Using Big Data to Estimate Dementia Prevalence in New Zealand: Protocol for an Observational Study - JMIR Research Protocols
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JMIR RESEARCH PROTOCOLS Rivera-Rodriguez et al
Protocol
Using Big Data to Estimate Dementia Prevalence in New Zealand:
Protocol for an Observational Study
Claudia Rivera-Rodriguez1, PhD; Gary Cheung2, PhD; Sarah Cullum2, PhD
1
Department of Statistics, University of Auckland, Auckland, New Zealand
2
Department of Psychological Medicine, University of Auckland, Auckland, New Zealand
Corresponding Author:
Claudia Rivera-Rodriguez, PhD
Department of Statistics
University of Auckland
2/1576 Great North Road
Waterview
Auckland, 1026
New Zealand
Phone: 64 0223920565
Email: [email protected]
Abstract
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
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KEYWORDS
routinely collected data; repeated measures; dementia; Alzheimer disease; modeling; complex sampling
interRAI-HC are hypertension, head injury, air pollution, and
Introduction education. Dementia diagnosis data collected in interRAI
Dementia is a global public health priority [1]. There are assessments show a high degree of accuracy when compared
currently 50 million people living with dementia worldwide, with clinical records [7]. The interRAI-CA is a shorter geriatric
and this number is projected to increase to 82 million in 2030 assessment used to assess clients urgently, reliably, and
and 152 million in 2050 [2]. The current global cost of dementia efficiently and identify the complexity of the older adult’s
care is over US $1 trillion per year, and 40% of the cost is due condition. It is a basic screening assessment that provides
to informal care provided by unpaid carers, who are usually clinical information to support decision making about the need
family members [2]. Dementia is a neurodegenerative disease and urgency for a comprehensive assessment, support, and
that affects a person’s memory, thinking, behavior, and specialized rehabilitation services.
day-to-day functioning. Dementia is recognized as a significant Initially, older adults referred to NASC agencies are classified
health care challenge in New Zealand that will have major social as urgent or nonurgent, and urgent cases are immediately
and economic impacts in the coming years [3]. Since age is a assessed using interRAI-HC [8,9]. Nonurgent cases are only
main risk factor for dementia, dementia prevalence will increase assessed using interRAI-CA but could be reclassified as urgent
as the baby boomer populations in New Zealand and other at a later time, for example, when they are reassessed annually.
Western countries enter the older age cohort. However, there Therefore, several observations are available for each client as
is no previous large-scale epidemiological study examining the long as they remain in receipt of support services. The
extent or impact of dementia in New Zealand. There is an urgent assessments can be used to inform care planning, resource
need to study dementia prevalence and outcomes to inform allocation decisions, and economic evaluations [7,10]. The
public policy and health services planning. Two particular interRAI-HC has good convergent validity as compared with
motivations for our research are the potential to estimate the Resource Utilization in Dementia Lite instrument to estimate
dementia prevalence using health administrative data and the the societal cost of resource utilization in community-dwelling
use of a novel statistical model to evaluate the informal cost of older adults [10].
dementia care for people with support needs in New Zealand
[4]. Methods
The New Zealand Ministry of Health routinely collects
information on people with support needs in the Needs
Study Aims and Objectives
Assessment and Service Coordination (NASC) database. This This study will investigate the prevalence, risks factors, and
database contains data that are collected by publicly funded informal cost of dementia in New Zealand.
NASC agencies, including basic demographic and health Objective 1 is to produce an integrated data set by linking the
information. However, the information in the NASC database NASC and interRAI data sets between July 1, 2014, and June
alone is not sufficiently detailed to study the specific needs of 30, 2019. Objective 2 is to produce a descriptive analysis of the
people with dementia. We therefore propose linking the NASC routinely collected data for people registered in NASC and
data with another Ministry of Health data set, the International interRAI in New Zealand. Objective 3 is to evaluate the risk
Residential Assessment Instrument (interRAI). factors for dementia and the drivers of informal cost. Objective
There is a suite of interRAI assessments that are currently in 4 is to calculate an estimate of the prevalence and average
use in New Zealand. This study will focus solely on interRAI informal cost of dementia.
Home Care (interRAI-HC) and interRAI Contact Assessment
Study Design
(interRAI-CA). The interRAI-HC is a comprehensive geriatric
assessment developed by a network of health researchers in The study is an observational study comprising 5 years of
over 30 countries. It aims to provide a clinical assessment of longitudinal data.
medical, rehabilitation, and support needs and abilities. It Study Population
contains information on about 250 demographic, clinical
The study population is people who were registered in the NASC
(including the diagnosis of Alzheimer disease and dementia),
database between July 1, 2014, and June 30, 2019. Patients are
and psychosocial factors, which can be used to support care
referred to NASCs by medical practitioners when they are
planning, resource allocation, quality measurement, and outcome
considered to have needs and requirements for services such as
evaluation. New Zealand has implemented a mandated
home care or long-term care. The NASC data set contains
interRAI-HC assessment for all older adults who are being
demographic information, such as age, gender, and ethnicity,
assessed for publicly funded home support services since 2012
along with information on whether the patient was classified as
and long-term aged residential care since 2016 [5]. Data on
urgent or nonurgent at their first evaluation by NASC.
informal costs are collected as informal hours of unpaid care.
Additionally, the interRAI-HC captures 8 of the 12 known risk Study Sample
factors for dementia [6]: diabetes, smoking, obesity, physical The study sample is people who are registered in the NASC
inactivity, depression, alcohol, hearing impairment, and lack of database and were assessed with at least one interRAI-HC or
social contact. Dementia risk factors that are not captured by
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interRAI-CA between July 1, 2014, and June 30, 2019. This cost is measured by the interRAI in hours, to which standard
sample contains all urgent cases and a sample of nonurgent unit costs for informal care are applied.
cases from the NASC database.
Theory and Models
Eligibility Criteria For objective 2, we will use basic descriptive statistics and
Repeated assessments or observations on the same patient will hypothesis tests, such as 2-tailed t tests and F tests. For objective
be included in the analysis. Patients included in the sample for 3, we will use marginal regression models obtained from
analysis will only be those in the NASC database with at least generalized estimating equations (GEEs) for 2 outcomes:
one interRAI assessment between July 1, 2014, and June 30, dementia presence and number of hours of informal care. We
2019. will evaluate risk factors and drivers of the cost, such as
ethnicity, gender, severity of the diseases, age, marital status,
Ethical Considerations and comorbidities. GEE models are used for data structures that
This study has been approved by the New Zealand Health and have repeated observations. In order to correct for nonresponses
Disability Ethics Committee (reference 19/STH/206). The and missing data, we will use the calibrated sampling weights
research team will ensure the research meets or exceeds method [12-14], where each observation is given a weight w
established ethical standards determined by the committee. that compensates for differential nonresponses and missing data.
For this project, the weights will be estimated using
Data Management
demographic information, such as age, gender, ethnicity, and
Data Sources urgency of the case in the sample of people with dementia and
The primary data source is the Integrated Data Infrastructure in the whole NASC population. These weights will be
(IDI) [11]. The IDI is a large research database. It holds incorporated into GEE models using a loss function that yields
microdata about people and households in New Zealand. The the minimum loss. The choice of a loss function is usually a
data are about life events, such as education, income, benefits, balance between the goal of the analysis and the efficiency and
migration, justice, and health, and come from government complexity of the function. GEE is a well-known method for
agencies, Statistics New Zealand surveys, and nongovernment regression in the presence of correlated data or repeated
organizations. Data on an individual person are linked together, measures [15,16]. The efficiency of GEE depends on the
or integrated, to form the IDI. Researchers gain access to the assumptions made about the variability of the data. For example,
IDI data labs by formally applying for a research project. Data a straightforward choice would be independence. Such
in the IDI are deidentified. Numbers that can be used to identify assumptions are crucial for the second part of the theoretical
people are encrypted. development or inference. This is the vital step in which we
draw valid conclusions from the data.
Information from interRAI and NASC is available in the IDI.
We have been granted approval to access these data (project For objective 4, dementia prevalence will be calculated as a
No. MAA2020-02). weighted total using the calibrated sampling weights mentioned
above. The resulting quantity will then be divided by the number
The interRAI and NASC data have encrypted identifiers that of person-years calculated using the longitudinal data.
are consistent in both data sets. The linkage will be conducted
in the Statistics New Zealand Data Lab at the University of Informal cost estimates will be calculated as weighted averages
Auckland. An integrated data set will be generated. This will using calibrated sampling weights. The resulting quantity will
result in 3 data sets: (1) the interRAI data set, (2) the NASC then be divided by the number of person-years calculated using
data set, and (3) the integrated data set. the longitudinal data. All codes will be programmed in R (The
R Foundation).
Time and Data Storage
The 3 resulting data sets (interRAI, NASC, and integrated) will Results
be stored in the Statistics New Zealand Data Lab at the
University of Auckland, which is part of the IDI in New As of November 2020, there have been no dementia prevalence
Zealand. studies or studies on informal care costs of dementia using
national data from New Zealand. All existing studies have used
Data Analysis data from other populations, for which the demographic
Statistical analysis will address two different elements: (1) data distribution is significantly different. This study will identify
cleaning and integration and (2) the theory and models. the risk factors and informal costs of dementia (unpaid care) in
people 65 years or older who have been assessed for care needs
Data Cleaning and Integration in New Zealand. We will also explore the potential of using
The data cleaning and integration step will focus on data linkage routinely collected health data to provide a proxy measure of
and data cleaning. For objective 1, the information to be linked dementia.
is the information from NASC (which contains demographics)
We have obtained ethics approval from the New Zealand Health
and the information from interRAI (which contains data on
and Disability Ethics Committee (reference 19/STH/206). The
dementia diagnoses, physical and psychosocial health, and
complex sampling design method will be employed in this study
informal care). Informal care includes the care provided by
to extrapolate the results to the population with disabilities in
unpaid (informal) carers, usually family members. The informal
New Zealand. The population data frame will be the New
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Zealand NASC database, and the complex sample will be the Māori individuals present at a younger age than non-Māori
interRAI data set (a subset of the NASC data set). This offers individuals to a tertiary memory service [17]. This might be
the potential to extrapolate results from the interRAI to NASC expected, as Māori are at greater risk of dementia due to
by using the screening processes to calculate sampling weights. increased prevalence of risk factors such as diabetes and
We hope that this approach will provide much needed statistics cardiovascular disease. The only epidemiological study that has
regarding potential health inequities, which can then be used examined differences in dementia between Māori and non-Māori
by decision makers to change policy and practice. We also hope individuals is the Life and Living in Advanced Age, a Cohort
that this opens doors to future research in which larger Study in New Zealand (LiLACS NZ) study, a longitudinal study
populations or surveys are linked to interRAI data. on the health and well-being of octogenarians [18]. LiLACS
NZ examined around 500 Māori and 500 non-Māori
Discussion octogenarians. They found that more Māori people scored below
the cutoff in a well-known cognitive screening tool (the
The aim of this study is to investigate the prevalence, risks Mini-Mental State Examination [MMSE] [19]), but that the
factors, and informal cost of dementia in New Zealand. The prevalence of dementia using a specialist diagnostic assessment
number of people living with dementia in the world has been was no different between the two groups. This indicates that
estimated to be 50 million and is expected to almost double the MMSE is culturally biased against Māori individuals and
every 20 years [2]. There has never been a study examining the overestimates the prevalence of dementia in Māori populations
prevalence, risk factors, or cost of dementia in New Zealand. [18]. We agree and have been careful to seek consultation
A Deloitte report [3] estimated the prevalence of dementia and regarding not only the collection and analysis of routinely
identified the main risk factors of dementia by extrapolating collected data but also the responsible dissemination of findings
epidemiological data from other countries. Our proposed study as they might pertain to Māori individuals. We have consulted
will advance current research on dementia in New Zealand by with a senior cultural Māori advisor at a district health board
using routinely collected local data to estimate the prevalence regarding the use of both local and national health data and the
of dementia. This study will provide insight into the prevalence dissemination of findings. This advisor supports this study and
of dementia in the main ethnic groups in New Zealand, our endeavor to use routinely collected health data to highlight
especially those considered to have a higher risk of dementia, and address health inequities and suggests that we collaborate
such as Māori and Pacific Islander people. with local marae and Māori health centers to discuss how best
It is mandatory in New Zealand to have Māori consultation for to present the findings of the study to decision makers,
studies that involve data pertaining to Māori people. For the academics, and the public. We have also consulted with a Māori
Māori community, there are concerns that policies can occur statistician and researcher, who also supports this study and has
without a robust Māori data governance partnership that is agreed to be an advisor on this study in order to ensure the
representative and inclusive and provides accountability back safeguarding and sovereignty of data and the responsible
to Māori communities. It has been previously demonstrated that dissemination of the study findings pertaining to Māori
populations.
Acknowledgments
We thank Mr Andrew Sporle for his advice on Māori consultation. This team has received funding from the University of Auckland
Science/Faculty Research Development Fund New Staff Grant (3716994).
Conflicts of Interest
None declared.
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Abbreviations
GEE: generalized estimating equation
IDI: Integrated Data Infrastructure
interRAI: International Residential Assessment Instrument
interRAI-CA: International Residential Assessment Instrument Contact Assessment
interRAI-HC: International Residential Assessment Instrument Home Care
LiLACS NZ: Life and Living in Advanced Age, a Cohort Study in New Zealand
MMSE: Mini-Mental State Examination
NASC: Needs Assessment and Service Coordination
Edited by G Eysenbach; submitted 13.05.20; peer-reviewed by N Mohammad Gholi Mezerji, C Sinclair; comments to author 21.08.20;
revised version received 06.09.20; accepted 24.11.20; published 06.01.21
Please cite as:
Rivera-Rodriguez C, Cheung G, Cullum S
Using Big Data to Estimate Dementia Prevalence in New Zealand: Protocol for an Observational Study
JMIR Res Protoc 2021;10(1):e20225
URL: https://www.researchprotocols.org/2021/1/e20225
doi: 10.2196/20225
PMID:
©Claudia Rivera-Rodriguez, Gary Cheung, Sarah Cullum. Originally published in JMIR Research Protocols
(http://www.researchprotocols.org), 06.01.2021. This is an open-access article distributed under the terms of the Creative Commons
Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction
in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic
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