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suck abstract from ncbi


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pmid33927179      Rev+Esp+Salud+Publica 2021 ; 95 (ä): ä
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  • COVID-19 y genero: certezas e incertidumbres en la monitori-zacion de la pandemia #MMPMID33927179
  • Martin U; Bacigalupe A; Jimenez Carrillo M
  • Rev Esp Salud Publica 2021[Apr]; 95 (ä): ä PMID33927179show ga
  • OBJECTIVE: Highlighting gender inequalities during the pandemic and its relationship with other axes of social inequality will be decisive for its adequate monitoring. The aim of this study was to assess the differences between men and women in the main measures of infection and mortality by COVID-19, considering its temporal evolution, raising awareness about the weaknesses and contradictions between sources of information. METHODS: Cross-sectional analysis based on the microdata on COVID cases notified by the National Epidemiological Surveillance Network (RENAVE), the Death Statistics of the National Statistical Institute (INE) and the estimates of excess mortality from the INE and the Daily Mortality Monitoring System (MoMo) microdata. Standardized rates, prevalences and and ratios by sex were calculated for each indicator. The percentage of excess mortality without COVID-19 diagnosis in each sex was calculated. Male/female ratios for symptoms and risk factors of COVID-19 were also calculated. RESULTS: The rate of infection by COVID-19 was higher in women in the three waves of the pandemic, reaching 65% of infections during April and May 2020. Complications were between 1.5 and 2.5 times higher in men, especially in ICU admissions, which were 2.5 times more frequent than in women. Although mortality rates and excess mortality were also higher in men (around 1.8 times), the percentage of excess mortality without COVID-19 diagnosis was higher in women (44% in men vs. 52% in women the first wave). With regard to the symptoms of COVID-19, fever, cough, and dyspnoea were more frequent in men (20%, 10% and 19% more, respectively) compared to sore throat, vomiting or diarrhea that were more prevalent in women (90%, 40% and 10% more, respectively). CONCLUSIONS: The analysis disaggregated by sex has made it possible to identify differences between men and women in the diagnosis, presentation and severity of the COVID-19 that can help a better clinical and epidemiological approach to the disease. However, official sources present important gaps when presenting information disaggregated by sex. It is therefore necessary to advance in the inclusion of a gender perspective in the statistics on COVID-19, starting with a necessary but not sufficient condition such as the disaggregation by sex of the data.
  • |*Health Status Disparities[MESH]
  • |*Pandemics[MESH]
  • |Adult[MESH]
  • |Aged[MESH]
  • |Aged, 80 and over[MESH]
  • |COVID-19 Testing[MESH]
  • |COVID-19/diagnosis/*epidemiology/etiology[MESH]
  • |Cross-Sectional Studies[MESH]
  • |Female[MESH]
  • |Humans[MESH]
  • |Male[MESH]
  • |Middle Aged[MESH]
  • |Prevalence[MESH]
  • |Risk Factors[MESH]
  • |Severity of Illness Index[MESH]
  • |Sex Distribution[MESH]
  • |Sex Factors[MESH]


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