Search

We envisage the development of new Brain Health Services to achieve primary and secondary dementia prevention. These services will complement existing memory clinics by targeting cognitively unimpaired individuals, where the focus is on risk profiling and personalized risk reduction interventions rather than diagnosing and treating late-stage disease. In this article, we review key potentially modifiable risk factors and genetic risk factors and discuss assessment of risk factors as well as additional fluid and imaging biomarkers that may enhance risk profiling. We then outline multidomain measures and risk profiling and provide practical guidelines for Brain Health Services, with consideration of outstanding uncertainties and challenges. Users of Brain Health Services should undergo risk profiling tailored to their age, level of risk, and availability of local resources. Initial risk assessment should incorporate a multidomain risk profiling measure. For users aged 39–64, we recommend the Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) Dementia Risk Score, whereas for users aged 65 and older, we recommend the Brief Dementia Screening Indicator (BDSI) and the Australian National University Alzheimer's Disease Risk Index (ANU-ADRI). The initial assessment should also include potentially modifiable risk factors including sociodemographic, lifestyle, and health factors. If resources allow, apolipoprotein E ɛ4 status testing and structural magnetic resonance imaging should be conducted. If this initial assessment indicates a low dementia risk, then low intensity interventions can be implemented. If the user has a high dementia risk, additional investigations should be considered if local resources allow. Common variant polygenic risk of late-onset AD can be tested in middle-aged or older adults. Rare variants should only be investigated in users with a family history of early-onset dementia in a first degree relative. Advanced imaging with 18-fluorodeoxyglucose positron emission tomography (FDG-PET) or amyloid PET may be informative in high risk users to clarify the nature and burden of their underlying pathologies. Cerebrospinal fluid biomarkers are not recommended for this setting, and blood-based biomarkers need further validation before clinical use. As new technologies become available, advances in artificial intelligence are likely to improve our ability to combine diverse data to further enhance risk profiling. Ultimately, Brain Health Services have the potential to reduce the future burden of dementia through risk profiling, risk communication, personalized risk reduction, and cognitive enhancement interventions. [ABSTRACT FROM AUTHOR]

Background: Strongyloides stercoralis and hookworm are two soil-transmitted helminths (STH) that are highly prevalent in Cambodia. Strongyloides stercoralis causes long-lasting infections and significant morbidity but is largely neglected, while hookworm causes the highest public health burden among STH. The two parasites have the same infection route, i.e. skin penetration. The extent of co-distribution, which could result in potential high co-morbidities, is unknown in highly endemic settings like Cambodia. The aim of this study was to predict the spatial distribution of S. stercoralis-hookworm co-infection risk and to investigate determinants of co-infection in Preah Vihear Province, North Cambodia. Methods: A cross-sectional survey was conducted in 2010 in 60 villages of Preah Vihear Province. Diagnosis was performed on two stool samples, using combined Baermann technique and Koga agar culture plate for S. stercoralis and Kato-Katz technique for hookworm. Bayesian multinomial geostatistical models were used to assess demographic, socioeconomic, and behavioural determinants of S. stercoralis-hookworm co-infection and to predict co-infection risk at non-surveyed locations. Results: Of the 2576 participants included in the study, 48.6% and 49.0% were infected with S. stercoralis and hookworm, respectively; 43.8% of the cases were co-infections. Females, preschool aged children, adults aged 19-49 years, and participants who reported regularly defecating in toilets, systematically boiling drinking water and having been treated with anthelmintic drugs had lower odds of co-infection. While S. stercoralis infection risk did not appear to be spatially structured, hookworm mono-infection and co-infection exhibited spatial correlation at about 20 km. Co-infection risk was positively associated with longer walking distances to a health centre and exhibited a small clustering tendency. The association was only partly explained by climatic variables, suggesting a role for underlying factors, such as living conditions and remoteness. Conclusions: Both parasites were ubiquitous in the province, with co-infections accounting for almost half of all cases. The high prevalence of S. stercoralis calls for control measures. Despite several years of school-based de-worming programmes, hookworm infection levels remain high. Mebendazole efficacy, as well as coverage of and compliance to STH control programmes should be investigated. [ABSTRACT FROM AUTHOR]

Background: Soil-transmitted helminthiasis affects more than a billion people in the world and accounts for a global burden of 5.1 million disability-adjusted life years. The objectives of this study were (i) to map and predict the risk of soil-transmitted helminth infections among school-aged children in Côte d'Ivoire; (ii) to estimate school-aged children population-adjusted risk; and (iii) to estimate annual needs for preventive chemotherapy. Methods: In late 2011/early 2012, a cross-sectional survey was carried out among school-aged children in 92 localities of Côte d'Ivoire. Children provided a single stool sample that was subjected to duplicate Kato-Katz thick smears for the diagnosis of soil-transmitted helminths. A Bayesian geostatistical variable selection approach was employed to identify environmental and socioeconomic risk factors for soil-transmitted helminth infections. Bayesian kriging was used to predict soil-transmitted helminth infections on a grid of 1 x 1 km spatial resolution. The number of school-aged children infected with soil-transmitted helminths and the amount of doses needed for preventive chemotherapy according to World Health Organization guidelines were estimated. Results: Parasitological data were available from 5246 children aged 5-16 years. Helminth infections with hookworm were predominant (17.2 %). and were rarely found; overall Ascaris lumbricoides Trichuris trichiura prevalences were 1.9 % and 1.2 %, respectively. Bayesian geostatistical variable selection identified rural setting for hookworm, soil acidity and soil moisture for, and rainfall coefficient of variation for as A. lumbricoides T. trichiura main predictors of infection. The estimated school-aged children population-adjusted risk of soil-transmitted helminth infection in Côte d'Ivoire is 15.5 % (95 % confidence interval: 14.2-17.0 %). We estimate that approximately 1.3 million doses of albendazole or mebendazole are required for school-based preventive chemotherapy, and we provide school-aged children-adjusted risk aggregated at health district level. Conclusion: We provide the first soil-transmitted helminthiasis risk profile for entire Côte d'Ivoire, based on a robust Bayesian geostatistical framework. Our model-based estimates of treatment needs and risk maps on health district level may guide the national control program in spatial targeting of annual interventions. [ABSTRACT FROM AUTHOR]

Background: Schistosomiasis is a water-based disease that affects an estimated 250 million people, mainly in sub-Saharan Africa. The transmission of schistosomiasis is spatially and temporally restricted to freshwater bodies that contain schistosome cercariae released from specific snails that act as intermediate hosts. Our objective was to assess the contribution of remote sensing applications and to identify remaining challenges in its optimal application for schistosomiasis risk profiling in order to support public health authorities to better target control interventions. Methods: We reviewed the literature (i) to deepen our understanding of the ecology and the epidemiology of schistosomiasis, placing particular emphasis on remote sensing; and (ii) to fill an identified gap, namely interdisciplinary research that bridges different strands of scientific inquiry to enhance spatially explicit risk profiling. As a first step, we reviewed key factors that govern schistosomiasis risk. Secondly, we examined remote sensing data and variables that have been used for risk profiling of schistosomiasis. Thirdly, the linkage between the ecological consequence of environmental conditions and the respective measure of remote sensing data were synthesised. Results: We found that the potential of remote sensing data for spatial risk profiling of schistosomiasis is - in principle - far greater than explored thus far. Importantly though, the application of remote sensing data requires a tailored approach that must be optimised by selecting specific remote sensing variables, considering the appropriate scale of observation and modelling within ecozones. Interestingly, prior studies that linked prevalence of Schistosoma infection to remotely sensed data did not reflect that there is a spatial gap between the parasite and intermediate host snail habitats where disease transmission occurs, and the location (community or school) where prevalence measures are usually derived from. Conclusions: Our findings imply that the potential of remote sensing data for risk profiling of schistosomiasis and other neglected tropical diseases has yet to be fully exploited. [ABSTRACT FROM AUTHOR]

Background Targeted interventions for the long-term sick-listed may prevent permanent exclusion from the labour force. We aimed to develop a prediction method for identifying high risk groups for continued or recurrent long-term sickness absence, unemployment, or disability among persons on long-term sick leave. Methods We obtained individual characteristics and follow-up data from the Danish Register of Sickness Absence Compensation Benefits and Social Transfer Payments (RSS) during 2004 to 2010 for 189,279 Danes who experienced a period of long-term sickness absence (4+ weeks). In a learning data set, statistical prediction methods were built using logistic regression and a discrete event simulation approach for a one year prediction horizon. Personalized risk profiles were obtained for five outcomes: employment, unemployment, recurrent sickness absence, continuous long-term sickness absence, and early retirement from the labour market. Predictor variables included gender, age, socio-economic position, job type, chronic disease status, history of sickness absence, and prior history of unemployment. Separate models were built for times of economic growth (2005-2007) and times of recession (2008-2010). The accuracy of the prediction models was assessed with analyses of Receiver Operating Characteristic (ROC) curves and the Brier score in an independent validation data set. Results In comparison with a null model which ignored the predictor variables, logistic regression achieved only moderate prediction accuracy for the five outcome states. Results obtained with discrete event simulation were comparable with logistic regression. Conclusions Only moderate prediction accuracy could be achieved using the selected information from the Danish register RSS. Other variables need to be included in order to establish a prediction method which provides more accurate risk profiles for long-term sick-listed persons. [ABSTRACT FROM AUTHOR]