Estimation of the epidemiology of dementia and associated neuropsychiatric symptoms by applying machine learning to real-world data

Javier Mar; Ania Gorostiza; Arantzazu Arrospide; Igor Larrañaga; Ane Alberdi; Carlos Cernuda; Álvaro Iruin; Mikel Tainta; Lorea Mar-Barrutia; Oliver Ibarrondo

doi:10.1016/j.rpsm.2021.03.001

Estimation of the epidemiology of dementia and associated neuropsychiatric symptoms by applying machine learning to real-world data

Rev Psiquiatr Salud Ment (Engl Ed). 2021 Mar 25:S1888-9891(21)00032-X. doi: 10.1016/j.rpsm.2021.03.001. Online ahead of print.

[Article in English, Spanish]

Authors

Affiliations

¹ Basque Health Service (Osakidetza), Debagoiena Integrated Healthcare Organisation, Research Unit, Arrasate-Mondragón, Gipuzkoa, Spain; Kronikgune Institute for Health Service Research, Barakaldo, Bizkaia, Spain; Biodonostia Health Research Institute, Donostia-San Sebastián, Gipuzkoa, Spain; Health Services Research on Chronic Patients Network (REDISSEC), Bilbao, Bizkaia, Spain. Electronic address: javier.marmedina@osakidetza.eus.
² Basque Health Service (Osakidetza), Debagoiena Integrated Healthcare Organisation, Research Unit, Arrasate-Mondragón, Gipuzkoa, Spain; Kronikgune Institute for Health Service Research, Barakaldo, Bizkaia, Spain.
³ Basque Health Service (Osakidetza), Debagoiena Integrated Healthcare Organisation, Research Unit, Arrasate-Mondragón, Gipuzkoa, Spain; Kronikgune Institute for Health Service Research, Barakaldo, Bizkaia, Spain; Biodonostia Health Research Institute, Donostia-San Sebastián, Gipuzkoa, Spain; Health Services Research on Chronic Patients Network (REDISSEC), Bilbao, Bizkaia, Spain.
⁴ Mondragon Unibertsitatea, Faculty of Engineering, Electronics and Computing Department, Arrasate-Mondragón, Gipuzkoa, Spain.
⁵ Basque Health Service (Osakidetza), Gipuzkoa Mental Health Network, Donostia-San Sebastián, Gipuzkoa, Spain; Biodonostia Health Research Institute, Donostia-San Sebastián, Gipuzkoa, Spain.
⁶ Basque Health Service (Osakidetza), Goierri-Urola Garaia Integrated Healthcare Organisation, Department of Neurology, Zumarraga, Gipuzkoa, Spain; Fundación CITA-Alzheimer Fundazioa, Donostia-San Sebastián, Gipuzkoa, Spain.
⁷ Psiquiatry Service, Hospital Bellvitge, Hospitalet de Llobregat, Barcelona, Spain.
⁸ Basque Health Service (Osakidetza), Debagoiena Integrated Healthcare Organisation, Research Unit, Arrasate-Mondragón, Gipuzkoa, Spain; Biodonostia Health Research Institute, Donostia-San Sebastián, Gipuzkoa, Spain; RS-Statistics, Arrasate-Mondragón, Gipuzkoa, Spain.

PMID: 33774222
DOI: 10.1016/j.rpsm.2021.03.001

Abstract

Introduction: Incidence rates of dementia-related neuropsychiatric symptoms (NPS) are not known and this hampers the assessment of their population burden. The objective of this study was to obtain an approximate estimate of the population incidence and prevalence of both dementia and NPS.

Methods: Given the dynamic nature of the population with dementia, a retrospective study was conducted within the database of the Basque Health Service (real-world data) at the beginning and end of 2019. Validated random forest models were used to identify separately depressive and psychotic clusters according to their presence in the electronic health records of all patients diagnosed with dementia.

Results: Among the 631,949 individuals over 60 years registered, 28,563 were diagnosed with dementia, of whom 15,828 (55.4%) showed psychotic symptoms and 19,461 (68.1%) depressive symptoms. The incidence of dementia in 2019 was 6.8/1000 person-years. Most incident cases of depressive (72.3%) and psychotic (51.9%) NPS occurred in cases of incident dementia. The risk of depressive-type NPS grows with years since dementia diagnosis, living in a nursing home, and female sex, but falls with older age. In the psychotic cluster model, the effects of male sex, and older age are inverted, both increasing the probability of this type of symptoms.

Conclusions: The stigmatization factor conditions the social and attitudinal environment, delaying the diagnosis of dementia, preventing patients from receiving adequate care and exacerbating families' suffering. This study evidences the synergy between big data and real-world data for psychiatric epidemiological research.

Keywords: Aprendizaje automático; Demencia; Dementia; Incidence; Incidencia; Machine learning; Neuropsychiatric symptoms; Prevalence; Prevalencia; Síntomas neuropsiquiátricos.