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.

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.

CogDrisk, ANU-ADRI, CAIDE, and LIBRA Risk Scores for Estimating Dementia Risk.

Importance: While the Australian National University-Alzheimer Disease Risk Index (ANU-ADRI), Cardiovascular Risk Factors, Aging, and Dementia (CAIDE), and Lifestyle for Brain Health (LIBRA) dementia risk tools have been widely used, a large body of new evidence has emerged since their publication. Recently, Cognitive Health and Dementia Risk Index (CogDrisk) and CogDrisk for Alzheimer disease (CogDrisk-AD) risk tools have been developed for the assessment of dementia and AD risk, respectively, using contemporary evidence; comparison of the relative performance of these risk tools is limited. Objective: To evaluate the performance of CogDrisk, ANU-ADRI, CAIDE, LIBRA, and modified LIBRA (LIBRA with age and sex estimates from ANU-ADRI) in estimating dementia and AD risks (with CogDrisk-AD and ANU-ADRI). Design, Setting, and Participants: This population-based cohort study obtained data from the Rush Memory and Aging Project (MAP), the Cardiovascular Health Study Cognition Study (CHS-CS), and the Health and Retirement Study-Aging, Demographics and Memory Study (HRS-ADAMS). Participants who were free of dementia at baseline were included. The factors were component variables in the risk tools that included self-reported baseline demographics, medical risk factors, and lifestyle habits. The study was conducted between November 2021 and March 2023, and statistical analysis was performed from January to June 2023. Main outcomes and measures: Risk scores were calculated based on available factors in each of these cohorts. Area under the receiver operating characteristic curve (AUC) was calculated to measure the performance of each risk score. Multiple imputation was used to assess whether missing data may have affected estimates for dementia risk. Results: Among the 6107 participants in 3 validation cohorts included for this study, 2184 participants without dementia at baseline were available from MAP (mean [SD] age, 80.0 [7.6] years; 1606 [73.5%] female), 548 participants without dementia at baseline were available from HRS-ADAMS (mean [SD] age, 79.5 [6.3] years; 288 [52.5%] female), and 3375 participants without dementia at baseline were available from CHS-CS (mean [SD] age, 74.8 [4.9] years; 1994 [59.1%] female). In all 3 cohorts, a similar AUC for dementia was obtained using CogDrisk, ANU-ADRI, and modified LIBRA (MAP cohort: CogDrisk AUC, 0.65 [95% CI, 0.61-0.69]; ANU-ADRI AUC, 0.65 [95% CI, 0.61-0.69]; modified LIBRA AUC, 0.65 [95% CI, 0.61-0.69]; HRS-ADAMS cohort: CogDrisk AUC, 0.75 [95% CI, 0.71-0.79]; ANU-ADRI AUC, 0.74 [95% CI, 0.70-0.78]; modified LIBRA AUC, 0.75 [95% CI, 0.71-0.79]; CHS-CS cohort: CogDrisk AUC, 0.70 [95% CI, 0.67-0.72]; ANU-ADRI AUC, 0.69 [95% CI, 0.66-0.72]; modified LIBRA AUC, 0.70 [95% CI, 0.68-0.73]). The CAIDE and LIBRA also provided similar but lower AUCs than the 3 aforementioned tools (eg, MAP cohort: CAIDE AUC, 0.50 [95% CI, 0.46-0.54]; LIBRA AUC, 0.53 [95% CI, 0.48-0.57]). The performance of CogDrisk-AD and ANU-ADRI in estimating AD risks was also similar. Conclusions and relevance: CogDrisk and CogDrisk-AD performed similarly to ANU-ADRI in estimating dementia and AD risks. These results suggest that CogDrisk and CogDrisk-AD, with a greater range of modifiable risk factors compared with other risk tools in this study, may be more informative for risk reduction.

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.

A Technology-Enriched Approach to Studying Microlongitudinal Aging Among Adults Aged 18 to 85 Years: Protocol for the Labs Without Walls Study.

BACKGROUND: Traditional longitudinal aging research involves studying the same individuals over a long period, with measurement intervals typically several years apart. App-based studies have the potential to provide new insights into life-course aging by improving the accessibility, temporal specificity, and real-world integration of data collection. We developed a new research app for iOS named Labs Without Walls to facilitate the study of life-course aging. Combined with data collected using paired smartwatches, the app collects complex data including data from one-time surveys, daily diary surveys, repeated game-like cognitive and sensory tasks, and passive health and environmental data. OBJECTIVE: The aim of this protocol is to describe the research design and methods of the Labs Without Walls study conducted between 2021 and 2023 in Australia. METHODS: Overall, 240 Australian adults will be recruited, stratified by age group (18-25, 26-35, 36-45, 46-55, 56-65, 66-75, and 76-85 years) and sex at birth (male and female). Recruitment procedures include emails to university and community networks, as well as paid and unpaid social media advertisements. Participants will be invited to complete the study onboarding either in person or remotely. Participants who select face-to-face onboarding (n=approximately 40) will be invited to complete traditional in-person cognitive and sensory assessments to be cross-validated against their app-based counterparts. Participants will be sent an Apple Watch and headphones for use during the study period. Participants will provide informed consent within the app and then begin an 8-week study protocol, which includes scheduled surveys, cognitive and sensory tasks, and passive data collection using the app and a paired watch. At the conclusion of the study period, participants will be invited to rate the acceptability and usability of the study app and watch. We hypothesize that participants will be able to successfully provide e-consent, input survey data through the Labs Without Walls app, and have passive data collected over 8 weeks; participants will rate the app and watch as user-friendly and acceptable; the app will allow for the study of daily variability in self-perceptions of age and gender; and data will allow for the cross-validation of app- and laboratory-based cognitive and sensory tasks. RESULTS: Recruitment began in May 2021, and data collection was completed in February 2023. The publication of preliminary results is anticipated in 2023. CONCLUSIONS: This study will provide evidence regarding the acceptability and usability of the research app and paired watch for studying life-course aging processes on multiple timescales. The feedback obtained will be used to improve future iterations of the app, explore preliminary evidence for intraindividual variability in self-perceptions of aging and gender expression across the life span, and explore the associations between performance on app-based cognitive and sensory tests and that on similar traditional cognitive and sensory tests. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/47053.

Have We Been Underestimating Modifiable Dementia Risk? An Alternative Approach for Calculating the Combined Population Attributable Fraction for Modifiable Dementia Risk Factors.

Estimating the fraction of dementia cases in a population attributable to a risk factor or combination of risk factors (the population attributable fraction (PAF)) informs the design and choice of dementia risk-reduction activities. It is directly relevant to dementia prevention policy and practice. Current methods employed widely in the dementia literature to combine PAFs for multiple dementia risk factors assume a multiplicative relationship between factors and rely on subjective criteria to develop weightings for risk factors. In this paper we present an alternative approach to calculating the PAF based on sums of individual risk. It incorporates individual risk factor interrelationships and enables a range of assumptions about the way in which multiple risk factors will combine to affect dementia risk. Applying this method to global data demonstrates that the previous estimate of 40% is potentially too conservative an estimate of modifiable dementia risk and would necessitate subadditive interaction between risk factors. We calculate a plausible conservative estimate of 55.7% (95% confidence interval: 55.2, 56.1) based on additive risk factor interaction.

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 ≥55years 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 64.3years for GBM without HIV and 62.1years 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 1km (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.

Development of the CogDrisk tool to assess risk factors for dementia.

Introduction: We aimed to develop a comprehensive risk assessment tool for Alzheimer's disease (AD), vascular dementia (VaD), and any dementia, that will be applicable in high and low resource settings. Method: Risk factors which can easily be assessed in most settings, and their effect sizes, were identified from an umbrella review, or estimated using meta-analysis where new data were available. Results: Seventeen risk/protective factors met criteria for the algorithm to estimate risk for any dementia including age, sex, education, hypertension, midlife obesity, midlife high cholesterol, diabetes, insufficient physical activity, depression, traumatic brain injury, atrial fibrillation, smoking, social engagement, cognitive engagement, fish consumption (diet), stroke, and insomnia. A version for AD excluded atrial fibrillation and insomnia due to insufficient evidence and included pesticide exposure. There was insufficient evidence for a VaD risk score. Discussion: Validation of the tool on external datasets is planned. The assessment tool will assist with implementing risk reduction guidelines.

Gender differences in cognitive development in cohorts of young, middle, and older adulthood over 12 years.

Understanding gender differences in human cognitive development may contribute to understanding the gender differences in outcomes in cognitive ageing. However, evaluation of this topic has been hindered by a lack of representative, longitudinal data from different aged cohorts measured on the same cognitive tests. Gender differences in cognitive abilities were evaluated in three population-based cohorts (baseline age-span 20 to 76, 52% female, 94% Caucasian, 5% Asian and 1% other ethnic background, baseline N = 7,485), initially drawn from the electoral role in Australia where voting is compulsory, that were assessed four times over 12 years on measures of verbal memory, processing speed, working memory, verbal ability, and reaction time. Linear mixed models showed that within each cohort, women had better verbal memory and men had better working memory and faster reaction times. Verbal ability and processing speed showed variable gender differences in the young and middle-aged cohorts but no difference in the oldest cohort. In young and middle age, there were no gender differences in rates of change in verbal memory, processing speed, reaction time, verbal ability, or working memory. In old age, the gender differences were only observed in rates of change in verbal memory. Women showed more verbal memory decline between the 8-year and 12-year follow-ups than men, despite retaining higher average memory performance than men. We conclude that from ages 20-76, gender differences in cognitive abilities are stable except for faster memory ageing among women in the eighth decade. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

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 (ß= -0.04, 99% CI: -0.08, 0.00) and processing speed (ß= -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.

Association of sex differences in dementia risk factors with sex differences in memory decline in a population-based cohort spanning 20-76 years.

Sex differences in late-life memory decline may be explained by sex differences in dementia risk factors. Episodic memory and dementia risk factors were assessed in young, middle-aged and older adults over 12 years in a population-based sample (N = 7485). For men in midlife and old age, physical, cognitive and social activities were associated with less memory decline, and financial hardship was associated with more. APOE e4 and vascular risk factors were associated with memory decline for women in midlife. Depression, cognitive and physical activity were associated with memory change in older women. Incident midlife hypertension (ß = - 0.48, 95% CI - 0.87, - 0.09, p = 0.02) was associated with greater memory decline in women and incident late-life stroke accounted for greater memory decline in men (ß = - 0.56, 95% CI - 1.12, - 0.01), p = 0.05). Women have fewer modifiable risk factors than men. Stroke and hypertension explained sex differences in memory decline for men and women respectively.

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.

Protocol for a single patient therapy plan: A randomised, double-blind, placebo-controlled N-of-1 trial to assess the efficacy of cannabidiol in patients with intractable epilepsy.

AIM: This paper describes the use of the single patient therapy plan (SPTP). The SPTP has been designed to assess the efficacy at an individual level of a commercially available cannabinoid product, cannabidiol, in reducing seizure frequency in paediatric patients with intractable epilepsy. METHODS: The SPTP is a randomised, double-blind, placebo-controlled N-of-1 trial designed to assess the efficacy of treatment in a neurology outpatient setting. The primary objective of the SPTP is to assess the efficacy of cannabidiol in reducing seizure frequency in each patient with intractable epilepsy, with change in seizure frequency being the primary outcome of interest. The analysis adopts a Bayesian approach, which provides results in the form of posterior probabilities that various levels of benefit (based on the primary outcome measure, seizure frequency) have been achieved under active treatment compared to placebo, accompanied by decision rules that provide thresholds for deciding whether treatment has been successful in the individual patient. The SPTP arrangement is most accurately considered part of clinical practice rather than research, since it is aimed at making clinical treatment decisions for individual patients and is not testing a hypothesis or collecting aggregate data. Therefore, Human Research Ethics Committee approval was considered not to be required, although it is recommended that hospital Clinical Ethics Committees provide ethical oversight. CONCLUSION: These SPTP resources are made available so that they may inform clinical practice in the treatment of severe epilepsy or adapted for use in other conditions.

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.

Multiple imputation methods for handling incomplete longitudinal and clustered data where the target analysis is a linear mixed effects model.

Multiple imputation (MI) is increasingly popular for handling multivariate missing data. Two general approaches are available in standard computer packages: MI based on the posterior distribution of incomplete variables under a multivariate (joint) model, and fully conditional specification (FCS), which imputes missing values using univariate conditional distributions for each incomplete variable given all the others, cycling iteratively through the univariate imputation models. In the context of longitudinal or clustered data, it is not clear whether these approaches result in consistent estimates of regression coefficient and variance component parameters when the analysis model of interest is a linear mixed effects model (LMM) that includes both random intercepts and slopes with either covariates or both covariates and outcome contain missing information. In the current paper, we compared the performance of seven different MI methods for handling missing values in longitudinal and clustered data in the context of fitting LMMs with both random intercepts and slopes. We study the theoretical compatibility between specific imputation models fitted under each of these approaches and the LMM, and also conduct simulation studies in both the longitudinal and clustered data settings. Simulations were motivated by analyses of the association between body mass index (BMI) and quality of life (QoL) in the Longitudinal Study of Australian Children (LSAC). Our findings showed that the relative performance of MI methods vary according to whether the incomplete covariate has fixed or random effects and whether there is missingnesss in the outcome variable. We showed that compatible imputation and analysis models resulted in consistent estimation of both regression parameters and variance components via simulation. We illustrate our findings with the analysis of LSAC data.

Giardia duodenalis infection in the context of a community-based deworming and water, sanitation and hygiene trial in Timor-Leste.

BACKGROUND: Giardiasis is a common diarrhoeal disease caused by the protozoan Giardia duodenalis. It is prevalent in low-income countries in the context of inadequate access to water, sanitation and hygiene (WASH), and is frequently co-endemic with neglected tropical diseases such as soil-transmitted helminth (STH) infections. Large-scale periodic deworming programmes are often implemented in these settings; however, there is limited evidence for the impact of regular anthelminthic treatment on G. duodenalis infection. Additionally, few studies have examined the impact of WASH interventions on G. duodenalis. METHODS: The WASH for WORMS cluster randomised controlled trial was conducted in remote communities in Manufahi municipality, Timor-Leste, between 2012 and 2016. All study communities received four rounds of deworming with albendazole at six-monthly intervals. Half were randomised to additionally receive a community-level WASH intervention following study baseline. We measured G. duodenalis infection in study participants every six months for two years, immediately prior to deworming, as a pre-specified secondary outcome of the trial. WASH access and behaviours were measured using questionnaires. RESULTS: There was no significant change in G. duodenalis prevalence in either study arm between baseline and the final study follow-up. We found no additional benefit of the community-level WASH intervention on G. duodenalis infection (relative risk: 1.05, 95% CI: 0.72-1.54). Risk factors for G. duodenalis infection included living in a household with a child under five years of age (adjusted odds ratio, aOR: 1.35, 95% CI: 1.04-1.75), living in a household with more than six people (aOR: 1.32, 95% CI: 1.02-1.72), and sampling during the rainy season (aOR: 1.23, 95% CI: 1.04-1.45). Individuals infected with the hookworm Necator americanus were less likely to have G. duodenalis infection (aOR: 0.71, 95% CI: 0.57-0.88). CONCLUSIONS: Prevalence of G. duodenalis was not affected by a community WASH intervention or by two years of regular deworming with albendazole. Direct household contacts appear to play a dominant role in driving transmission. We found evidence of antagonistic effects between G. duodenalis and hookworm infection, which warrants further investigation in the context of global deworming efforts. Trial registration Australian New Zealand Clinical Trials Registry, ACTRN12614000680662. Registered 27 June 2014, retrospectively registered. https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=366540 .

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

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.

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.

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

OBJECTIVE: To estimate prevalence and persistence of 19 common paediatric conditions from infancy to 14-15 years. DESIGN: Population-based prospective cohort study. SETTING: Australia. PARTICIPANTS: Parallel cohorts assessed biennially from 2004 to 2014 from ages 0-1 and 4-5 years to 10-11 and 14-15 years, respectively, in the Longitudinal Study of Australian Children. MAIN 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 years; persistence was especially high for obesity beyond 6-7 (91.3%-95.1% persisting at 14-15). CONCLUSIONS: Beyond infancy, most Australian children are experiencing at least one ongoing health condition at any given time. This study'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.