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Spatio-temporal modelling of routine health facility data for malaria risk micro-stratification in mainland Tanzania
As malaria transmission declines, the need to monitor the heterogeneity of malaria risk at finer scales becomes critical to guide community-based targeted interventions. Although routine health facility (HF) data can provide epidemiological evidence at high spatial and temporal resolution, its incom...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10313820/ https://www.ncbi.nlm.nih.gov/pubmed/37391538 http://dx.doi.org/10.1038/s41598-023-37669-x |
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author | Thawer, Sumaiyya G. Golumbeanu, Monica Lazaro, Samwel Chacky, Frank Munisi, Khalifa Aaron, Sijenunu Molteni, Fabrizio Lengeler, Christian Pothin, Emilie Snow, Robert W. Alegana, Victor A. |
author_facet | Thawer, Sumaiyya G. Golumbeanu, Monica Lazaro, Samwel Chacky, Frank Munisi, Khalifa Aaron, Sijenunu Molteni, Fabrizio Lengeler, Christian Pothin, Emilie Snow, Robert W. Alegana, Victor A. |
author_sort | Thawer, Sumaiyya G. |
collection | PubMed |
description | As malaria transmission declines, the need to monitor the heterogeneity of malaria risk at finer scales becomes critical to guide community-based targeted interventions. Although routine health facility (HF) data can provide epidemiological evidence at high spatial and temporal resolution, its incomplete nature of information can result in lower administrative units without empirical data. To overcome geographic sparsity of data and its representativeness, geo-spatial models can leverage routine information to predict risk in un-represented areas as well as estimate uncertainty of predictions. Here, a Bayesian spatio-temporal model was applied on malaria test positivity rate (TPR) data for the period 2017–2019 to predict risks at the ward level, the lowest decision-making unit in mainland Tanzania. To quantify the associated uncertainty, the probability of malaria TPR exceeding programmatic threshold was estimated. Results showed a marked spatial heterogeneity in malaria TPR across wards. 17.7 million people resided in areas where malaria TPR was high (≥ 30; 90% certainty) in the North-West and South-East parts of Tanzania. Approximately 11.7 million people lived in areas where malaria TPR was very low (< 5%; 90% certainty). HF data can be used to identify different epidemiological strata and guide malaria interventions at micro-planning units in Tanzania. These data, however, are imperfect in many settings in Africa and often require application of geo-spatial modelling techniques for estimation. |
format | Online Article Text |
id | pubmed-10313820 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-103138202023-07-02 Spatio-temporal modelling of routine health facility data for malaria risk micro-stratification in mainland Tanzania Thawer, Sumaiyya G. Golumbeanu, Monica Lazaro, Samwel Chacky, Frank Munisi, Khalifa Aaron, Sijenunu Molteni, Fabrizio Lengeler, Christian Pothin, Emilie Snow, Robert W. Alegana, Victor A. Sci Rep Article As malaria transmission declines, the need to monitor the heterogeneity of malaria risk at finer scales becomes critical to guide community-based targeted interventions. Although routine health facility (HF) data can provide epidemiological evidence at high spatial and temporal resolution, its incomplete nature of information can result in lower administrative units without empirical data. To overcome geographic sparsity of data and its representativeness, geo-spatial models can leverage routine information to predict risk in un-represented areas as well as estimate uncertainty of predictions. Here, a Bayesian spatio-temporal model was applied on malaria test positivity rate (TPR) data for the period 2017–2019 to predict risks at the ward level, the lowest decision-making unit in mainland Tanzania. To quantify the associated uncertainty, the probability of malaria TPR exceeding programmatic threshold was estimated. Results showed a marked spatial heterogeneity in malaria TPR across wards. 17.7 million people resided in areas where malaria TPR was high (≥ 30; 90% certainty) in the North-West and South-East parts of Tanzania. Approximately 11.7 million people lived in areas where malaria TPR was very low (< 5%; 90% certainty). HF data can be used to identify different epidemiological strata and guide malaria interventions at micro-planning units in Tanzania. These data, however, are imperfect in many settings in Africa and often require application of geo-spatial modelling techniques for estimation. Nature Publishing Group UK 2023-06-30 /pmc/articles/PMC10313820/ /pubmed/37391538 http://dx.doi.org/10.1038/s41598-023-37669-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Thawer, Sumaiyya G. Golumbeanu, Monica Lazaro, Samwel Chacky, Frank Munisi, Khalifa Aaron, Sijenunu Molteni, Fabrizio Lengeler, Christian Pothin, Emilie Snow, Robert W. Alegana, Victor A. Spatio-temporal modelling of routine health facility data for malaria risk micro-stratification in mainland Tanzania |
title | Spatio-temporal modelling of routine health facility data for malaria risk micro-stratification in mainland Tanzania |
title_full | Spatio-temporal modelling of routine health facility data for malaria risk micro-stratification in mainland Tanzania |
title_fullStr | Spatio-temporal modelling of routine health facility data for malaria risk micro-stratification in mainland Tanzania |
title_full_unstemmed | Spatio-temporal modelling of routine health facility data for malaria risk micro-stratification in mainland Tanzania |
title_short | Spatio-temporal modelling of routine health facility data for malaria risk micro-stratification in mainland Tanzania |
title_sort | spatio-temporal modelling of routine health facility data for malaria risk micro-stratification in mainland tanzania |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10313820/ https://www.ncbi.nlm.nih.gov/pubmed/37391538 http://dx.doi.org/10.1038/s41598-023-37669-x |
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