Résumé
Background: To reduce the severity of the disease among COVID-19 patients with co-morbidities is the need of the hour. Obesity may contribute to adverse outcomes in coronavirus disease 2019 (COVID-19). This study aimedto find the association between the COVID-19 biomarker values and the underlying comorbid conditions and assess the severity of the disease according to the patients' body mass index (BMI).Methods: A single centre cross-sectional study was conducted in a tertiary hospital among 184 COVID-19 patients admitted for one month (June-July 2021).Results: The results show a statistically significant association between the COVID-19 severity and co-morbidity status of the patients, with diabetes mellitus being the most prevalent co-morbidity among the patients, followed by diabetes with hypertension. A statistically significant association was also seen between age and co-morbidity and biomarkers and co-morbidity. Conclusions: Obesity and other comorbid conditions like diabetes mellitus and hypertension should be given utmost importance during treatment among COVID-19 patients. Biomarker screening should be routinely done in patients with co-morbidities and obesity. Awareness among the COVID-19 patients regarding the comorbid conditions and BMI is the need of the hour.
Résumé
Background: The correlation between income inequality and life expectancy was demonstrated 10 years ago, but later, several studies suggested that the negative impact of a low economic income on the health status was disappearing. Aim: To assess the independent effects of community income inequality on self rated health in Chile. Material and methods : Multilevel analysis of the 2000 National Socio Economic Characterization Survey (CASEN) data from Chile. Individual level information included self rated health, age, sex, ethnicity, marital status, education, income, type of health insurance and residential setting (urban/rural). Community level variables included the Gini coefficient and median income. The main outcome measure was dichotomized self rated health (0 if excellent, very good or good; 1 if fair or poor). Results: 101,374 individuals (at level 1) aged 18 and above, nested within 285 communities (at level 2) and 13 regions (at level 3) were studied. Controlling for a range of individual level predictors, a significant gradient was observed between income and poor self rated health, with very poor most likely to report poor health (10.5 percent) followed by poor (9.5 percent) low (9 percent) middle (7 percent), high (6 percent) and very high (4.5 percent) income earners. Controlling for individual and community effects of income, a significant non linear effect of community income inequality was observed, with the most unequal communities being associated with approximately 5 percent higher likelihood of reporting poor health compared to the most equal communities. Conclusions : Individual income does not explain any of the between community differences and neither does it wash the significant effects of income inequality on poor self rated health. The contextual effect of inequality is almost as large as the differential observed in poor health comparing the very poor to the very rich individual income categories