Cargando…

Cost-effectiveness of community diabetes screening: Application of Akaike information criterion in rural communities of Nigeria

BACKGROUND: The prevalence of diabetes mellitus (DM) is increasing globally, and this requires several approaches to screening. There are reports of alternative indices for prediction of DM, besides fasting blood glucose (FBG) level. This study, investigated the ability of combination of biochemical...

Descripción completa

Detalles Bibliográficos
Autores principales: Anyasodor, Anayochukwu Edward, Nwose, Ezekiel Uba, Bwititi, Phillip Taderera, Richards, Ross Stuart
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9357922/
https://www.ncbi.nlm.nih.gov/pubmed/35958851
http://dx.doi.org/10.3389/fpubh.2022.932631
_version_ 1784763815996424192
author Anyasodor, Anayochukwu Edward
Nwose, Ezekiel Uba
Bwititi, Phillip Taderera
Richards, Ross Stuart
author_facet Anyasodor, Anayochukwu Edward
Nwose, Ezekiel Uba
Bwititi, Phillip Taderera
Richards, Ross Stuart
author_sort Anyasodor, Anayochukwu Edward
collection PubMed
description BACKGROUND: The prevalence of diabetes mellitus (DM) is increasing globally, and this requires several approaches to screening. There are reports of alternative indices for prediction of DM, besides fasting blood glucose (FBG) level. This study, investigated the ability of combination of biochemical and anthropometric parameters and orodental disease indicators (ODIs) to generate models for DM prediction, using Akaike information criterion (AIC) to substantiate health economics of diabetes screening. METHODS: Four hundred and thirty-three subjects were enrolled in the study in Ndokwa communities, Delta State, Nigeria, and their glycaemic status was determined, using the CardioChek analyser(®) and previous data from the Prediabetes and Cardiovascular Complications Study were also used. The cost of screening for diabetes (NGN 300 = $0.72) in a not-for-profit organization/hospital was used as basis to calculate the health economics of number of individuals with DM in 1,000 participants. Data on the subjects' anthropometric, biochemical and ODI parameters were used to generate different models, using R statistical software (version 4.0.0). The different models were evaluated for their AIC values. Lowest AIC was considered as best model. Microsoft Excel software (version 2020) was used in preliminary analysis. RESULT: The cost of identifying <2 new subjects with hyperglycemia, in 1,000 people was ≥NGN 300,000 ($ 716). A total of 4,125 models were generated. AIC modeling indicates FBG test as the best model (AIC = 4), and the least being combination of random blood sugar + waist circumference + hip circumference (AIC ≈ 34). Models containing ODI parameters had AIC values >34, hence considered as not recommendable. CONCLUSION: The cost of general screening for diabetes in rural communities may appear high and burdensome in terms of health economics. However, the use of prediction models involving AIC is of value in terms of cost-benefit and cost-effectiveness to the healthcare consumers, which favors health economics.
format Online
Article
Text
id pubmed-9357922
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-93579222022-08-10 Cost-effectiveness of community diabetes screening: Application of Akaike information criterion in rural communities of Nigeria Anyasodor, Anayochukwu Edward Nwose, Ezekiel Uba Bwititi, Phillip Taderera Richards, Ross Stuart Front Public Health Public Health BACKGROUND: The prevalence of diabetes mellitus (DM) is increasing globally, and this requires several approaches to screening. There are reports of alternative indices for prediction of DM, besides fasting blood glucose (FBG) level. This study, investigated the ability of combination of biochemical and anthropometric parameters and orodental disease indicators (ODIs) to generate models for DM prediction, using Akaike information criterion (AIC) to substantiate health economics of diabetes screening. METHODS: Four hundred and thirty-three subjects were enrolled in the study in Ndokwa communities, Delta State, Nigeria, and their glycaemic status was determined, using the CardioChek analyser(®) and previous data from the Prediabetes and Cardiovascular Complications Study were also used. The cost of screening for diabetes (NGN 300 = $0.72) in a not-for-profit organization/hospital was used as basis to calculate the health economics of number of individuals with DM in 1,000 participants. Data on the subjects' anthropometric, biochemical and ODI parameters were used to generate different models, using R statistical software (version 4.0.0). The different models were evaluated for their AIC values. Lowest AIC was considered as best model. Microsoft Excel software (version 2020) was used in preliminary analysis. RESULT: The cost of identifying <2 new subjects with hyperglycemia, in 1,000 people was ≥NGN 300,000 ($ 716). A total of 4,125 models were generated. AIC modeling indicates FBG test as the best model (AIC = 4), and the least being combination of random blood sugar + waist circumference + hip circumference (AIC ≈ 34). Models containing ODI parameters had AIC values >34, hence considered as not recommendable. CONCLUSION: The cost of general screening for diabetes in rural communities may appear high and burdensome in terms of health economics. However, the use of prediction models involving AIC is of value in terms of cost-benefit and cost-effectiveness to the healthcare consumers, which favors health economics. Frontiers Media S.A. 2022-07-25 /pmc/articles/PMC9357922/ /pubmed/35958851 http://dx.doi.org/10.3389/fpubh.2022.932631 Text en Copyright © 2022 Anyasodor, Nwose, Bwititi and Richards. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Anyasodor, Anayochukwu Edward
Nwose, Ezekiel Uba
Bwititi, Phillip Taderera
Richards, Ross Stuart
Cost-effectiveness of community diabetes screening: Application of Akaike information criterion in rural communities of Nigeria
title Cost-effectiveness of community diabetes screening: Application of Akaike information criterion in rural communities of Nigeria
title_full Cost-effectiveness of community diabetes screening: Application of Akaike information criterion in rural communities of Nigeria
title_fullStr Cost-effectiveness of community diabetes screening: Application of Akaike information criterion in rural communities of Nigeria
title_full_unstemmed Cost-effectiveness of community diabetes screening: Application of Akaike information criterion in rural communities of Nigeria
title_short Cost-effectiveness of community diabetes screening: Application of Akaike information criterion in rural communities of Nigeria
title_sort cost-effectiveness of community diabetes screening: application of akaike information criterion in rural communities of nigeria
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9357922/
https://www.ncbi.nlm.nih.gov/pubmed/35958851
http://dx.doi.org/10.3389/fpubh.2022.932631
work_keys_str_mv AT anyasodoranayochukwuedward costeffectivenessofcommunitydiabetesscreeningapplicationofakaikeinformationcriterioninruralcommunitiesofnigeria
AT nwoseezekieluba costeffectivenessofcommunitydiabetesscreeningapplicationofakaikeinformationcriterioninruralcommunitiesofnigeria
AT bwititiphilliptaderera costeffectivenessofcommunitydiabetesscreeningapplicationofakaikeinformationcriterioninruralcommunitiesofnigeria
AT richardsrossstuart costeffectivenessofcommunitydiabetesscreeningapplicationofakaikeinformationcriterioninruralcommunitiesofnigeria