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Introducción: Una nueva intervención de salud pública, como la introducción de una vacuna, implica el monitoreo de indicadores que aseguren una intervención efectiva, y que exista la posibilidad de cuantificar sus beneficios. Obtener estimaciones precisas del impacto de una intervención de salud se considera un desafío importante. Objetivos: Estimar el impacto causal de uno de los productos líderes del Instituto Finlay de La Habana: la vacuna cubana registrada y comercializada VA-MENGOC-BC®. Métodos: Se seleccionaron datos en los anuarios estadísticos de salud desde 2009 hasta 2017. Se usaron como variable de interés, la tasa de incidencia de la enfermedad meningocócica en Cuba ( 100 000 habitantes y un conjunto de covariables que no se afectaron por la intervención: tasas de incidencia de fiebre tifoidea, de incidencia de tuberculosis, de mortalidad por enfermedades infecciosas (parasitarias e intestinales) todas ( 100 000 habitantes. Se consideró 1989 como año de la intervención. Se aplicó el método bayesiano de series temporales estructurales, que evaluó el impacto causal de la vacunación sostenida con VA-MENGOC-BC® desde 1989 hasta el presente. Resultados: Se estimó un impacto causal acumulativo significativo en la reducción de la incidencia de la enfermedad meningocócica. Se verificó que se produjo una disminución de la enfermedad en 97,2 %. Conclusiones: La aplicación del método de series de tiempo estructural bayesina para estimar el impacto de la vacuna VA-MENGOC-BC®, constituyó una herramienta novedosa para evaluar el contrafactual. Se proporcionó una apreciación del impacto de la vacunación con VA-MENGOC-BC®, una vacuna implementada y reconocida a nivel mundial.

Introduction: A new public health intervention, such as the introduction of a vaccine, implies monitoring the indicators that guarantee its effectiveness, and the possibility of quantifying its benefits. Obtaining accurate estimates of the impact of a health intervention is considered a major challenge. Objective: To estimate the causal impact of one of the leading products of the Finlay Institute in Havana: the registered and marketed Cuban vaccine VA-MENGOC-BC®. Methods: Data from the health statistics yearbooks from 2009 to 2017 were selected. The incidence rate of the meningococcal disease in Cuba per 100 000 population and a set of co-variables that were not affected by the intervention, such as incidence rate of typhoid fever, tuberculosis, and fatality cases due to infectious diseases (parasitic or intestinal) per 100 000 population were used as variables of interest. The intervention year was 1989. The Bayesian structural time series model was applied to evaluate the causal impact of the continued vaccination with VA-MENGOC-BC® from 1989 to date. Results: A significant cumulative causal impact in reducing the incidence of meningococcal disease was estimated. A decrease of 97.2% in the disease was verified. Conclusions: The application of the Bayesian structural time series model to estimate the impact of the vaccine VA-MENGOC-BC® was a novel tool to estimate the counterfactual. It was provided an estimate of the impact of the vaccination with VA-MENGOC-BC®, an implemented and globally well-known vaccine.

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Background: Non-pharmaceutical interventions (NPIs) against COVID-19 may prevent the spread of other infectious diseases. Our purpose was to assess the effects of NPIs against COVID-19 on infectious diarrhea in Xi'an, China. Methods: Based on the surveillance data of infectious diarrhea, and the different periods of emergence responses for COVID-19 in Xi'an from 2011 to 2021, we applied Bayesian structural time series model and interrupted time series model to evaluate the effects of NPIs against COVID-19 on the epidemiological characteristics and the causative pathogens of infectious diarrhea. Findings: A total of 102,051 cases of infectious diarrhea were reported in Xi'an from 2011 to 2021. The Bayesian structural time series model results demonstrated that the cases of infectious diarrhea during the emergency response period was 40.38% lower than predicted, corresponding to 3,211 fewer cases, during the COVID-19 epidemic period of 2020-2021. The reduction exhibited significant variations in the demography, temporal and geographical distribution. The decline in incidence was especially evident in children under 5-years-old, with decreases of 34.09% in 2020 and 33.99% in 2021, relative to the 2017-2019 average. Meanwhile, the incidence decreased more significantly in industrial areas. Interpretation: NPIs against COVID-19 were associated with short- and long-term reductions in the incidence of infectious diarrhea, and this effect exhibited significant variations in epidemiological characteristics.

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Enterovirus A71 (EV71) vaccination program was introduced in 2016 in China. Based on a longitudinal surveillance dataset from 2012 to 2019 in Guangdong, China, we estimated the impact of the EV71 vaccination program on hand, foot, and mouth disease (HFMD) incidence, by using a counterfactual prediction made from synthetic control approach integrated with a Bayesian time-series model. We observed a relative reduction of 41.4% for EV71-associated HFMD cases during the post-vaccination period of 2017-2019, corresponding to 26,226 cases averted. The reduction of EV71-associated HFMD cases raised with the elevation of EV71 vaccine coverage by year. We found an indirect effect for the children aged 6-14 years who were less likely to be vaccinated. Whereas, the EV71 vaccine may not protect against non-EV71-associated HFMD. This study provides a template for ongoing public health surveillance of EV71 vaccine effectiveness with a counterfactual study design. Our results show strong evidence of the EV71 vaccination program working on reducing EV71-associated HFMD in real-world settings. The finding will benefit policy-making of EV71 vaccination and the prevention of HFMD.

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Although long-term ecosystem monitoring provides essential knowledge for practicing ecosystem management, analyses of the causal effects of ecological impacts from large-scale observational data are still in an early stage of development. We used causal impact analysis (CIA)-a synthetic control method that enables estimation of causal impacts from unrepeated, long-term observational data-to evaluate the causal impacts of extreme water-level drawdowns during summer on subsequent water quality. We used more than 100 years of transparency and water level monitoring data from Lake Biwa, Japan. The results of the CIA showed that the most extreme drawdown in recorded history, which occurred in 1994, had a significant positive effect on transparency (a maximum increase of 1.75 m on average over the following year) in the north basin of the lake. The extreme drawdown in 1939 was also shown to be a trigger for an increase in transparency in the north basin, whereas that in 1984 had no significant effects on transparency. In the south basin, contrary to the pattern in the north basin, the extreme drawdown had a significant negative effect on transparency shortly after the extreme drawdown. These different impacts of the extreme drawdowns were considered to be affected by the timing and magnitude of the extreme drawdowns and the depths of the basins. Our approach of inferring the causal impacts of past events on ecosystems will be helpful in implementing water-level management for ecosystem management and improving water quality in lakes.

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