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Background: Scrub typhus (ST, also known as tsutsugamushi disease) is a common febrile vector-borne disease in South Korea and commonly known as autumn- and female-dominant disease. Although understanding changes in seasonality and sex differences in ST is essential for preparing health interventions, previous studies have not dealt with variations in periodicity and demographic characteristics in detail. Therefore, we aimed to quantify the temporal dynamics of seasonal patterns and sex differences in the incidence of ST in South Korea. Methods: We extracted epidemiological week (epi-week)-based ST cases from 2003 to 2019 Korean National Health Insurance Service data (ICD-10-CM code: A75.3). To determine changes in seasonality and sex differences, year-, sex-, and age-group-stratified male-to-female ratios and wavelet transform analyses were conducted. Results: Between 2003 and 2019, 213,976 ST cases were identified. The incidence per 100,000 population increased by 408.8% from 9.1 in 2003 to 37.2 in 2012, and subsequently decreased by 59.7% from 2012 to 15.0 in 2019. According to the continuous wavelet transform results, ST exhibited a dual seasonal pattern with dominant seasonality in autumn and smaller seasonality in spring from 2005 to 2019. Overall, the periodicity of seasonality decreased, whereas its strength decreased in autumn and increased in spring. With an overall male-to-female ratio being 0.68:1, the ratio has increased from 0.67:1 in 2003 to 0.78:1 in 2019 (Kendall's τ = 0.706, p < 0.001). However, interestingly, the ratio varied significantly across different age groups. Conclusions: Our findings quantitatively demonstrated changes in seasonality with dual seasonal pattern and shortened overall periodicity and a decrease in sex differences of ST in South Korea. Our study suggests the need for continuous surveillance on populations of vector and host to address ST dynamics to preemptively prepare against global warming. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • There has been an exponential growth in the attention given to the effects of climate change on schistosomiasis in the peer-reviewed literature, but little consensus about the outcomes. • The sum of observations indicates an overall decrease in the central areas of schistosomiasis' environmental niche (tropical Africa), while an increase is expected at the cooler range margins. • A geographical bias is noted, with most studies from China, although schistosomiasis remains a much larger public health challenge currently in Africa. • Schistosoma haematobium may lose more ground in a warmer Africa compared to S. mansoni , but could gain momentum in cooler areas outside its current range. • Differences in thermal performance across parasite and snail species should be taken into account in future CC-schistosomiasis studies. Abstract The exact impact of climate change on schistosomiasis, a disease caused by a blood fluke that affects more than 250 million people mainly in tropical and subtropical countries, is currently unknown, but likely to vary with the snail-parasite species' specific ecologies and the spatio-temporal scale of investigation. Here, by means of a systematic review to identify studies reporting on impacts of climate change on the agents of schistosomiasis, we provide an updated synthesis of the current knowledge about the climate change-schistosomiasis relation. We found that, despite a recent increase in scientific studies that discuss the potential impact of climate change on schistosomiasis, only a handful of reports have applied modelling and predictive forecasting that provide a quantitative estimate of potential outcomes. The volume and type of evidence associated with climate change responses were found to be variable across geographical regions and snail-parasite taxonomic groups. Indeed, the strongest evidence stems from the People's Republic of China pertaining to Schistosoma japonicum. Some evidence is also available from eastern Africa, mainly for Schistosoma mansoni. While studies focused on the northern and southern range margins for schistosomiasis indicate an increase in transmission range as the most likely outcome, there was less agreement about the direction of outcomes from the central and eastern parts of Africa. The current lack of consensus suggests that climate change is more likely to shift than to expand the geographic ranges of schistosomiasis. A comparison between the current geographical distributions and the thermo-physiological limitations of the two main African schistosome species (Schistosoma haematobium and S. mansoni) offered additional insights, and showed that both species already exist near their thermo-physiological niche boundaries. The African species both stand to move considerably out of their "thermal comfort zone" in a future, warmer Africa, but S. haematobium in particular is likely to experience less favourable climatic temperatures. The consequences for schistosomiasis transmission will, to a large extent, depend on the parasites and snails ability to adapt or move. Based on the identified geographical trends and knowledge gaps about the climate change-schistosomiasis relation, we propose to align efforts to close the current knowledge gaps and focus on areas considered to be the most vulnerable to climate change. [ABSTRACT FROM AUTHOR]

One year after the new WHO Global Air Quality Guidelines were published, the European Commission proposed revisions to the Ambient Air Quality Directives, seeking to align European Union air quality standards more closely with WHO recommendations. Here, we employed Bayesian geostatistical regression models to estimate at 1 km 2 spatial resolution the number of Europeans that would be currently exposed to levels above the new suggested thresholds for the three most harmful pollutants, namely PM 2. 5 , PM 10 , and NO 2. The results have shown that in 2021, 97.6% (95% credible interval: 95.1, 99.2%) of the total European population was exposed to PM 2. 5 levels exceeding the new WHO annual limit values, while 47.5% (36.2, 59.5%) lived in areas where the revised EU thresholds were exceeded. For PM 10 , the proportion of people exposed to concentrations above the WHO annual thresholds was estimated at 62.5% (54.0, 70.9%) and at 29.6 (21.9, 37.0%) above the EU values, whereas for NO 2 , the estimates were at 67.4% (57.9, 75.8%) and 20.2% (13.3, 28.5%), respectively. Our high-resolution model-based predictions are readily available to support policy makers and scientists working toward achieving the ambitious European zero-pollution vision for 2050. • Assessment of areas exceeding the new WHO and EU limit values for PM 2. 5 , PM 10 , and NO 2 concentrations at 1 km 2 resolution in 2021. • 97.6% and 47.5% of Europeans were exposed to PM 2. 5 concentration levels above the new annual WHO and EU thresholds, respectively. • The proportion of Europeans living in areas with PM 10 and NO 2 concentrations above these levels is lower than for PM 2. 5 but still high. [ABSTRACT FROM AUTHOR]

Snail-borne trematodiases, such as fascioliasis and schistosomiasis, belong to the neglected tropical diseases; yet, millions of people and livestock are affected. The spatial and temporal distribution of intermediate host snails plays an important role in the epidemiology and control of trematodiases. Snail distribution is influenced by numerous environmental and anthropomorphic factors. The aim of this study was to assess the distribution and constitution of the snail fauna during the dry season in constructed and natural water bodies in the Tchologo region, northern Côte d’Ivoire, and to relate these findings to environmental factors and human infections. Snails were collected using standard procedures and environmental parameters were assessed from a total of 50 water bodies in and around 30 randomly selected villages. A canonical correspondence analysis was performed to establish the relationship between snail occurrence and environmental factors. Furthermore, a total of 743 people from the same 30 villages and nearby settlements were invited for stool and urine examination for the diagnosis of Fasciola spp., Schistosoma haematobium and Schistosoma mansoni. Snails of medical importance of the genera Biomphalaria, Bulinus, Lymnaea and Physa were found. Differences in snail occurrence from sites sampled in December 2014 and snails sampled in February 2015, as well as between the northern and southern part of the study area, were revealed. Various environmental factors, such as temperature and human activities, were related to the occurrence of intermediate host snail species in the region. Only 2.3% of human participants tested positive for schistosomiasis, while no Fasciola eggs were found in stool samples. We conclude that intermediate host snails of Fasciola and Schistosoma co-occur in water bodies in the Tchologo region and that the distribution of these snails correlates not only with environmental factors, but also with the presence of humans and animals and the environmental contamination of their excreta. [ABSTRACT FROM AUTHOR]

Several studies are pointing out that exposure to elevated air pollutants could contribute to increased COVID-19 mortality. However, literature on the associations between air pollution exposure and COVID-19 severe morbidity is rather sparse. In addition, the majority of the studies used an ecological study design and were applied in regions with rather high air pollution levels. Here, we study the differential effects of long-term exposure to air pollution on severe morbidity and mortality risks from COVID-19 in various population subgroups in Switzerland, a country known for clean air. We perform individual-level analyses using data covering the first two major waves of COVID-19 between February 2020 and May 2021. High-resolution maps of particulate matter ( P M 2.5) and nitrogen dioxide ( N O 2) concentrations were produced for the 6 years preceding the pandemic using Bayesian geostatistical models. Air pollution exposure for each patient was measured by the long-term average concentration across the municipality of residence. The models were adjusted for the effects of individual characteristics, socio-economic, health-system, and climatic factors. The variables with an important association to COVID-19 case-severity were identified using Bayesian spatial variable selection. The results have shown that the individual-level characteristics are important factors related to COVID-19 morbidity and mortality in all the models. Long-term exposure to air pollution appears to influence the severity of the disease only when analyzing data during the first wave; this effect is attenuated upon adjustment for health-system related factors during the entire study period. Our findings suggest that the burden of air pollution increased the risks of COVID-19 in Switzerland during the first wave of the pandemic, but not during the second wave, when the national health system was better prepared. [Display omitted] • Bayesian spatial variable selection identified risk factors of COVID-19 severity. • Air pollution exposure influences disease severity only during first wave of pandemic. • Long-term exposure to N O 2 increases the odds of entering intensive care unit. • Both N O 2 and P M 2.5 are associated to increased risk of COVID-19 mortality. • Magnitude of air pollution effect is lower than that of known individual risk factors. [ABSTRACT FROM AUTHOR]

Background: Mansonella perstans is a widespread, but relatively unknown human filarial parasite transmitted by Culicoides biting midges. Although it is found in many parts of sub-Saharan Africa, only few studies have been carried out to deepen the understanding of its ecology, epidemiology, and health consequences. Hence, knowledge about ecological drivers of the vector and parasite distribution, integral to develop spatially explicit models for disease prevention, control, and elimination strategies, is limited. Methodology: We analyzed data from a comprehensive nationwide survey of M. perstans infection conducted in 76 schools across Uganda in 2000–2003, to identify environmental drivers. A suite of Bayesian geostatistical regression models was fitted, and the best fitting model based on the deviance information criterion was utilized to predict M. perstans infection risk for all of Uganda. Additionally, we investigated co-infection rates and co-distribution with Wuchereria bancrofti and Plasmodium spp. infections observed at the same survey by mapping geographically overlapping areas. Principal Findings: Several bioclimatic factors were significantly associated with M. perstans infection levels. A spatial Bayesian regression model showed the best fit, with diurnal temperature range, normalized difference vegetation index, and cattle densities identified as significant covariates. This model was employed to predict M. perstans infection risk at non-sampled locations. The level of co-infection with W. bancrofti was low (0.3%), due to limited geographic overlap. However, where the two infections did overlap geographically, a positive association was found. Conclusions/Significance: This study presents the first geostatistical risk map for M. perstans in Uganda. We confirmed a widespread distribution of M. perstans, and identified important potential drivers of risk. The results provide new insight about the ecologic preferences of this otherwise poorly known filarial parasite and its Culicoides vector species in Uganda, which might be relevant for other settings in sub-Saharan Africa. [ABSTRACT FROM AUTHOR]

Continuous monitoring in health and demographic surveillance sites (HDSS) allows for collection of longitudinal demographic data, health related, and socio-economic indicators of the site population. We sought to use household survey data collected between 2002 and 2006 in the Kenya Medical Research Institute in collaboration with Centers for Disease Control and prevention (KEMRI/CDC) HDSS site in Asembo and Gem Western Kenya to estimate socio-economic status (SES) and assess changes of SES over time and space. Data on household assets and characteristics, mainly source of drinking water, cooking fuel, and occupation of household head was annually collected from 44,313 unique households during the study period. An SES index was calculated as a weighted average of assets using weights generated via Principal Component Analysis (PCA), Polychoric PCA, and Multiple Correspondence Analysis (MCA) methods applied to the pooled data. The index from the best method was used to rank households into SES quintiles and assess their transition over time across SES categories. Kriging was employed to produce SES maps at the start and the end of the study period. First component of PCA, Polychoric PCA, and MCA accounted for 13.7%, 31.8%, and 47.3%, respectively of the total variance of all variables. The gap between the poorest and the least poor increased from 1% at the start to 6% at the end of the study period. Spatial analysis revealed that the increase in least poor households was centered in the lower part of study area (Asembo) over time. No significant changes were observed in Gem. The HDSS sites can provide a platform to assess spatial–temporal changes in the SES status of the population. Evidence on how SES varied over time and space within the same geographical area may provide a useful tool to design interventions in health and other areas that have a close bearing to the SES of the population. [ABSTRACT FROM AUTHOR]

Background: Among the common soil-transmitted helminth infections, hookworm causes the highest burden. Previous research in the southern part of Lao People's Democratic Republic (Lao PDR) revealed high prevalence rates of hookworm infection. The purpose of this study was to predict the spatial distribution of hookworm infection and intensity, and to investigate risk factors in the Champasack province, southern Lao PDR. Methodology: A cross-sectional parasitological and questionnaire survey was conducted in 51 villages. Data on demography, socioeconomic status, water, sanitation, and behavior were combined with remotely sensed environmental data. Bayesian mixed effects logistic and negative binomial models were utilized to investigate risk factors and spatial distribution of hookworm infection and intensity, and to make predictions for non-surveyed locations. Principal Findings: A total of 3,371 individuals were examined with duplicate Kato-Katz thick smears and revealed a hookworm prevalence of 48.8%. Most infections (91.7%) were of light intensity (1-1,999 eggs/g of stool). Lower hookworm infection levels were associated with higher socioeconomic status. The lowest infection levels were found in preschool-aged children. Overall, females were at lower risk of infection, but women aged 50 years and above harbored the heaviest hookworm infection intensities. Hookworm was widespread in Champasack province with little evidence for spatial clustering. Infection risk was somewhat lower in the lowlands, mostly along the western bank of the Mekong River, while infection intensity was homogeneous across the Champasack province. Conclusions/Significance: Hookworm transmission seems to occur within, rather than between villages in Champasack province. We present spatial risk maps of hookworm infection and intensity, which suggest that control efforts should be intensified in the Champasack province, particularly in mountainous areas. Author Summary: Hookworm is a parasitic worm that can infect the human intestine. Infections are common in low- and middle-income tropical and subtropical countries, particularly where sanitation conditions are poor. Control programs aim at preventing heavy infection intensities, which are associated with morbidity. The aim of this study was to investigate risk factors for hookworm infection and intensity, and to predict their distribution in the Champasack province, southern Lao People's Democratic Republic. We found that almost half of the 3,371 people examined were infected with hookworms. Better-off people had a lower risk and intensity of infection. Females showed lower prevalence and intensity of hookworm infection than males, except women aged 50 years and above, who were the most heavily infected. Preschool-aged children had the lowest infection levels. Hookworm transmission appeared to occur within, rather than between villages. Hookworm infections are widespread in the Champasack province, with homogeneous infection intensity and somewhat lower infection risk west of the Mekong River, which offers higher living conditions. Hookworm control should be intensified in the Champasack province, particularly in the mountainous areas. [ABSTRACT FROM AUTHOR]

Background: Among the common soil-transmitted helminth infections, hookworm causes the highest burden. Previous research in the southern part of Lao People’s Democratic Republic (Lao PDR) revealed high prevalence rates of hookworm infection. The purpose of this study was to predict the spatial distribution of hookworm infection and intensity, and to investigate risk factors in the Champasack province, southern Lao PDR. Methodology: A cross-sectional parasitological and questionnaire survey was conducted in 51 villages. Data on demography, socioeconomic status, water, sanitation, and behavior were combined with remotely sensed environmental data. Bayesian mixed effects logistic and negative binomial models were utilized to investigate risk factors and spatial distribution of hookworm infection and intensity, and to make predictions for non-surveyed locations. Principal Findings: A total of 3,371 individuals were examined with duplicate Kato-Katz thick smears and revealed a hookworm prevalence of 48.8%. Most infections (91.7%) were of light intensity (1-1,999 eggs/g of stool). Lower hookworm infection levels were associated with higher socioeconomic status. The lowest infection levels were found in preschool-aged children. Overall, females were at lower risk of infection, but women aged 50 years and above harbored the heaviest hookworm infection intensities. Hookworm was widespread in Champasack province with little evidence for spatial clustering. Infection risk was somewhat lower in the lowlands, mostly along the western bank of the Mekong River, while infection intensity was homogeneous across the Champasack province. Conclusions/Significance: Hookworm transmission seems to occur within, rather than between villages in Champasack province. We present spatial risk maps of hookworm infection and intensity, which suggest that control efforts should be intensified in the Champasack province, particularly in mountainous areas. [ABSTRACT FROM AUTHOR]

Abstract: We compared prevalence estimates of self-rated health (SRH) derived indirectly using four different small area estimation methods for the Vadu (small) area from the national Study on Global AGEing (SAGE) survey with estimates derived directly from the Vadu SAGE survey. The indirect synthetic estimate for Vadu was 24% whereas the model based estimates were 45.6% and 45.7% with smaller prediction errors and comparable to the direct survey estimate of 50%. The model based techniques were better suited to estimate the prevalence of SRH than the indirect synthetic method. We conclude that a simplified mixed effects regression model can produce valid small area estimates of SRH. [Copyright &y& Elsevier]

Highlights: ▶ Investigation into micro-scale variations of gastropod community diversity. ▶ Bayesian multivariate modeling to predict Biomphalaria abundance. ▶ DNA sequence analysis of S. mansoni cercariae from wild-infected snails. ▶ Population genetics analysis of Biomphalaria at a micro-scale level. ▶ Comparison of micro-scale patterns to lake-wide observations. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

Background: After many years of neglect, schistosomiasis control is going to scale. The strategy of choice is preventive chemotherapy, that is the repeated large-scale administration of praziquantel (a safe and highly efficacious drug) to at-risk populations. The frequency of praziquantel administration is based on endemicity, which usually is defined by prevalence data summarized at an arbitrarily chosen administrative level. Methodology: For an ensemble of 29 West and East African countries, we determined the annualized praziquantel treatment needs for the school-aged population, adhering to World Health Organization guidelines. Different administrative levels of prevalence aggregation were considered; country, province, district, and pixel level. Previously published results on spatially explicit schistosomiasis risk in the selected countries were employed to classify each area into distinct endemicity classes that govern the frequency of praziquantel administration. Principal Findings: Estimates of infection prevalence adjusted for the school-aged population in 2010 revealed that most countries are classified as moderately endemic for schistosomiasis (prevalence 10–50%), while four countries (i.e., Ghana, Liberia, Mozambique, and Sierra Leone) are highly endemic (>50%). Overall, 72.7 million annualized praziquantel treatments (50% confidence interval (CI): 68.8–100.7 million) are required for the school-aged population if country-level schistosomiasis prevalence estimates are considered, and 81.5 million treatments (50% CI: 67.3–107.5 million) if estimation is based on a more refined spatial scale at the provincial level. Conclusions/Significance: Praziquantel treatment needs may be over- or underestimated depending on the level of spatial aggregation. The distribution of schistosomiasis in Ethiopia, Liberia, Mauritania, Uganda, and Zambia is rather uniform, and hence country-level risk estimates are sufficient to calculate treatment needs. On the other hand, countries like Burkina Faso, Mali, Mozambique, Sudan, and Tanzania show large spatial heterogeneity in schistosomiasis risk, which should be taken into account for calculating treatment requirements. Author Summary: More than 200 million people are affected by the snailborne disease schistosomiasis. The main strategy to control schistosomiasis is to regularly treat school-aged children with the drug praziquantel. The frequency of praziquantel treatment depends on the average prevalence of schistosomiasis, which can be defined as low (prevalence <10%), moderate (10–50%), or high (>50%). However, it remains unclear at which geographical scale these prevalence levels should be considered to avoid unnecessary treatments but still comply with local needs. We investigated the effect of the geographical scale for an ensemble of 29 West and East African countries using previously published model-based schistosomiasis risk estimates obtained at high spatial resolution. These estimates allow spatial risk aggregation at different geographical scales (i.e., country, region, district, or pixel level). More than 70 million praziquantel treatments are required every year for school-aged children if countrylevel estimates are used. On a more refined geographical scale (i.e., province), annualized praziquantel treatments increase by 12%. Depending on the averaged schistosomiasis prevalence and the spatial risk variation across a country, the difference in the estimated amount of praziquantel between country-level aggregation and other geographical scales might be very important, as for example in Burkina Faso, Ghana, and Mali. [ABSTRACT FROM AUTHOR]

Background: After many years of neglect, schistosomiasis control is going to scale. The strategy of choice is preventive chemotherapy, that is the repeated large-scale administration of praziquantel (a safe and highly efficacious drug) to at-risk populations. The frequency of praziquantel administration is based on endemicity, which usually is defined by prevalence data summarized at an arbitrarily chosen administrative level. Methodology: For an ensemble of 29 West and East African countries, we determined the annualized praziquantel treatment needs for the school-aged population, adhering to World Health Organization guidelines. Different administrative levels of prevalence aggregation were considered; country, province, district, and pixel level. Previously published results on spatially explicit schistosomiasis risk in the selected countries were employed to classify each area into distinct endemicity classes that govern the frequency of praziquantel administration. Principal Findings: Estimates of infection prevalence adjusted for the school-aged population in 2010 revealed that most countries are classified as moderately endemic for schistosomiasis (prevalence 10-50%), while four countries (i.e., Ghana, Liberia, Mozambique, and Sierra Leone) are highly endemic (>50%). Overall, 72.7 million annualized praziquantel treatments (50% confidence interval (CI): 68.8-100.7 million) are required for the school-aged population if country-level schistosomiasis prevalence estimates are considered, and 81.5 million treatments (50% CI: 67.3-107.5 million) if estimation is based on a more refined spatial scale at the provincial level. Conclusions/Significance: Praziquantel treatment needs may be over- or underestimated depending on the level of spatial aggregation. The distribution of schistosomiasis in Ethiopia, Liberia, Mauritania, Uganda, and Zambia is rather uniform, and hence country-level risk estimates are sufficient to calculate treatment needs. On the other hand, countries like Burkina Faso, Mali, Mozambique, Sudan, and Tanzania show large spatial heterogeneity in schistosomiasis risk, which should be taken into account for calculating treatment requirements. [ABSTRACT FROM AUTHOR]

Intestinal schistosomiasis is a global disease of enormous public health importance. Assessing the local risk of transmission of the parasite causing the disease requires an appraisal of the distribution of its intermediate snail hosts, Biomphalaria spp.. In East Africa, the Lake Victorian Basin is a major freshwater ecosystem and highly endemic for intestinal schistosomiasis, although detailed distribution data for Biomphalaria have not been collected. We used on-the-ground malacological surveys and conjoint measurement of environmental determinants to develop models for analysis of variables associated with distribution and predictive snail mapping into data-deficient areas. Four expeditions were undertaken to collect snails along the Lake Victoria shoreline, visiting 223 sites overall. Environmental measurements were recorded at the time of inspection and water samples taken for quantification of anion and cation concentrations. The spatial distributions of Biomphalaria choanomphala and Biomphalaria sudanica were modelled in two Bayesian multivariate models: one non-spatial and one spatial with random effects. The results showed that chloride, nitrate, sulphate and the number of sympatric snail species were significant predictors of B. choanomphala whereas habitat, water depth, pH and sulphate were significant predictors for B. sudanica. The range of spatial autocorrelation was large (572.9 km for B. choanomphala and 175.3 km for B. sudanica). Interpolating snail abundance data by kriging revealed two 'hot-spots' of high abundance of Biomphalaria. These areas should be targeted in future expeditions to ground-truth the model's predictions. Our study is the first to use Bayesian methods for determination of biomedically important snail distributions and sets crucial limnological baseline data for assessment of future changes in snail biodiversity in the Lake Victoria Basin. [ABSTRACT FROM AUTHOR]

The most common assumption in geostatistical modeling of malaria is stationarity, that is spatial correlation is a function of the separation vector between locations. However, local factors (environmental or human-related activities) may influence geographical dependence in malaria transmission differently at different locations, introducing non-stationarity. Ignoring this characteristic in malaria spatial modeling may lead to inaccurate estimates of the standard errors for both the covariate effects and the predictions. In this paper, a model based on random Voronoi tessellation that takes into account non-stationarity was developed. In particular, the spatial domain was partitioned into sub-regions (tiles), a stationary spatial process was assumed within each tile and between-tile correlation was taken into account. The number and configuration of the sub-regions are treated as random parameters in the model and inference is made using reversible jump Markov chain Monte Carlo simulation. This methodology was applied to analyze malaria survey data from Mali and to produce a country-level smooth map of malaria risk. [ABSTRACT FROM AUTHOR]

Background: The Zambia Malaria Indicator Survey (ZMIS) of 2006 was the first nation-wide malaria survey, which combined parasitological data with other malaria indicators such as net use, indoor residual spraying and household related aspects. The survey was carried out by the Zambian Ministry of Health and partners with the objective of estimating the coverage of interventions and malaria related burden in children less than five years. In this study, the ZMIS data were analysed in order (i) to estimate an empirical high-resolution parasitological risk map in the country and (ii) to assess the relation between malaria interventions and parasitaemia risk after adjusting for environmental and socio-economic confounders. Methods: The parasitological risk was predicted from Bayesian geostatistical and spatially independent models relating parasitaemia risk and environmental/climatic predictors of malaria. A number of models were fitted to capture the (potential) non-linearity in the malaria-environment relation and to identify the elapsing time between environmental effects and parasitaemia risk. These models included covariates (a) in categorical scales and (b) in penalized and basis splines terms. Different model validation methods were used to identify the best fitting model. Model-based risk predictions at unobserved locations were obtained via Bayesian predictive distributions for the best fitting model. Results: Model validation indicated that linear environmental predictors were able to fit the data as well as or even better than more complex non-linear terms and that the data do not support spatial dependence. Overall the averaged population-adjusted parasitaemia risk was 20.0% in children less than five years with the highest risk predicted in the northern (38.3%) province. The odds of parasitaemia in children living in a household with at least one bed net decreases by 40% (CI: 12%, 61%) compared to those without bed nets. Conclusions: The map of parasitaemia risk together with the prediction error and the population at risk give an important overview of the malaria situation in Zambia. These maps can assist to achieve better resource allocation, health management and to target additional interventions to reduce the burden of malaria in Zambia significantly. Repeated surveys will enable the evaluation of the effectiveness of on-going interventions. [ABSTRACT FROM AUTHOR]

Background: Bovine tuberculosis (BTB) today primarily affects developing countries. In Africa, the disease is present essentially on the whole continent; however, little accurate information on its distribution and prevalence is available. Also, attempts to evaluate diagnostic tests for BTB in naturally infected cattle are scarce and mostly complicated by the absence of knowledge of the true disease status of the tested animals. However, diagnostic test evaluation in a given setting is a prerequisite for the implementation of local surveillance schemes and control measures. Methodology/Principal Findings: We subjected a slaughterhouse population of 954 Chadian cattle to single intra-dermal comparative cervical tuberculin (SICCT) testing and two recently developed fluorescence polarization assays (FPA). Using a Bayesian modeling approach we computed the receiver operating characteristic (ROC) curve of each diagnostic test, the true disease prevalence in the sampled population and the disease status of all sampled animals in the absence of knowledge of the true disease status of the sampled animals. In our Chadian setting, SICCT performed better if the cut-off for positive test interpretation was lowered from >4 mm (OIE standard cut-off) to >2 mm. Using this cut-off, SICCT showed a sensitivity and specificity of 66% and 89%, respectively. Both FPA tests showed sensitivities below 50% but specificities above 90%. The true disease prevalence was estimated at 8%. Altogether, 11% of the sampled animals showed gross visible tuberculous lesions. However, modeling of the BTB disease status of the sampled animals indicated that 72% of the suspected tuberculosis lesions detected during standard meat inspections were due to other pathogens than Mycobacterium bovis. Conclusions/Significance: Our results have important implications for BTB diagnosis in a high incidence sub-Saharan African setting and demonstrate the practicability of our Bayesian approach for diagnostic test evaluation. [ABSTRACT FROM AUTHOR]

Background: Rainfall data have potential use for malaria prediction. However, the relationship between rainfall and the number of malaria cases is indirect and complex. Methods: The statistical relationships between monthly malaria case count data series and monthly mean rainfall series (extracted from interpolated station data) over the period 1972 - 2005 in districts in Sri Lanka was explored in four analyses: cross-correlation; cross-correlation with pre-whitening; inter-annual; and seasonal inter-annual regression. Results: For most districts, strong positive correlations were found for malaria time series lagging zero to three months behind rainfall, and negative correlations were found for malaria time series lagging four to nine months behind rainfall. However, analysis with pre-whitening showed that most of these correlations were spurious. Only for a few districts, weak positive (at lags zero and one) or weak negative (at lags two to six) correlations were found in pre-whitened series. Inter-annual analysis showed strong negative correlations between malaria and rainfall for a group of districts in the centre-west of the country. Seasonal inter-annual analysis showed that the effect of rainfall on malaria varied according to the season and geography. Conclusion: Seasonally varying effects of rainfall on malaria case counts may explain weak overall cross-correlations found in pre-whitened series, and should be taken into account in malaria predictive models making use of rainfall as a covariate. [ABSTRACT FROM AUTHOR]

Background: Malaria in Sri Lanka is unstable and fluctuates in intensity both spatially and temporally. Although the case counts are dwindling at present, given the past history of resurgence of outbreaks despite effective control measures, the control programmes have to stay prepared. The availability of long time series of monitored/diagnosed malaria cases allows for the study of forecasting models, with an aim to developing a forecasting system which could assist in the efficient allocation of resources for malaria control. Methods: Exponentially weighted moving average models, autoregressive integrated moving average (ARIMA) models with seasonal components, and seasonal multiplicative autoregressive integrated moving average (SARIMA) models were compared on monthly time series of district malaria cases for their ability to predict the number of malaria cases one to four months ahead. The addition of covariates such as the number of malaria cases in neighbouring districts or rainfall were assessed for their ability to improve prediction of selected (seasonal) ARIMA models. Results: The best model for forecasting and the forecasting error varied strongly among the districts. The addition of rainfall as a covariate improved prediction of selected (seasonal) ARIMA models modestly in some districts but worsened prediction in other districts. Improvement by adding rainfall was more frequent at larger forecasting horizons. Conclusion: Heterogeneity of patterns of malaria in Sri Lanka requires regionally specific prediction models. Prediction error was large at a minimum of 22% (for one of the districts) for one month ahead predictions. The modest improvement made in short term prediction by adding rainfall as a covariate to these prediction models may not be sufficient to merit investing in a forecasting system for which rainfall data are routinely processed. [ABSTRACT FROM AUTHOR]

Background: Insecticide treated nets (ITN) have been proven to be an effective tool in reducing the burden of malaria. Few randomized clinical trials examined the spatial effect of ITNs on child mortality at a high coverage level, hence it is essential to better understand these effects in real-life situation with varying levels of coverage. We analyzed for the first time data from a large follow-up study in an area of high perennial malaria transmission in southern Tanzania to describe the spatial effects of bednets on all-cause child mortality. Methods: The study was carried out between October 2001 and September 2003 in 25 villages in Kilombero Valley, southern Tanzania. Bayesian geostatistical models were fitted to assess the effect of different bednet density measures on child mortality adjusting for possible confounders. Results: In the multivariate model addressing potential confounding, the only measure significantly associated with child mortality was the bed net density at household level; we failed to observe additional community effect benefit from bed net coverage in the community. Conclusion: In this multiyear, 25 village assessment, despite substantial known inadequate insecticide-treatment for bed nets, the density of household bed net ownership was significantly associated with all cause child mortality reduction. The absence of community effect of bednets in our study area might be explained by (1) the small proportion of nets which are treated with insecticide, and (2) the relative homogeneity of coverage with nets in the area. To reduce malaria transmission for both users and non-users it is important to increase the ITNs and long-lasting nets coverage to at least the present untreated nets coverage. [ABSTRACT FROM AUTHOR]

Abstract: Models that accurately estimate the age-specific infection prevalence of Schistosoma mansoni can be useful for schistosomiasis control programmes, particularly with regard to whether mass drug administration or selected treatment should be employed. We developed a Bayesian formulation of an immigration-death model that has been previously proposed, which used maximum likelihood inference for estimating the age-specific S. mansoni prevalence in a dataset from Egypt. For comparative purposes, we first applied the Bayesian formulation of the immigration-death model to the dataset from Egypt. We further analysed data obtained from a cross-sectional parasitological survey that determined the infection prevalence of S. mansoni among 447 individuals in a village in Côte d’Ivoire. Three consecutive stool samples were collected from each participant and analysed by the Kato-Katz technique. In the Côte d’Ivoire study, the observed S. mansoni infection prevalence was 41.6% and varied with age. The immigration-death model was able to correctly predict 50% of the observed age group-specific point prevalences. The model presented here can be utilized to estimate S. mansoni community infection prevalences, which in turn helps in the strategic planning of schistosomiasis control. [Copyright &y& Elsevier]

Multiple-species parasitic infections are pervasive in the developing world, yet resources for their control are scarce. We present an integrated approach for risk profiling and spatial prediction of coinfection with Schistosoma mansoni and hookworm for western Côte d'lvoire. Our approach combines demographic, environmental, and socioeconomic data; incorporates them into a geographic information system; and employs spatial statistics. Demographic and socioeconomic data were obtained from education registries and from a questionnaire administered to schoolchildren. Environmental data were derived from remotely sensed satellite images and digitized ground maps. Parasitologic data, obtained from fecal examination by using two different diagnostic approaches, served as the outcome measure. Bayesian variogram models were used to assess risk factors and spatial variation of S. mansoni-hookworm coinfection in relation to demographic, environmental, and socioeconomic variables. Coinfections were found in 680 of 3,578 schoolchildren (19.0%) with complete data records. The prevalence of monoinfections with either hookworm or S. mansoni was 24.3 % and 24.1%, respectively. Multinomial Bayesian spatial models showed that age, sex, socioeconomic status, and elevation were good predictors for the spatial distribution of S. mansoni-hookworm coinfection. We conclude that our integrated approach, employing a diversity of data sources, geographic information system and remote sensing technologies, and Bayesian spatial statistics, is a powerful tool for risk profiling and spatial prediction of S. mansoni-hookworm coinfection. More generally, this approach facilitates risk mapping and prediction of other parasite combinations and multiparasitism, and hence can guide integrated disease control programs in resource-constrained settings. [ABSTRACT FROM AUTHOR]

Models for geostatistical data introduce spatial dependence in the covariance matrix of location-specific random effects. This is usually defined to be a parametric function of the distances between locations. Bayesian formulations of such models overcome asymptotic inference and estimation problems involved in maximum likelihood-based approaches and can be fitted using Markov chain Monte Carlo (MCMC) simulation. The MCMC implementation, however, requires repeated inversions of the covariance matrix which makes the problem computationally intensive, especially for large number of locations. In the present work, we propose to convert the spatial covariance matrix to a sparse matrix and compare a number of numerical algorithms especially suited within the MCMC framework in order to accelerate large matrix inversion. The algorithms are assessed empirically on simulated datasets of different size and sparsity. We conclude that the band solver applied after ordering the distance matrix reduces the computational time in inverting covariance matrices substantially. [ABSTRACT FROM AUTHOR]

Abstract: This study is aimed at investigating the risk factors for hookworm infection among schoolchildren in a rural area of western Côte d''Ivoire and predicting and mapping the spatial distribution of infection. We used demographic and socio-economic data from a cross-sectional survey of 6–16-year-old schoolchildren from 56 schools. Infection with hookworm was determined by microscopic examination of stool samples employing the Kato–Katz technique and an ether-concentration method. Environmental data were derived from satellite images and digitised maps. Bayesian variogram models were applied to investigate the variation of hookworm infection in relation to demographic, socio-economic and environmental factors. The overall hookworm infection prevalence, based on the pooled microscopic diagnoses, was 43.3% and ranged from 5.4 to 79.1% in the schools surveyed. Bivariate analyses showed that sex, age, socio-economic status, elevation, rainfall and land cover were significantly associated with the spatial distribution of hookworm infection. The final multivariate spatial model consisted of the covariates age, sex, socio-economic status, elevation and land cover. When assuming non-stationary underlying spatial dependency, the results of the model suggested that spatial correlation depended on the location only marginally. We conclude that, at the current resolution, it seems more reasonable to target interventions based on well-established epidemiologic risk factors, rather than on spatial factors. [Copyright &y& Elsevier]

Background: On the fringes of endemic zones climate is a major determinant of inter-annual variation in malaria incidence. Quantitative description of the space-time effect of this association has practical implications for the development of operational malaria early warning system (MEWS) and malaria control. We used Bayesian negative binomial models for spatio-temporal analysis of the relationship between annual malaria incidence and selected climatic covariates at a district level in Zimbabwe from 1988-1999. Results: Considerable inter-annual variations were observed in the timing and intensity of malaria incidence. Annual mean values of average temperature, rainfall and vapour pressure were strong positive predictors of increased annual incidence whereas maximum and minimum temperature had the opposite effects. Our modelling approach adjusted for unmeasured space-time varying risk factors and showed that while year to year variation in malaria incidence is driven mainly by climate, the resultant spatial risk pattern may to large extent be influenced by other risk factors except during high and low risk years following the occurrence of extremely wet and dry conditions, respectively. Conclusion: Our model revealed a spatially varying risk pattern that is not attributable only to climate. We postulate that only years characterized by extreme climatic conditions may be important for developing climate based MEWS and for delineating areas prone to climate driven epidemics. However, the predictive value of climatic risk factors identified in this study still needs to be evaluated. [ABSTRACT FROM AUTHOR]

Abstract: Geographic information system (GIS) and remote sensing (RS) technologies offer new opportunities for rapid assessment of endemic areas, provision of reliable estimates of populations at risk, prediction of disease distributions in areas that lack baseline data and are difficult to access, and guidance of intervention strategies, so that scarce resources can be allocated in a cost-effective manner. Here, we focus on the epidemiology and control of schistosomiasis in China and review GIS and RS applications to date. These include mapping prevalence and intensity data of Schistosoma japonicum at a large scale, and identifying and predicting suitable habitats for Oncomelania hupensis, the intermediate host snail of S. japonicum, at a small scale. Other prominent applications have been the prediction of infection risk due to ecological transformations, particularly those induced by floods and water resource developments, and the potential impact of climate change. We also discuss the limitations of the previous work, and outline potential new applications of GIS and RS techniques, namely quantitative GIS, WebGIS, and utilization of emerging satellite information, as they hold promise to further enhance infection risk mapping and disease prediction. Finally, we stress current research needs to overcome some of the remaining challenges of GIS and RS applications for schistosomiasis, so that further and sustained progress can be made to control this disease in China and elsewhere. [Copyright &y& Elsevier]

Abstract: Spatio-temporal variations of Schistosoma japonicum infection risk in Jiangsu province, China, were examined and the relationships between key climatic factors and infection prevalence at the county level were determined. The parasitological data were collected annually by means of cross-sectional surveys carried out in 47 counties from 1990 to 1998. Climatic factors, namely land surface temperature (LST) and normalized difference vegetation index (NDVI), were obtained from remote sensing satellite sensors. Bayesian spatio-temporal models were employed to analyze the data. The best fitting model showed that spatial autocorrelation in Jiangsu province decreased dramatically from 1990 to 1992 and increased gradually thereafter. A likely explanation of this finding arises from the large-scale administration of praziquantel for morbidity control of schistosomiasis. Our analysis suggested a negative association between NDVI and risk of S. japonicum infection. On the other hand, an increase in LST contributed to a significant increase in S. japonicum infection prevalence. We conclude that combining geographic information system, remote sensing and Bayesian-based statistical approaches facilitate integrated risk modeling of S. japonicum, which in turn is of relevance for allocation of scarce resources for control of schistosomiasis japonica in Jiangsu province and elsewhere in China, where the disease remains of public health and economic significance. [Copyright &y& Elsevier]

Bayesian hierarchical models are developed to estimate the frequencies of the alleles at the HLA-C locus in the presence of non-identifiable alleles and possible spatial correlations in a large but sparse, spatially defined database from Papua New Guinea. Bayesian model selection methods are applied to investigate the effects of altitude and language on the genetic diversity of HLA-C alleles. The general model includes fixed altitudinal effects, random language effects and random spatially structured location effects. Conditional autoregressive priors are used to incorporate the geographical structure of the map, and Markov chain Monte Carlo simulation methods are applied for estimation and inference. The results show that HLA-C allele frequencies are explained more by linguistic than altitudinal differences, indicating that genetic diversity at this locus in Papua New Guinea probably tracks population movements and is less influenced by natural selection than is variation at HLA-A and HLA-B. [ABSTRACT FROM AUTHOR]

Praziquantel exhibits activity against all major human schistosome parasites and has become the cornerstone for treatment and morbidity control of schistosomiasis. Praziquantel is also active against a wide range of trematodes, human and veterinary cestodes and displays cysticidal effects. To the best of our knowledge anthelminthic properties have never been documented. Here, we report a study among 96 schoolchildren from an area highly endemic for Schistosoma mansoni and hookworm infection, and place particular emphasis on the effect of praziquantel on the prevalence and intensity of hookworm infections. Stool specimens were screened over several consecutive days prior and 4 weeks after systematic administration of praziquantel. We found a significant reduction in the prevalence of hookworm infection from 75.0 to 40.6% (odds ratio (OR)=0.21; 95% confidence interval (CI): 0.11–0.40). Infection intensities, expressed by geometric mean egg counts of all children, were also reduced significantly from 10.7 to 2.0 eggs per gram stool (paired t-test=7.78, P<0.001). If these findings are confirmed in other epidemiological settings – following a similarly sensitive diagnostic approach – they might become of considerable relevance. In areas where both schistosome and hookworm coexist, and praziquantel is being recommended for schistosomiasis control, large-scale application of this drug might also reduce the burden of hookworms. [Copyright &y& Elsevier]

Background: The burden of malaria in Kenya was showing a declining trend, but appears to have reached a plateau in recent years. This study estimated changes in the geographical distribution of malaria parasite risk in the country between the years 2015 and 2020, and quantified the contribution of malaria control interventions and climatic/ environmental factors to these changes. Methods: Bayesian geostatistical models were used to analyse the Kenyan 2015 and 2020 Malaria Indicator Survey (MIS) data. Bivariate models were fitted to identify the most important control intervention indicators and climatic/environmental predictors of parasitaemia risk by age groups (6–59 months and 5–14 years). Parasitaemia risk and the number of infected children were predicted over a 1 × 1 km2 grid. The probability of the decline in parasitaemia risk in 2020 compared to 2015 was also evaluated over the gridded surface and factors associated with changes in parasitaemia risk between the two surveys were evaluated. Results: There was a significant decline in the coverage of most malaria indicators related to Insecticide Treated Nets (ITN) and Artemisinin Combination Therapies (ACT) interventions. Overall, there was a 31% and 26% reduction in malaria prevalence among children aged < 5 and 5–14 years, respectively. Among younger children, the highest reduction (50%) and increase (41%) were in the low-risk and semi-arid epi zones, respectively; while among older children there was increased risk in both the low-risk (83%) and semi-arid (100%) epi zones. Increase in nightlights and the proportion of individuals using ITNs in 2020 were associated with reduced parasitaemia risk. Conclusion: Increased nightlights and ITN use could have led to the reduction in parasitaemia risk. However, the reduction is heterogeneous and there was increased risk in northern Kenya. Taken together, these results suggest that constant surveillance and re-evaluation of parasite and vector control measures in areas with increased transmission is necessary. The methods used in this analysis can be employed in other settings. [ABSTRACT FROM AUTHOR]

• Predictions of NO 2 concentrations across Europe at 1 km2 spatial resolution. • Estimates of population exposed to NO 2 levels exceeding WHO and EU thresholds. • Tropospheric column to-surface conversion of satellite NO 2 using GEOS-Chem. • Column to-surface conversion improved slightly geostatistical predictions of NO 2. • CAMS-Ensemble regional CTM simulations further improved the estimates. 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 ( NO 2 ) concentrations at smaller areas of investigation. Here, we applied these models to estimate yearly averaged NO 2 concentrations at 1 km 2 spatial resolution across 44 European countries, integrating information from in situ monitoring stations, satellites and chemical transport model (CTM) simulations. The tropospheric values of NO 2 derived from the ozone monitoring instrument (OMI) onboard the National Aeronautics and Space Administration's (NASA's) Aura satellite were converted to near ground NO 2 concentration proxies using simulations from the 3-D global CTM (GEOS-Chem) at 0.5° × 0.625° spatial resolution and surface-to-column NO 2 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 NO 2 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 NO 2 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 NO 2 . Our estimates are readily available to policy makers and scientists assessing the burden of disease attributable to NO 2 in 2016. [ABSTRACT FROM AUTHOR]

Background: Malaria remains an important public health problem, particularly in sub-Saharan Africa. In Rwanda, where malaria ranks among the leading causes of mortality and morbidity, disease transmission is influenced by climatic factors. However, there is a paucity of studies investigating the link between climate change and malaria dynamics, which hinders the development of effective national malaria response strategies. Addressing this critical gap, this study analyses how climatic factors influence malaria transmission across Rwanda, thereby informing tailored interventions and enhancing disease management frameworks. Methods: The study analysed the potential impact of temperature and cumulative rainfall on malaria incidence in Rwanda from 2012 to 2021 using meteorological data from the Rwanda Meteorological Agency and malaria case records from the Rwanda Health Management and Information System. The analysis was performed in two stages. First, district-specific generalized linear models with a quasi-Poisson distribution were applied, which were enhanced by distributed lag non-linear models to explore non-linear and lagged effects. Second, random effects multivariate meta-analysis was employed to pool the estimates and to refine them through best linear unbiased predictions. Results: A 1-month lag with specific temperature and rainfall thresholds influenced malaria incidence across Rwanda. Average temperature of 18.5 °C was associated with higher malaria risk, while temperature above 23.9 °C reduced the risk. Rainfall demonstrated a dual effect on malaria risk: conditions of low (below 73 mm per month) and high (above 223 mm per month) precipitation correlated with lower risk, while moderate rainfall (87 to 223 mm per month) correlated with higher risk. Seasonal patterns showed increased malaria risk during the major rainy season, while the short dry season presented lower risk. Conclusion: The study underscores the influence of temperature and rainfall on malaria transmission in Rwanda and calls for tailored interventions that are specific to location and season. The findings are crucial for informing policy that enhance preparedness and contribute to malaria elimination efforts. Future research should explore additional ecological and socioeconomic factors and their differential contribution to malaria transmission. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: In Northwestern Switzerland, recent legislation tackles the needs of community-dwelling older adults by creating Information and Advice Centers (IACs). IACs are a new service in the community that aims to assess the needs and provide information on age-related issues to community-dwelling older adults and their families. Previous studies reported difficulties in reaching community-dwelling older adults for community-based programs. We aimed to: 1) systematically identify implementation strategies to promote the IAC among community care providers, older adults and informal caregivers; 2) monitor the delivery of these strategies by the IAC management; and 3) describe the impact of those strategies on reach of community-dwelling older adults. This study was conducted as part of the TRANS-SENIOR project. Methods: As part of the INSPIRE feasibility assessment, we conducted a pre-test post-test study between March and September 2022. The sample included 8,840 older adults aged 65 + visiting/calling or being referred to the IAC for the first time. Implementation strategies were selected using implementation mapping and organized in bundles for each group of community care providers and older adults/caregivers. Our evaluation included: estimation of fidelity to the delivery of implementation strategies and bundles by the IAC management and their coverage; referral source of older adults to the IAC; and impact of the strategies on reach of the IAC on the 65 + population living in the care region. Adaptations to the strategies were documented using the FRAME-IS. Descriptive statistics were calculated and reported. Results: Seven implementation strategies were selected and organized in bundles for each community care provider and older adults and their caregivers. The lowest fidelity score was found in implementation strategies selected for nursing homes whereas the highest score corresponded to strategies targeting older adults and caregivers. "Informational visits" was the strategy with the lowest coverage (2.5% for nursing homes and 10.5% for hospitals and specialized clinics). The main referral sources were self-referrals and referrals by caregivers, followed by nursing homes. The IAC reach among the 65 + population was 5.4%. Conclusion: We demonstrated the use of implementation mapping to select implementation strategies to reach community-dwelling older adults. The reach was low suggesting that higher fidelity to the delivery of the strategies, and reflection on the causal pathway of the implementation strategies might be needed. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

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 FROM AUTHOR]

Background: Strongyloides 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 findings: A 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 / Significance: S. 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. [ABSTRACT FROM AUTHOR]

Background: In 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. Methodology: Bayesian 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 findings: The 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/Significance: For 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. Author summary: Bangladesh was in 2003 one of the world's countries that was most the affected by soil-transmitted helminthiasis, an infection of parasitic intestinal worms, with prevalence estimates as high as 55%. Following recommendations put forth by the World Health Organization (WHO), Bangladesh initiated a national deworming program based on school mass drug administration at six-month intervals, which started in 2008. Data collected between 2017 and 2020 showed that this program has lead to a striking decrease in prevalence, which was now between 10.5% and 1% depending on the species. Entering a close to elimination setting, the disease's control strategy can be refined according to local endemicity to reduce overtreatment. To inform such a shift, we predict the prevalence across Bangladesh at high spatial resolution and highlight zones of different endemicity. We also show the potential reduction of overtreatment and financial resources that could be achieved by treating according to WHO recommendations at the predicted endemicity levels. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Burkina Faso conducted its first nationally representative household malaria survey in 2010/2011. The survey collected among others, information on malaria interventions, treatment choices and malaria parasite prevalence in children aged 6-59 months. Methods: In this study, Bayesian geostatistical models were employed to assess the effects of health interventions related to insecticide-treated bed nets (ITN), indoor residual spray (IRS), artemisinin-based combination therapy (ACT) coverage associated with childhood malaria parasite risk at national and sub-national level, after taking into account geographical disparities of climatic/environmental and socio-economic factors. Several ITN coverage measures were calculated and Bayesian variable selection was used to identify the most important ones. Parasitaemia risk depicting spatial patterns of infections were estimated. Results: The results show that the predicted population-adjusted parasitaemia risk ranges from 4.04% in Kadiogo province to 82% in Kompienga province. The effect of ITN coverage was not important at national level; however ITNs have an important protective effect in Ouagadougou as well as in three districts in the western part of the country with high parasitaemia prevalence and low to moderate coverage. There is a large variation in ACT coverage between the districts. Although at national level the ACT effects on parasitaemia risk was not important, at sub-national level 18 districts around Ouagadougou deliver effective treatment. Conclusion: The produced maps show great variations in parasitaemia risk across the country and identify the districts where interventions are being effective. These outputs are valuable tools that can help improve malaria control in Burkina Faso. [ABSTRACT FROM AUTHOR]

Background: Hookworm 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). Methodology: A 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 findings: Taking 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/Significance: Our 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. Author summary: Human hookworm infections are primarily caused by two parasitic worm species, namely Ancylostoma duodenale and Necator americanus. Already moderate infection intensities can impair cognitive and physical development of children, and reduce fertility in women of reproductive age. The World Health Organization set the global target to eliminate morbidity due to hookworm infections in preschool-age and school-age children by 2030. To reach this goal, different public health interventions are carried out. The most widely used strategy is mass drug administration. To assess the efficacy of treatments, epidemiologic studies and clinical trials usually employ the Kato-Katz thick smear technique to microscopically examine stool samples of individuals before and after treatment. Of note, this diagnostic test is not 100% accurate such that not all positive individuals are detected. We developed a mathematical model, which takes into account the diagnostic error to compare the 'true' efficacy of different drugs and treatment regimens against hookworm infection. Furthermore, we computed how well the test detects infected individuals in relation to infection intensity. We found that the diagnostic sensitivity of the Kato-Katz thick smear technique increases considerably if two stool specimens from different days are collected and multiple Kato-Katz thick smears are analyzed instead of one. [ABSTRACT FROM AUTHOR]

Background: Reagent 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. Methods: We 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 findings: Reagent 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/Significance: Trace 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. Author summary: Control of schistosomiasis, a parasitic worm infection affecting more than 200 million people worldwide, relies mainly on mass drug administration of praziquantel to school-age children as well as adults in areas where the disease is particularly rampant. The World Health Organization has set thresholds of observed prevalence that require intervention to reach the goal of eliminating schistosomiasis as a public health problem by 2025. Intervention thresholds are defined based on parasitologic methods, which is urine filtration for Schistosoma haematobium. There are alternative diagnostic methods to detect S. haematobium, such as the detection of blood in urine that is a common symptom of urogenital schistosomiasis. We determined the diagnostic sensitivity and specificity of a reagent strip to detect microhematuria using data from two villages in Tanzania at multiple time points (once before and several times after treatment with the deworming drug praziquantel) and translate the urine filtration intervention thresholds to reagent strip equivalents. We show that the reagent strip including trace results is almost perfectly sensitive for infections above 15 eggs of S. haematobium per 10 ml of urine and that a 10% observed prevalence by urine filtration corresponds to 17.6% observed prevalence by reagent strip including traces. [ABSTRACT FROM AUTHOR]

Abstract: The geographical ranges of most species, including many infectious disease agents and their vectors and intermediate hosts, are assumed to be constrained by climatic tolerances, mainly temperature. It has been suggested that global warming will cause an expansion of the areas potentially suitable for infectious disease transmission. However, the transmission of infectious diseases is governed by a myriad of ecological, economic, evolutionary and social factors. Hence, a deeper understanding of the total disease system (pathogens, vectors and hosts) and its drivers is important for predicting responses to climate change. Here, we combine a growing degree day model for Schistosoma mansoni with species distribution models for the intermediate host snail (Biomphalaria spp.) to investigate large-scale environmental determinants of the distribution of the African S. mansoni-Biomphalaria system and potential impacts of climatic changes. Snail species distribution models included several combinations of climatic and habitat-related predictors; the latter divided into “natural” and “human-impacted” habitat variables to measure anthropogenic influence. The predictive performance of the combined snail–parasite model was evaluated against a comprehensive compilation of historical S. mansoni parasitological survey records, and then examined for two climate change scenarios of increasing severity for 2080. Future projections indicate that while the potential S. mansoni transmission area expands, the snail ranges are more likely to contract and/or move into cooler areas in the south and east. Importantly, we also note that even though climate per se matters, the impact of humans on habitat play a crucial role in determining the distribution of the intermediate host snails in Africa. Thus, a future contraction in the geographical range size of the intermediate host snails caused by climatic changes does not necessarily translate into a decrease or zero-sum change in human schistosomiasis prevalence. [Copyright &y& Elsevier]

Introduction: With the renewed drive towards malaria elimination, there is a need for improved surveillance tools. While time series analysis is an important tool for surveillance, prediction and for measuring interventions’ impact, approximations by commonly used Gaussian methods are prone to inaccuracies when case counts are low. Therefore, statistical methods appropriate for count data are required, especially during “consolidation” and “pre-elimination” phases. Methods: Generalized autoregressive moving average (GARMA) models were extended to generalized seasonal autoregressive integrated moving average (GSARIMA) models for parsimonious observation-driven modelling of non Gaussian, non stationary and/or seasonal time series of count data. The models were applied to monthly malaria case time series in a district in Sri Lanka, where malaria has decreased dramatically in recent years. Results: The malaria series showed long-term changes in the mean, unstable variance and seasonality. After fitting negative-binomial Bayesian models, both a GSARIMA and a GARIMA deterministic seasonality model were selected based on different criteria. Posterior predictive distributions indicated that negative-binomial models provided better predictions than Gaussian models, especially when counts were low. The G(S)ARIMA models were able to capture the autocorrelation in the series. Conclusions: G(S)ARIMA models may be particularly useful in the drive towards malaria elimination, since episode count series are often seasonal and non-stationary, especially when control is increased. Although building and fitting GSARIMA models is laborious, they may provide more realistic prediction distributions than do Gaussian methods and may be more suitable when counts are low. [ABSTRACT FROM AUTHOR]

Background: Opisthorchis viverrini is a food-borne trematode species that might give rise to biliary diseases and the fatal cholangiocarcinoma. In Lao PDR, an estimated 2.5 million individuals are infected with O. viverrini, but epidemiological studies are scarce and the spatial distribution of infection remains to be determined. Our aim was to map the distribution of O. viverrini in southern Lao PDR, identify underlying risk factors, and predict the prevalence of O. viverrini at non-surveyed locations. Methodology: A cross-sectional parasitological and questionnaire survey was carried out in 51 villages in Champasack province in the first half of 2007. Data on demography, socioeconomic status, water supply, sanitation, and behavior were combined with remotely sensed environmental data and fed into a geographical information system. Bayesian geostatistical models were employed to identify risk factors and to investigate the spatial pattern of O. viverrini infection. Bayesian kriging was utilized to predict infection risk at non-surveyed locations. Principal Findings: The prevalence of O. viverrini among 3,371 study participants with complete data records was 61.1%. Geostatistical models identified age, Lao Loum ethnic group, educational attainment, occupation (i.e., rice farmer, fisherman, and animal breeder), and unsafe drinking water source as risk factors for infection. History of praziquantel treatment, access to sanitation, and distance to freshwater bodies were found to be protective factors. Spatial patterns of O. viverrini were mainly governed by environmental factors with predictive modeling identifying two different risk profiles: low risk of O. viverrini in the mountains and high risk in the Mekong corridor. Conclusions/Significance: We present the first risk map of O. viverrini infection in Champasack province, which is important for spatial targeting of control efforts. Infection with O. viverrini appears to be strongly associated with exposure to the second intermediate host fish, human behavior and culture, whereas high transmission is sustained by the lack of sanitation. Author Summary: The liver fluke Opisthorchis viverrini mainly occurs in Lao PDR and Thailand. Humans become infected through the consumption of raw or insufficiently cooked freshwater fish. Chronic infections may lead to severe liver (bile duct) diseases that eventually develop into a bile duct cancer with extremely poor prognosis. Current control efforts aim at preventing heavy morbidity and mortality. In recent years, spatial modeling, using data from well designed surveys, has been employed to better understand the distribution and determinants of parasitic diseases for guiding subsequent control. However, a spatial modeling approach has not been used for O. viverrini before. The purpose of the current study was to map the distribution of O. viverrini infection in Champasack province in southern Lao PDR, to identify risk factors of infection, and to predict the distribution at non-surveyed locations. We found that the risk of O. viverrini infection is higher for people living in close proximity to freshwater bodies, whereas the lack of sanitation sustained environmental contamination and transmission. High risk zones in Champasack province are concentrated in the Mekong River corridor, and hence control efforts should be targeted along the Mekong River. [ABSTRACT FROM AUTHOR]

Background: Opisthorchis viverrini is a food-borne trematode species that might give rise to biliary diseases and the fatal cholangiocarcinoma. In Lao PDR, an estimated 2.5 million individuals are infected with O. viverrini, but epidemiological studies are scarce and the spatial distribution of infection remains to be determined. Our aim was to map the distribution of O. viverrini in southern Lao PDR, identify underlying risk factors, and predict the prevalence of O. viverrini at non-surveyed locations. Methodology: A cross-sectional parasitological and questionnaire survey was carried out in 51 villages in Champasack province in the first half of 2007. Data on demography, socioeconomic status, water supply, sanitation, and behavior were combined with remotely sensed environmental data and fed into a geographical information system. Bayesian geostatistical models were employed to identify risk factors and to investigate the spatial pattern of O. viverrini infection. Bayesian kriging was utilized to predict infection risk at non-surveyed locations. Principal Findings: The prevalence of O. viverrini among 3,371 study participants with complete data records was 61.1%. Geostatistical models identified age, Lao Loum ethnic group, educational attainment, occupation (i.e., rice farmer, fisherman, and animal breeder), and unsafe drinking water source as risk factors for infection. History of praziquantel treatment, access to sanitation, and distance to freshwater bodies were found to be protective factors. Spatial patterns of O. viverrini were mainly governed by environmental factors with predictive modeling identifying two different risk profiles: low risk of O. viverrini in the mountains and high risk in the Mekong corridor. Conclusions/Significance: We present the first risk map of O. viverrini infection in Champasack province, which is important for spatial targeting of control efforts. Infection with O. viverrini appears to be strongly associated with exposure to the second intermediate host fish, human behavior and culture, whereas high transmission is sustained by the lack of sanitation. [ABSTRACT FROM AUTHOR]

The 2006-2007 Angola Malaria Indicator Survey (AMIS) is the first nationally representative household survey in the country assessing coverage of the key malaria control interventions and measuring malaria-related burden among children under 5 years of age. In this paper, the Angolan MIS data were analyzed to produce the first smooth map of parasitaemia prevalence based on contemporary nationwide empirical data in the country. Bayesian geostatistical models were fitted to assess the effect of interventions after adjusting for environmental, climatic and socio-economic factors. Non-linear relationships between parasitaemia risk and environmental predictors were modeled by categorizing the covariates and by employing two non-parametric approaches, the B-splines and the P-splines. The results of the model validation showed that the categorical model was able to better capture the relationship between parasitaemia prevalence and the environmental factors. Model fit and prediction were handled within a Bayesian framework using Markov chain Monte Carlo (MCMC) simulations. Combining estimates of parasitaemia prevalence with the number of children under 5 we obtained estimates of the number of infected children in the country. The population-adjusted prevalence ranges from 3.76% in Namibe province to 32.65% in Malanje province. The odds of parasitaemia in children living in a household with at least 0.2 ITNs per person was by 41% lower (CI: 14%, 60%) than in those with fewer ITNs. The estimates of the number of parasitaemic children produced in this paper are important for planning and implementing malaria control interventions and for monitoring the impact of prevention and control activities. [ABSTRACT FROM AUTHOR]

Background: Detailed information regarding the spatial and/or spatial-temporal distribution of mortality is required for the efficient implementation and targeting of public health interventions. Objectives: Identify high risk clusters of mortality within the Agincourt subdistrict for targeting of public health interventions, and highlight areas for further research. Design: Mortality data were extracted from the Agincourt health and socio-demographic surveillance system (HDSS) for the period 1992-2007. Mortality rates by age group and time were calculated assuming a Poisson distribution and using precise person-time contribution estimates. A spatial scan statistic (Kulldorff) was used to test for clusters of age group specific all-cause and cause-specific mortality both in space and time. Results: Many statistically significant clusters of higher all-cause and cause-specific mortality were identified both in space and time. Specific areas were consistently identified as high risk areas; namely, the east/southeast and upper east central regions. This corresponds to areas with higher mortality due to communicable causes (especially HIV/TB and diarrheal disease) and indicates a non-random element to the distribution of potential underlying causative factors e.g. settlements comprising former Mozambican refugees in east/southeast of the site, corresponding higher poverty areas, South African villages with higher HIV prevalence, etc. Clusters of older adult mortality were also observed indicating potential non-random distribution of non-communicable disease mortality. Conclusion: This study has highlighted distinct clusters of all-cause and cause-specific mortality within the Agincourt subdistrict. It is a first step in prioritizing areas for further, more detailed research as well as for future public health follow-on efforts such as targeting of vertical prevention of HIV/TB and antiretroviral rollout in significant child and adult mortality clusters; and assessment and provision of adequate water and sanitation in the child mortality clusters particularly in the south-east where diarrheal mortality appears high. Underlying causative factors need to be identified and accurately quantified. Other questions for more detailed research are discussed. [ABSTRACT FROM AUTHOR]