Improving the spatial measurement of conflict exposure for population health research: Comparing kernel density estimation against conventional approaches in Mali

Improving the spatial measurement of conflict exposure for population health research: Comparing kernel density estimation against conventional approaches in Mali | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Improving the spatial measurement of conflict exposure for population health research: Comparing kernel density estimation against conventional approaches in Mali Mariame O. Ouédraogo, Peter M. Macharia, Birama A. Ly, Hamidou Niangaly, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9588657/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Precise measurement of conflict exposure is required to understand its health impacts. Common approaches to operationalizing conflicts, such as counting events within administrative boundaries or applying a fixed radius around events to capture their spatial influence, can obscure important spatial and temporal patterns of conflict. Using Mali as a case study, we constructed a conflict intensity measure based on kernel density estimation (KDE) and compared it with standard approaches by examining population exposure and associations with key reproductive, maternal, and child health (RMCH) outcomes. Methods We compiled georeferenced conflict events from global datasets. Using KDE, we produced gridded annual conflict intensity surfaces (10x10 km) from 2000 to 2024 and aggregated them to district and health-facility levels. We also measured conflict exposure as the number of events per district and within a 50-km radius of each health facility. For selected years (2013, 2018, and 2023), we compared the proportion of population subgroups exposed to conflict across approaches, using percent agreement and Cohen’s Kappa. We also examined associations between conflict measures and key RMCH indicators, using correlation coefficients and key model performance metrics (coefficient of determination (R²) and root mean square error (RMSE)). Results In 2013 and 2018, KDE and standard approaches yielded similar estimates of the proportion of the population exposed to conflict, around 30%. In 2023, the measures diverged substantially: standard approaches classified 88% of the population as exposed to conflict, while KDE classified 38%. Agreement in classifying conflict-affected population groups was on average moderate, with kappa values ranging from 0.37 in 2023 to 0.75 in 2018. KDE revealed a log-linear dose-response relationship between conflict intensity and selected RMCH indicators in 2013 and 2018, with correlation coefficients ranging from 0.31 to 0.75. Standard approaches showed weaker relationships, with correlations ranging from 0.03 to 0.64. Across selected years, KDE yielded higher R² values and lower RMSE than the standard approaches. Conclusions KDE outperformed standard methods, enabling the unmasking of health impacts at district and facility levels. Its potential at fine spatial scales remains to be explored. Armed conflict Health Spatial analysis Kernel density estimation Mali Sahel Africa Full Text Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterials01May2026.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9588657","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":636526270,"identity":"277b6de9-77bd-4d78-941f-7e341f062366","order_by":0,"name":"Mariame O. 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Common approaches to operationalizing conflicts, such as counting events within administrative boundaries or applying a fixed radius around events to capture their spatial influence, can obscure important spatial and temporal patterns of conflict. Using Mali as a case study, we constructed a conflict intensity measure based on kernel density estimation (KDE) and compared it with standard approaches by examining population exposure and associations with key reproductive, maternal, and child health (RMCH) outcomes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe compiled georeferenced conflict events from global datasets. Using KDE, we produced gridded annual conflict intensity surfaces (10x10 km) from 2000 to 2024 and aggregated them to district and health-facility levels. We also measured conflict exposure as the number of events per district and within a 50-km radius of each health facility. For selected years (2013, 2018, and 2023), we compared the proportion of population subgroups exposed to conflict across approaches, using percent agreement and Cohen\u0026rsquo;s Kappa. We also examined associations between conflict measures and key RMCH indicators, using correlation coefficients and key model performance metrics (coefficient of determination (R\u0026sup2;) and root mean square error (RMSE)).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn 2013 and 2018, KDE and standard approaches yielded similar estimates of the proportion of the population exposed to conflict, around 30%. In 2023, the measures diverged substantially: standard approaches classified 88% of the population as exposed to conflict, while KDE classified 38%. Agreement in classifying conflict-affected population groups was on average moderate, with kappa values ranging from 0.37 in 2023 to 0.75 in 2018. KDE revealed a log-linear dose-response relationship between conflict intensity and selected RMCH indicators in 2013 and 2018, with correlation coefficients ranging from 0.31 to 0.75. Standard approaches showed weaker relationships, with correlations ranging from 0.03 to 0.64. Across selected years, KDE yielded higher R\u0026sup2; values and lower RMSE than the standard approaches.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eKDE outperformed standard methods, enabling the unmasking of health impacts at district and facility levels. Its potential at fine spatial scales remains to be explored.\u003c/p\u003e","manuscriptTitle":"Improving the spatial measurement of conflict exposure for population health research: Comparing kernel density estimation against conventional approaches in Mali","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 03:05:12","doi":"10.21203/rs.3.rs-9588657/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"80b1d527-b143-4a87-bfda-048f652f2e46","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[{"type":"editorAssigned","content":"","date":"2026-05-08T02:22:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-08T02:22:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Conflict and Health","date":"2026-05-01T18:49:29+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T03:05:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 03:05:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9588657","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9588657","identity":"rs-9588657","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}