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

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Childhood malaria in the Gambia: a case-study in model-based geostatistics. / Rowlingson, Barry; Diggle, Peter; Moyeed, Rana; Thomson, Madeleine.

In: Journal of the Royal Statistical Society: Series C (Applied Statistics), Vol. 51, No. 4, 01.10.2002, p. 493-506.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Rowlingson, B, Diggle, P, Moyeed, R & Thomson, M 2002, 'Childhood malaria in the Gambia: a case-study in model-based geostatistics.', Journal of the Royal Statistical Society: Series C (Applied Statistics), vol. 51, no. 4, pp. 493-506. https://doi.org/10.1111/1467-9876.00283

APA

Rowlingson, B., Diggle, P., Moyeed, R., & Thomson, M. (2002). Childhood malaria in the Gambia: a case-study in model-based geostatistics. Journal of the Royal Statistical Society: Series C (Applied Statistics), 51(4), 493-506. https://doi.org/10.1111/1467-9876.00283

Vancouver

Rowlingson B, Diggle P, Moyeed R, Thomson M. Childhood malaria in the Gambia: a case-study in model-based geostatistics. Journal of the Royal Statistical Society: Series C (Applied Statistics). 2002 Oct 1;51(4):493-506. https://doi.org/10.1111/1467-9876.00283

Author

Rowlingson, Barry ; Diggle, Peter ; Moyeed, Rana ; Thomson, Madeleine. / Childhood malaria in the Gambia: a case-study in model-based geostatistics. In: Journal of the Royal Statistical Society: Series C (Applied Statistics). 2002 ; Vol. 51, No. 4. pp. 493-506.

Bibtex

@article{3415168f612f4164b5c1008886d690fd,
title = "Childhood malaria in the Gambia: a case-study in model-based geostatistics.",
abstract = "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.",
author = "Barry Rowlingson and Peter Diggle and Rana Moyeed and Madeleine Thomson",
note = "RAE_import_type : Journal article RAE_uoa_type : Statistics and Operational Research",
year = "2002",
month = oct,
day = "1",
doi = "10.1111/1467-9876.00283",
language = "English",
volume = "51",
pages = "493--506",
journal = "Journal of the Royal Statistical Society: Series C (Applied Statistics)",
issn = "0035-9254",
publisher = "Wiley-Blackwell",
number = "4",

}

RIS

TY - JOUR

T1 - Childhood malaria in the Gambia: a case-study in model-based geostatistics.

AU - Rowlingson, Barry

AU - Diggle, Peter

AU - Moyeed, Rana

AU - Thomson, Madeleine

N1 - RAE_import_type : Journal article RAE_uoa_type : Statistics and Operational Research

PY - 2002/10/1

Y1 - 2002/10/1

N2 - 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.

AB - 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.

U2 - 10.1111/1467-9876.00283

DO - 10.1111/1467-9876.00283

M3 - Journal article

VL - 51

SP - 493

EP - 506

JO - Journal of the Royal Statistical Society: Series C (Applied Statistics)

JF - Journal of the Royal Statistical Society: Series C (Applied Statistics)

SN - 0035-9254

IS - 4

ER -