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To screen or not to screen: an interactive framework for comparing costs of mass malaria treatment interventions

BACKGROUND: Mass drug administration and mass-screen-and-treat interventions have been used to interrupt malaria transmission and reduce burden in sub-Saharan Africa. Determining which strategy will reduce costs is an important challenge for implementers; however, model-based simulations and field s...

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Autores principales: Millar, Justin, Toh, Kok Ben, Valle, Denis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7304173/
https://www.ncbi.nlm.nih.gov/pubmed/32552743
http://dx.doi.org/10.1186/s12916-020-01609-7
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author Millar, Justin
Toh, Kok Ben
Valle, Denis
author_facet Millar, Justin
Toh, Kok Ben
Valle, Denis
author_sort Millar, Justin
collection PubMed
description BACKGROUND: Mass drug administration and mass-screen-and-treat interventions have been used to interrupt malaria transmission and reduce burden in sub-Saharan Africa. Determining which strategy will reduce costs is an important challenge for implementers; however, model-based simulations and field studies have yet to develop consensus guidelines. Moreover, there is often no way for decision-makers to directly interact with these data and/or models, incorporate local knowledge and expertise, and re-fit parameters to guide their specific goals. METHODS: We propose a general framework for comparing costs associated with mass drug administrations and mass screen and treat based on the possible outcomes of each intervention and the costs associated with each outcome. We then used publicly available data from six countries in western Africa to develop spatial-explicit probabilistic models to estimate intervention costs based on baseline malaria prevalence, diagnostic performance, and sociodemographic factors (age and urbanicity). In addition to comparing specific scenarios, we also develop interactive web applications which allow managers to select data sources and model parameters, and directly input their own cost values. RESULTS: The regional-level models revealed substantial spatial heterogeneity in malaria prevalence and diagnostic test sensitivity and specificity, indicating that a “one-size-fits-all” approach is unlikely to maximize resource allocation. For instance, urban communities in Burkina Faso typically had lower prevalence rates compared to rural communities (0.151 versus 0.383, respectively) as well as lower diagnostic sensitivity (0.699 versus 0.862, respectively); however, there was still substantial regional variation. Adjusting the cost associated with false negative diagnostic results to included additional costs, such as delayed treated and potential lost wages, undermined the overall costs associated with MSAT. CONCLUSIONS: The observed spatial variability and dependence on specified cost values support not only the need for location-specific intervention approaches but also the need to move beyond standard modeling approaches and towards interactive tools which allow implementers to engage directly with data and models. We believe that the framework demonstrated in this article will help connect modeling efforts and stakeholders in order to promote data-driven decision-making for the effective management of malaria, as well as other diseases.
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spelling pubmed-73041732020-06-22 To screen or not to screen: an interactive framework for comparing costs of mass malaria treatment interventions Millar, Justin Toh, Kok Ben Valle, Denis BMC Med Research Article BACKGROUND: Mass drug administration and mass-screen-and-treat interventions have been used to interrupt malaria transmission and reduce burden in sub-Saharan Africa. Determining which strategy will reduce costs is an important challenge for implementers; however, model-based simulations and field studies have yet to develop consensus guidelines. Moreover, there is often no way for decision-makers to directly interact with these data and/or models, incorporate local knowledge and expertise, and re-fit parameters to guide their specific goals. METHODS: We propose a general framework for comparing costs associated with mass drug administrations and mass screen and treat based on the possible outcomes of each intervention and the costs associated with each outcome. We then used publicly available data from six countries in western Africa to develop spatial-explicit probabilistic models to estimate intervention costs based on baseline malaria prevalence, diagnostic performance, and sociodemographic factors (age and urbanicity). In addition to comparing specific scenarios, we also develop interactive web applications which allow managers to select data sources and model parameters, and directly input their own cost values. RESULTS: The regional-level models revealed substantial spatial heterogeneity in malaria prevalence and diagnostic test sensitivity and specificity, indicating that a “one-size-fits-all” approach is unlikely to maximize resource allocation. For instance, urban communities in Burkina Faso typically had lower prevalence rates compared to rural communities (0.151 versus 0.383, respectively) as well as lower diagnostic sensitivity (0.699 versus 0.862, respectively); however, there was still substantial regional variation. Adjusting the cost associated with false negative diagnostic results to included additional costs, such as delayed treated and potential lost wages, undermined the overall costs associated with MSAT. CONCLUSIONS: The observed spatial variability and dependence on specified cost values support not only the need for location-specific intervention approaches but also the need to move beyond standard modeling approaches and towards interactive tools which allow implementers to engage directly with data and models. We believe that the framework demonstrated in this article will help connect modeling efforts and stakeholders in order to promote data-driven decision-making for the effective management of malaria, as well as other diseases. BioMed Central 2020-06-19 /pmc/articles/PMC7304173/ /pubmed/32552743 http://dx.doi.org/10.1186/s12916-020-01609-7 Text en © The Author(s) 2020 Open AccessThis 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/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Millar, Justin
Toh, Kok Ben
Valle, Denis
To screen or not to screen: an interactive framework for comparing costs of mass malaria treatment interventions
title To screen or not to screen: an interactive framework for comparing costs of mass malaria treatment interventions
title_full To screen or not to screen: an interactive framework for comparing costs of mass malaria treatment interventions
title_fullStr To screen or not to screen: an interactive framework for comparing costs of mass malaria treatment interventions
title_full_unstemmed To screen or not to screen: an interactive framework for comparing costs of mass malaria treatment interventions
title_short To screen or not to screen: an interactive framework for comparing costs of mass malaria treatment interventions
title_sort to screen or not to screen: an interactive framework for comparing costs of mass malaria treatment interventions
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7304173/
https://www.ncbi.nlm.nih.gov/pubmed/32552743
http://dx.doi.org/10.1186/s12916-020-01609-7
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