The use of bivariate spatial modeling of questionnaire and parasitology data to predict the distribution of Schistosoma haematobium in Coastal Kenya

Multilevel bivariate analysis of the association between high-risk fertility behaviors of birth and stunting with associated risk factors in Ethiopia

Introduction

Currently, the linkage between high-risk fertility behavior of birth and the occurrence of stunting among children under the age of 5 continues to be a significant public health problem in developing countries, including Ethiopia. This issue poses a threat to the health and overall wellbeing of under-five children. Thus, the main objective of this study was to examine the association between high-risk fertility behavior of birth and the stunting status of children and associated factors.

Methods

The data used for this study were extracted from the recent Ethiopian Mini Demographic and Health Survey data in 2019. A total weighted sample of 4,969 under-five children was included in this study, and the relevant data were extracted from those samples. The multilevel bivariate analysis was used to assess the association between high-risk fertility behavior of birth and the stunting status of under-five children in Ethiopia.

Results

It was found that, out of 4,997 under-five children, 24% of under-five children experienced stunting as a result of high-risk fertility behavior of birth. Our study also revealed an intra-class correlation of 0.2, indicating that 20% of the variability in both high-risk fertility behaviors of birth and stunting can be attributed to differences between communities. Furthermore, there was a statistically significant association between high-risk fertility behavior of birth and the stunting status of children under the age of 5 years [AOR = 8.5, 95% CI: (5.58, 18.70)]. Similarly, the stunting status of birth among boys was 1.36 times greater than the estimated odds of the stunting status of birth among girls [AOR = 1.36, 95% CI: (1.19, 1.55)].

Conclusion

This study found that there was a significant statistical association between high-risk fertility behavior of birth and stunting status of under-five children. Specifically, children born to mothers under 18 years and in households with high parity were identified as the main risk factors for child stunting. Furthermore, health-related education, improved access to maternal healthcare, and training interventions were associated with high-risk fertility behavior during birth and child stunting. The study suggests that regular health assessments and early interventions for infants born to mothers with high-risk reproductive characteristics are crucial to reducing the impact of child stunting under 5 years of age.

Estimating spatially disaggregated probability of severe COVID-19 and the impact of handwashing interventions: The case of Zimbabwe

INTRODUCTION

The severity of COVID-19 disease varies substantially between individuals, with some infections being asymptomatic while others are fatal. Several risk factors have been identified that affect the progression of SARS-CoV-2 to severe COVID-19. They include age, smoking and presence of underlying comorbidities such as respiratory illness, HIV, anemia and obesity. Given that respiratory illness is one such comorbidity and is affected by hand hygiene, it is plausible that improving access to handwashing could lower the risk of severe COVID-19 among a population. In this paper, we estimate the potential impact of improved access to handwashing on the risk of respiratory illness and its knock-on impact on the risk of developing severe COVID-19 disease across Zimbabwe.

METHODS

Spatial generalized additive models were applied to cluster level data from the 2015 Demographic and Health Survey. These models were used to generate continuous (1km resolution) estimates of risk factors for severe COVID-19, including prevalence of major comorbidities (respiratory illness, HIV without viral load suppression, anemia and obesity) and prevalence of smoking, which were aggregated to district level alongside estimates of the proportion of the population under 50 from Worldpop data. The risk of severe COVID-19 was then calculated for each district using published estimates of the relationship between comorbidities, smoking and age (under 50) and severe COVID-19. Two scenarios were then simulated to see how changing access to handwashing facilities could have knock on implications for the prevalence of severe COVID-19 in the population.

RESULTS

This modeling conducted in this study shows that (1) current risk of severe disease is heterogeneous across the country, due to differences in individual characteristics and household conditions and (2) that if the quantifiable estimates on the importance of handwashing for transmission are sound, then improvements in handwashing access could lead to reductions in the risk of severe COVID-19 of up to 16% from the estimated current levels across all districts.

CONCLUSIONS

Taken alongside the likely impact on transmission of SARS-CoV-2 itself, as well as countless other pathogens, this result adds further support for the expansion of access to handwashing across the country. It also highlights the spatial differences in risk of severe COVID-19, and thus the opportunity for better planning to focus limited resources in high-risk areas in order to potentially reduce the number of severe cases.

Programmatic Implications for Schistosomiasis Elimination Based on Community-Based Survey in the Blue Nile, North Kordofan, and Sennar States, Sudan

Schistosomiasis prevalence has remained high in some areas due to reinfection despite repeated mass drug administration interventions. We aimed to explore its risk factors in order to help to design adequate interventions in such high-transmission areas. A total of 6225 individuals residing in 60 villages in 8 districts of North Kordofan, Blue Nile, or Sennar States, Sudan participated in the community-based survey in March 2018. First, we investigated Schistosoma haematobium and Schistosoma mansoni prevalences among school-aged children and adults. Second, the associations between risk factors and schistosomiasis were explored. Those without any type of latrine in their households had higher odds of being infected with schistosomiasis than those with a latrine (odds ratio (OR) = 1.53; 95% confidence interval (CI) 1.20-1.94; p = 0.001), and the odds of being positive for schistosomiasis among people living in a household without an improved latrine were higher than for their counterparts with an improved latrine (OR = 1.63; CI 1.05-2.55; p = 0.03). Furthermore, people with households or outside compounds found to contain human faeces had higher odds of being infected with schistosomiasis than their counterparts (OR = 1.36, 95% CI 1.01-1.83, p = 0.04). Installing an improved latrine and eliminating open defecation should be highlighted in schistosomiasis elimination projects in high-transmission areas.

Schistosoma haematobium infection and environmental factors in Southwestern Tanzania: A cross-sectional, population-based study

Schistosomiasis is a leading cause of morbidity in Africa. Understanding the disease ecology and environmental factors that influence its distribution is important to guide control efforts. Geographic information systems have increasingly been used in the field of schistosomiasis environmental epidemiology. This study reports prevalences of Schistosoma haematobium infection and uses remotely sensed and questionnaire data from over 17000 participants to identify environmental and socio-demographic factors that are associated with this parasitic infection. Data regarding socio-demographic status and S. haematobium infection were obtained between May 2006 and May 2007 from 17280 participants (53% females, median age = 17 years) in the Mbeya Region, Tanzania. Combined with remotely sensed environmental data (vegetation cover, altitude, rainfall etc.) this data was analyzed to identify environmental and socio-demographic factors associated with S. haematobium infection, using mixed effects logistic regression and geostatistical modelling. The overall prevalence of S. haematobium infection was 5.3% (95% confidence interval (CI): 5.0-5.6%). Multivariable analysis revealed increased odds of infection for school-aged children (5-15 years, odds ratio (OR) = 7.8, CI: 5.9-10.4) and the age groups 15-25 and 25-35 years (15-25 years: OR = 5.8, CI: 4.3-8.0, 25-35 years: OR = 1.6, CI: 1.1-2.4) compared to persons above 35 years of age, for increasing distance to water courses (OR = 1.4, CI: 1.2-1.6 per km) and for proximity to Lake Nyasa (<1 km, OR = 4.5, CI: 1.8-11.4; 1-2 km, OR = 3.5, CI: 1.7-7.5; 2-4 km; OR = 3.3, CI: 1.7-6.6), when compared to distances >4 km. Odds of infection decreased with higher altitude (OR = 0.7, CI: 0.6-0.8 per 100 m increase) and with increasing enhanced vegetation index EVI (OR = 0.2, CI: 0.1-0.4 per 0.1 units). When additionally adjusting for spatial correlation population density became a significant predictor of schistosomiasis infection (OR = 1.3, CI: 1.1-1.5 per 1000 persons/km2) and altitude turned non-significant. We found highly focal geographical patterns of S. haematobium infection in Mbeya Region in Southwestern Tanzania. Despite low overall prevalence our spatially heterogeneous results show that some of the study sites suffer from a considerable burden of S. haematobium infection, which is related to various socio-demographic and environmental factors. Our results could help to design more effective control strategies in the future, especially targeting school-aged children living in low altitude sites and/or crowded areas as the persons at highest need for preventive chemotherapy.

Finding hotspots: development of an adaptive spatial sampling approach

The identification of disease hotspots is an increasingly important public health problem. While geospatial modeling offers an opportunity to predict the locations of hotspots using suitable environmental and climatological data, little attention has been paid to optimizing the design of surveys used to inform such models. Here we introduce an adaptive sampling scheme optimized to identify hotspot locations where prevalence exceeds a relevant threshold. Our approach incorporates ideas from Bayesian optimization theory to adaptively select sample batches. We present an experimental simulation study based on survey data of schistosomiasis and lymphatic filariasis across four countries. Results across all scenarios explored show that adaptive sampling produces superior results and suggest that similar performance to random sampling can be achieved with a fraction of the sample size.

Modeling Schistosoma japonicum Infection under Pure Specification Bias: Impact of Environmental Drivers of Infection

Uncertainties in spatial modeling studies of schistosomiasis (SCH) are relevant for the reliable identification of at-risk populations. Ecological fallacy occurs when ecological or group-level analyses, such as spatial aggregations at a specific administrative level, are carried out for an individual-level inference. This could lead to the unreliable identification of at-risk populations, and consequently to fallacies in the drugs’ allocation strategies and their cost-effectiveness. A specific form of ecological fallacy is pure specification bias. The present research aims to quantify its effect on the parameter estimates of various environmental covariates used as drivers for SCH infection. This is done by (i) using a spatial convolution model that removes pure specification bias, (ii) estimating group and individual-level covariate regression parameters, and (iii) quantifying the difference between the parameter estimates and the predicted disease outcomes from the convolution and ecological models. We modeled the prevalence of Schistosoma japonicum using group-level health outcome data, and city-level environmental data as a proxy for individual-level exposure. We included environmental data such as water and vegetation indexes, distance to water bodies, day and night land surface temperature, and elevation. We estimated and compared the convolution and ecological model parameter estimates using Bayesian statistics. Covariate parameter estimates from the convolution and ecological models differed between 0.03 for the nearest distance to water bodies (NDWB), and 0.28 for the normalized difference water index (NDWI). The convolution model presented lower uncertainties in most of the parameter estimates, except for NDWB. High differences in uncertainty were found in night land surface temperature (0.23) and elevation (0.13). No significant differences were found between the predicted values and their uncertainties from both models. The proposed convolution model is able to correct for a pure specification bias by presenting less uncertain parameter estimates. It shows a good predictive performance for the mean prevalence values and for a positive number of infected people. Further research is needed to better understand the spatial extent and support of analysis to reliably explore the role of environmental variables.

Joint spatial modelling of disease risk using multiple sources: an application on HIV prevalence from antenatal sentinel and demographic and health surveys in Namibia

Background

In disease mapping field, researchers often encounter data from multiple sources. Such data are fraught with challenges such as lack of a representative sample, often incomplete and most of which may have measurement errors, and may be spatially and temporally misaligned. This paper presents a joint model in the effort to deal with the sampling bias and misalignment.

Methods

A joint (bivariate) spatial model was applied to estimate HIV prevalence using two sources: 2014 National HIV Sentinel survey (NHSS) among pregnant women aged 15–49 years attending antenatal care (ANC) and the 2013 Namibia Demographic and Health Surveys (NDHS).

Results

Findings revealed that health districts and constituencies in the northern part of Namibia were found to be highly associated with HIV infection. Also, the study showed that place of residence, gender, gravida, marital status, number of kids dead, wealth index, education, and condom use were significantly associated with HIV infection in Namibia.

Conclusion

This study had shown determinants of HIV infection in Namibia and had revealed areas at high risk through HIV prevalence mapping. Moreover, a joint modelling approach was used in order to deal with spatially misaligned data. Finally, it was shown that prediction of HIV prevalence using the NDHS data source can be enhanced by jointly modelling other HIV data such as NHSS data. These findings would help Namibia to tailor national intervention strategies for specific regions and groups of population.

Mapping Soil Transmitted Helminths and Schistosomiasis under Uncertainty: A Systematic Review and Critical Appraisal of Evidence

BACKGROUND

Spatial modelling of STH and schistosomiasis epidemiology is now commonplace. Spatial epidemiological studies help inform decisions regarding the number of people at risk as well as the geographic areas that need to be targeted with mass drug administration; however, limited attention has been given to propagated uncertainties, their interpretation, and consequences for the mapped values. Using currently published literature on the spatial epidemiology of helminth infections we identified: (1) the main uncertainty sources, their definition and quantification and (2) how uncertainty is informative for STH programme managers and scientists working in this domain.

METHODOLOGY/PRINCIPAL FINDINGS

We performed a systematic literature search using the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) protocol. We searched Web of Knowledge and PubMed using a combination of uncertainty, geographic and disease terms. A total of 73 papers fulfilled the inclusion criteria for the systematic review. Only 9% of the studies did not address any element of uncertainty, while 91% of studies quantified uncertainty in the predicted morbidity indicators and 23% of studies mapped it. In addition, 57% of the studies quantified uncertainty in the regression coefficients but only 7% incorporated it in the regression response variable (morbidity indicator). Fifty percent of the studies discussed uncertainty in the covariates but did not quantify it. Uncertainty was mostly defined as precision, and quantified using credible intervals by means of Bayesian approaches.

CONCLUSION/SIGNIFICANCE

None of the studies considered adequately all sources of uncertainties. We highlighted the need for uncertainty in the morbidity indicator and predictor variable to be incorporated into the modelling framework. Study design and spatial support require further attention and uncertainty associated with Earth observation data should be quantified. Finally, more attention should be given to mapping and interpreting uncertainty, since they are relevant to inform decisions regarding the number of people at risk as well as the geographic areas that need to be targeted with mass drug administration.

Mapping Malaria Risk in Low Transmission Settings: Challenges and Opportunities

As malaria transmission declines, it becomes increasingly focal and prone to outbreaks. Understanding and predicting patterns of transmission risk becomes an important component of an effective elimination campaign, allowing limited resources for control and elimination to be targeted cost-effectively. Malaria risk mapping in low transmission settings is associated with some unique challenges. This article reviews the main challenges and opportunities related to risk mapping in low transmission areas including recent advancements in risk mapping low transmission malaria, relevant metrics, and statistical approaches and risk mapping in post-elimination settings.

Analysing risk factors of co-occurrence of schistosomiasis haematobium and hookworm using bivariate regression models: Case study of Chikwawa, Malawi

Schistosomiasis and soil-transmitted helminth (STH) infections constitute a major public health problem in many parts of sub-Saharan Africa. In areas where prevalence of geo-helminths and schistosomes is high, co-infection with multiple parasite species is common, resulting in disproportionately elevated burden compared with single infections. Determining risk factors of co-infection intensity is important for better design of targeted interventions. In this paper, we examined risk factors of hookworm and S. haematobium co-infection intensity, in Chikwawa district, southern Malawi in 2005, using bivariate count models. Results show that hookworm and S. haematobium infections were much localised with small proportion of individuals harbouring more parasites especially among school-aged children. The risk of co-intensity with both hookworm and S. haematobium was high for all ages, although this diminished with increasing age, increased with fishing (hookworm: coefficient. = 12.29; 95% CI = 11.50-13.09; S. haematobium: 0.040; 95% CI = 0.0037, 3.832). Both infections were abundant in those with primary education (hookworm: coef. = 0.072; 95% CI = 0.056, 0.401 and S. haematobium: coef. = 0.286; 95% CI = 0.034, 0.538). However, much lower risk was observed for those who were farmers (hookworm: coef. = - 0.349, 95% CI = - 0.547,-0.150; S. haematobium: coef. - 0.239, 95% CI = - 0.406, - 0.072). In conclusion, our findings suggest that efforts to control helminths infection should be co-integrated and health promotion campaigns should be aimed at school-going children and adults who are in constant contact with water.

Enhancing collaboration between China and African countries for schistosomiasis control

Schistosomiasis remains an important public health issue, with a large number of cases reported across sub-Saharan Africa, and parts of Asia and Latin America. China was once highly endemic, but has made substantial progress and is moving towards elimination of schistosomiasis. Meanwhile, despite long-term, repeated, school-based chemotherapy in many African countries, more than 90% of all schistosomiasis cases are concentrated in Africa, and hence, this continent constitutes the key challenge for schistosomiasis control. Opportunities and issues for international collaboration in the fight against schistosomiasis are outlined with a focus on China's experiences, including the role of public health authorities and intersectoral collaboration, use of new and effective snail control approaches and diagnostic tools adapted to the specific stage of control, as well as the strengthening of risk mapping and surveillance-response mechanisms. Training courses targeting African governmental officials and professionals, coupled with field visits of African scientists and control programme managers to China, and vice versa, are considered important for improved schistosomiasis control and elimination. The crucial question remains whether the Chinese experience can be translated and applied in African countries to improve the effectiveness of health interventions and scale-up.

Schistosoma haematobium and soil-transmitted Helminths in Tana Delta District of Kenya: infection and morbidity patterns in primary schoolchildren from two isolated villages

BACKGROUND

Schistosomes and soil-transmitted helminths (STH) (hookworm, Trichuris trichiura and Ascaris lumbricoides) are widely distributed in developing countries where they infect over 230 million and 1.5 billion people, respectively. The parasites are frequently co-endemic and many individuals are co-infected with two or more of the species, but information on how the parasites interact in co-infected individuals is scarce. The present study assessed Schistosoma haematobium and STH infection and morbidity patterns among school children in a hyper-endemic focus in the Tana River delta of coastal Kenya.

METHODS

Two hundred and sixty-two children aged 5-12 years from two primary schools were enrolled in the study. For each child, urine was examined for S. haematobium eggs and haematuria, stool was examined for STH eggs, peripheral blood was examined for eosinophilia and haemoglobin level, the urinary tract was ultrasound-examined for S. haematobium-related pathology, and the height and weight was measured and used to calculate the body mass index (BMI).

RESULTS

Prevalences of S. haematobium, hookworm, T. trichiura and A. lumbricoides infection were 94, 81, 88 and 46 %, respectively. There was no significant association between S. haematobium and STH infection but intensity of hookworm infection significantly increased with that of T. trichiura. Lower BMI scores were associated with high intensity of S. haematobium (difference =-0.48, p > 0.05) and A. lumbricoides (difference =-0.67, p < 0.05). Haematuria (both macro and micro) was common and associated with S. haematobium infection, while anaemia was associated with high intensity of S. haematobium (OR = 2.08, p < 0.05) and high hookworm infections OR = 4.75; p < 0.001). The majority of children had eosinophilia, which was significantly associated with high intensity of hookworm infection (OR = 5.34, p < 0.05). Overall 38 % of the children had ultrasound-detectable urinary tract morbidity, which was associated with high intensity of S. haematobium infection (OR = 3.13, p < 0.05).

CONCLUSION

Prevalences of S. haematobium and STH infections among the primary school children were high and the parasites were responsible for significant morbidity. A clear synergistic interaction was observed between hookworm and T. trichiura infections. Increased coverage in administration of praziquantel and albendazole in the area is recommended to control morbidity due to these infections.

Schistosoma haematobium infection and asymptomatic bacteriuria in young South African females

Schistosoma haematobium eggs can induce lesions in the urinary and genital tract epithelia, as eggs pass through or get trapped in the tissue. Local inflammatory reactions induced by S. haematobium eggs might affect the ability of bacteria to establish mucosal super-infection foci. S. haematobium infection and asymptomatic bacteriuria can both portray haematuria, proteinuria and leukocyturia. This shared set of proxy diagnostic markers could fuel routine misdiagnosis in S. haematobium endemic areas. Furthermore, S. haematobium infected individuals might be at a higher risk of contracting bacterial urinary tract infections, which could manifest either as symptomatic or asymptomatic bacteriuria. The aim of the current study was to explore whether schistosomal lesions are susceptible to super-infection by bacteria measured as asymptomatic bacteriuria. S. haematobium infection was determined by microscopy of urine samples. Furthermore, urine samples were tested with dipslides for asymptomatic bacteriuria and with dipsticks for haematuria, proteinuria and leukocytes. We found no association between asymptomatic bacteriuria and S. haematobium infection in a sample of 1040 female primary and high school students from a schistosomiasis endemic area in KwaZulu-Natal, South Africa. Furthermore, it was demonstrated that asymptomatic bacteriuria is not a bias for use of micro-haematuria as a proxy diagnostic measure for S. haematobium infection in this population.

Fine-scale malaria risk mapping from routine aggregated case data

Background

Mapping malaria risk is an integral component of efficient resource allocation. Routine health facility data are convenient to collect, but without information on the locations at which transmission occurred, their utility for predicting variation in risk at a sub-catchment level is presently unclear.

Methods

Using routinely collected health facility level case data in Swaziland between 2011–2013, and fine scale environmental and ecological variables, this study explores the use of a hierarchical Bayesian modelling framework for downscaling risk maps from health facility catchment level to a fine scale (1 km x 1 km). Fine scale predictions were validated using known household locations of cases and a random sample of points to act as pseudo-controls.

Results

Results show that fine-scale predictions were able to discriminate between cases and pseudo-controls with an AUC value of 0.84. When scaled up to catchment level, predicted numbers of cases per health facility showed broad correspondence with observed numbers of cases with little bias, with 84 of the 101 health facilities with zero cases correctly predicted as having zero cases.

Conclusions

This method holds promise for helping countries in pre-elimination and elimination stages use health facility level data to produce accurate risk maps at finer scales. Further validation in other transmission settings and an evaluation of the operational value of the approach is necessary.

Electronic supplementary material

The online version of this article (doi:10.1186/1475-2875-13-421) contains supplementary material, which is available to authorized users.

A brief review of spatial analysis concepts and tools used for mapping, containment and risk modelling of infectious diseases and other illnesses

Fast response and decision making about containment, management, eradication and prevention of diseases, are increasingly important aspects of the work of public health officers and medical providers. Diseases and the agents causing them are spatially and temporally distributed, and effective countermeasures rely on methods that can timely locate the foci of infection, predict the distribution of illnesses and their causes, and evaluate the likelihood of epidemics. These methods require the use of large datasets from ecology, microbiology, health and environmental geography. Geodatabases integrating data from multiple sets of information are managed within the frame of geographic information systems (GIS). Many GIS software packages can be used with minimal training to query, map, analyse and interpret the data. In combination with other statistical or modelling software, predictive and spatio-temporal modelling can be carried out. This paper reviews some of the concepts and tools used in epidemiology and parasitology. The purpose of this review is to provide public health officers with the critical tools to decide about spatial analysis resources and the architecture for the prevention and surveillance systems best suited to their situations.