Accepted for/Published in: JMIR Research Protocols
Date Submitted: May 13, 2020
Date Accepted: Nov 24, 2020
Using big data to estimate dementia prevalence in New Zealand: Study protocol
ABSTRACT
Background:
Data on people with support needs in New Zealand are collected by the Needs Assessment and Service Coordination services (NASC). Basic data and demographics are stored in the SOCRATES database administered by the Ministry of Health. Using a series of screening procedures, some clients are selected for more detailed data collection using InterRAI assessments. Each client can have several assessments over time. Therefore, the resulting sample is a complex sample with potential repeated measures.
Objective:
Objectives This study investigates the prevalence, risks factors and cost of informal care for people with dementia in people identified in the SOCRATES database.
Methods:
This study aims to analyze secondary data routinely collected in the SOCRATES and InterRAI (contact and home care assessments) databases between 1st July 2014 and 1st July 2019 in New Zealand [1; 2]. The databases will be linked to produce an integrated dataset which will be used to: 1. investigate the sociodemographic and clinical risk factors associated with dementia and other neurological conditions in people registered in the SOCRATES database; 2. assess whether the prevalence of dementia can be estimated from these data using weighting methods for complex samples; 3. estimate the informal cost per client (in number of hours of care provided by unpaid carers) according to demographic characteristics and type of neurological conditions. The methods used will be design-based survey methods for prevalence and generalized estimating equations for regression models and correlated/longitudinal data.
Results:
The results will provide much needed statistics regarding dementia prevalence, 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:
Using routinely collected health data for regression modeling may be a cost-effective way to investigate risk factors of neurological conditions. It has the potential to improve health outcomes and to better inform policy and planning. Clinical Trial: Trial registration New Zealand HDEC: 19/STH/206
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