RESUMEN
Linking records of the same person from different sources makes it possible to build administrative cohorts and perform longitudinal analyzes, as an alternative to traditional cohort studies, and have important practical implications in producing knowledge in public health. We implemented the Fellegi-Sunter probabilistic linkage method to a sample of records from the Mexican Automated System for Hospital Discharges and the Statistical and Epidemiological System for Deaths and evaluated its performance. The records in each source were randomly divided into a training sample (25%) and a validation sample (75%). We evaluated different types of blocking in terms of complexity reduction and pairs completeness, and record linkage in terms of sensitivity and positive predictive value. In the validation sample, a blocking scheme based on trigrams of the full name achieved 95.76% pairs completeness and 99.9996% complexity reduction. After pairs classification, we achieved a sensitivity of 90.72% and a positive predictive value of 97.10% in the validation sample. Both values were about one percentage point higher than that obtained in the automatic classification without clerical review of potential pairs. We concluded that the linkage algorithm achieved a good performance in terms of sensitivity and positive predictive value and can be used to build administrative cohorts for the epidemiological analysis of populations with records in health information systems.
RESUMEN
Background: The death toll after SARS-CoV-2 emergence includes deaths directly or indirectly associated with COVID-19. Mexico reported 325,415 excess deaths, 34.4% of them not directly related to COVID-19 in 2020. In this work, we aimed to analyse temporal changes in the distribution of the leading causes of mortality produced by COVID-19 pandemic in Mexico to understand excess mortality not directly related to the virus infection. Methods: We did a longitudinal retrospective study of the leading causes of mortality and their variation with respect to cause-specific expected deaths in Mexico from January 2020 through December 2021 using death certificate information. We fitted a Poisson regression model to predict cause-specific mortality during the pandemic period, based on the 2015-2019 registered mortality. We estimated excess deaths as a weekly difference between expected and observed deaths and added up for the entire period. We expressed all-cause and cause-specific excess mortality as a percentage change with respect to predicted deaths by our model. Findings: COVID-19 was the leading cause of death in 2020-2021 (439,582 deaths). All-cause total excess mortality was 600,590 deaths (38â 2% [95% CI: 36·0 to 40·4] over expected). The largest increases in cause-specific mortality, occurred in diabetes (36·8% over expected), respiratory infections (33·3%), ischaemic heart diseases (32·5%) and hypertensive diseases (25·0%). The cause-groups that experienced significant decreases with respect to the expected pre-pandemic mortality were infectious and parasitic diseases (-20·8%), skin diseases (-17·5%), non-traffic related accidents (-16·7%) and malignant neoplasm (-5·3%). Interpretation: Mortality from COVID-19 became the first cause of death in 2020-2021, the increase in other causes of death may be explained by changes in the health service utilization patterns caused by hospital conversion or fear of the population using them. Cause-misclassification cannot be ruled out. Funding: This study was funded by Conacyt.
RESUMEN
En el sector salud, las brechas entre la generación del conocimiento científico y su uso para la toma de decisiones comprometen la calidad de la atención brindada. Estas brechas se han relacionado con diferentes factores, entre ellos el capital social. Este trabajo explora el papel del capital social en el uso del conocimiento en el sector salud. Para hacerlo se recurrió a una revisión de la literatura de los últimos veinte años. Los hallazgos reportaron que las tres dimensiones del capital social, es decir, el capital estructural, el capital relacional y el capital cognitivo, se han relacionado teóricamente con la gestión y el uso del conocimiento y que el capital social se considera un predictor significativo de la práctica basada en evidencias. Se concluye que fomentar la confianza y los lazos de comunicación entre los empleados, así como buscar relaciones con investigadores, potencian el uso de las evidencias científicas en la toma de decisiones. Estas acciones se traducen en beneficios para los usuarios finales del sistema de salud(AU)
In the health sector, gaps persist between scientific knowledge´s production and its use for decision-making; and these gaps compromise the quality of provided care. These gaps have been related to different factors, including social capital. This paper explores the role of social capital in the use of knowledge in the health sector. To do so, a review of the literature of the last twenty years was conducted. Among the main findings, three dimensions of social capital (i.e. structural capital, relational capital and cognitive capital) have been theoretically related to knowledge use and management; and social capital is highlighted as a significant predictor of evidence-based practice. Encouraging trust and strengthening communication links among employees, as well as seeking relationships with researchers have the potential to foster scientific knowledge use in decision-making. These actions can be translated into benefits for final health system´s users(AU)
