Cargando…

Improving estimates of the burden of severe wasting: analysis of secondary prevalence and incidence data from 352 sites

INTRODUCTION: Estimates of incident cases of severe wasting among young children are not available for most settings but are needed for optimal planning of treatment programmes and burden estimation. To improve programme planning, global guidance recommends a single ‘incidence correction factor’ of...

Descripción completa

Detalles Bibliográficos
Autores principales: Isanaka, Sheila, Andersen, Christopher T, Cousens, Simon, Myatt, Mark, Briend, André, Krasevec, Julia, Hayashi, Chika, Mayberry, Amy, Mwirigi, Louise, Guerrero, Saul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7929878/
https://www.ncbi.nlm.nih.gov/pubmed/33653730
http://dx.doi.org/10.1136/bmjgh-2020-004342
_version_ 1783659996951609344
author Isanaka, Sheila
Andersen, Christopher T
Cousens, Simon
Myatt, Mark
Briend, André
Krasevec, Julia
Hayashi, Chika
Mayberry, Amy
Mwirigi, Louise
Guerrero, Saul
author_facet Isanaka, Sheila
Andersen, Christopher T
Cousens, Simon
Myatt, Mark
Briend, André
Krasevec, Julia
Hayashi, Chika
Mayberry, Amy
Mwirigi, Louise
Guerrero, Saul
author_sort Isanaka, Sheila
collection PubMed
description INTRODUCTION: Estimates of incident cases of severe wasting among young children are not available for most settings but are needed for optimal planning of treatment programmes and burden estimation. To improve programme planning, global guidance recommends a single ‘incidence correction factor’ of 1.6 be applied to available prevalence estimates to account for incident cases. This study aimed to update estimates of the incidence correction factor to improve programme planning and inform the approach to burden estimation for severe wasting. METHODS: A global call was issued for secondary data from severe wasting treatment programmes including prevalence, population size, programme admission and programme coverage through a UNICEF-led effort. Site-specific incidence correction factors were calculated as the number of incident cases (annual programme admissions/programme coverage) divided by the number of prevalent cases (prevalence*population size). Estimates were aggregated by country, region and overall using inverse-variance weighted random-effects meta-analysis. RESULTS: We estimated incidence correction factors from 352 sites in 20 countries. Estimates aggregated by country ranged from 1.3 (Nigeria) to 30.1 (Burundi). Excluding implausible values, the overall incidence correction factor was 3.6 (95% CI 3.4 to 3.9). CONCLUSION: Our results suggest that incidence correction factors vary between sites and that the burden of severe wasting will often be underestimated using the currently recommended incidence correction factor of 1.6. Application of updated incidence correction factors represents a simple way to improve programme planning when incidence data are not available and could inform the approach to burden estimation.
format Online
Article
Text
id pubmed-7929878
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher BMJ Publishing Group
record_format MEDLINE/PubMed
spelling pubmed-79298782021-03-19 Improving estimates of the burden of severe wasting: analysis of secondary prevalence and incidence data from 352 sites Isanaka, Sheila Andersen, Christopher T Cousens, Simon Myatt, Mark Briend, André Krasevec, Julia Hayashi, Chika Mayberry, Amy Mwirigi, Louise Guerrero, Saul BMJ Glob Health Original Research INTRODUCTION: Estimates of incident cases of severe wasting among young children are not available for most settings but are needed for optimal planning of treatment programmes and burden estimation. To improve programme planning, global guidance recommends a single ‘incidence correction factor’ of 1.6 be applied to available prevalence estimates to account for incident cases. This study aimed to update estimates of the incidence correction factor to improve programme planning and inform the approach to burden estimation for severe wasting. METHODS: A global call was issued for secondary data from severe wasting treatment programmes including prevalence, population size, programme admission and programme coverage through a UNICEF-led effort. Site-specific incidence correction factors were calculated as the number of incident cases (annual programme admissions/programme coverage) divided by the number of prevalent cases (prevalence*population size). Estimates were aggregated by country, region and overall using inverse-variance weighted random-effects meta-analysis. RESULTS: We estimated incidence correction factors from 352 sites in 20 countries. Estimates aggregated by country ranged from 1.3 (Nigeria) to 30.1 (Burundi). Excluding implausible values, the overall incidence correction factor was 3.6 (95% CI 3.4 to 3.9). CONCLUSION: Our results suggest that incidence correction factors vary between sites and that the burden of severe wasting will often be underestimated using the currently recommended incidence correction factor of 1.6. Application of updated incidence correction factors represents a simple way to improve programme planning when incidence data are not available and could inform the approach to burden estimation. BMJ Publishing Group 2021-03-02 /pmc/articles/PMC7929878/ /pubmed/33653730 http://dx.doi.org/10.1136/bmjgh-2020-004342 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ. https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.
spellingShingle Original Research
Isanaka, Sheila
Andersen, Christopher T
Cousens, Simon
Myatt, Mark
Briend, André
Krasevec, Julia
Hayashi, Chika
Mayberry, Amy
Mwirigi, Louise
Guerrero, Saul
Improving estimates of the burden of severe wasting: analysis of secondary prevalence and incidence data from 352 sites
title Improving estimates of the burden of severe wasting: analysis of secondary prevalence and incidence data from 352 sites
title_full Improving estimates of the burden of severe wasting: analysis of secondary prevalence and incidence data from 352 sites
title_fullStr Improving estimates of the burden of severe wasting: analysis of secondary prevalence and incidence data from 352 sites
title_full_unstemmed Improving estimates of the burden of severe wasting: analysis of secondary prevalence and incidence data from 352 sites
title_short Improving estimates of the burden of severe wasting: analysis of secondary prevalence and incidence data from 352 sites
title_sort improving estimates of the burden of severe wasting: analysis of secondary prevalence and incidence data from 352 sites
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7929878/
https://www.ncbi.nlm.nih.gov/pubmed/33653730
http://dx.doi.org/10.1136/bmjgh-2020-004342
work_keys_str_mv AT isanakasheila improvingestimatesoftheburdenofseverewastinganalysisofsecondaryprevalenceandincidencedatafrom352sites
AT andersenchristophert improvingestimatesoftheburdenofseverewastinganalysisofsecondaryprevalenceandincidencedatafrom352sites
AT cousenssimon improvingestimatesoftheburdenofseverewastinganalysisofsecondaryprevalenceandincidencedatafrom352sites
AT myattmark improvingestimatesoftheburdenofseverewastinganalysisofsecondaryprevalenceandincidencedatafrom352sites
AT briendandre improvingestimatesoftheburdenofseverewastinganalysisofsecondaryprevalenceandincidencedatafrom352sites
AT krasevecjulia improvingestimatesoftheburdenofseverewastinganalysisofsecondaryprevalenceandincidencedatafrom352sites
AT hayashichika improvingestimatesoftheburdenofseverewastinganalysisofsecondaryprevalenceandincidencedatafrom352sites
AT mayberryamy improvingestimatesoftheburdenofseverewastinganalysisofsecondaryprevalenceandincidencedatafrom352sites
AT mwirigilouise improvingestimatesoftheburdenofseverewastinganalysisofsecondaryprevalenceandincidencedatafrom352sites
AT guerrerosaul improvingestimatesoftheburdenofseverewastinganalysisofsecondaryprevalenceandincidencedatafrom352sites