Background Clinical trials and individual-level observational data in Israel demonstrated approximately 95% effectiveness of mRNA-based vaccines against symptomatic SARS-CoV-2 infection. Individual-level data are not available in many countries, particularly low- and middle- income countries. Using a novel Poisson regression model, we analyzed ecologic data in Costa Rica to estimate vaccine effectiveness and assess the usefulness of this approach. Methods We used national data from December 1, 2020 to May 13, 2021 to ascertain incidence, hospitalizations and deaths within ecologic units defined by 14 age groups, gender, 105 geographic areas, and day of the epidemic. Within each unit we used the proportions of the population with one and with two vaccinations, primarily tozinameran. Using a non-standard Poisson regression model that included an ecologic-unit-specific rate factor to describe rates without vaccination and a factor that depended on vaccine effectiveness parameters and proportions vaccinated, we estimated vaccine effectiveness. Results In 3.621 million persons aged 20 or older, there were 125,031 incident cases, 7716 hospitalizations, and 1929 deaths following SARS-CoV-2 diagnosis; 73% of those aged ≥ 75 years received two doses. For one dose, estimated effectiveness was 59% (95% confidence interval 53% to 64%) for SARS-CoV-2 incidence, 76% (68% to 85%) for hospitalizations, and 63% (47% to 80%) for deaths. For two doses, the respective estimates of effectiveness were 93% (90% to 96%), 100% (97% to 100%), and 100% (97% to 100%). Conclusions These effectiveness estimates agree well with findings from clinical trials and individual-level observational studies and indicate high effectiveness in the general population of Costa Rica. This novel statistical approach is promising for countries where ecologic, but not individual-level, data are available. The method could also be adapted to monitor vaccine effectiveness over calendar time.

Variability in household secondary attack rates (SAR) and transmission risks factors of SARS-CoV-2 remain poorly understood. To characterize SARS-CoV-2 transmission in a household setting, we conducted a household serologic study of SARS-CoV-2 in Costa Rica, with SARS-CoV-2 index cases selected from a larger prospective cohort study and their household contacts were enrolled. A total of 719 household contacts of 304 household index cases were enrolled from November 21, 2020, through July 31, 2021. Demographic, clinical, and behavioral information was collected from the index cases and their household contacts. Blood specimens were collected from contacts within 30-60 days of index case diagnosis; and serum was tested for presence of spike and nucleocapsid SARS-CoV-2 IgG antibodies. Evidence of SARS-CoV-2 prior infections among household contacts was defined based on the presence of both spike and nucleocapsid antibodies. To avoid making strong assumptions that the index case was the sole source of infections among household contacts, we fitted a chain binomial model to the serologic data, which allowed us to account for exogenous community infection risk as well as potential multi-generational transmissions within the household. Overall seroprevalence was 53% (95% confidence interval (CI) 48% – 58%) among household contacts The estimated household secondary attack rate (SAR) was 32% (95% CI 5% – 74%) and the average community infection risk was 19% (95% CI 14% - 26%). Mask wearing by the index case was associated with the household transmission risk reduction by 67% (adjusted odds ratio = 0.33 with 95% CI: 0.09-0.75) and sleeping in a separate bedroom from the index case reduced the risk of household transmission by 78% (adjusted odds ratio = 0.22 with 95% CI 0.10-0.41). The estimated distribution of household secondary attack rates was highly heterogeneous across index cases, with 30% of index cases being the source for 80% of secondary cases. Modeling analysis suggests behavioral factors were important drivers of the observed SARS-CoV-2 transmission heterogeneity within the household.

Aim: To establish an initial approach to identify individual and group variables that determine adequate interventions on diabetic patients by the Basic Integral Healthcare Units of the Costa Rican social security system.  Methods: The study design is non-experimental, cross-sectional and applies multi-level logistic regression. The data were obtained from a sample chosen from the Health Services Purchasing Direction, and includes diabetic patients cared for at local levels from January to December 2004. The information was used at 2 levels of analysis: level 1 (individual) and level 2 (group), and glycosylated hemoglobin was used as a dependent variable. Results: Forty-nine percent of patients were controlled, with a median age above 60 years, 66% were women, and 76,6% had a body mass index reflecting overweight or obesity. On average, the Basic Integral Healthcare Units in the study had 7 years since initiating the reform process, and their average score on the “Commitment of Management” for the last 5 years was 87%. Moreover, the average population covered was 40 thousand inhabitants, and 22% of them had high school education. Multilevel logistic regression revealed that as diabetic patients age the probability of achieving control of the disease also increases. Women had a lower probability of being under control as compared to men. Diabetic patients belonging to the Basic Integral Healthcare Units that initiated their reform earlier, and those belonging to health services achieving higher scores on the “Commitment of Management” had a higher probability of being under control, even though this finding was not statistically significant, with respect to the other variables in the model. Discussion: Approximately between 6% and 10% of the variance in the control of diabetic patients is explained by differences within local health services, after controlling for other intervening variables