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

Could Country-Level Factors Explain Sex Differences in Dementia Incidence and Prevalence? A Systematic Review and Meta-Analysis

Background: Despite rising interest in sex differences in dementia, it is unclear whether sex differences in dementia incidence and prevalence are apparent globally. Objective: We examine sex differences in incidence and prevalence of Any dementia, Alzheimer's disease (AD), and vascular dementia (VaD), and evaluate whether country-level indicators of gender inequality account for differences. Methods: Systematic review with meta-analysis was used to obtain estimates of incidence and prevalence of Any dementia, AD, and VaD using random effects meta-analysis, and population-based studies with clinical or validated dementia measures. Meta-regression was used to evaluate how country-specific factors of life expectancy, education, and gender differences in development, unemployment, and inequality indices influenced estimates. Results: We identified 205 eligible studies from 8,731 articles, representing 998,187 participants across 43 countries. There were no sex differences in the incidence of Any dementia, AD, or VaD, except in the 90+ age group (women higher). When examined by 5-year age bands, the only sex difference in prevalence of Any dementia was in the 85+ group and there was no sex difference in VaD. AD was more prevalent in women at most ages. Globally, the overall prevalence of dementia in adults 65 + was higher for women (80.22/1000, 95% CI 62.83-97.61) than men (54.86/1000, 95% CI 43.55-66.17). Meta-regression revealed that sex differences in Any dementia prevalence were associated with gender differences in life expectancy and in education. Conclusion: Globally, there are no sex differences in age-specific dementia incidence, but prevalence of AD is higher in women. Country-level factors like life expectancy and gender differences in education may explain variability in sex differences

No clear associations between subjective memory concerns and subsequent change in cognitive function : the PATH through life study

The literature on subjective memory concerns (SMC) as a predictor for future cognitive decline is varied. Furthermore, recent research has pointed to additional complexity arising from variability in the experience of SMC themselves (i.e. whether they are remitting or sustained over time). We investigated the associations between SMC and objectively measured cognition in an Australian population-based cohort. Four waves (4-year intervals between waves) of data from 1236 participants (aged 62.4 ± 1.5 years, 53% male) were used. We categorized participants as experiencing SMC, when they indicated that their memory problems might interfere with their day-to-day life and/or they had seen a doctor about their memory. SMC was categorized as “no” reported SMC, “remitting”, “new-onset” or “sustained” SMC. Cognitive assessment of immediate and delayed recall, working memory, psychomotor speed, attention and processing speed were assessed using a neuropsychological battery. Eighteen percent of participants were characterised as having SMC: 6% (77) “remitting”, 6% (77) “new-onset” and 6% (69) “sustained” SMC. There was no consistent evidence for an association between SMC and subsequent decline in cognition. However, SMC was associated with poorer performance on contemporaneous tasks of attention and processing speed compared to “no” SMC. Asking about SMC may indicate a current decline in cognitive function but, in this sample at least, did not indicate an increased risk of future decline

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

Measuring Coverage in MNCH:A Prospective Validation Study in Pakistan and Bangladesh on Measuring Correct Treatment of Childhood Pneumonia

Antibiotic treatment for pneumonia as measured by Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS) is a key indicator for tracking progress in achieving Millennium Development Goal 4. Concerns about the validity of this indicator led us to perform an evaluation in urban and rural settings in Pakistan and Bangladesh.Caregivers of 950 children under 5 y with pneumonia and 980 with "no pneumonia" were identified in urban and rural settings and allocated for DHS/MICS questions 2 or 4 wk later. Study physicians assigned a diagnosis of pneumonia as reference standard; the predictive ability of DHS/MICS questions and additional measurement tools to identify pneumonia versus non-pneumonia cases was evaluated. Results at both sites showed suboptimal discriminative power, with no difference between 2- or 4-wk recall. Individual patterns of sensitivity and specificity varied substantially across study sites (sensitivity 66.9% and 45.5%, and specificity 68.8% and 69.5%, for DHS in Pakistan and Bangladesh, respectively). Prescribed antibiotics for pneumonia were correctly recalled by about two-thirds of caregivers using DHS questions, increasing to 72% and 82% in Pakistan and Bangladesh, respectively, using a drug chart and detailed enquiry.Monitoring antibiotic treatment of pneumonia is essential for national and global programs. Current (DHS/MICS questions) and proposed new (video and pneumonia score) methods of identifying pneumonia based on maternal recall discriminate poorly between pneumonia and children with cough. Furthermore, these methods have a low yield to identify children who have true pneumonia. Reported antibiotic treatment rates among these children are therefore not a valid proxy indicator of pneumonia treatment rates. These results have important implications for program monitoring and suggest that data in its current format from DHS/MICS surveys should not be used for the purpose of monitoring antibiotic treatment rates in children with pneumonia at the present time

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