BACKGROUND: The COVID-19 pandemic revealed the complex relationship between pre-existing health conditions, age, and disease severity. Comorbidities emerged as key determinants of clinical outcomes, including hospitalization, ICU admission, and mechanical ventilation. Understanding these dynamics is vital for effective public health strategies and optimizing patient care.METHOD: This retrospective observational study analyzed a publicly available dataset of 1,048,575 individuals in Mexico to evaluate associations between pre-existing conditions and severe COVID-19 outcomes. Logistic regression and Chi-square tests were used to assess the impact of age (categorized into eight groups from 0 -10 to 70+), comorbidities, hospitalization, ICU admission, and intubation.RESULTS: Prevalent comorbidities among diagnosed COVID-19 patients included hypertension (15.5%), obesity (15.2%), Pneumonia (13.4%), and diabetes (11.9%). Logistic regression analysis revealed that pneumonia (OR = 1.853), obesity (OR = 1.312), diabetes (OR = 1.143), and hypertension (OR = 1.037) were significantly associated with an increased risk of infection (p < 0.001). Conversely, COPD (OR = 0.691), asthma (OR = 0.896), immunosuppression (OR = 0.702), and cardiovascular conditions (OR = 0.757) were associated with a lower likelihood of COVID-19 diagnosis. Age demonstrated a non-linear association with infection severity, with risk gradually increasing from ages 1 to 70, peaking in the 61-70 group (Exp(B) = 5.815) and then declining in individuals over 70 (Exp(B) = 5.518). Chi-square analysis confirmed strong associations between comorbidities and severe outcomes, particularly for pneumonia, obesity, diabetes, and hypertension, which were linked to increased ICU admissions and intubation.CONCLUSION: Pneumonia, obesity, diabetes, and hypertension significantly contributed to severe COVID-19 outcomes. Middle-aged individuals are more susceptible to this condition than older adults. Although these comorbidities are key risk factors, their moderate effect sizes suggest that other clinical and sociodemographic factors also influence severity. These findings support the need for targeted prevention, early intervention, and comprehensive risk assessment to better protect vulnerable populations in current and future pandemics.
Sandhya Bohini (Thu,) studied this question.