The Influence of Comorbidities, General Health Status, and Self-Care Self-Efficacy on COVID-19 Symptoms During the Omicron Wave.

Background The emergence of the less virulent COVID-19 strains such as Omicron and its subvariants shifted the paradigm of COVID-19 treatment from inpatient treatment to regular outpatient care. The individual health determinants affecting COVID-19 disease severity among vulnerable adults treated in outpatient settings are an under-researched area. Methods This study conducted in an outpatient COVID-19 antibody infusion center employed a cross-sectional survey design to explore the impact of comorbidities, general health status, and self-care self-efficacy on COVID-19 symptom severity. We recruited 120 COVID-19-positive participants over 40 years of age, of which 117 completed the study with 87 providing complete data. After the screening and consenting process, the participants completed the following surveys in a secure REDCap survey software (Vanderbilt University, Nashville, USA) on an iPad (Apple Inc., Cupertino, USA): 1) sociodemographic questionnaire, 2) Charlson Comorbidity Index (CCI) to capture comorbidities, 3) Medical Outcomes Study Short-Form (SF-12) to assess general health including physical (PCS) and mental (MCS) health subscales, 4) Self-Care Self-Efficacy Scale (SCSES) to measure self-care self-efficacy, and 5) the COVID-19 Symptom rating scale (COVID-19 SRS). Statistical analysis used were Chi-square and Pearson correlations.  Results As evidenced by CCI, the top five comorbidities were hypertension (42%), diabetes mellitus (31%), pulmonary disease (19%), depression (14%), and solid tumors (11%). Age was statistically significantly correlated to comorbidity burden (p<0.0001). Severe COVID-19 symptoms reported were fatigue, myalgia, cough, runny nose, and sore throat. The general health status measure (SF-12) subscales showed that the patient's mental component summary (MCS) was more statistically significant to COVID-19 symptom severity than the physical component summary (PCS). The MCS demonstrated a statistically significant correlation with fatigue and myalgia (p<0.0001), headache and breathing difficulties (p<0.001), nausea/vomiting (p<0.01), and abdominal pain/diarrhea (p<0.05). The PCS showed a lesser statistically significant correlation with fatigue, myalgia, headaches (p<0.01), fever/chills, cough, congestion/runny nose, night sweats, breathing difficulties, nausea/vomiting, and abdominal pain/diarrhea (p<0.05). Interestingly, the 'loss of smell' which is the hallmark symptom of COVID-19 was the only symptom that showed a statically significant correlation with the Charlson Comorbidity Index (p<0.05), and it did not show any association with either mental (SF-12 MCS) or physical (SF-12 PCS) health status. The SF-12 MCS also showed a statistically significant correlation with a diagnosis of depression (p< 0.01), validating it as a true measure of mental health among vulnerable adults. The SCSES was not correlated with any of the COVID-19 symptoms. Conclusions The patient's general health status, especially mental health was more statistically significant to COVID-19 symptoms. The COVID-19 hallmark symptom of 'loss of smell' was the only symptom that showed statistical significance with comorbidities. Within the limitations of a cross-sectional survey design and convenient sampling methods, this study calls to tailor general health status, especially mental health, and cumulative comorbidity burden to risk assessment/risk stratification of COVID-19 care.

Health determinants and risk factors for coronary artery disease among older Filipinos in rural communities.

AIMS: Lifestyle modifications and healthy behavioural regimens are critical in preventing coronary artery disease (CAD) and other important health conditions. Little is known about the risk for CAD and health behaviour among older adults (>60 years) living in rural areas in the Philippines. Compare risk profiles and health behaviours of Filipinos at low- vs. moderate-to-high-risk for CAD and examine the association between demographic variables, risk profiles, and health behaviours. METHODS AND RESULTS: A comparative, cross-sectional study was conducted using a convenient sample of 427 Filipinos (≥60 years old). Data on sociodemographic characteristics, risk profiles, and health behaviours (e.g. diet, physical activity, smoking status, and alcohol use) were collected. Ten-year CAD risk was estimated using the non-laboratory-based Framingham algorithm. Of the 427 participants [mean age was 69.2 ± 6.7 years, primarily women (65%), married (52.8%)], 319 (75%) were at low risk, and 108 (25%) were at moderate-to-high-risk for CAD. Filipinos at moderate to high risk were more likely to have cardiometabolic diseases (e.g. hypertension, hyperlipidaemia, diabetes, and obesity, all P's < 0.001). Health behaviours did not differ between the two groups except for the consumption of ≥5 servings of fruit, higher in the low-risk group. CONCLUSION: Data showed highly consistent and convergent evidence among older Filipinos living in rural areas at high risk for CAD and other health conditions. These findings underscore the need for culturally sensitive guidance to improve CAD outcomes for moderate to high-risk older adults living in rural areas, including education and counselling on risk and risk-reducing strategies.