{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T13:14:15Z","timestamp":1788786855266,"version":"build-2803163510"},"reference-count":41,"publisher":"BMJ","issue":"9","license":[{"start":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T00:00:00Z","timestamp":1788739200000},"content-version":"unspecified","delay-in-days":6,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["BMJ Open"],"accepted":{"date-parts":[[2026,8,12]]},"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To examine the predictive contributions of clinical, non-clinical and policy-related factors to cesarean section (C-section) utilisation under Indonesia\u2019s National Health Insurance (JKN) and to assess whether the predictive relationships differed between the periods before and after the 2023 Indonesia Case-Based Groups (INA-CBG) tariff reform. We developed and temporally evaluated machine learning models to determine whether non-clinical and tariff-related variables provided additional predictive information beyond clinical risk factors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design<\/jats:title>\n                    <jats:p>Retrospective observational study using Random Forest and Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression models applied to national hospital claims data from 2022 and 2023. Models were developed separately by year and evaluated within and across years to assess discrimination, calibration, predictor selection and temporal transportability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Setting<\/jats:title>\n                    <jats:p>Under JKN, coverage is limited to medically indicated C-sections, reimbursed through the INA-CBG payment system. In 2023, INA-CBG tariffs were recalibrated nationwide, increasing payment levels and introducing regional payment reclassification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Participants<\/jats:title>\n                    <jats:p>A total of 39\u2009049 delivery episodes involving 38\u2009535\u2009women with recorded mode of delivery in JKN-affiliated referral hospitals between January 2022 and December 2023.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Primary and secondary outcome measures<\/jats:title>\n                    <jats:p>The primary outcome was mode of delivery (C-section vs vaginal birth). Model discrimination was evaluated using the area under the receiver operating characteristic curve, with bootstrap CIs. Calibration was evaluated using decile-based calibration plots. Secondary analytical measures included Random Forest variable-importance rankings, predictors retained by LASSO logistic regression and differences in model performance across within-year and cross-year evaluations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>C-section rates remained high and stable at approximately 66% across the study period. Models demonstrated consistent discriminatory performance across years. Clinical risk factors remained the strongest predictors of C-section use. However, non-clinical characteristics\u2014including subsidised insurance status, inpatient class, referral pathways and private facility ownership\u2014gained greater predictive prominence following the 2023 tariff reform. Cross-year evaluation showed reduced discrimination when models were applied to data from the alternate year.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Clinical risk remained the dominant predictor of C-section utilisation under JKN, while non-clinical, health-system and tariff-related variables provided additional predictive information. Cross-year analyses indicated modest temporal differences in predictive relationships and limited model transportability across the two study years. Future studies should use longitudinal and quasi-experimental designs incorporating procedure-specific tariff changes to evaluate causal mechanisms.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/bmjopen-2026-118641","type":"journal-article","created":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T13:00:53Z","timestamp":1788786053000},"page":"e118641","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":0,"title":["Predictive patterns of cesarean section utilisation under Indonesia\u2019s National Health Insurance following tariff reform: a machine learning analysis of hospital claims"],"prefix":"10.1136","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1566-7724","authenticated-orcid":false,"given":"Nuzulul Kusuma","family":"Putri","sequence":"first","affiliation":[{"name":"Department of Health Policy and Administration, Faculty of Public Health, Universitas Airlangga, Surabaya, East Java, Indonesia"},{"name":"Department of International Health and Sustainable Development, Celia Scott Weatherhead School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dinda","family":"Zhafira","sequence":"additional","affiliation":[{"name":"Universitas Airlangga Hospital, Surabaya, East Java, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Ardian Cahya","family":"Laksana","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Faculty of Medicine, Universitas Airlangga, Surabaya, East Java, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"239","published-online":{"date-parts":[[2026,9,7]]},"reference":[{"key":"2026090706001973000_16.9.e118641.1","doi-asserted-by":"crossref","DOI":"10.1136\/bmjgh-2021-005671","article-title":"Trends and projections of caesarean section rates: global and regional estimates","volume":"6","author":"Betran","year":"2021","journal-title":"BMJ Glob Health"},{"key":"2026090706001973000_16.9.e118641.2","unstructured":"World Health Organization . WHO statement on caesarean section rates. Geneva: World Health Organization, 2015."},{"key":"2026090706001973000_16.9.e118641.3","unstructured":"National Security Council . 2023 annual report jaminan kesehatan nasional. 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