The achievement of universal health coverage has put Primary Health Care back at the center of policy orientations, particularly by identifying factors likely to improve the organization of peripheral facilities. However, this objective depends on the econometric methods used, especially for cross-sectional data and small sample sizes. This study aims to examine the sensitivity of the most usual estimation methods (Stochastic Frontier Analysis (SFA), Data Envelopment Analysis (DEA), DEA double bootstrap, Tobit, Truncated Standard Regression) for evaluating the scores and determinants of technical inefficiency of Primary Health Care Facilities (PHCF) in Côte d’Ivoire. Estimates show average technical efficiency scores of 94.13% for the DEA versus 89.61% for the SFA and 82.24% for the DEA double bootstrap. The results also indicate a proportion of determinants of technical inefficiency, in decreasing order of importance, with the DEA double bootstrap, the SFA, truncated regression and Tobit. This technical inefficiency can be improved in policies to promote basic health care by: increasing the proportion of nurses in the medical staff, the nurse/inhabitant ratio, the adult literacy rate by region, controlling the average capacity of the PHCFs, improving their geographical accessibility and reducing the rate of extreme poverty by health region.