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Development and validation of a predictive ecological model for TB prevalence

BACKGROUND: Nationally representative tuberculosis (TB) prevalence surveys provide invaluable empirical measurements of TB burden but are a massive and complex undertaking. Therefore, methods that capitalize on data from these surveys are both attractive and imperative. The aim of this study was to...

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Autores principales: Alba, Sandra, Rood, Ente, Bakker, Mirjam I, Straetemans, Masja, Glaziou, Philippe, Sismanidis, Charalampos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6208279/
https://www.ncbi.nlm.nih.gov/pubmed/30124858
http://dx.doi.org/10.1093/ije/dyy174
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author Alba, Sandra
Rood, Ente
Bakker, Mirjam I
Straetemans, Masja
Glaziou, Philippe
Sismanidis, Charalampos
author_facet Alba, Sandra
Rood, Ente
Bakker, Mirjam I
Straetemans, Masja
Glaziou, Philippe
Sismanidis, Charalampos
author_sort Alba, Sandra
collection PubMed
description BACKGROUND: Nationally representative tuberculosis (TB) prevalence surveys provide invaluable empirical measurements of TB burden but are a massive and complex undertaking. Therefore, methods that capitalize on data from these surveys are both attractive and imperative. The aim of this study was to use existing TB prevalence estimates to develop and validate an ecological predictive statistical model to indirectly estimate TB prevalence in low- and middle-income countries without survey data. METHODS: We included national and subnational estimates from 30 nationally representative surveys and 2 district-level surveys in India, resulting in 50 data points for model development (training set). Ecological predictors included TB notification and programmatic data, co-morbidities and socio-environmental factors extracted from online data repositories. A random-effects multivariable binomial regression model was developed using the training set and was used to predict bacteriologically confirmed TB prevalence in 63 low- and middle-income countries across Africa and Asia in 2015. RESULTS: Out of the 111 ecological predictors considered, 14 were retained for model building (due to incompleteness or collinearity). The final model retained for predictions included five predictors: continent, percentage retreated cases out of all notified, all forms TB notification rates per 100 000 population, population density and proportion of the population under the age of 15. Cross-fold validations in the training set showed very good average fit (R-sq = 0.92). CONCLUSION: Predictive ecological modelling is a useful complementary approach to indirectly estimating TB burden and can be considered alongside other methods in countries with limited robust empirical measurements of TB among the general population.
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spelling pubmed-62082792018-11-05 Development and validation of a predictive ecological model for TB prevalence Alba, Sandra Rood, Ente Bakker, Mirjam I Straetemans, Masja Glaziou, Philippe Sismanidis, Charalampos Int J Epidemiol Methods BACKGROUND: Nationally representative tuberculosis (TB) prevalence surveys provide invaluable empirical measurements of TB burden but are a massive and complex undertaking. Therefore, methods that capitalize on data from these surveys are both attractive and imperative. The aim of this study was to use existing TB prevalence estimates to develop and validate an ecological predictive statistical model to indirectly estimate TB prevalence in low- and middle-income countries without survey data. METHODS: We included national and subnational estimates from 30 nationally representative surveys and 2 district-level surveys in India, resulting in 50 data points for model development (training set). Ecological predictors included TB notification and programmatic data, co-morbidities and socio-environmental factors extracted from online data repositories. A random-effects multivariable binomial regression model was developed using the training set and was used to predict bacteriologically confirmed TB prevalence in 63 low- and middle-income countries across Africa and Asia in 2015. RESULTS: Out of the 111 ecological predictors considered, 14 were retained for model building (due to incompleteness or collinearity). The final model retained for predictions included five predictors: continent, percentage retreated cases out of all notified, all forms TB notification rates per 100 000 population, population density and proportion of the population under the age of 15. Cross-fold validations in the training set showed very good average fit (R-sq = 0.92). CONCLUSION: Predictive ecological modelling is a useful complementary approach to indirectly estimating TB burden and can be considered alongside other methods in countries with limited robust empirical measurements of TB among the general population. Oxford University Press 2018-10 2018-08-16 /pmc/articles/PMC6208279/ /pubmed/30124858 http://dx.doi.org/10.1093/ije/dyy174 Text en © The Author(s) 2018. Published by Oxford University Press on behalf of the International Epidemiological Association. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Methods
Alba, Sandra
Rood, Ente
Bakker, Mirjam I
Straetemans, Masja
Glaziou, Philippe
Sismanidis, Charalampos
Development and validation of a predictive ecological model for TB prevalence
title Development and validation of a predictive ecological model for TB prevalence
title_full Development and validation of a predictive ecological model for TB prevalence
title_fullStr Development and validation of a predictive ecological model for TB prevalence
title_full_unstemmed Development and validation of a predictive ecological model for TB prevalence
title_short Development and validation of a predictive ecological model for TB prevalence
title_sort development and validation of a predictive ecological model for tb prevalence
topic Methods
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6208279/
https://www.ncbi.nlm.nih.gov/pubmed/30124858
http://dx.doi.org/10.1093/ije/dyy174
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