Treatment-seeking rates in malaria endemic countries

BACKGROUND: The proportion of individuals who seek treatment for fever is an important quantity in understanding access to and use of health systems, as well as for interpreting data on disease incidence from routine surveillance systems. For many malaria endemic countries (MECs), treatment-seeking information is available from national household surveys. The aim of this paper was to assemble sub-national estimates of treatment-seeking behaviours and to predict national treatment-seeking measures for all MECs lacking household survey data. METHODS: Data on treatment seeking for fever were obtained from Demographic and Health Surveys, Malaria Indicator Surveys and Multiple Cluster Indicator Surveys for every MEC and year that data were available. National-level social, economic and health-related variables were gathered from the World Bank as putative covariates of treatment-seeking rates. A generalized additive mixed model (GAMM) was used to estimate treatment-seeking behaviours for countries where survey data were unavailable. Two separate models were developed to predict the proportion of fever cases that would seek treatment at (1) a public health facility or (2) from any kind of treatment provider. RESULTS: Treatment-seeking data were available for 74 MECs and modelled for the remaining 24. GAMMs found that the percentage of pregnant women receiving prenatal care, vaccination rates, education level, government health expenditure, and GDP growth were important predictors for both categories of treatment-seeking outcomes. Treatment-seeking rates, which varied both within and among regions, revealed that public facilities were not always the primary facility type used. CONCLUSIONS: Estimates of treatment-seeking rates show how health services are utilized and help correct reported malaria case numbers to obtain more accurate measures of disease burden. The assembled and modelled data demonstrated that while treatment-seeking rates have overall increased over time, access remains low in some malaria endemic regions and utilization of government services is in some areas limited

Improved prediction accuracy for disease risk mapping using Gaussian process stacked generalization.

Maps of infectious disease-charting spatial variations in the force of infection, degree of endemicity and the burden on human health-provide an essential evidence base to support planning towards global health targets. Contemporary disease mapping efforts have embraced statistical modelling approaches to properly acknowledge uncertainties in both the available measurements and their spatial interpolation. The most common such approach is Gaussian process regression, a mathematical framework composed of two components: a mean function harnessing the predictive power of multiple independent variables, and a covariance function yielding spatio-temporal shrinkage against residual variation from the mean. Though many techniques have been developed to improve the flexibility and fitting of the covariance function, models for the mean function have typically been restricted to simple linear terms. For infectious diseases, known to be driven by complex interactions between environmental and socio-economic factors, improved modelling of the mean function can greatly boost predictive power. Here, we present an ensemble approach based on stacked generalization that allows for multiple nonlinear algorithmic mean functions to be jointly embedded within the Gaussian process framework. We apply this method to mapping Plasmodium falciparum prevalence data in sub-Saharan Africa and show that the generalized ensemble approach markedly outperforms any individual method

Electrochemically-triggered spatially and temporally resolved multi-component gels

Spatial control over gelation with low molecular weight gelators is possible using an electrochemically-driven pH triggering method. Gelation occurs at the electrode surface. We show here that composition control in multi-component low molecular weight hydrogels can also be achieved, allowing simultaneous spatial, temporal and compositional control

Functional complementarity of ancient plant–fungal mutualisms: contrasting nitrogen, phosphorus and carbon exchanges between Mucoromycotina and Glomeromycotina fungal symbionts of liverworts

Liverworts, which are amongst the earliest divergent plant lineages and important ecosystem pioneers, often form nutritional mutualisms with arbuscular mycorrhiza‐forming Glomeromycotina and fine‐root endophytic Mucoromycotina fungi, both of which coevolved with early land plants. Some liverworts, in common with many later divergent plants, harbour both fungal groups, suggesting these fungi may complementarily improve plant access to different soil nutrients. We tested this hypothesis by growing liverworts in single and dual fungal partnerships under a modern atmosphere and under 1500 ppm [CO2], as experienced by early land plants. Access to soil nutrients via fungal partners was investigated with 15N‐labelled algal necromass and 33P orthophosphate. Photosynthate allocation to fungi was traced using 14CO2. Only Mucoromycotina fungal partners provided liverworts with substantial access to algal 15N, irrespective of atmospheric CO2 concentration. Both symbionts increased 33P uptake, but Glomeromycotina were often more effective. Dual partnerships showed complementarity of nutrient pool use and greatest photosynthate allocation to symbiotic fungi. We show there are important functional differences between the plant–fungal symbioses tested, providing new insights into the functional biology of Glomeromycotina and Mucoromycotina fungal groups that form symbioses with plants. This may explain the persistence of the two fungal lineages in symbioses across the evolution of land plants

Likelihood inference for exponential-trawl processes

Integer-valued trawl processes are a class of serially correlated, stationary and infinitely divisible processes that Ole E. Barndorff-Nielsen has been working on in recent years. In this Chapter, we provide the first analysis of likelihood inference for trawl processes by focusing on the so-called exponential-trawl process, which is also a continuous time hidden Markov process with countable state space. The core ideas include prediction decomposition, filtering and smoothing, complete-data analysis and EM algorithm. These can be easily scaled up to adapt to more general trawl processes but with increasing computation efforts.Comment: 29 pages, 6 figures, forthcoming in: "A Fascinating Journey through Probability, Statistics and Applications: In Honour of Ole E. Barndorff-Nielsen's 80th Birthday", Springer, New Yor

The Role of Executive Functions in Socioeconomic Attainment Gaps: Results From a Randomized Controlled Trial

The socioeconomic attainment gap in mathematics starts early and increases over time. This study aimed to examine why this gap exists. Four‐year‐olds from diverse backgrounds were randomly allocated to a brief intervention designed to improve executive functions (N = 87) or to an active control group (N = 88). The study was preregistered and followed CONSORT guidelines. Executive functions and mathematical skills were measured at baseline, 1 week, 3 months, 6 months, and 1 year posttraining. Executive functions mediated the relation between socioeconomic status and mathematical skills. Children improved over training, but this did not transfer to untrained executive functions or mathematics. Executive functions may explain socioeconomic attainment gaps, but cognitive training directly targeting executive functions is not an effective way to narrow this gap

A bi-directional relationship between obesity and health-related quality of life : evidence from the longitudinal AusDiab study

Objective: To assess the prospective relationship between obesity and health-related quality of life, including a novel assessment of the impact of health-related quality of life on weight gain.Design and setting: Longitudinal, national, population-based Australian Diabetes, Obesity and Lifestyle (AusDiab) study, with surveys conducted in 1999/2000 and 2004/2005.Participants: A total of 5985 men and women aged 25 years at study entry.Main outcome measure(s): At both time points, height, weight and waist circumference were measured and self-report data on health-related quality of life from the SF-36 questionnaire were obtained. Cross-sectional and bi-directional, prospective associations between obesity categories and health-related quality of life were assessed.Results: Higher body mass index (BMI) at baseline was associated with deterioration in health-related quality of life over 5 years for seven of the eight health-related quality of life domains in women (all P0.01, with the exception of mental health, P&gt;0.05), and six out of eight in men (all P&lt;0.05, with the exception of role-emotional, P=0.055, and mental health, P&gt;0.05). Each of the quality-of-life domains related to mental health as well as the mental component summary were inversely associated with BMI change (all P&lt;0.0001 for women and P0.01 for men), with the exception of vitality, which was significant in women only (P=0.008). For the physical domains, change in BMI was inversely associated with baseline general health in women only (P=0.023).Conclusions: Obesity was associated with a deterioration in health-related quality of life (including both physical and mental health domains) in this cohort of Australian adults followed over 5 years. Health-related quality of life was also a predictor of weight gain over 5 years, indicating a bi-directional association between obesity and health-related quality of life. The identification of those with poor health-related quality of life may be important in assessing the risk of future weight gain, and a focus on health-related quality of life may be beneficial in weight management strategies.<br /