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      Long-Term Associations between Disaster-Related Home Loss and Health and Well-Being of Older Survivors: Nine Years after the 2011 Great East Japan Earthquake and Tsunami

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          Abstract

          Background:

          Little research has examined associations between disaster-related home loss and multiple domains of health and well-being, with extended long-term follow-up and comprehensive adjustment for pre-disaster characteristics of survivors.

          Objectives:

          We examined the longitudinal associations between disaster-induced home loss and 34 indicators of health and well-being, assessed 9 y post-disaster.

          Methods:

          We used data from a preexisting cohort study of Japanese older adults in an area directly impacted by the 2011 Japan Earthquake ( n = 3,350 and n = 2,028 , depending on the outcomes). The study was initiated in 2010, and disaster-related home loss status was measured in 2013 retrospectively. The 34 outcomes were assessed in 2020 and covered dimensions of physical health, mental health, health behaviors/sleep, social well-being, cognitive social capital, subjective well-being, and prosocial/altruistic behaviors. We estimated the associations between disaster-related home loss and the outcomes, using targeted maximum likelihood estimation and SuperLearner. We adjusted for pre-disaster characteristics from the wave conducted 7 months before the disaster (i.e., 2010), including prior outcome values that were available.

          Results:

          After Bonferroni correction for multiple testing, we found that home loss (vs. no home loss) was associated with increased posttraumatic stress symptoms ( standardized difference = 0.50 ; 95% CI: 0.35, 0.65), increased daily sleepiness (0.38; 95% CI: 0.21, 0.54), lower trust in the community ( 0.36 ; 95% CI: 0.53 , 0.18 ), lower community attachment ( 0.60 ; 95% CI: 0.75 , 0.45 ), and lower prosociality ( 0.39 ; 95% CI: 0.55 , 0.24 ). We found modest evidence for the associations with increased depressive symptoms, increased hopelessness, more chronic conditions, higher body mass index, lower perceived mutual help in the community, and decreased happiness. There was little evidence for associations with the remaining 23 outcomes.

          Discussion:

          Home loss due to a disaster may have long-lasting adverse impacts on the cognitive social capital, mental health, and prosociality of older adult survivors. https://doi.org/10.1289/EHP10903

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          Most cited references42

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          Sensitivity Analysis in Observational Research: Introducing the E-Value.

          Sensitivity analysis is useful in assessing how robust an association is to potential unmeasured or uncontrolled confounding. This article introduces a new measure called the "E-value," which is related to the evidence for causality in observational studies that are potentially subject to confounding. The E-value is defined as the minimum strength of association, on the risk ratio scale, that an unmeasured confounder would need to have with both the treatment and the outcome to fully explain away a specific treatment-outcome association, conditional on the measured covariates. A large E-value implies that considerable unmeasured confounding would be needed to explain away an effect estimate. A small E-value implies little unmeasured confounding would be needed to explain away an effect estimate. The authors propose that in all observational studies intended to produce evidence for causality, the E-value be reported or some other sensitivity analysis be used. They suggest calculating the E-value for both the observed association estimate (after adjustments for measured confounders) and the limit of the confidence interval closest to the null. If this were to become standard practice, the ability of the scientific community to assess evidence from observational studies would improve considerably, and ultimately, science would be strengthened.
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            Is Open Access

            Principles of confounder selection

            Selecting an appropriate set of confounders for which to control is critical for reliable causal inference. Recent theoretical and methodological developments have helped clarify a number of principles of confounder selection. When complete knowledge of a causal diagram relating all covariates to each other is available, graphical rules can be used to make decisions about covariate control. Unfortunately, such complete knowledge is often unavailable. This paper puts forward a practical approach to confounder selection decisions when the somewhat less stringent assumption is made that knowledge is available for each covariate whether it is a cause of the exposure, and whether it is a cause of the outcome. Based on recent theoretically justified developments in the causal inference literature, the following proposal is made for covariate control decisions: control for each covariate that is a cause of the exposure, or of the outcome, or of both; exclude from this set any variable known to be an instrumental variable; and include as a covariate any proxy for an unmeasured variable that is a common cause of both the exposure and the outcome. Various principles of confounder selection are then further related to statistical covariate selection methods.
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              XGBoost: A Scalable Tree Boosting System

              Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems. KDD'16 changed all figures to type1
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                Author and article information

                Journal
                Environ Health Perspect
                Environ Health Perspect
                EHP
                Environmental Health Perspectives
                Environmental Health Perspectives
                0091-6765
                1552-9924
                1 July 2022
                July 2022
                : 130
                : 7
                : 077001
                Affiliations
                [ 1 ]Department of Epidemiology, Harvard T.H. Chan School of Public Health , Boston, Massachusetts, USA
                [ 2 ]Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health , Boston, Massachusetts, USA
                [ 3 ]Human Flourishing Program, Institute for Quantitative Social Science, Harvard University , Cambridge, Massachusetts, USA
                [ 4 ]Division of Public Health, Kitasato University School of Medicine , Kanagawa, Japan
                [ 5 ]Department of Biostatistics, Harvard T.H. Chan School of Public Health , Boston, Massachusetts, USA
                [ 6 ]Department of Urban Studies and Planning, Massachusetts Institute of Technology , Cambridge, Massachusetts, USA
                [ 7 ]Institute for Analytical Sociology, Linköping University , Linköping, Sweden
                [ 8 ]Division of Data Science and Artificial Intelligence, Department of Computer Science and Engineering, Chalmers University of Technology , Gothenburg, Sweden
                [ 9 ]Department of Epidemiology and Prevention, Center for Clinical Sciences, National Center for Global Health and Medicine , Tokyo, Japan
                [ 10 ]Faculty of Science, Western University , London, Ontario, Canada
                [ 11 ]Department of Oral Health Promotion, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University , Tokyo, Japan
                [ 12 ]Department of Gerontological Evaluation, National Center for Geriatrics and Gerontology, Aichi, Japan
                [ 13 ]Department of Social Preventive Medical Sciences, Center for Preventive Medical Sciences, Chiba University , Chiba, Japan
                Author notes
                Address correspondence to Koichiro Shiba, 677 Huntington Ave., Boston, MA 02115 USA, Telephone: (617) 642-8046. Email: shiba_k@ 123456g.harvard.edu
                Author information
                https://orcid.org/0000-0001-7956-6485
                Article
                EHP10903
                10.1289/EHP10903
                9249145
                35776697
                1f15e5f6-253f-4ee0-b66c-0435ac1978a8

                EHP is an open-access journal published with support from the National Institute of Environmental Health Sciences, National Institutes of Health. All content is public domain unless otherwise noted.

                History
                : 06 January 2022
                : 26 May 2022
                : 21 June 2022
                Categories
                Research

                Public health
                Public health

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