Malaria Mapping Using Transmission Models: Application to Survey Data from Mali

Geographic mapping of the distribution of malaria is complicated by the limitations of the available data. The most widely available data are from prevalence surveys, but these surveys are generally carried out at arbitrary locations and include nonstandardized and overlapping age groups. To achieve comparability between different surveys, the authors propose the use of transmission models, particularly the Garki model, to convert heterogeneous age prevalence data to a common scale of estimated entomological inoculation rates, vectorial capacity, or force of infection. They apply this approach to the analysis of survey data from Mali, collected in 1965-1998, extracted from the Mapping Malaria Risk in Africa database. They use Bayesian geostatistical models to produce smooth maps of estimates of the entomological inoculation rates obtained from the Garki model, allowing for the effect of environmental covariates. Again using the Garki model, they convert kriged entomological inoculation rates values to age-specific malaria prevalence. The approach makes more efficient use of the available data than do previous malaria mapping methods, and it produces highly plausible maps of malaria distributio

Spatial Patterns of Infant Mortality in Mali: The Effect of Malaria Endemicity

A spatial analysis was carried out to identify factors related to geographic differences in infant mortality risk in Mali by linking data from two spatially structured databases: the Demographic and Health Surveys of 1995-1996 and the Mapping Malaria Risk in Africa database for Mali. Socioeconomic factors measured directly at the individual level and site-specific malaria prevalence predicted for the Demographic and Health Surveys' locations by a spatial model fitted to the Mapping Malaria Risk in Africa database were examined as possible risk factors. The analysis was carried out by fitting a Bayesian hierarchical geostatistical logistic model to infant mortality risk, by Markov chain Monte Carlo simulation. It confirmed that mother's education, birth order and interval, infant's sex, residence, and mother's age at infant's birth had a strong impact on infant mortality risk in Mali. The residual spatial pattern of infant mortality showed a clear relation to well-known foci of malaria transmission, especially the inland delta of the Niger River. No effect of estimated parasite prevalence could be demonstrated. Possible explanations include confounding by unmeasured covariates and sparsity of the source malaria data. Spatial statistical models of malaria prevalence are useful for indicating approximate levels of endemicity over wide areas and, hence, for guiding intervention strategies. However, at points very remote from those sampled, it is important to consider prediction erro

Integrating fragmented evidence by network meta-analysis: relative effectiveness of psychological interventions for adults with post-traumatic stress disorder

Background. To summarize the available evidence on the effectiveness of psychological interventions for patients with post-traumatic stress disorder (PTSD). Method. We searched bibliographic databases and reference lists of relevant systematic reviews and meta-analyses for randomized controlled trials that compared specific psychological interventions for adults with PTSD symptoms either head-to-head or against control interventions using non-specific intervention components, or against wait-list control. Two investigators independently extracted the data and assessed trial characteristics. Results. The analyses included 4190 patients in 66 trials. An initial network meta-analysis showed large effect sizes (ESs) for all specific psychological interventions (ESs between −1.10 and −1.37) and moderate effects of psychological interventions that were used to control for non-specific intervention effects (ESs −0.58 and −0.62). ES differences between various types of specific psychological interventions were absent to small (ES differences between 0.00 and 0.27). Considerable between-trial heterogeneity occurred (τ 2=0.30). Stratified analyses revealed that trials that adhered to DSM-III/IV criteria for PTSD were associated with larger ESs. However, considerable heterogeneity remained. Heterogeneity was reduced in trials with adequate concealment of allocation and in large-sized trials. We found evidence for small-study bias. Conclusions. Our findings show that patients with a formal diagnosis of PTSD and those with subclinical PTSD symptoms benefit from different psychological interventions. We did not identify any intervention that was consistently superior to other specific psychological interventions. However, the robustness of evidence varies considerably between different psychological interventions for PTSD, with most robust evidence for cognitive behavioral and exposure therapie

Spatially Explicit Burden Estimates of Malaria in Tanzania: Bayesian Geostatistical Modeling of the Malaria Indicator Survey Data

A national HIV/AIDS and malaria parasitological survey was carried out in Tanzania in 2007–2008. In this study the parasitological data were analyzed: i) to identify climatic/environmental, socio-economic and interventions factors associated with child malaria risk and ii) to produce a contemporary, high spatial resolution parasitaemia risk map of the country. Bayesian geostatistical models were fitted to assess the association between parasitaemia risk and its determinants. Bayesian kriging was employed to predict malaria risk at unsampled locations across Tanzania and to obtain the uncertainty associated with the predictions. Markov chain Monte Carlo (MCMC) simulation methods were employed for model fit and prediction. Parasitaemia risk estimates were linked to population data and the number of infected children at province level was calculated. Model validation indicated a high predictive ability of the geostatistical model, with 60.00% of the test locations within the 95% credible interval. The results indicate that older children are significantly more likely to test positive for malaria compared with younger children and living in urban areas and better-off households reduces the risk of infection. However, none of the environmental and climatic proxies or the intervention measures were significantly associated with the risk of parasitaemia. Low levels of malaria prevalence were estimated for Zanzibar island. The population-adjusted prevalence ranges from in Kaskazini province (Zanzibar island) to in Mtwara region. The pattern of predicted malaria risk is similar with the previous maps based on historical data, although the estimates are lower. The predicted maps could be used by decision-makers to allocate resources and target interventions in the regions with highest burden of malaria in order to reduce the disease transmission in the country

Shifting suitability for malaria vectors across Africa with warming climates

<p>Abstract</p> <p>Background</p> <p>Climates are changing rapidly, producing warm climate conditions globally not previously observed in modern history. Malaria is of great concern as a cause of human mortality and morbidity, particularly across Africa, thanks in large part to the presence there of a particularly competent suite of mosquito vector species.</p> <p>Methods</p> <p>I derive spatially explicit estimates of human populations living in regions newly suitable climatically for populations of two key <it>Anopheles gambiae </it>vector complex species in Africa over the coming 50 years, based on ecological niche model projections over two global climate models, two scenarios of climate change, and detailed spatial summaries of human population distributions.</p> <p>Results</p> <p>For both species, under all scenarios, given the changing spatial distribution of appropriate conditions and the current population distribution, the models predict a reduction of 11.3–30.2% in the percentage of the overall population living in areas climatically suitable for these vector species in coming decades, but reductions and increases are focused in different regions: malaria vector suitability is likely to decrease in West Africa, but increase in eastern and southern Africa.</p> <p>Conclusion</p> <p>Climate change effects on African malaria vectors shift their distributional potential from west to east and south, which has implications for overall numbers of people exposed to these vector species. Although the total is reduced, malaria is likely to pose novel public health problems in areas where it has not previously been common.</p

Standardizing estimates of the Plasmodium falciparum parasite rate

<p>Abstract</p> <p>Background</p> <p>The <it>Plasmodium falciparum </it>parasite rate (PfPR) is a commonly reported index of malaria transmission intensity. PfPR rises after birth to a plateau before declining in older children and adults. Studies of populations with different age ranges generally report average PfPR, so age is an important source of heterogeneity in reported PfPR data. This confounds simple comparisons of PfPR surveys conducted at different times or places.</p> <p>Methods</p> <p>Several algorithms for standardizing PfPR were developed using 21 studies that stratify in detail PfPR by age. An additional 121 studies were found that recorded PfPR from the same population over at least two different age ranges; these paired estimates were used to evaluate these algorithms. The best algorithm was judged to be the one that described most of the variance when converting the PfPR pairs from one age-range to another.</p> <p>Results</p> <p>The analysis suggests that the relationship between PfPR and age is predictable across the observed range of malaria endemicity. PfPR reaches a peak after about two years and remains fairly constant in older children until age ten before declining throughout adolescence and adulthood. The PfPR pairs were poorly correlated; using one to predict the other would explain only 5% of the total variance. By contrast, the PfPR predicted by the best algorithm explained 72% of the variance.</p> <p>Conclusion</p> <p>The PfPR in older children is useful for standardization because it has good biological, epidemiological and statistical properties. It is also historically consistent with the classical categories of hypoendemic, mesoendemic and hyperendemic malaria. This algorithm provides a reliable method for standardizing PfPR for the purposes of comparing studies and mapping malaria endemicity. The scripts for doing so are freely available to all.</p

Baseline spatial distribution of malaria prior to an elimination programme in Vanuatu

BACKGROUND: The Ministry of Health in the Republic of Vanuatu has implemented a malaria elimination programme in Tafea Province, the most southern and eastern limit of malaria transmission in the South West Pacific. Tafea Province is comprised of five islands with malaria elimination achieved on one of these islands (Aneityum) in 1998. The current study aimed to establish the baseline distribution of malaria on the most malarious of the province's islands, Tanna Island, to guide the implementation of elimination activities. METHODS: A parasitological survey was conducted in Tafea Province in 2008. On Tanna Island there were 4,716 participants from 220 villages, geo-referenced using a global position system. Spatial autocorrelation in observed prevalence values was assessed using a semivariogram. Backwards step-wise regression analysis was conducted to determine the inclusion of environmental and climatic variables into a prediction model. The Bayesian geostatistical logistic regression model was used to predict malaria risk, and associated uncertainty across the island. RESULTS: Overall, prevalence on Tanna was 1.0% for Plasmodium falciparum (accounting for 32% of infections) and 2.2% for Plasmodium vivax (accounting for 68% of infections). Regression analysis showed significant association with elevation and distance to coastline for P. vivax and P. falciparum, but no significant association with NDVI or TIR. Colinearity was observed between elevation and distance to coastline with the later variable included in the final Bayesian geostatistical model for P. vivax and the former included in the final model for P. falciparum. Model validation statistics revealed that the final Bayesian geostatistical model had good predictive ability. CONCLUSION: Malaria in Tanna Island, Vanuatu, has a focal and predominantly coastal distribution. As Vanuatu refines its elimination strategy, malaria risk maps represent an invaluable resource in the strategic planning of all levels of malaria interventions for the island

Assembling a global database of malaria parasite prevalence for the Malaria Atlas Project

BACKGROUND: Open access to databases of information generated by the research community can synergize individual efforts and are epitomized by the genome mapping projects. Open source models for outputs of scientific research funded by tax-payers and charities are becoming the norm. This has yet to be extended to malaria epidemiology and control. METHODS: The exhaustive searches and assembly process for a global database of malaria parasite prevalence as part of the Malaria Atlas Project (MAP) are described. The different data sources visited and how productive these were in terms of availability of parasite rate (PR) data are presented, followed by a description of the methods used to assemble a relational database and an associated geographic information system. The challenges facing spatial data assembly from varied sources are described in an effort to help inform similar future applications. RESULTS: At the time of writing, the MAP database held 3,351 spatially independent PR estimates from community surveys conducted since 1985. These include 3,036 Plasmodium falciparum and 1,347 Plasmodium vivax estimates in 74 countries derived from 671 primary sources. More than half of these data represent malaria prevalence after the year 2000. CONCLUSION: This database will help refine maps of the global spatial limits of malaria and be the foundation for the development of global malaria endemicity models as part of MAP. A widespread application of these maps is envisaged. The data compiled and the products generated by MAP are planned to be released in June 2009 to facilitate a more informed approach to global malaria control