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Adaptive geostatistical sampling enables efficient identification of malaria hotspots in repeated cross-sectional surveys in rural Malawi.

ABSTRACT: INTRODUCTION:In the context of malaria elimination, interventions will need to target high burden areas to further reduce transmission. Current tools to monitor and report disease burden lack the capacity to continuously detect fine-scale spatial and temporal variations of disease distribution exhibited by malaria. These tools use random sampling techniques that are inefficient for capturing underlying heterogeneity while health facility data in resource-limited settings are inaccurate. Continuous community surveys of malaria burden provide real-time results of local spatio-temporal variation. Adaptive geostatistical design (AGD) improves prediction of outcome of interest compared to current random sampling techniques. We present findings of continuous malaria prevalence surveys using an adaptive sampling design. METHODS:We conducted repeated cross sectional surveys guided by an adaptive sampling design to monitor the prevalence of malaria parasitaemia and anaemia in children below five years old in the communities living around Majete Wildlife Reserve in Chikwawa district, Southern Malawi. AGD sampling uses previously collected data to sample new locations of high prediction variance or, where prediction exceeds a set threshold. We fitted a geostatistical model to predict malaria prevalence in the area. FINDINGS:We conducted five rounds of sampling, and tested 876 children aged 6-59 months from 1377 households over a 12-month period. Malaria prevalence prediction maps showed spatial heterogeneity and presence of hotspots-where predicted malaria prevalence was above 30%; predictors of malaria included age, socio-economic status and ownership of insecticide-treated mosquito nets. CONCLUSIONS:Continuous malaria prevalence surveys using adaptive sampling increased malaria prevalence prediction accuracy. Results from the surveys were readily available after data collection. The tool can assist local managers to target malaria control interventions in areas with the greatest health impact and is ready for assessment in other diseases.

SUBMITTER: Kabaghe AN

PROVIDER: S-EPMC5308819 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Adaptive geostatistical sampling enables efficient identification of malaria hotspots in repeated cross-sectional surveys in rural Malawi.

Kabaghe Alinune N AN Chipeta Michael G MG McCann Robert S RS Phiri Kamija S KS van Vugt Michèle M Takken Willem W Diggle Peter P Terlouw Anja D AD

PloS one 20170214 2

<h4>Introduction</h4>In the context of malaria elimination, interventions will need to target high burden areas to further reduce transmission. Current tools to monitor and report disease burden lack the capacity to continuously detect fine-scale spatial and temporal variations of disease distribution exhibited by malaria. These tools use random sampling techniques that are inefficient for capturing underlying heterogeneity while health facility data in resource-limited settings are inaccurate. ...[more]

PMID: 28196105

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Project description:OBJECTIVE:The objective of this analysis was to examine trends in malaria parasite prevalence and related socioeconomic inequalities in malaria indicators from 2006 to 2013 during a period of intensification of malaria control interventions in Siaya County, western Kenya. METHODS:Data were analysed from eight independent annual cross-sectional surveys from a combined sample of 19 315 individuals selected from 7253 households. Study setting was a health and demographic surveillance area of western Kenya. Data collected included demographic factors, household assets, fever and medication use, malaria parasitaemia by microscopy, insecticide-treated bed net (ITN) use and care-seeking behaviour. Households were classified into five socioeconomic status and dichotomised into poorest households (poorest 60%) and less poor households (richest 40%). Adjusted prevalence ratios (aPR) were calculated using a multivariate generalised linear model accounting for clustering and cox proportional hazard for pooled data assuming constant follow-up time. RESULTS:Overall, malaria infection prevalence was 36.5% and was significantly higher among poorest individuals compared with the less poor (39.9% vs 33.5%, aPR=1.17; 95% CI 1.11 to 1.23) but no change in prevalence over time (trend p value <0.256). Care-seeking (61.1% vs 62.5%, aPR=0.99; 95% CI 0.95 to 1.03) and use of any medication were similar among the poorest and less poor. Poorest individuals were less likely to use Artemether-Lumefantrine or quinine for malaria treatment (18.8% vs 22.1%, aPR=0.81, 95% CI 0.72 to 0.91) while use of ITNs was lower among the poorest individuals compared with less poor (54.8% vs 57.9%; aPR=0.95; 95% CI 0.91 to 0.99), but the difference was negligible. CONCLUSIONS:Despite attainment of equity in ITN use over time, socioeconomic inequalities still existed in the distribution of malaria. This might be due to a lower likelihood of treatment with an effective antimalarial and lower use of ITNs by poorest individuals. Additional strategies are necessary to reduce socioeconomic inequities in prevention and control of malaria in endemic areas in order to achieve universal health coverage and sustainable development goals.

Project description:BACKGROUND:Accurate radiation dose estimates are critical for determining eligibility for therapies by timely triaging of exposed individuals after large-scale radiation events. However, the universal assessment of a large population subjected to a nuclear spill incident or detonation is not feasible. Even with high-throughput dosimetry analysis, test volumes far exceed the capacities of first responders to measure radiation exposures directly, or to acquire and process samples for follow-on biodosimetry testing. AIM:To significantly reduce data acquisition and processing requirements for triaging of treatment-eligible exposures in population-scale radiation incidents. METHODS:Physical radiation plumes modelled nuclear detonation scenarios of simulated exposures at 22 US locations. Models assumed only location of the epicenter and historical, prevailing wind directions/speeds. The spatial boundaries of graduated radiation exposures were determined by targeted, multistep geostatistical analysis of small population samples. Initially, locations proximate to these sites were randomly sampled (generally 0.1% of population). Empirical Bayesian kriging established radiation dose contour levels circumscribing these sites. Densification of each plume identified critical locations for additional sampling. After repeated kriging and densification, overlapping grids between each pair of contours of successive plumes were compared based on their diagonal Bray-Curtis distances and root-mean-square deviations, which provided criteria (<10% difference) to discontinue sampling. RESULTS/CONCLUSIONS:We modeled 30 scenarios, including 22 urban/high-density and 2 rural/low-density scenarios under various weather conditions. Multiple (3-10) rounds of sampling and kriging were required for the dosimetry maps to converge, requiring between 58 and 347 samples for different scenarios. On average, 70±10% of locations where populations are expected to receive an exposure ?2Gy were identified. Under sub-optimal sampling conditions, the number of iterations and samples were increased, and accuracy was reduced. Geostatistical mapping limits the number of required dose assessments, the time required, and radiation exposure to first responders. Geostatistical analysis will expedite triaging of acute radiation exposure in population-scale nuclear events.

Dataset Information

Adaptive geostatistical sampling enables efficient identification of malaria hotspots in repeated cross-sectional surveys in rural Malawi.

Publications

Adaptive geostatistical sampling enables efficient identification of malaria hotspots in repeated cross-sectional surveys in rural Malawi.

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