{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T23:04:35Z","timestamp":1793315075861,"version":"4.5.0"},"reference-count":107,"publisher":"BMJ","issue":"3","license":[{"start":{"date-parts":[[2024,3,15]],"date-time":"2024-03-15T00:00:00Z","timestamp":1710460800000},"content-version":"unspecified","delay-in-days":14,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Sapien Labs","award":["N\/A"],"award-info":[{"award-number":["N\/A"]}],"id":[{"id":"https:\/\/ror.org\/015agz903","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["BMJ Open"],"accepted":{"date-parts":[[2024,2,16]]},"published-print":{"date-parts":[[2024,3]]},"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To understand the extent to which various demographic and social determinants predict mental health status and their relative hierarchy of predictive power in order to prioritise and develop population-based preventative approaches.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design<\/jats:title>\n                    <jats:p>Cross-sectional analysis of survey data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Setting<\/jats:title>\n                    <jats:p>Internet-based survey from 32 countries across North America, Europe, Latin America, Middle East and North Africa, Sub-Saharan Africa, South Asia and Australia, collected between April 2020 and December 2021.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Participants<\/jats:title>\n                    <jats:p>270\u2009000 adults aged 18\u201385+ years who participated in the Global Mind Project.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Outcome measures<\/jats:title>\n                    <jats:p>We used 120+ demographic\u2009and social determinants to predict aggregate mental health status and scores of individuals (mental health quotient (MHQ)) and determine their relative predictive influence using various machine learning models including gradient boosting and random forest classification for various demographic stratifications by age, gender, geographical region and language. Outcomes reported include model performance metrics of accuracy, precision, recall, F1 scores and importance of individual factors determined by reduction in the squared error attributable to that factor.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Across all demographic classification models, 80% of those with negative MHQs were correctly identified, while regression models predicted specific MHQ scores within \u00b115% of the position on the scale. Predictions were higher for older ages (0.9+ accuracy, 0.9+ F1 Score; 65+ years) and poorer for younger ages (0.68 accuracy, 0.68 F1 Score; 18\u201324 years). Across all age groups, genders, regions and language groups, lack of social interaction and sufficient sleep were several times more important than all other factors. For younger ages (18\u201324 years), other highly predictive factors included cyberbullying and sexual abuse while not being able to work was high for ages 45\u201354 years.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Social determinants of traumas, adversities and lifestyle can account for 60%\u201390% of mental health challenges. However, additional factors are at play, particularly for younger ages, that are not included in these data and need further investigation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/bmjopen-2023-075095","type":"journal-article","created":{"date-parts":[[2024,3,15]],"date-time":"2024-03-15T10:50:49Z","timestamp":1710499849000},"page":"e075095","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":18,"title":["Hierarchy of demographic and social determinants of mental health: analysis of cross-sectional survey data from the Global Mind Project"],"prefix":"10.1136","volume":"14","author":[{"given":"Jerzy","family":"Bala","sequence":"first","affiliation":[{"name":"Sapien Labs, Arlington, Virginia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6732-5991","authenticated-orcid":false,"given":"Jennifer Jane","family":"Newson","sequence":"additional","affiliation":[{"name":"Sapien Labs, Arlington, Virginia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tara C","family":"Thiagarajan","sequence":"additional","affiliation":[{"name":"Sapien Labs, Arlington, Virginia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"239","published-online":{"date-parts":[[2024,3,15]]},"reference":[{"key":"2025101813130637000_14.3.e075095.1","unstructured":"WHO . 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