Mapping Malaria Risk In Africa Research Articles

Estimating malaria burden in Nigeria: a geostatistical modelling approach.

This study has produced a map of malaria prevalence in Nigeria based on available data from the Mapping Malaria Risk in Africa (MARA) database, including all malaria prevalence surveys in Nigeria that could be geolocated, as well as data collected during fieldwork in Nigeria between March and June 2007. Logistic regression was fitted to malaria prevalence to identify significant demographic (age) and environmental covariates in STATA. The following environmental covariates were included in the spatial model: the normalized difference vegetation index, the enhanced vegetation index, the leaf area index, the land surface temperature for day and night, land use/landcover (LULC), distance to water bodies, and rainfall. The spatial model created suggests that the two main environmental covariates correlating with malaria presence were land surface temperature for day and rainfall. It was also found that malaria prevalence increased with distance to water bodies up to 4 km. The malaria risk map estimated from the spatial model shows that malaria prevalence in Nigeria varies from 20% in certain areas to 70% in others. The highest prevalence rates were found in the Niger Delta states of Rivers and Bayelsa, the areas surrounding the confluence of the rivers Niger and Benue, and also isolated parts of the north-eastern and north-western parts of the country. Isolated patches of low malaria prevalence were found to be scattered around the country with northern Nigeria having more such areas than the rest of the country. Nigeria's belt of middle regions generally has malaria prevalence of 40% and above.

Mapping malaria risk in West Africa using a Bayesian nonparametric non-stationary model

Malaria transmission is highly influenced by environmental and climatic conditions but their effects are often not linear. The climate-malaria relation is unlikely to be the same over large areas covered by different agro-ecological zones. Similarly, spatial correlation in malaria transmission arisen mainly due to spatially structured covariates (environmental and human made factors), could vary across the agro-ecological zones, introducing non-stationarity. Malaria prevalence data from West Africa extracted from the “Mapping Malaria Risk in Africa” database were analyzed to produce regional parasitaemia risk maps. A non-stationary geostatistical model was developed assuming that the underlying spatial process is a mixture of separate stationary processes within each zone. Non-linearity in the environmental effects was modeled by separate P-splines in each agro-ecological zone. The model allows smoothing at the borders between the zones. The P-splines approach has better predictive ability than categorizing the covariates as an alternative of modeling non-linearity. Model fit and prediction was handled within a Bayesian framework, using Markov chain Monte Carlo (MCMC) simulations.

Spatial analysis of the relationship between early childhood mortality and malaria endemicity in Malawi

Spatial differences in mortality have been reported in Africa amongst children under-five years of age. Risk factors contributing to this geographical variation include bio-demographic and socio-economic factors, the prevalence of infectious diseases and the variability in the quality of child health care. This paper is concerned with investigating the link between early childhood mortality and malaria risk. We used data from the Mapping Malaria Risk in Africa (MARA) and Demographic and Health Survey (DHS) databases to explore this relationship. The DHS survey included questions on bio-demographic and socio-economic status, complete birth histories and survival time of each child within the five years preceding the survey. Survival times were computed in months until death or until the survey was done. The malaria risk was based on prevalence data estimated at the precise DHS sampling location. A spatial Cox regression model was applied to analyze child survival, assessing the influence of both individual-specific factors, malaria endemicity and group-specific environmental factors, approximated by geographical location. Geographical location was considered at subdistrict level. Our analysis shows that although malaria endemicity is not associated with the risk of infant mortality, it is an important risk factor for child mortality. The results confirm the effects of bio-demographic and socio-economic variables (maternal education, maternal age, birth order and place of residence) on infant and child mortality. The subdistrict-specific variation of infant and child mortality shows a rural-urban distinction with a relatively lower risk of mortality in main urban areas.

Developing a spatial-statistical model and map of historical malaria prevalence in Botswana using a staged variable selection procedure

BackgroundSeveral malaria risk maps have been developed in recent years, many from the prevalence of infection data collated by the MARA (Mapping Malaria Risk in Africa) project, and using various environmental data sets as predictors. Variable selection is a major obstacle due to analytical problems caused by over-fitting, confounding and non-independence in the data. Testing and comparing every combination of explanatory variables in a Bayesian spatial framework remains unfeasible for most researchers. The aim of this study was to develop a malaria risk map using a systematic and practicable variable selection process for spatial analysis and mapping of historical malaria risk in Botswana.ResultsOf 50 potential explanatory variables from eight environmental data themes, 42 were significantly associated with malaria prevalence in univariate logistic regression and were ranked by the Akaike Information Criterion. Those correlated with higher-ranking relatives of the same environmental theme, were temporarily excluded. The remaining 14 candidates were ranked by selection frequency after running automated step-wise selection procedures on 1000 bootstrap samples drawn from the data. A non-spatial multiple-variable model was developed through step-wise inclusion in order of selection frequency. Previously excluded variables were then re-evaluated for inclusion, using further step-wise bootstrap procedures, resulting in the exclusion of another variable. Finally a Bayesian geo-statistical model using Markov Chain Monte Carlo simulation was fitted to the data, resulting in a final model of three predictor variables, namely summer rainfall, mean annual temperature and altitude. Each was independently and significantly associated with malaria prevalence after allowing for spatial correlation. This model was used to predict malaria prevalence at unobserved locations, producing a smooth risk map for the whole country.ConclusionWe have produced a highly plausible and parsimonious model of historical malaria risk for Botswana from point-referenced data from a 1961/2 prevalence survey of malaria infection in 1–14 year old children. After starting with a list of 50 potential variables we ended with three highly plausible predictors, by applying a systematic and repeatable staged variable selection procedure that included a spatial analysis, which has application for other environmentally determined infectious diseases. All this was accomplished using general-purpose statistical software.

Developing an Evidence-Based Decision Support System for Rational Insecticide Choice in the Control of African Malaria Vectors

The emergence of Anopheles species resistant to insecticides widely used in vector control has the potential to impact directly on the control of malaria. This may have a particularly dramatic effect in Africa, where pyrethroids impregnated onto bed-nets are the dominant insecticides used for vector control. Because the same insecticides are used for crop pests, the extensive use and misuse of insecticides for agriculture has contributed to the resistance problem in some vectors. The potential for resistance to develop in African vectors has been apparent since the 1950s, but the scale of the problem has been poorly documented. A geographical information system-based decision support system for malaria control has recently been established in Africa and used operationally in Mozambique. The system incorporates climate data and disease transmission rates, but to date it has not incorporated spatial or temporal data on vector abundance or insecticide resistance. As a first step in incorporating this information, available published data on insecticide resistance in Africa has now been collated and incorporated into this decision support system. Data also are incorporated onto the openly available Mapping Malaria Risk in Africa (MARA) Web site (http://www.mara.org.za). New data, from a range of vector population-monitoring initiatives, can now be incorporated into this open access database to allow a spatial understanding of resistance distribution and its potential impact on disease transmission to benefit vector control programs.

Mapping malaria transmission in West and Central Africa

We have produced maps of Plasmodium falciparum malaria transmission in West and Central Africa using the Mapping Malaria Risk in Africa (MARA) database comprising all malaria prevalence surveys in these regions that could be geolocated. The 1846 malaria surveys analysed were carried out during different seasons, and were reported using different age groupings of the human population. To allow comparison between these, we used the Garki malaria transmission model to convert the malaria prevalence data at each of the 976 locations sampled to a single estimate of transmission intensity E, making use of a seasonality model based on Normalized Difference Vegetation Index (NDVI), temperature and rainfall data. We fitted a Bayesian geostatistical model to E using further environmental covariates and applied Bayesian kriging to obtain smooth maps of E and hence of age-specific prevalence. The product is the first detailed empirical map of variations in malaria transmission intensity that includes Central Africa. It has been validated by expert opinion and in general confirms known patterns of malaria transmission, providing a baseline against which interventions such as insecticide-treated nets programmes and trends in drug resistance can be evaluated. There is considerable geographical variation in the precision of the model estimates and, in some parts of West Africa, the predictions differ substantially from those of other risk maps. The consequent uncertainties indicate zones where further survey data are needed most urgently. Malaria risk maps based on compilations of heterogeneous survey data are highly sensitive to the analytical methodology.

SPATIAL STATISTICAL ANALYSIS OF MALARIA PREVALENCE DATA IN BOTSWANA

ISEE-300 Introduction: Recent years have seen increased application of spatial statistical methods in modelling and mapping the distribution of vector-borne diseases. In sub-Saharan Africa the MARA (Mapping Malaria Risk in Africa) project has been working towards a malaria risk atlas to guide rational and targeted control. Historical malaria prevalence data have been collated from malarious countries and a number of malaria risk maps have been produced at different scales, using different data sets and analytical approaches. For malaria risk maps to be useful to control managers, they have to depict the malaria situation with certain real-life accuracy, accepting that some residual variation will always remain unexplained. Retrospective analysis of observational data requires many decisions concerning the explanatory and outcome variables and the statistical methods, which impact on the model outcome and the resulting risk map. Aim: In this paper we grapple with the issue of biological plausibility versus statistical accuracy by investigating the sensitivity of malaria risk maps to model formulation. We focus on Botswana, which lies at the southern edge of the historical malaria distribution. Methods: The 1063 age-specific malaria prevalence rates collected for this country differed with regard to age groups, month of survey, area covered and subject selection method. To reduce bias from various sources we focussed on the 1961/62 national survey, involving 125 prevalence results for the 1-14 year age group for 116 unique locations fairly well distributed across the whole country. Potential explanatory variables were generated from various environmental data sets available for the African continent. Simple, non-spatial uni-variate and multi-variate logistic regression analysis was carried out against the available environmental co-variates. Spatial statistical analysis was also carried out using the most plausible non-spatial multi-variate models as a starting point. We used both Bayesian estimation implemented via Markov chain Monte Carlo methods, and a combination of Bayesian and geo-statistical methods. Risk maps were generated using the model results. Results: Of over 60 potential explanatory variables, the large majority were strongly associated with malaria prevalence in uni-variate analysis. Different co-variate selection methods resulted in several very different multi-variate regression models. Though good overall fit could be achieved, the best-fitting models did not produce the most plausible risk maps. Conclusion: The choice of co-variates had a greater impact on the risk map outcome than using spatial versus non-spatial statistical methods. Different model outcomes will be presented and the issue of biological plausibility versus statistical accuracy will be discussed.

<i>Plasmodium falciparum</i> parasite prevalence in East Africa: A Review

Empirical data on malaria endemicity are rarely available for public domain use to guide effective malaria control. This paper describes the work carried in East Africa since 1997 as part of a pan-African collaboration to map the risk of malaria, Mapping Malaria Risk in Africa (MARA) aimed at redressing deficiency. Studies of cross-sectional community estimates of Plasmodium falciparum prevalence among children aged 0-15 years were identified from a variety of sources including electronic searches of published material, manual review of pre-electronic peer reviewed journals and searches of libraries and archives in Kenya, Tanzania and Uganda. Each survey source, infection prevalence, date, longitude and latitude and survey characteristics were recorded. All data were subjected to a number of selection criteria including minimum sample sizes, samples randomly selected, community-based surveys, age ranges of sampled communities within 0-15 years, and surveys that were spatially unique. Of the 2,003 survey data points identified since 1907 in East Africa, only 503 were eligible for inclusion in the analysis dating from 1927 to 2003. The spatial plots of the data demonstrate the paucity of information on malaria prevalence from a number of densely populated areas and highlight the concentration of empirical data in concert with research centres in the sub-region. Models are required to define malaria risk in areas of East Africa where no empirical data are available so that limited resources can be better targeted to those in greatest need.

URBANIZATION IN SUB-SAHARAN AFRICA AND IMPLICATION FOR MALARIA CONTROL

Malaria not only remains a leading cause of morbidity and mortality, but it also impedes socioeconomic development, particularly in sub-Saharan Africa. Rapid and unprecedented urbanization, going hand-in-hand with often declining economies, might have profound implications for the epidemiology and control of malaria, as the relative disease burden increases among urban dwellers. Reviewing the literature and using a modeling approach, we find that entomologic inoculation rates in cities range from 0 to 54 per year, depending on the degree of urbanization, the spatial location within a city, and overall living conditions. Using the latest United Nations figures on urbanization prospects, nighttime light remotely sensed images, and the "Mapping Malaria Risk in Africa" results on climate suitability for stable malaria transmission, we estimate that 200 million people (24.6% of the total African population) currently live in urban settings where they are at risk of contracting the disease. Importantly, the estimated total surface area covered by these urban settings is only approximately 1.1-1.6% of the total African surface. Considering different plausible scenarios, we estimate an annual incidence of 24.8-103.2 million cases of clinical malaria attacks among urban dwellers in Africa. These figures translate to 6-28% of the estimated global annual disease incidence. Against this background, basic health care delivery systems providing early diagnosis and early treatment and preventive actions through mother and child health programs and the promotion of insecticide-treated bed nets for the rapidly growing numbers of the urban poor must be improved alongside well-tailored and integrated malaria control strategies. We propose environmental management and larviciding within well-specified productive sites as a main feature for such an integrated control approach. Mitigation of the current burden of malaria in urban African settings, in turn, is a necessity for stimulating environmentally and socially sustainable development.

Progress of the MARA/ARMA (mapping malaria risk in Africa) project

Progress of the MARA/ARMA (mapping malaria risk in Africa) project