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Background Malaria mortality is influenced by several factors including climatic and environmental factors, interventions, socioeconomic status (SES) and access to health systems. Here, we investigated the joint effects of climatic and non-climatic factors on under-five malaria mortality at different spatial scales using data from a Health and Demographic Surveillance System (HDSS) in western Kenya.Methods We fitted Bayesian spatiotemporal (zero-inflated) negative binomial models to monthly mortality data aggregated at the village scale and over the catchment areas of the health facilities within the HDSS, between 2008 and 2019. First order autoregressive temporal and conditional autoregressive spatial processes were included as random effects to account for temporal and spatial variation. Remotely sensed climatic and environmental variables, bed net use, SES, travel time to health facilities, proximity from water bodies/streams and altitude were included in the models to assess their association with malaria mortality.Results Increase in rainfall (mortality rate ratio (MRR)=1.12, 95% Bayesian credible interval (BCI): 1.04–1.20), Normalized Difference Vegetation Index (MRR=1.16, 95% BCI: 1.06–1.28), crop cover (MRR=1.17, 95% BCI: 1.11–1.24) and travel time to the hospital (MRR=1.09, 95% BCI: 1.04–1.13) were associated with increased mortality, whereas increase in bed net use (MRR=0.84, 95% BCI: 0.70–1.00), distance to the nearest streams (MRR=0.89, 95% BCI: 0.83–0.96), SES (MRR=0.95, 95% BCI: 0.91–1.00) and altitude (MRR=0.86, 95% BCI: 0.81–0.90) were associated with lower mortality. The effects of travel time and SES were no longer significant when data was aggregated at the health facility catchment level.Conclusion Despite the relatively small size of the HDSS, there was spatial variation in malaria mortality that peaked every May–June. The rapid decline in malaria mortality was associated with bed nets, and finer spatial scale analysis identified additional important variables. Time and spatially targeted control interventions may be helpful, and fine spatial scales should be considered when data are available.

Abstract Background In Burkina Faso, the prevalence of malaria has decreased over the past two decades, following the scale-up of control interventions. The successful development of malaria parasites depends on several climatic factors. Intervention gains may be reversed by changes in climatic factors. In this study, we investigated the role of malaria control interventions and climatic factors in influencing changes in the risk of malaria parasitaemia. Methods Bayesian logistic geostatistical models were fitted on Malaria Indicator Survey data from Burkina Faso obtained in 2014 and 2017/2018 to estimate the effects of malaria control interventions and climatic factors on the temporal changes of malaria parasite prevalence. Additionally, intervention effects were assessed at regional level, using a spatially varying coefficients model. Results Temperature showed a statistically important negative association with the geographic distribution of parasitaemia prevalence in both surveys; however, the effects of insecticide-treated nets (ITNs) use was negative and statistically important only in 2017/2018. Overall, the estimated number of infected children under the age of 5 years decreased from 704,202 in 2014 to 290,189 in 2017/2018. The use of ITNs was related to the decline at national and regional level, but coverage with artemisinin-based combination therapy only at regional level. Conclusion Interventions contributed more than climatic factors to the observed change of parasitaemia risk in Burkina Faso during the period of 2014 to 2017/2018. Intervention effects varied in space. Longer time series analyses are warranted to determine the differential effect of a changing climate on malaria parasitaemia risk.

Abstract Assessment of the relative impact of climate change on malaria dynamics is a complex problem. Climate is a well-known factor that plays a crucial role in driving malaria outbreaks in epidemic transmission areas. However, its influence in endemic environments with intensive malaria control interventions is not fully understood, mainly due to the scarcity of high-quality, long-term malaria data. The demographic surveillance systems in Africa offer unique platforms for quantifying the relative effects of weather variability on the burden of malaria. Here, using a process-based stochastic transmission model, we show that in the lowlands of malaria endemic western Kenya, variations in climatic factors played a key role in driving malaria incidence during 2008–2019, despite high bed net coverage and use among the population. The model captures some of the main mechanisms of human, parasite, and vector dynamics, and opens the possibility to forecast malaria in endemic regions, taking into account the interaction between future climatic conditions and intervention scenarios.

BackgroundIn Bangladesh, preventive chemotherapy targeting soil-transmitted helminth (STH) infections in school-age children has been implemented since 2008. To evaluate the success of this strategy, surveys were conducted between 2017 and 2020 in 10 out of 64 districts. We estimate the geographic distribution of STH infections by species at high spatial resolution, identify risk factors, and estimate treatment needs at different population subgroups.MethodologyBayesian geostatistical models were fitted to prevalence data of each STH species. Climatic, environmental, and socioeconomic predictors were extracted from satellite images, open-access, model-based databases, and demographic household surveys, and used to predict the prevalence of infection over a gridded surface at 1 x 1 km spatial resolution across the country, via Bayesian kriging. These estimates were combined with gridded population data to estimate the number of required treatments for different risk groups.Principal findingsThe population-adjusted prevalence of Ascaris lumbricoides, Trichuris trichiura, and hookworm across all ages is estimated at 9.9% (95% Bayesian credible interval: 8.0-13.0%), 4.3% (3.0-7.3%), and 0.6% (0.4-0.9%), respectively. There were 24 out of 64 districts with an estimated population-adjusted STH infection prevalence above 20%. The proportion of households with improved sanitation showed a statistically important, protective association for both, A. lumbricoides and T. trichiura prevalence. Precipitation in the driest month of the year was negatively associated with A. lumbricoides prevalence. High organic carbon concentration in the soil's fine earth fraction was related to a high hookworm prevalence. Furthermore, we estimated that 30.5 (27.2; 36.0) million dosages of anthelmintic treatments for school-age children were required per year in Bangladesh.Conclusions/significanceFor each of the STH species, the prevalence was reduced by at least 80% since treatment was scaled up more than a decade ago. The current number of deworming dosages could be reduced by up to 61% if the treatment strategy was adapted to the local prevalence.

Background: Despite considerable progress made over the past 20 years in reducing the global burden of malaria, the disease remains a major public health problem and there is concern that climate change might expand suitable areas for transmission. This study investigated the relative effect of climate variability on malaria incidence after scale-up of interventions in western Kenya. Methods: Bayesian negative binomial models were fitted to monthly malaria incidence data, extracted from records of patients with febrile illnesses visiting the Lwak Mission Hospital between 2008 and 2019. Data pertaining to bed net use and socio-economic status (SES) were obtained from household surveys. Climatic proxy variables obtained from remote sensing were included as covariates in the models. Bayesian variable selection was used to determine the elapsing time between climate suitability and malaria incidence. Results: Malaria incidence increased by 50% from 2008 to 2010, then declined by 73% until 2015. There was a resurgence of cases after 2016, despite high bed net use. Increase in daytime land surface temperature was associated with a decline in malaria incidence (incidence rate ratio [IRR] = 0.70, 95% Bayesian credible interval [BCI]: 0.59–0.82), while rainfall was associated with increased incidence (IRR = 1.27, 95% BCI: 1.10–1.44). Bed net use was associated with a decline in malaria incidence in children aged 6–59 months (IRR = 0.78, 95% BCI: 0.70–0.87) but not in older age groups, whereas SES was not associated with malaria incidence in this population. Conclusions: Variability in climatic factors showed a stronger effect on malaria incidence than bed net use. Bed net use was, however, associated with a reduction in malaria incidence, especially among children aged 6–59 months after adjusting for climate effects. To sustain the downward trend in malaria incidence, this study recommends continued distribution and use of bed nets and consideration of climate-based malaria early warning systems when planning for future control interventions.

Abstract Background The economic impact of schistosomiasis and the underlying tradeoffs between water resources development and public health concerns have yet to be quantified. Schistosomiasis exerts large health, social and financial burdens on infected individuals and households. While irrigation schemes are one of the most important policy responses designed to reduce poverty, particularly in sub-Saharan Africa, they facilitate the propagation of schistosomiasis and other diseases. Methods We estimate the economic impact of schistosomiasis in Burkina Faso via its effect on agricultural production. We create an original dataset that combines detailed household and agricultural surveys with high-resolution geo-statistical disease maps. We develop new methods that use the densities of the intermediate host snails of schistosomiasis as instrumental variables together with panel, spatial and machine learning techniques. Results We estimate that the elimination of schistosomiasis in Burkina Faso would increase average crop yields by around 7%, rising to 32% for high infection clusters. Keeping schistosomiasis unchecked, in turn, would correspond to a loss of gross domestic product of approximately 0.8%. We identify the disease burden as a shock to the agricultural productivity of farmers. The poorest households engaged in subsistence agriculture bear a far heavier disease burden than their wealthier counterparts, experiencing an average yield loss due to schistosomiasis of between 32 and 45%. We show that the returns to water resources development are substantially reduced once its health effects are taken into account: villages in proximity of large-scale dams suffer an average yield loss of around 20%, and this burden decreases as distance between dams and villages increases. Conclusions This study provides a rigorous estimation of how schistosomiasis affects agricultural production and how it is both a driver and a consequence of poverty. It further quantifies the tradeoff between the economics of water infrastructures and their impact on public health. Although we focus on Burkina Faso, our approach can be applied to any country in which schistosomiasis is endemic. Graphical Abstract

BackgroundHookworm infections, caused by Ancylostoma duodenale and Necator americanus, are of considerable public health importance. The World Health Organization recommends preventive chemotherapy as the key strategy for morbidity control. Meta-analyses have been conducted to estimate treatment efficacy of available drugs and drug combinations. However, in most studies, the relation between the diagnostic error and infection intensity have not been considered, resulting in an overestimation of cure rates (CRs).MethodologyA Bayesian model was developed to compare the 'true' CR and egg reduction rate of different treatment regimens for hookworm infections taking into account the error of the recommended Kato-Katz thick smear diagnostic technique. It was fitted to the observed egg count data which was linked to the distribution of worms, considered the day-to-day variation of hookworm egg excretion and estimated the infection intensity-dependent sensitivity. The CR was obtained by defining the prevalence of infection at follow-up as the probability of having at least one fertilized female worm. The model was applied to individual-level egg count data available from 17 treatments and six clinical trials.Principal findingsTaking the diagnostic error into account resulted in considerably lower CRs than previously reported. Overall, of all treatments analyzed, mebendazole administered in six dosages of 100 mg each was the most efficacious treatment with a CR of 88% (95% Bayesian credible interval: 79-95%). Furthermore, diagnostic sensitivity varied with the infection intensity and sampling effort. For an infection intensity of 50 eggs per gram of stool, the sensitivity is close to 60%; for two Kato-Katz thick smears it increased to approximately 76%.Conclusions/significanceOur model-based estimates provide the true efficacy of different treatment regimens against hookworm infection taking into account the diagnostic error of the Kato-Katz method. Estimates of the diagnostic sensitivity for different number of stool samples and thick smears are obtained. To accurately assess efficacy in clinical trials with the Kato-Katz method, at least two stool samples on consecutive days should be collected.

Abstract Background Most older adults prefer to continue living at home despite increasing care needs and demand for services. To aid in maintaining independence, integrated care models for community-dwelling older people are promoted as the most cost-effective approach. The implementation of such care models is challenging and often the end-users are not involved or their needs are not considered. We conducted a population survey in order to understand the needs and preferences of home-dwelling older adults living in Canton Basel-Landschaft, Switzerland. The aims of this paper are to chronicle the development of the INSPIRE Population Survey, outline its variables and measurements, describe the marketing strategy utilized for survey dissemination and report on the response rate and respondent characteristics. Methods The INSPIRE Population Survey, conducted between March and August 2019, is a cross-sectional survey of older adults aged 75 and older living at home in Canton Basel-Landschaft. The questionnaire was developed by expert input and stakeholder involvement. Its readability and acceptability were pilot-tested with older people. To ensure the likelihood of a high and representative response rate, a meticulous step-by-step marketing strategy was developed prior to the dissemination of the questionnaire. Results The overall response rate was 30.7% (n = 8,846), with variations between 20.6 and 34.5% across the different care regions in the canton. A generally higher response rate was found in the care regions with a higher density and which bordered the urban city of Basel. We received support from local stakeholders, policy makers and media through using a broad combination of marketing channels and targeting our community partners who have a strong relationship with our target audience. Conclusions Although recruiting older adults in research is challenging, our study shows that a high response rate can be achieved by developing the survey through expert input and by involving all important stakeholders, including older adults, throughout the entire process.

BackgroundReagent strip to detect microhematuria as a proxy for Schistosoma haematobium infections has been considered an alternative to urine filtration for individual diagnosis and community-based estimates of treatment needs for preventive chemotherapy. However, the diagnostic accuracy of reagent strip needs further investigation, particularly at low infection intensity levels.MethodsWe used existing data from a study conducted in Tanzania that employed urine filtration and reagent strip testing for S. haematobium in two villages, including a baseline and six follow-up surveys after praziquantel treatment representing a wide range of infection prevalence. We developed a Bayesian model linking individual S. haematobium egg count data based on urine filtration to reagent strip binary test results available on multiple days and estimated the relation between infection intensity and sensitivity of reagent strip. Furthermore, we simulated data from 3,000 hypothetical populations with varying mean infection intensity to infer on the relation between prevalence observed by urine filtration and the interpretation of reagent strip readings.Principal findingsReagent strip showed excellent sensitivity even for single measurement reaching 100% at around 15 eggs of S. haematobium per 10 ml of urine when traces on reagent strip were considered positive. The corresponding specificity was 97%. When traces were considered negative, the diagnostic accuracy of the reagent strip was equivalent to urine filtration data obtained on a single day. A 10% and 50% urine filtration prevalence based on a single day sampling corresponds to 11.2% and 48.6% prevalence by reagent strip, respectively, when traces were considered negative, and 17.6% and 57.7%, respectively, when traces were considered positive.Conclusions/significanceTrace results should be included in reagent strip readings when high sensitivity is required, but excluded when high specificity is needed. The observed prevalence of reagent strip results, when traces are considered negative, is a good proxy for prevalence estimates of S. haematobium infection by urine filtration on a single day.

Abstract Background The Service Availability and Readiness Assessment surveys generate data on the readiness of health facility services. We constructed a readiness index related to malaria services and determined the association between health facility malaria readiness and malaria mortality in children under the age of 5 years in Burkina Faso. Methods Data on inpatients visits and malaria-related deaths in under 5-year-old children were extracted from the national Health Management Information System in Burkina Faso. Bayesian geostatistical models with variable selection were fitted to malaria mortality data. The most important facility readiness indicators related to general and malaria-specific services were determined. Multiple correspondence analysis (MCA) was employed to construct a composite facility readiness score based on multiple factorial axes. The analysis was carried out separately for 112 medical centres and 546 peripheral health centres. Results Malaria mortality rate in medical centres was 4.8 times higher than that of peripheral health centres (3.5% vs. 0.7%, p

Abstract Background Schistosomiasis control programmes primarily use school-based surveys to identify areas for mass drug administration of preventive chemotherapy. However, as the spatial distribution of schistosomiasis can be highly focal, transmission may not be detected by surveys implemented at districts or larger spatial units. Improved mapping strategies are required to accurately and cost-effectively target preventive chemotherapy to remaining foci across all possible spatial distributions of schistosomiasis. Methods Here, we use geostatistical models to quantify the spatial heterogeneity of Schistosoma haematobium and S. mansoni across sub-Saharan Africa using the most comprehensive dataset available on school-based surveys. Applying this information to parameterise simulations, we assess the accuracy and cost of targeting alternative implementation unit sizes across the range of plausible schistosomiasis distributions. We evaluate the consequences of decisions based on survey designs implemented at district and subdistrict levels sampling different numbers of schools. Cost data were obtained from field surveys conducted across multiple countries and years, with cost effectiveness evaluated as the cost per correctly identified school. Results Models identified marked differences in prevalence and spatial distributions between countries and species; however, results suggest implementing surveys at subdistrict level increase the accuracy of treatment classifications across most scenarios. While sampling intensively at the subdistrict level resulted in the highest classification accuracy, this sampling strategy resulted in the highest costs. Alternatively, sampling the same numbers of schools currently recommended at the district level but stratifying by subdistrict increased cost effectiveness. Conclusions This study provides a new tool to evaluate schistosomiasis survey designs across a range of transmission settings. Results highlight the importance of considering spatial structure when designing sampling strategies, illustrating that a substantial proportion of children may be undertreated even when an implementation unit is correctly classified. Control programmes need to weigh the increased accuracy of more detailed mapping strategies against the survey costs and treatment priorities.

Abstract Background In 2015, a China-UK-Tanzania tripartite pilot project was implemented in southeastern Tanzania to explore a new model for reducing malaria burden and possibly scaling-out the approach into other malaria-endemic countries. The 1,7-malaria Reactive Community-based Testing and Response (1,7-mRCTR) which is a locally-tailored approach for reporting febrile malaria cases in endemic villages was developed to stop transmission and Plasmodium life-cycle. The (1,7-mRCTR) utilizes existing health facility data and locally trained community health workers to conduct community-level testing and treatment. Methods The pilot project was implemented from September 2015 to June 2018 in Rufiji District, southern Tanzania. The study took place in four wards, two with low incidence and two with a higher incidence. One ward of each type was selected for each of the control and intervention arms. The control wards implemented the existing Ministry of Health programmes. The 1,7-mRCTR activities implemented in the intervention arm included community testing and treatment of malaria infection. Malaria case-to-suspect ratios at health facilities (HF) were aggregated by villages, weekly to identify the village with the highest ratio. Community-based mobile test stations (cMTS) were used for conducting mass testing and treatment. Baseline (pre) and endline (post) household surveys were done in the control and intervention wards to assess the change in malaria prevalence measured by the interaction term of ‘time’ (post vs pre) and arm in a logistic model. A secondary analysis also studied the malaria incidence reported at the HFs during the intervention. Results Overall the 85 rounds of 1,7-mRCTR conducted in the intervention wards significantly reduced the odds of malaria infection by 66% (adjusted OR 0.34, 95% CI 0.26,0.44, p

Introduction: The majority of emergency paediatric death in African countries occur within the first 24 h of admission. A coloured triage system is widely implemented in high-income countries and the emergency triage and assessment treatment (ETAT) is recommended by the World Health Organization, but not put into practice in Mozambique. We implemented a three-colour triage system in a rural district hospital with lay-staff workers conducting the first triage. Methods: A retrospective, before and after, mortality analysis was performed using routine patient files from the district hospital between 2014 and 2017. The triage system was implemented in August 2016. Inclusion criteria were children under 15 years of age that entered the emergency centre. Primary outcome was child mortality rate. Secondary outcomes included the percentage agreement between the clinical and non-clinical staff and the duration from triage to first treatment. We used a negative binomial model in STATA 15 to compare mortality rates, and Kappa statistics to estimate the agreement between clinical and non-clinical staff. Results: 4176 admissions were included. The mortality rate ratio (MMR) was 45% lower after the start of the intervention (2016; MRR = 0.55; 0.38, 0.81; p = 0.002), compared to before. To estimate the agreement between non-clinical and clinical staff, 548 (of the 671) patient files were included. The agreement was estimated at 88.7% (Kappa = 0.644; p

Abstract Background With increasing spatial heterogeneity of malaria transmission and a shift of the disease burden towards older children and adults, pregnant women attending antenatal care (ANC) have been proposed as a pragmatic sentinel population for malaria surveillance. However, the representativeness of routine ANC malaria test-positivity and its relationship with prevalence in other population subgroups are yet to be investigated. Methods Monthly ANC malaria test-positivity data from all Tanzanian health facilities for January 2014 to May 2016 was compared to prevalence data from the School Malaria Parasitaemia Survey 2015, the Malaria Indicator Survey (MIS) 2015/16, the Malaria Atlas Project 2015, and a Bayesian model fitted to MIS data. Linear regression was used to describe the difference between malaria test-positivity in pregnant women and respective comparison groups as a function of ANC test-positivity and potential covariates. Results The relationship between ANC test-positivity and survey prevalence in children follows spatially and biologically meaningful patterns. However, the uncertainty of the relationship was substantial, particularly in areas with high or perennial transmission. In comparison, modelled data estimated higher prevalence in children at low transmission intensities and lower prevalence at higher transmission intensities. Conclusions Pregnant women attending ANC are a pragmatic sentinel population to assess heterogeneity and trends in malaria prevalence in Tanzania. Yet, since ANC malaria test-positivity cannot be used to directly predict the prevalence in other population subgroups, complementary community-level measurements remain highly relevant.

Abstract Background To reduce the under-five mortality (U5M), fine-gained spatial assessment of the effects of health interventions is critical because national averages can obscure important sub-national disparities. In turn, sub-national estimates can guide control programmes for spatial targeting. The purpose of our study is to quantify associations of interventions with U5M rate at national and sub-national scales in Uganda and to identify interventions associated with the largest reductions in U5M rate at the sub-national scale. Methods Spatially explicit data on U5M, interventions and sociodemographic indicators were obtained from the 2011 Uganda Demographic and Health Survey (DHS). Climatic data were extracted from remote sensing sources. Bayesian geostatistical Weibull proportional hazards models with spatially varying effects at sub-national scales were utilized to quantify associations between all-cause U5M and interventions at national and regional levels. Bayesian variable selection was employed to select the most important determinants of U5M. Results At the national level, interventions associated with the highest reduction in U5M were artemisinin-based combination therapy (hazard rate ratio (HRR) = 0.60; 95% Bayesian credible interval (BCI): 0.11, 0.79), initiation of breastfeeding within 1 h of birth (HR = 0.70; 95% BCI: 0.51, 0.86), intermittent preventive treatment (IPTp) (HRR = 0.74; 95% BCI: 0.67, 0.97) and access to insecticide-treated nets (ITN) (HRR = 0.75; 95% BCI: 0.63, 0.84). In Central 2, Mid-Western and South-West, largest reduction in U5M was associated with access to ITNs. In Mid-North and West-Nile, improved source of drinking water explained most of the U5M reduction. In North-East, improved sanitation facilities were associated with the highest decline in U5M. In Kampala and Mid-Eastern, IPTp had the largest associated with U5M. In Central1 and East-Central, oral rehydration solution and postnatal care were associated with highest decreases in U5M respectively. Conclusion Sub-national estimates of the associations between U5M and interventions can guide control programmes for spatial targeting and accelerate progress towards mortality-related Sustainable Development Goals.

BackgroundRecent research suggests that schistosomiasis targets for morbidity control and elimination as a public health problem could benefit from a reanalysis. These analyses would define evidence-based targets that control programs could use to confidently assert that they had controlled or eliminated schistosomiasis as a public health problem. We estimated how low Schistosoma haematobium infection levels diagnosed by urine filtration in school-age children should be decreased so that microhematuria prevalence was at, or below, a "background" level of morbidity.MethodologyData obtained from school-age children in Burkina Faso, Mali, Niger, Tanzania, and Zambia who participated in schistosomiasis monitoring and evaluation cohorts were reanalyzed before and after initiation of preventive chemotherapy. Bayesian models estimated the infection level prevalence probabilities associated with microhematuria thresholds ≤10%, 13%, or 15%.Principal findingsAn infection prevalence of 5% could be a sensible target for urogenital schistosomiasis morbidity control in children as microhematuria prevalence was highly likely to be below 10% in all surveys. Targets of 8% and 11% infection prevalence were highly likely to result in microhematuria levels less than 13% and 15%, respectively. By contrast, measuring heavy-intensity infections only achieves these thresholds at impractically low prevalence levels.Conclusions/significanceA target of 5%, 8%, or 11% urogenital schistosomiasis infection prevalence in school-age children could be used to determine whether a geographic area has controlled or eliminated schistosomiasis as a public health problem depending on the local background threshold of microhematuria.

BackgroundWorld Health Organization (WHO) guidelines for measuring global progress in schistosomiasis control classify individuals with Schistosoma spp. infections based on the concentration of excreted eggs. We assessed the associations between WHO infection intensity categories and morbidity prevalence for selected S. haematobium and S. mansoni morbidities in school-age children.MethodologyA total of 22,488 children aged 6-15 years from monitoring and evaluation cohorts in Burkina Faso, Mali, Niger, Uganda, Tanzania, and Zambia from 2003-2008 were analyzed using Bayesian logistic regression. Models were utilized to evaluate associations between intensity categories and the prevalence of any urinary bladder lesion, any upper urinary tract lesion, microhematuria, and pain while urinating (for S. haematobium) and irregular hepatic ultrasound image pattern (C-F), enlarged portal vein, laboratory-confirmed diarrhea, and self-reported diarrhea (for S. mansoni) across participants with infection and morbidity data.Principal findingsS. haematobium infection intensity categories possessed consistent morbidity prevalence across surveys for multiple morbidities and participants with light infections had elevated morbidity levels, compared to negative participants. Conversely, S. mansoni infection intensity categories lacked association with prevalence of the morbidity measures assessed.Conclusions/significanceCurrent status infection intensity categories for S. haematobium were associated with morbidity levels in school-age children, suggesting urogenital schistosomiasis morbidity can be predicted by an individual's intensity category. Conversely, S. mansoni infection intensity categories were not consistently indicative of childhood morbidity at baseline or during the first two years of a preventive chemotherapy control program.

BackgroundThe prevalence of Schistosoma mansoni infection is usually assessed by the Kato-Katz diagnostic technique. However, Kato-Katz thick smears have low sensitivity, especially for light infections. Egg count models fitted on individual level data can adjust for the infection intensity-dependent sensitivity and estimate the 'true' prevalence in a population. However, application of these models is complex and there is a need for adjustments that can be done without modeling expertise. This study provides estimates of the 'true' S. mansoni prevalence from population summary measures of observed prevalence and infection intensity using extensive simulations parametrized with data from different settings in sub-Saharan Africa.MethodologyAn individual-level egg count model was applied to Kato-Katz data to determine the S. mansoni infection intensity-dependent sensitivity for various sampling schemes. Observations in populations with varying forces of transmission were simulated, using standard assumptions about the distribution of worms and their mating behavior. Summary measures such as the geometric mean infection, arithmetic mean infection, and the observed prevalence of the simulations were calculated, and parametric statistical models fitted to the summary measures for each sampling scheme. For validation, the simulation-based estimates are compared with an observational dataset not used to inform the simulation.Principal findingsOverall, the sensitivity of Kato-Katz in a population varies according to the mean infection intensity. Using a parametric model, which takes into account different sampling schemes varying from single Kato-Katz to triplicate slides over three days, both geometric and arithmetic mean infection intensities improve estimation of sensitivity. The relation between observed and 'true' prevalence is remarkably linear and triplicate slides per day on three consecutive days ensure close to perfect sensitivity.Conclusions/significanceEstimation of 'true' S. mansoni prevalence is improved when taking into account geometric or arithmetic mean infection intensity in a population. We supply parametric functions and corresponding estimates of their parameters to calculate the 'true' prevalence for sampling schemes up to 3 days with triplicate Kato-Katz thick smears per day that allow estimation of the 'true' prevalence.

Abstract Background Parasite prevalence has been used widely as a measure of malaria transmission, especially in malaria endemic areas. However, its contribution and relationship to malaria mortality across different age groups has not been well investigated. Previous studies in a health and demographic surveillance systems (HDSS) platform in western Kenya quantified the contribution of incidence and entomological inoculation rates (EIR) to mortality. The study assessed the relationship between outcomes of malaria parasitaemia surveys and mortality across age groups. Methods Parasitological data from annual cross-sectional surveys from the Kisumu HDSS between 2007 and 2015 were used to determine malaria parasite prevalence (PP) and clinical malaria (parasites plus reported fever within 24 h or temperature above 37.5 °C). Household surveys and verbal autopsy (VA) were used to obtain data on all-cause and malaria-specific mortality. Bayesian negative binomial geo-statistical regression models were used to investigate the association of PP/clinical malaria with mortality across different age groups. Estimates based on yearly data were compared with those from aggregated data over 4 to 5-year periods, which is the typical period that mortality data are available from national demographic and health surveys. Results Using 5-year aggregated data, associations were established between parasite prevalence and malaria-specific mortality in the whole population (RRmalaria = 1.66; 95% Bayesian Credible Intervals: 1.07–2.54) and children 1–4 years (RRmalaria = 2.29; 1.17–4.29). While clinical malaria was associated with both all-cause and malaria-specific mortality in combined ages (RRall-cause = 1.32; 1.01–1.74); (RRmalaria = 2.50; 1.27–4.81), children 1–4 years (RRall-cause = 1.89; 1.00–3.51); (RRmalaria = 3.37; 1.23–8.93) and in older children 5–14 years (RRall-cause = 3.94; 1.34–11.10); (RRmalaria = 7.56; 1.20–39.54), no association was found among neonates, adults (15–59 years) and the elderly (60+ years). Distance to health facilities, socioeconomic status, elevation and survey year were important factors for all-cause and malaria-specific mortality. Conclusion Malaria parasitaemia from cross-sectional surveys was associated with mortality across age groups over 4 to 5 year periods with clinical malaria more strongly associated with mortality than parasite prevalence. This effect was stronger in children 5–14 years compared to other age-groups. Further analyses of data from other HDSS sites or similar platforms would be useful in investigating the relationship between malaria and mortality across different endemicity levels.

Air quality monitoring across Europe is mainly based on in situ ground stations, which are too sparse to accurately assess the exposure effects of air pollution for the entire continent. The demand for precise predictive models that estimate gridded geophysical parameters of ambient air at high spatial resolution has rapidly grown. Here, we investigate the potential of satellite-derived products to improve particulate matter (PM) estimates. Bayesian geostatistical models addressing confounding between the spatial distribution of pollutants and remotely sensed predictors were developed to estimate yearly averages of both, fine (PM2.5) and coarse (PM10) surface PM concentrations, at 1 km2 spatial resolution over 46 European countries. Model outcomes were compared to geostatistical, geographically weighted and land-use regression formulations. Rigorous model selection identified the Earth observation data which contribute most to pollutants' estimation. Geostatistical models outperformed the predictive ability of the frequently employed land-use regression. The resulting estimates of PM10 and PM2.5, which represent the main air quality indicators for the urban Sustainable Development Goal, indicate that in 2016, 66.2% of the European population was breathing air above the WHO air quality guidelines thresholds. Our estimates are readily available to policy makers and scientists assessing the effects of long-term exposure to pollution on human and ecosystem health. Keywords: Particulate matter, Bayesian geostatistics, Integrated nested Laplace approximation, Aerosol optical depth, MAIAC, Copernicus

BackgroundSchistosomiasis is a parasitic disease caused by trematode worms of the genus Schistosoma and belongs to the neglected tropical diseases. The disease has been reported in 78 countries, with around 290.8 million people in need of treatment in 2018. Schistosomiasis is predominantly considered a rural disease with a subsequent focus of research and control activities in rural settings. Over the past decades, occurrence and even expansion of schistosomiasis foci in peri-urban and urban settings have increasingly been observed. Rural-urban migration in low- and middle-income countries and subsequent rapid and unplanned urbanization are thought to explain these observations. Fifty-five percent (55%) of the world population is already estimated to live in urban areas, with a projected increase to 68% by 2050. In light of rapid urbanization and the efforts to control morbidity and ultimately achieve elimination of schistosomiasis, it is important to deepen our understanding of the occurrence, prevalence, and transmission of schistosomiasis in urban and peri-urban settings. A systematic literature review looking at urban and peri-urban schistosomiasis was therefore carried out as a first step to address the research and mapping gap.MethodologyFollowing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic computer-aided literature review was carried out using PubMed, ScienceDirect, and the World Health Organization Database in November 2019, which was updated in March 2020. Only papers for which at least the abstract was available in English were used. Relevant publications were screened, duplicates were removed, guidelines for eligibility were applied, and eligible studies were reviewed. Studies looking at human Schistosoma infections, prevalence, and intensity of infection in urban and peri-urban settings were included as well as those focusing on the intermediate host snails.Principal findingsA total of 248 publications met the inclusion criteria. The selected studies confirm that schistosomiasis is prevalent in peri-urban and urban areas in the countries assessed. Earlier studies report higher prevalence levels in urban settings compared to data extracted from more recent publications, yet the challenge of migration, rapid uncontrolled urbanization, and resulting poor living conditions highlight the potential for continuous or even newly established transmission to take place.ConclusionsThe review indicates that schistosomiasis has long existed in urban and peri-urban areas and remains a public health problem. There is, however, a challenge of comparability of settings due to the lack of a clear definition of what constitutes urban and peri-urban. There is a pressing need for improved monitoring of schistosomiasis in urban communities and consideration of treatment strategies.

BackgroundTreatment needs for Schistosoma haematobium are commonly evaluated using urine filtration with detection of parasite eggs under a microscope. A common symptom of S. haematobium is hematuria, the passing of blood in urine. Hence, the use of hematuria-based diagnostic techniques as a proxy for the assessment of treatment needs has been considered. This study evaluates data from a national survey in Zimbabwe, where three hematuria-based diagnostic techniques, that is microhematuria, macrohematuria, and an anamnestic questionnaire pertaining to self-reported blood in urine, have been included in addition to urine filtration in 280 schools across 70 districts.MethodologyWe developed an egg count model, which evaluates the infection intensity-dependent sensitivity and the specificity of each diagnostic technique without relying on a 'gold' standard. Subsequently, we determined prevalence thresholds for each diagnostic technique, equivalent to a 10% urine filtration-based prevalence and compared classification of districts according to treatment strategy based on the different diagnostic methods.Principal findingsA 10% urine filtration prevalence threshold corresponded to a 17.9% and 13.3% prevalence based on questionnaire and microhematuria, respectively. Both the questionnaire and the microhematuria showed a sensitivity and specificity of more than 85% for estimating treatment needs at the above thresholds. For diagnosis at individual level, the questionnaire showed the highest sensitivity (70.0%) followed by urine filtration (53.8%) and microhematuria (52.2%).Conclusions/significanceThe high sensitivity and specificity of a simple questionnaire to estimate treatment needs of S. haematobium suggests that it can be used as a rapid, low-cost method to estimate district prevalence. Our modeling approach can be expanded to include setting-dependent specificity of the technique and should be assessed in relation to other diagnostic methods due to potential cross-reaction with other diseases.

# Background Despite massive investment in diseases responsible for fever among children less than five years in Uganda, the burden of the fever symptom remains high and disproportionately distributed across regions. In Uganda, studies assessing the relationship between fever prevalence and multiple childhood diseases, especially at a local scale are scanty. We analyse the 2016 Demographic and Health Survey (DHS) data to quantify the associations and contribution of childhood diseases to fever prevalence among children less than five years in Uganda at the national and regional level. Findings can lead to the development and implementation of preventive and treatment measures especially at a local scale to minimize fever-related diseases among the under-fives in Uganda. # Methods Bayesian geostatistical logistic regression models with spatially varying coefficients were fitted to determine the associations and contribution of childhood diseases on fever prevalence at the national and regional levels. Region-specific spatially varying coefficients were modeled by a conditional autoregressive distribution. Cluster-specific random effects were introduced into the model to take into account spatial dependence in fever prevalence. Bayesian geostatistical stochastic search variable selection was applied to determine the most important predictors in explaining variation in fever prevalence. The contribution of childhood diseases to fever prevalence was estimated using population attributable fractions. # Results The prevalence of fever was highest in Busoga and Teso regions and lowest in Bunyoro region. At the national level, the population attribution fraction of diarrhoea, acute respiratory infections (ARI) and malaria to the prevalence of fever in the under-five was 38.12 (95% Bayesian credible intervals, BCI=25.15-41.59), 30.99 (95% BCI=9.82-34.26) and 9.50 (95% BCI=2.34-25.15), respectively. The attribution of diarrhoea was common in all regions except Bunyoro, while ARI was more common in Bugisu, Karamoja and West Nile, and malaria was commonest in Bunyoro. In Lango, the attribution of diarrhoea and ARI was similar. # Conclusions Majority of fevers among the under-five are due to diarrhoea, followed by ARI. Hand washing with soap and water/detergent should be strengthened in all regions. Vaccination against ARI should be encouraged, in the regions of Central 2, Bugisu, Teso, Karamoja, Lango, West-Nile and Tooro. The health system should be reinforced to treat diarrhoea and ARI.

Bayesian geostatistical regression (GR) models estimate air pollution exposure at high spatial resolution, quantify the prediction uncertainty and provide probabilistic inference on the exceedance of air quality thresholds. However, due to high computational burden, previous GR models have provided gridded ambient nitrogen dioxide (NO2) concentrations at smaller areas of investigation. Here, we applied these models to estimate yearly averaged NO2 concentrations at 1 km2 spatial resolution across 44 European countries, integrating information from in situ monitoring stations, satellites and chemical transport model (CTM) simulations. The tropospheric values of NO2 derived from the ozone monitoring instrument (OMI) onboard the National Aeronautics and Space Administration’s (NASA’s) Aura satellite were converted to near ground NO2 concentration proxies using simulations from the 3-D global CTM (GEOS-Chem) at 0.5° × 0.625°spatial resolution and surface-to-column NO2 ratios. Simulations from the Ensemble of regional CTMs at spatial resolution of 0.1° × 0.1°were extracted from the Copernicus atmosphere monitoring service (CAMS). The contribution of these covariates to the predictive capability of geostatistical models was for the first time evaluated here through a rigorous model selection procedure along with additional continental high-resolution satellite-derived products, including novel data from the pan-European Copernicus land monitoring service (CLMS). The results have shown that the conversion of columnar NO2 values to surface quasi-observations yielded models with slightly better predictive ability and lower uncertainty. Nonetheless, the use of higher resolution CAMS-Ensemble simulations as covariates in GR models granted the most accurate surface NO2 estimates, showing that, in 2016, 16.17 (95% C.I. 6.34–29.96) million people in Europe, representing 2.97% (95% C.I. 1.16% - 5.50%) of the total population, were exposed to levels above the EU directive and WHO air quality guidelines threshold for NO2. Our estimates are readily available to policy makers and scientists assessing the burden of disease attributable to NO2 in 2016. Keywords: Nitrogen dioxide, Bayesian geostatistics, Ozone monitoring instrument (OMI), Chemical transport models (GEOS-Chem, CAMS-Ensemble), Copernicus, Air quality guidelines

Diarrhoeal diseases remain a major public health problem, causing more than half a million child deaths every year, particularly in low- and middle-income countries (LMICs). Despite existing knowledge on the aetiologies and causes of diarrhoeal diseases, relatively little is known about its spatial patterns in LMICs, including Senegal. In the present study, data from a cross-sectional survey carried out in 2016 were analysed to describe the spatial pattern of diarrhoeal prevalence in children under the age of 5 years in the secondary city of Mbour in the south-western part of Senegal. Bayesian conditional autoregressive (CAR) models with spatially varying coefficients were employed to determine the effect of sociodemographic, economic and climate parameters on diarrhoeal prevalence. We observed substantial spatial heterogeneities in diarrhoea prevalence. Risk maps, stratified by age group, showed that diarrhoeal prevalence was higher in children aged 25-59 months compared to their younger counterparts with the highest risk observed in the north and south peripheral neighbourhoods, especially in Grand Mbour, Médine, Liberté and Zone Sonatel. The posterior relative risk estimate obtained from the Bayesian CAR model indicated that a unit increase in the proportion of people with untreated stored drinking water was associated with a 29% higher risk of diarrhoea. A unit increase in rainfall was also associated with an increase in diarrhoea risk. Our findings suggest that public health officials should integrate disease mapping and cluster analyses and consider the varying effects of sociodemographic factors in developing and implementing areaspecific interventions for reducing diarrhoea.

Abstract Background Electronic reporting of routine health facility data in Uganda began with the adoption of the District Health Information Software System version 2 (DHIS2) in 2011. This has improved health facility reporting and overall data quality. In this study, the effects of case management with artemisinin-based combination therapy (ACT) and vector control interventions on space–time patterns of disease incidence were determined using DHIS2 data reported during 2013–2016. Methods Bayesian spatio-temporal negative binomial models were fitted on district-aggregated monthly malaria cases, reported by two age groups, defined by a cut-off age of 5 years. The effects of interventions were adjusted for socio-economic and climatic factors. Spatial and temporal correlations were taken into account by assuming a conditional autoregressive and a first-order autoregressive AR(1) process on district and monthly specific random effects, respectively. Fourier trigonometric functions were incorporated in the models to take into account seasonal fluctuations in malaria transmission. Results The temporal variation in incidence was similar in both age groups and depicted a steady decline up to February 2014, followed by an increase from March 2015 onwards. The trends were characterized by a strong bi-annual seasonal pattern with two peaks during May–July and September-December. Average monthly incidence in children

Abstract Background In 2011, the demographic and health survey (DHS) in Cameroon was combined with the multiple indicator cluster survey. Malaria parasitological data were collected, but the survey period did not overlap with the high malaria transmission season. A malaria indicator survey (MIS) was also conducted during the same year, within the malaria peak transmission season. This study compares estimates of the geographical distribution of malaria parasite risk and of the effects of interventions obtained from the DHS and MIS survey data. Methods Bayesian geostatistical models were applied on DHS and MIS data to obtain georeferenced estimates of the malaria parasite prevalence and to assess the effects of interventions. Climatic predictors were retrieved from satellite sources. Geostatistical variable selection was used to identify the most important climatic predictors and indicators of malaria interventions. Results The overall observed malaria parasite risk among children was 33 and 30% in the DHS and MIS data, respectively. Both datasets identified the Normalized Difference Vegetation Index and the altitude as important predictors of the geographical distribution of the disease. However, MIS selected additional climatic factors as important disease predictors. The magnitude of the estimated malaria parasite risk at national level was similar in both surveys. Nevertheless, DHS estimates lower risk in the North and Coastal areas. MIS did not find any important intervention effects, although DHS revealed that the proportion of population with an insecticide-treated nets access in their household was statistically important. An important negative relationship between malaria parasitaemia and socioeconomic factors, such as the level of mother’s education, place of residence and the household welfare were captured by both surveys. Conclusion Timing of the malaria survey influences estimates of the geographical distribution of disease risk, especially in settings with seasonal transmission. In countries with different ecological zones and thus different seasonal patterns, a single survey may not be able to identify all high risk areas. A continuous MIS or a combination of MIS, health information system data and data from sentinel sites may be able to capture the disease risk distribution in space across different seasons.

Abstract 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 Background Malaria transmission reduction is a goal of many malaria control programmes. Little is known of how much mortality can be reduced by specific reductions in transmission. Verbal autopsy (VA) is widely used for estimating malaria specific mortality rates, but does not reliably distinguish malaria from other febrile illnesses. Overall malaria attributable mortality includes both direct and indirect deaths. It is unclear what proportion of the deaths averted by reducing malaria transmission are classified as malaria in VA. Methods Both all-cause, and cause-specific mortality reported by VA for children under 5 years of age, were assembled from the KEMRI/CDC health and demographic surveillance system in Siaya county, rural Western Kenya for the years 2002–2004. These were linked to household-specific estimates of the Plasmodium falciparum entomological inoculation rate (EIR) based on high resolution spatio-temporal geostatistical modelling of entomological data. All-cause and malaria specific mortality (by VA), were analysed in relation to EIR, insecticide-treated net use (ITN), socioeconomic status (SES) and parameters describing space–time correlation. Time at risk for each child was analysed using Bayesian geostatistical Cox proportional hazard models, with time-dependent covariates. The outputs were used to estimate the diagnostic performance of VA in measuring mortality that can be attributed to malaria exposure. Results The overall under-five mortality rate was 80 per 1000 person-years during the study period. Eighty-one percent of the total deaths were assigned causes of death by VA, with malaria assigned as the main cause of death except in the neonatal period. Although no trend was observed in malaria-specific mortality assessed by VA, ITN use was associated with reduced all-cause mortality in infants (hazard ratio 0.15, 95% CI 0.02, 0.63) and the EIR was strongly associated with both all-cause and malaria-specific mortality. 48.2% of the deaths could be attributed to malaria by analysing the exposure–response relationship, though only 20.5% of VAs assigned malaria as the cause and the sensitivity of VAs was estimated to be only 26%. Although VAs assigned some deaths to malaria even in areas where there was estimated to be no exposure, the specificity of the VAs was estimated to be 85%. Conclusion Interventions that reduce P. falciparum transmission intensity will not only significantly reduce malaria-diagnosed mortality, but also mortality assigned to other causes in under-5 year old children in endemic areas. In this setting, the VA tool based on clinician review substantially underestimates the number of deaths that could be averted by reducing malaria exposure in childhood, but has a reasonably high specificity. This suggests that malaria transmission-reducing interventions such as ITNs can potentially reduce overall child mortality by as much as twice the total direct malaria burden estimated from VAs.

Abstract Background In Uganda, malaria vector control interventions and case management with Artemisinin Combination Therapies (ACTs) have been scaled up over the last few years as a result of increased funding. Data on parasitaemia prevalence among children less than 5 years old and coverage of interventions was collected during the first two Malaria Indicator Surveys (MIS) conducted in 2009 and 2014, respectively. In this study, we quantify the effects of control interventions on parasitaemia risk changes between the two MIS in a spatio-temporal analysis. Methods Bayesian geostatistical and temporal models were fitted on the MIS data of 2009 and 2014. The models took into account geographical misalignment in the locations of the two surveys and adjusted for climatic changes and socio-economic differentials. Parasitaemia risk was predicted over a 2 × 2 km2 grid and the number of infected children less than 5 years old was estimated. Geostatistical variable selection was applied to identify the most important ITN coverage indicators. A spatially varying coefficient model was used to estimate intervention effects at sub-national level. Results The coverage of Insecticide Treated Nets (ITNs) and ACTs more than doubled at country and sub-national levels during the period 2009–2014. The coverage of Indoor Residual Spraying (IRS) remained static at all levels. ITNs, IRS, and ACTs were associated with a reduction in parasitaemia odds of 19% (95% BCI: 18–29%), 78% (95% BCI: 67–84%), and 34% (95% BCI: 28–66%), respectively. Intervention effects varied with region. Higher socio-economic status and living in urban areas were associated with parasitaemia odds reduction of 46% (95% BCI: 0.51–0.57) and 57% (95% BCI: 0.40–0.53), respectively. The probability of parasitaemia risk decline in the country was 85% and varied from 70% in the North-East region to 100% in Kampala region. The estimated number of children infected with malaria declined from 2,480,373 in 2009 to 825,636 in 2014. Conclusions Interventions have had a strong effect on the decline of parasitaemia risk in Uganda during 2009–2014, albeit with varying magnitude in the regions. This success should be sustained by optimizing ITN coverage to achieve universal coverage.

Abstract Background Diarrhoeal diseases remain an important cause of mortality and morbidity among children, particularly in low- and middle-income countries. In Senegal, diarrhoea is responsible for 15% of all deaths in children under the age of five and is the third leading cause of childhood deaths. For targeted planning and implementation of prevention strategies, a context-specific understanding of the determinants of diarrhoeal diseases is needed. The aim of this study was to identify risk factors of diarrhoeal diseases in children under the age of five in Mbour, Senegal. Methods Between February and March 2014, a cross-sectional survey was conducted in four zones of Mbour to estimate the burden of diarrhoeal diseases (i.e. diarrhoea episodes in the 2 weeks preceding the survey) and associated risk factors. The zones covered urban central, peri-central, north peripheral and south peripheral areas. Overall, 596 households were surveyed by a questionnaire, yielding information on sociodemographic, environmental and hygiene behavioural factors. Univariable and multivariable logistic regression analyses were used to identify risk factors associated with the occurrence of diarrhoea. Results The reported prevalence of diarrhoea among children under the age of five during the 2 weeks preceding the survey was 26%. Without adjustment, the highest diarrhoea prevalence rates were observed in the peri-central (44.8%) and urban central zones (36.3%). Multivariable regression revealed significant associations between diarrhoeal diseases and unemployment of mothers (adjusted odds ratio [aOR] = 1.62, 95% confidence interval [CI]: 1.18–2.23), use of open bags for storing household waste (aOR = 1.75, 95% CI: 1.00–3.02), evacuation of household waste in public streets (aOR = 2.07, 95% CI: 1.20–3.55), no treatment of stored drinking water (aOR = 1.69, 95% CI: 1.11–2.56) and use of shared toilets (aOR = 1.69, 95% CI: 1.11–2.56). Conclusion We found a high prevalence of diarrhoea in children under the age of five in Mbour, with the highest prevalence occurring in the central and peri-central areas. These findings underscore the need for public health interventions to alleviate the burden of diarrhoea among vulnerable groups. Promotion of solid waste disposal and reduction of wastewater exposure should be implemented without delay.

BackgroundIn South Asia, hundreds of millions of people are infected with soil-transmitted helminths (Ascaris lumbricoides, hookworm, and Trichuris trichiura). However, high-resolution risk profiles and the estimated number of people infected have yet to be determined. In turn, such information will assist control programs to identify priority areas for allocation of scarce resource for the control of soil-transmitted helminth infection.MethodologyWe pursued a systematic review to identify prevalence surveys pertaining to soil-transmitted helminth infections in four mainland countries (i.e., Bangladesh, India, Nepal, and Pakistan) of South Asia. PubMed and ISI Web of Science were searched from inception to April 25, 2019, without restriction of language, study design, and survey date. We utilized Bayesian geostatistical models to identify environmental and socioeconomic predictors, and to estimate infection risk at high spatial resolution across the study region.Principal findingsA total of 536, 490, and 410 georeferenced surveys were identified for A. lumbricoides, hookworm, and T. trichiura, respectively. We estimate that 361 million people (95% Bayesian credible interval (BCI) 331-395 million), approximately one-quarter of the South Asia population, was infected with at least one soil-transmitted helminth species in 2015. A. lumbricoides was the predominant species. Moderate to high prevalence (>20%) of any soil-transmitted helminth infection was predicted in the northeastern part and some northern areas of the study region, as well as the southern coastal areas of India. The annual treatment needs for the school-age population requiring preventive chemotherapy was estimated at 165 million doses (95% BCI: 146-185 million).Conclusions/significanceOur risk maps provide an overview of the geographic distribution of soil-transmitted helminth infection in four mainland countries of South Asia and highlight the need for up-to-date surveys to accurately evaluate the disease burden in the region.

BackgroundStrongyloides stercoralis is a neglected soil-transmitted helminth that occurs worldwide, though it is particularly endemic in tropical and subtropical areas. It can cause long-lasting and potentially fatal infections due to its ability to replicate within its host. S. stercoralis causes gastrointestinal and dermatological morbidity. The objective of this study was to assess the S. stercoralis infection risk and, using geostatistical models, to predict its geographical distribution in Cambodia.Methodology / principal findingsA nation-wide, community-based parasitological survey was conducted among the Cambodian population, aged 6 years and older. S. stercoralis was diagnosed using a serological diagnostic test that detects IgG antibodies in urine. Data on demography, hygiene and knowledge about helminth infection were collected. S. stercoralis prevalence among 7,246 participants with a complete data record was 30.5%, ranging from 10.9% to 48.2% across provinces. The parasite was ubiquitous in Cambodia; only five south-eastern provinces had prevalence rates below 20%. Infection risk increased with age for both men and women, although girls under the age of 13 and women aged 50 years and over had lower odds of infection than their male counterparts. Open defecation was associated with higher odds of infection, while having some knowledge of the health problems caused by worms was a protective factor. Infection risk was positively associated with nighttime maximum temperature, minimum rainfall, and distance to water; it was negatively associated with land occupied by rice fields.Conclusions / significanceS. stercoralis infection is rampant in Cambodia. Control programs delivering ivermectin are needed to manage the parasite. However, the high cost of this drug in Cambodia currently precludes the implementation of control initiatives. Donations, subsidies or affordable generics are needed so that S. stercoralis, which infects almost a third of the Cambodian population, can be addressed through an adequate control program.

Introduction: Information on the causes of death among under-five children is key in designing and implementation of appropriate interventions. In Uganda, civil death registration is incomplete which limits the estimation of disease-related mortality burden especially at a local scale. In the absence of routine cause-specific data, we used household surveys to quantify the effects and contribution of main childhood diseases such as malaria, severe or moderate anaemia, severe or moderate malnutrition, diarrhoea and acute respiratory infections (ARIs) on all-cause under-five mortality (U5M) at national and sub-national levels. We related all-cause U5M with risks of childhood diseases after adjusting for geographical disparities in coverages of health interventions, socio-economic, environmental factors and disease co-endemicities. Methods: Data on U5M, disease prevalence, socio-economic and intervention coverage indicators were obtained from the 2011 Demographic and Health Survey, while data on malaria prevalence were extracted from the 2009 Malaria Indicator Survey. Bayesian geostatistical Weibull proportional hazards models with spatially varying disease effects at sub-national scales were fitted to quantify the associations between childhood diseases and the U5M. Spatial correlation between clusters was incorporated via locational random effects while region-specific random effects with conditional autoregressive prior distributions modeled the geographical variation in the effects of childhood diseases. The models addressed geographical misalignment in the locations of the two surveys. The contribution of childhood diseases to under-five mortality was estimated using population attributable fractions. Results: The overall U5M rate was 90 deaths per 1000 live births. Large regional variations in U5M rates were observed, lowest in Kampala at 56 and highest in the North-East at 152 per 1000 live births. National malaria parasitemia prevalence was 42%, with Kampala experiencing the lowest of 5% and the Mid-North the highest of 62%. About 27% of Ugandan children aged 6–59 months were severely or moderately anaemic; lowest in South-West (8%) and highest in East-Central (46%). Overall, 17% of children were either severely or moderately malnourished. The percentage of moderately/severely malnourished children varied by region with Kampala having the lowest (8%) and North-East the highest (45%). Nearly a quarter of the children under-five years were reported to have diarrhoea at national level, and this proportion was highest in East-Central (32%) and Mid-Eastern (33%) and lowest in South-West (14%). Overall, ARIs in the two weeks before the survey was 15%; highest in Mid-North (22%) and lowest in Central 1 (9%). At national level, the U5M was associated with prevalence of malaria (hazard ratio (HR) = 1.74; 95% BCI: 1.42, 2.16), severe or moderate anaemia (HR =1.37; 95% BCI: 1.20, 1.75), severe or moderate malnutrition (HR = 1.49; 95% BCI: 1.25, 1.66) and diarrhoea (HR = 1.61; 95% BCI: 1.31, 2.05). The relationship between malaria and U5M was important in the regions of Central 2, East-Central, Mid-North, North-East and West-Nile. Diarrhoea was associated with under-five deaths in Central 2, East-central, Mid-Eastern and Mid-Western. Moderate/severe malnutrition was associated with U5M in East-Central, Mid-Eastern and North-East. Moderate/severe anaemia was associated with deaths in Central 1, Kampala, Mid-North, Mid-Western, North-East, South-West and West-Nile.At the national level, 97% (PAF = 96.9; 95%BCI: 94.4, 98.0), 91% (PAF = 90.9; 95%BCI: 84.4, 95.3), 89% (PAF = 89.3; 95%BCI: 76.0,93.8) and 93% (PAF = 93.3 95%BCI: 87.7,96.0) of the deaths among children less than five years in Uganda were attributable to malaria, severe/moderate anaemia, severe/moderate malnutrition and diarrhoea respectively. The attribution of malaria was comparable in Central 2, East-Central, Mid-North, North-East and West-Nile while severe/moderate anaemia was more common in all regions except Central 2, East-Central and Mid-Eastern. The attribution of diarrhoea in Central 2, East-Central, Mid-Eastern and Mid-Western was similar. The attribution of severe/moderate malnutrition was common in East-Central, Mid-Eastern and North-East. Conclusion: In Uganda, the contribution and effects of childhood diseases on U5M vary by region. Majority of the under-five deaths are due to malaria, followed by diarrhoea, severe/moderate anaemia and severe/moderate malnutrition. Thus, strengthening disease-specific interventions especially in the affected regions may be an important strategy to accelerate progress towards the reduction of the U5M as per the SDG target by 2030. In particular, Indoor Residual Spraying, iron supplementation, deworming, exclusive breastfeeding, investment in nutrition and education in nutrition practices, oral rehydration therapy or recommended home fluid, improved sanitation facilities should be improved. Keywords: DHS, Under-five mortality, Malaria, Anaemia, Malnutrition, Diarrhoea, Respiratory infections, Population attributable fractions, Bayesian geostatistical inference, Uganda

BACKGROUND:Over the past 15 years, scaling up of cost effective interventions resulted in a remarkable decline of under-five mortality rates (U5MR) in sub-Saharan Africa. However, the reduction shows considerable heterogeneity. We estimated the association of child, maternal, and household interventions with U5MR in Burkina Faso at national and subnational levels and identified the regions with least effective interventions. METHODS:Data on health-related interventions and U5MR were extracted from the Burkina Faso Demographic and Health Survey (DHS) 2010. Bayesian geostatistical proportional hazards models with a Weibull baseline hazard were fitted on the mortality outcome. Spatially varying coefficients were considered to assess the geographical variation in the association of the health interventions with U5MR. The analyses were adjusted for child, maternal, and household characteristics, as well as climatic and environmental factors. FINDINGS:The average U5MR was as high as 128 per 1000 ranging from 81 (region of Centre-Est) to 223 (region of Sahel). At national level, DPT3 immunization and baby post-natal check within 24 hours after birth had the most important association with U5MR (hazard rates ratio (HRR) = 0.89, 95% Bayesian credible interval (BCI): 0.86-0.98 and HRR = 0.89, 95% BCI: 0.86-0.92, respectively). At sub-national level, the most effective interventions are the skilled birth attendance, and improved drinking water, followed by baby post-natal check within 24 hours after birth, vitamin A supplementation, antenatal care visit and all-antigens immunization (including BCG, Polio3, DPT3, and measles immunization). Centre-Est, Sahel, and Sud-Ouest were the regions with the highest number of effective interventions. There was no intervention that had a statistically important association with child survival in the region of Hauts Bassins. INTERPRETATION:The geographical variation in the magnitude and statistical importance of the association between health interventions and U5MR raises the need to deliver and reinforce health interventions at a more granular level. Priority interventions are DPT3 immunization, skilled birth attendance, baby post-natal visits in the regions of Sud-Ouest, Sahel, and Hauts Bassins, respectively. Our methodology could be applied to other national surveys, as it allows an incisive, data-driven and specific decision-making approach to optimize the allocation of health interventions at subnational level.

BackgroundIntervention guidelines against Schistosoma mansoni are based on the Kato-Katz technique. However, Kato-Katz thick smears show low sensitivity, especially for light-intensity infections. The point-of-care circulating cathodic antigen (POC-CCA) is a promising rapid diagnostic test detecting antigen output of living worms in urine and results are reported as trace, 1+, 2+, and 3+. The use of POC-CCA for schistosomiasis mapping, control, and surveillance requires translation of the Kato-Katz prevalence thresholds into POC-CCA relative treatment cut-offs. Furthermore, the infection status of egg-negative but antigen-positive individuals and the intensity-dependent sensitivity of POC-CCA should be estimated to determine its suitability for verification of disease elimination efforts.MethodologyWe used data from settings in Africa and the Americas characterized by a wide range of S. mansoni endemicity. We estimated infection intensity-dependent sensitivity and specificity of each test at the unit of the individual, using a hierarchical Bayesian egg-count model that removes the need to define a 'gold' standard applied to data with multiple Kato-Katz thick smears and POC-CCA urine cassette tests. A simulation study was carried out based on the model estimates to assess the relation of the two diagnostic tests for different endemicity scenarios.Principal findingsPOC-CCA showed high specificity (> 95%), and high sensitivity (> 95%) for moderate and heavy infection intensities, and moderate sensitivity (> 75%) for light infection intensities, and even for egg-negative but antigen-positive infections. A 10% duplicate slide Kato-Katz thick smear prevalence corresponded to a 15-40% prevalence of ≥ trace-positive POC-CCA, and 10-20% prevalence of ≥ 1+ POC-CCA. The prevalence of ≥ 2+ POC-CCA corresponded directly to single slide Kato-Katz prevalence for all prevalence levels.Conclusions/significanceThe moderate sensitivity of POC-CCA, even for very light S. mansoni infections where the sensitivity of Kato-Katz is very low, and the identified relationship between Kato-Katz and POC-CCA prevalence thresholds render the latter diagnostic tool useful for surveillance and initial estimation of elimination of S. mansoni. For prevalence below 10% based on a duplicate slide Kato-Katz thick smear, we suggest using POC-CCA including trace results to evaluate treatment needs and propose new intervention thresholds that need to be validated in different settings.

Background: Although malaria burden in Uganda has declined since 2009 following the scale-up of interventions, the disease is still the leading cause of hospitalization and death. Transmission remains high and is driven by suitable weather conditions. There is a real concern that intervention gains may be reversed by climatic changes in the country. In this study, we investigate the effects of climate on the spatio-temporal trends of malaria incidence in Uganda during 2013–2017. Methods: Bayesian spatio-temporal negative binomial models were fitted on district-aggregated monthly malaria cases, reported by two age groups, defined by a cut-off age of 5 years. Weather data was obtained from remote sensing sources including rainfall, day land surface temperature (LSTD) and night land surface temperature (LSTN), Normalized Difference Vegetation Index (NDVI), altitude, land cover, and distance to water bodies. Spatial and temporal correlations were taken into account by assuming a conditional autoregressive and a first-order autoregressive process on district and monthly specific random effects, respectively. Fourier trigonometric functions modeled seasonal fluctuations in malaria transmission. The effects of climatic changes on the malaria incidence changes between 2013 and 2017 were estimated by modeling the difference in time varying climatic conditions at the two time points and adjusting for the effects of intervention coverage, socio-economic status and health seeking behavior. Results: Malaria incidence declined steadily from 2013 to 2015 and then increased in 2016. The decrease was by over 38% and 20% in children

Over one billion people are infected with soil-transmitted helminths (STH), i.e. Ascaris lumbricoides, hookworm and Trichuris trichiura. For estimating drug efficacy and monitoring anthelminthic drug resistance, accurate diagnostic methods are critical. FECPAKG2 is a new remote-diagnostic tool used in veterinary medicine, which produces an image of the stool sample that can be stored on an internet cloud. We compared for the first time FECPAKG2 with the recommended Kato-Katz method.Two stool samples were collected from adolescent participants (age 15-18 years) at baseline and 14 to 21 days after treatment in the framework of a randomized clinical trial on Pemba Island, Tanzania. Stool samples were analyzed with different diagnostic efforts: i) one or ii) two Kato-Katz thick smears from the first sample, iii) two Kato-Katz thick smears from two samples and iv) FECPAKG2 from the first sample. Parameters were calculated based on a hierarchical Bayesian egg count model. Complete data for all diagnostic efforts were available from 615 participants at baseline and 231 hookworm-positive participants at follow-up. At baseline FECPAKG2 revealed a sensitivity of 75.6% (72.0-77.7) for detecting A. lumbricoides, 71.5% (67.4-95.3) for hookworm and 65.8% (64.9-66.2) for T. trichiura, which was significantly lower (all p

The Kato-Katz technique is the most widely used diagnostic method in epidemiologic surveys and drug efficacy trials pertaining to intestinal schistosomiasis and soil-transmitted helminthiasis. However, the sensitivity of the technique is low, particularly for the detection of light-intensity helminth infections. Examination of multiple stool samples reduces the diagnostic error; yet, most studies rely on a single Kato-Katz thick smear, thus underestimating infection prevalence. We present a model which estimates the sensitivity of the Kato-Katz technique in Schistosoma mansoni and hookworm, as a function of infection intensity for repeated stool sampling and provide estimates of the age-dependent 'true' prevalence. We find that the sensitivity for S. mansoni diagnosis is dominated by missed light infections, which have a low probability to be diagnosed correctly even through repeated sampling. The overall sensitivity strongly depends on the mean infection intensity. In particular at an intensity of 100 eggs per gram of stool (EPG), we estimate a sensitivity of 50% and 80% for one and two samples, respectively. At an infection intensity of 300 EPG, we estimate a sensitivity of 62% for one sample and 90% for two samples. The sensitivity for hookworm diagnosis is dominated by day-to-day variation with typical values for one, two, three, and four samples equal to 50%, 75%, 85%, and 95%, respectively, while it is only weakly dependent on the mean infection intensity in the population. We recommend taking at least two samples and estimate the 'true' prevalence of S. mansoni considering the dependence of the sensitivity on the mean infection intensity and the 'true' hookworm prevalence by taking into account the sensitivity given in the current study.

Abstract Background Quantification of malaria heterogeneity is very challenging, partly because of the underlying characteristics of mosquitoes and also because malaria is an environmentally driven disease. Furthermore, in order to assess the spatial and seasonal variability in malaria transmission, vector data need to be collected repeatedly over time (at fixed geographical locations). Measurements collected at locations close to each other and over time tend to be correlated because of common exposures such as environmental or climatic conditions. Non- spatial statistical methods, when applied to analyze such data, may lead to biased estimates. We developed rigorous methods for analyzing sparse and spatially correlated data. We applied Bayesian variable selection to identify the most important predictors as well as the elapsing time between climate suitability and changes in entomological indices. Methods Bayesian geostatistical zero-inflated binomial and negative binomial models including harmonic seasonal terms, temporal trends and climatic remotely sensed proxies were applied to assess spatio-temporal variation of sporozoite rate and mosquito density in the study area. Bayesian variable selection was employed to determine the most important climatic predictors and elapsing (lag) time between climatic suitability and malaria transmission. Bayesian kriging was used to predict mosquito density and sporozoite rate at unsampled locations. These estimates were converted to covariate and season-adjusted maps of entomological inoculation rates. Models were fitted using Markov chain Monte Carlo simulation. Results The results show that Anophele. gambiae is the most predominant vector (79.29%) and is more rain-dependant than its sibling Anophele. funestus (20.71%). Variable selection suggests that the two species react differently to different climatic conditions. Prediction maps of entomological inoculation rate (EIR) depict a strong spatial and temporal heterogeneity in malaria transmission risk despite the relatively small geographical extend of the study area. Conclusion Malaria transmission is very heterogeneous over the study area. The EIR maps clearly depict a strong spatial and temporal heterogeneity despite the relatively small geographical extend of the study area. Model based estimates of transmission can be used to identify high transmission areas in order to prioritise interventions and support research in malaria epidemiology.

Temperature, precipitation and humidity are known to be important factors for the development of schistosome parasites as well as their intermediate snail hosts. Climate therefore plays an important role in determining the geographical distribution of schistosomiasis and it is expected that climate change will alter distribution and transmission patterns. Reliable predictions of distribution changes and likely transmission scenarios are key to efficient schistosomiasis intervention-planning. However, it is often difficult to assess the direction and magnitude of the impact on schistosomiasis induced by climate change, as well as the temporal transferability and predictive accuracy of the models, as prevalence data is often only available from one point in time. We evaluated potential climate-induced changes on the geographical distribution of schistosomiasis in Zimbabwe using prevalence data from two points in time, 29 years apart; to our knowledge, this is the first study investigating this over such a long time period. We applied historical weather data and matched prevalence data of two schistosome species (Schistosoma haematobium and S. mansoni). For each time period studied, a Bayesian geostatistical model was fitted to a range of climatic, environmental and other potential risk factors to identify significant predictors that could help us to obtain spatially explicit schistosomiasis risk estimates for Zimbabwe. The observed general downward trend in schistosomiasis prevalence for Zimbabwe from 1981 and the period preceding a survey and control campaign in 2010 parallels a shift towards a drier and warmer climate. However, a statistically significant relationship between climate change and the change in prevalence could not be established.

BACKGROUND:Clonorchiasis, one of the most important food-borne trematodiases, affects more than 12 million people in the People's Republic of China (P.R. China). Spatially explicit risk estimates of Clonorchis sinensis infection are needed in order to target control interventions. METHODOLOGY:Georeferenced survey data pertaining to infection prevalence of C. sinensis in P.R. China from 2000 onwards were obtained via a systematic review in PubMed, ISI Web of Science, Chinese National Knowledge Internet, and Wanfang Data from January 1, 2000 until January 10, 2016, with no restriction of language or study design. Additional disease data were provided by the National Institute of Parasitic Diseases, Chinese Center for Diseases Control and Prevention in Shanghai. Environmental and socioeconomic proxies were extracted from remote-sensing and other data sources. Bayesian variable selection was carried out to identify the most important predictors of C. sinensis risk. Geostatistical models were applied to quantify the association between infection risk and the predictors of the disease, and to predict the risk of infection across P.R. China at high spatial resolution (over a grid with grid cell size of 5×5 km). PRINCIPAL FINDINGS:We obtained clonorchiasis survey data at 633 unique locations in P.R. China. We observed that the risk of C. sinensis infection increased over time, particularly from 2005 onwards. We estimate that around 14.8 million (95% Bayesian credible interval 13.8-15.8 million) people in P.R. China were infected with C. sinensis in 2010. Highly endemic areas (≥ 20%) were concentrated in southern and northeastern parts of the country. The provinces with the highest risk of infection and the largest number of infected people were Guangdong, Guangxi, and Heilongjiang. CONCLUSIONS/SIGNIFICANCE:Our results provide spatially relevant information for guiding clonorchiasis control interventions in P.R. China. The trend toward higher risk of C. sinensis infection in the recent past urges the Chinese government to pay more attention to the public health importance of clonorchiasis and to target interventions to high-risk areas.

The effect of malaria exposure on mortality using health facility incidence data as a measure of transmission has not been well investigated. Health and demographic surveillance systems (HDSS) routinely capture data on mortality, interventions and other household related indicators, offering a unique platform for estimating and monitoring the incidence-mortality relationship in space and time.Mortality data from the HDSS located in Western Kenya collected from 2007 to 2012 and linked to health facility incidence data were analysed using Bayesian spatio-temporal survival models to investigate the relation between mortality (all-cause/malaria-specific) and malaria incidence across all age groups. The analysis adjusted for insecticide-treated net (ITN) ownership, socio-economic status (SES), distance to health facilities and altitude. The estimates obtained were used to quantify excess mortality due to malaria exposure.Our models identified a strong positive relationship between slide positivity rate (SPR) and all-cause mortality in young children 1-4 years (HR = 4.29; 95% CI: 2.78-13.29) and all ages combined (HR = 1.55; 1.04-2.80). SPR had a strong positive association with malaria-specific mortality in young children (HR = 9.48; 5.11-37.94), however, in older children (5-14 years), it was associated with a reduction in malaria specific mortality (HR = 0.02; 0.003-0.33).SPR as a measure of transmission captures well the association between malaria transmission intensity and all-cause/malaria mortality. This offers a quick and efficient way to monitor malaria burden. Excess mortality estimates indicate that small changes in malaria incidence substantially reduce overall and malaria specific mortality.

BACKGROUND:Malaria burden in Uganda has declined disproportionately among regions despite overall high intervention coverage across all regions. The Uganda Malaria Indicator Survey (MIS) 2014-15 was the second nationally representative survey conducted to provide estimates of malaria prevalence among children less than 5 years, and to track the progress of control interventions in the country. In this present study, 2014-15 MIS data were analysed to assess intervention effects on malaria prevalence in Uganda among children less than 5 years, assess intervention effects at regional level, and estimate geographical distribution of malaria prevalence in the country. METHODS:Bayesian geostatistical models with spatially varying coefficients were used to determine the effect of interventions on malaria prevalence at national and regional levels. Spike-and-slab variable selection was used to identify the most important predictors and forms. Bayesian kriging was used to predict malaria prevalence at unsampled locations. RESULTS:Indoor Residual Spraying (IRS) and Insecticide Treated Nets (ITN) ownership had a significant but varying protective effect on malaria prevalence. However, no effect was observed for Artemisinin Combination-based Therapies (ACTs). Environmental factors, namely, land cover, rainfall, day and night land surface temperature, and area type were significantly associated with malaria prevalence. Malaria prevalence was higher in rural areas, increased with the child's age, and decreased with higher household socioeconomic status and higher level of mother's education. The highest prevalence of malaria in children less than 5 years was predicted for regions of East Central, North East and West Nile, whereas the lowest was predicted in Kampala and South Western regions, and in the mountainous areas in Mid-Western and Mid-Eastern regions. CONCLUSIONS:IRS and ITN ownership are important interventions against malaria prevalence in children less than 5 years in Uganda. The varying effects of the interventions calls for selective implementation of control tools suitable to regional ecological settings. To further reduce malaria burden and sustain malaria control in Uganda, current tools should be supplemented by health system strengthening, and socio-economic development.

Abstract Background In Brazil, preventive chemotherapy targeting soil-transmitted helminthiasis is being scaled-up. Hence, spatially explicit estimates of infection risks providing information about the current situation are needed to guide interventions. Available high-resolution national model-based estimates either rely on analyses of data restricted to a given period of time, or on historical data collected over a longer period. While efforts have been made to take into account the spatial structure of the data in the modelling approach, little emphasis has been placed on the temporal dimension. Methods We extracted georeferenced survey data on the prevalence of infection with soil-transmitted helminths (i.e. Ascaris lumbricoides, hookworm and Trichuris trichiura) in Brazil from the Global Neglected Tropical Diseases (GNTD) database. Selection of the most important predictors of infection risk was carried out using a Bayesian geostatistical approach and temporal models that address non-linearity and correlation of the explanatory variables. The spatial process was estimated through a predictive process approximation. Spatio-temporal models were built on the selected predictors with integrated nested Laplace approximation using stochastic partial differential equations. Results Our models revealed that, over the past 20 years, the risk of soil-transmitted helminth infection has decreased in Brazil, mainly because of the reduction of A. lumbricoides and hookworm infections. From 2010 onwards, we estimate that the infection prevalences with A. lumbricoides, hookworm and T. trichiura are 3.6%, 1.7% and 1.4%, respectively. We also provide a map highlighting municipalities in need of preventive chemotherapy, based on a predicted soil-transmitted helminth infection risk in excess of 20%. The need for treatments in the school-aged population at the municipality level was estimated at 1.8 million doses of anthelminthic tablets per year. Conclusions The analysis of the spatio-temporal aspect of the risk of infection with soil-transmitted helminths contributes to a better understanding of the evolution of risk over time. Risk estimates provide the soil-transmitted helminthiasis control programme in Brazil with useful benchmark information for prioritising and improving spatial and temporal targeting of interventions.

Background: Malaria transmission is measured using entomological inoculation rate (EIR), number of infective mosquito bites/person/unit time. Understanding heterogeneity of malaria transmission has been difficult due to a lack of appropriate data. A comprehensive entomological database compiled by the Malaria Transmission Intensity and Mortality Burden across Africa (MTIMBA) project (2001–2004) at several sites is the most suitable dataset for studying malaria transmission–mortality relations. The data are sparse and large, with small-scale spatial–temporal variation. Objective: This work demonstrates a rigorous approach for analysing large and highly variable entomological data for the study of malaria transmission heterogeneity, measured by EIR, within the Rufiji Demographic Surveillance System (DSS), MTIMBA project site in Tanzania. Design: Bayesian geostatistical binomial and negative binomial models with zero inflation were fitted for sporozoite rates (SRs) and mosquito density, respectively. The spatial process was approximated from a subset of locations. The models were adjusted for environmental effects, seasonality and temporal correlations and assessed based on their predictive ability. EIR was calculated using model-based predictions of SR and density. Results: Malaria transmission was mostly influenced by rain and temperature, which significantly reduces the probability of observing zero mosquitoes. High transmission was observed at the onset of heavy rains. Transmission intensity reduced significantly during Year 2 and 3, contrary to the Year 1, pronouncing high seasonality and spatial variability. The southern part of the DSS showed high transmission throughout the years. A spatial shift of transmission intensity was observed where an increase in households with very low transmission intensity and significant reduction of locations with high transmission were observed over time. Over 68 and 85% of the locations selected for validation for SR and density, respectively, were correctly predicted within 95% credible interval indicating good performance of the models. Conclusion: Methodology introduced here has the potential for efficient assessment of the contribution of malaria transmission in mortality and monitoring performance of control and intervention strategies.

Soil-transmitted helminths (Ascaris lumbricoides, Trichuris trichiura and hookworm) negatively impact the health and wellbeing of hundreds of millions of people, particularly in tropical and subtropical countries, including Brazil. Reliable maps of the spatial distribution and estimates of the number of infected people are required for the control and eventual elimination of soil-transmitted helminthiasis. We used advanced Bayesian geostatistical modelling, coupled with geographical information systems and remote sensing to visualize the distribution of the three soil-transmitted helminth species in Brazil. Remotely sensed climatic and environmental data, along with socioeconomic variables from readily available databases were employed as predictors. Our models provided mean prevalence estimates for A. lumbricoides, T. trichiura and hookworm of 15.6%, 10.1% and 2.5%, respectively. By considering infection risk and population numbers at the unit of the municipality, we estimate that 29.7 million Brazilians are infected with A. lumbricoides, 19.2 million with T. trichiura and 4.7 million with hookworm. Our model-based maps identified important risk factors related to the transmission of soiltransmitted helminths and confirm that environmental variables are closely associated with indices of poverty. Our smoothed risk maps, including uncertainty, highlight areas where soil-transmitted helminthiasis control interventions are most urgently required, namely in the North and along most of the coastal areas of Brazil. We believe that our predictive risk maps are useful for disease control managers for prioritising control interventions and for providing a tool for more efficient surveillance-response mechanisms.

Schistosomiasis prevalence data for Nigeria were extracted from peer-reviewed journals and reports, geo-referenced and collated in a nationwide geographical information system database for the generation of point prevalence maps. This exercise revealed that the disease is endemic in 35 of the country’s 36 states, including the federal capital territory of Abuja, and found in 462 unique locations out of 833 different survey locations. Schistosoma haematobium, the predominant species in Nigeria, was found in 368 locations (79.8%) covering 31 states, S. mansoni in 78 (16.7%) locations in 22 states and S. intercalatum in 17 (3.7%) locations in two states. S. haematobium and S. mansoni were found to be co-endemic in 22 states, while co-occurrence of all three species was only seen in one state (Rivers). The average prevalence for each species at each survey location varied between 0.5% and 100% for S. haematobium, 0.2% to 87% for S. mansoni and 1% to 10% for S. intercalatum. The estimated prevalence of S. haematobium, based on Bayesian geospatial predictive modelling with a set of bioclimatic variables, ranged from 0.2% to 75% with a mean prevalence of 23% for the country as a whole (95% confidence interval (CI): 22.8-23.1%). The model suggests that the mean temperature, annual precipitation and soil acidity significantly influence the spatial distribution. Prevalence estimates, adjusted for school-aged children in 2010, showed that the prevalence is

Tobacco smoking is a main cause of disease in Switzerland; lung cancer being the most common cancer mortality in men and the second most common in women. Although disease-specific mortality is decreasing in men, it is steadily increasing in women. The four language regions in this country might play a role in this context as they are influenced in different ways by the cultural and social behaviour of neighbouring countries. Bayesian hierarchical spatio-temporal, negative binomial models were fitted on subgroup-specific death rates indirectly standardized by national references to explore age- and gender-specific spatio-temporal patterns of mortality due to lung cancer and other tobacco-related cancers in Switzerland for the time period 1969-2002. Differences influenced by linguistic region and life in rural or urban areas were also accounted for. Male lung cancer mortality was found to be rather homogeneous in space, whereas women were confirmed to be more affected in urban regions. Compared to the German-speaking part, female mortality was higher in the French-speaking part of the country, a result contradicting other reports of similar comparisons between France and Germany. The spatio-temporal patterns of mortality were similar for lung cancer and other tobacco-related cancers. The estimated mortality maps can support the planning in health care services and evaluation of a national tobacco control programme. Better understanding of spatial and temporal variation of cancer of the lung and other tobacco-related cancers may help in allocating resources for more effective screening, diagnosis and therapy. The methodology can be applied to similar studies in other settings.

Background: South Africa accounts for more than a sixth of the global population of people infected with HIV and TB, ranking her highest in HIV/TB co-infection worldwide. Remote areas often bear the greatest burden of morbidity and mortality, yet there are spatial differences within rural settings. Objectives: The primary aim was to investigate HIV/TB mortality determinants and their spatial distribution in the rural Agincourt sub-district for children aged 1–5 years in 2004. Our secondary aim was to model how the associated factors were interrelated as either underlying or proximate factors of child mortality using pathway analysis based on a Mosley-Chen conceptual framework. Methods: We conducted a secondary data analysis based on cross-sectional data collected in 2004 from the Agincourt sub-district in rural northeast South Africa. Child HIV/TB death was the outcome measure derived from physician assessed verbal autopsy. Modelling used multiple logit regression models with and without spatial household random effects. Structural equation models were used in modelling the complex relationships between multiple exposures and the outcome (child HIV/TB mortality) as relayed on a conceptual framework. Results: Fifty-four of 6,692 children aged 1–5 years died of HIV/TB, from a total of 5,084 households. Maternal death had the greatest effect on child HIV/TB mortality (adjusted odds ratio=4.00; 95% confidence interval=1.01–15.80). A protective effect was found in households with better socio-economic status and when the child was older. Spatial models disclosed that the areas which experienced the greatest child HIV/TB mortality were those without any health facility. Conclusion: Low socio-economic status and maternal deaths impacted indirectly and directly on child mortality, respectively. These factors are major concerns locally and should be used in formulating interventions to reduce child mortality. Spatial prediction maps can guide policy makers to target interventions where they are most needed.