Addressing Dementia Through Analysis of Population Traits and Risk Factors (ADAPTOR) using Australia’s largest cohort study: a study protocol

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Strengthening the evidence base for dementia risk factors is critical for an effective and efficient public health and policy response. However, the number of Australians with dementia remains unknown, and there are significant gaps in the knowledge on risk factors in the Australian context. In this study we aim to develop reliable data on dementia incidence in Australia and investigate the associated risk factors, using a large population cohort. Specifically, we will assess the relative contribution of risk factors to dementia incidence as a basis for strengthening dementia prevention efforts. Methods We will use data from the 45 and Up Study that includes 267,358 residents of New South Wales, Australia, aged over 45 years, recruited between 2006-2009. To identify dementia cases we will link data from the 45 and Up Study with multiple health datasets containing information relevant to dementia case identification. We will estimate age- and sex-specific dementia incidence and model the association between dementia and risk factors related to socio-demographic characteristics, health conditions and health behaviours. We will also estimate the impact of various modifiable exposures on dementia incidence. Based on the results, we will produce a series of knowledge translation products providing advice on the contribution of identified risk factors to dementia incidence in Australia. Discussion Linking the 45 and Up Study data to multiple health datasets provides a unique opportunity to explore the role of risk factors on dementia incidence, including modelling the effect of modifiable risk factors on dementia incidence in the Australian population. We anticipate the results from this study to guide targeted and gradated strategies for population-level dementia prevention. Dementia ageing cohort study data linkage prevention modifiable risk factors Figures Figure 1 Background Dementia is a clinical syndrome that involves loss of cognitive functions, presenting with memory impairment and problems related to speech, thought, movement or mobility [1]. These symptoms are often accompanied by behavioural and psychological symptoms such as depression, psychosis, aggressive or agitated behaviour, sleep problems, and loss of inhibition [2, 3]. A recent estimate reported that more than 487,500 Australians were living with dementia in 2022 and is projected to increase to 1.1 million by 2058 [4]. Dementia is the second leading cause of death among Australians (after ischaemic heart disease), and the leading cause of death among Australian women [5]. Health and care costs (e.g. hospitalisations, general practitioner/primary healthcare and specialist visits, pharmaceuticals use, use of home care or nursing homes) associated with dementia was $9.1 billion in 2017 and is predicted to increase to $16.7 billion by 2036 [6]. Loss of income from dementia related lost productivity was $5.6 billion in 2017 and is estimated to increase to $9.1 billion by 2036 [6]. Dementia risk factors appear throughout the life course, with evidence indicating that risk factors presenting earlier in life play a major role in the development of dementia [7, 8]. Early-life risk factors such as lower education attainment contributes to increased risk by negatively affecting the cognitive reserve. Similarly, mid- to late-life risk factors (e.g. hypertension, alcohol misuse, smoking, obesity, diabetes, hearing loss, depression, social isolation) increase dementia risk by triggering age-related cognitive decline and neuropathological development [7-9]. The Lancet Commission on dementia prevention, intervention, and care estimated that, globally, managing 12 modifiable risk factors can prevent or delay up to 40% of dementia cases [8]. Despite the progress made in earlier dementia diagnosis and increases in knowledge about risk and protective factors, there remain significant evidence gaps that preclude effective prevention strategies, particularly at a population level. Recent Australian studies have shown varying dementia incidence rates that highlight the difficulty in estimating the exact number of Australians living with dementia [10-12]. This is likely due to consistent underreporting of cases and marked differences in case numbers between the administrative datasets (such as datasets on pharmaceutical benefits, aged care assessments, hospital/emergency department admissions, deaths). By linking administrative datasets, dementia cases can be captured at different stages of their trajectory and the quality of dementia monitoring can be improved [13]. Similarly, there are also considerable evidence gaps on dementia risk and protective factors. Most studies that have explored dementia risk factors have been on older adults, excluding exploration of exposure earlier in life [7, 8]. There is a need for long-term follow-up studies to understand the risk and protective factors across the lifespan. The Addressing Dementia Through Analysis of Population Traits and Risk Factors (ADAPTOR) project has been designed to address the limitations of existing approaches in estimating dementia incidence and identifying risk factors across the life course. Its specific aims are: 1) to estimate age- and sex-specific incidence of dementia; 2) to investigate the association between risk factors and dementia incidence; and 3) to model the impact of modifiable risk factor reduction on dementia incidence in the Australian population. This paper describes the protocol for the ADAPTOR project. Methods In the ADAPTOR project we will link survey data from a large Australian cohort study with multiple administrative and routinely collected datasets, as the basis for dementia incidence and risk factor assessment. We will use the results to generate resources and content, including a series of user-friendly briefs, to raise awareness and inform public health policies and interventions. Participants We will use data from the Sax Institute’s 45 and Up Study, a longitudinal study of 267,357 people, representing approximately 11% of people ≥45 years within New South Wales (NSW), Australia (https://www.saxinstitute.org.au/our-work/45-up-study/). The study methods and cohort have been described previously [14, 15]. Participants were recruited between February 2006 and December 2009 through Medicare Australia enrolment database. Medicare is the Australian universal national health insurance scheme that includes all Australian citizens and permanent residents. The Medicare database captures outpatient claims for general practitioner/primary healthcare and specialist visits, allied health services, procedures and diagnostic services, and certain private hospital procedures. Individuals with at least one Medicare claim in the past 2 years were randomly selected for the 45 and Up Study. Eligible individuals who had heard about the study could also volunteer to participate (the latter comprised 0.6% of the final sample). Participation in the study required informed consent to each of the following: 1) access to (linkage with) data from a comprehensive range of health-related databases; 2) provision of the participant’s Medicare number to assist with record linkage; 3) future contact for follow-up and potential participation in further research/sub studies. Participants in the study are followed up approximately every five years and may be included in further sub-studies. The participant response rate at baseline was at least 19%; the exact response rate is unknown as the number who were actually contacted is undetermined due to the potential for incorrect details in the Medicare database. In the final sample 45 and Up Study there was an overrepresentation of ≥80 year olds and those residing in rural and remote areas, and a lower proportion of participants identifying as Aboriginal or Torres Strait Islanders, compared to the overall NSW population (0.7% vs 1.4%) [14]. The population sample of the 45 and Up Study is ideal for identifying dementia cases and associated risk factors. As all participants provided consent for linkage with administrative health datasets there is no loss to follow-up for outcomes assessed through data linkage. The study collected data on a range of dementia risk factors at baseline, and participants were asked to indicate which of a series of medications, vitamins or supplements they had taken in the last four weeks. Furthermore, the study recruited adults from culturally and linguistically diverse communities, from rural and regional areas, and across the socioeconomic spectrum. This will allow the accurate capture of relative contributions of different risk factors to dementia development, enabling us to model the effect of reductions in risk factors on the wider Australian population. Moreover, the original intent of the 45 and Up Study – to expand the evidence base on healthy ageing – means that the cohort is well-aligned with the age profile of interest for dementia research. When the study commenced in 2006 the youngest participants were 45 years old, this aligns with the mid-life period that dementia risk factors start to exert significant influence [8]. Datasets and Data Linkage We will obtain data on participant characteristics and risk factors from 45 and Up Study’s baseline survey; this data can link with a multitude of health-related datasets. The multiple administrative datasets that will be used in the ADAPTOR project contain information relevant to dementia case identification, each dataset is described below. 1. Death datasets (three datasets): National Death Index (NDI): Contains information on deaths occurring throughout Australia (including NSW residents who die interstate), with ICD-10 coded cause of death. Cause of Death (COD) Unit Record File: Contains information on deaths occurring in NSW, with ICD-10 coded underlying and contributing causes of death. NSW Registry of Births, Deaths and Marriages (RBDM): Includes information on all recorded deaths registered in NSW. As this dataset only contains information on date of death, it is used to confirm/contradict dates of death in the NDI and COD datasets. 2. Pharmaceutical Benefits Schedule (PBS) dataset: Includes details of medications dispensed or subsidised as part of the Medicare health insurance scheme, but not the reason for prescription. The medications are coded according to the Anatomical Therapeutic Chemical (ATC) classification system. 3. NSW Mental Health Ambulatory (MHA) dataset: Contains information on all contacts between a clinician and a non-admitted patient attending a public mental health facility (e.g. mental health day programs, psychiatric outpatient and outreach services) for assessment, treatment, rehabilitation, or care. Data includes ICD-10-AM (Australian Modification) coded mental health diagnosis. 4. NSW Emergency Department Data Collection (EDDC): Contains information on all Emergency Department presentations to NSW public hospitals (i.e. public or government funded). It includes ICD-9-CM (Clinical Modification), ICD-10-AM, or SNOMED Clinical Terms (SNOMED CT) coded principal diagnosis. 5. NSW Hospital Admitted Patients Data Collection (APDC): Contains information on all admissions to Public Hospitals, Public Psychiatric Hospitals, Public Multi-Purpose Services, Private Hospitals, and Private Day Procedures Centres. The complete APDC data includes both Public and Private hospital admissions in a single dataset but the data collection periods differ for both, therefore, they need to be treated as separate data sources. They include ICD-10-AM coded diagnoses, and procedures and interventions classified according to the Australian Classification of Health Interventions (ACHI). 6. National Aged Care Data Clearinghouse: Contains numerous datasets with information on subsidised community support and residential aged care programs provided to eligible Australians. The datasets include diagnostic codes for dementia. The three relevant aged care datasets that will be used in the ADAPTOR project are: Aged Care Assessment Program (ACAP): Provides information on assessments undertaken by an Aged Care Assessment Team to determine eligibility of individuals for residential and other aged care programs. Up to 10 health conditions are recorded for each assessment. Home Care Package (HCP): Contains information on Commonwealth HCPs that provide one of four levels of personalised care to individuals living in their own homes. Aged Care Funding Instrument (ACFI): Contains information on care needs of individuals living in permanent residential aged care, and includes information on up to three mental or behavioural, and three medical conditions relevant to care needs. To maintain participant privacy and confidentiality, 45 and Up Study data will be linked to the administrative datasets using a specific Personal Identification Number generated for each participant in the Study. Approval has been obtained from all data custodians for use of the datasets. The Centre for Health Record Linkage at the NSW Ministry of Health provided linkage of the state datasets. The Data Integration Services Centre at the Australian Institute of Health and Welfare assisted with obtaining approval and linkage for the Commonwealth datasets. The research team will access data and conduct analysis on the Sax Institute’s Secure Unified Research Environment (SURE), a virtual environment which enables researchers to use de-identified data in a safe and secured workspace. Study period and rationale The study period for the ADAPTOR project commenced with enrolment to the 45 And Up Study (i.e. between February 2006 and December 2009), and ended on 30 th June 2018. Since enrolment to the study occurred over three years, enrolment date varies among participants. Figure 1 shows that the period covered by each administrative dataset varies. All datasets have data available until at least 30 th June 2018, except for COD data (available until early December 2017) and ACAP data (available until late May 2016). However, information contained in these two datasets is also available in the other death and aged care datasets, so very few, if any, cases of dementia will be missed when using the proposed end date. Definition of dementia Consistent with previous approaches that used routinely collected data for dementia diagnosis [10, 11], we will use data linkage to identify participants diagnosed with dementia since recruitment to 45 and Up Study. For each data source, we will classify individuals as having or not having dementia (codes used in dementia diagnosis in Supplementary Table 1). To define date of onset of dementia we will use the earliest record of dementia identified across the datasets. Accordingly, we will define an overall diagnosis of dementia as a dementia record in any of the datasets, with the date of onset as the earliest onset across all data sources. We will exclude prevalent cases i.e. study participants with dementia diagnosis within two years prior to enrolment in the 45 and Up Study. Two year timeframe is being used as all relevant datasets, except for Emergency Department (ED) data, are available from 2004 onwards (two years prior to 45 and Up Study enrolment); we do not anticipate that the ED data will be the sole source of many cases. While death data are not available until the start of 2006, this is irrelevant to assessing prevalent cases as participants must be alive for enrolment in the 45 and Up Study. Risk Factors Risk factors that we will investigate in the ADAPTOR project are informed by the literature and clinical expertise within the research team; we will limit investigations to the following risk factors that are available in 45 and Up baseline survey (Supplementary Table 2). Sociodemographic factors include sex, age at baseline survey (45-64; 65-74; 75+ years), education (did not complete school, high school/trade/certificate/diploma, university or higher), socioeconomic status (quintiles Index of Relative Socioeconomic Disadvantage based on residential postcode) [16], partner status (currently partnered i.e. married/defacto/living with a partner, or not currently partnered i.e. single/widowed/divorced/separated), and whether a language other than English was spoken at home. Health behaviours Smoking : We classified participants’ smoking status as ‘current smoker’, ‘past smoker’, or ‘never smoked’. Alcohol use : Utilizing the National Health and Medical Research Council guidelines we classified alcohol consumption as ‘non-drinker/low risk’ ( >0 and 0 and 14 drinks per week for females, and >28 drinks per week for males) [17]. Weight : Body Mass Index (BMI) is calculated as self-reported weight (in kilograms) divided by the square of self-reported height (in metres). We will categorise weight based on Kulminski recommendations for older people as high risk (BMI35) and missing. As this categorisation is based on mortality risk of older individuals and the World Health Organisation (WHO) guidelines are not necessarily relevant for the 65+ age group we used this recommendation [18]. Due to the high number of observations with missing height or weight values we included the missing category. Physical activity : Due to its validity and reliability we used the Active Australia Survey to measure physical activity [19, 20]. Based on the WHO guidelines we will categorise physical activity as inactive, moderate activity or high intensity activity [21]. Sleep : As per the Sabia groupings we will classify hours of sleep per night as ≤6, 6-8, or ≥8 hours [22]. Social engagement : This was measured by the Duke Social Interaction Subscale which includes three items assessing the number of different types of interaction with others in the last week and one item measuring the number of people the respondent can depend on or feel close to [23]. We will use the sum of the response options to generate a score which will be used as a continuous variable. Health conditions . Heart disease : We defined participants as having heart disease if they reported that they had been told by a doctor that they had heart disease, or had been treated for heart attack, angina or other heart disease last month. Stroke : We defined participants as having had stroke if they had ever been told by a doctor that they had a stroke. Diabetes : We classified participants as diabetic if they had ever been told by a doctor that they had diabetes. Hearing loss : We classified participants as having hearing loss if they self-reported having a hearing loss. Mental health : In the 45 And Up Study baseline survey, self-reported doctor diagnosed depression and anxiety were reported differently across the collection periods. Initially a combined item was reported: ‘Has a doctor ever told you that you have: depression/anxiety’. This was changed to two questions in later versions: ‘Has a doctor ever told you that you have: anxiety or depression’ and ‘In the last month have you been treated for: anxiety or depression’. We classified participants as having mental health disorder if they answered ‘yes’ to any of the questions. Cholesterol level : In the 45 And Up Study survey participants were asked ‘Have you taken any medications, vitamins or supplements for most of the last 4 weeks, including HRT and the pill?’ and ‘In the last month have you been treated for: specific chronic conditions’. We considered participants to have high cholesterol if they had answered ‘yes’ to having been treated for high cholesterol or taken either cholesterol-lowering medications in the month prior to the baseline survey. Blood pressure : We will generate three categories for high blood pressure: No high blood pressure: participants had not been diagnosed with high blood pressure by a doctor, or had not been treated for high blood pressure, or taken any blood pressure lowering medications in the last month; High blood pressure but not treated: participants diagnosed by a doctor with high blood pressure but not being treated for it; or Treatment for high blood pressure: participants on treatment for high blood pressure, or blood pressure lowering medications (e.g. Avapro Karvea, Coversyl, Lasmix Frusemide, Cardizem Vasocordol, Micardis, Norvasc, Tritace or Noten Tenormin Atenolol) in the last month. Psychological distress : The Kessler-10 (K-10) instrument was used in the 45 And Up Study survey to measure psychological distress [24]. This instrument includes 10 items on how often participants experienced a range of symptoms in the last month, with five possible response options ranging from none of the time (score of 1) to all of the time (score of 5). The response scores are summed to give an overall scale ranging from 10 to 50, which is then categorised as not probable psychosocial distress (10-22), mild psychosocial distress (22-30), or severe-very severe psychosocial distress (30-50). Statistical methods We will use the dementia definitions described previously to identify dementia cases and the date of the participant’s first documented diagnosis, and present sex and age-specific incidence as cases per 1000 person-years with 95% confidence intervals. Person-years, or follow-up time for an individual is calculated as the time between enrolment in the 45 and Up Study and the date of diagnosis, the date of death, or the end of the study period - whichever is the earliest. To investigate the association between socioeconomic factors, health behaviour and health conditions (full list in Supplementary Table 2), and incidence of dementia we will use Poisson regression with dementia diagnosis (yes/no) as the outcome and person-years as the offset (denominator). In the modelling stage we will account for risk factors found to be associated with dementia (p<0.25). We will also undertake time-to-event (survival) analysis, with time determined from enrolment to 45 and Up Study to dementia diagnosis or censoring (death or the end of the study period) [25]. Using Cox proportional hazards regression we will conduct time-to-event analyses. While this is semi-parametric and does not make any assumptions about distribution, it requires that the hazards are proportional over time. Inappropriate examination of the proportional assumptions may cause considerable bias in the estimates obtained from the models. Therefore, we will undertake assessment of Schoenfeld model residuals, including graphical displays of the covariate variables to assess the adequacy of the Cox regression models. To correct variables that violate the proportional hazards assumption we will use interactions with functions of time. To account for those participants who die prior to having a dementia diagnosis we will undertake secondary analyses using competing risks methods, using the Fine and Gray sub-distribution hazard function [26]. Our primary analysis will involve the study period from enrolment to the 45 and Up Study to 30 th June 2018; during this period we expect the datasets to contain the majority of dementia cases. We will undertake a sensitivity analysis for which the study period will be between the beginning of enrolment in the study and 31 st May 2016 (i.e. up to the period when all datasets are available). In an additional sensitivity analysis we will use a second definition of prevalent dementia i.e. participants diagnosed with dementia within two years of enrolling in the 45 and Up Study, and accordingly the study period will be two years following enrolment to the study. We will build a population model using simulation modelling to understand how prevention strategies targeting specific risk factors can affect dementia incidence in Australia. Based on the results we will undertake further modelling to assess the potential impact of eliminating individual and combination of risk factors on dementia incidence. We will use Population Attributable Fractions (PAFs) to determine the impact of reducing risk factors on dementia incidence [27], using risk factor prevalence from the best available Australian data, and associations between risk factors and dementia incidence data from the modelling undertaken above. To provide weighted PAFs, we will adjust rate ratios for associations between risk factors and dementia for overlap between risk factors (communality). Where relevant, we will supplement our estimates with data from other sources and will consider feasible and meaningful risk factor reductions based on the literature and discussions with policymakers. We will undertake a range of sensitivity analyses to assess the impact of varying prevalence of risk factors, risk factor reductions, and associations between risk factors and dementia. Discussion There are considerable limitations in the methods currently used for dementia risk factors and case identification in Australia. Current projections use simplistic extrapolation and microsimulation models that apply existing estimates of dementia prevalence into future projections, with most prediction models derived from small cohorts. Reliable dementia prediction models require multivariable modelling techniques that link self-reported risk factors with administrative datasets that capture healthcare-diagnosed dementia [28]. The ADAPTOR project will link data from a large Australian cohort study (i.e. 45 and Up Study) with administrative datasets, allowing population-level estimates, and sub-population comparisons [10, 11, 29]. The study’s large cohort, 15-year follow-up, and its capacity to link data on risk factors with health administrative datasets help address the gap in evidence on modifiable risk factors for dementia. Another strength of the project is that it will capture data from participants with cognitive decline or those who would otherwise be lost to follow-up. Additionally, loss to follow-up is not an issue in the project because all the administrative datasets used in the project contain information to help with dementia identification. While cohort studies are often not representative of the general population, they provide valid estimates of associations between risk factors and outcomes [30]. Accordingly, the ADAPTOR project will allow us to build large-scale population models to investigate how reducing specific risk factors might affect the prevalence and incidence of dementia, and to further examine the potential impact of risk factor elimination on healthcare expenditure. Lastly, we expect that linking new and existing risk factors with dementia cases will not only strengthen current evidence but will provide further evidence to address barriers to diagnosis, prevention and care, and help understand dementia further to allow introduction of approaches to mitigate stigmatisation. The main limitation of the ADAPTOR project is that certain established risk factors for dementia are not captured in the 45 and Up Study. For example, a larger social network can protect against cognitive decline in older women [31]. While this has not been addressed directly in the 45 and Up cohort, we will include a measure of social engagement. Similarly, hearing loss and sleep disturbances, which are known risk factors for dementia, are only partially captured in the study. Traumatic brain injury [32] and pesticide exposure [33] have been identified as dementia risk factors while cognitive activity as a protective factor [34], however no data is available on these factors in the datasets being used. There is a growing body of evidence demonstrating that Mediterranean diet protects against cognitive decline in older adults [35]. Although diet quality was assessed in the 45 and Up Study (e.g. eating red meat, seafood, raw vegetables, avoiding saturated fat), there was no information specific to the Mediterranean diet (e.g. olive oil, legumes, red wine). While a question was included in the survey on frequency of fish or seafood consumption per week, this was not available in the ADAPTOR dataset. Additionally, a well-known risk factor for dementia is APOE genotype, with polymorphisms being independently predictive of dementia and Alzheimer’s disease [36]. Collection of APOE genotype was not within the scope of the 45 and Up Study but this question may be addressed in future research through the collection of biospecimens, including saliva, whole blood and DNA Study. Dementia is widely underdiagnosed worldwide, particularly in the younger age groups, males, and those from low income backgrounds. In their 2017 meta-analysis Lang et al found that, globally, the rate of undetected dementia cases was >60% [37]. The current lack of a single valid and reliable dementia dataset means that dementia cases are likely to be underdiagnosed in the ADAPTOR project as well. However, the utilization of multiple data linkage in the ADAPTOR project can improve dementia identification and contribute to prevention efforts by strengthening evidence on risk factors. Abbreviations ACAP Aged Care Assessment Program ACFI Aged Care Funding Instrument ACHI Australian Classification of Health Interventions ADAPTOR Addressing Dementia Through Analysis of Population Traits and Risk Factors AIHW Australian Institute of Health and Welfare APDC Admitted Patients Data Collection APOE Apolipoprotein E ATC Anatomical Therapeutic Chemical BMI Body Mass Index BPSD Behavioural and Psychological Symptoms of Dementia CALD culturally and linguistically diverse CHeReL The Centre for Health Record Linkage ED Emergency Department EDDC Emergency Department Data Collection HCP Home Care Package ICD-10 (AM) International Classification of Diseases, 10th revision, Australian Modification ICD-9 (CM) International Classification of Diseases, Ninth Revision, Clinical Modification K-10 Kessler-10 MHA Mental Health Ambulatory NHMRC National Health and Medical Research Council PAF Population Attributable Fraction RBDM Registry of Births, Deaths and Marriages SNOMED CT SNOMED Clinical Terms SURE Secure Unified Research Environment WHO World Health Organisation Declarations Ethics approval and consent to participate The study will be conducted in accordance with the Declaration of Helsinki. Ethical approval has been obtained from the New South Wales Population and Health Service Research Ethics Committee (2019/ETH00118) and Australian Institute of Health and Welfare Ethics Committee (EO2019/4/1062). Data from the 45 And Up Study will be used for this data linkage study; all 45 And Up Study participants have provided informed consent to access to their data from a comprehensive range of health-related databases, their Medicare number to assist with record linkage, and future contact for follow-up and potential participation in further research/sub studies. Consent for publication Not applicable Availability of data and materials Data used for this study were received from the Australian Institute of Health and Welfare, The Centre for Health Record Linkage (CHeReL), and the Sax Institute. Data for the study will be generated within the Sax Institute’s Secure Unified Research Environment (SURE). The derived data supporting the findings of the study will be available from the corresponding author upon request. Competing interests The authors declare that they have no competing interests that are directly related to the content of this manuscript. Funding Funding for this research has been provided from the Australian Government’s Medical Research Future Fund (MRFF). The MRFF provides funding to support health and medical research and innovation, with the objective of improving the health and wellbeing of Australians. MRFF funding has been provided to The Australian Prevention Partnership Centre under the MRFF Boosting Preventive Health Research Program. Further information on the MRFF is available at www.health.gov.au/mrff. KJA is funded by ARC Fellowship FL190100011. HW is part funded by NHMRC Grant #1171279. Authors' contributions XDG, CD, DC, MM, HW, AG, HB, and KA contributed to the conception and design of the study. XDG, DC and SN drafted the manuscript, created tables, and revised the manuscript. All authors provided critical feedback on the manuscript and approved the final version of the manuscript. Acknowledgements The authors acknowledge The Australian Prevention Partnership Centre, funded by the NHMRC, Australian Government Department of Health, ACT Health, Cancer Council Australia, NSW Ministry of Health, Wellbeing SA, Tasmanian Department of Health, and VicHealth. The Prevention Centre is administered by the Sax Institute. References Duong S, Patel T, Chang F. Dementia: What pharmacists need to know. Can Pharm J (Ott). 2017;150(2):118-29. Brodaty H, Draper BM, Low LF. Behavioural and psychological symptoms of dementia: a seven‐tiered model of service delivery. Medical journal of Australia. 2003;178(5):231-4. Macfarlane S, O’Connor D. Managing behavioural and psychological symptoms in dementia. Australian prescriber. 2016;39(4):123. Dementia Australia. Dementia statistics 2022 [updated January 2022; cited 2022 25 November 2022]. Available from: https://www.dementia.org.au/statistics. Australian Bureau of Statistics. Causes of Death, Australia 2022 [updated 19 October 2022; cited 2022 25 November 2022]. Available from: https://www.abs.gov.au/statistics/health/causes-death/causes-death-australia/2021#key-statistics. Brown L, Hansnata E, La HA. Economic cost of dementia in Australia 2016-2056. 2017. Anstey KJ, Ee N, Eramudugolla R, Jagger C, Peters R. A systematic review of meta-analyses that evaluate risk factors for dementia to evaluate the quantity, quality, and global representativeness of evidence. Journal of Alzheimer's Disease. 2019;70(s1):S165-S86. Livingston G, Huntley J, Sommerlad A, Ames D, Ballard C, Banerjee S, et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet. 2020;396(10248):413-46. Anstey KJ, Eramudugolla R, Hosking DE, Lautenschlager NT, Dixon RA. Bridging the translation gap: from dementia risk assessment to advice on risk reduction. The journal of prevention of Alzheimer's disease. 2015;2(3):189. Welberry HJ, Brodaty H, Hsu B, Barbieri S, Jorm LR. Measuring dementia incidence within a cohort of 267,153 older Australians using routinely collected linked administrative data. Scientific Reports. 2020;10(1):8781. Waller M, Mishra GD, Dobson AJ. Estimating the prevalence of dementia using multiple linked administrative health records and capture–recapture methodology. Emerging themes in epidemiology. 2017;14(1):1-9. Australian Institute of Health and Welfare. Prevalence of dementia 2022 [updated 16 September 2022; cited 2022 25 November 2022]. Available from: https://www.aihw.gov.au/reports/dementia/dementia-in-aus/contents/population-health-impacts-of-dementia/prevalence-of-dementia. Chow EP, Hsu B, Waite LM, Blyth FM, Handelsman DJ, Le Couteur DG, et al. Diagnostic accuracy of linked administrative data for dementia diagnosis in community-dwelling older men in Australia. BMC geriatrics. 2022;22(1):858. Bleicher K, Summerhayes R, Baynes S, Swarbrick M, Navin Cristina T, Luc H, et al. Cohort profile update: the 45 and Up Study. International Journal of Epidemiology. 2022. and Up Study Collaborators. Cohort profile: the 45 and up study. International journal of epidemiology. 2008;37(5):941-7. Australian Bureau of Statistics. Socio-economic Indexes for Areas (SEIFA) 2016 2018 [updated 27 March 2018; cited 2022 18 November 2022]. Available from: https://www.abs.gov.au/ausstats/ [email protected] /mf/2033.0.55.001. National Health and Medical Research Council. Australian Guidelines to Reduce Health Risks from Drinking Alcohol. National Health and Medical Research Council; 2020. Kulminski AM, Ukraintseva SV, Culminskaya IV, Arbeev KG, Land KC, Akushevich L, et al. Cumulative Deficits and Physiological Indices as Predictors of Mortality and Long Life. The Journals of Gerontology: Series A. 2008;63(10):1053-9. Brown W, Trost S, Bauman A, Mummery K, Owen N. Test-retest reliability of four physical activity measures used in population surveys. Journal of Science and Medicine in Sport. 2004;7(2):205-15. Heesch KC, Hill RL, van Uffelen JG, Brown WJ. Are Active Australia physical activity questions valid for older adults? Journal of Science and Medicine in Sport. 2011;14(3):233-7. World Health Organisation. WHO Guidelines Approved by the Guidelines Review Committee. Geneva: World Health Organization; 2020. Sabia S, Fayosse A, Dumurgier J, van Hees VT, Paquet C, Sommerlad A, et al. Association of sleep duration in middle and old age with incidence of dementia. Nature Communications. 2021;12(1):2289. Koenig HG, Westlund RE, George LK, Hughes DC, Blazer DG, Hybels C. Abbreviating the Duke Social Support Index for use in chronically ill elderly individuals. Psychosomatics. 1993;34(1):61-9. Kessler RC, Andrews G, Colpe LJ, Hiripi E, Mroczek DK, Normand S-L, et al. Short screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychological medicine. 2002;32(6):959-76. Prentice RL, Cai J. Covariance and survivor function estimation using censored multivariate failure time data. Biometrika. 1992;79(3):495-512. Fine JP, Gray RJ. A Proportional Hazards Model for the Subdistribution of a Competing Risk. Journal of the American Statistical Association. 1999;94(446):496-509. Mukadam N, Sommerlad A, Huntley J, Livingston G. Population attributable fractions for risk factors for dementia in low-income and middle-income countries: an analysis using cross-sectional survey data. The Lancet Global Health. 2019;7(5):e596-e603. Fisher S, Hsu A, Mojaverian N, Taljaard M, Huyer G, Manuel DG, et al. Dementia Population Risk Tool (DemPoRT): study protocol for a predictive algorithm assessing dementia risk in the community. BMJ open. 2017;7(10):e018018. Withall A, Draper B, Seeher K, Brodaty H. The prevalence and causes of younger onset dementia in Eastern Sydney, Australia. Int Psychogeriatr. 2014;26(12):1955-65. Mealing NM, Banks E, Jorm LR, Steel DG, Clements MS, Rogers KD. Investigation of relative risk estimates from studies of the same population with contrasting response rates and designs. BMC medical research methodology. 2010;10:1-12. Crooks VC, Lubben J, Petitti DB, Little D, Chiu V. Social network, cognitive function, and dementia incidence among elderly women. Am J Public Health. 2008;98(7):1221-7. Li Y, Li Y, Li X, Zhang S, Zhao J, Zhu X, et al. Head injury as a risk factor for dementia and Alzheimer’s disease: a systematic review and meta-analysis of 32 observational studies. PloS one. 2017;12(1):e0169650. Jones N. Risk of dementia and Alzheimer disease increases with occupational pesticide exposure. Nature Reviews Neurology. 2010;6(7):353-. Duffner L, Deckers K, Cadar D, Steptoe A, De Vugt M, Köhler S. The role of cognitive and social leisure activities in dementia risk: assessing longitudinal associations of modifiable and non-modifiable risk factors. Epidemiology and Psychiatric Sciences. 2022;31:e5. Petersson SD, Philippou E. Mediterranean Diet, Cognitive Function, and Dementia: A Systematic Review of the Evidence. Advances in Nutrition. 2016;7(5):889-904. Rasmussen KL, Tybjærg‐Hansen A, Nordestgaard BG, Frikke‐Schmidt R. APOE and dementia–resequencing and genotyping in 105,597 individuals. Alzheimer's & Dementia. 2020;16(12):1624-37. Lang L, Clifford A, Wei L, Zhang D, Leung D, Augustine G, et al. Prevalence and determinants of undetected dementia in the community: a systematic literature review and a meta-analysis. BMJ open. 2017;7(2):e011146. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.docx SupplementaryTable2.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2525669","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Study protocol","associatedPublications":[],"authors":[{"id":175512243,"identity":"2a0a43f1-029d-488e-9a49-72971a375f0a","order_by":0,"name":"Martin 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for dementia diagnosis\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2525669/v1/04ff38362dfaa8df6083e272.png"},{"id":32969586,"identity":"e5001608-f3d6-410a-a840-c90b8f6be4e7","added_by":"auto","created_at":"2023-02-15 10:44:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":426225,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2525669/v1/6b7c1aa7-b754-4abb-a625-c1f2c1f27512.pdf"},{"id":32925014,"identity":"6a6263a0-2a3c-43d1-9468-3325c82507ee","added_by":"auto","created_at":"2023-02-14 15:58:50","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":30666,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-2525669/v1/3a1d6d21c9f489ade69c0caf.docx"},{"id":32926183,"identity":"60eb3438-e389-42b4-81dd-87dde3273f25","added_by":"auto","created_at":"2023-02-14 16:06:49","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":31372,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-2525669/v1/52c24b48f8beff764b11d07e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Addressing Dementia Through Analysis of Population Traits and Risk Factors (ADAPTOR) using Australia’s largest cohort study: a study protocol","fulltext":[{"header":"Background","content":"\u003cp\u003eDementia is a clinical syndrome that involves loss of cognitive functions, presenting with memory impairment and problems related to speech, thought, movement or mobility [1]. These symptoms are often accompanied by behavioural and psychological symptoms such as depression, psychosis, aggressive or agitated behaviour, sleep problems, and loss of inhibition [2, 3].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA recent estimate reported that more than 487,500 Australians were living with dementia in 2022 and is projected to increase to 1.1 million by 2058 [4]. Dementia is the second leading cause of death among Australians (after ischaemic heart disease), and the leading cause of death among Australian women [5]. Health and care costs (e.g. hospitalisations, general practitioner/primary healthcare and specialist visits, pharmaceuticals use, use of home care or nursing homes) associated with dementia was $9.1 billion in 2017 and is predicted to increase to $16.7 billion by 2036 [6]. Loss of income from dementia related lost productivity was $5.6 billion in 2017 and is estimated to increase to $9.1 billion by 2036 [6].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDementia risk factors appear throughout the life course, with evidence indicating that risk factors presenting earlier in life play a major role in the development of dementia [7, 8]. Early-life risk factors such as lower education attainment contributes to increased risk by negatively affecting the cognitive reserve. Similarly, mid- to late-life risk factors (e.g. hypertension, alcohol misuse, smoking, obesity, diabetes, hearing loss, depression, social isolation) increase dementia risk by triggering age-related cognitive decline and neuropathological development [7-9]. The Lancet Commission on dementia prevention, intervention, and care estimated that, globally, managing 12 modifiable risk factors can prevent or delay up to 40% of dementia cases [8].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite the progress made in earlier dementia diagnosis and increases in knowledge about risk and protective factors, there remain significant evidence gaps that preclude effective prevention strategies, particularly at a population level. Recent Australian studies have shown varying dementia incidence rates that highlight the difficulty in estimating the exact number of Australians living with dementia [10-12]. This is likely due to consistent underreporting of cases and marked differences in case numbers between the administrative datasets (such as datasets on pharmaceutical benefits, aged care assessments, hospital/emergency department admissions, deaths). By linking administrative datasets, dementia cases can be captured at different stages of their trajectory and the quality of dementia monitoring can be improved [13]. Similarly, there are also considerable evidence gaps on dementia risk and protective factors. Most studies that have explored dementia risk factors have been on older adults, excluding exploration of exposure earlier in life [7, 8]. There is a need for long-term follow-up studies to understand the risk and protective factors across the lifespan.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Addressing Dementia Through Analysis of Population Traits and Risk Factors (ADAPTOR) project has been designed to address the limitations of existing approaches in estimating dementia incidence and identifying risk factors across the life course. Its specific aims are: 1) to estimate age- and sex-specific incidence of dementia; 2) to investigate the association between risk factors and dementia incidence; and 3) to model the impact of modifiable risk factor reduction on dementia incidence in the Australian population. This paper describes the protocol for the ADAPTOR project.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eIn the ADAPTOR project we will link survey data from a large Australian cohort study with multiple administrative and routinely collected datasets, as the basis for dementia incidence and risk factor assessment. We will use the results to generate resources and content, including a series of user-friendly briefs, to raise awareness and inform public health policies and interventions.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe will use data from the Sax Institute\u0026rsquo;s 45 and Up Study, a longitudinal study of 267,357 people, representing approximately 11% of people \u0026ge;45 years within New South Wales (NSW), Australia (https://www.saxinstitute.org.au/our-work/45-up-study/). The study methods and cohort have been described previously [14, 15]. Participants were recruited between February 2006 and December 2009 through Medicare Australia enrolment database. Medicare is the Australian universal national health insurance scheme that includes all Australian citizens and permanent residents. The Medicare database captures outpatient claims for general practitioner/primary healthcare and specialist visits, allied health services, procedures and diagnostic services, and certain private hospital procedures. Individuals with at least one Medicare claim in the past 2 years were randomly selected for the 45 and Up Study. Eligible individuals who had heard about the study could also volunteer to participate (the latter comprised 0.6% of the final sample). Participation in the study required informed consent to each of the following: 1) access to (linkage with) data from a comprehensive range of health-related databases; 2) provision of the participant\u0026rsquo;s Medicare number to assist with record linkage; 3) future contact for follow-up and potential participation in further research/sub studies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eParticipants in the study are followed up approximately every five years and may be included in further sub-studies. The participant response rate at baseline was at least 19%; the exact response rate is unknown as the number who were actually contacted is undetermined due to the potential for incorrect details in the Medicare database. In the final sample 45 and Up Study there was an overrepresentation of \u0026ge;80 year olds and those residing in rural and remote areas, and a lower proportion of participants identifying as Aboriginal or Torres Strait Islanders, compared to the overall NSW population (0.7% vs 1.4%) [14].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe population sample of the 45 and Up Study is ideal for identifying dementia cases and associated risk factors. As all participants provided consent for linkage with administrative health datasets there is no loss to follow-up for outcomes assessed through data linkage. The study collected data on a range of dementia risk factors at baseline, and participants were asked to indicate which of a series of medications, vitamins or supplements they had taken in the last four weeks. Furthermore, the study recruited adults from culturally and linguistically diverse communities, from rural and regional areas, and across the socioeconomic spectrum. This will allow the accurate capture of relative contributions of different risk factors to dementia development, enabling us to model the effect of reductions in risk factors on the wider Australian population. Moreover, the original intent of the 45 and Up Study \u0026ndash; to expand the evidence base on healthy ageing \u0026ndash; means that the cohort is well-aligned with the age profile of interest for dementia research. When the study commenced in 2006 the youngest participants were 45 years old, this aligns with the mid-life period that dementia risk factors start to exert significant influence [8].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDatasets and Data Linkage\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe will obtain data on participant characteristics and risk factors from 45 and Up Study\u0026rsquo;s baseline survey; this data can link with a multitude of health-related datasets. The multiple administrative datasets that will be used in the ADAPTOR project contain information relevant to dementia case identification, each dataset is described below.\u003c/p\u003e\n\u003cp\u003e1. Death datasets (three datasets):\u003c/p\u003e\n\u003col style=\"list-style-type: upper-alpha;\"\u003e\n \u003cli\u003eNational Death Index (NDI): Contains information on deaths occurring throughout Australia (including NSW residents who die interstate), with ICD-10 coded cause of death.\u003c/li\u003e\n \u003cli\u003eCause of Death (COD) Unit Record File: Contains information on deaths occurring in NSW, with ICD-10 coded underlying and contributing causes of death.\u003c/li\u003e\n \u003cli\u003eNSW Registry of Births, Deaths and Marriages (RBDM): Includes information on all recorded deaths registered in NSW. As this dataset only contains information on date of death, it is used to confirm/contradict dates of death in the NDI and COD datasets.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e2. Pharmaceutical Benefits Schedule (PBS) dataset: Includes details of medications dispensed or subsidised as part of the Medicare health insurance scheme, but not the reason for prescription. The medications are coded according to the Anatomical Therapeutic Chemical (ATC) classification system.\u003c/p\u003e\n\u003cp\u003e3. NSW Mental Health Ambulatory\u0026nbsp;(MHA) dataset: Contains information on all contacts between a clinician and a non-admitted patient attending a public mental health facility (e.g. mental health day programs, psychiatric outpatient and outreach services) for assessment, treatment, rehabilitation, or care. Data includes ICD-10-AM (Australian Modification) coded mental health diagnosis.\u003c/p\u003e\n\u003cp\u003e4. NSW Emergency Department Data Collection (EDDC): Contains information on all Emergency Department presentations to NSW public hospitals (i.e. public or government funded). It includes ICD-9-CM (Clinical Modification), ICD-10-AM, or SNOMED Clinical Terms (SNOMED CT) coded principal diagnosis.\u003c/p\u003e\n\u003cp\u003e5. NSW Hospital Admitted Patients Data Collection (APDC): Contains information on all admissions to Public Hospitals, Public Psychiatric Hospitals, Public Multi-Purpose Services, Private Hospitals, and Private Day Procedures Centres. The complete APDC data includes both Public and Private hospital admissions in a single dataset but the data collection periods differ for both, therefore, they need to be treated as separate data sources. They include ICD-10-AM coded diagnoses, and procedures and interventions classified according to the Australian Classification of Health Interventions (ACHI).\u003c/p\u003e\n\u003cp\u003e6. National Aged Care Data Clearinghouse: Contains numerous datasets with information on subsidised community support and residential aged care programs provided to eligible Australians. The datasets include diagnostic codes for dementia. The three relevant aged care datasets that will be used in the ADAPTOR project are:\u003c/p\u003e\n\u003col style=\"list-style-type: upper-alpha;\"\u003e\n \u003cli\u003eAged Care Assessment Program (ACAP): Provides information on assessments undertaken by an Aged Care Assessment Team to determine eligibility of individuals for residential and other aged care programs. Up to 10 health conditions are recorded for each assessment.\u003c/li\u003e\n \u003cli\u003eHome Care Package (HCP): Contains information on Commonwealth HCPs that provide one of four levels of personalised care to individuals living in their own homes.\u003c/li\u003e\n \u003cli\u003eAged Care Funding Instrument (ACFI): Contains information on care needs of individuals living in permanent residential aged care, and includes information on up to three mental or behavioural, and three medical conditions relevant to care needs.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTo maintain participant privacy and confidentiality, 45 and Up Study data will be linked to the administrative datasets using a specific Personal Identification Number generated for each participant in the Study. Approval has been obtained from all data custodians for use of the datasets. The Centre for Health Record Linkage at the NSW Ministry of Health provided linkage of the state datasets. The Data Integration Services Centre at the Australian Institute of Health and Welfare assisted with obtaining approval and linkage for the Commonwealth datasets. The research team will access data and conduct analysis on the Sax Institute\u0026rsquo;s Secure Unified Research Environment (SURE), a virtual environment which enables researchers to use de-identified data in a safe and secured workspace.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy period and rationale\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study period for the ADAPTOR project commenced with enrolment to the 45 And Up Study (i.e.\u0026nbsp;between February 2006 and December 2009), and ended on 30\u003csup\u003eth\u003c/sup\u003e June 2018. Since enrolment to the study occurred over three years, enrolment date varies among participants. Figure 1 shows that the period covered by each administrative dataset varies. All datasets have data available until at least 30\u003csup\u003eth\u003c/sup\u003e June 2018, except for COD data (available until early December 2017) and ACAP data (available until late May 2016). However, information contained in these two datasets is also available in the other death and aged care datasets, so very few, if any, cases of dementia will be missed when using the proposed end date.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDefinition of dementia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsistent with previous approaches that used routinely collected data for dementia diagnosis [10, 11], we will use data linkage to identify participants diagnosed with dementia since recruitment to 45 and Up Study. For each data source, we will classify individuals as having or not having dementia (codes used in dementia diagnosis in Supplementary Table 1). To define date of onset of dementia we will use the earliest record of dementia identified across the datasets. Accordingly, we will define an overall diagnosis of dementia as a dementia record in any of the datasets, with the date of onset as the earliest onset across all data sources.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe will exclude prevalent cases i.e. study participants with dementia diagnosis within two years prior to enrolment in the 45 and Up Study. Two year timeframe is being used as all relevant datasets, except for Emergency Department (ED) data, are available from 2004 onwards (two years prior to 45 and Up Study enrolment); we do not anticipate that the ED data will be the sole source of many cases. While death data are not available until the start of 2006, this is irrelevant to assessing prevalent cases as participants must be alive for enrolment in the 45 and Up Study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRisk Factors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRisk factors that we will investigate in the ADAPTOR project are informed by the literature and clinical expertise within the research team; we will limit investigations to the following risk factors that are available in 45 and Up baseline survey (Supplementary Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSociodemographic factors include sex, age at baseline survey (45-64; 65-74; 75+ years), education (did not complete school, high school/trade/certificate/diploma, university or higher), socioeconomic status (quintiles Index of Relative Socioeconomic Disadvantage based on residential postcode) [16], partner status (currently partnered i.e. married/defacto/living with a partner, or not currently partnered i.e. single/widowed/divorced/separated), and whether a language other than English was spoken at home.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHealth behaviours\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eSmoking\u003c/u\u003e: We classified participants\u0026rsquo; smoking status as \u0026lsquo;current smoker\u0026rsquo;, \u0026lsquo;past smoker\u0026rsquo;, or \u0026lsquo;never smoked\u0026rsquo;.\u003cu\u003e\u0026nbsp;\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAlcohol use\u003c/u\u003e: Utilizing the National Health and Medical Research Council guidelines we classified alcohol consumption as \u0026lsquo;non-drinker/low risk\u0026rsquo; ( \u0026gt;0 and \u0026lt;7 drinks per week for females, and \u0026gt;0 and \u0026lt;14 drinks per week for males), \u0026lsquo;risky\u0026rsquo; (7-14 per week for females, and 15 to 28 drinks per week for males) or \u0026lsquo;high risk\u0026rsquo; (\u0026gt;14 drinks per week for females, and \u0026gt;28 drinks per week for males)\u0026nbsp;[17].\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eWeight\u003c/u\u003e: Body Mass Index (BMI) is calculated as self-reported weight (in kilograms) divided by the square of self-reported height (in metres). We will categorise weight based on Kulminski recommendations for older people as high risk (BMI\u0026lt;22), healthy weight (BMI=22-24.9), minimal risk (BMI=25-34.9), obese (BMI\u0026gt;35) and missing.\u0026nbsp;As this categorisation is based on mortality risk of older individuals and the World Health Organisation (WHO) guidelines are not necessarily relevant for the 65+ age group we used this recommendation\u0026nbsp;[18]. Due to the high number of observations with missing height or weight values we included the missing category.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003ePhysical activity\u003c/u\u003e: Due to its validity and reliability we used the Active Australia Survey to measure physical activity [19, 20]. Based on the WHO guidelines we will categorise physical activity as inactive, moderate activity or high intensity activity [21].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eSleep\u003c/u\u003e: As per the Sabia groupings we will classify hours of sleep per night as \u0026le;6, 6-8, or \u0026ge;8 hours\u0026nbsp;[22].\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eSocial engagement\u003c/u\u003e: This was measured by the Duke Social Interaction Subscale which includes three items assessing the number of different types of interaction with others in the last week and one item measuring the number of people the respondent can depend on or feel close to [23]. We will use the sum of the response options to generate a score which will be used as a continuous variable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHealth conditions\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eHeart disease\u003c/u\u003e: We defined participants as having heart disease if they reported that they had been told by a doctor that they had heart disease, or had been treated for heart attack, angina or other heart disease last month.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eStroke\u003c/u\u003e: We defined participants as having had stroke if they had ever been told by a doctor that they had a stroke.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eDiabetes\u003c/u\u003e: We classified participants as diabetic if they had ever been told by a doctor that they had diabetes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eHearing loss\u003c/u\u003e: We classified participants as having hearing loss if they self-reported having a hearing loss.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eMental health\u003c/u\u003e: In the 45 And Up Study baseline survey, self-reported doctor diagnosed depression and anxiety were reported differently across the collection periods. Initially a combined item was reported: \u0026lsquo;Has a doctor ever told you that you have: depression/anxiety\u0026rsquo;. This was changed to two questions in later versions: \u0026lsquo;Has a doctor ever told you that you have: anxiety or depression\u0026rsquo; and \u0026lsquo;In the last month have you been treated for: anxiety or depression\u0026rsquo;. We classified participants as having mental health disorder if they answered \u0026lsquo;yes\u0026rsquo; to any of the questions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eCholesterol level\u003c/u\u003e: In the 45 And Up Study survey participants were asked \u0026lsquo;Have you taken any medications, vitamins or supplements for most of the last 4 weeks, including HRT and the pill?\u0026rsquo; and \u0026lsquo;In the last month have you been treated for: specific chronic conditions\u0026rsquo;. We considered participants to have high cholesterol if they had answered \u0026lsquo;yes\u0026rsquo; to having been treated for high cholesterol or taken either cholesterol-lowering medications in the month prior to the baseline survey.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eBlood pressure\u003c/u\u003e: We will generate three categories for high blood pressure:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eNo high blood pressure: participants had not been diagnosed with high blood pressure by a doctor, or had not been treated for high blood pressure, or taken any blood pressure lowering medications in the last month;\u003c/li\u003e\n \u003cli\u003eHigh blood pressure but not treated: participants diagnosed by a doctor with high blood pressure but not being treated for it; or\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTreatment for high blood pressure: participants on treatment for high blood pressure, or blood pressure lowering medications (e.g. Avapro Karvea, Coversyl, Lasmix Frusemide, Cardizem Vasocordol, Micardis, Norvasc, Tritace or Noten Tenormin Atenolol) in the last month.\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cu\u003ePsychological distress\u003c/u\u003e: The Kessler-10 (K-10) instrument was used in the 45 And Up Study survey to measure psychological distress [24]. This instrument includes 10 items on how often participants experienced a range of symptoms in the last month, with five possible response options ranging from none of the time (score of 1) to all of the time (score of 5). The response scores are summed to give an overall scale ranging from 10 to 50, which is then categorised as not probable psychosocial distress (10-22), mild psychosocial distress (22-30), or severe-very severe psychosocial distress (30-50).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical methods\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe will use the dementia definitions described previously to identify dementia cases and the date of the participant\u0026rsquo;s first documented diagnosis, and present sex and age-specific incidence as cases per 1000 person-years with 95% confidence intervals. Person-years, or follow-up time for an individual is calculated as the time between enrolment in the 45 and Up Study and the date of diagnosis, the date of death, or the end of the study period - whichever is the earliest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo investigate the association between socioeconomic factors, health behaviour and health conditions (full list in Supplementary Table 2), and incidence of dementia we will use Poisson regression with dementia diagnosis (yes/no) as the outcome and person-years as the offset (denominator). In the modelling stage we will account for risk factors found to be associated with dementia (p\u0026lt;0.25). We will also undertake time-to-event (survival) analysis, with time determined from enrolment to 45 and Up Study to dementia diagnosis or censoring (death or the end of the study period) [25]. Using Cox proportional hazards regression we will conduct time-to-event analyses. While this is semi-parametric and does not make any assumptions about distribution, it requires that the hazards are proportional over time. Inappropriate examination of the proportional assumptions may cause considerable bias in the estimates obtained from the models. Therefore, we will undertake assessment of Schoenfeld model residuals, including graphical displays of the covariate variables to assess the adequacy of the Cox regression models. To correct variables that violate the proportional hazards assumption we will use interactions with functions of time.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo account for those participants who die prior to having a dementia diagnosis we will undertake secondary analyses using competing risks methods, using the Fine and Gray sub-distribution hazard function [26].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur primary analysis will involve the study period from enrolment to the 45 and Up Study to 30\u003csup\u003eth\u003c/sup\u003e June 2018; during this period we expect the datasets to contain the majority of dementia cases. We will undertake a sensitivity analysis for which the study period will be between the beginning of enrolment in the study and 31\u003csup\u003est\u003c/sup\u003e May 2016 (i.e. up to the period when all datasets are available). In an additional sensitivity analysis we will use a second definition of prevalent dementia i.e. participants diagnosed with dementia within two years of enrolling in the 45 and Up Study, and accordingly the study period will be two years following enrolment to the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe will build a population model using simulation modelling to understand how prevention strategies targeting specific risk factors can affect dementia incidence in Australia. Based on the results we will undertake further modelling to assess the potential impact of eliminating individual and combination of risk factors on dementia incidence. We will use Population Attributable Fractions (PAFs) to determine the impact of reducing risk factors on dementia incidence [27], using risk factor prevalence from the best available Australian data, and associations between risk factors and dementia incidence data from the modelling undertaken above. To provide weighted PAFs, we will adjust rate ratios for associations between risk factors and dementia for overlap between risk factors (communality). Where relevant, we will supplement our estimates with data from other sources and will consider feasible and meaningful risk factor reductions based on the literature and discussions with policymakers. We will undertake a range of sensitivity analyses to assess the impact of varying prevalence of risk factors, risk factor reductions, and associations between risk factors and dementia.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThere are considerable limitations in the methods currently used for dementia risk factors and case identification in Australia. Current projections use simplistic extrapolation and microsimulation models that apply existing estimates of dementia prevalence into future projections, with most prediction models derived from small cohorts. Reliable dementia prediction models require multivariable modelling techniques that link self-reported risk factors with administrative datasets that capture healthcare-diagnosed dementia [28].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe ADAPTOR project will link data from a large Australian cohort study (i.e. 45 and Up Study) with administrative datasets, allowing population-level estimates, and sub-population comparisons [10, 11, 29]. The study\u0026rsquo;s large cohort, 15-year follow-up, and its capacity to link data on risk factors with health administrative datasets help address the gap in evidence on modifiable risk factors for dementia. Another strength of the project is that it will capture data from participants with cognitive decline or those who would otherwise be lost to follow-up. Additionally, loss to follow-up is not an issue in the project because all the administrative datasets used in the project contain information to help with dementia identification. While cohort studies are often not representative of the general population, they provide valid estimates of associations between risk factors and outcomes [30]. Accordingly, the ADAPTOR project will allow us to build large-scale population models to investigate how reducing specific risk factors might affect the prevalence and incidence of dementia, and to further examine the potential impact of risk factor elimination on healthcare expenditure. Lastly, we expect that linking new and existing risk factors with dementia cases will not only strengthen current evidence but will provide further evidence to address barriers to diagnosis, prevention and care, and help understand dementia further to allow introduction of approaches to mitigate stigmatisation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe main limitation of the ADAPTOR project is that certain established risk factors for dementia are not captured in the 45 and Up Study. For example, a larger social network can protect against cognitive decline in older women [31]. While this has not been addressed directly in the 45 and Up cohort, we will include a measure of social engagement. Similarly, hearing loss and sleep disturbances, which are known risk factors for dementia, are only partially captured in the study. Traumatic brain injury [32] and pesticide exposure [33] have been identified as dementia risk factors while cognitive activity as a protective factor [34], however no data is available on these factors in the datasets being used. There is a growing body of evidence demonstrating that Mediterranean diet protects against cognitive decline in older adults [35]. Although diet quality was assessed in the 45 and Up Study (e.g. eating red meat, seafood, raw vegetables, avoiding saturated fat), there was no information specific to the Mediterranean diet (e.g. olive oil, legumes, red wine). While a question was included in the survey on frequency of fish or seafood consumption per week, this was not available in the ADAPTOR dataset. Additionally, a well-known risk factor for dementia is \u003cem\u003eAPOE\u0026nbsp;\u003c/em\u003egenotype, with polymorphisms being independently predictive of dementia and Alzheimer\u0026rsquo;s disease [36]. Collection of \u003cem\u003eAPOE\u003c/em\u003e genotype was not within the scope of the 45 and Up Study but this question may be addressed in future research through the collection of biospecimens, including saliva, whole blood and DNA Study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDementia is widely underdiagnosed worldwide, particularly in the younger age groups, males, and those from low income backgrounds. In their 2017 meta-analysis Lang et al found that, globally, the rate of undetected dementia cases was \u0026gt;60% [37]. The current lack of a single valid and reliable dementia dataset means that dementia cases are likely to be underdiagnosed in the ADAPTOR project as well. However, the utilization of multiple data linkage in the ADAPTOR project can improve dementia identification and contribute to prevention efforts by strengthening evidence on risk factors.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"\" cellpadding=\"0\" cellspacing=\"0\" width=\"603\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"17.081260364842453%\"\u003e\n \u003cp\u003e\u003cstrong\u003eACAP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"82.91873963515755%\"\u003e\n \u003cp\u003eAged Care Assessment Program\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"17.081260364842453%\"\u003e\n \u003cp\u003e\u003cstrong\u003eACFI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"82.91873963515755%\"\u003e\n \u003cp\u003eAged Care Funding Instrument\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"17.081260364842453%\"\u003e\n \u003cp\u003e\u003cstrong\u003eACHI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"82.91873963515755%\"\u003e\n \u003cp\u003eAustralian Classification of Health Interventions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"17.081260364842453%\"\u003e\n \u003cp\u003e\u003cstrong\u003eADAPTOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"82.91873963515755%\"\u003e\n \u003cp\u003eAddressing Dementia Through Analysis of Population Traits and Risk Factors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"17.081260364842453%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAIHW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"82.91873963515755%\"\u003e\n \u003cp\u003eAustralian Institute of Health and Welfare\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"17.081260364842453%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAPDC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"82.91873963515755%\"\u003e\n \u003cp\u003eAdmitted Patients Data Collection\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"17.081260364842453%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAPOE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"82.91873963515755%\"\u003e\n \u003cp\u003eApolipoprotein E\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"17.081260364842453%\"\u003e\n \u003cp\u003e\u003cstrong\u003eATC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"82.91873963515755%\"\u003e\n \u003cp\u003eAnatomical Therapeutic Chemical\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"17.081260364842453%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"82.91873963515755%\"\u003e\n \u003cp\u003eBody Mass Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"17.081260364842453%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBPSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"82.91873963515755%\"\u003e\n \u003cp\u003eBehavioural and Psychological Symptoms of Dementia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"17.081260364842453%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCALD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"82.91873963515755%\"\u003e\n \u003cp\u003eculturally and linguistically diverse\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"17.081260364842453%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCHeReL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"82.91873963515755%\"\u003e\n \u003cp\u003eThe Centre for Health Record Linkage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"17.081260364842453%\"\u003e\n \u003cp\u003e\u003cstrong\u003eED\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"82.91873963515755%\"\u003e\n \u003cp\u003eEmergency Department\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"17.081260364842453%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEDDC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"82.91873963515755%\"\u003e\n \u003cp\u003eEmergency Department Data Collection\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"17.081260364842453%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHCP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"82.91873963515755%\"\u003e\n \u003cp\u003eHome Care Package\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"17.081260364842453%\"\u003e\n \u003cp\u003e\u003cstrong\u003eICD-10 (AM)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"82.91873963515755%\"\u003e\n \u003cp\u003eInternational Classification of Diseases, 10th revision, Australian Modification\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"17.081260364842453%\"\u003e\n \u003cp\u003e\u003cstrong\u003eICD-9 (CM)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"82.91873963515755%\"\u003e\n \u003cp\u003eInternational Classification of Diseases, Ninth Revision, Clinical Modification\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"17.081260364842453%\"\u003e\n \u003cp\u003e\u003cstrong\u003eK-10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"82.91873963515755%\"\u003e\n \u003cp\u003eKessler-10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"17.081260364842453%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMHA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"82.91873963515755%\"\u003e\n \u003cp\u003eMental Health Ambulatory\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"17.081260364842453%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNHMRC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"82.91873963515755%\"\u003e\n \u003cp\u003eNational Health and Medical Research Council\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"17.081260364842453%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePAF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"82.91873963515755%\"\u003e\n \u003cp\u003ePopulation Attributable Fraction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"17.081260364842453%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRBDM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"82.91873963515755%\"\u003e\n \u003cp\u003eRegistry of Births, Deaths and Marriages\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"17.081260364842453%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNOMED CT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"82.91873963515755%\"\u003e\n \u003cp\u003eSNOMED Clinical Terms\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"17.081260364842453%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSURE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"82.91873963515755%\"\u003e\n \u003cp\u003eSecure Unified Research Environment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"17.081260364842453%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWHO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"82.91873963515755%\"\u003e\n \u003cp\u003eWorld Health Organisation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study will be conducted in accordance with the Declaration of Helsinki. Ethical approval has been obtained from the New South Wales Population and Health Service Research Ethics Committee (2019/ETH00118) and Australian Institute of Health and Welfare Ethics Committee (EO2019/4/1062). Data from the 45 And Up Study will be used for this data linkage study; all 45 And Up Study participants have provided informed consent to access to their data from a comprehensive range of health-related databases, their Medicare number to assist with record linkage, and future contact for follow-up and potential participation in further research/sub studies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData used for this study were received from the Australian Institute of Health and Welfare, The Centre for Health Record Linkage (CHeReL), and the Sax Institute. Data for the study will be generated within the Sax Institute\u0026rsquo;s Secure Unified Research Environment (SURE). The derived data supporting the findings of the study will be available from the corresponding author upon request.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests that are directly related to the content of this manuscript.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding for this research has been provided from the Australian Government\u0026rsquo;s Medical Research Future Fund (MRFF). The MRFF provides funding to support health and medical research and innovation, with the objective of improving the health and wellbeing of Australians. MRFF funding has been provided to The Australian Prevention Partnership Centre under the MRFF Boosting Preventive Health Research Program. Further information on the MRFF is available at www.health.gov.au/mrff. KJA is funded by ARC Fellowship FL190100011. HW is part funded by NHMRC Grant #1171279.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXDG, CD, DC, MM, HW, AG, HB, and KA contributed to the conception and design of the study. XDG, DC and SN drafted the manuscript, created tables, and revised the manuscript. All authors provided critical feedback on the manuscript and approved the final version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge The Australian Prevention Partnership Centre, funded by the NHMRC, Australian Government Department of Health, ACT Health, Cancer Council Australia, NSW Ministry of Health, Wellbeing SA, Tasmanian Department of Health, and VicHealth. The Prevention Centre is administered by the Sax Institute.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDuong S, Patel T, Chang F. Dementia: What pharmacists need to know. Can Pharm J (Ott). 2017;150(2):118-29.\u003c/li\u003e\n\u003cli\u003eBrodaty H, Draper BM, Low LF. Behavioural and psychological symptoms of dementia: a seven‐tiered model of service delivery. Medical journal of Australia. 2003;178(5):231-4.\u003c/li\u003e\n\u003cli\u003eMacfarlane S, O\u0026rsquo;Connor D. Managing behavioural and psychological symptoms in dementia. Australian prescriber. 2016;39(4):123.\u003c/li\u003e\n\u003cli\u003eDementia Australia. Dementia statistics 2022 [updated January 2022; cited 2022 25 November 2022]. Available from: https://www.dementia.org.au/statistics.\u003c/li\u003e\n\u003cli\u003eAustralian Bureau of Statistics. Causes of Death, Australia 2022 [updated 19 October 2022; cited 2022 25 November 2022]. Available from: https://www.abs.gov.au/statistics/health/causes-death/causes-death-australia/2021#key-statistics.\u003c/li\u003e\n\u003cli\u003eBrown L, Hansnata E, La HA. Economic cost of dementia in Australia 2016-2056. 2017.\u003c/li\u003e\n\u003cli\u003eAnstey KJ, Ee N, Eramudugolla R, Jagger C, Peters R. A systematic review of meta-analyses that evaluate risk factors for dementia to evaluate the quantity, quality, and global representativeness of evidence. Journal of Alzheimer\u0026apos;s Disease. 2019;70(s1):S165-S86.\u003c/li\u003e\n\u003cli\u003eLivingston G, Huntley J, Sommerlad A, Ames D, Ballard C, Banerjee S, et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet. 2020;396(10248):413-46.\u003c/li\u003e\n\u003cli\u003eAnstey KJ, Eramudugolla R, Hosking DE, Lautenschlager NT, Dixon RA. Bridging the translation gap: from dementia risk assessment to advice on risk reduction. The journal of prevention of Alzheimer\u0026apos;s disease. 2015;2(3):189.\u003c/li\u003e\n\u003cli\u003eWelberry HJ, Brodaty H, Hsu B, Barbieri S, Jorm LR. Measuring dementia incidence within a cohort of 267,153 older Australians using routinely collected linked administrative data. Scientific Reports. 2020;10(1):8781.\u003c/li\u003e\n\u003cli\u003eWaller M, Mishra GD, Dobson AJ. Estimating the prevalence of dementia using multiple linked administrative health records and capture\u0026ndash;recapture methodology. Emerging themes in epidemiology. 2017;14(1):1-9.\u003c/li\u003e\n\u003cli\u003eAustralian Institute of Health and Welfare. Prevalence of dementia 2022 [updated 16 September 2022; cited 2022 25 November 2022]. Available from: https://www.aihw.gov.au/reports/dementia/dementia-in-aus/contents/population-health-impacts-of-dementia/prevalence-of-dementia.\u003c/li\u003e\n\u003cli\u003eChow EP, Hsu B, Waite LM, Blyth FM, Handelsman DJ, Le Couteur DG, et al. Diagnostic accuracy of linked administrative data for dementia diagnosis in community-dwelling older men in Australia. BMC geriatrics. 2022;22(1):858.\u003c/li\u003e\n\u003cli\u003eBleicher K, Summerhayes R, Baynes S, Swarbrick M, Navin Cristina T, Luc H, et al. Cohort profile update: the 45 and Up Study. International Journal of Epidemiology. 2022.\u003c/li\u003e\n\u003cli\u003eand Up Study Collaborators. Cohort profile: the 45 and up study. International journal of epidemiology. 2008;37(5):941-7.\u003c/li\u003e\n\u003cli\u003eAustralian Bureau of Statistics. Socio-economic Indexes for Areas (SEIFA) 2016 2018 [updated 27 March 2018; cited 2022 18 November 2022]. Available from: https://www.abs.gov.au/ausstats/[email protected]/mf/2033.0.55.001.\u003c/li\u003e\n\u003cli\u003eNational Health and Medical Research Council. Australian Guidelines to Reduce Health Risks from Drinking Alcohol. National Health and Medical Research Council; 2020.\u003c/li\u003e\n\u003cli\u003eKulminski AM, Ukraintseva SV, Culminskaya IV, Arbeev KG, Land KC, Akushevich L, et al. Cumulative Deficits and Physiological Indices as Predictors of Mortality and Long Life. The Journals of Gerontology: Series A. 2008;63(10):1053-9.\u003c/li\u003e\n\u003cli\u003eBrown W, Trost S, Bauman A, Mummery K, Owen N. Test-retest reliability of four physical activity measures used in population surveys. Journal of Science and Medicine in Sport. 2004;7(2):205-15.\u003c/li\u003e\n\u003cli\u003eHeesch KC, Hill RL, van Uffelen JG, Brown WJ. Are Active Australia physical activity questions valid for older adults? Journal of Science and Medicine in Sport. 2011;14(3):233-7.\u003c/li\u003e\n\u003cli\u003eWorld Health Organisation. WHO Guidelines Approved by the Guidelines Review Committee. Geneva: World Health Organization; 2020.\u003c/li\u003e\n\u003cli\u003eSabia S, Fayosse A, Dumurgier J, van Hees VT, Paquet C, Sommerlad A, et al. Association of sleep duration in middle and old age with incidence of dementia. Nature Communications. 2021;12(1):2289.\u003c/li\u003e\n\u003cli\u003eKoenig HG, Westlund RE, George LK, Hughes DC, Blazer DG, Hybels C. Abbreviating the Duke Social Support Index for use in chronically ill elderly individuals. Psychosomatics. 1993;34(1):61-9.\u003c/li\u003e\n\u003cli\u003eKessler RC, Andrews G, Colpe LJ, Hiripi E, Mroczek DK, Normand S-L, et al. Short screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychological medicine. 2002;32(6):959-76.\u003c/li\u003e\n\u003cli\u003ePrentice RL, Cai J. Covariance and survivor function estimation using censored multivariate failure time data. Biometrika. 1992;79(3):495-512.\u003c/li\u003e\n\u003cli\u003eFine JP, Gray RJ. A Proportional Hazards Model for the Subdistribution of a Competing Risk. Journal of the American Statistical Association. 1999;94(446):496-509.\u003c/li\u003e\n\u003cli\u003eMukadam N, Sommerlad A, Huntley J, Livingston G. Population attributable fractions for risk factors for dementia in low-income and middle-income countries: an analysis using cross-sectional survey data. The Lancet Global Health. 2019;7(5):e596-e603.\u003c/li\u003e\n\u003cli\u003eFisher S, Hsu A, Mojaverian N, Taljaard M, Huyer G, Manuel DG, et al. Dementia Population Risk Tool (DemPoRT): study protocol for a predictive algorithm assessing dementia risk in the community. BMJ open. 2017;7(10):e018018.\u003c/li\u003e\n\u003cli\u003eWithall A, Draper B, Seeher K, Brodaty H. The prevalence and causes of younger onset dementia in Eastern Sydney, Australia. Int Psychogeriatr. 2014;26(12):1955-65.\u003c/li\u003e\n\u003cli\u003eMealing NM, Banks E, Jorm LR, Steel DG, Clements MS, Rogers KD. Investigation of relative risk estimates from studies of the same population with contrasting response rates and designs. BMC medical research methodology. 2010;10:1-12.\u003c/li\u003e\n\u003cli\u003eCrooks VC, Lubben J, Petitti DB, Little D, Chiu V. Social network, cognitive function, and dementia incidence among elderly women. Am J Public Health. 2008;98(7):1221-7.\u003c/li\u003e\n\u003cli\u003eLi Y, Li Y, Li X, Zhang S, Zhao J, Zhu X, et al. Head injury as a risk factor for dementia and Alzheimer\u0026rsquo;s disease: a systematic review and meta-analysis of 32 observational studies. PloS one. 2017;12(1):e0169650.\u003c/li\u003e\n\u003cli\u003eJones N. Risk of dementia and Alzheimer disease increases with occupational pesticide exposure. Nature Reviews Neurology. 2010;6(7):353-.\u003c/li\u003e\n\u003cli\u003eDuffner L, Deckers K, Cadar D, Steptoe A, De Vugt M, K\u0026ouml;hler S. The role of cognitive and social leisure activities in dementia risk: assessing longitudinal associations of modifiable and non-modifiable risk factors. Epidemiology and Psychiatric Sciences. 2022;31:e5.\u003c/li\u003e\n\u003cli\u003ePetersson SD, Philippou E. Mediterranean Diet, Cognitive Function, and Dementia: A Systematic Review of the Evidence. Advances in Nutrition. 2016;7(5):889-904.\u003c/li\u003e\n\u003cli\u003eRasmussen KL, Tybj\u0026aelig;rg‐Hansen A, Nordestgaard BG, Frikke‐Schmidt R. APOE and dementia\u0026ndash;resequencing and genotyping in 105,597 individuals. Alzheimer\u0026apos;s \u0026amp; Dementia. 2020;16(12):1624-37.\u003c/li\u003e\n\u003cli\u003eLang L, Clifford A, Wei L, Zhang D, Leung D, Augustine G, et al. Prevalence and determinants of undetected dementia in the community: a systematic literature review and a meta-analysis. BMJ open. 2017;7(2):e011146.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Dementia, ageing, cohort study, data linkage, prevention, modifiable risk factors","lastPublishedDoi":"10.21203/rs.3.rs-2525669/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2525669/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDementia is a leading cause of disease burden in Australia, with almost half a million people living with dementia and a steady increase over time in dementia-related deaths. Strengthening the evidence base for dementia risk factors is critical for an effective and efficient public health and policy response. However, the number of Australians with dementia remains unknown, and there are significant gaps in the knowledge on risk factors in the Australian context. In this study we aim to develop reliable data on dementia incidence in Australia and investigate the associated risk factors, using a large population cohort. Specifically, we will assess the relative contribution of risk factors to dementia incidence as a basis for strengthening dementia prevention efforts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe will use data from the 45 and Up Study that includes 267,358 residents of New South Wales, Australia, aged over 45 years, recruited between 2006-2009. To identify dementia cases we will link data from the 45 and Up Study with multiple health datasets containing information relevant to dementia case identification. We will estimate age- and sex-specific dementia incidence and model the association between dementia and risk factors related to socio-demographic characteristics, health conditions and health behaviours. We will also estimate the impact of various modifiable exposures on dementia incidence. Based on the results, we will produce a series of knowledge translation products providing advice on the contribution of identified risk factors to dementia incidence in Australia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLinking the 45 and Up Study data to multiple health datasets provides a unique opportunity to explore the role of risk factors on dementia incidence, including modelling the effect of modifiable risk factors on dementia incidence in the Australian population. We anticipate the results from this study to guide targeted and gradated strategies for population-level dementia prevention.\u003c/p\u003e","manuscriptTitle":"Addressing Dementia Through Analysis of Population Traits and Risk Factors (ADAPTOR) using Australia’s largest cohort study: a study protocol","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-02-14 15:58:44","doi":"10.21203/rs.3.rs-2525669/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"10133ca0-e39d-4fab-b6b1-6d767ac313ac","owner":[],"postedDate":"February 14th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-02-15T10:44:25+00:00","versionOfRecord":[],"versionCreatedAt":"2023-02-14 15:58:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2525669","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2525669","identity":"rs-2525669","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}