The aim of this work was to develop an indicator that estimates the population's access to imaging services (IS) of health care facilities in the Costa Rican Public Health System, taking into account five aspects: (a) health care facilities infrastructure, (b) capacity of the IS according to the installed technological resources, (c) epidemiological aspects, (d) demographic aspects, and (e) location of the health facility in relation to the Capital. To achieve this, 14 variables and 7 indicators were defined. These indicators were integrated into a mathematical function which resulted in a global indicator that throws quantitative data that represents the level of access of a population to the IS in their geographic region of influence. The application of the indicator was performed in eight health facility sub-networks with defined geographical regions within the territory of Costa Rica. The level of access to the imaging service of the inhabitants of the eight subnetworks results that three of them obtained a bad access, located in east and north-central of the country; other three sub-networks obtained regular access, located in the west; and the last two who obtained a good access are located in the center of the country. The results showed that the imaging services are not equitable in each sub-network. Knowing this, it is possible to work on healthcare technology management proposals in order to strengthen the regional imaging services, contributing to decentralizing the services of the general hospitals located in the Capital.

There is a need to develop methods to evaluate public health interventions. Therefore, this work proposed an intervention analysis on time series of breast cancer mortality rates to assess the effects of an action of the Brazilian Screening Programme. Methods: The analysed series was the monthly female breast cancer mortality rates from January 1996 to March 2016. The intervention was the establishment of the National Information System on Breast Cancer in June 2009. The Box-Tiao approach was used to build a Global Intervention Model (GIM) composed of a component that fits the series without the intervention, and a component that fits the effect with the intervention. The intervention's response time was estimated and used to define the length of the residual series to assess the predictive accuracy of the GIM, which was compared to a one-step-ahead forecasting approach. Results: The pre-intervention period was fitted to a SARIMA (0,1,2) (1,1,1)12 model and the intervention's effect to an ARIMA (1,1,0) model. The intervention led to an increase in the mortality rates, and its response time was 24 months. The forecast error (MAPE) for the GIM was 3.14%, and for the one-step-ahead forecast it was 2.15%. Conclusion: This work goes one step further in relation to the studies carried out to evaluate the Breast Cancer Screening Programme in Brazil, considering that it was possible to quantify the effects and the response time of the intervention, demonstrating the potential of the proposed method to be used to evaluate health interventions.

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