Geostatistical Modeling of Malaria Endemicity Using Serological Indicators of Exposure Collected Through School Surveys

Ashton, Ruth A.; Kefyalew, Takele; Rand, Alison; Sime, Heven; Assefa, Ashenafi; Mekasha, Addis; Edosa, Wasihun; Tesfaye, Gezahegn; Cano, Jorge; Teka, Hiwot; Reithinger, Richard; Pullan, Rachel L.; Drakeley, Chris; Brooker, Simon

doi:10.4269/ajtmh.14-0620

Cited by 24 publications

(24 citation statements)

References 48 publications

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“…This may provide a useful means to identify villages that are less responsive for any given reason to current interventions and may require more specific interventions, such as vector control, increased resources for coverage, or twice-a-year MDA. Seroprevalence is increasingly being used to generate models for estimation of transmission dynamics in near-elimination contexts, most notably in malaria [ 43 – 46 ].…”

Section: Discussionmentioning

confidence: 99%

Analysis of age-dependent trends in Ov16 IgG4 seroprevalence to onchocerciasis

et al. 2016

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BackgroundDiagnostics provide a means to measure progress toward disease elimination. Many countries in Africa are approaching elimination of onchocerciasis after successful implementation of mass drug administration programs as well as vector control. An understanding of how markers for infection such as skin snip microfilaria and Onchocerca volvulus-specific seroconversion perform in near-elimination settings informs how to best use these markers.MethodsAll-age participants from 35 villages in Togo were surveyed in 2013 and 2014 for skin snip Onchocerca volvulus microfilaria and IgG4 antibody response by enzyme-linked immunosorbent assay (ELISA) to the Onchocerca volvulus-specific antigen Ov16. A Gaussian mixture model applying the expectation-maximization (EM) algorithm was used to determine seropositivity from Ov16 ELISA data. For a subset of participants (n = 434), polymerase chain reaction (PCR) was performed on the skin snips taken during surveillance.ResultsWithin the 2,005 participants for which there was Ov16 ELISA data, O. volvulus microfilaremia prevalence and Ov16 seroprevalence were, 2.5 and 19.7 %, respectively, in the total population, and 1.6 and 3.6 % in children under 11. In the subset of 434 specimens for which ELISA, PCR, and microscopy data were generated, it was found that in children under 11 years of age, the anti-Ov16 IgG4 antibody response demonstrate a sensitivity and specificity of 80 and 97 %, respectively, against active infections as determined by combined PCR and microscopy on skin snips. Further analysis was performed in 34 of the 35 villages surveyed. These villages were stratified by all-age seroprevalence into three clusters: < 15 %; 15–20 %; and > 20 %. Age-dependence of seroprevalence for each cluster was best reflected by a two-phase force-of-infection (FOI) catalytic model. In all clusters, the lower of the two phases of FOI was associated with a younger age group, as reflected by the seroconversion rates for each phase. The age at which transition from lower to higher seroconversion, between the two phases of FOI, was found to be highest (older) for the cluster of villages with < 15 % seroprevalence and lowest (younger) for the cluster with the highest all-age seroprevalence.ConclusionsThe anti-Ov16 IgG4 antibody response is an accurate marker for active infection in children under 11 years of age in this population. Applying Ov16 surveillance to a broader age range provides additional valuable information for understanding progression toward elimination and can inform where targeted augmented interventions may be needed. Clustering of villages by all-age sero-surveillance allowed application of a biphasic FOI model to differentiate seroconversion rates for different age groups within the village cluster categories.Electronic supplementary materialThe online version of this article (doi:10.1186/s13071-016-1623-1) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Analysis of age-dependent trends in Ov16 IgG4 seroprevalence to onchocerciasis

et al. 2016

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“…Similar to the spatial random effects, the model range remained relatively constant across models, although quite a bit smaller compared to other spatial analyses of malaria prevalence in sub-Saharan Africa. Ashton and colleagues used spatial modelling with a Bayesian framework to assess the spatial variation of malaria ( Plasmodium falciparum and Plasmodium vivax ) among 5914 school children in Oromia Regional State, Ethiopia [ 51 ]. They described range as the distance at which similarities in climatic factors and ecology would be expected, and found that it was approximately 45 km in the P. falciparum model [ 51 ].…”

Section: Discussionmentioning

confidence: 99%

“…Ashton and colleagues used spatial modelling with a Bayesian framework to assess the spatial variation of malaria ( Plasmodium falciparum and Plasmodium vivax ) among 5914 school children in Oromia Regional State, Ethiopia [ 51 ]. They described range as the distance at which similarities in climatic factors and ecology would be expected, and found that it was approximately 45 km in the P. falciparum model [ 51 ]. Although the outcome assessed by Ashton and colleagues was similar to that of the Ghana trial ( P. falciparum parasitaemia), the range from each study may have been less comparable due to differences in key study characteristics.…”

Section: Discussionmentioning

confidence: 99%

Geo-spatial factors associated with infection risk among young children in rural Ghana: a secondary spatial analysis

Aimone

Brown

Zlotkin

et al. 2016

Malar J

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BackgroundDetermining the spatial patterns of infection among young children living in a malaria-endemic area may provide a means of locating high-risk populations who could benefit from additional resources for treatment and improved access to healthcare. The objective of this secondary analysis of baseline data from a cluster-randomized trial among 1943 young Ghanaian children (6–35 months of age) was to determine the geo-spatial factors associated with malaria and non-malaria infection status.MethodsSpatial analyses were conducted using a generalized linear geostatistical model with a Matern spatial correlation function and four definitions of infection status using different combinations of inflammation (C-reactive protein, CRP > 5 mg/L) and malaria parasitaemia (with or without fever). Potentially informative variables were included in a final model through a series of modelling steps, including: individual-level variables (Model 1); household-level variables (Model 2); and, satellite-derived spatial variables (Model 3). A final (Model 4) and maximal model (Model 5) included a set of selected covariates from Models 1 to 3.ResultsThe final models indicated that children with inflammation (CRP > 5 mg/L) and/or any evidence of malaria parasitaemia at baseline were more likely to be under 2 years of age, stunted, wasted, live further from a health facility, live at a lower elevation, have less educated mothers, and higher ferritin concentrations (corrected for inflammation) compared to children without inflammation or parasitaemia. Similar results were found when infection was defined as clinical malaria or parasitaemia with/without fever (definitions 3 and 4). Conversely, when infection was defined using CRP only, all covariates were non-significant with the exception of baseline ferritin concentration. In Model 5, all infection definitions that included parasitaemia demonstrated a significant interaction between normalized difference vegetation index and land cover type. Maps of the predicted infection probabilities and spatial random effect showed defined high- and low-risk areas that tended to coincide with elevation and cluster around villages.ConclusionsThe risk of infection among young children in a malaria-endemic area may have a predictable spatial pattern which is associated with geographical characteristics, such as elevation and distance to a health facility.Original trial registration clinicaltrials.gov (NCT01001871)Electronic supplementary materialThe online version of this article (doi:10.1186/s12936-016-1388-1) contains supplementary material, which is available to authorized users.

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“…Modelling could also be used to assess the level of hidden/unidentifiable cases/infections that would hinder (or would not hinder) elimination (e.g., asymptomatic or individuals with minor symptomology who would not seek treatment). As transmission declines, the addition of serological measures of past exposure [ 65 , 98 – 102 ] or active community-based transmission measurements and reactive case management [ 103 – 107 ] may be considered. Modelling can estimate the incremental benefit of adding specific surveillance activities to an already established surveillance system and could examine cost-effectiveness issues [ 48 , 108 ], specific epidemiologic aspects of contract tracing [ 109 ], and the target product profile of diagnostics [ 25 , 65 , 66 ] in case-investigation or foci-investigation settings.…”

Section: Opportunitiesmentioning

confidence: 99%

malERA: An updated research agenda for combination interventions and modelling in malaria elimination and eradication

Modelling¹

2017

PLoS Med

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This paper summarises key advances and priorities since the 2011 presentation of the Malaria Eradication Research Agenda (malERA), with a focus on the combinations of intervention tools and strategies for elimination and their evaluation using modelling approaches. With an increasing number of countries embarking on malaria elimination programmes, national and local decisions to select combinations of tools and deployment strategies directed at malaria elimination must address rapidly changing transmission patterns across diverse geographic areas. However, not all of these approaches can be systematically evaluated in the field. Thus, there is potential for modelling to investigate appropriate ‘packages’ of combined interventions that include various forms of vector control, case management, surveillance, and population-based approaches for different settings, particularly at lower transmission levels. Modelling can help prioritise which intervention packages should be tested in field studies, suggest which intervention package should be used at a particular level or stratum of transmission intensity, estimate the risk of resurgence when scaling down specific interventions after local transmission is interrupted, and evaluate the risk and impact of parasite drug resistance and vector insecticide resistance. However, modelling intervention package deployment against a heterogeneous transmission background is a challenge. Further validation of malaria models should be pursued through an iterative process, whereby field data collected with the deployment of intervention packages is used to refine models and make them progressively more relevant for assessing and predicting elimination outcomes.

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Geostatistical Modeling of Malaria Endemicity Using Serological Indicators of Exposure Collected Through School Surveys

Cited by 24 publications

References 48 publications

Analysis of age-dependent trends in Ov16 IgG4 seroprevalence to onchocerciasis

Analysis of age-dependent trends in Ov16 IgG4 seroprevalence to onchocerciasis

Geo-spatial factors associated with infection risk among young children in rural Ghana: a secondary spatial analysis

malERA: An updated research agenda for combination interventions and modelling in malaria elimination and eradication

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