Optimizing Access to Healthcare in Benin: A Thesis on Zero-Inflated Regression Models

Alpha Oumar Diallo from the University of Rennes has unlocked a scientific barrier regarding access to healthcare in Benin by demonstrating the effectiveness of zero-inflated regression models. This research, supported by INSA Rennes, offers concrete solutions to improve the use of health resources in the country, validated by rigorous simulations.

Alpha Oumar Diallo's thesis addresses the issue of access to healthcare in Benin, where a high number of individuals do not utilize health services, leading to an excess of zeros in count data. Zero-inflated regression models are identified as essential tools for analyzing this data and optimizing health interventions.

Diallo demonstrates the existence of the maximum likelihood estimator in the zero-inflated binomial model, proving its consistency and asymptotic normality. These results are validated by simulations on finite sample sizes, thereby reinforcing the credibility of the approach and its potential application in the health field.

The implications of this research are significant for decision-makers. The application of these models can improve the efficiency of health interventions in Benin but requires academic partnerships and adequate training to overcome resistance to change and integrate these solutions into health policies.

« « My study shows that zero-inflated regression models can transform the analysis of health data in Benin, optimizing access to healthcare for underserved populations. » »

« « The integration of zero-inflated regression models into health policies is an advancement that could transform access to healthcare in West Africa, enabling decision-makers to better target their interventions. » »