Provisional death counts of diabetes, coronavirus disease 2019 (COVID-19) and other select causes of death, by month, sex, and age.

Sample sizes of diabetes patients with COVID-19 hospitalization across different demographic groups.

With the onset of COVID-19, hospitals statewide saw a sharp drop in inpatient discharges, emergency department utilization, and ambulatory surgeries. These datasets contain monthly counts of encounters and in-hospital mortalities in those three settings and are also broken down by the following common health conditions/categories: anxiety, asthma, behavioral syndromes, cancer, cardiac arrest, chronic obstructive pulmonary disease (COPD), COVID-19, depression, diabetes, homeless, hypertension, mood disorders (excluding depression), non-mood psychotic disorders, nonpsychotic disorders (excluding anxiety), obesity, pneumonia, respiratory arrest/failure, sepsis, stroke, substance use disorders, and unspecified mental disorders.

Context

Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. Most people infected with COVID-19 virus will experience mild to moderate respiratory illness and recover without requiring special treatment. Older people, and those with underlying medical problems like cardiovascular disease, diabetes, chronic respiratory disease, and cancer are more likely to develop serious illness. During the entire course of the pandemic, one of the main problems that healthcare providers have faced is the shortage of medical resources and a proper plan to efficiently distribute them. In these tough times, being able to predict what kind of resource an individual might require at the time of being tested positive or even before that will be of immense help to the authorities as they would be able to procure and arrange for the resources necessary to save the life of that patient.

The main goal of this project is to build a machine learning model that, given a Covid-19 patient's current symptom, status, and medical history, will predict whether the patient is in high risk or not.

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The dataset was provided by the Mexican government (link). This dataset contains an enormous number of anonymized patient-related information including pre-conditions. The raw dataset consists of 21 unique features and 1,048,576 unique patients. In the Boolean features, 1 means "yes" and 2 means "no". values as 97 and 99 are missing data.

• sex: 1 for female and 2 for male.
• age: of the patient.
• classification: covid test findings. Values 1-3 mean that the patient was diagnosed with covid in different degrees. 4 or higher means that the patient is not a carrier of covid or that the test is inconclusive.
• patient type: type of care the patient received in the unit. 1 for returned home and 2 for hospitalization.
• pneumonia: whether the patient already have air sacs inflammation or not.
• pregnancy: whether the patient is pregnant or not.
• diabetes: whether the patient has diabetes or not.
• copd: Indicates whether the patient has Chronic obstructive pulmonary disease or not.
• asthma: whether the patient has asthma or not.
• inmsupr: whether the patient is immunosuppressed or not.
• hypertension: whether the patient has hypertension or not.
• cardiovascular: whether the patient has heart or blood vessels related disease.
• renal chronic: whether the patient has chronic renal disease or not.
• other disease: whether the patient has other disease or not.
• obesity: whether the patient is obese or not.
• tobacco: whether the patient is a tobacco user.
• usmr: Indicates whether the patient treated medical units of the first, second or third level.
• medical unit: type of institution of the National Health System that provided the care.
• intubed: whether the patient was connected to the ventilator.
• icu: Indicates whether the patient had been admitted to an Intensive Care Unit.
• date died: If the patient died indicate the date of death, and 9999-99-99 otherwise.

Associations of Long COVID documentation with clinical outcomes among Veterans with diabetes who do and do not use insulin, n = 1,896,080.

Multiple linear regression analysis of predictors of change (Δ) in %times in range and CV for blood glucose from pre-lockdown to lockdown for the combined adult and paediatric cohorts (n = 145).

BackgroundDiabetes mellitus (DM) is one of the most frequent comorbidities in patients suffering from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with a higher rate of severe course of coronavirus disease (COVID-19). However, data about post-COVID-19 syndrome (PCS) in patients with DM are limited.MethodsThis multicenter, propensity score-matched study compared long-term follow-up data about cardiovascular, neuropsychiatric, respiratory, gastrointestinal, and other symptoms in 8,719 patients with DM to those without DM. The 1:1 propensity score matching (PSM) according to age and sex resulted in 1,548 matched pairs.ResultsDiabetics and nondiabetics had a mean age of 72.6 ± 12.7 years old. At follow-up, cardiovascular symptoms such as dyspnea and increased resting heart rate occurred less in patients with DM (13.2% vs. 16.4%; p = 0.01) than those without DM (2.8% vs. 5.6%; p = 0.05), respectively. The incidence of newly diagnosed arterial hypertension was slightly lower in DM patients as compared to non-DM patients (0.5% vs. 1.6%; p = 0.18). Abnormal spirometry was observed more in patients with DM than those without DM (18.8% vs. 13; p = 0.24). Paranoia was diagnosed more frequently in patients with DM than in non-DM patients at follow-up time (4% vs. 1.2%; p = 0.009). The incidence of newly diagnosed renal insufficiency was higher in patients suffering from DM as compared to patients without DM (4.8% vs. 2.6%; p = 0.09). The rate of readmission was comparable in patients with and without DM (19.7% vs. 18.3%; p = 0.61). The reinfection rate with COVID-19 was comparable in both groups (2.9% in diabetics vs. 2.3% in nondiabetics; p = 0.55). Long-term mortality was higher in DM patients than in non-DM patients (33.9% vs. 29.1%; p = 0.005).ConclusionsThe mortality rate was higher in patients with DM type II as compared to those without DM. Readmission and reinfection rates with COVID-19 were comparable in both groups. The incidence of cardiovascular symptoms was higher in patients without DM.

Demographic and clinical characteristics of all patients with Covid-19.

Characteristics associated with overall Covid-19 illness severity* in the total sample (N = 442).

Characteristics associated with Covid-19 illness severity among all hospitalized patients.

The objective of this study was to compare the effect of diabetes and pathologies potentially related to diabetes on the risk of infection and death from COVID-19 among people from Highly-Developed-Country (HDC), including Italians, and immigrants from the High-Migratory-Pressure-Countries (HMPC). Among the population with diabetes, whose prevalence is known to be higher among immigrants, we compared the effect of body mass index among HDC and HMPC populations. A population-based cohort study was conducted, using population registries and routinely collected surveillance data. The population was stratified into HDC and HMPC, according to the place of birth; moreover, a focus was set on the South Asiatic population. Analyses restricted to the population with type-2 diabetes were performed. We reported incidence (IRR) and mortality rate ratios (MRR) and hazard ratios (HR) with 95% confidence interval (CI) to estimate the effect of diabetes on SARS-CoV-2 infection and COVID-19 mortality. Overall, IRR of infection and MRR from COVID-19 comparing HMPC with HDC group were 0.84 (95% CI 0.82–0.87) and 0.67 (95% CI 0.46–0.99), respectively. The effect of diabetes on the risk of infection and death from COVID-19 was slightly higher in the HMPC population than in the HDC population (HRs for infection: 1.37 95% CI 1.22–1.53 vs. 1.20 95% CI 1.14–1.25; HRs for mortality: 3.96 95% CI 1.82–8.60 vs. 1.71 95% CI 1.50–1.95, respectively). No substantial difference in the strength of the association was observed between obesity or other comorbidities and SARS-CoV-2 infection. Similarly for COVID-19 mortality, HRs for obesity (HRs: 18.92 95% CI 4.48–79.87 vs. 3.91 95% CI 2.69–5.69) were larger in HMPC than in the HDC population, but differences could be due to chance. Among the population with diabetes, the HMPC group showed similar incidence (IRR: 0.99 95% CI: 0.88–1.12) and mortality (MRR: 0.89 95% CI: 0.49–1.61) to that of HDC individuals. The effect of obesity on incidence was similar in both HDC and HMPC populations (HRs: 1.73 95% CI 1.41–2.11 among HDC vs. 1.41 95% CI 0.63–3.17 among HMPC), although the estimates were very imprecise. Despite a higher prevalence of diabetes and a stronger effect of diabetes on COVID-19 mortality in HMPC than in the HDC population, our cohort did not show an overall excess risk of COVID-19 mortality in immigrants.

ImportanceThe ongoing pandemic of COVID-19 is still affecting our life, but the effects of lockdown measures on gestational diabetes mellitus (GDM) in pregnant women remain unclear.AimTo investigate the association between COVID-19 lockdown and GDM.Subjects and MethodsMedical records of 140844 pregnant women during 2015-2020 were extracted from 5 hospitals in Guangdong Province, China. Pregnant women who underwent the COVID-19 Level I lockdown (1/23 - 2/24/2020) during pregnancy were defined as the exposed group (N=20472) and pregnant women who underwent the same calendar months during 2015-2019 (1/23 - 2/24) were defined as the unexposed group (N=120372). Subgroup analyses were used to explore the potential susceptible exposure window of COVID-19 lockdown on GDM. Cumulative exposure is quantitatively estimated by assigning different weights to response periods with different exposure intensities. A logistic regression model was used to estimate the association between COVID-19 lockdown exposure and GDM.ResultsThe rates of GDM in the exposed and unexposed groups were 15.2% and 12.4%, respectively. The overall analyses showed positive associations (odds ratio, OR=1.22, 95%CI: 1.17, 1.27) between lockdown exposure and GDM risk in all pregnant women. More pronounced associations were found in women who underwent the COVID-19 lockdown in their first four months of pregnancy, and the adjusted OR values ranged from 1.24 (95%CI: 1.10, 1.39) in women with 5-8 gestational weeks (GWs) to 1.35 (95%CI: 1.20, 1.52) with < 5 GWs. In addition, we found a positive exposure-response association of cumulative lockdown exposure with the risk of GDM.ConclusionsThe COVID-19 lockdown was associated with an increased risk of GDM, and the first four months of pregnancy may be the window for sensitive exposure.

2.1. Participants and procedure The participants were patients with DM from nine primary health care areas corresponding to four Cuban provinces belonging to different regions of the country (Pinar del Río, Havana, Ciego de Ávila and Santiago de Cuba), selected by means of non-probabilistic sampling. The inclusion criteria included: 1) having type 2 diabetes mellitus according to the criteria of the World Health Organization 2) being ≥18 years of age 3) being attended in the previously mentioned health areas where their clinical histories were located and 4) being willing to participate in the research study and answer the survey after signing the informed consent form. Patients with severe mental illness or cognitive deficits (dementia, psychosis or mental disabilities) or any other apparent condition that compromised their ability to understand and complete the questionnaire were not included in the study. The sample size was calculated with the Soper software [29], which indicated a number of 200 participants. For this we considered the number of observed variables (6 items), latent variables of the model to be evaluated (concern for COVID-19 contagion), the anticipated effect size (λ = 0.3), the probability (α = 0.05) and the statistical power (1 - β = 0.95). Finally, 219 people with type 2 DM were surveyed. The application of the survey was carried out between the months of January and April 2021, while the patients attended consultation or in their own homes by the researchers trained for the task and complying with strict COVID-19 prevention protocols. The Cuban panorama in the fight against COVID-19 during the period of data collection was not favorable, as the country was in a phase of resurgence characterized by high numbers of people infected with the virus, much higher compared to the diagnoses at a similar point during the first stage of the disease, in 2020. Although government health measures were strengthened to contain the pandemic, the population's perception of risk was on the rise. During those dates, more than 64,414 positive diagnoses and 384 deaths were reported. Participation in the study was voluntary and no financial compensation was provided. All participants signed informed consent and were allowed to withdraw at any time from the study without having to justify their decision. In addition, the data were guaranteed to be confidential and anonymous. The study received approval from the ethics committee of the Universidad Privada del Norte in Peru (registration number: 20213002). The majority of the participants were women (66.2%) with a mean age of 58.5 years old (SD = 18.2). Thirty-two point nine percent had higher education. Of the total participants, 37.9% were retired and 32% were state workers; while 43.4 had more than 10 years with the disease. The majority (68.9%) had no associated chronic complications and were receiving treatment for diabetes (98.2%). More details of the sociodemographic variables can be seen in Table 1. Table 1. Characteristics of the participants (n = 219). Characteristic n (%) Age 58.5 (18.2)a Sex Female 145 (66.2) Male 74 (33.8) Level of education University 72 (32.9) Pre-university 63 (28.8) Mid-level technical 39 (17.8) Secondary 25 (11.4) Primary 17 (7.8) No schooling 3 (1.4) Occupation Retired/pensioned 83 (37.9) State employee 70 (32.0) Self-employed 37 (17.0) Housewife 17 (7.8) Student 10 (4.6) Unemployed 2 (0.9) Time of evolution of diabetes (years) Less than 5 52 (23.7) From 5 to 10 72 (32.9) More than 10 95 (43.4) Associated chronic complications b None 151 (68.9) Diabetic foot 31 (14.2) Polyneuropathy 20 (9.1) Retinopathy 15 (6.8) Nephropathy 7 (3.2) Other 2 (0.9) Treatment of diabetes Yes 215 (98.2) No 4 (1.8) Comorbidities Yes 141 (64.4) No 78 (35.6) Family member or friend infected by COVID-19 Yes 110 (50.2) No 109 (49.8) Family member or friend deceased due to COVID-19 No 210 (95.9) Yes 9 (4.1) a: mean and standard deviation; b: a patient may have more than one complication. 2.2. Instruments Scale of Worry for Contagion of COVID-19 (PRE-COVID-19). The scale is comprised of 6 items that assess concern about becoming infected with COVID-19 and its impact on people's daily functioning, specifically on their mood and their ability to perform their daily activities. Each item presented 4 Likert-type response options (from 1 = never or rarely to 4 = almost all the time), with higher scores indicating greater concern about COVID-19 infection. Generalized Anxiety Disorder Scale-2 (GAD-2) [30]. The GAD-2 consists of 2 items that measure an emotional (feeling nervous) and cognitive (worry) symptom of generalized anxiety in the past 2 weeks. The 2 items have 4 response options using a Likert-type scale (from 0 = not at all to 3 = almost every day), where a higher score indicates a higher level of generalized anxiety. 2.3. Data analysis Confirmatory Factor Analysis (CFA) was performed using the Diagonally Weighted Least Squares with Mean and...

Prediction of severe COVID-19: Cross-validation of models chosen by stepwise regression.

Cox regression analysis of risk factors for mortality of diabetic patients with COVID-19.

Baseline demographics for the complete dataset and separate adult and paediatric cohorts.

ObjectiveStudies have shown an increased incidence of pediatric type 1 diabetes during the COVID-19 pandemic, but the detailed role of SARS-CoV-2 infection in the incidence increase in type 1 diabetes remains unclear. We investigated the spatiotemporal association of pediatric type 1 diabetes and COVID-19 incidence at the district level in Germany.MethodsFor the period from March 2020 to June 2022, nationwide data on incident type 1 diabetes among children and adolescents aged <20 years and daily documented COVID-19 infections in the total population were obtained from the German Diabetes Prospective Follow-up Registry and the Robert Koch Institute, respectively. Data were aggregated at district level and seven time periods related to COVID-19 pandemic waves. Spatiotemporal associations between indirectly standardized incidence rates of type 1 diabetes and COVID-19 were analyzed by Spearman correlation and Bayesian spatiotemporal conditional autoregressive Poisson models.ResultsStandardized incidence ratios of type 1 diabetes and COVID-19 in the pandemic period were not significantly correlated across districts and time periods. A doubling of the COVID-19 incidence rate was not associated with a significant increase in the incidence rate of type 1 diabetes (relative risk 1.006, 95% CI 0.987; 1.019).ConclusionOur findings based on data from the pandemic period indirectly indicate that a causal relationship between SARS-COV-2 infection and type 1 diabetes among children and adolescents is unlikely.

Objectives: Diabetes is a risk factor for poor COVID-19 prognosis. The analysis of related prognostic factors in diabetic patients with COVID-19 would be helpful for further treatment of such patients.Methods: This retrospective study involved 3623 patients with COVID-19 (325 with diabetes). Clinical characteristics and laboratory tests were collected and compared between the diabetic group and the non-diabetic group. Binary logistic regression analysis was applied to explore risk factors associated in diabetic patients with COVID-19. A prediction model was built based on these risk factors.Results: The risk factors for higher mortality in diabetic patients with COVID-19 were dyspnea, lung disease, cardiovascular diseases, neutrophil, PLT count, and CKMB. Similarly, dyspnea, cardiovascular diseases, neutrophil, PLT count, and CKMB were risk factors related to the severity of diabetes with COVID-19. Based on these factors, a risk score was built to predict the severity of disease in diabetic patients with COVID-19. Patients with a score of 7 or higher had an odds ratio of 7.616.Conclusions: Dyspnea is a critical clinical manifestation that is closely related to the severity of disease in diabetic patients with COVID-19. Attention should also be paid to the neutrophil, PLT count and CKMB levels after admission.

This dataset provides the Emergency Department ratio of encounters and treatment stations to represent the ED Burden. Smaller ratios indicate fewer ED visits per available treatment station and less burden. Larger ratios of ED visits per available treatment station indicate greater burden. The encounters are broken down by health-related conditions: Active COVID-19, Asthma, Cancer, Cardiac, COPD, COVID-19 History, Diabetes, Homeless, Hypertension, Mental Health, Obesity, Pneumonia, Respiratory, Sepsis, Stroke, and Substance Abuse.

Univariate associations of severe disease with demographic factors.