{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T19:42:54Z","timestamp":1769974974853,"version":"3.49.0"},"reference-count":51,"publisher":"Cambridge University Press (CUP)","license":[{"start":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T00:00:00Z","timestamp":1732492800000},"content-version":"unspecified","delay-in-days":329,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Disaster med. public health prep."],"published-print":{"date-parts":[[2024]]},"abstract":"<jats:title>Abstract<\/jats:title>\n\t  <jats:sec id=\"S193578932400288X_sec1\">\n\t    <jats:title>Objectives<\/jats:title>\n\t    <jats:p>The objective of this study was to explore the burden of disasters and adverse health outcomes during and following disasters in Bangladesh.<\/jats:p>\n\t  <\/jats:sec>\n\t  <jats:sec id=\"S193578932400288X_sec2\" sec-type=\"methods\">\n\t    <jats:title>Methods<\/jats:title>\n\t    <jats:p>We analyzed 6 788 947 respondents\u2019 data from a cross-sectional and nationally representative 2021 Bangladesh Disaster-related Statistics (BDRS). The key explanatory variables were the types of disasters respondents faced, while the outcome variables were the disease burden during and following disasters. Descriptive statistics were used to determine disease burden. A multilevel mixed-effects logistic regression model assessed the association between disease burden and disaster types, along with socio-demographic characteristics of respondents.<\/jats:p>\n\t  <\/jats:sec>\n\t  <jats:sec id=\"S193578932400288X_sec3\" sec-type=\"results\">\n\t    <jats:title>Results<\/jats:title>\n\t    <jats:p>Nearly 50% of respondents experienced diseases during disasters, rising to 53.4% afterward. Fever, cough and diarrhea were prevalent during and after disasters, with increases in skin diseases, malnutrition, and asthma post-disaster. Vulnerable groups, such as children aged 0\u20134, hijra individuals, those with lower education, people with disabilities, and rural residents, especially in Chattogram, Rangpur, and Sylhet divisions, were most affected. Floods, cyclones, thunderstorms, and hailstorms significantly increased disease likelihood during and after disasters.<\/jats:p>\n\t  <\/jats:sec>\n\t  <jats:sec id=\"S193578932400288X_sec4\" sec-type=\"conclusions\">\n\t    <jats:title>Conclusions<\/jats:title>\n\t    <jats:p>The study underscores the complex relationship between disasters and health outcomes in Bangladesh, stressing the need for targeted public health interventions, improved health care infrastructure, and evidence-based policies to mitigate disaster-related health risks.<\/jats:p>\n\t  <\/jats:sec>","DOI":"10.1017\/dmp.2024.288","type":"journal-article","created":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T06:17:02Z","timestamp":1732515422000},"source":"Crossref","is-referenced-by-count":3,"title":["Disease Burden in the Context of Disasters: Insights from Over 6.7 million Respondents in the Bangladesh Disaster-Related Statistics of 2021"],"prefix":"10.1017","volume":"18","author":[{"given":"Iqbal","family":"Kabir","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shohel","family":"Rana","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Badsha","family":"Alam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Atika Rahman","family":"Chowdhury","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mirza Afreen Fatima","family":"Lusha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shimlin Jahan","family":"Khanam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dewan Mashrur","family":"Hossain","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saiful","family":"Islam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4550-4363","authenticated-orcid":false,"given":"Nuruzzaman","family":"Khan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2024,11,25]]},"reference":[{"key":"S193578932400288X_r12","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2022.158332"},{"key":"S193578932400288X_r15","doi-asserted-by":"publisher","DOI":"10.1080\/17565529.2020.1772705"},{"key":"S193578932400288X_r23","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2021-051908"},{"key":"S193578932400288X_r40","doi-asserted-by":"publisher","DOI":"10.1186\/s12889-023-16497-8"},{"key":"S193578932400288X_r2","unstructured":"2. Eckstein, D , Kunzel, V , & Schafer, L , Global Climate Risk Index 2021. \u201cWho Suffers Most from Extreme Weather Events? Weather-related Loss Events in 2019 and 2000 to 2019\u201d.Germanwatch e.V. January 2021: Bonn, Germany. 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