Effects of underlying morbidities on the occurrence of deaths in COVID-19 patients: A systematic review and meta-analysis

Abstract

Background Coronavirus disease 2019 (COVID-19), the most hectic pandemic of the era, is increasing exponentially and taking thousands of lives worldwide. This study aimed to assess the prevalence of pre-existing comorbidities among COVID-19 patients and their mortality risks with each category of pre-existing comorbidity. Methods To conduct this systematic review and meta-analysis, Medline, Web of Science, Scopus, and CINAHL databases were searched using pre-specified search strategies. Further searches were conducted using the reference list of the selected studies, renowned preprint servers (eg, medRxiv, bioRxiv, SSRN), and relevant journals’ websites. Studies written in the English language included if those were conducted among COVID-19 patients with and without comorbidities and presented survivor vs non-survivor counts or hazard/odds of deaths or survivors with types of pre-existing comorbidities. Comorbidities reported in the selected studies were grouped into eight categories. The pooled likelihoods of deaths in each category were estimated using a fixed or random-effect model, based on the heterogeneity assessment. Publication bias was assessed by visual inspection of the funnel plot asymmetry and Egger’s regression test. Trim and Fill method was used if there any publication bias was found. Results A total of 41 studies included in this study comprised of 27 670 samples. The most common pre-existing comorbidities in COVID-19 patients were hypertension (39.5%), cardiovascular disease (12.4%), and diabetes (25.2%). The higher likelihood of deaths was found among COVID-19 patients who had pre-existing cardiovascular diseases (odds ratio (OR) = 3.42, 95% confidence interval (CI) = 2.86-4.09), immune and metabolic disorders (OR = 2.46, 95% CI = 2.03-2.85), respiratory diseases (OR = 1.94, 95% CI = 1.72-2.19), cerebrovascular diseases (OR = 4.12, 95% CI = 3.04-5.58), any types of cancers (OR = 2.22, 95% CI = 1.63-3.03), renal (OR = 3.02, 95% CI = 2.60-3.51), and liver diseases (OR = 2.35, 95% CI = 1.50-3.69). Conclusions This study provides evidence that COVID-19 patients with pre-existing comorbidities had a higher likelihood of death. These findings could potentially help health care providers to sort out the most susceptible COVID-19 patients by comorbidities, take precautionary measures during hospitalization, assess susceptibility to death, and prioritize their treatment, which could potentially reduce the number of fatalities in COVID-19.

Characteristics Associated With Racial/Ethnic Disparities in COVID-19 Outcomes in an Academic Health Care System

“… 54 Similarly, health conditions, such as obesity, cancer, type 2 diabetes, and renal conditions, were prevalent among patients with worse COVID-19 outcomes. 34 , 45 , 46 , 55 Notably, our findings largely agree with recent published work examining racial/ethnic differences in COVID-19 outcomes, which found Black patients had a higher hospitalization rate, 8 increased odds of positive test results, 12 and disproportionately high COVID-19 diagnosis rate 11 compared with White patients. Similar directional results but different strength of association with socioeconomic status variables are likely because we used a continuous metric as opposed to the categorical measures used in Price-Haywood et al 14 Moreover, we also identified type 2 diabetes and kidney disease as risk factors associated hospitalization.…”

“…Based on Centers for Disease Control and Prevention guidelines on risk factors for COVID-19 44 and previous studies, 34 , 45 , 46 we constructed COVID-19–related comorbid conditions using available International Classification of Diseases, Ninth Revision and Tenth Revision codes for 12 036 individuals (tested or diagnosed: 5225 individuals; untested comparison group: 6811 individuals) from their EHRs. Longitudinal time-stamped diagnoses were recoded as indicator variables for whether a patient ever had a given diagnosis code recorded by MM.…”

Asthma and COVID-19: What do we know now

“…5,6 This was reciprocated in a systematic review of n = 12 760 individuals by Khan et al, which found 1.4% (n = 355) of their COVID-19 positive cohort had asthma as a comorbidity. 7 Other studies worldwide have demonstrated an increased prevalence of asthma in COVID-positive individuals and have suggested that the lower prevalence of asthma reported within some studies could be attributed to underreporting, underdiagnosis or poor recognition of chronic respiratory disease in patients with COVID-19 infection. 8 Additionally, another explanation for the differing rates of comorbid asthma seen among studies may be due to the overall differences in rates of comorbidities including asthma in different countries.…”

Numerical simulation of the force of infection and the typical times of SARS-CoV-2 disease for different location countries

“…Recently, the model and the modified models were used for forecasting and spreading of the new coronavirus disease where (Malavika et al 2020 ) used SIR and logistic growth models for the forecasting of COVID-19 epidemic in India and high incidence states, (Al-Raeei 2020a , b ) found the basic reproduction number values of the disease for multiple countries, (Lifshits et al 2020 ) studied the forecasting of the pandemic in Russia, (Roy et al 2020 ) discussed the forecasting of the disease in India using ARIMA (autoregressive integrated moving average) model and the same model was applied for Russia by Fang et al ( 2020 ), Rejaur Rahman et al ( 2020 ) studied the forecasting of the disease in Bangladesh with geospatial modelling, Gupta et al ( 2020 ) discussed the effect of the geographical locations in India on the disease, Santosh ( 2020 ) discussed the prediction models with unexploited data of the disease, Neto et al . ( 2020 ) showed the new coronavirus industrial impact with the fourth industrial revolution, Aabed et al ( 2020 ) discussed the analytical study of the forecasting factors on the spread of the disease, Ali et al ( 2020 ) discussed the effects of the PM 2.5 on the spreading of the disease, Khan et al ( 2020 ) discussed the effects of underlying morbidities on the occurrence of deaths in COVID-19 patients, Al-Raeei ( 2020a , b ) discussed the forecasting of the disease for multiple countries with mortality and Bhadra et al ( 2020 ) discussed the effects of population density on the infection and the mortality of the disease. In this work, we use the SEIR epidemic-model for simulating force of infection of the new coronavirus disease values for multiple countries.…”