{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T11:34:34Z","timestamp":1777548874024,"version":"3.51.4"},"reference-count":30,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T00:00:00Z","timestamp":1775692800000},"content-version":"vor","delay-in-days":98,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"name":"National Institute on Aging of the National Institutes of Health","award":["R01AG059979"],"award-info":[{"award-number":["R01AG059979"]}]},{"name":"National Institute on Aging of the National Institutes of Health","award":["R01AG080486"],"award-info":[{"award-number":["R01AG080486"]}]},{"name":"National Institute on Aging of the National Institutes of Health","award":["R01AG086247"],"award-info":[{"award-number":["R01AG086247"]}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,4,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and Objectives<\/jats:title>\n                    <jats:p>Adults in low-resource neighborhoods face greater burden of preventable diseases than the general population. Methodologies to measure the impact of population-level efforts to reduce neighborhood-level disparities in health outcomes are lacking. We introduce a simulation modeling approach for incident disease and mortality risk over the life course and apply the model to assess the impact of a hypothetical population health initiative to reduce neighborhood-level disparities.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Research Design and Methods<\/jats:title>\n                    <jats:p>Using electronic health records, we constructed a probabilistic dynamic systems model that simulated the life course of each patient in a large primary care population, taking into account specific relationships by sex and Area Deprivation Index (ADI) quintile. The model predicted long-term incidence of 10 chronic conditions and all-cause mortality.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The model was reliable, with strong discrimination (C-statistic for 10-year mortality: 0.871) and calibration. Predicted median life expectancy was nearly a decade lower for patients ages 40-45 who resided in the highest (ADI quintile 5) vs the lowest (quintile 1) deprivation neighborhoods (difference [95% CI]; women, 8.6 [8.2, 9.0] years; men, 10.0 [9.6, 10.3]. A hypothetical initiative to reduce the number of smokers by 10% had minimal effect on disparities in life expectancy when implemented in the general patient population (women, \u22120.4 years; men, \u22120.2), but meaningfully narrowed disparities when focused on high-deprivation neighborhoods (women, \u22120.8 years; men, \u22120.6).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion and Implications<\/jats:title>\n                    <jats:p>In a broad patient population, it was feasible to measure socioeconomic disparities in health outcomes and life expectancy and evaluate interventions to improve health equity.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/geroni\/igag026","type":"journal-article","created":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T11:34:20Z","timestamp":1775561660000},"source":"Crossref","is-referenced-by-count":0,"title":["Simulation modeling of neighborhood-level differences in life course health outcomes: a proof of concept"],"prefix":"10.1093","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8336-1212","authenticated-orcid":false,"given":"Jarrod E","family":"Dalton","sequence":"first","affiliation":[{"name":"Department of Quantitative Health Sciences, Cleveland Clinic , Cleveland, Ohio,","place":["United States"]},{"name":"Cleveland Clinic Lerner College of Medicine of Case Western Reserve University , Cleveland, Ohio,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paul R","family":"Gunsalus","sequence":"additional","affiliation":[{"name":"Department of Quantitative Health Sciences, Cleveland Clinic , Cleveland, Ohio,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adam T","family":"Perzynski","sequence":"additional","affiliation":[{"name":"Population Health Research Institute, The MetroHealth System at Case Western Reserve University , Cleveland, Ohio,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lyla","family":"Mourany","sequence":"additional","affiliation":[{"name":"Department of Quantitative Health Sciences, Cleveland Clinic , Cleveland, Ohio,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3485-260X","authenticated-orcid":false,"given":"Elizabeth R","family":"Pfoh","sequence":"additional","affiliation":[{"name":"Cleveland Clinic Lerner College of Medicine of Case Western Reserve University , Cleveland, Ohio,","place":["United States"]},{"name":"Primary Care Institute, Cleveland Clinic , Cleveland, Ohio,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Madeleine M","family":"Blazel","sequence":"additional","affiliation":[{"name":"Cleveland Clinic Lerner College of Medicine of Case Western Reserve University , Cleveland, Ohio,","place":["United 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