Evidence map›Paper›PMID 41888757›Full record

ArticleBMC neurology2026

Parkinson's disease in real life healthcare organization database: a medication-based algorithm.

Hila Avisar, Ruth Djaldetti, Amir Krivoy, Anat Mirelman, Roy N Alcalay, Nir Giladi

Abstract read
In one paragraph

Article in BMC neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Hila AvisarData Research Center for Mental Health and Rehabilitation, Clalit Health Services, Petach Tikva, Israel. hila444@gmail.com.ORCID http://orcid.org/0000-0001-9328-6340
Ruth DjaldettiDepartment of Neurology, Movement Disorders Clinic, Rabin medical Center - Belinson Hospital, Petach Tikva, Israel.
Amir KrivoyGray Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv, Israel.
Anat MirelmanNeurological institute, division of movement disorders, Tel-Aviv Souraski Medical center, Tel Aviv, Israel.ORCID http://orcid.org/0000-0002-1520-2292
Roy N Alcalay *Neurological institute, division of movement disorders, Tel-Aviv Souraski Medical center, Tel Aviv, Israel.ORCID http://orcid.org/0000-0002-5717-4875
Nir Giladi *Gray Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv, Israel.ORCID http://orcid.org/0000-0003-1482-8522

Funding

Michael J. Fox Foundation grant # MJFF-023376
6 · The paper itself

Abstract

backgroundAccurate identification of Parkinson’s 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.

methodsUsing Clalit Health Services EHR data covering over five million individuals (2005–2025), 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.

resultsThe algorithm identified 34,368 PD patients (56.5% male; mean age at index 75.2 ± 10.5 years). Sensitivity was 94.8% (95% CI 90.4–97.2) in the FDOPA PET/CT cohort, 94.8% (95% CI 92.1–96.6) in the private clinic cohort, and 94.7% (95% CI 90.9–96.9) in the movement disorder clinic cohort. Specificity was 85.2% (95% CI 77.8–90.6). Incidence increased markedly with age but declined significantly over time (overall annual percent change [APC] - 4.47%, 95% CI -4.90 – -4.03). Age-adjusted incidence rates (≥20 years) declined 2.4-fold between 2005 and 2024 (55 [95% CI 53–58] to 23 [95% CI 21–24] per 100,000). Overall prevalence declined modestly (APC -0.78%, 95% CI -0.84 – -0.72), with increases in younger age groups and declines in older groups. Constipation, depression, and tremor diagnoses were more frequent years before diagnosis, whereas smoking-related codes were less frequent among future PD patients.

conclusionsThis 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.

Indexed as

AlgorithmsDatabases, FactualParkinson DiseaseAgedAged, 80 and overAntiparkinson AgentsElectronic Health RecordsFemaleHumansIncidenceMaleMiddle AgedPrevalenceRegistriesAntiparkinson AgentsAlgorithm for identifying Parkinson’s diseaseMedication- based algorithmParkinson’s diseaseParkinson’s disease incidenceProdromal phaseRegistry

Identifiers

PMID41888757
PMCPMC13185355

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.