DEVELOPMENT AND IMPLEMENTATION OF THE COGDRISK DEMENTIA RISK ASSESSMENT TOOL AND INTERACTIVE WEBSITE

We developed a comprehensive risk assessment tool for dementia – Cognitive Health and Dementia Risk Assessment (CogDrisk) and a version specifically for Alzheimer’s disease called CogDrisk-AD that could be applicable in low and high-resource settings. This tool incorporates risk and protective factors identified through systematic synthesis of observational studies that report risk ratios. Risk and protective factors included in the tool were selected on the strength of evidence as well as the availability of measures that are practicable in a range of clinical and research contexts. Seventeen risk/protective factors were identified for inclusion in the dementia algorithm to estimate the risk of dementia while sixteen factors were identified for the AD model, with an overlap in the majority of the factors. CogDrisk and the CogDrisk-AD were predictive of dementia and AD when validated across four high-quality international cohort studies. To enable the CogDrisk tool to be implemented in practice our team has developed an interactive website where individuals 18 years and above can complete the CogDrisk questionnaire, obtain a personalised risk profile, and receive feedback on their risk profile. The website was developed with the capacity to collect and store data. We anticipate that the tool can be used by members of the public, in clinical settings and as a screening or outcome measure for clinical trials

Physical function limitation among gay and bisexual men aged ≥55years with and without HIV: findings from the Australian Positive and Peers Longevity Evaluation Study (APPLES)

Background. As people living with HIV now have a life expectancy approaching that of the general population, clinical care focuses increasingly on the management and prevention of comorbidities and conditions associated with aging. We aimed to assess the prevalence of physical function (PF) limitation among gay and bisexual men (GBM) and determine whether HIV is associated with severe PF limitation in this population. Methods. We analysed cross-sectional data from GBM aged ≥55 years in the Australian Positive and Peers Longevity Evaluation Study who completed a self-administered survey on health and lifestyle factors. PF was measured using the Medical Outcomes Study–Physical Functioning scale. Factors associated with severe PF limitation were assessed using logistic regression. Results. The survey was completed by 381 men: 186 without HIV and 195 with HIV. Median age was 64.3 years for GBM without HIV and 62.1 years for GBM with HIV. Compared with men without HIV, those with HIV had higher proportions of severe (13.3% vs 8.1%) and moderate-to-severe (26.7% vs 24.2%) PF limitation. Severe PF limitation commonly involved difficulty with vigorous activity (95% with severe PF limitation described being limited a lot), climbing several flights of stairs (68.4% limited a lot), bending, kneeling or stooping (60.5% limited a lot), and walking 1 km (55.0% limited a lot). In a model adjusted for age, body mass index, typical duration of physical activity, psychological distress, and number of comorbidities, we found a significant association between HIV and severe PF limitation (adjusted odds ratio 3.3 vs not having HIV, 95% confidence interval 1.3–8.7). Conclusions. The biological mechanisms underlying this association require further investigation, particularly given the growing age of the HIV population and inevitable increase in the burden of PF limitation

Spatial modeling, covariate measurement error and design issues in environmental epidemiology

University of Technology Sydney. Faculty of Science.In this thesis we develop methods to resolve a series of problems motivated by the analysis of administrative data to help explain geographical variation in disease rates. The Conditional auto-regressive (CAR) structure within a hierarchical generalized linear model offers a robust, flexible, and popular class of models for the exploration and analysis of geographical variation across small areas. However, lack of modeling strategies for individual level covariate data is a limitation of the existing methodology. We propose an individual level covariate adjusted conditional auto-regressive (indiCAR) model to incorporate both individual and area level covariates while adjusting for spatial correlation in disease rates. We also extend the indiCAR method to a semiparametric mixed model framework that allows adjustment for smooth covariate effects (smooth-indiCAR). We illustrate the applicability of both methods in a distributed computing framework that enhances its application in the Big Data domain with a large number of individual/group level covariates involved. We evaluate the performance of indiCAR and smooth-indiCAR through simulation studies. Our results indicate that both methods provide reliable estimates of all the regression and random effect parameters. The estimated regression coefficient based on the CAR modeling, however, appears to be sensitive to the assumed spatial correlation structure. We hypothesize that such sensitivity is especially likely to occur when the covariate of interest has been measured with error. We quantify the biases of covariate measurement error, showing that the amount of attenuation depends on the degree of spatial correlation in both the covariate of interest and the assumed random error from the regression model. These results explain why the estimates obtained from spatial regression modeling are often so sensitive to the assumed model error structure. We propose and develop both a parametric and a semiparametric approach to obtain bias corrected estimate. Statistical analysis of administrative data often helps in uncovering trends and patterns that need to be followed up via traditional epidemiologic investigations. Case control studies are often the first choice. However, appropriate selection of controls and lack of power to detect interaction effect are the main concerns of a case control design. We propose a variant of the classical case-control design, the exposure enriched case-control (EECC) design, where not only cases, but also high (or low) exposed individuals are over-sampled, depending on the skewness of the exposure distribution. We show that the judicious oversampling of exposure is possible and can boost the study power particularly when susceptibility genes are rare and environmental exposure is highly skewed

On-road behavior in older drivers with mild cognitive impairment

Objectives: Dementia increases the risk of unsafe driving, but this is less apparent in preclinical stages such as mild cognitive impairment (MCI). There is, however, limited detailed data on the patterns of driving errors associated with MCI. Here, we examined whether drivers with MCI exhibited different on-road error profiles compared with cognitively normal (CN) older drivers. Design: Observational. Setting and Participants: A total of 296 licensed older drivers [mean age 75.5 (SD = 6.2) years, 120 (40.5%) women] recruited from the community. Method: Participants completed a health and driving history survey, a neuropsychological test battery, and an on-road driving assessment including driver-instructed and self-navigation components. Driving assessors were blind to participant cognitive status. Participants were categorized as safe or unsafe based on a validated on-road safety scale, and as having MCI based on International Working Group diagnostic criteria. Proportion of errors incurred as a function of error type and traffic context were compared across safe and unsafe MCI and CN drivers. Results: Compared with safe CN drivers (n = 225), safe MCI drivers (n = 45) showed a similar pattern of errors in different traffic contexts. Compared with safe CN drivers, unsafe CN drivers (n = 17) were more likely to make errors in observation, speed control, lane position, and approach, and at stop/give-way signs, lane changes, and curved driving. Unsafe MCI drivers (n = 9) had additional difficulties at intersections, roundabouts, parking, straight driving, and under self-navigation conditions. A higher proportion of unsafe MCI drivers had multidomain subtype [n = 6 (67%)] than safe MCI drivers [n = 11 (25%)], odds ratio 6.2 (95% confidence interval, 1.4–29.6). Conclusion and Implications: Among safe drivers, MCI and CN drivers exhibit similar on-road error profiles, suggesting driver restrictions based on MCI status alone are unwarranted. However, formal evaluation is recommended in such cases, as there is evidence drivers with multiple domains of cognitive impairment may require additional interventions to support safe driving.</p

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

Abstract Background Multiple 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. Method Using 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. Results In 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. Conclusion Both 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

Validation of brief screening tools to identify impaired driving among older adults in Australia

Importance: There is an urgent need to develop evidence-based assessments to identify older individuals who may be unsafe drivers. Objective: To validate 8 off-road brief screening tests to predict on-road driving ability and to identify which combination of these provides the best prediction of older adults who will not pass an on-road driving test. Design, Setting, and Participants: This prognostic study was conducted between October 31, 2013, and May 10, 2017, using the criterion standard for screening tests, an on-road driving test, with analysis conducted from August 1, 2019, to April 2, 2020. A volunteer sample of older drivers was recruited from community advertisements, rehabilitation and driver assessment clinics, and an optometry clinic in Canberra and Brisbane, Australia. Exposures: Off-road driver screening measures, including the Useful Field of View, DriveSafe/DriveAware, Multi-D battery, Trails B, Maze test, Hazard Perception Test, DriveSafe Intersection test, and 14-item Road Law test. Main Outcomes and Measures: Classification as unsafe on a standardized 50-minute on-road driving assessment administered by a driving instructor and an occupational therapist masked to the participant's clinical diagnosis and off-road test performance. Results: A total of 560 drivers aged 63 to 94 years (mean [SD] age, 74.7 [6.2] years]; 350 [62.5%] men) were assessed. Logistic regression and receiver operating characteristic analyses indicated the area under the curve was largest for a multivariate model comprising the Multi-D, Useful Field of View, and Hazard Perception Test, with an area under the curve of 0.89 (95% CI, 0.85-0.94), sensitivity of 80.4%, and specificity of 84.1% for predicting unsafe drivers. The Multi-D battery was the most accurate individual assessment and had an area under the curve of 0.85 (95% CI, 0.79-0.90), sensitivity of 77.1%, and specificity of 82.1%. The multivariate model had sensitivity of 83.3% and specificity of 91.8% in the cognitively impaired group and sensitivity of 87.5% and specificity of 70.8% in the visually impaired group. Conclusions and Relevance: These findings suggest that off-road screening tests can reliably identify older drivers with a strong probability of failing an on-road driving test. Implementation of these measures could enable better targeting of resources for managing older driver licensing and support injury prevention strategies in this group.</p

Parent-reported prevalence and persistence of 19 common child health conditions

To estimate prevalence and persistence of 19 common paediatric conditions from infancy to 14&ndash;15 years.Design : Population-based prospective cohort study.Setting : Australia.Participants : Parallel cohorts assessed biennially from 2004 to 2014 from ages 0&ndash;1 and 4&ndash;5 years to 10&ndash;11 and 14&ndash;15 years, respectively, in the Longitudinal Study of Australian ChildrenMain outcome measures : 19 health conditions: 17 parent-reported, 2 (overweight/obesity, obesity) directly assessed. Two general measures: health status, special health care needs. Analysis: (1) prevalence estimated in 2-year age-bands and (2) persistence rates calculated at each subsequent time point for each condition among affected children.Results : 10 090 children participated in Wave 1 and 6717 in all waves. From age 2, more than 60% of children were experiencing at least one health condition at any age. Distinct prevalence patterns by age-bands comprised eight conditions that steadily rose (overweight/obesity, obesity, injury, anxiety/depression, frequent headaches, abdominal pain, autism spectrum disorder, attention-deficit hyperactivity disorder). Six conditions fell with age (eczema, sleep problems, day-wetting, soiling, constipation, recurrent tonsillitis), three remained stable (asthma, diabetes, epilepsy) and two peaked in mid-childhood (dental decay, recurrent ear infections). Conditions were more likely to persist if present for 2&thinsp;years; persistence was especially high for obesity beyond 6&ndash;7 (91.3%&ndash;95.1% persisting at 14&ndash;15).Conclusions : Beyond infancy, most Australian children are experiencing at least one ongoing health condition at any given time. This study&rsquo;s age-specific estimates of prevalence and persistence should assist families and clinicians to plan care. Conditions showing little resolution (obesity, asthma, attention-deficit hyperactivity disorder) require long-term planning and management

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.Trial registration number ACTRN12621000977875

The use of driver screening tools to predict self-reported crashes and incidents in older drivers

There is a clear need to identify older drivers at increased crash risk, without additional burden on the individual or licensing system. Brief off-road screening tools have been used to identify unsafe drivers and drivers at risk of losing their license. The aim of the current study was to evaluate and compare driver screening tools in predicting prospective self-reported crashes and incidents over 24 months in drivers aged 60 years and older. 525 drivers aged 63–96 years participated in the prospective Driving Aging Safety and Health (DASH) study, completing an on-road driving assessment and seven off-road screening tools (Multi-D battery, Useful Field of View, 14-Item Road Law, Drive Safe, Drive Safe Intersection, Maze Test, Hazard Perception Test (HPT)), along with monthly self-report diaries on crashes and incidents over a 24-month period. Over the 24 months, 22% of older drivers reported at least one crash, while 42% reported at least one significant incident (e.g., near miss). As expected, passing the on-road driving assessment was associated with a 55% [IRR 0.45, 95% CI 0.29–0.71] reduction in self-reported crashes adjusting for exposure (crash rate), but was not associated with reduced rate of a significant incident. For the off-road screening tools, poorer performance on the Multi-D test battery was associated with a 22% [IRR 1.22, 95% CI 1.08–1.37] increase in crash rate over 24 months. Meanwhile, all other off-road screening tools were not predictive of rates of crashes or incidents reported prospectively. The finding that only the Multi-D battery was predictive of increased crash rate, highlights the importance of accounting for age-related changes in vision, sensorimotor skills and cognition, as well as driving exposure, in older drivers when using off-road screening tools to assess future crash risk