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Model-Based Geostatistical Methods Enable Efficient Design and Analysis of Prevalence Surveys for Soil-Transmitted Helminth Infection and Other Neglected Tropical Diseases

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Model-Based Geostatistical Methods Enable Efficient Design and Analysis of Prevalence Surveys for Soil-Transmitted Helminth Infection and Other Neglected Tropical Diseases. / Johnson, Olatunji; Fronterre, Claudio; Amoah, Benjamin et al.
In: Clinical Infectious Diseases, Vol. 72, No. Supplement_3, 14.06.2021, p. S172-S179.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Johnson, O, Fronterre, C, Amoah, B, Montresor, A, Giorgi, E, Midzi, N, Mutsaka-Makuvaza, MJ, Kargbo-Labor, I, Hodges, MH, Zhang, Y, Okoyo, C, Mwandawiro, C, Minnery, M & Diggle, PJ 2021, 'Model-Based Geostatistical Methods Enable Efficient Design and Analysis of Prevalence Surveys for Soil-Transmitted Helminth Infection and Other Neglected Tropical Diseases', Clinical Infectious Diseases, vol. 72, no. Supplement_3, pp. S172-S179. https://doi.org/10.1093/cid/ciab192

APA

Johnson, O., Fronterre, C., Amoah, B., Montresor, A., Giorgi, E., Midzi, N., Mutsaka-Makuvaza, M. J., Kargbo-Labor, I., Hodges, M. H., Zhang, Y., Okoyo, C., Mwandawiro, C., Minnery, M., & Diggle, P. J. (2021). Model-Based Geostatistical Methods Enable Efficient Design and Analysis of Prevalence Surveys for Soil-Transmitted Helminth Infection and Other Neglected Tropical Diseases. Clinical Infectious Diseases, 72(Supplement_3), S172-S179. https://doi.org/10.1093/cid/ciab192

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@article{a1c95e35a40f4426b975f790be5e3e93,
title = "Model-Based Geostatistical Methods Enable Efficient Design and Analysis of Prevalence Surveys for Soil-Transmitted Helminth Infection and Other Neglected Tropical Diseases",
abstract = "Maps of the geographical variation in prevalence play an important role in large-scale programs for the control of neglected tropical diseases. Precontrol mapping is needed to establish the appropriate control intervention in each area of the country in question. Mapping is also needed postintervention to measure the success of control efforts. In the absence of comprehensive disease registries, mapping efforts can be informed by 2 kinds of data: empirical estimates of local prevalence obtained by testing individuals from a sample of communities within the geographical region of interest, and digital images of environmental factors that are predictive of local prevalence. In this article, we focus on the design and analysis of impact surveys, that is, prevalence surveys that are conducted postintervention with the aim of informing decisions on what further intervention, if any, is needed to achieve elimination of the disease as a public health problem. We show that geospatial statistical methods enable prevalence surveys to be designed and analyzed as efficiently as possible so as to make best use of hard-won field data. We use 3 case studies based on data from soil-transmitted helminth impact surveys in Kenya, Sierra Leone, and Zimbabwe to compare the predictive performance of model-based geostatistics with methods described in current World Health Organization (WHO) guidelines. In all 3 cases, we find that model-based geostatistics substantially outperforms the current WHO guidelines, delivering improved precision for reduced field-sampling effort. We argue from experience that similar improvements will hold for prevalence mapping of other neglected tropical diseases.",
author = "Olatunji Johnson and Claudio Fronterre and Benjamin Amoah and Antonio Montresor and Emanuele Giorgi and Nicholas Midzi and Mutsaka-Makuvaza, {Masceline Jenipher} and Ibrahim Kargbo-Labor and Hodges, {Mary H} and Yaobi Zhang and Collins Okoyo and Charles Mwandawiro and Mark Minnery and Diggle, {Peter J}",
year = "2021",
month = jun,
day = "14",
doi = "10.1093/cid/ciab192",
language = "English",
volume = "72",
pages = "S172--S179",
journal = "Clinical Infectious Diseases",
issn = "1058-4838",
publisher = "BioMed Central",
number = "Supplement_3",

}

RIS

TY - JOUR

T1 - Model-Based Geostatistical Methods Enable Efficient Design and Analysis of Prevalence Surveys for Soil-Transmitted Helminth Infection and Other Neglected Tropical Diseases

AU - Johnson, Olatunji

AU - Fronterre, Claudio

AU - Amoah, Benjamin

AU - Montresor, Antonio

AU - Giorgi, Emanuele

AU - Midzi, Nicholas

AU - Mutsaka-Makuvaza, Masceline Jenipher

AU - Kargbo-Labor, Ibrahim

AU - Hodges, Mary H

AU - Zhang, Yaobi

AU - Okoyo, Collins

AU - Mwandawiro, Charles

AU - Minnery, Mark

AU - Diggle, Peter J

PY - 2021/6/14

Y1 - 2021/6/14

N2 - Maps of the geographical variation in prevalence play an important role in large-scale programs for the control of neglected tropical diseases. Precontrol mapping is needed to establish the appropriate control intervention in each area of the country in question. Mapping is also needed postintervention to measure the success of control efforts. In the absence of comprehensive disease registries, mapping efforts can be informed by 2 kinds of data: empirical estimates of local prevalence obtained by testing individuals from a sample of communities within the geographical region of interest, and digital images of environmental factors that are predictive of local prevalence. In this article, we focus on the design and analysis of impact surveys, that is, prevalence surveys that are conducted postintervention with the aim of informing decisions on what further intervention, if any, is needed to achieve elimination of the disease as a public health problem. We show that geospatial statistical methods enable prevalence surveys to be designed and analyzed as efficiently as possible so as to make best use of hard-won field data. We use 3 case studies based on data from soil-transmitted helminth impact surveys in Kenya, Sierra Leone, and Zimbabwe to compare the predictive performance of model-based geostatistics with methods described in current World Health Organization (WHO) guidelines. In all 3 cases, we find that model-based geostatistics substantially outperforms the current WHO guidelines, delivering improved precision for reduced field-sampling effort. We argue from experience that similar improvements will hold for prevalence mapping of other neglected tropical diseases.

AB - Maps of the geographical variation in prevalence play an important role in large-scale programs for the control of neglected tropical diseases. Precontrol mapping is needed to establish the appropriate control intervention in each area of the country in question. Mapping is also needed postintervention to measure the success of control efforts. In the absence of comprehensive disease registries, mapping efforts can be informed by 2 kinds of data: empirical estimates of local prevalence obtained by testing individuals from a sample of communities within the geographical region of interest, and digital images of environmental factors that are predictive of local prevalence. In this article, we focus on the design and analysis of impact surveys, that is, prevalence surveys that are conducted postintervention with the aim of informing decisions on what further intervention, if any, is needed to achieve elimination of the disease as a public health problem. We show that geospatial statistical methods enable prevalence surveys to be designed and analyzed as efficiently as possible so as to make best use of hard-won field data. We use 3 case studies based on data from soil-transmitted helminth impact surveys in Kenya, Sierra Leone, and Zimbabwe to compare the predictive performance of model-based geostatistics with methods described in current World Health Organization (WHO) guidelines. In all 3 cases, we find that model-based geostatistics substantially outperforms the current WHO guidelines, delivering improved precision for reduced field-sampling effort. We argue from experience that similar improvements will hold for prevalence mapping of other neglected tropical diseases.

U2 - 10.1093/cid/ciab192

DO - 10.1093/cid/ciab192

M3 - Journal article

VL - 72

SP - S172-S179

JO - Clinical Infectious Diseases

JF - Clinical Infectious Diseases

SN - 1058-4838

IS - Supplement_3

ER -