Dataset Information

Geostatistical modeling of malaria endemicity using serological indicators of exposure collected through school surveys.

ABSTRACT: Ethiopia has a diverse ecology and geography resulting in spatial and temporal variation in malaria transmission. Evidence-based strategies are thus needed to monitor transmission intensity and target interventions. A purposive selection of dried blood spots collected during cross-sectional school-based surveys in Oromia Regional State, Ethiopia, were tested for presence of antibodies against Plasmodium falciparum and P. vivax antigens. Spatially explicit binomial models of seroprevalence were created for each species using a Bayesian framework, and used to predict seroprevalence at 5 km resolution across Oromia. School seroprevalence showed a wider prevalence range than microscopy for both P. falciparum (0-50% versus 0-12.7%) and P. vivax (0-53.7% versus 0-4.5%), respectively. The P. falciparum model incorporated environmental predictors and spatial random effects, while P. vivax seroprevalence first-order trends were not adequately explained by environmental variables, and a spatial smoothing model was developed. This is the first demonstration of serological indicators being used to detect large-scale heterogeneity in malaria transmission using samples from cross-sectional school-based surveys. The findings support the incorporation of serological indicators into periodic large-scale surveillance such as Malaria Indicator Surveys, and with particular utility for low transmission and elimination settings.

SUBMITTER: Ashton RA

PROVIDER: S-EPMC4497890 | biostudies-other | 2015 Jul

REPOSITORIES: biostudies-other

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Geostatistical modeling of malaria endemicity using serological indicators of exposure collected through school surveys.

Ashton Ruth A RA Kefyalew Takele T Rand Alison A Sime Heven H Assefa Ashenafi A Mekasha Addis A Edosa Wasihun W Tesfaye Gezahegn G Cano Jorge J Teka Hiwot H Reithinger Richard R Pullan Rachel L RL Drakeley Chris J CJ Brooker Simon J SJ

The American journal of tropical medicine and hygiene 20150511 1

Ethiopia has a diverse ecology and geography resulting in spatial and temporal variation in malaria transmission. Evidence-based strategies are thus needed to monitor transmission intensity and target interventions. A purposive selection of dried blood spots collected during cross-sectional school-based surveys in Oromia Regional State, Ethiopia, were tested for presence of antibodies against Plasmodium falciparum and P. vivax antigens. Spatially explicit binomial models of seroprevalence were c ...[more]

PMID: 25962770

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Project description:Background Targeted research on residual malaria transmission is important to improve strategies in settings pursuing elimination, where transmission reductions prove challenging. This study aimed to detect and characterize spatial heterogeneity and factors associated with Plasmodium falciparum infections and exposure, P. falciparum apical membrane antigen 1 (PfAMA1) antibody (Ab) response, in the Central Highlands of Madagascar (CHL). Methods From May to July 2014, a cross-sectional school-based survey was carried out in 182 fokontany (villages) within 7 health districts of the CHL. Rapid diagnostic tests (RDTs) and a bead-based immunoassay including PfAMA1 antigen biomarker were used to estimate malaria prevalence and seroprevalence, respectively. Local Moran’s I index was used to detect spatial “hotspots”. Remotely sensed environmental data—temperature, vegetation indices, land covers, and elevation—were used in multivariable mixed-effects logistic regression models to characterize factors associated with malaria infection and cumulative exposure. Results Among 6,293 school-children ages 2–14 years surveyed, RDT prevalence was low at 0.8% (95% CI 0.6–1.1%), while PfAMA1 Ab seroprevalence was 7.0% (95% CI 6.4–7.7%). Hotspots of PfAMA1 Ab seroprevalence were observed in two districts (Ankazobe and Mandoto). Seroprevalence increased for children living > 5 km from a health centre (adjusted odds ratio (OR) = 1.6, 95% CI 1.2–2.2), and for those experiencing a fever episode in the previous 2 weeks (OR 1.7, 95% CI 1.2–2.4), but decreased at higher elevation (for each 100-m increase, OR = 0.7, 95% CI 0.6–0.8). A clear age pattern was observed whereby children 9–10 years old had an OR of 1.8 (95% CI 1.2–2.4), children 11–12 years an OR of 3.7 (95% CI 2.8–5.0), and children 13–14 years an OR of 5.7 (95% CI 4.0–8.0) for seropositivity, compared with younger children (2–8 years). Conclusion The use of serology in this study provided a better understanding of malaria hotspots and associated factors, revealing a pattern of higher transmission linked to geographical barriers in health care access. The integration of antibody-assays into existing surveillance activities could improve exposure assessment, and may help to monitor the effectiveness of malaria control efforts and adapt elimination interventions. Supplementary Information The online version contains supplementary material available at 10.1186/s12936-022-04260-0.

Project description:ObjectiveTo design and implement surveys of malaria infection and coverage of malaria control interventions among school children in Kenya in order to contribute towards a nationwide assessment of malaria.MethodsThe country was stratified into distinct malaria transmission zones based on a malaria risk map and 480 schools were visited between October 2008 and March 2010. Surveys were conducted in two phases: an initial opportunistic phase whereby schools were selected for other research purposes; and a second phase whereby schools were purposively selected to provide adequate spatial representation across the country. Consent for participation was based on passive, opt-out consent rather than written, opt-in consent because of the routine, low-risk nature of the survey. All children were diagnosed for Plasmodium infection using rapid diagnostic tests, assessed for anaemia and were interviewed about mosquito net usage, recent history of illness, and socio-economic and household indicators. Children's responses were entered electronically in the school and data transmitted nightly to Nairobi using a mobile phone modem connection. RDT positive results were corrected by microscopy and all results were adjusted for clustering using random effect regression modelling.Results49,975 children in 480 schools were sampled, at an estimated cost of US$ 1,116 per school. The overall prevalence of malaria and anaemia was 4.3% and 14.1%, respectively, and 19.0% of children reported using an insecticide-treated net (ITN). The prevalence of infection showed marked variation across the country, with prevalence being highest in Western and Nyanza provinces, and lowest in Central, North Eastern and Eastern provinces. Nationally, 2.3% of schools had reported ITN use >60%, and low reported ITN use was a particular problem in Western and Nyanza provinces. Few schools reported having malaria health education materials or ongoing malaria control activities.ConclusionSchool malaria surveys provide a rapid, cheap and sustainable approach to malaria surveillance which can complement household surveys, and in Kenya, show that large areas of the country do not merit any direct school-based control, but school-based interventions, coupled with strengthened community-based strategies, are warranted in western and coastal Kenya. The results also provide detailed baseline data to inform evaluation of school-based malaria control in Kenya.

Project description:BackgroundEffective targeting and evaluation of interventions that protect against adult malaria vectors requires an understanding of how gaps in personal protection arise. An improved understanding of human and mosquito behaviour, and how they overlap in time and space, is critical to estimating the impact of insecticide-treated nets (ITNs) and determining when and where supplemental personal protection tools are needed. Methods for weighting estimates of human exposure to biting Anopheles mosquitoes according to where people spend their time were first developed over half a century ago. However, crude indoor and outdoor biting rates are still commonly interpreted as indicative of human-vector contact patterns without any adjustment for human behaviour or the personal protection effects of ITNs.Main textA small number of human behavioural variables capturing the distribution of human populations indoors and outdoors, whether they are awake or asleep, and if and when they use an ITN over the course of the night, can enable a more accurate representation of human biting exposure patterns. However, to date no clear guidance is available on what data should be collected, what indicators should be reported, or how they should be calculated. This article presents an integrated perspective on relevant indicators of human-vector interactions, the critical entomological and human behavioural data elements required to quantify human-vector interactions, and recommendations for collecting and analysing such data.ConclusionsIf collected and used consistently, this information can contribute to an improved understanding of how malaria transmission persists in the context of current intervention tools, how exposure patterns may change as new vector control tools are introduced, and the potential impact and limitations of these tools. This article is intended to consolidate understanding around work on this topic to date and provide a consistent framework for building upon it. Additional work is needed to address remaining questions, including further development and validation of methods for entomological and human behavioural data collection and analysis.

Project description:BackgroundIn Ethiopia, malaria transmission is seasonal and unstable, with both Plasmodium falciparum and Plasmodium vivax endemic. Such spatial and temporal clustering of malaria only serves to underscore the importance of regularly collecting up-to-date malaria surveillance data to inform decision-making in malaria control. Cross-sectional school-based malaria surveys were conducted across Oromia Regional State to generate up-to-date data for planning malaria control interventions, as well as monitoring and evaluation of operational programme implementation.MethodsTwo hundred primary schools were randomly selected using a stratified and weighted sampling frame; 100 children aged five to 18 years were then randomly chosen within each school. Surveys were carried out in May 2009 and from October to December 2009, to coincide with the peak of malaria transmission in different parts of Oromia. Each child was tested for malaria by expert microscopy, their haemoglobin measured and a simple questionnaire completed. Satellite-derived environmental data were used to assess ecological correlates of Plasmodium infection; Bayesian geostatistical methods and Kulldorff's spatial scan statistic were employed to investigate spatial heterogeneity.ResultsA total 20,899 children from 197 schools provided blood samples, two selected schools were inaccessible and one school refused to participate. The overall prevalence of Plasmodium infection was found to be 0.56% (95% CI: 0.46-0.67%), with 53% of infections due to P. falciparum and 47% due to P. vivax. Of children surveyed, 17.6% (95% CI: 17.0-18.1%) were anaemic, while 46% reported sleeping under a mosquito net the previous night. Malaria was found at 30 (15%) schools to a maximum elevation of 2,187 metres, with school-level Plasmodium prevalence ranging between 0% and 14.5%. Although environmental variables were only weakly associated with P. falciparum and P. vivax infection, clusters of infection were identified within Oromia.ConclusionThese findings demonstrate the marked spatial heterogeneity of malaria in Oromia and, in general, Ethiopia, and provide a strong epidemiological basis for planning as well as monitoring and evaluating malaria control in a setting with seasonal and unstable malaria transmission.

Project description:BACKGROUND:Plasmodium vivax imposes substantial morbidity and mortality burdens in endemic zones. Detailed understanding of the contemporary spatial distribution of this parasite is needed to combat it. We used model based geostatistics (MBG) techniques to generate a contemporary map of risk of Plasmodium vivax malaria in Indonesia in 2010. METHODS:Plasmodium vivax Annual Parasite Incidence data (2006-2008) and temperature masks were used to map P. vivax transmission limits. A total of 4,658 community surveys of P. vivax parasite rate (PvPR) were identified (1985-2010) for mapping quantitative estimates of contemporary endemicity within those limits. After error-checking a total of 4,457 points were included into a national database of age-standardized 1-99 year old PvPR data. A Bayesian MBG procedure created a predicted PvPR(1-99) endemicity surface with uncertainty estimates. Population at risk estimates were derived with reference to a 2010 human population surface. RESULTS:We estimated 129.6 million people in Indonesia lived at risk of P. vivax transmission in 2010. Among these, 79.3% inhabited unstable transmission areas and 20.7% resided in stable transmission areas. In western Indonesia, the predicted P. vivax prevalence was uniformly low. Over 70% of the population at risk in this region lived on Java and Bali islands, where little malaria transmission occurs. High predicted prevalence areas were observed in the Lesser Sundas, Maluku and Papua. In general, prediction uncertainty was relatively low in the west and high in the east. CONCLUSION:Most Indonesians living with endemic P. vivax experience relatively low risk of infection. However, blood surveys for this parasite are likely relatively insensitive and certainly do not detect the dormant liver stage reservoir of infection. The prospects for P. vivax elimination would be improved with deeper understanding of glucose-6-phosphate dehydrogenase deficiency (G6PDd) distribution, anti-relapse therapy practices and manageability of P. vivax importation risk, especially in Java and Bali.

Project description:BackgroundMalaria control programs require a detailed understanding of the contemporary spatial distribution of infection risk to efficiently allocate resources. We used model based geostatistics (MBG) techniques to generate a contemporary map of Plasmodium falciparum malaria risk in Indonesia in 2010.MethodsPlasmodium falciparum Annual Parasite Incidence (PfAPI) data (2006-2008) were used to map limits of P. falciparum transmission. A total of 2,581 community blood surveys of P. falciparum parasite rate (PfPR) were identified (1985-2009). After quality control, 2,516 were included into a national database of age-standardized 2-10 year old PfPR data (PfPR(2-10)) for endemicity mapping. A Bayesian MBG procedure was used to create a predicted surface of PfPR(2-10) endemicity with uncertainty estimates. Population at risk estimates were derived with reference to a 2010 human population count surface.ResultsWe estimate 132.8 million people in Indonesia, lived at risk of P. falciparum transmission in 2010. Of these, 70.3% inhabited areas of unstable transmission and 29.7% in stable transmission. Among those exposed to stable risk, the vast majority were at low risk (93.39%) with the reminder at intermediate (6.6%) and high risk (0.01%). More people in western Indonesia lived in unstable rather than stable transmission zones. In contrast, fewer people in eastern Indonesia lived in unstable versus stable transmission areas.ConclusionWhile further feasibility assessments will be required, the immediate prospects for sustained control are good across much of the archipelago and medium term plans to transition to the pre-elimination phase are not unrealistic for P. falciparum. Endemicity in areas of Papua will clearly present the greatest challenge. This P. falciparum endemicity map allows malaria control agencies and their partners to comprehensively assess the region-specific prospects for reaching pre-elimination, monitor and evaluate the effectiveness of future strategies against this 2010 baseline and ultimately improve their evidence-based malaria control strategies.

Dataset Information

Geostatistical modeling of malaria endemicity using serological indicators of exposure collected through school surveys.

Publications

Geostatistical modeling of malaria endemicity using serological indicators of exposure collected through school surveys.

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