{"id":2533,"date":"2020-08-13T18:04:22","date_gmt":"2020-08-13T18:04:22","guid":{"rendered":"http:\/\/daphnia.ecology.uga.edu\/drakelab\/?p=2533"},"modified":"2020-08-13T18:05:38","modified_gmt":"2020-08-13T18:05:38","slug":"socio-demographic-and-not-environmental-risk-factors-explain-fine-scale-spatial-patterns-of-diarrheal-disease-in-ifanadiana-rural-madagascar","status":"publish","type":"post","link":"https:\/\/daphnia.ecology.uga.edu\/drakelab\/?p=2533","title":{"rendered":"Diarrheal disease in rural Madagascar"},"content":{"rendered":"\n<h4 class=\"wp-block-heading\">Socio-demographic, and not environmental, risk factors explain fine-scale spatial patterns of diarrheal disease in Ifanadiana, rural Madagascar<\/h4>\n\n\n\n<p>Diarrheal disease (DD) is responsible  for over 700,000 child deaths annually and, in Madagascar, is the most  common cause of death across all ages. Because the pathogens that cause  DD are environmentally transmitted, spatial patterns in the burden of  disease are thought to be strongly related to spatial patterns in  underlying environmental variables. For example, moist, warm conditions  may increase the survival and growth rate of bacterial pathogens in  water and soil, increasing the risk of transmission to people. Precision  health mapping is an approach that leverages the spatial relationships  between socio-ecological variables and disease to predict hotspots of  disease risk. This tends to be done at global or multi-national scales,  combining remotely sensed climate and landcover data with national  health surveys to predict across a broad spatial extent. However, many  public health interventions, particularly in Madagascar, take place at  the local district scale, not the national scale, and it is unknown if  these relationships found at a broad scale can be downscaled to the fine  spatial scale required by district hospitals and healthcare workers.\u00a0<\/p>\n\n\n\n<p>In this study, we used two fine-scale\n health datasets to identify socio-ecological covariates associated with\n DD and assess the utility of precision health mapping at the scale of \nthe health district. These datasets were collected through a \ncollaboration between the Madagascar Ministry of Health and Pivot, a \nhealth system strengthening NGO based in Ifanadiana district, \nMadagascar. We found that disease risk was most strongly associated with\n socio-demographic variables, such as a child\u2019s sex, age or wealth. \nEnvironmental variables, specifically rainfall and temperature, were \nonly able to predict seasonal trends in DD, and not spatial patterns. \nSocio-demographic variables are rarely collected at fine spatial scales \nin developing countries and often lack spatial structure in rural areas,\n and so have limited use in precision health mapping. However, our study\n found that the environmental variables that precision health mapping \nrelies on are not strongly associated with DD risk in this context, \nsuggesting that precision health mapping may not be appropriate for use \nat these scales if not adapted to the local context.<\/p>\n\n\n\n<p>This work was a collaboration between Michelle Evans, John Drake, and Courtney Murdock,  and researchers from Pivot, Harvard Medical School, the Madagascar  Ministry of Health, and the Madagascar Institute of Statistics. A  pre-print of the manuscript can be found here [ <a href=\"https:\/\/www.medrxiv.org\/content\/10.1101\/2020.04.02.20051151v1\">https:\/\/www.medrxiv.org\/content\/10.1101\/2020.04.02.20051151v1<\/a>]\n","protected":false},"excerpt":{"rendered":"<p>Socio-demographic, and not environmental, risk factors explain fine-scale spatial patterns of diarrheal disease in Ifanadiana, rural Madagascar Diarrheal disease (DD) is responsible for over 700,000&#8230;<\/p>\n","protected":false},"author":3,"featured_media":2535,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[53,43],"tags":[],"class_list":["post-2533","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-infectious-diseases","category-new-paper"],"_links":{"self":[{"href":"https:\/\/daphnia.ecology.uga.edu\/drakelab\/index.php?rest_route=\/wp\/v2\/posts\/2533","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/daphnia.ecology.uga.edu\/drakelab\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/daphnia.ecology.uga.edu\/drakelab\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/daphnia.ecology.uga.edu\/drakelab\/index.php?rest_route=\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/daphnia.ecology.uga.edu\/drakelab\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=2533"}],"version-history":[{"count":3,"href":"https:\/\/daphnia.ecology.uga.edu\/drakelab\/index.php?rest_route=\/wp\/v2\/posts\/2533\/revisions"}],"predecessor-version":[{"id":2569,"href":"https:\/\/daphnia.ecology.uga.edu\/drakelab\/index.php?rest_route=\/wp\/v2\/posts\/2533\/revisions\/2569"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/daphnia.ecology.uga.edu\/drakelab\/index.php?rest_route=\/wp\/v2\/media\/2535"}],"wp:attachment":[{"href":"https:\/\/daphnia.ecology.uga.edu\/drakelab\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2533"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/daphnia.ecology.uga.edu\/drakelab\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2533"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/daphnia.ecology.uga.edu\/drakelab\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2533"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}