AUTHOR=Anyasodor Anayochukwu Edward , Nwose Ezekiel Uba , Bwititi Phillip Taderera , Richards Ross Stuart 

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

JOURNAL=Frontiers in Public Health

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.932631

DOI=10.3389/fpubh.2022.932631

ISSN=2296-2565

ABSTRACT=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 non-for-profit organisation/hospital was taken into consideration. Data on the subjects’ biochemical and anthropometric parameters as well as ODIs were analysed to generate different 4125 models, using R statistical software (version 4.0.0). Microsoft Excel software (version 2020) was used in preliminary analysis.
Result: The cost of identifying <2 new subjects with hyperglycaemia, in 1 000 people was ≥NGN 300 000 ($ 716). The best model was FBG, with the lowest AIC value of 4, and the least was a combination of random blood sugar + waist circumference + hip circumference (AIC: ≈ 34). ODI models had high AIC values, and therefore were not considered in the result.
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 favours health economics.