Comparing field-collected versus remotely-sensed variables to model malaria risk in the highlands of western Uganda
Abstract Background Malaria risk is not uniform across relatively small geographic areas, such as within a village. This heterogeneity in risk is associated with factors including demographic characteristics, individual behaviours, home construction, and environmental conditions, the importance of w...
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ftdoajarticles:oai:doaj.org/article:285f7ef8a7674d5489be519dbdfd2fec 2023-07-30T04:02:09+02:00 Comparing field-collected versus remotely-sensed variables to model malaria risk in the highlands of western Uganda Brandon D. Hollingsworth Hilary Sandborn Emmanuel Baguma Emmanuel Ayebare Moses Ntaro Edgar M. Mulogo Ross M. Boyce 2023-06-01T00:00:00Z https://doi.org/10.1186/s12936-023-04628-w https://doaj.org/article/285f7ef8a7674d5489be519dbdfd2fec EN eng BMC https://doi.org/10.1186/s12936-023-04628-w https://doaj.org/toc/1475-2875 doi:10.1186/s12936-023-04628-w 1475-2875 https://doaj.org/article/285f7ef8a7674d5489be519dbdfd2fec Malaria Journal, Vol 22, Iss 1, Pp 1-11 (2023) Malaria Uganda Micro-epidemiology Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 article 2023 ftdoajarticles https://doi.org/10.1186/s12936-023-04628-w 2023-07-09T00:37:51Z Abstract Background Malaria risk is not uniform across relatively small geographic areas, such as within a village. This heterogeneity in risk is associated with factors including demographic characteristics, individual behaviours, home construction, and environmental conditions, the importance of which varies by setting, making prediction difficult. This study attempted to compare the ability of statistical models to predict malaria risk at the household level using either (i) free easily-obtained remotely-sensed data or (ii) results from a resource-intensive household survey. Methods The results of a household malaria survey conducted in 3 villages in western Uganda were combined with remotely-sensed environmental data to develop predictive models of two outcomes of interest (1) a positive ultrasensitive rapid diagnostic test (uRDT) and (2) inpatient admission for malaria within the last year. Generalized additive models were fit to each result using factors from the remotely-sensed data, the household survey, or a combination of both. Using a cross-validation approach, each model’s ability to predict malaria risk for out-of-sample households (OOS) and villages (OOV) was evaluated. Results Models fit using only environmental variables provided a better fit and higher OOS predictive power for uRDT result (AIC = 362, AUC = 0.736) and inpatient admission (AIC = 623, AUC = 0.672) compared to models using household variables (uRDT AIC = 376, Admission AIC = 644, uRDT AUC = 0.667, Admission AUC = 0.653). Combining the datasets did not result in a better fit or higher OOS predictive power for uRDT results (AIC = 367, AUC = 0.671), but did for inpatient admission (AIC = 615, AUC = 0.683). Household factors performed best when predicting OOV uRDT results (AUC = 0.596) and inpatient admission (AUC = 0.553), but not much better than a random classifier. Conclusions These results suggest that residual malaria risk is driven more by the external environment than home construction within the study area, possibly due to ... Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic Malaria Journal 22 1 |
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ftdoajarticles |
language |
English |
topic |
Malaria Uganda Micro-epidemiology Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 |
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Malaria Uganda Micro-epidemiology Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 Brandon D. Hollingsworth Hilary Sandborn Emmanuel Baguma Emmanuel Ayebare Moses Ntaro Edgar M. Mulogo Ross M. Boyce Comparing field-collected versus remotely-sensed variables to model malaria risk in the highlands of western Uganda |
topic_facet |
Malaria Uganda Micro-epidemiology Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 |
description |
Abstract Background Malaria risk is not uniform across relatively small geographic areas, such as within a village. This heterogeneity in risk is associated with factors including demographic characteristics, individual behaviours, home construction, and environmental conditions, the importance of which varies by setting, making prediction difficult. This study attempted to compare the ability of statistical models to predict malaria risk at the household level using either (i) free easily-obtained remotely-sensed data or (ii) results from a resource-intensive household survey. Methods The results of a household malaria survey conducted in 3 villages in western Uganda were combined with remotely-sensed environmental data to develop predictive models of two outcomes of interest (1) a positive ultrasensitive rapid diagnostic test (uRDT) and (2) inpatient admission for malaria within the last year. Generalized additive models were fit to each result using factors from the remotely-sensed data, the household survey, or a combination of both. Using a cross-validation approach, each model’s ability to predict malaria risk for out-of-sample households (OOS) and villages (OOV) was evaluated. Results Models fit using only environmental variables provided a better fit and higher OOS predictive power for uRDT result (AIC = 362, AUC = 0.736) and inpatient admission (AIC = 623, AUC = 0.672) compared to models using household variables (uRDT AIC = 376, Admission AIC = 644, uRDT AUC = 0.667, Admission AUC = 0.653). Combining the datasets did not result in a better fit or higher OOS predictive power for uRDT results (AIC = 367, AUC = 0.671), but did for inpatient admission (AIC = 615, AUC = 0.683). Household factors performed best when predicting OOV uRDT results (AUC = 0.596) and inpatient admission (AUC = 0.553), but not much better than a random classifier. Conclusions These results suggest that residual malaria risk is driven more by the external environment than home construction within the study area, possibly due to ... |
format |
Article in Journal/Newspaper |
author |
Brandon D. Hollingsworth Hilary Sandborn Emmanuel Baguma Emmanuel Ayebare Moses Ntaro Edgar M. Mulogo Ross M. Boyce |
author_facet |
Brandon D. Hollingsworth Hilary Sandborn Emmanuel Baguma Emmanuel Ayebare Moses Ntaro Edgar M. Mulogo Ross M. Boyce |
author_sort |
Brandon D. Hollingsworth |
title |
Comparing field-collected versus remotely-sensed variables to model malaria risk in the highlands of western Uganda |
title_short |
Comparing field-collected versus remotely-sensed variables to model malaria risk in the highlands of western Uganda |
title_full |
Comparing field-collected versus remotely-sensed variables to model malaria risk in the highlands of western Uganda |
title_fullStr |
Comparing field-collected versus remotely-sensed variables to model malaria risk in the highlands of western Uganda |
title_full_unstemmed |
Comparing field-collected versus remotely-sensed variables to model malaria risk in the highlands of western Uganda |
title_sort |
comparing field-collected versus remotely-sensed variables to model malaria risk in the highlands of western uganda |
publisher |
BMC |
publishDate |
2023 |
url |
https://doi.org/10.1186/s12936-023-04628-w https://doaj.org/article/285f7ef8a7674d5489be519dbdfd2fec |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Malaria Journal, Vol 22, Iss 1, Pp 1-11 (2023) |
op_relation |
https://doi.org/10.1186/s12936-023-04628-w https://doaj.org/toc/1475-2875 doi:10.1186/s12936-023-04628-w 1475-2875 https://doaj.org/article/285f7ef8a7674d5489be519dbdfd2fec |
op_doi |
https://doi.org/10.1186/s12936-023-04628-w |
container_title |
Malaria Journal |
container_volume |
22 |
container_issue |
1 |
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1772812881017962496 |