The burden and outcomes of COVID-19 among patients with co-morbid disease in Africa: protocol for a systematic review and meta-analysis

The Effect of Comorbidities and Complications on COVID-19 Mortality: A Detailed Retrospective Study in Western Romania

COVID-19 continues to impact global health systems even after being declared over, with some patients exhibiting severe complications linked to pre-existing conditions. This study aimed to investigate the association between comorbidities, complications, and survival outcomes among COVID-19 survivors in Western Romania. Our hypothesis posited that comorbidities and complications significantly influence survival rates. We conducted a retrospective analysis of 1948 COVID-19 survivors admitted from January to December 2021, with 192 selected for detailed analysis based on inclusion and exclusion criteria. The severity of COVID-19 was classified according to WHO guidelines, and conditions like hypertension and obesity were defined using criteria from the European Society of Hypertension (ESH), the European Society of Cardiology (ESC), and WHO, respectively. Among the 192 patients, 33 had mild, 62 had moderate, and 97 had severe COVID-19. The median age across the severity groups was 63.2 years. Patients undergoing tracheostomy had a mortality rate of 83.3% versus 22.2% for non-tracheostomy patients (p < 0.001) and presented with significantly higher lung injury, hospitalization duration, and complications. Remarkably, tracheostomized patients were 17.50 times more likely to succumb to the disease (95% CI 4.39-116.91, p < 0.001). Furthermore, pneumothorax increased the mortality risk significantly (OR 22.11, 95% CI 5.72-146.03, p < 0.001). Intriguingly, certain conditions like grade I hypertension and grade II obesity showed a protective effect against mortality, whereas type 2 diabetes mellitus increased mortality risk (univariate OR 2.89, p = 0.001). The presence of certain comorbidities and complications significantly impacts the survival rates of COVID-19 patients in Western Romania. Notably, tracheostomy, pneumothorax, and T2DM were associated with increased mortality. This study underscores the importance of personalized patient care and provides insights for healthcare policymakers in Western Romania to improve clinical management strategies.

Changes in self-care behaviors of Iranian patients with type 2 diabetes using insulin pens during COVID-19 pandemic

Aims

The COVID-19 pandemic as a serious public health concern has been accompanied with changes and restrictions in everyday life. This can affect directly or indirectly health behaviors and disease management, particularly in developing countries with low resources. This study aimed to compare self-care behaviors of patients with type 2 diabetes using insulin pens before and after the COVID-19 pandemic.

Methods

This was a prospective cohort study involving 300 patients with type 2 diabetes who had been referred to a referral tertiary care diabetes clinic during 2018–2019. The Summary of Diabetes Self-Care Activities Assessment (SDSCA) questionnaire was used for the evaluation of 5 self-care activities.

Results

The mean total self-care score before and one year after the onset of the COVID-19 crisis was 37.63 (SD, 10.89) and 26.14 (SD, 10.99), respectively. Before the COVID-19 crisis, 27%, 54.3%, and 18.7% of patients had poor, moderate, and good self-care, respectively. One year after the onset of the epidemic, however, these rates were 66.3%, 29%, and 4.7%, respectively. There was a significant difference between the mean score of 5 indices of self-care behaviors before and after the COVID-19 crisis (p < 0.01).

Conclusion

Our findings suggest the deterioration of self-care behaviors amidst the COVID-19 pandemic in an Iranian population. Continuous follow-up of patients with diabetes and the design of effective educational programs for these patient can prevent or delay the long-term consequences of diabetes, especially in the context of the COVID-19 crisis.

Leveraging Artificial Intelligence and Big Data to Optimize COVID-19 Clinical Public Health and Vaccination Roll-Out Strategies in Africa

COVID-19 is imposing massive health, social and economic costs. While many developed countries have started vaccinating, most African nations are waiting for vaccine stocks to be allocated and are using clinical public health (CPH) strategies to control the pandemic. The emergence of variants of concern (VOC), unequal access to the vaccine supply and locally specific logistical and vaccine delivery parameters, add complexity to national CPH strategies and amplify the urgent need for effective CPH policies. Big data and artificial intelligence machine learning techniques and collaborations can be instrumental in an accurate, timely, locally nuanced analysis of multiple data sources to inform CPH decision-making, vaccination strategies and their staged roll-out. The Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC) has been established to develop and employ machine learning techniques to design CPH strategies in Africa, which requires ongoing collaboration, testing and development to maximize the equity and effectiveness of COVID-19-related CPH interventions.