Temporal variation of excess deaths from diabetes during the COVID-19 pandemic in the United States

Mathematical model for understanding the relationship between diabetes and novel coronavirus

A new model is proposed to explore interactions between diabetes and novel coronavirus. The model accounted for both the omicron variant and variants varying from omicron. The model investigated compartments such as hospitalization, diabetes, co-infection, omicron variant, and quarantine. Additionally, the impact of different vaccination doses is assessed. Sensitivity analysis is carried out to determine disease prevalence and control options, emphasizing the significance of knowing epidemics and their characteristics. The model is validated using actual data from Japan. The parameters are fitted with the help of "Least Square Curve Fitting" method to describe the dynamic behavior of the proposed model. Simulation results and theoretical findings demonstrate the dynamic behavior of novel coronavirus and diabetes mellitus (DM). Biological illustrations that illustrate impact of model parameters are evaluated. Furthermore, effect of vaccine efficacy and vaccination rates for the vaccine's first, second, and booster doses is conducted. The impact of various preventive measures, such as hospitalization rate, quarantine or self-isolation rate, vaccine dose-1, dose-2, and booster dose, is considered for diabetic individuals in contact with symptomatic or asymptomatic COVID-19 infectious people in the proposed model. The findings demonstrate the significance of vaccine doses on people with diabetes and individuals infectious with omicron variant. The proposed work helps with subsequent prevention efforts and the design of a vaccination policy to mitigate the effect of the novel coronavirus.

COVID-19 epidemic investigation study of a follow-up cohort of patients with diabetic kidney disease

Introduction

The impact of coronavirus disease 2019 (COVID-19) on diabetic kidney disease (DKD) patients in China is not fully understood. This study aimed to investigate infection status in a DKD cohort post-renal biopsy and analyze vaccination and infection rates, as well as symptom severity, across various renal pathologies in DKD patients.

Methods

This epidemiological survey, centered on COVID-19, employed a Chinese DKD and renal puncture follow-up cohort. A customized questionnaire enabled standardized data gathering. It collected data on clinical characteristics, vaccination and infection statuses, and diverse pathological types. The study analyzed the relationship between vaccination and infection statuses across various pathological types, evaluating characteristics and treatment outcomes in patients with infections.

Results

In total, 437 patients with DKD from 26 Chinese provinces were followed up for a median of 44.6 ± 20 months. COVID-19 infection, vaccination, and novel coronavirus pneumonia (NCP) rates were 73.68%, 59.3%, and 6.63%, respectively. Ten patients with NCP had severe pneumonia or died of COVID-19. Renal pathology revealed that 167 (38.22%) patients had diabetic nephropathy (DN), 171 (39.13%) had non-diabetic renal disease (NDRD), and 99 had DN and NDRD (22.65%). The DN group had the lowest vaccination (54.5%), highest all-cause mortality (3.6%), and highest endpoint rates (34.10%). Compared to patients who were not vaccinated pre-infection (117 cases), vaccinated patients (198 cases) had reduced NCP (6.6% vs. 13.7%), severity (1.0% vs. 3.4%), and endpoint (9.10% vs. 31.60%) rates.

Conclusion

Vaccination can prevent infection and diminish COVID-19 severity in patients with DKD; therefore, increasing vaccination rates is particularly important.

Clinical Trial registration

ClinicalTrails.gov, NCT05888909.

Changes in Substance Use Diagnoses in the Great Plains during the COVID-19 Pandemic

As drug overdose mortality rises in the United States, healthcare visits present critical opportunities to mitigate this trend. This study examines changes in healthcare visits for substance use disorders (SUDs) and remission prior to and during the COVID-19 pandemic in the Great Plains, with a focus on identifying the characteristics of those served. Data were analyzed from 109,671 patient visits (mode = one visit per patient), encompassing diverse demographics, including sex, age, race, ethnicity, and geographic location. Visits analyzed included those for Alcohol Use Disorder (AUD), Opioid Use Disorder (OUD), or Stimulant Use Disorder (StUD) and those in remission of these disorders between March 2019 and March 2021. Patient demographic information and geographic factors, like rurality and Medicaid expansion status, were considered, and logistic regression was utilized. Visits were primarily by White (70.83%) and Native American (21.39%) patients, non-Hispanic (91.70%) patients, and males (54.16%). Various demographic, geographic, and temporal trends were observed. Findings indicated that males were more likely to receive an AUD diagnosis, while females were more likely to receive an OUD or StUD diagnosis. Metropolitan-residing patients were more likely to receive an AUD diagnosis, while non-metropolitan patients were more likely to receive an OUD diagnosis. Remission odds increased for StUD during the pandemic but decreased for AUD and OUD. These findings illuminate the demographic and geographic patterns of SUD-related healthcare visits, suggesting critical touchpoints for intervention. The results emphasize the urgent need for targeted healthcare strategies, especially in rural and underserved areas, to address persistent health disparities.

The impact of the COVID-19 pandemic and associated disruptions in health-care provision on clinical outcomes in people with diabetes: a systematic review

The COVID-19 pandemic triggered disruptions to health care and lifestyles that could conceivably impact diabetes management. We set out to identify the impact of disruptions caused by COVID-19 on clinical outcomes in people with diabetes. We performed a systematic review of the available literature in the MEDLINE and OVID databases from Jan 1, 2020, to June 7, 2023, and included 138 studies (n>1 000 000 people). All but five studies were judged to be at some risk of bias. All studies compared prepandemic with pandemic periods. All-cause mortality (six studies) and diabetes-related mortality (13 studies) showed consistent increases, and most studies indicated increases in sight loss (six studies). In adult and mixed samples, data generally suggested no difference in diabetic ketoacidosis frequency or severity, whereas in children and adolescents most studies showed increases with some due to new-onset diabetes (69 studies). Data suggested decreases in hospital admissions in adults but increases in diabetes-related admissions to paediatric intensive care units (35 studies). Data were equivocal on diabetic foot ulcer presentations (nine studies), emergency department admissions (nine studies), and overall amputation rates (20 studies). No studies investigated renal failure. Where reported, the impact was most pronounced for females, younger people, and racial and ethnic minority groups. Further studies are needed to investigate the longer-term impact of the pandemic and the on potential differential impacts, which risk further exacerbating existing inequalities within people with diabetes.

A comprehensive analysis of all-cause and cause-specific excess deaths in 30 countries during 2020

The impact of COVID-19 on mortality from specific causes of death remains poorly understood. This study analysed cause-of-death data provided by the World Health Organization from 2011 to 2019 to estimate excess deaths in 2020 in 30 countries. Over-dispersed Poisson regression models were used to estimate the number of deaths that would have been expected if the pandemic had not occurred, separately for men and women. The models included year and age categories to account for temporal trends and changes in size and age structure of the populations. Excess deaths were calculated by subtracting observed deaths from expected ones. Our analysis revealed significant excess deaths from ischemic heart diseases (IHD) (in 10 countries), cerebrovascular diseases (CVD) (in 10 countries), and diabetes (in 19 countries). The majority of countries experienced excess mortality greater than 10%, including Mexico (+ 38·8% for IHD, + 34·9% for diabetes), Guatemala (+ 30·0% for IHD, + 10·2% for CVD, + 39·7% for diabetes), Cuba (+ 18·8% for diabetes), Brazil (+ 12·9% for diabetes), the USA (+ 15·1% for diabetes), Slovenia (+ 33·8% for diabetes), Poland (+ 30·2% for IHD, + 19·5% for CVD, + 26 1% for diabetes), Estonia (+ 26·9% for CVD, + 34·7% for diabetes), Bulgaria (+ 22·8% for IHD, + 11·4% for diabetes), Spain (+ 19·7% for diabetes), Italy (+ 18·0% for diabetes), Lithuania (+ 17·6% for diabetes), Finland (+ 13·2% for diabetes) and Georgia (+ 10·7% for IHD, + 19·0% for diabetes). In 2020, 22 out of 30 countries had a significant increase in total mortality. Some of this excess was attributed to COVID-19, but a substantial increase was also observed in deaths attributed to cardiovascular diseases and diabetes.

Unique SARS-CoV-2 Variants, Tourism Metrics, and B.1.2 Emergence in Early COVID-19 Pandemic: A Correlation Analysis in South Dakota

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) virus, which is the source of the coronavirus disease 2019 (COVID-19), was declared a pandemic in the March of 2020. Travel and tourism were severely impacted as restrictions were imposed to help slow the disease spread, but some states took alternative approaches to travel restrictions. This study investigated the spread of COVID-19 in South Dakota during the early pandemic period to better understand how tourism affected the movement of the virus within the region. Sequences from the fall of 2020 were retrieved from public sources. CDC and other sources were used to determine infections, deaths, and tourism metrics during this time. The data were analyzed using correlation and logistic regression. This study found that the number of unique variants per month was positively correlated with hotel occupancy, but not with the number of cases or deaths. Interestingly, the emergence of the B.1.2 variant in South Dakota was positively correlated with increased case numbers and deaths. Data show that states with a shelter-in-place order were associated with a slower emergence of the B.1.2 variant compared to states without such an order, including South Dakota. Findings suggest complex relationships between tourism, SARS-CoV-2 infections, and mitigation strategies. The unique approach that South Dakota adopted provided insights into the spread of the disease in areas without state-wide restrictions. Our results suggest both positive and negative aspects of this approach. Finally, our data highlight the need for future surveillance efforts, including efforts focused on identifying variants with known increased transmission potential to produce effective population health management.