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Public Health"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\n                      During the global COVID-19 pandemic (2020\u20132021), excess mortality varied substantially across countries. Notably, upper-middle-income countries experienced greater variability in excess mortality than both low- and high-income countries, despite reporting fewer COVID-19 cases than high-income countries but more than low-income countries. This disconnect between case numbers and mortality suggests more complex structural vulnerabilities. Socioeconomic conditions and healthcare system performance, collectively referred to as National Framework Conditions (\n                      <jats:italic>NFCs<\/jats:italic>\n                      ), are likely key determinants of pandemic outcomes. However, the specific relationship between these factors and excess mortality remains poorly understood.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We constructed a predictive model of excess mortality using reported COVID-19 case counts and a wide array of\n                      <jats:italic>NFCs<\/jats:italic>\n                      derived from the World Development Indicators (WDI), employing a tree-based machine learning method (XGBoost). To reduce dimensionality, we applied a non-linear method (e-Isomap), extracting latent components called compressed National Framework Conditions (\n                      <jats:italic>cNFCs<\/jats:italic>\n                      ). We applied SHapley Additive exPlanations (SHAP) values to estimate the feature importance and quantify the contribution of each\n                      <jats:italic>cNFC<\/jats:italic>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Our machine learning model explained nearly half of the global variance in excess mortality (\n                      <jats:inline-formula>\n                        <jats:alternatives>\n                          <jats:tex-math>$$R^2$$<\/jats:tex-math>\n                          <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                            <mml:msup>\n                              <mml:mi>R<\/mml:mi>\n                              <mml:mn>2<\/mml:mn>\n                            <\/mml:msup>\n                          <\/mml:math>\n                        <\/jats:alternatives>\n                      <\/jats:inline-formula>\n                      : median 49.7; interquartile range (IQR): 10.9). SHAP analysis revealed that\n                      <jats:italic>cNFCs<\/jats:italic>\n                      contributed most strongly to model predictions of excess mortality (SHAP: median 8.1; IQR 1.2), followed by\n                      <jats:italic>pandemic indicators<\/jats:italic>\n                      , such as reported COVID-19 cases (SHAP: median 6.4; IQR 0.7). Using explainable artificial intelligence (XAI), we further identified how interconnected socioeconomic conditions, including labor force participation age and health spending, shaped mortality outcomes.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      Our findings demonstrate that\n                      <jats:italic>cNFCs<\/jats:italic>\n                      outperform conventional epidemiological or preparedness metrics, in explaining cross-country differences in COVID-19 excess mortality during 2020\u20132021. By capturing latent socioeconomic structures, the\n                      <jats:italic>cNFC<\/jats:italic>\n                      framework reveals systemic vulnerabilities that reported COVID-19 cases and other indicators fail to detect. This approach offers a new perspective on structural resilience and pandemic preparedness.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s44263-026-00255-0","type":"journal-article","created":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T23:03:15Z","timestamp":1772406195000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Mortality risk during the COVID-19 pandemic is shaped by human development"],"prefix":"10.1186","volume":"4","author":[{"given":"Kolja","family":"Nenoff","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sarah","family":"Habershon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miguel D.","family":"Mahecha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sabine","family":"Attinger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Khalil","family":"Teber","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guido","family":"Kraemer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,2]]},"reference":[{"issue":"7942","key":"255_CR1","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1038\/s41586-022-05522-2","volume":"613","author":"W Msemburi","year":"2023","unstructured":"Msemburi W, Karlinsky A, Knutson V, Aleshin-Guendel S, Chatterji S, Wakefield J. 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