Strengthening Essential Health Services Recovery Policy after the COVID-19 Pandemic. Evidence from the Mexican Institute of Social Security.

AIM: To evaluate the progress of the Mexican Institute of Social Security Recovery Policy (IMSS-RP) in addressing the decline in essential health services caused by the COVID-19 pandemic. METHODS: We analyzed eleven indicators of essential health services from 35 IMSS state delegations. The assessment included ambulatory and hospital care indicators such as breast and cervical cancer screening, family medicine, dental and specialty visits, diabetes and hypertension visits and health outcomes, deliveries, and elective surgeries. We analyzed the period before (January 2018-March 2021) and during (April 2021-June 2023) the implementation of the IMSS-RP. Statistical analysis to determine the association of the policy with service indicators and the change in their trends included an interrupted time series analysis and Poisson Generalized Estimating Equation models. RESULTS: The volume of services showed substantial declines during the first year of the COVID-19 pandemic, reaching between 11 and 81% of pre-pandemic levels. All services increased significantly during the first 27 months of the IMSS-RP implementation; specialty visits, cervical and breast cancer screening, and diabetes control exceeded pre-pandemic levels (103%,112%,103%, and 138%, respectively). However, only deliveries and the percentage of patients with controlled diabetes and hypertension showed a stable increase following the IMSS-RP implementation, whereas the remaining services showed an initial increase but began to decrease over time. CONCLUSIONS: After 27 months of implementation, IMSS-RP achieved progress in increasing the volume of essential health services and improving chronic disease control. However, declining trends in several services signal the need to focalize the policy.

A Geospatial Model to Identify Areas Associated with Late- Stage Breast Cancer: A Spatial Epidemiology Approach.

OBJECTIVE: The aim of this study was to show how a geospatial model can be used to identify areas with a higher probability for late-stage breast cancer (BC) diagnoses. METHODS: Our study considered an ecological design. Clinical records at a tertiary care hospital were reviewed in order to obtain the place of residence and stage of the disease, which was classified as early (0-IIA) and late (IIB-IV) and whose diagnoses were made during the 2013-2017 period. Then, they were geolocated to identify the distribution and spatial trend. Subsequently, the pattern of location, i.e. scattered, random and concentrated, was statistically assessed and a geospatial model was elaborated to determine the probability of late diagnoses in the state of Jalisco, Mexico. RESULT: There were 1 954 (N) geolocated BC diagnoses: 58.3% were late. During the five-year period, a southwest-northeast trend was identified, nearly 9.5% of the surface of Jalisco, where 6 out of 10 (n= 751) late- stage diagnoses were concentrated. A concentrated and statistically significant pattern was identified in the southern, central and northern Pacific area of Jalisco, where the geospatial model delimited the places with the highest probability of late clinical stages (p <0.05). CONCLUSION: The geographical differences associated with the late diagnoses of BC suggest it is necessary to adapt and focus the strategies for early detection as an alternative to create a major impact on the population. Reproducible analysis tools were used in other contexts where geolocation data are available to complement public policies and strategies aimed to control BC.