Identifying children with excess malaria episodes after adjusting for variation in exposure: identification from a longitudinal study using statistical count models.

Francis Maina Ndungu,Juliana Wambua,Gregory Fegan,Alex Macharia,Sophie Uyoga,Kevin Marsh,George Nyangweso,Thomas N Williams,Chris Nyundo,Philip Bejon,Edna Ogada,Tabitha Mwangi

doi:10.1186/s12916-015-0422-4

Francis Maina Ndungu, Juliana Wambua + Show 10 more

Open Access

https://doi.org/10.1186/s12916-015-0422-4

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Abstract

BackgroundThe distribution of Plasmodium falciparum clinical malaria episodes is over-dispersed among children in endemic areas, with more children experiencing multiple clinical episodes than would be expected based on a Poisson distribution. There is consistent evidence for micro-epidemiological variation in exposure to P. falciparum. The aim of the current study was to identify children with excess malaria episodes after controlling for malaria exposure.MethodsWe selected the model that best fit the data out of the models examined and included the following covariates: age, a weighted local prevalence of infection as an index of exposure, and calendar time to predict episodes of malaria on active surveillance malaria data from 2,463 children of under 15 years of age followed for between 5 and 15 years each. Using parameters from the zero-inflated negative binomial model which best fitted our data, we ran 100 simulations of the model based on our population to determine the variation that might be seen due to chance.ResultsWe identified 212 out of 2,463 children who had a number of clinical episodes above the 95th percentile of the simulations run from the model, hereafter referred to as “excess malaria (EM)”. We then identified exposure-matched controls with “average numbers of malaria” episodes, and found that the EM group had higher parasite densities when asymptomatically infected or during clinical malaria, and were less likely to be of haemoglobin AS genotype.ConclusionsOf the models tested, the negative zero-inflated negative binomial distribution with exposure, calendar year, and age acting as independent predictors, fitted the distribution of clinical malaria the best. Despite accounting for these factors, a group of children suffer excess malaria episodes beyond those predicted by the model. An epidemiological framework for identifying these children will allow us to study factors that may explain excess malaria episodes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12916-015-0422-4) contains supplementary material, which is available to authorized users.

Highlights

The distribution of Plasmodium falciparum clinical malaria episodes is over-dispersed among children in endemic areas, with more children experiencing multiple clinical episodes than would be expected based on a Poisson distribution
Junju is under moderate malaria transmission intensity with P. falciparum parasite prevalence from crosssectional surveys at 30 % during January to May [17, 18], while Ngerenya is in an area in which malaria transmission has fallen to very low levels since 2004 [19], such that older children were historically exposed but younger ones have not been
As expected from previous studies [2], the number of clinical episodes varied with increasing age showing an initial increase and subsequent decrease in risk, consistent with initially increased mosquito biting rates as the child grows and loss of protective maternal antibodies, and subsequent acquired natural immunity to clinical malaria (Fig. 1a). This increase and decline was most evident among the children living with the highest exposure to P. falciparum parasites in the microenvironment, followed by those in the medium, and the lowest of the three exposure index tertiles

Summary

Introduction

The distribution of Plasmodium falciparum clinical malaria episodes is over-dispersed among children in endemic areas, with more children experiencing multiple clinical episodes than would be expected based on a Poisson distribution. The aim of the current study was to identify children with excess malaria episodes after controlling for malaria exposure. The current antimalarial drugs and insecticide-dependent control methods are at risk from the emergence of resistant parasites and mosquitoes, respectively. Additional control methods, such as preventative vaccines, are required. Through continuous exposure to malaria parasites, children acquire immunity to clinical malaria as they grow older [2]. The distribution of clinical malaria is highly heterogeneous, even among children of similar ages as demonstrated in Kenya [3, 4] and in Senegal, where the numbers of clinical episodes ranged from zero to 40 per child over a 5-year period of surveillance in the same village [5]. A subgroup of children suffered malaria attacks every 4 to 6 weeks over many years for unexplained reasons [6, 7]

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