Using Big Data to Estimate Dementia Prevalence in New Zealand: Protocol for an Observational Study.
In: Journal of Medical Internet Research, Jg. 23 (2021), Heft 1, S. N.PAG
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Background: Dementia describes a cluster of symptoms that includes memory loss; difficulties with thinking, problem solving, or language; and functional impairment. Dementia can be caused by a number of neurodegenerative diseases, such as Alzheimer disease and cerebrovascular disease. Currently in New Zealand, most of the systematically collected and detailed information on dementia is obtained through a suite of International Residential Assessment Instrument (interRAI) assessments, including the home care, contact assessment, and long-term care facility versions. These versions of interRAI are standardized comprehensive geriatric assessments. Patients are referred to have an interRAI assessment by the Needs Assessment and Service Coordination (NASC) services after a series of screening processes. Previous estimates of the prevalence and costs of dementia in New Zealand have been based on international studies with different populations and health and social care systems. This new local knowledge will have implications for estimating the demographic distribution and socioeconomic impact of dementia in New Zealand. Objective: This study investigates the prevalence of dementia, risk factors for dementia, and drivers of the informal cost of dementia among people registered in the NASC database in New Zealand. Methods: This study aims to analyze secondary data routinely collected by the NASC and interRAI (home care and contact assessment versions) databases between July 1, 2014, and July 1, 2019, in New Zealand. The databases will be linked to produce an integrated data set, which will be used to (1) investigate the sociodemographic and clinical risk factors associated with dementia and other neurological conditions, (2) estimate the prevalence of dementia using weighting methods for complex samples, and (3) identify the cost of informal care per client (in number of hours of care provided by unpaid carers) and the drivers of such costs. We will use design-based survey methods for the estimation of prevalence and generalized estimating equations for regression models and correlated and longitudinal data. Results: The results will provide much needed statistics regarding dementia prevalence and risk factors and the cost of informal care for people living with dementia in New Zealand. Potential health inequities for different ethnic groups will be highlighted, which can then be used by decision makers to inform the development of policy and practice. Conclusions: As of November 2020, there were no dementia prevalence studies or studies on informal care costs of dementia using national data from New Zealand. All existing studies have used data from other populations with substantially different demographic distributions. This study will give insight into the actual prevalence, risk factors, and informal care costs of dementia for the population with support needs in New Zealand. It will provide valuable information to improve health outcomes and better inform policy and planning. International Registered Report Identifier (IRRID): DERR1-10.2196/20225 JMIR Res Protoc 2021;10(1):e20225 doi:10.2196/20225 We help JMIR researchers to raise funds to pursue their research and development aimed at tackling important health and technology challenges. If you would like to show your support for this author, please donate using the button below. The funds raised will directly benefit the corresponding author of this article (minus 8% admin fees). Your donations will help this author to continue publishing open access papers in JMIR journals. Donations of over $100 may also be acknowledged in future publications. Suggested contribution levels: $20/$50/$100 [ABSTRACT FROM AUTHOR]
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Titel: |
Using Big Data to Estimate Dementia Prevalence in New Zealand: Protocol for an Observational Study.
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Autor/in / Beteiligte Person: | Rivera-Rodriguez, Claudia ; Cheung, Gary ; Cullum, Sarah |
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Zeitschrift: | Journal of Medical Internet Research, Jg. 23 (2021), Heft 1, S. N.PAG |
Veröffentlichung: | 2021 |
Medientyp: | academicJournal |
ISSN: | 1439-4456 (print) |
DOI: | 10.2196/20225 |
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