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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 (PM 2.5 ) and nitrogen dioxide (NO 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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Introduction: The majority of emergency paediatric death in African countries occur within the first 24 h of admission. A coloured triage system is widely implemented in high-income countries and the emergency triage and assessment treatment (ETAT) is recommended by the World Health Organization, but not put into practice in Mozambique. We implemented a three-colour triage system in a rural district hospital with lay-staff workers conducting the first triage., Methods: A retrospective, before and after, mortality analysis was performed using routine patient files from the district hospital between 2014 and 2017. The triage system was implemented in August 2016. Inclusion criteria were children under 15 years of age that entered the emergency centre. Primary outcome was child mortality rate. Secondary outcomes included the percentage agreement between the clinical and non-clinical staff and the duration from triage to first treatment. We used a negative binomial model in STATA 15 to compare mortality rates, and Kappa statistics to estimate the agreement between clinical and non-clinical staff., Results: 4176 admissions were included. The mortality rate ratio (MMR) was 45% lower after the start of the intervention (2016; MRR = 0.55; 0.38, 0.81; p = 0.002), compared to before. To estimate the agreement between non-clinical and clinical staff, 548 (of the 671) patient files were included. The agreement was estimated at 88.7% (Kappa = 0.644; p < 0.001). The median waiting time decreased with urgency of the triage: 2 h33 for 'green'/least serious (IQR 1 h58-3 h30), 21 min for yellow/serious (IQR 0 h10-0 h58) and nine minutes for 'red'/urgent (IQR 2-40 min)., Conclusion: In a rural setting with nurse-led clinical care and non-clinician staff working at the triage reception, implementation of a three-coloured triage system was feasible. Triage and ETAT training was associated with a decrease of 45% of paediatric deaths. The impact on mortality, low cost, and ease of the implementation supports scaling this intervention in similar settings., (2019 African Federation for Emergency Medicine. Publishing services provided by Elsevier.)

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., (Copyright © 2018 Elsevier B.V. All rights reserved.)

The soil-transmitted nematode, Strongyloides stercoralis is one of the most-neglected of all neglected tropical diseases. It is globally distributed, favouring the humid, wet climates of the tropics and subtropics. Inadequate sanitary conditions promote the spread of S. stercoralis infection. In South-East Asia, many countries provide the ideal ecological and economic setting for high S. stercoralis infection rates. Yet, in most of these countries, little is known about the actual prevalence and distribution of S. stercoralis. One reason for this lack of knowledge pertains to the time- and resource-intensive diagnostic methods used to detect S. stercoralis infection. The Koga Agar culture method and the Baermann method are considered to be the best coprological diagnostic methods for field settings today. Both detect the parasite with high sensitivity. This sensitivity can be increased further by examining stool samples for several consecutive days, thereby increasing the chances of detecting low-intensity chronic infections. Diagnostic challenges, however, lead to the omission of S. stercoralis in studies of soil-transmitted helminths and few studies focus on S. stercoralis, specifically. These factors lead to an underreporting of the nematode's prevalence, not only in South-East Asia but worldwide. We have reviewed the scientific literature of the last 25 years and estimated country-wide prevalence rates for South-East Asia. We aim to summarise what is known today about the prevalence of S. stercoralis in South-East Asia, as well as to ascertain the risk factors and diagnostic methods most commonly applied., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Smoking is the leading cause of lung cancer. Non-smoking factors have been associated with the disease. Existing Swiss survey data only capture the country partially and temporal coverage does not allow for a time lag between exposure to tobacco and lung cancer outbreak. Knowledge about the distribution of tobacco-use is essential to estimate its contribution to disease burden. Bayesian regression models were applied to estimate spatial smoking patterns. Data were provided from the Swiss Health Survey (14521 participants). Regression models with spatial random effects (SREs) were employed to obtain smoking proxies based on mortality rates and SREs adjusted for environmental exposures. Population attributable fractions were estimated to assess the burden of tobacco-use on lung cancer mortality. Correlation between observed smoking prevalence with smoking proxies was moderate and stronger in females. In the absence of sufficient survey data, smooth unadjusted mortality rates can be used to assess smoking patterns in Switzerland., (Copyright © 2015. Published by Elsevier Ltd.)

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., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Soil-transmitted helminth infections are intimately connected with poverty. Yet, there is a paucity of using socioeconomic proxies in spatially explicit risk profiling. We compiled household-level socioeconomic data pertaining to sanitation, drinking-water, education and nutrition from readily available Demographic and Health Surveys, Multiple Indicator Cluster Surveys and World Health Surveys for Cambodia and aggregated the data at village level. We conducted a systematic review to identify parasitological surveys and made every effort possible to extract, georeference and upload the data in the open source Global Neglected Tropical Diseases database. Bayesian geostatistical models were employed to spatially align the village-aggregated socioeconomic predictors with the soil-transmitted helminth infection data. The risk of soil-transmitted helminth infection was predicted at a grid of 1×1km covering Cambodia. Additionally, two separate individual-level spatial analyses were carried out, for Takeo and Preah Vihear provinces, to assess and quantify the association between soil-transmitted helminth infection and socioeconomic indicators at an individual level. Overall, we obtained socioeconomic proxies from 1624 locations across the country. Surveys focussing on soil-transmitted helminth infections were extracted from 16 sources reporting data from 238 unique locations. We found that the risk of soil-transmitted helminth infection from 2000 onwards was considerably lower than in surveys conducted earlier. Population-adjusted prevalences for school-aged children from 2000 onwards were 28.7% for hookworm, 1.5% for Ascaris lumbricoides and 0.9% for Trichuris trichiura. Surprisingly, at the country-wide analyses, we did not find any significant association between soil-transmitted helminth infection and village-aggregated socioeconomic proxies. Based also on the individual-level analyses we conclude that socioeconomic proxies might not be good predictors at an aggregated large-scale analysis due to their large between- and within-village heterogeneity. Specific information of both the infection risk and potential predictors might be needed to obtain any existing association. The presented soil-transmitted helminth infection risk estimates for Cambodia can be used for guiding and evaluating control and elimination efforts., (Copyright © 2014. Published by Elsevier B.V.)

Schistosomiasis is one of the most common parasitic diseases in tropical and subtropical areas, including Brazil. A national control programme was initiated in Brazil in the mid-1970s and proved successful in terms of morbidity control, as the number of cases with hepato-splenic involvement was reduced significantly. To consolidate control and move towards elimination, there is a need for reliable maps on the spatial distribution of schistosomiasis, so that interventions can target communities at highest risk. The purpose of this study was to map the distribution of Schistosoma mansoni in Brazil. We utilized readily available prevalence data from the national schistosomiasis control programme for the years 2005-2009, derived remotely sensed climatic and environmental data and obtained socioeconomic data from various sources. Data were collated into a geographical information system and Bayesian geostatistical models were developed. Model-based maps identified important risk factors related to the transmission of S. mansoni and confirmed that environmental variables are closely associated with indices of poverty. Our smoothed predictive risk map, including uncertainty, highlights priority areas for intervention, namely the northern parts of North and Southeast regions and the eastern part of Northeast region. Our predictive risk map provides a useful tool for to strengthen existing surveillance-response mechanisms., (Copyright © 2014. Published by Elsevier B.V.)

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., (© 2013 Published by Elsevier Ltd.)

A study to investigate the effect of environmental predictors on abundance of Biomphalaria, the intermediate host snails of Schistosoma mansoni, was carried out on two small islands in Lake Victoria, Uganda. Malacological surveys were performed at 40 shoreline sites on Kimi and Ngamba islands documenting occurrence of Biomphalaria; other environmental and limnological conditions were also recorded, including gastropod community diversity. Snails were examined for shedding of schistosomes and emerging cercariae were DNA 'barcoded'. For population genetics analysis of Biomphalaria, snails from four populations from each island were also sequenced. Aquatic phosphate concentrations were higher on Kimi island, confirming greater anthropogenic influence, although, snail species diversity and community assemblages were not significantly different between islands. Bayesian geostatistical models were fitted to assess the effect of environmental factors on Biomphalaria abundance and snails' risk of shedding schistosome or non-schistosome cercariae. No factors were found to be significant in the spatial model. The local population genetics of S. mansoni and Biomphalaria on each island followed similar patterns as that seen in previous studies on a lake-wide basis. These findings suggest that smaller scale studies may prove useful as proxies for regional level investigations, with reduced logistical and resource output required. However, further research should also include surveys of terminal host parasite burden, as these will affect even micro-scale dynamics of parasite-intermediate host interactions, as well as be important from a public health perspective in their own right., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Schistosomiasis remains one of the most prevalent parasitic diseases in the tropics and subtropics, but current statistics are outdated due to demographic and ecological transformations and ongoing control efforts. Reliable risk estimates are important to plan and evaluate interventions in a spatially explicit and cost-effective manner. We analysed a large ensemble of georeferenced survey data derived from an open-access neglected tropical diseases database to create smooth empirical prevalence maps for Schistosoma mansoni and Schistosoma haematobium for a total of 13 countries of eastern Africa. Bayesian geostatistical models based on climatic and other environmental data were used to account for potential spatial clustering in spatially structured exposures. Geostatistical variable selection was employed to reduce the set of covariates. Alignment factors were implemented to combine surveys on different age-groups and to acquire separate estimates for individuals aged ≤20 years and entire communities. Prevalence estimates were combined with population statistics to obtain country-specific numbers of Schistosoma infections. We estimate that 122 million individuals in eastern Africa are currently infected with either S. mansoni, or S. haematobium, or both species concurrently. Country-specific population-adjusted prevalence estimates range between 12.9% (Uganda) and 34.5% (Mozambique) for S. mansoni and between 11.9% (Djibouti) and 40.9% (Mozambique) for S. haematobium. Our models revealed that infection risk in Burundi, Eritrea, Ethiopia, Kenya, Rwanda, Somalia and Sudan might be considerably higher than previously reported, while in Mozambique and Tanzania, the risk might be lower than current estimates suggest. Our empirical, large-scale, high-resolution infection risk estimates for S. mansoni and S. haematobium in eastern Africa can guide future control interventions and provide a benchmark for subsequent monitoring and evaluation activities., (Copyright © 2011 Elsevier B.V. All rights reserved.)

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 © 2011 Elsevier B.V. All rights reserved.)

There is a paucity of research on micro-level assessment of the dynamics of socio-economic status following health interventions. The use of household asset data to determine wealth indices is a common procedure for estimating socio-economic position in low-income countries. Indeed, in such settings information about income is usually lacking and the collection of individual consumption or expenditure data would require in-depth interviews, posing a considerable risk of bias. In this study, we determined the socio-economic status of 159 households in a village in north-western Tanzania before and 1 year after participatory hygiene and sanitation transformation (PHAST) intervention to control schistosomiasis. We constructed a household 'wealth index' based on durable assets ownership (e.g. bicycle and radio) and household characteristics dealing with ownership of land and house construction features (e.g. type of walls and roof). We employed principal components analysis and classified households into wealth quintiles. The study revealed that asset variables with positive factor scores were associated with higher socio-economic status, whereas asset variables with negative factor scores were associated with lower socio-economic status. Overall, households which were rated as the poorest and very poor were on the decrease, whereas those rated as poor, less poor and the least poor were on the increase after PHAST intervention. This decrease/increase was significant. The median shifted from -0.761 to -0.448, and the mean from -0.204 (standard deviation (SD) 1.924) to 0.193 (SD 2.079) between pre- and post-intervention phases. The difference in socio-economic status of the people comparing the pre- and post-intervention phases was highly statistically significant (p<0.001). This observation was confirmed by a multinomial model with a random effect on the households. We argue that significant changes in the socio-economic status observed in our study are attributable to the PHAST intervention, despite other sporadic interventions against schistosomiasis., (Copyright © 2013 Elsevier B.V. All rights reserved.)

For schistosomiasis and other neglected tropical diseases for which resources for control are still limited, model-based maps are needed for prioritising spatial targeting of control interventions and surveillance of control programmes. Bayesian geostatistical modelling has been widely and effectively used to generate smooth empirical risk maps. In this paper, we review important issues related to the modelling of schistosomiasis risk, including Bayesian computation of large datasets, heterogeneity of historical survey data, stationary and isotropy assumptions and novel approaches for Bayesian geostatistical variable selection. We provide an example of advanced Bayesian geostatistical variable selection based on historical prevalence data of Schistosoma mansoni in Côte d'Ivoire. We include a "parameter expanded normal mixture of inverse-gamma" prior for the regression coefficients, which in turn allows selection of blocks of covariates, particularly categorical variables. The implemented Bayesian geostatistical variable selection provided a rigorous approach for the selection of predictors within a Bayesian geostatistical framework, identified the most important predictors of S. mansoni infection risk and led to a more parsimonious model compared to traditional selection approaches that ignore the spatial structure in the data. In conclusion, statistical advances in Bayesian geostatistical modelling offer unique opportunities to account for important inherent characteristics of the Schistosoma infection, and hence Bayesian geostatistical models can guide the spatial targeting of control interventions., (Copyright © 2013 Elsevier B.V. All rights reserved.)

South Africa is experiencing a major burden of HIV/TB. We used longitudinal data from the Agincourt sub-district in rural northeast South Africa over the years 2000 to 2005. A total of 187 HIV/TB deaths were observed among 16,844 children aged 1-5 years coming from 8,863 households. In this paper we used Bayesian models to assess risk factors for child HIV/TB mortality taking into account the presence of spatial correlation. Bayesian zero inflated spatiotemporal models were able to detect hidden patterns within the data. Our main finding was that maternal orphans experienced a threefold greater risk of HIV/TB death compared to those with living mothers (AHR=2.93, 95% CI[1.29;6.93]). Risk factor analyses which adjust for person, place and time provide evidence for policy makers that includes a spatial distribution of risk. Child survival is dependent on the mother's survival; hence programs that promote maternal survival are critical., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

The proportion of malaria vectors harboring the infectious stage of the parasite (the sporozoite rates) is an important component of measures of malaria transmission. Variation in time and/or space in sporozoite rates contribute substantially to spatio-temporal variation in transmission. However, because most vectors test negative for sporozoites, sporozoite rate data are sparse with large number of observed zeros across locations or over time in the case of longitudinal data. Rarely are appropriate methods and models used in analyzing such data. In this study, Bayesian zero inflated binomial (ZIB) geostatistical models were developed and compared with standard binomial analogues to analyze sporozoite data obtained from the KEMRI/CDC health and demographic surveillance system (HDSS) site in rural Western Kenya during 2002-2004. ZIB models showed a better predictive ability, identified more significant covariates and obtained narrower credible intervals for all parameters compared to standard geostatistical binomial model., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Anemia is common among children in sub-Saharan Africa and its etiology is multifactorial. Likely causes of anemia are low bioavailability of dietary iron, malaria, and helminth infection. In this study, we aimed to assess the effect of iron fortification, intermittent preventive treatment (IPT) of malaria, and anthelmintic treatment on hemoglobin concentration and anemia prevalence among school children. The study was a 6-mo, randomized, double-blind, controlled trial enrolling 591 6- to 14-y-old school children in Côte d'Ivoire using the following: 1) iron-fortified biscuits providing an additional 20 mg iron/d as electrolytic iron 4 times/wk; 2) IPT of malaria with sulfadoxine-pyrimethamine at 0 and 3 mo; and 3) anthelmintic treatment at 0 and 3 mo as the interventions. Prevalence of anemia, iron deficiency, malaria parasitemia, and helminth infection was 70.4, 9.3, 57.7, and 54.8%, respectively. Iron fortification did not improve iron status, IPT of malaria did not affect malaria burden, and neither had an impact on anemia prevalence. Anthelmintics significantly reduced the burden of helminth infections and decreased anemia prevalence (odds ratio: 0.4, 95% CI: 0.3, 0.7). The low prevalence of iron deficiency and an extended dry season that decreased malaria transmission likely reduced the potential impact of iron fortification and IPT. In this setting, anthelmintic treatment was the only intervention that modestly decreased rates of anemia.

Agricultural activities, among other factors, can influence the transmission of malaria. In two villages of central Côte d'Ivoire (Tiémélékro and Zatta) with distinctively different agro-ecological characteristics, we assessed Plasmodium prevalence rates, fever and clinically confirmed malaria episodes among children aged 15 years and below by means of repeated cross-sectional surveys. Additionally, presumptive malaria cases were monitored in dispensaries for a 4-year period. In Tiémélékro, we observed a decrease in malaria prevalence rates from 2002 to 2005, which might be partially explained by changes in agricultural activities from subsistence farming to cash crop production. In Zatta, where an irrigated rice perimeter is located in close proximity to human habitations, malaria prevalence rates in 2003 were significantly lower than in 2002 and 2005, which coincided with the interruption of irrigated rice farming in 2003/2004. Although malaria transmission differed by an order of magnitude in the two villages in 2003, there was no statistically significant difference between the proportions of severe malaria episodes (i.e. axillary temperature>37.5 degrees C plus parasitaemia>5000 parasites/microl blood). Our study underscores the complex relationship between malaria transmission, prevalence rate and the dynamics of malaria episodes. A better understanding of local contextual determinants, including the effect of agricultural activities, will help to improve the local epidemiology and control of malaria.

Whilst infections with soil-transmitted helminths are common across China, the public-health significance of Schistosoma japonicum and food-borne helminths is more focalized. Only few studies have investigated the local epidemiology of helminth infections in rural China, including risk factor analysis. We collected stool and blood samples from 3220 individuals, aged 5-88 years, from 35 randomly selected villages in Eryuan county, Yunnan province, China. Stool samples were subjected to the Kato-Katz technique and examined for helminth eggs. Blood samples were tested for Trichinella spp., S. japonicum and cysticerci-specific antibodies. Data on individual and family-level risk factors were collected using questionnaires. The prevalence of Ascaris lumbricoides, Taenia spp., Trichuris trichiura and hookworms was 15.4%, 3.5%, 1.7% and 0.3%, respectively. The seroprevalence of Trichinella spp. was 58.8% and that of cysticercosis 18.5%. The egg positivity rate of S. japonicum in the 13 known endemic villages was 2.7%, and the corresponding seroprevalence was 49.5%. We observed a strong spatial heterogeneity in the families' economic status. S. japonicum infections were more prevalent among the Han than Bai nationality (odds ratio (OR)=3.77, 95% confidence interval (CI)=1.97-7.23) and tobacco growers (OR=3.66, 95% CI=1.77-7.60) and was only found at elevations below 2150 m above sea level. A. lumbricoides and Taenia spp. infections were more prevalent at altitudes above 2150 m when compared to lower settings (OR=1.51, 95% CI=1.24-1.84 and OR=5.32, 95% CI=3.42-8.28, respectively). The opposite was found for T. trichiura (OR=0.31, 95% CI=0.14-0.70). Our findings can guide the design and spatial targeting of control interventions against helminth infections in Eryuan county.

The type of water management and drainage system could be a potential reason for variation in malaria transmission in rice cultivation areas. To investigate this we have compared the population dynamics of Anopheles mosquitoes (Diptera, Culicidae) in rice plots with controlled and uncontrolled water depth, i.e. casiers and hors-casiers, respectively in the Office du Niger, Mali. We also compared malaria transmission in areas with mixed and casiers plots. Larval collection was performed fortnightly with the standard WHO dipping technique. Adult Anopheles were collected both by pyrethrum spray and landing catches. During the dry season rice cultivation cycle, the larval density in the hors-casier was significantly higher than in the casier plots. The larval peak in the casier plots was considerably smaller than the one in the hors-casier. During the rainy season, no significant difference was observed between the two plot types. However, larval densities begin to rise approximately one month earlier in the casier then in the hors-casier plots, and continued to increase trough the rice development phases until the grain filling/maturation phase, declining thereafter. In contrast, in the hors-casier rice plots larval density increased throughout the rice development. This difference was not significantly reflected in the adult vector density and man biting rate. However, high relative frequencies of Anopheles funestus, survival and entomological inoculation rates of An. gambiae s.l. were observed in the mixed plot sector.

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.

A model was developed using remote sensing and geographic information system technologies for habitat identification of Oncomelania hupensis, the intermediate host snail of Schistosoma japonicum, in the Poyang Lake area, China. In a first step, two multi-temporal Landsat TM 5 satellite images, one from the wet and the second from the dry season, were visually classified into different land-use types. Next, the normalized difference vegetation index was extracted from the images and the tasseled-cap transformation was employed to derive the wetness feature. Our model predicted an estimated 709 km2 of the marshlands in Poyang Lake as potential habitats for O. hupensis. Near-ground temperature measurements in April and August yielded a range of 22.8-24.2 degrees C, and pH values of 6.0-8.5 were derived from existing records. Both climatic features represent suitable breeding conditions for the snails. Preliminary validation of the model at 10 sites around Poyang Lake revealed an excellent accuracy for predicting the presence of O. hupensis. We used the predicted snail habitats as centroids and established buffer zones around them. Villages with an overall prevalence of S. japonicum below 3% were located more than 1200m away from the centroids. Furthermore, a gradient of high-to-low prevalence was observed with increasing distance from the centroids. In conclusion, the model holds promise for identifying high risk areas of schistosomiasis japonica and may become an important tool for the ongoing national schistosomiasis control programme. The model is of particular relevance for schistosome-affected regions that lack accurate surveillance capabilities and are large enough to be detected at most commercially available remote sensing scales.