Unusually High Risks of COVID-19 Mortality with Age-Related Comorbidities: An Adjusted Meta-Analysis Method to Improve the Risk Assessment of Mortality Using the Comorbid Mortality Data

Abstract

Background: The pandemic of Coronavirus Disease 2019 (COVID-19) has been a threat to global health. In the US, the Centers for Disease Control and Prevention (CDC) has listed 12 comorbidities within the first tier that increase with the risk of severe illness from COVID-19, including the comorbidities that are common with increasing age (referred to as age-related comorbidities) and other comorbidities. However, the current method compares a population with and without a particular disease (or disorder), which may result in a bias in the results. Thus, comorbidity risks of COVID-19 mortality may be underestimated.

Objective: To re-evaluate the mortality data from the US and estimate the odds ratios of death by major comorbidities with COVID-19, we incorporated the control population with no comorbidity reported and assessed the risk of COVID-19 mortality with a comorbidity.

Methods: We collected all the comorbidity data from the public health websites of fifty US States and Washington DC (originally accessed on December 2020). The timing of the data collection should minimize bias from the COVID-19 vaccines and new COVID-19 variants. The comorbidity demographic data were extracted from the state public health data made available online. Using the inverse variance random-effects model, we performed a comparative analysis and estimated the odds ratio of deaths by COVID-19 with pre-existing comorbidities.

Results: A total of 39,451 COVID-19 deaths were identified from four States that had comorbidity data, including Alabama, Louisiana, Mississippi, and New York. 92.8% of the COVID-19 deaths were associated with a pre-existing comorbidity. The risk of mortality associated with at least one comorbidity combined was 1113 times higher than that with no comorbidity. The comparative analysis identified nine comorbidities with odds ratios of up to 35 times higher than no comorbidities. Of them, the top four comorbidities were: hypertension (odds ratio 34.73; 95% CI 3.63-331.91; p = 0.002), diabetes (odds ratio 20.16; 95% CI 5.55-73.18; p < 0.00001), cardiovascular disease (odds ratio 18.91; 95% CI 2.88-124.38; p = 0.002), and chronic kidney disease (odds ratio 12.34; 95% CI 9.90-15.39; p < 0.00001). Interestingly, lung disease added only a modest increase in risk (odds ratio 6.69; 95% CI 1.06-42.26; p < 0.00001).

Conclusion: The aforementioned comorbidities show surprisingly high risks of COVID-19 mortality when compared to the population with no comorbidity. Major comorbidities were enriched with pre-existing comorbidities that are common with increasing age (cardiovascular disease, diabetes, and hypertension). The COVID-19 deaths were mostly associated with at least one comorbidity, which may be a source of the bias leading to the underestimation of the mortality risks previously reported. We note that the method has limitations stemming primarily from the availability of the data. Taken together, this type of study is useful to approximate the risks, which most likely provide an updated awareness of age-related comorbidities.

Keywords: COVID-19; age-related comorbidity; mortality; risk assessment.

Figure 1

A flow diagram of this study, originally accessed on June 2020. The PRISMA template for researchers [5] was modified and used.

Figure 2

A total number of COVID-19 deaths first separated by state and then by comorbidity. A list of COVID-19 deaths was identified from a total of four US states in 2020—Alabama, Louisiana, Mississippi, and New York.

Figure 3

Summary of the mortality risks by COVID-19 comorbidities.

Figure 4

Assessment of the risk of bias using ROBINS-1. Shown are the traffic light plot (above) and the summary plot (below). We used comorbidity as an intervention and death as an outcome. No information (Blue) is not applicable to this study. We used a visualization tool as described in [17].

Figure 5

Meta-analysis of the comorbidities with COVID-19. (A) Cardiovascular disease (CVD). This odds ratio includes atrial fibrillation, coronary artery disease, hypertension, and stroke from New York’s comorbidity data. (B) Chronic kidney disease (CKD). (C) Diabetes. (D) Hypertension. (E) Immunocompromised condition. (F) Liver disease. (G) Lung disease. This odds ratio includes chronic lung disease from Alabama’s data and COPD from New York’s data. (H) Neurological Disease. This odds ratio includes Alzheimer’s disease and stroke data from New York. (I) Obesity. (J) Renal disease. This odds ratio includes CKD data from Alabama and Louisiana. “Total” columns in the model represent the numerical amount of the total deaths from COVID-19. The “Events” column on the left represents the number of deaths that included the cardiovascular disease comorbidity in that respective state. The “Events” column on the right represents the number of deaths without a comorbidity in that respective state.

Figure 5

Meta-analysis of the comorbidities with COVID-19. (A) Cardiovascular disease (CVD). This odds ratio includes atrial fibrillation, coronary artery disease, hypertension, and stroke from New York’s comorbidity data. (B) Chronic kidney disease (CKD). (C) Diabetes. (D) Hypertension. (E) Immunocompromised condition. (F) Liver disease. (G) Lung disease. This odds ratio includes chronic lung disease from Alabama’s data and COPD from New York’s data. (H) Neurological Disease. This odds ratio includes Alzheimer’s disease and stroke data from New York. (I) Obesity. (J) Renal disease. This odds ratio includes CKD data from Alabama and Louisiana. “Total” columns in the model represent the numerical amount of the total deaths from COVID-19. The “Events” column on the left represents the number of deaths that included the cardiovascular disease comorbidity in that respective state. The “Events” column on the right represents the number of deaths without a comorbidity in that respective state.