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Modelling longitudinal binary outcomes with outcome dependent observation times: an application to a malaria cohort study

BACKGROUND : In spite of the global reduction of 21% in malaria incidence between 2010 and 2015, the disease still threatens many lives of children and pregnant mothers in African countries. A correct assessment and evaluation of the impact of malaria control strategies still remains quintessential...

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Autores principales: Martins, Adelino, Herzog, Sereina A., Mugenyi, Levicatus, Faes, Christel, Hens, Niel, Abrams, Steven
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9741489/
https://www.ncbi.nlm.nih.gov/pubmed/36496382
http://dx.doi.org/10.1186/s12936-022-04386-1
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author Martins, Adelino
Herzog, Sereina A.
Mugenyi, Levicatus
Faes, Christel
Hens, Niel
Abrams, Steven
author_facet Martins, Adelino
Herzog, Sereina A.
Mugenyi, Levicatus
Faes, Christel
Hens, Niel
Abrams, Steven
author_sort Martins, Adelino
collection PubMed
description BACKGROUND : In spite of the global reduction of 21% in malaria incidence between 2010 and 2015, the disease still threatens many lives of children and pregnant mothers in African countries. A correct assessment and evaluation of the impact of malaria control strategies still remains quintessential in order to eliminate the disease and its burden. Malaria follow-up studies typically involve routine visits at pre-scheduled time points and/or clinical visits whenever individuals experience malaria-like symptoms. In the latter case, infection triggers outcome assessment, thereby leading to outcome-dependent sampling (ODS). Commonly used methods to analyze such longitudinal data ignore ODS and potentially lead to biased estimates of malaria-specific transmission parameters, hence, inducing an incorrect assessment and evaluation of malaria control strategies. METHODS : In this paper, a new method is proposed to handle ODS by use of a joint model for the longitudinal binary outcome measured at routine visits and the clinical event times. The methodology is applied to malaria parasitaemia data from a cohort of [Formula: see text] Ugandan children aged 0.5–10 years from 3 regions (Walukuba—300 children, Kihihi—355 children and Nagongera—333 children) with varying transmission intensities (entomological inoculation rate equal to 2.8, 32 and 310 infectious bites per unit year, respectively) collected between 2011–2014. RESULTS : The results indicate that malaria parasite prevalence and force of infection (FOI) increase with age in the region of high malaria intensity with highest FOI in age group 5–10 years. For the region of medium intensity, the prevalence slightly increases with age and the FOI for the routine process is highest in age group 5–10 years, yet for the clinical infections, the FOI gradually decreases with increasing age. For the region with low intensity, both the prevalence and FOI peak at the age of 1 year after which the former remains constant with age yet the latter suddenly decreases with age for the clinically observed infections. CONCLUSION : Malaria parasite prevalence and FOI increase with age in the region of high malaria intensity. In all study sites, both the prevalence and FOI are highest among previously asymptomatic children and lowest among their symptomatic counterparts. Using a simulation study inspired by the malaria data at hand, the proposed methodology shows to have the smallest bias, especially when consecutive positive malaria parasitaemia presence results within a time period of 35 days were considered to be due to the same infection. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12936-022-04386-1.
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spelling pubmed-97414892022-12-12 Modelling longitudinal binary outcomes with outcome dependent observation times: an application to a malaria cohort study Martins, Adelino Herzog, Sereina A. Mugenyi, Levicatus Faes, Christel Hens, Niel Abrams, Steven Malar J Research BACKGROUND : In spite of the global reduction of 21% in malaria incidence between 2010 and 2015, the disease still threatens many lives of children and pregnant mothers in African countries. A correct assessment and evaluation of the impact of malaria control strategies still remains quintessential in order to eliminate the disease and its burden. Malaria follow-up studies typically involve routine visits at pre-scheduled time points and/or clinical visits whenever individuals experience malaria-like symptoms. In the latter case, infection triggers outcome assessment, thereby leading to outcome-dependent sampling (ODS). Commonly used methods to analyze such longitudinal data ignore ODS and potentially lead to biased estimates of malaria-specific transmission parameters, hence, inducing an incorrect assessment and evaluation of malaria control strategies. METHODS : In this paper, a new method is proposed to handle ODS by use of a joint model for the longitudinal binary outcome measured at routine visits and the clinical event times. The methodology is applied to malaria parasitaemia data from a cohort of [Formula: see text] Ugandan children aged 0.5–10 years from 3 regions (Walukuba—300 children, Kihihi—355 children and Nagongera—333 children) with varying transmission intensities (entomological inoculation rate equal to 2.8, 32 and 310 infectious bites per unit year, respectively) collected between 2011–2014. RESULTS : The results indicate that malaria parasite prevalence and force of infection (FOI) increase with age in the region of high malaria intensity with highest FOI in age group 5–10 years. For the region of medium intensity, the prevalence slightly increases with age and the FOI for the routine process is highest in age group 5–10 years, yet for the clinical infections, the FOI gradually decreases with increasing age. For the region with low intensity, both the prevalence and FOI peak at the age of 1 year after which the former remains constant with age yet the latter suddenly decreases with age for the clinically observed infections. CONCLUSION : Malaria parasite prevalence and FOI increase with age in the region of high malaria intensity. In all study sites, both the prevalence and FOI are highest among previously asymptomatic children and lowest among their symptomatic counterparts. Using a simulation study inspired by the malaria data at hand, the proposed methodology shows to have the smallest bias, especially when consecutive positive malaria parasitaemia presence results within a time period of 35 days were considered to be due to the same infection. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12936-022-04386-1. BioMed Central 2022-12-10 /pmc/articles/PMC9741489/ /pubmed/36496382 http://dx.doi.org/10.1186/s12936-022-04386-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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
Martins, Adelino
Herzog, Sereina A.
Mugenyi, Levicatus
Faes, Christel
Hens, Niel
Abrams, Steven
Modelling longitudinal binary outcomes with outcome dependent observation times: an application to a malaria cohort study
title Modelling longitudinal binary outcomes with outcome dependent observation times: an application to a malaria cohort study
title_full Modelling longitudinal binary outcomes with outcome dependent observation times: an application to a malaria cohort study
title_fullStr Modelling longitudinal binary outcomes with outcome dependent observation times: an application to a malaria cohort study
title_full_unstemmed Modelling longitudinal binary outcomes with outcome dependent observation times: an application to a malaria cohort study
title_short Modelling longitudinal binary outcomes with outcome dependent observation times: an application to a malaria cohort study
title_sort modelling longitudinal binary outcomes with outcome dependent observation times: an application to a malaria cohort study
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9741489/
https://www.ncbi.nlm.nih.gov/pubmed/36496382
http://dx.doi.org/10.1186/s12936-022-04386-1
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