Bayesian geostatistical modelling of malaria and lymphatic filariasis infections in Uganda: predictors of risk and geographical patterns of co-endemicity

A Systematic Review of Joint Spatial and Spatiotemporal Models in Health Research

With the advancement of spatial analysis approaches, methodological research addressing the technical and statistical issues related to joint spatial and spatiotemporal models has increased. Despite the benefits of spatial modelling of several interrelated outcomes simultaneously, there has been no published systematic review on this topic, specifically when such models would be useful. This systematic review therefore aimed at reviewing health research published using joint spatial and spatiotemporal models. A systematic search of published studies that applied joint spatial and spatiotemporal models was performed using six electronic databases without geographic restriction. A search with the developed search terms yielded 4077 studies, from which 43 studies were included for the systematic review, including 15 studies focused on infectious diseases and 11 on cancer. Most of the studies (81.40%) were performed based on the Bayesian framework. Different joint spatial and spatiotemporal models were applied based on the nature of the data, population size, the incidence of outcomes, and assumptions. This review found that when the outcome is rare or the population is small, joint spatial and spatiotemporal models provide better performance by borrowing strength from related health outcomes which have a higher prevalence. A framework for the design, analysis, and reporting of such studies is also needed.

Spatial parasitology and the unmapped human helminthiases

Helminthiases are a class of neglected tropical diseases that affect at least 1 billion people worldwide, with a disproportionate impact on resource-poor areas with limited disease surveillance. Geospatial methods can offer valuable insights into the burden of these infections, particularly given that many are subject to strong ecological influences on the environmental, vector-borne or zoonotic stages of their life cycle. In this study, we screened 6829 abstracts and analysed 485 studies that use maps to document, infer or predict transmission patterns for over 200 species of parasitic worms. We found that quantitative mapping methods are increasingly used in medical parasitology, drawing on One Health surveillance data from the community scale to model geographic distributions and burdens up to the regional or global scale. However, we found that the vast majority of the human helminthiases may be entirely unmapped, with research effort focused disproportionately on a half-dozen infections that are targeted by mass drug administration programmes. Entire regions were also surprisingly under-represented in the literature, particularly southern Asia and the Neotropics. We conclude by proposing a shortlist of possible priorities for future research, including several neglected helminthiases with a burden that may be underestimated.

Prevalence, probability, and characteristics of malaria and filariasis co-infections: A systematic review and meta-analysis

BACKGROUND

Malaria and filariasis are significant vector-borne diseases that are co-endemic in the same human populations. This study aims to collate the evidence, probability, and characteristics of malaria and filariasis co-infections in participants among studies reporting the co-occurrence of both diseases.

METHODS

We searched for potentially relevant articles reporting the co-occurrence of malaria and filariasis in five electronic databases (Embase, PubMed, Scopus, Medline, and CENTRAL) from inception to May 22, 2022. We estimated the pooled prevalence and probability of malaria and filariasis co-infections among study participants using random-effects meta-analyses and synthesized the characteristics of patients with co-infections narratively.

RESULTS

We identified 951 articles, 24 of which (96,838 participants) met eligibility criteria and were included in the systematic review. Results of the meta-analysis showed a pooled prevalence of malaria and filariasis co-infections among participants of 11%. The prevalence of co-infections was 2.3% in Africa, 0.2% in Asia, and 1.6% in South America. The pooled prevalences of malaria and Wuchereria bancrofti, malaria and Loa loa, malaria and Mansonella perstans co-infections were 0.7%, 1.2%, and 1.0%, respectively. The meta-analysis results showed that the co-infections between two parasites occurred by probability (P = 0.001). Patients with co-infections were at increased risk of having an enlarged spleen, a lower rate of severe anemia, lower parasite density, and more asymptomatic clinical status. Patients with co-infections had decreased levels of C-X-C motif chemokine 5, tumor necrosis factor-α, interleukin-4, c4 complement, and interleukin-10. In addition, patients with co-infections had a lower interleukin-10/tumor necrosis factor-α ratio and higher interleukin-10/interleukin-6 ratio.

CONCLUSION

The present study showed that the prevalence of malaria and filariasis co-infections was low and varied between geographical areas in the selected articles. Co-infections tended to occur with a low probability. Further studies investigating the outcomes and characteristics of co-infections are needed.

Combining school-catchment area models with geostatistical models for analysing school survey data from low-resource settings: Inferential benefits and limitations

School-based sampling has been used to inform targeted responses for malaria and neglected tropical diseases. Standard geostatistical methods for mapping disease prevalence use the school location to model spatial correlation, which is questionable since exposure to the disease is more likely to occur in the residential location. In this paper, we propose to overcome the limitations of standard geostatistical methods by introducing a modelling framework that accounts for the uncertainty in the location of the residence of the students. By using cost distance and cost allocation models to define spatial accessibility and in absence of any information on the travel mode of students to school, we consider three school catchment area models that assume walking only, walking and bicycling and, walking and motorized transport. We illustrate the use of this approach using two case studies of malaria in Kenya and compare it with the standard approach that uses the school locations to build geostatistical models. We argue that the proposed modelling framework presents several inferential benefits, such as the ability to combine data from multiple surveys some of which may also record the residence location, and to deal with ecological bias when estimating the effects of malaria risk factors. However, our results show that invalid assumptions on the modes of travel to school can worsen the predictive performance of geostatistical models. Future research in this area should focus on collecting information on the modes of transportation to school which can then be used to better parametrize the catchment area models.

Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions

Spatial connectivity plays an important role in mosquito-borne disease transmission. Connectivity can arise for many reasons, including shared environments, vector ecology and human movement. This systematic review synthesizes the spatial methods used to model mosquito-borne diseases, their spatial connectivity assumptions and the data used to inform spatial model components. We identified 248 papers eligible for inclusion. Most used statistical models (84.2%), although mechanistic are increasingly used. We identified 17 spatial models which used one of four methods (spatial covariates, local regression, random effects/fields and movement matrices). Over 80% of studies assumed that connectivity was distance-based despite this approach ignoring distant connections and potentially oversimplifying the process of transmission. Studies were more likely to assume connectivity was driven by human movement if the disease was transmitted by an Aedes mosquito. Connectivity arising from human movement was more commonly assumed in studies using a mechanistic model, likely influenced by a lack of statistical models able to account for these connections. Although models have been increasing in complexity, it is important to select the most appropriate, parsimonious model available based on the research question, disease transmission process, the spatial scale and availability of data, and the way spatial connectivity is assumed to occur.

Spatiotemporal patterns of cutaneous leishmaniasis in the district upper and lower Dir, Khyber Pakhtunkhwa, Pakistan: A GIS-based spatial approaches

While Cutaneous leishmaniasis (CL) is not a life-threatening disease, it leads to devastating effects on local community. CL is widely scattered manifesting a noticeable epidemiological pattern around the globe. The present study was planned to address the role of Geographic Information System (GIS) using CL clinico-epidemiological data to determine the high-risk areas of CL. Recorded data (2014-2018) of 3630 positive individuals was collected from Basic Health Units of the Upper and Lower Dir Districts, Khyber Pakhtunkhwa, Pakistan. Descriptive and statistical analysis was used for clinico-epidemiological characterization. For spatial analysis, ArcGIS V.10.3 was used and the CL average incidence was tagged on the proportional, choropleth, and digital elevation model maps. For focal transmission and high-risk zones, Inverse Density Weight (IDW) spatial interpolation, focal statistics, hot spot, cluster and outlier, and Bayesian geostatistical analysis were used. The trend of CL cases was elevated from 2014 to 2016 except for 2017 and 2018. Individuals of both genders younger than 20 years old were highly susceptible. Single lesions were more prominent (56%) and frequently affected facial parts (51%). The size and pretreatment duration of the CL lesion was significantly associated. Spatially, a choropleth map displayed the maximum CL incidences in Tehsil Balambat, Khal, and Termergara (31%-13%) located within a range of 948-1947m elevation in the central regions with proximal CL transmissions. Hot spot and cluster and outlier analysis affirmed that Tehsil Khal was the high-risk CL foci. The Bayesian geostatistical analysis revealed high temperature, less altitude, and annual precipitation as important risk factors. An increasing trend in CL transmission has become evident, affecting both genders and <20 years old children. GIS resolute the concealed CL hubs in the least elevated central regions which warrant the control strategies to restrict future epidemics.

Risk factors for lymphatic filariasis and mass drug administration non-participation in Mandalay Region, Myanmar

BACKGROUND

Myanmar commenced a lymphatic filariasis (LF) elimination programme in 2000. Whilst the country has made considerable progress since then, a number of districts have demonstrated persistent transmission after many rounds of mass drug administration (MDA). The causes of unsuccessful MDA have been examined elsewhere; however, there remains little information on the factors that contribute in Myanmar.

METHODS

We conducted an analysis of factors associated with persistent infection, LF-related hydrocoele and MDA participation in an area with ongoing transmission in 2015. A cross-sectional household survey was undertaken in 24 villages across four townships of Mandalay Region. Participants were screened for circulating filarial antigen (CFA) using immunochromatographic tests and, if positive, for microfilaria by night-time thick blood slide. Individuals 15 year and older were assessed for filariasis morbidity (lymphoedema and, if male, hydrocoele) by ultrasound-assisted clinical examination. A pre-coded questionnaire was used to assess risk factors for LF and for non-participation (never taking MDA). Significant variables identified in univariate analyses were included in separate step-wise multivariate logistic regressions for each outcome.

RESULTS

After adjustment for covariates and survey design, being CFA positive was significantly associated with age [odds ratio (OR) 1.03, 95% CI 1.01-1.06), per year], male gender (OR 3.14, 1.27-7.76), elevation (OR 0.96, 0.94-0.99, per metre) and the density of people per household room (OR 1.59, 1.31-1.92). LF-related hydrocoele was associated with age (OR 1.06, 1.03-1.09, per year) and residing in Amarapura Township (OR 8.93, 1.37-58.32). Never taking MDA was associated with male gender [OR 6.89 (2.13-22.28)] and age, particularly in females, with a significant interaction term. Overall, compared to those aged 30-44 years, the proportion never taking MDA was higher in all age groups (OR highest in those < 5 years and > 60 years, ranging from 3.37 to 12.82). Never taking MDA was also associated with residing in Amarapura township (OR 2.48, 1.15-5.31), moving to one's current village from another (OR 2.62, 1.12-6.11) and ever having declined medication (OR 11.82, 4.25-32.91). Decreased likelihood of never taking MDA was associated with a higher proportion of household members being present during the last MDA round (OR 0.16, 0.03-0.74) and the number visits by the MDA programme (OR 0.69, 0.48-1.00).

CONCLUSIONS

These results contribute to the understanding of LF and MDA participation-related risk factors and will assist Myanmar to improve its elimination and morbidity management programmes.

Statistical methods for linking geostatistical maps and transmission models: Application to lymphatic filariasis in East Africa

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Novel methodology for combining geostatistical mapping and transmission modelling.

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Guide the planning of spatial control programmes by identifying affected areas.

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Current intervention strategy will not be sufficient to eliminate LF in most areas.

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Alternative strategies will be required to accelerate LF elimination in East Africa.

Infectious diseases remain one of the major causes of human mortality and suffering. Mathematical models have been established as an important tool for capturing the features that drive the spread of the disease, predicting the progression of an epidemic and hence guiding the development of strategies to control it. Another important area of epidemiological interest is the development of geostatistical methods for the analysis of data from spatially referenced prevalence surveys. Maps of prevalence are useful, not only for enabling a more precise disease risk stratification, but also for guiding the planning of more reliable spatial control programmes by identifying affected areas. Despite the methodological advances that have been made in each area independently, efforts to link transmission models and geostatistical maps have been limited. Motivated by this fact, we developed a Bayesian approach that combines fine-scale geostatistical maps of disease prevalence with transmission models to provide quantitative, spatially-explicit projections of the current and future impact of control programs against a disease. These estimates can then be used at a local level to identify the effectiveness of suggested intervention schemes and allow investigation of alternative strategies. The methodology has been applied to lymphatic filariasis in East Africa to provide estimates of the impact of different intervention strategies against the disease.

Global estimation of anti-malarial drug effectiveness for the treatment of uncomplicated Plasmodium falciparum malaria 1991-2019

BACKGROUND

Anti-malarial drugs play a critical role in reducing malaria morbidity and mortality, but their role is mediated by their effectiveness. Effectiveness is defined as the probability that an anti-malarial drug will successfully treat an individual infected with malaria parasites under routine health care delivery system. Anti-malarial drug effectiveness (AmE) is influenced by drug resistance, drug quality, health system quality, and patient adherence to drug use; its influence on malaria burden varies through space and time.

METHODS

This study uses data from 232 efficacy trials comprised of 86,776 infected individuals to estimate the artemisinin-based and non-artemisinin-based AmE for treating falciparum malaria between 1991 and 2019. Bayesian spatiotemporal models were fitted and used to predict effectiveness at the pixel-level (5 km × 5 km). The median and interquartile ranges (IQR) of AmE are presented for all malaria-endemic countries.

RESULTS

The global effectiveness of artemisinin-based drugs was 67.4% (IQR: 33.3-75.8), 70.1% (43.6-76.0) and 71.8% (46.9-76.4) for the 1991-2000, 2006-2010, and 2016-2019 periods, respectively. Countries in central Africa, a few in South America, and in the Asian region faced the challenge of lower effectiveness of artemisinin-based anti-malarials. However, improvements were seen after 2016, leaving only a few hotspots in Southeast Asia where resistance to artemisinin and partner drugs is currently problematic and in the central Africa where socio-demographic challenges limit effectiveness. The use of artemisinin-based combination therapy (ACT) with a competent partner drug and having multiple ACT as first-line treatment choice sustained high levels of effectiveness. High levels of access to healthcare, human resource capacity, education, and proximity to cities were associated with increased effectiveness. Effectiveness of non-artemisinin-based drugs was much lower than that of artemisinin-based with no improvement over time: 52.3% (17.9-74.9) for 1991-2000 and 55.5% (27.1-73.4) for 2011-2015. Overall, AmE for artemisinin-based and non-artemisinin-based drugs were, respectively, 29.6 and 36% below clinical efficacy as measured in anti-malarial drug trials.

CONCLUSIONS

This study provides evidence that health system performance, drug quality and patient adherence influence the effectiveness of anti-malarials used in treating uncomplicated falciparum malaria. These results provide guidance to countries' treatment practises and are critical inputs for malaria prevalence and incidence models used to estimate national level malaria burden.

Mapping and monitoring for a lymphatic filariasis elimination program: a systematic review

Lymphatic filariasis (LF) is targeted for elimination by the year 2020. The Global Programme for Elimination of LF (GPELF) aims to achieve elimination by interrupting transmission through annual mass drug administration (MDA) of albendazole with ivermectin or diethylcarbamazine. The program has successfully eliminated the disease in 11 of the 72 endemic countries, putting in enormous efforts on systematic planning and implementation of the strategy. Mapping areas endemic for LF is a pre-requisite for implementing MDA, monitoring and evaluation are the components of programme implementation. This review was undertaken to assess how the mapping and impact monitoring activities have evolved to become more robust over the years and steered the LF elimination programme towards its goal. The findings showed that the WHO recommended mapping strategy aided 17 countries to delimit, plan and implement MDA in only those areas endemic for LF thereby saving resources. Availability of serological tools for detecting infection in humans (antigen/antibody assays) and molecular xenomonitoring (MX) in vectors greatly facilitated programme monitoring and evaluation in endemic countries. Results of this review are discussed on how these existing mapping and monitoring procedures can be used for re-mapping of unsurveyed and uncertain areas to ensure there is no resurgence during post-MDA surveillance. Further the appropriateness of the tests (Microfilaria (Mf)/antigenemia (Ag)/antibody(Ab) surveys in humans or MX of vectors for infection) used currently for post-MDA surveillance and their role in the development of a monitoring and evaluation strategy for the recently WHO recommended triple drug regimen in MDA for accelerated LF elimination are discussed.

Non-invasive surveillance of Plasmodium infection by real-time PCR analysis of ethanol preserved faeces from Ugandan school children with intestinal schistosomiasis

Background

As part of ongoing co-surveillance of intestinal schistosomiasis and malaria in Ugandan school children, a non-invasive detection method for amplification of Plasmodium DNA using real-time (rt)PCR analysis of ethanol preserved faeces (EPF) was assessed. For diagnostic tabulations, results were compared to rtPCR analysis of dried blood spots (DBS) and field-based point-of-care (POC) rapid diagnostic tests (RDTs).

Methods

A total of 247 school children from 5 primary schools along the shoreline of Lake Albert were examined with matched EPF and DBS obtained. Mean prevalence and prevalence by school was calculated by detection of Plasmodium DNA by rtPCR using a 18S rDNA Taqman^® probe. Diagnostic sensitivity, specificity, positive and negative predictive values were tabulated and compared against RDTs.

Results

By rtPCR of EPF and DBS, 158 (63.9%; 95% CI 57.8–69.7) and 198 (80.1%, 95% CI 74.7–84.6) children were positive for Plasmodium spp. By RDT, 138 (55.8%; 95% CI 49.6–61.9) and 45 (18.2%; 95% CI 13.9–23.5) children were positive for Plasmodium falciparum, and with non-P. falciparum co-infections, respectively. Using RDT results as a convenient field-based reference, the sensitivity of rtPCR of EPF and DBS was 73.1% (95% CI 65.2–79.8) and 94.2% (95% CI 88.9–97.0) while specificity was 47.7% (95% CI 38.5–57.0) and 37.6% (95% CI 29.0–46.9), respectively. With one exception, school prevalence estimated by analysis of EPF was higher than that by RDT. Positive and negative predictive values were compared and discussed.

Conclusions

In this high transmission setting, EPF sampling with rtPCR analysis has satisfactory diagnostic performance in estimation of mean prevalence and prevalence by school upon direct comparison with POC-RDTs. Although analysis of EPF was judged inferior to that of DBS, it permits an alternative non-invasive sampling regime that could be implemented alongside general monitoring and surveillance for other faecal parasites. EPF analysis may also have future value in passive surveillance of low transmission settings.

Risk mapping of clonorchiasis in the People's Republic of China: A systematic review and Bayesian geostatistical analysis

BACKGROUND

Clonorchiasis, one of the most important food-borne trematodiases, affects more than 12 million people in the People's Republic of China (P.R. China). Spatially explicit risk estimates of Clonorchis sinensis infection are needed in order to target control interventions.

METHODOLOGY

Georeferenced survey data pertaining to infection prevalence of C. sinensis in P.R. China from 2000 onwards were obtained via a systematic review in PubMed, ISI Web of Science, Chinese National Knowledge Internet, and Wanfang Data from January 1, 2000 until January 10, 2016, with no restriction of language or study design. Additional disease data were provided by the National Institute of Parasitic Diseases, Chinese Center for Diseases Control and Prevention in Shanghai. Environmental and socioeconomic proxies were extracted from remote-sensing and other data sources. Bayesian variable selection was carried out to identify the most important predictors of C. sinensis risk. Geostatistical models were applied to quantify the association between infection risk and the predictors of the disease, and to predict the risk of infection across P.R. China at high spatial resolution (over a grid with grid cell size of 5×5 km).

PRINCIPAL FINDINGS

We obtained clonorchiasis survey data at 633 unique locations in P.R. China. We observed that the risk of C. sinensis infection increased over time, particularly from 2005 onwards. We estimate that around 14.8 million (95% Bayesian credible interval 13.8-15.8 million) people in P.R. China were infected with C. sinensis in 2010. Highly endemic areas (≥ 20%) were concentrated in southern and northeastern parts of the country. The provinces with the highest risk of infection and the largest number of infected people were Guangdong, Guangxi, and Heilongjiang.

CONCLUSIONS/SIGNIFICANCE

Our results provide spatially relevant information for guiding clonorchiasis control interventions in P.R. China. The trend toward higher risk of C. sinensis infection in the recent past urges the Chinese government to pay more attention to the public health importance of clonorchiasis and to target interventions to high-risk areas.

The Role of Spatial Statistics in the Control and Elimination of Neglected Tropical Diseases in Sub-Saharan Africa: A Focus on Human African Trypanosomiasis, Schistosomiasis and Lymphatic Filariasis

Disease control and elimination programmes can benefit greatly from accurate information on the spatial variability of disease risk, particularly when risk is highly spatially heterogeneous. Due to advances in statistical methodology, coupled with the increased availability of geospatial technology, this information is becoming increasingly accessible. In this chapter we describe recent advancements in spatial methods associated with the analysis of disease data measured at the point-level and demonstrate their application to the control and elimination of neglected tropical diseases (NTDs). We further provide information on spatially referenced data sources and software that can be used to create NTD risk maps, concentrating on those that can be freely obtained. Examples relating to three NTDs affecting populations in sub-Saharan Africa are presented throughout the chapter, i.e., human African trypanosomiasis, schistosomiasis and lymphatic filariasis. These three diseases, with differing routes of transmission, control methods and level of spatial heterogeneity, demonstrate the flexibility and applicability of the methods described.

Co-endemicity of Pulmonary Tuberculosis and Intestinal Helminth Infection in the People's Republic of China

Both pulmonary tuberculosis (PTB) and intestinal helminth infection (IHI) affect millions of individuals every year in China. However, the national-scale estimation of prevalence predictors and prevalence maps for these diseases, as well as co-endemic relative risk (RR) maps of both diseases' prevalence are not well developed. There are co-endemic, high prevalence areas of both diseases, whose delimitation is essential for devising effective control strategies. Bayesian geostatistical logistic regression models including socio-economic, climatic, geographical and environmental predictors were fitted separately for active PTB and IHI based on data from the national surveys for PTB and major human parasitic diseases that were completed in 2010 and 2004, respectively. Prevalence maps and co-endemic RR maps were constructed for both diseases by means of Bayesian Kriging model and Bayesian shared component model capable of appraising the fraction of variance of spatial RRs shared by both diseases, and those specific for each one, under an assumption that there are unobserved covariates common to both diseases. Our results indicate that gross domestic product (GDP) per capita had a negative association, while rural regions, the arid and polar zones and elevation had positive association with active PTB prevalence; for the IHI prevalence, GDP per capita and distance to water bodies had a negative association, the equatorial and warm zones and the normalized difference vegetation index had a positive association. Moderate to high prevalence of active PTB and low prevalence of IHI were predicted in western regions, low to moderate prevalence of active PTB and low prevalence of IHI were predicted in north-central regions and the southeast coastal regions, and moderate to high prevalence of active PTB and high prevalence of IHI were predicted in the south-western regions. Thus, co-endemic areas of active PTB and IHI were located in the south-western regions of China, which might be determined by socio-economic factors, such as GDP per capita.

Ecological Drivers of Mansonella perstans Infection in Uganda and Patterns of Co-endemicity with Lymphatic Filariasis and Malaria

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.

Modelling the distribution and transmission intensity of lymphatic filariasis in sub-Saharan Africa prior to scaling up interventions: integrated use of geostatistical and mathematical modelling

BACKGROUND

Lymphatic filariasis (LF) is one of the neglected tropical diseases targeted for global elimination. The ability to interrupt transmission is, partly, influenced by the underlying intensity of transmission and its geographical variation. This information can also help guide the design of targeted surveillance activities. The present study uses a combination of geostatistical and mathematical modelling to predict the prevalence and transmission intensity of LF prior to the implementation of large-scale control in sub-Saharan Africa.

METHODS

A systematic search of the literature was undertaken to identify surveys on the prevalence of Wuchereria bancrofti microfilaraemia (mf), based on blood smears, and on the prevalence of antigenaemia, based on the use of an immuno-chromatographic card test (ICT). Using a suite of environmental and demographic data, spatiotemporal multivariate models were fitted separately for mf prevalence and ICT-based prevalence within a Bayesian framework and used to make predictions for non-sampled areas. Maps of the dominant vector species of LF were also developed. The maps of predicted prevalence and vector distribution were linked to mathematical models of the transmission dynamics of LF to infer the intensity of transmission, quantified by the basic reproductive number (R0).

RESULTS

The literature search identified 1267 surveys that provide suitable data on the prevalence of mf and 2817 surveys that report the prevalence of antigenaemia. Distinct spatial predictions arose from the models for mf prevalence and ICT-based prevalence, with a wider geographical distribution when using ICT-based data. The vector distribution maps demonstrated the spatial variation of LF vector species. Mathematical modelling showed that the reproduction number (R0) estimates vary from 2.7 to 30, with large variations between and within regions.

CONCLUSIONS

LF transmission is highly heterogeneous, and the developed maps can help guide intervention, monitoring and surveillance strategies as countries progress towards LF elimination.

Malaria endemicity and co-infection with tissue-dwelling parasites in Sub-Saharan Africa: a review

Mechanisms and outcomes of host-parasite interactions during malaria co-infections with gastrointestinal helminths are reasonably understood. In contrast, very little is known about such mechanisms in cases of malaria co-infections with tissue-dwelling parasites. This is lack of knowledge is exacerbated by misdiagnosis, lack of pathognomonic clinical signs and the chronic nature of tissue-dwelling helminthic infections. A good understanding of the implications of tissue-dwelling parasitic co-infections with malaria will contribute towards the improvement of the control and management of such co-infections in endemic areas. This review summarises and discusses current information available and gaps in research on malaria co-infection with gastro-intestinal helminths and tissue-dwelling parasites with emphasis on helminthic infections, in terms of the effects of migrating larval stages and intra and extracellular localisations of protozoan parasites and helminths in organs, tissues, and vascular and lymphatic circulations.

Missing in space: an evaluation of imputation methods for missing data in spatial analysis of risk factors for type II diabetes

Background

Spatial analysis is increasingly important for identifying modifiable geographic risk factors for disease. However, spatial health data from surveys are often incomplete, ranging from missing data for only a few variables, to missing data for many variables. For spatial analyses of health outcomes, selection of an appropriate imputation method is critical in order to produce the most accurate inferences.

Methods

We present a cross-validation approach to select between three imputation methods for health survey data with correlated lifestyle covariates, using as a case study, type II diabetes mellitus (DM II) risk across 71 Queensland Local Government Areas (LGAs). We compare the accuracy of mean imputation to imputation using multivariate normal and conditional autoregressive prior distributions.

Results

Choice of imputation method depends upon the application and is not necessarily the most complex method. Mean imputation was selected as the most accurate method in this application.

Conclusions

Selecting an appropriate imputation method for health survey data, after accounting for spatial correlation and correlation between covariates, allows more complete analysis of geographic risk factors for disease with more confidence in the results to inform public policy decision-making.

Electronic supplementary material

The online version of this article (doi:10.1186/1476-072X-13-47) contains supplementary material, which is available to authorized users.

Multilevel and geo-statistical modeling of malaria risk in children of Burkina Faso

Background

Previous research on determinants of malaria in Burkina Faso has largely focused on individual risk factors. Malaria risk, however, is also shaped by community, health system, and climatic/environmental characteristics. The aims of this study were: i) to identify such individual, household, community, and climatic/environmental risk factors for malaria in children under five years of age, and ii) to produce a parasitaemia risk map of Burkina Faso.

Methods

The 2010 Demographic and Health Survey (DHS) was the first in Burkina Faso that tested children for malaria parasitaemia. Multilevel and geo-statistical models were used to explore determinants of malaria using this nationally representative database.

Results

Parasitaemia was collected from 6,102 children, of which 66.0% (95% confidence interval (CI): 64.0-68.0%) were positive for Plasmodium spp. Older children (>23 months) were more likely to be parasitaemic than younger ones, while children from wealthier households and whose mother had higher education were at a lower risk. At the community level, living in a district with a rate of attendance to health facilities lower than 2 visits per year was significantly associated with greater odds of being infected. Malaria prevalence was also associated with higher normalized difference vegetation index, lower average monthly rainfall, and lower population densities. Predicted malaria parasitaemia was spatially variable with locations falling within an 11%-92% prevalence range. The number of parasitaemic children was nonetheless concentrated in areas of high population density, albeit malaria risk was notably higher in the sparsely populated rural areas.

Conclusion

Malaria prevalence in Burkina Faso is considerably higher than in neighbouring countries. Our spatially-explicit population-based estimates of malaria risk and infected number of children could be used by local decision-makers to identify priority areas where control efforts should be enhanced.

Electronic supplementary material

The online version of this article (doi:10.1186/1756-3305-7-350) contains supplementary material, which is available to authorized users.

Filaria zoogeography in Africa: ecology, competitive exclusion, and public health relevance

Six species of filariae infect humans in sub-Saharan Africa. We hypothesise that these nematodes are able to polyparasitise human hosts by having successfully, through competitive exclusion, adapted to distinct niches. Despite inhabiting the same host, adult stages reside in different tissue sites. Microfilariae of some species exhibit temporal separation by reaching peak levels in the blood at specific times of day. Spatial and temporal distributions in microfilaria location are exploited by the vector feeding-behaviour whereas adult survival is enhanced by occupying exclusive 'ecological' niches of the body. We present specific examples to demonstrate this concept, which is not only important from the biological aspect but important in the context of elimination programmes.

Mapping the geographical distribution of lymphatic filariasis in Zambia

BACKGROUND

Past case reports have indicated that lymphatic filariasis (LF) occurs in Zambia, but knowledge about its geographical distribution and prevalence pattern, and the underlying potential environmental drivers, has been limited. As a background for planning and implementation of control, a country-wide mapping survey was undertaken between 2003 and 2011. Here the mapping activities are outlined, the findings across the numerous survey sites are presented, and the ecological requirements of the LF distribution are explored.

METHODOLOGY/PRINCIPAL FINDINGS

Approximately 10,000 adult volunteers from 108 geo-referenced survey sites across Zambia were examined for circulating filarial antigens (CFA) with rapid format ICT cards, and a map indicating the distribution of CFA prevalences in Zambia was prepared. 78% of survey sites had CFA positive cases, with prevalences ranging between 1% and 54%. Most positive survey sites had low prevalence, but six foci with more than 15% prevalence were identified. The observed geographical variation in prevalence pattern was examined in more detail using a species distribution modeling approach to explore environmental requirements for parasite presence, and to predict potential suitable habitats over unsurveyed areas. Of note, areas associated with human modification of the landscape appeared to play an important role for the general presence of LF, whereas temperature (measured as averaged seasonal land surface temperature) seemed to be an important determinant of medium-high prevalence levels.

CONCLUSIONS/SIGNIFICANCE

LF was found to be surprisingly widespread in Zambia, although in most places with low prevalence. The produced maps and the identified environmental correlates of LF infection will provide useful guidance for planning and start-up of geographically targeted and cost-effective LF control in Zambia.

Global numbers of infection and disease burden of soil transmitted helminth infections in 2010

BACKGROUND

Quantifying the burden of parasitic diseases in relation to other diseases and injuries requires reliable estimates of prevalence for each disease and an analytic framework within which to estimate attributable morbidity and mortality. Here we use data included in the Global Atlas of Helminth Infection to derive new global estimates of numbers infected with intestinal nematodes (soil-transmitted helminths, STH: Ascaris lumbricoides, Trichuris trichiura and the hookworms) and use disability-adjusted life years (DALYs) to estimate disease burden.

METHODS

Prevalence data for 6,091 locations in 118 countries were sourced and used to estimate age-stratified mean prevalence for sub-national administrative units via a combination of model-based geostatistics (for sub-Saharan Africa) and empirical approaches (for all other regions). Geographical variation in infection prevalence within these units was approximated using modelled logit-normal distributions, and numbers of individuals with infection intensities above given thresholds estimated for each species using negative binomial distributions and age-specific worm/egg burden thresholds. Finally, age-stratified prevalence estimates for each level of infection intensity were incorporated into the Global Burden of Disease Study 2010 analytic framework to estimate the global burden of morbidity and mortality associated with each STH infection.

RESULTS

Globally, an estimated 438.9 million people (95% Credible Interval (CI), 406.3 - 480.2 million) were infected with hookworm in 2010, 819.0 million (95% CI, 771.7 - 891.6 million) with A. lumbricoides and 464.6 million (95% CI, 429.6 - 508.0 million) with T. trichiura. Of the 4.98 million years lived with disability (YLDs) attributable to STH, 65% were attributable to hookworm, 22% to A. lumbricoides and the remaining 13% to T. trichiura. The vast majority of STH infections (67%) and YLDs (68%) occurred in Asia. When considering YLDs relative to total populations at risk however, the burden distribution varied more considerably within major global regions than between them.

CONCLUSION

Improvements in the cartography of helminth infection, combined with mathematical modelling approaches, have resulted in the most comprehensive contemporary estimates for the public health burden of STH. These numbers form an important benchmark upon which to evaluate future scale-up of major control efforts.

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.

How well are malaria maps used to design and finance malaria control in Africa?

Introduction

Rational decision making on malaria control depends on an understanding of the epidemiological risks and control measures. National Malaria Control Programmes across Africa have access to a range of state-of-the-art malaria risk mapping products that might serve their decision-making needs. The use of cartography in planning malaria control has never been methodically reviewed.

Materials and Methods

An audit of the risk maps used by NMCPs in 47 malaria endemic countries in Africa was undertaken by examining the most recent national malaria strategies, monitoring and evaluation plans, malaria programme reviews and applications submitted to the Global Fund. The types of maps presented and how they have been used to define priorities for investment and control was investigated.

Results

91% of endemic countries in Africa have defined malaria risk at sub-national levels using at least one risk map. The range of risk maps varies from maps based on suitability of climate for transmission; predicted malaria seasons and temperature/altitude limitations, to representations of clinical data and modelled parasite prevalence. The choice of maps is influenced by the source of the information. Maps developed using national data through in-country research partnerships have greater utility than more readily accessible web-based options developed without inputs from national control programmes. Although almost all countries have stratification maps, only a few use them to guide decisions on the selection of interventions allocation of resources for malaria control.

Conclusion

The way information on the epidemiology of malaria is presented and used needs to be addressed to ensure evidence-based added value in planning control. The science on modelled impact of interventions must be integrated into new mapping products to allow a translation of risk into rational decision making for malaria control. As overseas and domestic funding diminishes, strategic planning will be necessary to guide appropriate financing for malaria control.

Malaria risk mapping for control in the republic of Sudan

Evidence shows that malaria risk maps are rarely tailored to address national control program ambitions. Here, we generate a malaria risk map adapted for malaria control in Sudan. Community Plasmodium falciparum parasite rate (PfPR) data from 2000 to 2010 were assembled and were standardized to 2-10 years of age (PfPR(2-10)). Space-time Bayesian geostatistical methods were used to generate a map of malaria risk for 2010. Surfaces of aridity, urbanization, irrigation schemes, and refugee camps were combined with the PfPR(2-10) map to tailor the epidemiological stratification for appropriate intervention design. In 2010, a majority of the geographical area of the Sudan had risk of < 1% PfPR(2-10). Areas of meso- and hyperendemic risk were located in the south. About 80% of Sudan's population in 2011 was in the areas in the desert, urban centers, or where risk was < 1% PfPR(2-10). Aggregated data suggest reducing risks in some high transmission areas since the 1960s.

Spatial parasite ecology and epidemiology: a review of methods and applications

The distributions of parasitic diseases are determined by complex factors, including many that are distributed in space. A variety of statistical methods are now readily accessible to researchers providing opportunities for describing and ultimately understanding and predicting spatial distributions. This review provides an overview of the spatial statistical methods available to parasitologists, ecologists and epidemiologists and discusses how such methods have yielded new insights into the ecology and epidemiology of infection and disease. The review is structured according to the three major branches of spatial statistics: continuous spatial variation; discrete spatial variation; and spatial point processes.

Filariasis attenuates anemia and proinflammatory responses associated with clinical malaria: a matched prospective study in children and young adults

Background

Wuchereria bancrofti (Wb) and Mansonella perstans (Mp) are blood-borne filarial parasites that are endemic in many countries of Africa, including Mali. The geographic distribution of Wb and Mp overlaps considerably with that of malaria, and coinfection is common. Although chronic filarial infection has been shown to alter immune responses to malaria parasites, its effect on clinical and immunologic responses in acute malaria is unknown.

Methodology/Principal Findings

To address this question, 31 filaria-positive (FIL+) and 31 filaria-negative (FIL−) children and young adults, matched for age, gender and hemoglobin type, were followed prospectively through a malaria transmission season. Filarial infection was defined by the presence of Wb or Mp microfilariae on calibrated thick smears performed between 10 pm and 2 am and/or by the presence of circulating filarial antigen in serum. Clinical malaria was defined as axillary temperature ≥37.5°C or another symptom or sign compatible with malaria infection plus the presence of asexual malaria parasites on a thick blood smear. Although the incidence of clinical malaria, time to first episode, clinical signs and symptoms, and malaria parasitemia were comparable between the two groups, geometric mean hemoglobin levels were significantly decreased in FIL− subjects at the height of the transmission season compared to FIL+ subjects (11.4 g/dL vs. 12.5 g/dL, p<0.01). Plasma levels of IL-1ra, IP-10 and IL-8 were significantly decreased in FIL+ subjects at the time of presentation with clinical malaria (99, 2145 and 49 pg/ml, respectively as compared to 474, 5522 and 247 pg/ml in FIL− subjects).

Conclusions/Significance

These data suggest that pre-existent filarial infection attenuates immune responses associated with severe malaria and protects against anemia, but has little effect on susceptibility to or severity of acute malaria infection. The apparent protective effect of filarial infection against anemia is intriguing and warrants further study in a larger cohort.

In many regions of the world, including sub-Saharan Africa, concomitant infection with multiple parasites is common. In order to examine the effects of filariasis, a chronic helminth infection, on immune responses and clinical manifestations of acute malaria infection, the authors followed 31 filaria-infected (FIL+) and 31 filaria-uninfected (FIL–) individuals living in a malaria-endemic area of Mali through an entire malaria transmission season for the development of clinical malaria (fever or other symptoms of malaria in the setting of detectable blood parasites). Serum levels of inflammatory cytokines previously associated with severe malaria were decreased in FIL+ subjects at the time of acute clinical malaria. Although there were no differences between FIL+ and FIL– subjects with respect to the time of first episode of malaria or the number or severity of malaria episodes, filarial infection appeared to protect against the development of anemia during the malaria transmission season. These findings demonstrate that chronic filarial infection modulates the immune response to acute malaria. The apparent effect on anemia is intriguing and deserves further study.

Mapping the receptivity of malaria risk to plan the future of control in Somalia

OBJECTIVES

To measure the receptive risks of malaria in Somalia and compare decisions on intervention scale-up based on this map and the more widely used contemporary risk maps.

DESIGN

Cross-sectional community Plasmodium falciparum parasite rate (PfPR) data for the period 2007-2010 corrected to a standard age range of 2 to <10 years (PfPR(2-10)) and used within a Bayesian space-time geostatistical framework to predict the contemporary (2010) mean PfPR(2-10) and the maximum annual mean PfPR(2-10) (receptive) from the highest predicted PfPR(2-10) value over the study period as an estimate of receptivity.

SETTING

Randomly sampled communities in Somalia.

PARTICIPANTS

Randomly sampled individuals of all ages.

MAIN OUTCOME MEASURE

Cartographic descriptions of malaria receptivity and contemporary risks in Somalia at the district level.

RESULTS

The contemporary annual PfPR(2-10) map estimated that all districts (n=74) and population (n=8.4 million) in Somalia were under hypoendemic transmission (≤10% PfPR(2-10)). Of these, 23% of the districts, home to 13% of the population, were under transmission of <1% PfPR(2-10). About 58% of the districts and 55% of the population were in the risk class of 1% to <5% PfPR(2-10). In contrast, the receptivity map estimated 65% of the districts and 69% of the population were under mesoendemic transmission (>10%-50% PfPR(2-10)) and the rest as hypoendemic.

CONCLUSION

Compared with maps of receptive risks, contemporary maps of transmission mask disparities of malaria risk necessary to prioritise and sustain future control. As malaria risk declines across Africa, efforts must be invested in measuring receptivity for efficient control planning.