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Fox Foundation","doi-asserted-by":"crossref","award":["grant # MJFF-023376"],"award-info":[{"award-number":["grant # MJFF-023376"]}],"id":[{"id":"10.13039\/100000864","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Neurol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate identification of Parkinson\u2019s disease (PD) in large electronic health record (EHR) population-based databases is challenging due to diagnostic heterogeneity in routine care, with a substantial proportion of individuals diagnosed with PD had not been diagnosed by a specialist. Our aim was to develop and validate a simplified rule-based medication algorithm to identify PD in a nationwide healthcare registry and apply it to estimate long-term incidence, prevalence, and pre-diagnostic diagnoses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Using Clalit Health Services EHR data covering over five million individuals (2005\u20132025), we constructed a medication-based algorithm incorporating predefined inclusion and exclusion criteria and two levels of diagnostic certainty (probable\/possible PD). Validation was performed against two independent specialist-confirmed PD cohorts and FDOPA PET\/CT and a non-PD neurological cohort. Incidence rates per 100,000 were calculated annually with 95% confidence intervals (CIs) assuming a Poisson distribution. Age-adjusted incidence rates were computed using the WHO standard population. motor and non-motor diagnoses preceding PD were examined up to 18 years before the index date using matched controls.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The algorithm identified 34,368 PD patients (56.5% male; mean age at index 75.2\u2009\u00b1\u200910.5 years). Sensitivity was 94.8% (95% CI 90.4\u201397.2) in the FDOPA PET\/CT cohort, 94.8% (95% CI 92.1\u201396.6) in the private clinic cohort, and 94.7% (95% CI 90.9\u201396.9) in the movement disorder clinic cohort. Specificity was 85.2% (95% CI 77.8\u201390.6).<\/jats:p>\n                    <jats:p>Incidence increased markedly with age but declined significantly over time (overall annual percent change [APC] - 4.47%, 95% CI -4.90 \u2013 -4.03). Age-adjusted incidence rates (\u226520 years) declined 2.4-fold between 2005 and 2024 (55 [95% CI 53\u201358] to 23 [95% CI 21\u201324] per 100,000). Overall prevalence declined modestly (APC -0.78%, 95% CI -0.84 \u2013 -0.72), with increases in younger age groups and declines in older groups.<\/jats:p>\n                    <jats:p>Constipation, depression, and tremor diagnoses were more frequent years before diagnosis, whereas smoking-related codes were less frequent among future PD patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This validated medication-based algorithm provides a reproducible framework for PD identification in large registries. Applied over two decades in a nationwide cohort, it demonstrated high diagnostic performance and revealed age-dependent declines in PD incidence alongside heterogeneous prevalence trends.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12883-026-04840-6","type":"journal-article","created":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T03:18:21Z","timestamp":1774581501000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Parkinson\u2019s disease in real life healthcare organization database: a medication-based algorithm"],"prefix":"10.1186","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9328-6340","authenticated-orcid":false,"given":"Hila","family":"Avisar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruth","family":"Djaldetti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amir","family":"Krivoy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1520-2292","authenticated-orcid":false,"given":"Anat","family":"Mirelman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5717-4875","authenticated-orcid":false,"given":"Roy N.","family":"Alcalay","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1482-8522","authenticated-orcid":false,"given":"Nir","family":"Giladi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,27]]},"reference":[{"issue":"1","key":"4840_CR1","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1038\/s41531-018-0058-0","volume":"4","author":"C Marras","year":"2018","unstructured":"Marras C, Beck JC, Bower JH, Roberts E, Ritz B, Ross GW, Abbott RD, Savica R, Van Den Eeden SK, Willis AW, Tanner CM. 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