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Stable and Unstable Malaria Hotspots in Longitudinal Cohort Studies in Kenya

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      Abstract

      Philip Bejon and colleagues document the clustering of malaria episodes and malarial parasite infection. These patterns may enable future prediction of hotspots of malaria infection and targeting of treatment or preventive interventions.

      Abstract

      BackgroundInfectious diseases often demonstrate heterogeneity of transmission among host populations. This heterogeneity reduces the efficacy of control strategies, but also implies that focusing control strategies on “hotspots” of transmission could be highly effective.Methods and FindingsIn order to identify hotspots of malaria transmission, we analysed longitudinal data on febrile malaria episodes, asymptomatic parasitaemia, and antibody titres over 12 y from 256 homesteads in three study areas in Kilifi District on the Kenyan coast. We examined heterogeneity by homestead, and identified groups of homesteads that formed hotspots using a spatial scan statistic. Two types of statistically significant hotspots were detected; stable hotspots of asymptomatic parasitaemia and unstable hotspots of febrile malaria. The stable hotspots were associated with higher average AMA-1 antibody titres than the unstable clusters (optical density [OD] = 1.24, 95% confidence interval [CI] 1.02–1.47 versus OD = 1.1, 95% CI 0.88–1.33) and lower mean ages of febrile malaria episodes (5.8 y, 95% CI 5.6–6.0 versus 5.91 y, 95% CI 5.7–6.1). A falling gradient of febrile malaria incidence was identified in the penumbrae of both hotspots. Hotspots were associated with AMA-1 titres, but not seroconversion rates. In order to target control measures, homesteads at risk of febrile malaria could be predicted by identifying the 20% of homesteads that experienced an episode of febrile malaria during one month in the dry season. That 20% subsequently experienced 65% of all febrile malaria episodes during the following year. A definition based on remote sensing data was 81% sensitive and 63% specific for the stable hotspots of asymptomatic malaria.ConclusionsHotspots of asymptomatic parasitaemia are stable over time, but hotspots of febrile malaria are unstable. This finding may be because immunity offsets the high rate of febrile malaria that might otherwise result in stable hotspots, whereas unstable hotspots necessarily affect a population with less prior exposure to malaria.Please see later in the article for the Editors' Summary

      Editors' Summary

      BackgroundMalaria, a mosquito-borne parasitic disease, is a major global public-health problem. About half the world's population is at risk of malaria and about one million people (mainly children living in sub-Saharan Africa) die each year from the disease. Malaria is transmitted to people through the bite of an infected mosquito. Initially, the parasite replicates inside human liver cells but, about a week after infection, these cells release “merozoites” (one of the life-stages of the parasite), which invade red blood cells. Here, the merozoites replicate rapidly before bursting out after 2–3 days and infecting more red blood cells. The cyclical and massive increase in parasitemia (parasites in the bloodstream) that results from this pattern of replication is responsible for malaria's recurring fevers and can cause life-threatening organ damage and anemia (a lack of red blood cells). Malaria can be prevented by controlling the mosquitoes that spread the parasite and by avoiding mosquito bites. Effective treatment with antimalarial drugs can also reduce malaria transmission.Why Was This Study Done?Like many other infectious diseases, the transmission of malaria is heterogeneous. That is, even in places where malaria is always present, there are “hotspots” of transmission, areas where the risk of catching malaria is particularly high. The existence of these hotspots, which are caused by a combination of genetic factors (for example, host susceptibility to infection) and environmental factors (for example, distance from mosquito breeding sites), reduces the efficacy of control strategies. However, mathematical models suggest that focusing control strategies on transmission hotspots might be an effective way to reduce overall malaria transmission. Efforts have been made to identify such hotspots using environmental data collected by satellites but with limited success. In this study, therefore, the researchers investigate the heterogeneity of malaria transmission in the Kilifi District of Kenya over time by analyzing data collected over up to 12 years (“longitudinal” data) on malaria episodes and parasitemia in three groups (cohorts) of children living in 256 homesteads.What Did the Researchers Do and Find?The researchers identified febrile malaria episodes in the homesteads by taking blood from children with fever (febrile children) to analyze for parasitemia. They took blood once a year from all the study participants just before the rainy season (when malaria peaks) to look for symptom-free parasitemia and they also looked for antibodies (proteins made by the immune system that fight disease) against malaria parasites in the blood of the participants. They then used a “spatial scan statistic” to look for heterogeneity of transmission and to identify transmission hotspots (groups of homesteads where the observed incidence of malaria or parasitemia was higher than would be expected if cases were evenly distributed). The researchers identified two types of hotspots—stable hotspots of symptom-free parasitemia that were still hotspots several years later and unstable hotspots of febrile malaria that rarely stayed in the same place for more than a year or two. Children living in the stable hotspots had slightly higher average amounts of antimalaria antibodies and developed malaria at a slightly lower average age than children living in the unstable hotspots.What Do These Findings Mean?These findings show that in Kilifi District, Kenya, hotspots of symptom-free parasitemia are stable over time but hotspots of febrile malaria are unstable. The researchers suggest that rapid acquisition of immunity in the stable hotspots reduces the occurrence of febrile malaria whereas in the unstable hotspots there is a high incidence of febrile malaria because lack of previous exposure to the parasite means there is a low level of immunity. Targeted strategies for malaria control should target both types of hotspots, suggest the researchers. Stable hotspots of symptom-free parasitemia (which can be identified by parasite or antibody surveys or by remote environmental sensing) should be targeted because mosquito dispersion probably increases malaria transmission rates near these hotspots. Unstable hotspots of febrile disease should be targeted to reduce both the burden of disease and transmission in the wider community. Unstable hotspots of febrile malaria, the researchers suggest, could be efficiently identified in Kilifi District (and maybe elsewhere) by determining which homesteads had malaria outbreaks during September (part of the dry season) one year and then focusing control interventions on these homesteads the next year.Additional InformationPlease access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000304.Information is available from the World Health Organization on malaria (in several languages)The US Centers for Disease Control and Prevention provide information on malaria (in English and Spanish)MedlinePlus provides links to additional information on malaria (in English and Spanish)Information is available from the Roll Back Malaria Partnership on the global control of malaria (in English and French) and on malaria in Kenya

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      A modified poisson regression approach to prospective studies with binary data.

       Guangyong Zou (2004)
      Relative risk is usually the parameter of interest in epidemiologic and medical studies. In this paper, the author proposes a modified Poisson regression approach (i.e., Poisson regression with a robust error variance) to estimate this effect measure directly. A simple 2-by-2 table is used to justify the validity of this approach. Results from a limited simulation study indicate that this approach is very reliable even with total sample sizes as small as 100. The method is illustrated with two data sets.
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        Estimating medium- and long-term trends in malaria transmission by using serological markers of malaria exposure.

        The implementation and evaluation of malaria control programs would be greatly facilitated by new tools for the rapid assessment of malaria transmission intensity. Because acquisition and maintenance of antimalarial antibodies depend on exposure to malaria infection, such antibodies might be used as proxy measures of transmission intensity. We have compared the prevalence of IgG antibodies with three Plasmodium falciparum asexual stage antigens in individuals of all ages living at varying altitudes encompassing a range of transmission intensities from hyper- to hypoendemic in northeastern Tanzania, with alternative measures of transmission intensity. The prevalence of antibodies to merozoite surface protein-1(19) was significantly more closely correlated with altitude than either point-prevalence malaria parasitemia or single measures of hemoglobin concentration. Analysis of age-specific seroprevalence rates enabled differentiation of recent (seasonal) changes in transmission intensity from longer-term transmission trends and, using a mathematical model of the annual rate of seroconversion, estimation of the longevity of the antibody response. Thus, serological tools allow us to detect variations in malaria transmission over time. Such tools will be invaluable for monitoring trends in malaria endemicity and the effectiveness of malaria control programs.
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          Effect of a fall in malaria transmission on morbidity and mortality in Kilifi, Kenya

          Summary Background As efforts to control malaria are expanded across the world, understanding the role of transmission intensity in determining the burden of clinical malaria is crucial to the prediction and measurement of the effectiveness of interventions to reduce transmission. Furthermore, studies comparing several endemic sites led to speculation that as transmission decreases morbidity and mortality caused by severe malaria might increase. We aimed to assess the epidemiological characteristics of malaria in Kilifi, Kenya, during a period of decreasing transmission intensity. Methods We analyse 18 years (1990–2007) of surveillance data from a paediatric ward in a malaria-endemic region of Kenya. The hospital has a catchment area of 250 000 people. Clinical data and blood-film results for more than 61 000 admissions are reported. Findings Hospital admissions for malaria decreased from 18·43 per 1000 children in 2003 to 3·42 in 2007. Over 18 years of surveillance, the incidence of cerebral malaria initially increased; however, malaria mortality decreased overall because of a decrease in incidence of severe malarial anaemia since 1997 (4·75 to 0·37 per 1000 children) and improved survival among children admitted with non-severe malaria. Parasite prevalence, the mean age of children admitted with malaria, and the proportion of children with cerebral malaria began to change 10 years before hospitalisation for malaria started to fall. Interpretation Sustained reduction in exposure to infection leads to changes in mean age and presentation of disease similar to those described in multisite studies. Changes in transmission might not lead to immediate reductions in incidence of clinical disease. However, longitudinal data do not indicate that reductions in transmission intensity lead to transient increases in morbidity and mortality. Funding Wellcome Trust, Kenya Medical Research Institute.
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            Author and article information

            Affiliations
            [1 ]Kilifi KEMRI–Wellcome Trust Collaborative Research Programme, Centre for Geographic Medicine Research–Coast, Kilifi, Kenya
            [2 ]Centre for Clinical Vaccinology and Tropical Medicine, University of Oxford, Churchill Hospital, Oxford, United Kingdom
            [3 ]Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
            [4 ]Malaria Public Health & Epidemiology Group, Centre for Geographic Medicine Research–Coast, Kenya Medical Research Institute/Wellcome Trust Research Programme, Nairobi, Kenya
            [5 ]Spatial Ecology and Epidemiology Group, Tinbergen Building, Department of Zoology, University of Oxford, Oxford, United Kingdom
            Swiss Tropical Institute, Switzerland
            Author notes

            ICMJE criteria for authorship read and met: PB TNW AL AMN JW EO AO FHO SIH AF KM. Agree with the manuscript's results and conclusions: PB TNW AL AMN JW EO AO FHO SIH AF KM. Designed the experiments/the study: PB FHO KM. Analyzed the data: PB. Collected data/did experiments for the study: PB TNW AL JW EO AO FHO SIH. Enrolled patients: PB TNW JW EO. Wrote the first draft of the paper: PB. Contributed to the writing of the paper: PB TNW AL AMN AO FHO SIH KM. Overall data manager for the cohorts from which these data were derived and was responsible for data integrity: EO. Contributed data, interpretation, and writing of the paper: AF.

            Contributors
            Role: Academic Editor
            Journal
            PLoS Med
            PLoS
            plosmed
            PLoS Medicine
            Public Library of Science (San Francisco, USA )
            1549-1277
            1549-1676
            July 2010
            July 2010
            6 July 2010
            : 7
            : 7
            2897769
            20625549
            10-PLME-RA-4436R2
            10.1371/journal.pmed.1000304
            (Academic Editor)
            Bejon et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
            Counts
            Pages: 14
            Categories
            Research Article
            Infectious Diseases/Protozoal Infections
            Public Health and Epidemiology/Infectious Diseases

            Medicine

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