{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,7]],"date-time":"2026-11-07T14:47:15Z","timestamp":1794062835693,"version":"4.8.0"},"reference-count":25,"publisher":"BMJ","license":[{"start":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T00:00:00Z","timestamp":1727049600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["BMJ"],"accepted":{"date-parts":[[2024,8,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p\/>\n                    <jats:p>\n                      To investigate whether health insurance generated improvements in cardiovascular risk factors (blood pressure and hemoglobin A\n                      <jats:sub>1c<\/jats:sub>\n                      (HbA\n                      <jats:sub>1c<\/jats:sub>\n                      ) levels) for identifiable subpopulations, and using machine learning to identify characteristics of people predicted to benefit highly.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design<\/jats:title>\n                    <jats:p\/>\n                    <jats:p>Secondary analysis of randomized controlled trial.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Setting<\/jats:title>\n                    <jats:p\/>\n                    <jats:p>Medicaid insurance coverage in 2008 for adults on low incomes (defined as lower than the federal-defined poverty line) in Oregon who were uninsured.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Participants<\/jats:title>\n                    <jats:p\/>\n                    <jats:p>12\u2009134 participants from the Oregon Health Insurance Experiment with in-person data for health outcomes for both treatment and control groups.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Interventions<\/jats:title>\n                    <jats:p\/>\n                    <jats:p>Health insurance (Medicaid) coverage.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Main outcomes and measures<\/jats:title>\n                    <jats:p\/>\n                    <jats:p>\n                      The conditional local average treatment effects of Medicaid coverage on systolic blood pressure and HbA\n                      <jats:sub>1c<\/jats:sub>\n                      using a machine learning causal forest algorithm (with instrumental variables). Characteristics of individuals with positive predicted benefits of Medicaid coverage based on the algorithm were compared with the characteristics of others. The effect of Medicaid coverage was calculated on blood pressure and HbA\n                      <jats:sub>1c<\/jats:sub>\n                      among individuals with high predicted benefits.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p\/>\n                    <jats:p>\n                      In the in-person interview survey, mean systolic blood pressure was 119 (standard deviation 17) mmHg and mean HbA\n                      <jats:sub>1c<\/jats:sub>\n                      concentrations was 5.3% (standard deviation 0.6%). Our causal forest model showed heterogeneity in the effect of Medicaid coverage on systolic blood pressure. Individuals with lower baseline healthcare charges, for example, had higher predicted benefits from gaining Medicaid coverage. Medicaid coverage significantly lowered systolic blood pressure (\u22122.93 mmHg (95% confidence interval \u22125.82 to \u22120.32)) for people predicted to benefit highly. No evidence showed that Medicaid coverage lowered HbA\n                      <jats:sub>1c<\/jats:sub>\n                      for people with high predicted benefits.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p\/>\n                    <jats:p>Although Medicaid coverage did not improve cardiovascular risk factors on average, improvements were noted in blood pressure among a subset of individuals with higher predicted benefits. These individuals were more likely to have no or low prior healthcare charges, for example. The findings suggest that Medicaid coverage leads to improved blood pressure for some people, but those benefits may be diluted by individuals who did not benefit. Although the effect size may be of limited clinical significance for any individual, at a broad population level that includes individuals who are both hypertensive and normotensive, the findings may be of public health importance for policy interventions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/bmj-2024-079377","type":"journal-article","created":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T06:55:14Z","timestamp":1727074514000},"page":"e079377","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":22,"title":["Heterogeneous effects of Medicaid coverage on cardiovascular risk factors: secondary analysis of randomized controlled trial"],"prefix":"10.1136","volume":"386","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9614-8103","authenticated-orcid":false,"given":"Kosuke","family":"Inoue","sequence":"first","affiliation":[{"name":"Department of Social Epidemiology, Graduate School of Medicine, Kyoto University, Kyoto, Japan"},{"name":"Hakubi Center, Kyoto University, Kyoto, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susan","family":"Athey","sequence":"additional","affiliation":[{"name":"Graduate School of Business, Stanford University, Stanford, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katherine","family":"Baicker","sequence":"additional","affiliation":[{"name":"University of Chicago, Chicago, IL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1937-4833","authenticated-orcid":false,"given":"Yusuke","family":"Tsugawa","sequence":"additional","affiliation":[{"name":"Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA"},{"name":"Department of Health Policy and Management, UCLA Fielding School of Public Health, Los Angeles, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"239","published-online":{"date-parts":[[2024,9,23]]},"reference":[{"key":"2025110102444958000_386.sep23_5.e079377.1","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.p1029"},{"key":"2025110102444958000_386.sep23_5.e079377.2","unstructured":"Newhouse JP Group RCIE . 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