Estimating the burden of malaria in Senegal: Bayesian zero-inflated binomial geostatistical modeling of the MIS 2008 data

The Research Center for Human Development in Dakar (CRDH) with the technical assistance of ICF Macro and the National Malaria Control Programme (NMCP) conducted in 2008/2009 the Senegal Malaria Indicator Survey (SMIS), the first nationally representative household survey collecting parasitological data and malaria-related indicators. In this paper, we present spatially explicit parasitaemia risk estimates and number of infected children below 5 years. Geostatistical Zero-Inflated Binomial models (ZIB) were developed to take into account the large number of zero-prevalence survey locations (70%) in the data. Bayesian variable selection methods were incorporated within a geostatistical framework in order to choose the best set of environmental and climatic covariates associated with the parasitaemia risk. Model validation confirmed that the ZIB model had a better predictive ability than the standard Binomial analogue. Markov chain Monte Carlo (MCMC) methods were used for inference. Several insecticide treated nets (ITN) coverage indicators were calculated to assess the effectiveness of interventions. After adjusting for climatic and socio-economic factors, the presence of at least one ITN per every two household members and living in urban areas reduced the odds of parasitaemia by 86% and 81% respectively. Posterior estimates of the ORs related to the wealth index show a decreasing trend with the quintiles. Infection odds appear to be increasing with age. The population-adjusted prevalence ranges from 0.12% in Thillé-Boubacar to 13.1% in Dabo. Tambacounda has the highest population-adjusted predicted prevalence (8.08%) whereas the region with the highest estimated number of infected children under the age of 5 years is Kolda (13940). The contemporary map and estimates of malaria burden identify the priority areas for future control interventions and provide baseline information for monitoring and evaluation. Zero-Inflated formulations are more appropriate in modeling sparse geostatistical survey data, expected to arise more frequently as malaria research is focused on elimination.     

Spatially explicit analyses of anopheline mosquitoes indoor resting density: implications for malaria control

Background

The question of sampling and spatial aggregation of malaria vectors is central to vector control efforts and estimates of transmission. Spatial patterns of anopheline populations are complex because mosquitoes'' habitats and behaviors are strongly heterogeneous. Analyses of spatially referenced counts provide a powerful approach to delineate complex distribution patterns, and contributions of these methods in the study and control of malaria vectors must be carefully evaluated.

Methodology/Principal Findings

We used correlograms, directional variograms, Local Indicators of Spatial Association (LISA) and the Spatial Analysis by Distance IndicEs (SADIE) to examine spatial patterns of Indoor Resting Densities (IRD) in two dominant malaria vectors sampled with a 5×5 km grid over a 2500 km² area in the forest domain of Cameroon. SADIE analyses revealed that the distribution of Anopheles gambiae was different from regular or random, whereas there was no evidence of spatial pattern in Anopheles funestus (Ia = 1.644, Pa<0.05 and Ia = 1.464, Pa>0.05, respectively). Correlograms and variograms showed significant spatial autocorrelations at small distance lags, and indicated the presence of large clusters of similar values of abundance in An. gambiae while An. funestus was characterized by smaller clusters. The examination of spatial patterns at a finer spatial scale with SADIE and LISA identified several patches of higher than average IRD (hot spots) and clusters of lower than average IRD (cold spots) for the two species. Significant changes occurred in the overall spatial pattern, spatial trends and clusters when IRDs were aggregated at the house level rather than the locality level. All spatial analyses unveiled scale-dependent patterns that could not be identified by traditional aggregation indices.

Conclusions/Significance

Our study illustrates the importance of spatial analyses in unraveling the complex spatial patterns of malaria vectors, and highlights the potential contributions of these methods in malaria control.     

Heteroskedasticity as a leading indicator of desertification in spatially explicit data

Regime shifts are abrupt transitions between alternate ecosystem states including desertification in arid regions due to drought or overgrazing. Regime shifts may be preceded by statistical anomalies such as increased autocorrelation, indicating declining resilience and warning of an impending shift. Tests for conditional heteroskedasticity, a type of clustered variance, have proven powerful leading indicators for regime shifts in time series data, but an analogous indicator for spatial data has not been evaluated. A spatial analog for conditional heteroskedasticity might be especially useful in arid environments where spatial interactions are critical in structuring ecosystem pattern and process. We tested the efficacy of a test for spatial heteroskedasticity as a leading indicator of regime shifts with simulated data from spatially extended vegetation models with regular and scale‐free patterning. These models simulate shifts from extensive vegetative cover to bare, desert‐like conditions. The magnitude of spatial heteroskedasticity increased consistently as the modeled systems approached a regime shift from vegetated to desert state. Relative spatial autocorrelation, spatial heteroskedasticity increased earlier and more consistently. We conclude that tests for spatial heteroskedasticity can contribute to the growing toolbox of early warning indicators for regime shifts analyzed with spatially explicit data.     

Molecular malaria epidemiology: mapping and burden estimates for the Democratic Republic of the Congo, 2007

Background

Epidemiologic data on malaria are scant in many high-burden countries including the Democratic Republic of the Congo (DRC), which suffers the second-highest global burden of malaria. Malaria control efforts in regions with challenging infrastructure require reproducible and efficient surveillance. We employed new high-throughput molecular testing to characterize the state of malaria control in the DRC and estimate childhood mortality attributable to excess malaria transmission.

Methods and Findings

The Demographic and Health Survey was a cross-sectional, population-based cluster household survey of adults aged 15–59 years in 2007 employing structured questionnaires and dried blood spot collection. Parasitemia was detected by real-time PCR, and survey responses measured adoption of malaria control measures and under-5 health indices. The response rate was 99% at the household level, and 8,886 households were surveyed in 300 clusters; from 8,838 respondents molecular results were available. The overall prevalence of parasitemia was 33.5% (95% confidence interval [C.I.] 32–34.9); P. falciparum was the most prevalent species, either as monoinfection (90.4%; 95% C.I. 88.8–92.1) or combined with P. malariae (4.9%; 95% C.I. 3.7–5.9) or P. ovale (0.6%; 95% C.I. 0.1–0.9). Only 7.7% (95% CI 6.8–8.6) of households with children under 5 owned an insecticide-treated bednet (ITN), and only 6.8% (95% CI 6.1–7.5) of under-fives slept under an ITN the preceding night. The overall under-5 mortality rate was 147 deaths per 1,000 live births (95% C.I. 141–153) and between clusters was associated with increased P. falciparum prevalence; based on the population attributable fraction, 26,488 yearly under-5 deaths were attributable to excess malaria transmission.

Conclusions

Adult P. falciparum prevalence is substantial in the DRC and is associated with under-5 mortality. Molecular testing offers a new, generalizable, and efficient approach to characterizing malaria endemicity in underserved countries.     

Spatially explicit estimates of N2O emissions from croplands suggest climate mitigation opportunities from improved fertilizer management

With increasing nitrogen (N) application to croplands required to support growing food demand, mitigating N₂O emissions from agricultural soils is a global challenge. National greenhouse gas emissions accounting typically estimates N₂O emissions at the country scale by aggregating all crops, under the assumption that N₂O emissions are linearly related to N application. However, field studies and meta‐analyses indicate a nonlinear relationship, in which N₂O emissions are relatively greater at higher N application rates. Here, we apply a super‐linear emissions response model to crop‐specific, spatially explicit synthetic N fertilizer and manure N inputs to provide subnational accounting of global N₂O emissions from croplands. We estimate 0.66 Tg of N₂O‐N direct global emissions circa 2000, with 50% of emissions concentrated in 13% of harvested area. Compared to estimates from the IPCC Tier 1 linear model, our updated N₂O emissions range from 20% to 40% lower throughout sub‐Saharan Africa and Eastern Europe, to >120% greater in some Western European countries. At low N application rates, the weak nonlinear response of N₂O emissions suggests that relatively large increases in N fertilizer application would generate relatively small increases in N₂O emissions. As aggregated fertilizer data generate underestimation bias in nonlinear models, high‐resolution N application data are critical to support accurate N₂O emissions estimates.     

Spatially explicit estimation of occupancy,detection probability and survey effort needed to inform conservation planning

Aim It is increasingly recognized the importance of accounting for imperfect detection in species distribution modelling and conservation planning. However, the integration of detectability into a spatially explicit frame has received little attention. We aim (1) to show how to develop distribution maps of both detection probability and survey effort required to reliably determine a species presence/absence and (2) to increase awareness of the spatial variation of detection error inherent in studies of species occurrence. Location North‐western Spain. Methods  We registered the presence/absence of the endangered Egyptian vulture (Neophron percnopterus) in 213 surveys performed in 40 of 104 territories once known to be occupied. We model simultaneously both detection probability and occurrence, using site occupancy modelling. With the resulting regression equations, we developed distribution maps of both detection probability and required sampling effort throughout the area. Results Of the studied territories, 72.5% were detected as occupied, but after accounting for imperfect detection, the proportion of sites truly occupied was 79%. Detectability decreased in territories with higher topographical irregularity and increased with both the time of day of the survey and the progress of the season. Spatial distribution of detectability showed a mainly north–south gradient following the distribution of slope in the area. The likelihood of occupancy increased with rockier, less forested surface and less topographical irregularity within the territory. A minimum of five surveys, on average, are needed to assess, with 95% probability, the occupancy status of a site, ranging from ≤ 3 to > 24 visits/territory depending on survey‐ and site‐specific features. Main conclusions Accounting for detectability and its sources of variation allows us to elaborate distribution maps of detectability‐based survey effort. These maps are useful tools to reliably assess (e.g. with 95% probability) occupancy status throughout a landscape and provide guidance for species conservation planning.     

Linking hyphal growth to colony dynamics: Spatially explicit models of mycelia

The processes responsible for the growth of fungal mycelia act over a vast range of spatial scales; while nutrient uptake occurs at the molecular level, the fungal colony can expand by the order of centimetres each day. Although experiments can provide exceptionally detailed information on processes at specific scales, it can be difficult to relate those processes between different spatial scales. In this article a series of mathematical models are described that link the different spatial scales found within a mycelium. The models are all closely related to each other and are applied to a range of growth environments, and the advantages and limitations of each modelling approach are discussed.     

Spatially explicit estimate of nitrogen effects on soil respiration across the globe

Soil respiration (Rs), as the second largest flux of carbon dioxide (CO₂) between terrestrial ecosystems and the atmosphere, is vulnerable to global nitrogen (N) enrichment. However, the global distribution of the N effects on Rs remains uncertain. Here, we compiled a new database containing 1282 observations of Rs and its heterotrophic component (Rh) in field N manipulative experiments from 317 published papers. Using this up-to-date database, we first performed a formal meta-analysis to explore the responses of Rs and Rh to N addition, and then presented a global spatially explicit quantification of the N effects using a Random Forest model. Our results showed that experimental N addition significantly increased Rs but had a minimal impact on Rh, not supporting the prevailing view that N enrichment inhibits soil microbial respiration. For the major biomes, the magnitude of N input was the main determinant of the spatial variation in Rs response, while the most important predictors for Rh response were biome specific. Based on the key predictors, global mapping visually demonstrated a positive N effect in the regions with higher anthropogenic N inputs (i.e., atmospheric N deposition and agricultural fertilization). Overall, our analysis not only provides novel insight into the N effects on soil CO₂ fluxes, but also presents a spatially explicit assessment of the N effects at the global scale, which are pivotal for understanding ecosystem carbon dynamics in future scenarios with more frequent anthropogenic activities.     

Using community-owned resource persons to provide early diagnosis and treatment and estimate malaria burden at community level in north-eastern Tanzania

ABSTRACT: BACKGROUND: Although early diagnosis and prompt treatment is an important strategy for control of malaria, using fever to initiate presumptive treatment with expensive artemisinin combination therapy is a major challenge; particularly in areas with declining burden of malaria. This study was conducted using community-owned resource persons (CORPs) to provide early diagnosis and treatment of malaria, and collect data for estimation of malaria burden in four villages of Korogwe district, north-eastern Tanzania. METHODS: In 2006, individuals with history of fever within 24 hours or fever (axillary temperature [greater than or equal to]37.5degreesC) at presentation were presumptively treated using sulphadoxine/pyrimethamine. Between 2007 and 2010, individuals aged five years and above, with positive rapid diagnostic tests (RDTs) were treated with artemether/lumefantrine (AL) while under-fives were treated irrespective of RDT results. Reduction in anti-malarial consumption was determined by comparing the number of cases that would have been presumptively treated and those that were actually treated based on RDTs results. Trends of malaria incidence and slide positivity rates were compared between lowlands and highlands. RESULTS: Of 15,729 cases attended, slide positivity rate was 20.4% and declined by >72.0% from 2008, reaching <10.0% from 2009 onwards; and the slide positivity rates were similar in lowlands and highlands from 2009 onwards. Cases with fever at presentation declined slightly, but remained at >40.0% in under-fives and >20.0% among individuals aged five years and above. With use of RDTs, cases treated with AL decreased from <58.0% in 2007 to <11.0% in 2010 and the numbers of adult courses saved were 3,284 and 1,591 in lowlands and highlands respectively. Malaria incidence declined consistently from 2008 onwards; and the highest incidence of malaria shifted from children aged <10 years to individuals aged 10-19 years from 2009. CONCLUSIONS: With basic training, supervision and RDTs, CORPs successfully provided early diagnosis and treatment and reduced consumption of anti-malarials. Progressively declining malaria incidence and slide positivity rates suggest that all fever cases should be tested with RDTs before treatment. Data collected by CORPs was used to plan phase 1b MSP3 malaria vaccine trial and will be used for monitoring and evaluation of different health interventions. The current situation indicates that there is a remarkable changing pattern of malaria and these areas might be moving from control to pre-elimination levels.     

Periodicity in spatial data and geostatistical models: autocorrelation between patches

Several recent studies in landscape ecology have found periodicity in correlograms or semi-variograms calculated, for instance, from spatial data of soils, forests, or animal populations. Some of the studies interpreted this as an indication of regular or periodic landscape patterns. This interpretation is in disagreement with other studies that doubt whether such analysis is valid. The objective of our study was to explore the relationship between periodicity in landscape patterns and geostatistical models. We were especially interested in the validity of the assumption that periodicity in geostatistical models indicates periodicity in landscape pattern, and whether the former can characterize frequency and magnitude of the latter. We created maps containing various periodic spatial patterns, derived correlograms from these, and examined periodicity in the correlograms. We also created non-regular maps that we suspected would cause periodicity in correlograms. Our results demonstrate that a) various periodic spatial patterns produce periodicity in correlograms derived from them, b) the distance-lags at which correlograms peak correspond to the average distances between patch centers, c) periodicity is strongest when the diameter of patches is equal to the distance between patch edges, d) periodicity in omni-directional correlograms of complex spatial patterns (such as checkerboards) are combinations of several waves because inter-patch distances differ with direction; multiple directional correlograms can decompose such complexity, and e) periodicity in correlograms can also be caused when the number of patches in a study site is small. These results highlight that correlograms can be used to detect and describe regular spatial patterns. However, it is crucial to ensure that the assumption of stationarity is not violated, i.e., that the study area contains a sufficiently large number of patches to avoid incorrect conclusions.     

Spatially explicit fate factors of phosphorous emissions to freshwater at the global scale

Purpose

The location of a phosphorus emission can strongly affect its expected fate in freshwater. To date, in Life Cycle Assessment (LCA), fate factors for phosphorus emissions have been derived for continents or large countries and had limited spatial resolution. These fate factors do not account sufficiently for local variations and are not applicable globally. In this paper, fate factors for freshwater eutrophication are derived for phosphorus emissions to freshwater on a global scale with a half-degree resolution.

Methods

For this purpose, a new global fate model for phosphorus has been developed on a half-degree resolution. The removal processes taken into account are grid-specific advection, phosphorus retention and water use. Aggregated fate factors based on archetypes and on administrative units are presented.

Results and discussion

The derived fate factors represent the persistence of phosphorus in the freshwater environment. The typical fate factor of phosphorus emissions to freshwater is 10?days and can vary more than 2 orders of magnitude among the grid cells (the 5th and 95th percentile are 0.8 and 310?days, respectively). Advection is the dominant removal process of phosphorus in freshwater (67.5%), followed by retention (27.6%) and water use (4.9%).

Conclusions

The results demonstrate inclusion of information on the location of phosphorus emissions to freshwater can improve the comparative power of the fate factor implementation in LCAs. The fate factors enable consistent assessment and comparison of freshwater eutrophication impacts at different locations across the globe.     

Quantifying the burden of rhodesiense sleeping sickness in Urambo District, Tanzania

Background

Human African trypanosomiasis is a severely neglected vector-borne disease that is always fatal if untreated. In Tanzania it is highly focalised and of major socio-economic and public health importance in affected communities.

Objectives

This study aimed to estimate the public health burden of rhodesiense HAT in terms of DALYs and financial costs in a highly disease endemic area of Tanzania using hospital records.

Materials and Methods

Data was obtained from 143 patients admitted in 2004 for treatment for HAT at Kaliua Health Centre, Urambo District. The direct medical and other indirect costs incurred by individual patients and by the health services were calculated. DALYs were estimated using methods recommended by the Global Burden of Disease Project as well as those used in previous rhodesiense HAT estimates assuming HAT under reporting of 45%, a figure specific for Tanzania.

Results

The DALY estimate for HAT in Urambo District with and without age-weighting were 215.7 (95% CI: 155.3–287.5) and 281.6 (95% CI: 209.1–362.6) respectively. When 45% under-reporting was included, the results were 622.5 (95% CI: 155.3–1098.9) and 978.9 (95% CI: 201.1–1870.8) respectively. The costs of treating 143 patients in terms of admission costs, diagnosis, hospitalization and sleeping sickness drugs were estimated at US$ 15,514, of which patients themselves paid US$ 3,673 and the health services US$ 11,841. The burden in terms of indirect non-medical costs for the 143 patients was estimated at US$ 9,781.

Conclusions

This study shows that HAT imposes a considerable burden on affected rural communities in Tanzania and stresses the urgent need for location- and disease-specific burden estimates tailored to particular rural settings in countries like Tanzania where a considerable number of infectious diseases are prevalent and, due to their focal nature, are often concentrated in certain locations where they impose an especially high burden.     

Spatially explicit predictions of blood parasites in a widely distributed African rainforest bird

Critical to the mitigation of parasitic vector-borne diseases is the development of accurate spatial predictions that integrate environmental conditions conducive to pathogen proliferation. Species of Plasmodium and Trypanosoma readily infect humans, and are also common in birds. Here, we develop predictive spatial models for the prevalence of these blood parasites in the olive sunbird (Cyanomitra olivacea). Since this species exhibits high natural parasite prevalence and occupies diverse habitats in tropical Africa, it represents a distinctive ecological model system for studying vector-borne pathogens. We used PCR and microscopy to screen for haematozoa from 28 sites in Central and West Africa. Species distribution models were constructed to associate ground-based and remotely sensed environmental variables with parasite presence. We then used machine-learning algorithm models to identify relationships between parasite prevalence and environmental predictors. Finally, predictive maps were generated by projecting model outputs to geographically unsampled areas. Results indicate that for Plasmodium spp., the maximum temperature of the warmest month was most important in predicting prevalence. For Trypanosoma spp., seasonal canopy moisture variability was the most important predictor. The models presented here visualize gradients of disease prevalence, identify pathogen hotspots and will be instrumental in studying the effects of ecological change on these and other pathogens.