Impact of the COVID-19 Pandemic on Diabetes Care Among a North Carolina Patient Population

Understanding excess mortality during COVID in Belgium: the influence of pre-existing health status and social factors

BACKGROUND

This study aims to investigate how pre-existing health status and social background contribute to excess mortality during the COVID-19 crisis in Belgium.

METHODS

The study population consists of almost 1.4 million adult members of Solidaris, the second largest health insurance fund in Belgium. Pre-existing health status was identified using health care reimbursement data, including medication use. Social characteristics included a proxy for low socio-economic status, nationality of origin, and living arrangement. Excess mortality during the COVID-19 crisis was measured by computing the relative difference between all-cause mortality in 2020 or 2021 and the average yearly mortality in 2015-2019. Directly Standardised Mortality Rates (DSMRs) were calculated to investigate absolute mortality inequalities. Mortality Rate Ratios (MRRs) were computed using Poisson regression analyses to investigate relative mortality inequalities.

RESULTS

DSMRs show that persons with no previous disease experienced significant excess mortality in 2020, like men with one, two or three diseases and women with various numbers of pre-existing diseases. Results by specific disease show heterogenous results. After adjusting for age, sex and social characteristics, persons with cancer experienced a significant mortality deficit of 17% in 2020 and of 9% in 2021. For persons with cancer and asthma or COPD, significant mortality deficits of 10% and  3% were observed in 2020 and 2021, respectively.

CONCLUSION

The study provides insights into the complex dynamics of mortality during the COVID-19 crisis, emphasising the need to consider individual-level information on pre-existing health and social background jointly.

Artificial Intelligence-Based Digital Biomarkers for Type 2 Diabetes: A Review

Type 2 diabetes mellitus (T2DM), a complex metabolic disorder that burdens the health care system, requires early detection and treatment. Recent strides in digital health technologies, coupled with artificial intelligence (AI), may have the potential to revolutionize T2DM screening, diagnosis of complications, and management through the development of digital biomarkers. This review provides an overview of the potential applications of AI-driven biomarkers in the context of screening, diagnosing complications, and managing patients with T2DM. The benefits of using multisensor devices to develop digital biomarkers are discussed. The summary of these findings and patterns between model architecture and sensor type are presented. In addition, we highlight the pivotal role of AI techniques in clinical intervention and implementation, encompassing clinical decision support systems, telemedicine interventions, and population health initiatives. Challenges such as data privacy, algorithm interpretability, and regulatory considerations are also highlighted, alongside future research directions to explore the use of AI-driven digital biomarkers in T2DM screening and management.

Factors Associated with Diabetic Complication Index among Type 2 Diabetes Patients: Focusing on Regular Outpatient Follow-up and HbA1c Variability

PURPOSE

Preventing diabetic complications involves regular outpatient follow-up and maintaining low variability in hemoglobin A1c (HbA1c) levels. This study investigated the factors associated with diabetic complications, with a specific focus on the impact of regular outpatient follow-up and HbA1c variability, among patients with type 2 diabetes.

METHODS

The study design was secondary data analysis of electronic medical records from a university hospital in Korea. It included patients aged 40-79 with type 2 diabetes who were prescribed diabetes medication within three months of their first HbA1c test by an endocrinologist and were followed up for at least five years. Follow-up regularity, adjusted standard deviation of HbA1c levels, and diabetic complication indices were collected. Data were analyzed using the Chi-square test, independent t-test, repeated measures analysis of variance, and multiple regression analysis.

RESULTS

The study included 1566 patients. Lower follow-up regularity was observed in patients of older age, with comorbidities, diabetic complications, insulin treatment, a history of hospitalization, lower baseline estimated glomerular filtration rate (eGFR) and total cholesterol (TC), and higher HbA1c variability. Higher HbA1c variability was observed in younger patients without comorbidity but with insulin treatment, a history of hospitalization, higher baseline blood glucose (BG), HbA1c, TC, and triglyceride levels. HbA1c variability had the strongest influence on BG and HbA1c levels at the five-year follow-up. Baseline eGFR and TC were the most influential factors for their respective levels at the five-year follow-up. Follow-up regularity significantly affected BG, HbA1c, eGFR, and TC at five-year follow-up.

CONCLUSIONS

It has been shown that several variables besides regular follow-up and HbA1c variability have an influence. However, these are the two that can be corrected through nursing intervention and are important, so intervention on these is important.