Abstract: Background
In 2002, Chile introduced a major health reform, designed to level out inequities in healthcare coverage, access and opportunities. In particular, the opportunity guarantees ensure a maximum time to receive the appropriate diagnosis and treatment, and thus, gender bias should not be observed.
Objective
To explore the existence of differences in the timeliness of treatment between women and men under the Chilean public health insurance system. We controlled by other observable variables, including age, insurance holder status, provider complexity and health district.
Methods
We used an individual level database that includes all interactions for the diseases covered under the national plan from 2014 to 2019. We excluded from the analysis the diseases affecting only men, women, and infants. To study the waiting time differences between women and men, we first perform a Welch two sample t-test. Then, we used a multilevel hierarchical regression model to further explore the impact of gender in waiting time. At the individual level, we included gender, insurance holder status, age, and the interaction between gender and age. For the aggregate levels, we used the specific opportunity guarantee, the type of provider, and health district.
Results
From the Welch two sample t-test, we found significant differences in waiting times between women and men, in seven opportunity guarantees. From the multilevel regression, the individual variables: holder status, ages between 35 and 49, and the interaction between gender and age for ages between 40 and 54 were statistically significant at 95% level. We remark that the major differences in waiting times between women and men were observed for individuals between ages from 40 to 54, with women waiting significantly longer.
Conclusion
Results show the existence of bias in the timeliness of treatment, proving that universal guarantees are not enough to reduce gender inequalities in health care.

Abstract: Objectives: We develop a patient prioritization scheme for treating patients infected with hepatitis C virus (HCV) and study under which scenarios it outperforms the current practices in Spain and Chile.
Study design: We use simulation to evaluate the performance of prioritization rules under two HCV patient cohorts, constructed using secondary data of public records from Chile and Spain, during 2015-2016.
Methods: We use the results of a mathematical model, which determines individual optimal HCV treatment policies as an input for constructing a patient prioritization rule, when limited resources are present. The prioritization is based on marginal analysis on cost increases and health-outcome gains. We construct the Chilean and Spanish case studies and used Monte Carlo simulation to evaluate the performance of our methodology in these two scenarios.
Results: The resulting prioritizations for the Chilean and Spanish patients are similar, despite the significant differences of both countries, in terms of epidemiological profiles and cost structures. Furthermore, when resources are scarce compared with the number of patients in need of the new drug, our prioritization significantly outperforms current practices of treating sicker patients first, both in terms of cost and healthcare indicators: for the Chilean case, we have an increase in the quality-adjusted life years (QALYs) of 0.83 with a cost reduction of 8176 euros per patient, with a budget covering 2.5% of the patients in the cohort. This difference slowly decreases when increasing the available resources, converging to the performance indicators obtained when all patients are treated immediately: for the Spanish case, we have a decrease in the QALYs of 0.17 with a cost reduction of 1134 euros per patient, with a budget covering 20% of the patients in the cohort.
Conclusion: Decision science can provide useful analytical tools for designing efficient public policies that can excel in terms of quantitative health performance indicators.