Impact of health insurance on the use of oral health services in the Peruvian population 2015-2019 - Jorge Alfredo Herrera Ruiz; Nilthon Pisfil-Benites; Diego Azañedo; Akram Hernández-Vásquez (BMC, 2024)
Abstract Background The high treatment cost of oral diseases is a barrier for accessing oral health services (OHS), particularly in low-income countries. Therefore, this study aimed to evaluate the impact of health insurance on the use of OHS in the Peruvian population from 2015 to 2019. Methods We conducted a prospective, longitudinal study of secondary data using the National Household Survey (ENAHO) 2015-2019 panel databases, which collected information from the same participants during each of the five years. The dependent variable was the use of OHS in the three months prior to the survey (yes/no). The independent variable was health insurance affiliation (four years or less/all five years). Both were measured by survey questions. Generalized estimating equation (GEE) Poisson regression models with robust standard errors were used to estimate the relative risk (RR) associated with use of OHS. Results We included 4064 individuals distributed in 1847 households, who responded to the survey during each of the five years. The adjusted GEE model showed that those who had health insurance during all five years without interruption were more likely to attend OHS than those who had insurance for four years or less (adjusted relative risk [aRR]: 1.30; 95%CI: 1.13-1.50). In addition, we carried out a sensitivity analysis by recategorizing the independent variable into three categories (never/some years/ all five years), which also showed (aRR: 1.45; 95%CI: 1.11-1.89) that participants with health insurance during all five years were more likely to have used OHS than those who never had insurance. Conclusion Therefore, in the Peruvian context, health insurance affiliation was associated with greater use of OHS. The panel data used derives from a subsample of consecutive nationally representative samples, which may have led to a loss of representativeness. Furthermore, the data was collected between 2015 and 2019, prior to the onset of the COVID-19 pandemic, and insurance conditions may have changed.