Use of synthetic population to assess alcohol abuse in small areas

This study evaluated the use of synthetic populations, generated by conditional generative adversarial networks (cGAN), to estimate abusive alcohol consumption in small areas of Belo Horizonte (MG).

Data from the Vigitel survey from 2006 to 2018 were used. Sociodemographic and lifestyle variables were considered, and the synthetic records were assigned to nine vulnerability clusters.

The inclusion of synthetic data reduced standard errors and narrowed confidence intervals, especially in more vulnerable areas. Higher prevalence was observed in less vulnerable areas.

The approach proved promising for overcoming representativeness limitations and enhancing risk factor monitoring, but it still depends on the quality of the original data, may introduce biases, and requires external validation and cautious use.