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We estimated prevalence from three types of adverse event separately: opioid mortality, opioid\u2010poisoning hospitalizations and opioid\u2010related charges. We extended the model and produced prevalence estimates from a \u2018multi\u2010source\u2019 model based on all three types of adverse event data.<\/jats:p><\/jats:sec><jats:sec><jats:title>Setting, Participants and Measurements<\/jats:title><jats:p>This study was conducted in NSW, Australia, 2014\u201316 using data from the Opioid Agonist Treatment and Safety (OATS) study, which included all people who had received treatment for opioid dependence in NSW. Aggregate data were obtained on numbers of adverse events in NSW. Rates of each adverse event type within the OATS cohort were modelled. Population data were provided by State and Commonwealth agencies.<\/jats:p><\/jats:sec><jats:sec><jats:title>Findings<\/jats:title><jats:p>Prevalence of opioid dependence among those aged 15\u201364\u2009years in 2016 was estimated to be 0.96% (95% credible interval [CrI]\u2009=\u20090.82%, 1.12%) from the mortality model, 0.75% (95% CrI\u2009=\u20090.70%, 0.83%) from hospitalizations, 0.95% (95% CrI\u2009=\u20090.90%, 0.99%) from charges and 0.92% (95% CrI\u2009=\u20090.88%, 0.96%) from the multi\u2010source model. Of the estimated 46\u2009460 (95% CrI\u2009=\u200944\u2009680, 48\u2009410) people with opioid dependence in 2016 from the multi\u2010source model, approximately one\u2010third (16\u2009750, 95% CrI\u2009=\u200914\u2009960, 18\u2009690) had no record of opioid agonist treatment within the last 4\u00a0years. From the multi\u2010source model, prevalence in 2016 was estimated to be 1.24% (95% CrI\u2009=\u20091.18%, 1.31%) in men aged 15\u201344, 1.22% (95% CrI\u2009=\u20091.14%, 1.31%) in men 45\u201364, 0.63% (95% CrI\u2009=\u20090.59%, 0.68%) in women aged 15\u201344 and 0.56% (95% CrI\u2009=\u20090.50%, 0.63%) in women aged 45\u201364.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>A Bayesian statistical approach to estimate prevalence from multiple adverse event types simultaneously calculates that the estimated prevalence of opioid dependence in NSW, Australia in 2016 was 0.92%, higher than previous estimates.<\/jats:p><\/jats:sec>","DOI":"10.1111\/add.16268","type":"journal-article","created":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T05:56:40Z","timestamp":1686290200000},"page":"1994-2006","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Prevalence of opioid dependence in New South Wales, Australia, 2014\u201316: Indirect estimation from multiple data sources using a Bayesian approach"],"prefix":"10.1111","volume":"118","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7106-1033","authenticated-orcid":false,"given":"Beatrice C.","family":"Downing","sequence":"first","affiliation":[{"name":"Population Health Sciences, Bristol Medical School University of Bristol  Bristol UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9864-459X","authenticated-orcid":false,"given":"Matthew","family":"Hickman","sequence":"additional","affiliation":[{"name":"Population Health Sciences, Bristol Medical School University of Bristol  Bristol UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5742-4598","authenticated-orcid":false,"given":"Nicola R.","family":"Jones","sequence":"additional","affiliation":[{"name":"National Drug and Alcohol Research Centre University of New South 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