Year: 2026
Type: article
Abstract: Objectives To analyze the effect of using ARTIFICIAL INTELLIGENCE and synthetic populations in population surveys to improve the accuracy of estimates for small areas. Methods Ecological study with data from Vigitel from 2006 to 2018 for Belo Horizonte, State Capital in Minas Gerais, Brazil. The outcome was the prevalence of current smokers. The interviews were georeferenced to the census tracts of the 2010 Census and grouped into nine vulnerability clusters derived from the Social Vulnerability Index (SVI). To address the lack of interviews in more vulnerable areas, synthetic populations were generated using conditional adversarial generative networks (cGANs), incorporated into the clusters with the fewest observations. The quality of the synthetic data was assessed for structure and statistical similarity, and the estimates were weighted by post-stratification (rake) based on the 2010 Census. Prevalences and their respective 95% confidence intervals were estimated for three periods, comparing real and combined data. Results The clusters with greater vulnerability had fewer interviews and a higher standard error. The incorporation of 700 synthetic records per cluster substantially reduced the standard error (to 1.7%) and improved the accuracy of the estimates, especially in the most vulnerable areas. The similarity between real and synthetic data was high (KSComplement = 0.968). The highest smoking prevalences were concentrated in the clusters with greater vulnerability, with a tendency to decrease over the analyzed period. Conclusions This strategy represents advances in the monitoring of NCDs and their risk factors, allowing for more accurate and representative estimates in contexts of high vulnerability, and supporting public policies and actions to identify tobacco use and other risk factors. Key messages • The study represents advances in the monitoring of NCDs and risk factors, by using artificial intelligence techniques, such as synthetic populations, to reduce variability in small area estimates. • The study showed Tobbaco prevelence in areas of greater vulnerability. This can support health promotion policies and programs that encourage reduce Tobbaco use.
Institution: Universidade Federal de Minas Gerais
Language: English
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,181
OpenAlex ID: W7223666678