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Childhood malaria in the Gambia: a case‐study in model‐based geostatistics

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  • Peter Diggle
  • Rana Moyeed
  • Barry Rowlingson
  • Madeleine Thomson

Abstract

Summary. The paper develops a spatial generalized linear mixed model to describe the variation in the prevalence of malaria among a sample of village resident children in the Gambia. The response from each child is a binary indicator of the presence of malarial parasites in a blood sample. The model includes terms for the effects of child level covariates (age and bed net use), village level covariates (inclusion or exclusion from the primary health care system and greenness of surrounding vegetation as derived from satellite information) and separate components for residual spatial and non‐spatial extrabinomial variation. The results confirm and quantify the progressive increase in prevalence with age, and the protective effects of bed nets. They also show that the extrabinomial variation is spatially structured, suggesting an environmental effect rather than variation in familial susceptibility. Neither inclusion in the primary health care system nor the greenness of the surrounding vegetation appeared to affect the prevalence of malaria. The method of inference was Bayesian using vague priors and a Markov chain Monte Carlo implementation.

Suggested Citation

  • Peter Diggle & Rana Moyeed & Barry Rowlingson & Madeleine Thomson, 2002. "Childhood malaria in the Gambia: a case‐study in model‐based geostatistics," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 51(4), pages 493-506, October.
    • Handle: RePEc:bla:jorssc:v:51:y:2002:i:4:p:493-506
    DOI: 10.1111/1467-9876.00283
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    Cited by:

    1. S. M. Niaz Arifin & Rumana Reaz Arifin & Dilkushi De Alwis Pitts & M. Sohel Rahman & Sara Nowreen & Gregory R. Madey & Frank H. Collins, 2015. "Landscape Epidemiology Modeling Using an Agent-Based Model and a Geographic Information System," Land, MDPI, vol. 4(2), pages 1-35, May.
    2. Sahar Zarmehri & Ephraim M. Hanks & Lin Lin, 2021. "A Sample Covariance-Based Approach For Spatial Binary Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(2), pages 220-249, June.
    3. Peter W Gething & Anand P Patil & Simon I Hay, 2010. "Quantifying Aggregated Uncertainty in Plasmodium falciparum Malaria Prevalence and Populations at Risk via Efficient Space-Time Geostatistical Joint Simulation," PLOS Computational Biology, Public Library of Science, vol. 6(4), pages 1-12, April.
    4. Victor De Oliveira, 2017. "Geostatistical Binary Data: Models, Properties And Connections," Working Papers 0151mss, College of Business, University of Texas at San Antonio.
    5. Kandala, Ngianga-Bakwin & Magadi, Monica Akinyi & Madise, Nyovani Janet, 2006. "An investigation of district spatial variations of childhood diarrhoea and fever morbidity in Malawi," Social Science & Medicine, Elsevier, vol. 62(5), pages 1138-1152, March.
    6. Peter J. Diggle & Emanuele Giorgi, 2016. "Model-Based Geostatistics for Prevalence Mapping in Low-Resource Settings," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 1096-1120, July.
    7. Yun Bai & Jian Kang & Peter X.-K. Song, 2014. "Efficient pairwise composite likelihood estimation for spatial-clustered data," Biometrics, The International Biometric Society, vol. 70(3), pages 661-670, September.

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