{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T15:34:16Z","timestamp":1772033656683,"version":"3.50.1"},"reference-count":0,"publisher":"Swansea University","issue":"1","license":[{"start":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T00:00:00Z","timestamp":1771977600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJPDS"],"abstract":"<jats:p>BackgroundAmbient temperature may alter the risk of gout flare. We assessed the association between daily maximum temperature on the immediate-to-delayed (lag) hospitalisations for gout flare at the whole-population-level.\nMethodsData were extracted from the Western Australian Hospital Morbidity Data Collection for this time series study for metropolitan Perth (Australia) from 1980-2014, with meteorological data obtained from the Scientific Information for Land Owners dataset. We examined the association (relative risk [RR] and 95% confidence intervals [CI]) between hospitalisation for gout flare, daily maximum temperature and lag days since exposure using quasi-Poisson regression and the distributed lag non-linear model.\nResultsAverage daily maximum temperature was 24\u00b0C (5th percentile=17\u00b0C; 95th percentile=36\u00b0C). Maximum temperatures of 17\u00b0C and 36\u00b0C were associated with 6.2 and 6.8 gout admissions per 1,000,000 population over lag day 0 to lag day 21 following exposure, respectively. Risk of hospitalisation for gout (reference=24\u00b0C) with maximum temperature was modified by age and sex. Males aged \u226575 years had higher risks of gout hospitalisation following hotter days (36\u00b0C) beginning immediately on lag day 0 (RR=1.39; 95% CI: 1.02-1.88) and from lag day 7 (RR=1.11; 95% CI: 1.01-1.21) to lag day 10 after colder days (\u226415\u00b0C). Females aged \u226575 years had higher risk at 35\u00b0C from lag day 3 (RR=1.12; 95% CI: 1.02-1.23) to lag day 4. Males aged &lt;75 years had higher risks after 35\u00b0C days from lag day 6 (RR=1.06; 95% CI: 1.01-1.12) to lag Day 8 but lower risk from lag day 7 (RR=0.96: 95% CI: 0.92-0.99) to lag day 8 following colder days (17\u00b0C).\nConclusionOur study shows associations between extreme temperatures, both hot and cold, and immediate-to-delayed gout flare hospitalisations. Our findings will inform and guide public health measures and health system preparedness during the impending extremes of temperature with particular attention to older people.<\/jats:p>","DOI":"10.23889\/ijpds.v11i1.3149","type":"journal-article","created":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T13:55:50Z","timestamp":1772027750000},"source":"Crossref","is-referenced-by-count":0,"title":["Association between daily maximum temperature and immediate-to-delayed gout flare hospitalisations: a population-level time series study in metropolitan Perth, Australia"],"prefix":"10.23889","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0677-0420","authenticated-orcid":false,"given":"Derrick","family":"Lopez","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8805-8498","authenticated-orcid":false,"given":"Ross J.","family":"Marriott","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2833-7997","authenticated-orcid":false,"given":"Johannes","family":"Nossent","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Helen I.","family":"Keen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Charles","family":"Inderjeeth","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David B.","family":"Preen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"9980","published-online":{"date-parts":[[2026,2,25]]},"container-title":["International Journal of Population Data Science"],"original-title":[],"link":[{"URL":"https:\/\/ijpds.org\/article\/download\/3149\/6991","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ijpds.org\/article\/download\/3149\/6992","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ijpds.org\/article\/download\/3149\/6991","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T13:55:51Z","timestamp":1772027751000},"score":1,"resource":{"primary":{"URL":"https:\/\/ijpds.org\/article\/view\/3149"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,25]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1,14]]}},"URL":"https:\/\/doi.org\/10.23889\/ijpds.v11i1.3149","relation":{},"ISSN":["2399-4908"],"issn-type":[{"value":"2399-4908","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,25]]}}}