Individual level covariate adjusted conditional autoregressive (indiCAR) model for disease mapping

© 2016 The Author(s). Background: Mapping disease rates over a region provides a visual illustration of underlying geographical variation of the disease and can be useful to generate new hypotheses on the disease aetiology. However, methods to fit the popular and widely used conditional autoregressive (CAR) models for disease mapping are not feasible in many applications due to memory constraints, particularly when the sample size is large. We propose a new algorithm to fit a CAR model that can accommodate both individual and group level covariates while adjusting for spatial correlation in the disease rates, termed indiCAR. Our method scales well and works in very large datasets where other methods fail. Results: We evaluate the performance of the indiCAR method through simulation studies. Our simulation results indicate that the indiCAR provides reliable estimates of all the regression and random effect parameters. We also apply indiCAR to the analysis of data on neutropenia admissions in New South Wales (NSW), Australia. Our analyses reveal that lower rates of neutropenia admissions are significantly associated with individual level predictors including higher age, male gender, residence in an outer regional area and a group level predictor of social disadvantage, the socio-economic index for areas. A large value for the spatial dependence parameter is estimated after adjusting for individual and area level covariates. This suggests the presence of important variation in the management of cancer patients across NSW. Conclusions: Incorporating individual covariate data in disease mapping studies improves the estimation of fixed and random effect parameters by utilizing information from multiple sources. Health registries routinely collect individual and area level information and thus could benefit by using indiCAR for mapping disease rates. Moreover, the natural applicability of indiCAR in a distributed computing framework enhances its application in the Big Data domain with a large number of individual/group level covariates. CI NSW Study Reference Number: 2012/07/410. Dated: July 2012

Spatial regression with covariate measurement error: A semiparametric approach

© 2016, The International Biometric Society. Spatial data have become increasingly common in epidemiology and public health research thanks to advances in GIS (Geographic Information Systems) technology. In health research, for example, it is common for epidemiologists to incorporate geographically indexed data into their studies. In practice, however, the spatially defined covariates are often measured with error. Naive estimators of regression coefficients are attenuated if measurement error is ignored. Moreover, the classical measurement error theory is inapplicable in the context of spatial modeling because of the presence of spatial correlation among the observations. We propose a semiparametric regression approach to obtain bias-corrected estimates of regression parameters and derive their large sample properties. We evaluate the performance of the proposed method through simulation studies and illustrate using data on Ischemic Heart Disease (IHD). Both simulation and practical application demonstrate that the proposed method can be effective in practice

MyCOACH (COnnected Advice for Cognitive Health): a digitally delivered multidomain intervention for cognitive decline and risk of dementia in adults with mild cognitive impairment or subjective cognitive decline–study protocol for a randomised controlled trial

Introduction Digital health interventions are cost-effective and easily accessible, but there is currently a lack of effective online options for dementia prevention especially for people at risk due to mild cognitive impairment (MCI) or subjective cognitive decline (SCD). Methods and analysis MyCOACH (COnnected Advice for Cognitive Health) is a tailored online dementia risk reduction programme for adults aged ≥65 living with MCI or SCD. The MyCOACH trial aims to evaluate the programme’s effectiveness in reducing dementia risk compared with an active control over a 64-week period (N=326). Eligible participants are randomly allocated to one of two intervention arms for 12 weeks: (1) the MyCOACH intervention programme or (2) email bulletins with general healthy ageing information (active control). The MyCOACH intervention programme provides participants with information about memory impairments and dementia, memory strategies and different lifestyle factors associated with brain ageing as well as practical support including goal setting, motivational interviewing, brain training, dietary and exercise consultations, and a 26-week post-intervention booster session. Follow-up assessments are conducted for all participants at 13, 39 and 65 weeks from baseline, with the primary outcome being exposure to dementia risk factors measured using the Australian National University-Alzheimer’s Disease Risk Index. Secondary measures include cognitive function, quality of life, functional impairment, motivation to change behaviour, self-efficacy, morale and dementia literacy. Ethics and dissemination Ethical approval was obtained from the University of New South Wales Human Research Ethics Committee (HC210012, 19 February 2021). The results of the study will be disseminated in peer-reviewed journals and research conferences

Study protocol for development and validation of a single tool to assess risks of stroke, diabetes mellitus, myocardial infarction and dementia: DemNCD-Risk

Introduction Current efforts to reduce dementia focus on prevention and risk reduction by targeting modifiable risk factors. As dementia and cardiometabolic non-communicable diseases (NCDs) share risk factors, a single risk-estimating tool for dementia and multiple NCDs could be cost-effective and facilitate concurrent assessments as compared with a conventional single approach. The aim of this study is to develop and validate a new risk tool that estimates an individual's risk of developing dementia and other NCDs including diabetes mellitus, stroke and myocardial infarction. Once validated, it could be used by the public and general practitioners. Methods and analysis Ten high-quality cohort studies from multiple countries were identified, which met eligibility criteria, including large representative samples, long-term follow-up, data on clinical diagnoses of dementia and NCDs, recognised modifiable risk factors for the four NCDs and mortality data. Pooled harmonised data from the cohorts will be used, with 65% randomly allocated for development of the predictive model and 35% for testing. Predictors include sociodemographic characteristics, general health risk factors and lifestyle/behavioural risk factors. A subdistribution hazard model will assess the risk factors' contribution to the outcome, adjusting for competing mortality risks. Point-based scoring algorithms will be built using predictor weights, internally validated and the discriminative ability and calibration of the model will be assessed for the outcomes. Sensitivity analyses will include recalculating risk scores using logistic regression. Ethics and dissemination Ethics approval is provided by the University of New South Wales Human Research Ethics Committee (UNSW HREC; protocol numbers HC200515, HC3413). All data are deidentified and securely stored on servers at Neuroscience Research Australia. Study findings will be presented at conferences and published in peer-reviewed journals. The tool will be accessible as a public health resource. Knowledge translation and implementation work will explore strategies to apply the tool in clinical practice

Validation of the CogDrisk Instrument as Predictive of Dementia in Four General Community-Dwelling Populations

Background: Lack of external validation of dementia risk tools is a major limitation for generalizability and translatability of prediction scores in clinical practice and research. Objectives: We aimed to validate a new dementia prediction risk tool called CogDrisk and a version, CogDrisk-AD for predicting Alzheimer’s disease (AD) using cohort studies. Design, Setting, Participants and Measurements: Four cohort studies were identified that included majority of the dementia risk factors from the CogDrisk tool. Participants who were free of dementia at baseline were included. The predictors were component variables in the CogDrisk tool that include self-reported demographics, medical risk factors and lifestyle habits. Risk scores for Any Dementia and AD were computed and Area Under the Curve (AUC) was assessed. To examine modifiable risk factors for dementia, the CogDrisk tool was tested by excluding age and sex estimates from the model. Results: The performance of the tool varied between studies. The overall AUC and 95% CI for predicting dementia was 0.77 (0.57, 0.97) for the Swedish National study on Aging and Care in Kungsholmen, 0.76 (0.70, 0.83) for the Health and Retirement Study - Aging, Demographics and Memory Study, 0.70 (0.67,0.72) for the Cardiovascular Health Study Cognition Study, and 0.66 (0.62,0.70) for the Rush Memory and Aging Project. Conclusions: The CogDrisk and CogDrisk-AD performed well in the four studies. Overall, this tool can be used to assess individualized risk factors of dementia and AD in various population settings

A comparison of multiple imputation methods for missing data in longitudinal studies

BackgroundMultiple imputation (MI) is now widely used to handle missing data in longitudinal studies. Several MI techniques have been proposed to impute incomplete longitudinal covariates, including standard fully conditional specification (FCS-Standard) and joint multivariate normal imputation (JM-MVN), which treat repeated measurements as distinct variables, and various extensions based on generalized linear mixed models. Although these MI approaches have been implemented in various software packages, there has not been a comprehensive evaluation of the relative performance of these methods in the context of longitudinal data.MethodUsing both empirical data and a simulation study based on data from the six waves of the Longitudinal Study of Australian Children (N = 4661), we investigated the performance of a wide range of MI methods available in standard software packages for investigating the association between child body mass index (BMI) and quality of life using both a linear regression and a linear mixed-effects model.ResultsIn this paper, we have identified and compared 12 different MI methods for imputing missing data in longitudinal studies. Analysis of simulated data under missing at random (MAR) mechanisms showed that the generally available MI methods provided less biased estimates with better coverage for the linear regression model and around half of these methods performed well for the estimation of regression parameters for a linear mixed model with random intercept. With the observed data, we observed an inverse association between child BMI and quality of life, with available data as well as multiple imputation.ConclusionBoth FCS-Standard and JM-MVN performed well for the estimation of regression parameters in both analysis models. More complex methods that explicitly reflect the longitudinal structure for these analysis models may only be needed in specific circumstances such as irregularly spaced data

Smooth individual level covariates adjustment in disease mapping.

Spatial models for disease mapping should ideally account for covariates measured both at individual and area levels. The newly available "indiCAR" model fits the popular conditional autoregresssive (CAR) model by accommodating both individual and group level covariates while adjusting for spatial correlation in the disease rates. This algorithm has been shown to be effective but assumes log-linear associations between individual level covariates and outcome. In many studies, the relationship between individual level covariates and the outcome may be non-log-linear, and methods to track such nonlinearity between individual level covariate and outcome in spatial regression modeling are not well developed. In this paper, we propose a new algorithm, smooth-indiCAR, to fit an extension to the popular conditional autoregresssive model that can accommodate both linear and nonlinear individual level covariate effects while adjusting for group level covariates and spatial correlation in the disease rates. In this formulation, the effect of a continuous individual level covariate is accommodated via penalized splines. We describe a two-step estimation procedure to obtain reliable estimates of individual and group level covariate effects where both individual and group level covariate effects are estimated separately. This distributed computing framework enhances its application in the Big Data domain with a large number of individual/group level covariates. We evaluate the performance of smooth-indiCAR through simulation. Our results indicate that the smooth-indiCAR method provides reliable estimates of all regression and random effect parameters. We illustrate our proposed methodology with an analysis of data on neutropenia admissions in New South Wales (NSW), Australia

Association between Anxiety and Cognitive Decline over 12 Years in a Population-Based Cohort

Background: Findings on the associations between anxiety and cognitive decline are mixed and often confounded. Objective: We studied whether anxiety symptoms were associated with the risk of cognitive decline after adequate adjustment of confounding factors. Methods: Our study consists of 2,551 community-dwelling older adults recruited between the ages of 60-64 years and followed up for 12 years in the PATH Through Life cohort study. Anxiety symptoms were measured using the Goldberg Anxiety Scale (GAS; range 0-9). General cognitive function, episodic memory, working memory, verbal intelligence, processing speed, and psychomotor speed were measured. Multilevel analyses were carried out to investigate the association between anxiety symptoms and cognitive decline over 12 years, taking into account confounding variables. Results: We did not find a significant association between baseline anxiety symptoms and cognitive decline over 12 years. Although some associations between anxiety symptoms with psychomotor speed (=-0.04, 99% CI: -0.08, 0.00) and processing speed (=-0.27, 99% CI: -0.48, -0.07) were found, these were attenuated after adjusting for depression. We also did not find an association between cumulative anxiety and decline in cognitive performance. Conclusion: In this sample of cognitively healthy men and women aged 60 years and above, anxiety symptoms were not associated with the risk of cognitive decline. Long follow-up study time, appropriate selection of confounding factors, and estimating the effect of cumulative anxiety are important to establish the association between anxiety and cognitive symptoms