{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T18:55:15Z","timestamp":1772045715978,"version":"3.50.1"},"reference-count":30,"publisher":"BMJ","issue":"3","license":[{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100013235","name":"NIHR Greater Manchester Patient Safety Translational Research Centre","doi-asserted-by":"publisher","award":["Not applicable"],"award-info":[{"award-number":["Not applicable"]}],"id":[{"id":"10.13039\/501100013235","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013374","name":"NIHR School for Primary Care Research","doi-asserted-by":"crossref","award":["Not applicable"],"award-info":[{"award-number":["Not applicable"]}],"id":[{"id":"10.13039\/501100013374","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["BMJ Open"],"accepted":{"date-parts":[[2023,2,5]]},"published-print":{"date-parts":[[2023,3]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Age and socioeconomic status (SES) predict several health-related outcomes, including prescription opioid use. Contrasting findings from previous literature found higher prevalence of opioid use in both people over 65 years old and the working-age population of 35\u201355 years old. This study aimed to analyse if the association between age and opioid use is non-linear and differs in adults with different SES levels.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This cohort study used the Health Survey for England waves 1997\u20132014 data to investigate the shape of the correlation between reported opioid use and income decile, employment status and educational level. A semiparametric Generalised Additive Model was employed, so that linearity of correlation was not assumed. The shape of the relationship was assessed using the effective degrees of freedom (EDF).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Positive correlation between age and reported opioid use, more linear in people in the highest income decile (EDF: 1.01, p&lt;0.001) and higher education (EDF: 2.03, p&lt;0.001) was observed. In people on lower income and with lower levels of education, the highes probability of reported opioid use was at around 40\u201360 years old and slowly decreased after that. Higher income decile and higher levels of education were predictors of a lower probability of reported opioid use (OR: 0.27, 95%\u2009CI: 0.21 to 0.36 and OR: 0.48, 95%\u2009CI: 0.41 to 0.57, respectively). There was no statistically significant difference in opioid use between employed and unemployed people.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The relationship between age and the probability of prescribed opioid use varies greatly across different income and educations strata, highlighting different drivers in opioid prescribing across population groups. More research is needed into exploring patterns in opioid use in older people, particularly from disadvantaged socioeconomic backgrounds.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/bmjopen-2021-057428","type":"journal-article","created":{"date-parts":[[2023,3,8]],"date-time":"2023-03-08T10:43:34Z","timestamp":1678272214000},"page":"e057428","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":2,"title":["Examining non-linearity in the association between age and reported opioid use in different socioeconomic strata: cohort study using Health Survey for England waves from 1997 to 2014"],"prefix":"10.1136","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1386-2534","authenticated-orcid":false,"given":"Magdalena","family":"Nowakowska","sequence":"first","affiliation":[{"name":"NIHR School for Primary Care Research, The University of Manchester, Manchester, UK"},{"name":"Division of Informatics, Imaging and Data Sciences; Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK"},{"name":"NIHR Greater Manchester Patient Safety Translational Research Centre, The University of Manchester, Manchester, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7978-1094","authenticated-orcid":false,"given":"Salwa S","family":"Zghebi","sequence":"additional","affiliation":[{"name":"NIHR School for Primary Care Research, The University of Manchester, Manchester, UK"},{"name":"Division of Population Health, Health Services Research and Primary Care; Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li-Chia","family":"Chen","sequence":"additional","affiliation":[{"name":"Division of Pharmacy and Optometry; Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Darren M","family":"Ashcroft","sequence":"additional","affiliation":[{"name":"NIHR School for Primary Care Research, The University of Manchester, Manchester, UK"},{"name":"NIHR Greater Manchester Patient Safety Translational Research Centre, The University of Manchester, Manchester, UK"},{"name":"Division of Pharmacy and Optometry; Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6450-5815","authenticated-orcid":false,"given":"Evangelos","family":"Kontopantelis","sequence":"additional","affiliation":[{"name":"NIHR School for Primary Care Research, The University of Manchester, Manchester, UK"},{"name":"Division of Informatics, Imaging and Data Sciences; Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"239","published-online":{"date-parts":[[2023,3,1]]},"reference":[{"key":"2025121605034558000_13.3.e057428.1","doi-asserted-by":"crossref","unstructured":"Chou R , Hartung D , Turner J , et al . n.d. 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