpretation, except under the principal stratification framework-that is, this comparison can only be interpreted as causal for a nondiscernible stratum of the population. As the epidemiology of coronavirus disease 2019 is now nearly entirely dominated by reinfections, including in vaccinated individuals, and possibly caused by different Omicron subvariants, it has become even more important to design studies on the effects of vaccination on postacute sequelae that address precise causal questions and quantify effects corresponding to implementable interventions.
{"title":"Interpretations of Studies on SARS-CoV-2 Vaccination and Post-acute COVID-19 Sequelae.","authors":"Bronner P Gonçalves, Piero L Olliaro, Peter Horby, Laura Merson, Benjamin J Cowling","doi":"10.1097/ede.0000000000001720","DOIUrl":"https://doi.org/10.1097/ede.0000000000001720","url":null,"abstract":"This article discusses causal interpretations of epidemiologic studies of the effects of vaccination on sequelae after acute severe acute respiratory syndrome coronavirus 2 infection. To date, researchers have tried to answer several different research questions on this topic. While some studies assessed the impact of postinfection vaccination on the presence of or recovery from post-acute coronavirus disease 2019 syndrome, others quantified the association between preinfection vaccination and postacute sequelae conditional on becoming infected. However, the latter analysis does not have a causal interpretation, except under the principal stratification framework-that is, this comparison can only be interpreted as causal for a nondiscernible stratum of the population. As the epidemiology of coronavirus disease 2019 is now nearly entirely dominated by reinfections, including in vaccinated individuals, and possibly caused by different Omicron subvariants, it has become even more important to design studies on the effects of vaccination on postacute sequelae that address precise causal questions and quantify effects corresponding to implementable interventions.","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140628029","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 : 2024-04-18DOI: 10.1097/EDE.0000000000001710
Sonia Hernández-Díaz, Krista F Huybrechts, Miguel A Hernán
{"title":"Emulating a Target Trial of Interventions Initiated During Pregnancy With Healthcare Databases: The Example of COVID-19 Vaccination. The Authors Respond.","authors":"Sonia Hernández-Díaz, Krista F Huybrechts, Miguel A Hernán","doi":"10.1097/EDE.0000000000001710","DOIUrl":"https://doi.org/10.1097/EDE.0000000000001710","url":null,"abstract":"","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140688880","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 : 2024-04-18DOI: 10.1097/ede.0000000000001713
Ruth H Keogh, Nan Van Geloven
Predictions under interventions are estimates of what a person's risk of an outcome would be if they were to follow a particular treatment strategy, given their individual characteristics. Such predictions can give important input to medical decision-making. However, evaluating the predictive performance of interventional predictions is challenging. Standard ways of evaluating predictive performance do not apply when using observational data, because prediction under interventions involves obtaining predictions of the outcome under conditions that are different from those that are observed for a subset of individuals in the validation dataset. This work describes methods for evaluating counterfactual performance of predictions under interventions for time-to-event outcomes. This means we aim to assess how well predictions would match the validation data if all individuals had followed the treatment strategy under which predictions are made. We focus on counterfactual performance evaluation using longitudinal observational data, and under treatment strategies that involve sustaining a particular treatment regime over time. We introduce an estimation approach using artificial censoring and inverse probability weighting that involves creating a validation dataset mimicking the treatment strategy under which predictions are made. We extend measures of calibration, discrimination (c-index and cumulative/dynamic AUCt) and overall prediction error (Brier score) to allow assessment of counterfactual performance. The methods are evaluated using a simulation study, including scenarios in which the methods should detect poor performance. Applying our methods in the context of liver transplantation shows that our procedure allows quantification of the performance of predictions supporting crucial decisions on organ allocation.
{"title":"Prediction Under Interventions: Evaluation of Counterfactual Performance Using Longitudinal Observational Data.","authors":"Ruth H Keogh, Nan Van Geloven","doi":"10.1097/ede.0000000000001713","DOIUrl":"https://doi.org/10.1097/ede.0000000000001713","url":null,"abstract":"Predictions under interventions are estimates of what a person's risk of an outcome would be if they were to follow a particular treatment strategy, given their individual characteristics. Such predictions can give important input to medical decision-making. However, evaluating the predictive performance of interventional predictions is challenging. Standard ways of evaluating predictive performance do not apply when using observational data, because prediction under interventions involves obtaining predictions of the outcome under conditions that are different from those that are observed for a subset of individuals in the validation dataset. This work describes methods for evaluating counterfactual performance of predictions under interventions for time-to-event outcomes. This means we aim to assess how well predictions would match the validation data if all individuals had followed the treatment strategy under which predictions are made. We focus on counterfactual performance evaluation using longitudinal observational data, and under treatment strategies that involve sustaining a particular treatment regime over time. We introduce an estimation approach using artificial censoring and inverse probability weighting that involves creating a validation dataset mimicking the treatment strategy under which predictions are made. We extend measures of calibration, discrimination (c-index and cumulative/dynamic AUCt) and overall prediction error (Brier score) to allow assessment of counterfactual performance. The methods are evaluated using a simulation study, including scenarios in which the methods should detect poor performance. Applying our methods in the context of liver transplantation shows that our procedure allows quantification of the performance of predictions supporting crucial decisions on organ allocation.","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140627696","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 : 2024-04-18DOI: 10.1097/EDE.0000000000001716
{"title":"Erratum: \"The Effect of Mobile Stroke Unit Care on Functional Outcomes: An Application of the Front-door Formula\".","authors":"","doi":"10.1097/EDE.0000000000001716","DOIUrl":"https://doi.org/10.1097/EDE.0000000000001716","url":null,"abstract":"","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140688864","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 : 2024-04-18DOI: 10.1097/ede.0000000000001728
Kate R Weinberger, Nina Veeravalli, Xiao Wu, Nicholas J Nassikas, Keith R Spangler, Nina R Joyce, Gregory A Wellenius
Tropical cyclones are associated with acute increases in mortality and morbidity, but few studies have examined their longer-term health consequences. We assessed whether tropical cyclones are associated with a higher frequency of symptom exacerbation among children with asthma in the following 12 months in eastern United States counties, 2000-2018.
{"title":"Long-term Impact of Tropical Cyclones on Disease Exacerbation Among Children with Asthma in the Eastern United States, 2000-2018.","authors":"Kate R Weinberger, Nina Veeravalli, Xiao Wu, Nicholas J Nassikas, Keith R Spangler, Nina R Joyce, Gregory A Wellenius","doi":"10.1097/ede.0000000000001728","DOIUrl":"https://doi.org/10.1097/ede.0000000000001728","url":null,"abstract":"Tropical cyclones are associated with acute increases in mortality and morbidity, but few studies have examined their longer-term health consequences. We assessed whether tropical cyclones are associated with a higher frequency of symptom exacerbation among children with asthma in the following 12 months in eastern United States counties, 2000-2018.","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140627695","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}
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