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. 2013 Oct 5:12:355.
doi: 10.1186/1475-2875-12-355.

Analysis of partial and complete protection in malaria cohort studies

Matthew E Cairns  1 , Kwaku Poku Asante, Seth Owusu-Agyei, Daniel Chandramohan, Brian M Greenwood, Paul J Milligan
Affiliations

Analysis of partial and complete protection in malaria cohort studies

Matthew E Cairns et al. Malar J. .

Abstract

Background: Malaria transmission is highly heterogeneous and analysis of incidence data must account for this for correct statistical inference. Less widely appreciated is the occurrence of a large number of zero counts (children without a malaria episode) in malaria cohort studies. Zero-inflated regression methods provide one means of addressing this issue, and also allow risk factors providing complete and partial protection to be disentangled.

Methods: Poisson, negative binomial (NB), zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) regression models were fitted to data from two cohort studies of malaria in children in Ghana. Multivariate models were used to understand risk factors for elevated incidence of malaria and for remaining malaria-free, and to estimate the fraction of the population not at risk of malaria.

Results: ZINB models, which account for both heterogeneity in individual risk and an unexposed sub-group within the population, provided the best fit to data in both cohorts. These approaches gave additional insight into the mechanism of factors influencing the incidence of malaria compared to simpler approaches, such as NB regression. For example, compared to urban areas, rural residence was found to both increase the incidence rate of malaria among exposed children, and increase the probability of being exposed. In Navrongo, 34% of urban residents were estimated to be at no risk, compared to 3% of rural residents. In Kintampo, 47% of urban residents and 13% of rural residents were estimated to be at no risk.

Conclusion: These results illustrate the utility of zero-inflated regression methods for analysis of malaria cohort data that include a large number of zero counts. Specifically, these results suggest that interventions that reach mainly urban residents will have limited overall impact, since some urban residents are essentially at no risk, even in areas of high endemicity, such as in Ghana.

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Figures

Figure 1
Figure 1
Number of malaria attacks experienced by 24 months of age. The figures show the number of malaria attacks experienced by 24 months of age in A) Navrongo and B) Kintampo, for all residents, and by area of residence (urban or rural).
Figure 2
Figure 2
Time to first malaria episode according to place of residence. Figures show Kaplan-Meier estimate of time to first malaria episode in urban and rural areas for A) Navrongo and B) Kintampo cohorts. Tables show number of children remaining at risk at 6-month intervals. For clarity of presentation, the three rural areas in Navrongo (rocky highland, lowland rural, irrigated rural) were combined. Malaria incidence rates on the same time scale are shown in the Additional files.
Figure 3
Figure 3
Poisson, negative binomial, ZIP and ZINB model fits to data - Navrongo.
Figure 4
Figure 4
Poisson, negative binomial and ZINB model fits to data – Kintampo.
See this image and copyright information in PMC

References

    1. Woolhouse ME, Dye C, Etard JF, Smith T, Charlwood JD, Garnett GP, Hagan P, Hii JL, Ndhlovu PD, Quinnell RJ, Watts CH, Chandiwana SK, Anderson RM. Heterogeneities in the transmission of infectious agents: implications for the design of control programs. Proc Natl Acad Sci USA. 1997;94:338–342. - PMC - PubMed
    1. Bousema T, Drakeley C, Gesase S, Hashim R, Magesa S, Mosha F, Otieno S, Carneiro I, Cox J, Msuya E, Kleinschmidt I, Maxwell C, Greenwood B, Riley E, Sauerwein R, Chandramohan D, Gosling R. Identification of hot spots of malaria transmission for targeted malaria control. J Infect Dis. 2010;201:1764–1774. - PubMed
    1. Mwangi TW, Fegan G, Williams TN, Kinyanjui SM, Snow RW, Marsh K. Evidence for over-dispersion in the distribution of clinical malaria episodes in children. PLoS ONE. 2008;3:e2196. - PMC - PubMed
    1. White MT, Griffin JT, Drakeley CJ, Ghani AC. Heterogeneity in malaria exposure and vaccine response: implications for the interpretation of vaccine efficacy trials. Malar J. 2010;9:82. - PMC - PubMed
    1. Hilbe JM. Negative Binomial Regression. 2. Cambridge: Cambridge University Press; 2012.

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