CMPH Journal

Introduction

The coronavirus disease 2019 (COVID-19) pandemic disproportionately affected the older adult population and inflicted significantly higher mortality among the older adult population with comorbidities. Pre-existing comorbidities heightened the older adult’s vulnerability to COVID-19 infection and its complications. During the first two waves of COVID-19 infection, research documented cardiometabolic comorbidities such as diabetes mellitus, obesity, cardiovascular disease, and hypertension as the significant comorbidities contributing to greater fatalities among older adults with COVID-19. However, most of those studies were conducted in the inpatient settings because COVID-19 specific outpatient treatments were unavailable during that period [1,2]. The landscape of total comorbidity burden did not come to the forefront as a significant threat to the prognosis of a vulnerable adult fighting a COVID-19 infection and receiving treatment in an outpatient setting. With the emergence of multiple COVID-19 treatment modalities that can be provided in an outpatient setting, the paradigm for COVID-19 disease management is shifting to more outpatient care than hospitalization today. It is essential to understand the prevalent comorbidities to estimate the risk of poor prognosis among community-dwelling older adults with multiple comorbid conditions, being treated in an outpatient setting [3]. Pre-existing comorbidities increase the risk of contracting the virus, lead to critical illness (ie, admission to intensive care unit or death), and complicate recovery [4].

We conducted a cross-sectional study to assess the Influence of Comorbidities, General Health Status, and Self-Care Self-Efficacy on COVID-19 Symptoms severity among adults over 40 years of age during Omicron Wave. The objective of the study was 1) to assess the prevalence of common comorbidities among adults with symptomatic COVID-19 disease and 2) to assess the impact of selfcare, self-efficacy, general well-being and the comorbidity burden on COVID-19 symptoms among adults treated in an outpatient clinic. [5].  This paper is describing the comorbidity burden and its relationship to sociodemographic factors among adults with symptomatic COVID-19 in an outpatient setting.

The Charlson Comorbidity Index (CCI) is reliable in predicting the impact of comorbidities in recovery from COVID-19 in the general population. Strong research evidence through rigorous studies conducted in multiple countries established that COVID-19 patients with pre-existing comorbidities were more likely to die, especially those with advanced age. This simple tool can be applied in the outpatient setting and the results obtained could help healthcare providers identify the most susceptible patients to COVID-19 by comorbidities and age. A robust knowledge of patients’ risk for mortality and morbidity can aid treatment plan to significantly reduce the number of fatalities from COVID-19 in an outpatient setting [4,6,7].

Multiple meta-analyses revealed that advanced age, comorbidities, and abnormal inflammatory biomarkers predicted poor outcomes. Cardiometabolic comorbidities contribute to higher mortality among patients with COVID-19 disease. Hypertension, diabetes, cardiovascular diseases, and chronic kidney disease were risk factors for patients with severe COVID-19 infection [1,2,8-10]. However, the list of comorbidities assessed was constructed based on clinical observations rather than structured instruments like the CCI, which can compute the compounding effect of multiple comorbidities in a person. Additionally, employing clinical observations alone might result in a possible bias towards physical health and exclusion of mental health in the list of relevant comorbidities.

Furthermore, most studies were conducted in inpatient settings among patients admitted with severe infection during the first and second waves of the coronavirus pandemic with most virulent alpha and delta strains of the COVID-19 virus respectively. However, the third wave of COVID-19 infection, with predominantly less severe Omicron virus and its later variants, presented a unique opportunity to observe the elderly patients treated in the outpatient setting and to develop evidence-based risk assessment and riskspecific protocols to support the recovery of COVID-19 patients within the community cost-effectively. Along with pre-existing comorbidities, the lack of self-care self-efficacy, poor general health, and inadequate social support can be possible social determinants of mortality from COVID-19 disease. Still, these factors have not been systematically assessed using standardized instruments in a population with COVID-19 disease.

This study explored the impact of relevant risk factors such as comorbidities, general health status, and self-care practices on COVID-19 burden among at-risk adults who contracted COVID-19.

Methods

We employed a cross-sectional study to assess the role of selected sociodemographic and cardiometabolic risk factors in the burden of COVID-19 disease with a sample of 120 patients presenting for COVID-19 treatment at the “REGEN-COV” monoclonal antibody treatment centers. We recruited cognitively intact and hemodynamically stable COVID-19-positive adults aged 40 years and above who are not taking any other COVID-19 treatment for this study. The study was approved by the Institutional Review Board (IRB) at the University of Texas Medical Branch (UTMB [University of Texas Medical Branch, IRB approval No. 21-0259 ].

Potential participants who met the eligibility criteria were screened with the Short Portable Mental Status Questionnaire (SPMSQ) to screen for cognitive impairment. Once the potential participant cleared the screening process, we collected self-reported data using standardized questionnaires presented in a preordered sequence. We used the Charlson Comorbidity Index (CCI) to capture the weight of pre-existing comorbidities, the RAND Medical Outcomes Study Short-Form (SF-12) to assess general physical and mental health, the Self-Care Self-Efficacy Scale (SCSES) to measure the confidence of the participant in self-care, and the Social Support Survey (SSS) to gauge the available social support [11-15]. The CCI assigns differential weights to different comorbidities that reduce a patient’s chance of survival. This instrument has been validated in diverse patient populations, including patients with COVID-19, and the reliability coefficient varies between 0.5 to 0.86 for predicting mortality based on comorbidities. The SF-12 taps eight health concepts: physical functioning, bodily pain, role limitations due to physical health problems, role limitations due to personal or emotional problems, emotional well-being, social functioning, energy/fatigue, and general health perceptions. It also includes a single item that indicates the perceived change in health. The reliability coefficient of SF-12 has been consistently above 0.7, demonstrating good reliability. The SCSES was newly validated as a self-report measure for self-care self-efficacy among adults with chronic illness (mean age ranging from 65-77 years) and found high reliability with Cronbach’s alpha coefficients ranging from 0.89- 0.93. The SSS is a brief, self-administered instrument developed for patients in the Medical Outcomes Study, a two-year study of patients with chronic conditions and is reliable (all alphas >0.91) and stable. The COVID-19 Symptom Severity Scale (COVID-SRS) was developed in this study for patients to rate their symptoms.

The questionnaires were available in English and Spanish and was completed by the participants in an electronic format using an iPad. Responses were scored and saved on the Research Electronic Data Capture (REDCap, Fort Lauderdale, US) software database. This paper describes the incidence of comorbidities as measured by charlson comorbidity index and its relationship to the sociodemographic factors of the study sample. 

Data Analysis

Data collection and storage were done using RedCap survey software. Data analysis was done using SAS software. A priori sample size calculation in the ANOVA model using four predictors with the alpha at 0.05 and medium effect size (f=.35), the sample size required was 94 to gain the power of 0.80. Providing for approximately 20% dropout after screening and incomplete data, we recruited 120 participants for this study. One hundred and seventeen participants completed the survey instruments to determine the role of selected sociodemographic risk factors in COVID-19 disease. After testing for the normal distribution and adjusting for missing data, we used nonparametric Fisher’s exact test for categorical data in computing the relationship between the top five comorbidities and the major demographic variables queried in this study. Furthermore, we used Analysis of Variance (ANOVA) to quantify the compounding weight of comorbidities to estimate the relationship between the CCI score and the demographic variables.

Results

The top five comorbidities among the population studied are shown in Table 1. The cardiometabolic disease spectrum was the most prevalent comorbidity among the COVID-19-positive adults presented for the COVID-19 antibody infusion therapy, including hypertension (42%) and diabetes without organ damage (31%). Other top comorbidities include, pulmonary disease (19%), depression (14%), and cancer (solid tumor – 11%).

Table 1: Descriptive Statistics of the Studied Population.

The distribution of the most prevalent five comorbidities across selected demographics (Table 2) showed a better picture of the pattern of comorbidities among at-risk adults who presented to receive COVID-19 infusion therapy. The prevalence of depression was higher among younger age groups (p=0.0019), with more women reporting depression than men (p=0.0183). Pulmonary disease/asthma prevalence was higher among those of higher educational levels (master/doctorate – p=0.0371). It is also notable that the prevalence of pulmonary diseases was reported by 50% of Asians, 24% of Hispanics and lesser in other races (p=0.0757). The prevalence of solid tumor cancers showed an insignificant increase with age (p>0.05).

Table 2: Demographics of the Top 5 Comorbidities.

It is important to consider that many adults may suffer from multiple comorbidities, and the cumulative impact of comorbidities on an individual might be significantly different than the effect of a single comorbidity considered alone. We used the CCI to measure the cumulative impact of comorbidities among selected demographics to the CCI (Table 3). Older age had the highest impact on comorbidity burden in at-risk individuals who contracted COVID-19 and required COVID-19 antibody infusion therapy (p<0.0001), with their CCI almost doubling from age 51-60 years (mean 3.2 SD 1.1) to age 81-90 years range (mean 6.9 SD 2.3). Combined family income had the second highest impact on comorbidity burden, especially among those with an income <$40,000 (mean 6.4 SD 3.6 - p<0.0089).

Table 3: The Charlson Comorbidity Index (CCI) of Selected Demographics.

Considering race and ethnicity, the majority of the sample were Caucasians, and they had a higher comorbidity burden (mean 4.6 SD 2.3) compared to Native Americans (mean 4.0 SD 1.7), Hispanics (mean 3.6 SD 1.1) and African Americans (mean 3.3 SD 1.4), yet this difference did not mount to statistical significance (p=0.0963). Gender and education were not associated with the comorbidity burden in this analysis.