This dataset provides COVID-19 mortality data with details on age groups, sex, and pre-existing conditions such as diabetes and hypertensive diseases. It includes the date of death, COVID-19 diagnosis, and comorbidities, helping to analyze the impact of COVID-19 on different demographics and health conditions. The dataset is valuable for epidemiological research, healthcare policy planning, and understanding the role of comorbidities in COVID-19-related deaths.

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

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.

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.

content

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.

It was estimated that around 30 percent of those aged 80 years and older who had COVID-19 in the United States from January 22 to May 30, 2020 died from the disease. Deaths due to COVID-19 are much higher among those with underlying health conditions such as cardiovascular disease, chronic lung disease, or diabetes. This statistic shows the percentage of people in the U.S. who had COVID-19 from January 22 to May 30, 2020 who died, by age.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

IntroductionDiabetes is one of the comorbidities associated with poor prognosis in hospitalized COVID-19 patients. In this nationwide retrospective study, we evaluated the risk of in-hospital death attributed to diabetes.MethodsWe analyzed data from discharge reports of patients hospitalized with COVID-19 in 2020 as submitted to the Polish National Health Fund. Several multivariate logistic regression models were used. In each model, in-hospital death was estimated with explanatory variables. Models were built either on the whole cohorts or cohorts matched with propensity score matching (PSM). The models examined either the main effects of diabetes itself or the interaction of diabetes with other variables.ResultsWe included 174,621 patients with COVID-19 who were hospitalized in the year 2020. Among them, there were 40,168 diabetic patients (DPs), and the proportion of DPs in this group was higher than in the general population (23.0% vs. 9.5%, p

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.

It was estimated that around 20 percent of those with underlying health conditions who had COVID-19 in the United States from January 22 to May 30, 2020 died from the disease, compared to just 2 percent of COVID-patients without underlying health conditions. Underlying health conditions such as cardiovascular disease, chronic lung disease, or diabetes greatly increase the chance of death due to COVID-19. This statistic shows the percentage of people in the U.S. who had COVID-19 from January 22 to May 30, 2020 with and without underlying health conditions who died, by age.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Logistic regression analysis on the relationships of comorbidities with deaths for COVID-19a.

This study at Eka Kotebe Hospital in Addis Ababa, Ethiopia, examined the impact of diabetes on COVID-19 mortality. We conducted a matched-retrospective cohort study of consecutive patients admitted with COVID-19. We compared severity markers and outcomes to determine the risk of death in patients with diabetes compared to matched controls. We used descriptive statistics, chi-square, and Poisson regression. In a univariate comparison, a p-value less than 0.05 was considered significant. Ethics approval was obtained from the Eka Kotebe Hospital Institutional Ethics Committee. The study involved 284 patients, with a 1:1 proportion of diabetics and non-diabetics. Results showed that diabetic patients had a higher number of severe and critical cases but did not have a higher mortality rate. Mortality was associated with malignancy, HIV, and a lymphocyte count <1000/µL.

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

Cardiovascular diseases (CVDs) continue to be the leading cause of death worldwide. Over the past couple of years and with the surge of the COVID-19 pandemic, mortality from CVDs has been slightly overshadowed by those due to COVID-19, although it was during the peak of the pandemic. In the present study, patients with CVDs (CVDs; n = 41,883) were analyzed to determine which comorbidities had the largest impact on overall patient mortality due to their association with both diseases (n = 3,637). Obesity, hypertension, and diabetes worsen health in patients diagnosed positive for COVID-19. Hence, they were included in the overview of all patients with CVD. Our findings showed that 1,697 deaths were attributable to diabetes (p < 0.001) and 987 deaths to obesity (p < 0.001). Lastly, 2,499 deaths were attributable to hypertension (p < 0.001). Using logistic regression modeling, we found that diabetes (OR: 1.744, p < 0.001) and hypertension (OR: 2.179, p < 0.001) significantly affected the mortality rate of patients. Hence, having a CVD diagnosis, with hypertension and/or diabetes, seems to increase the likelihood of complications, leading to death in patients diagnosed positive for COVID-19.

Global Covid-19 Data

Global Covid-19 data on cases, deaths, vaccinations, and more

By Valtteri Kurkela [source]

About this dataset

The dataset is constantly updated and synced hourly to ensure up-to-date information. With over several columns available for analysis and exploration purposes, users can extract valuable insights from this extensive dataset.

Some of the key metrics covered in the dataset include:

1. Vaccinations: The dataset covers total vaccinations administered worldwide as well as breakdowns of people vaccinated per hundred people and fully vaccinated individuals per hundred people.

2. Testing & Positivity: Information on total tests conducted along with new tests conducted per thousand people is provided. Additionally, details on positive rate (percentage of positive Covid-19 tests out of all conducted) are included.

3. Hospital & ICU: Data on ICU patients and hospital patients are available along with corresponding figures normalized per million people. Weekly admissions to intensive care units and hospitals are also provided.

4. Confirmed Cases: The number of confirmed Covid-19 cases globally is captured in both absolute numbers as well as normalized values representing cases per million people.

5.Confirmed Deaths: Total confirmed deaths due to Covid-19 worldwide are provided with figures adjusted for population size (total deaths per million).

6.Reproduction Rate: The estimated reproduction rate (R) indicates the contagiousness of the virus within a particular country or region.

7.Policy Responses: Besides healthcare-related metrics, this comprehensive dataset includes policy responses implemented by countries or regions such as lockdown measures or travel restrictions.

8.Other Variables of InterestThe data encompasses various socioeconomic factors that may influence Covid-19 outcomes including population density,membership in a continent,gross domestic product(GDP)per capita;

For demographic factors: -Age Structure : percentage populations aged 65 and older,aged (70)older,median age -Gender-specific factors: Percentage of female smokers -Lifestyle-related factors: Diabetes prevalence rate and extreme poverty rate

1. Excess Mortality: The dataset further provides insights into excess mortality rates, indicating the percentage increase in deaths above the expected number based on historical data.

The dataset consists of numerous columns providing specific information for analysis, such as ISO code for countries/regions, location names,and units of measurement for different parameters.

Overall,this dataset serves as a valuable resource for researchers, analysts, and policymakers seeking to explore various aspects related to Covid-19

How to use the dataset

Introduction:

• Understanding the Basic Structure:

  • The dataset consists of various columns containing different data related to vaccinations, testing, hospitalization, cases, deaths, policy responses, and other key variables.
  • Each row represents data for a specific country or region at a certain point in time.

• Selecting Desired Columns:

  • Identify the specific columns that are relevant to your analysis or research needs.
  • Some important columns include population, total cases, total deaths, new cases per million people, and vaccination-related metrics.

• Filtering Data:

  • Use filters based on specific conditions such as date ranges or continents to focus on relevant subsets of data.
  • This can help you analyze trends over time or compare data between different regions.

• Analyzing Vaccination Metrics:

  • Explore variables like total_vaccinations, people_vaccinated, and people_fully_vaccinated to assess vaccination coverage in different countries.
  • Calculate metrics such as people_vaccinated_per_hundred or total_boosters_per_hundred for standardized comparisons across populations.

• Investigating Testing Information:

  • Examine columns such as total_tests, new_tests, and tests_per_case to understand testing efforts in various countries.
  • Calculate rates like tests_per_case to assess testing efficiency or identify changes in testing strategies over time.

• Exploring Hospitalization and ICU Data:

  • Analyze variables like hosp_patients, icu_patients, and hospital_beds_per_thousand to understand healthcare systems' strain.
  • Calculate rates like icu_patients_per_million or hosp_patients_per_million for cross-country comparisons.

• Assessing Covid-19 Cases and Deaths:

  • Analyze variables like total_cases, new_ca...

The full text of this article can be freely accessed on the publisher's website.

Heart conditions were the most common causes of death in Mexico in 2023. During that period, more than ******* people died in the North American country as a result from said conditions. Diabetes mellitus ranked second, with over ******* deaths registered that year. Obesity in MexicoObesity and being overweight can worsen many risk factors for developing heart conditions, prediabetes, type 2 diabetes, and gestational diabetes, which in the case of a COVID-19 infection can lead to a severe course of the disease. In 2020, Mexico was reported as having one of the largest overweight and/or obese population in Latin America, with ** percent of people in the country having a body mass index higher than 25. In 2022, obesity was announced as being one of the most common illnesses experienced in Mexico, with over ******* cases estimated. In a decade from now, it is predicted that about *** million children in Mexico will suffer from obesity. If estimations are correct, this North American country will belong to the world’s top 10 countries with the most obese children in 2030. Physical activity in MexicoIt is not only a matter of food intake. A 2023 survey found, for instance, that only **** percent of Mexican population practiced sports and physical activities in their free time, a figure that has decreased in comparison to 2013. Less than ** percent of the physically active Mexicans practice sports for fun. However, the vast majority were motivated by health reasons.

Context

A novel Coronavirus found its First case in December 2019, and after that, coronavirus cases are increasing with each subsequent day. As we all know, many people have lost their lives in the first wave of COVID-19, and the number of Deaths increased in the 2nd Wave of COVID-19.

COVID-19 is commonly mild and self-limiting, but in a considerable portion of patients the disease is severe and fatal. Determining which patients are at high risk of severe illness or mortality is essential for appropriate clinical decision-making.

Content

The data file contains information on demographics, comorbidities, admission laboratory values, admission medications, admission supplemental oxygen orders, discharge, and mortality. The data were derived from a healthcare surveillance software package (Clinical Looking Glass [CLG]; Streamline Health, Atlanta, Georgia) and review of the primary medical records. The data relate to COVID-19 patients admitted to a single healthcare system, over a specific period of time, and separated into the 1st 3 weeks of the pandemic and the 2nd 3 weeks of the pandemic. Some of the variables included in the dataset are: length of hospital stay (LOS), myocardial infraction (MI), peripheral vascular disease (PVD), congestive heart failure (CHF), cardiovascular disease (CVD), dementia (Dement), Chronic obstructive pulmonary disease (COPD), diabetes mellitus simple (DM simple), diabetes mellitus complicated (DM complicated), oxygen saturation (OsSats), mean arterial pressure, in mmHg (MAP), D-dimer, in mg/ml (Ddimer), platelets, in k per mm3 (Plts), international normalized ratio (INR), blood urea nitrogen, in mg/dL (BUN), alanine aminotransferase, in U/liter (AST), while blood cells, in per mm3 (WBC) and interleukin-6, in pg/ml (IL-6).

Acknowledgements

I would like to Thanks Scientific Reports for the study on Covid-19 patients.

Inspiration

This Dataset can help in predicting the Mortality Risk or Severe Covid-19 Patients in the Early Stages when they just get admitted into the hospital. By early prediction of Severe covid-19 patients it can help overburdened hospitals to arrange the resources like Oxygen cylinders and ICU beds accordingly which can save the life of patient.

ObjectiveTo identify clinical and biochemical characteristics associated with 7- & 30-day mortality and intensive care admission amongst diabetes patients admitted with COVID-19.Research Design and MethodsWe conducted a cohort study collecting data from medical notes of hospitalised people with diabetes and COVID-19 in 7 hospitals within the Mersey-Cheshire region from 1 January to 30 June 2020. We also explored the impact on inpatient diabetes team resources. Univariate and multivariate logistic regression analyses were performed and optimised by splitting the dataset into a training, test, and validation sets, developing a robust predictive model for the primary outcome.ResultsWe analyzed data from 1004 diabetes patients (mean age 74.1 (± 12.6) years, predominantly men 60.7%). 45% belonged to the most deprived population quintile in the UK. Median BMI was 27.6 (IQR 23.9-32.4) kg/m2. The primary outcome (7-day mortality) occurred in 24%, increasing to 33% by day 30. Approximately one in ten patients required insulin infusion (9.8%). In univariate analyses, patients with type 2 diabetes had a higher risk of 7-day mortality [p < 0.05, OR 2.52 (1.06, 5.98)]. Patients requiring insulin infusion had a lower risk of death [p = 0.02, OR 0.5 (0.28, 0.9)]. CKD in younger patients (

It was estimated that around 6 percent of males and 4.8 percent of females who had COVID-19 in the United States from January 22 to May 30, 2020 died from the disease. Deaths due to COVID-19 are much higher among those with underlying health conditions such as cardiovascular disease, chronic lung disease, or diabetes. This statistic shows the percentage of people in the U.S. who had COVID-19 from January 22 to May 30, 2020 who died, by gender.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

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...

As of March 10, 2023, the state with the highest rate of COVID-19 cases was Rhode Island followed by Alaska. Around 103.9 million cases have been reported across the United States, with the states of California, Texas, and Florida reporting the highest numbers of infections.

From an epidemic to a pandemic The World Health Organization declared the COVID-19 outbreak as a pandemic on March 11, 2020. The term pandemic refers to multiple outbreaks of an infectious illness threatening multiple parts of the world at the same time; when the transmission is this widespread, it can no longer be traced back to the country where it originated. The number of COVID-19 cases worldwide is roughly 683 million, and it has affected almost every country in the world.

The symptoms and those who are most at risk Most people who contract the virus will suffer only mild symptoms, such as a cough, a cold, or a high temperature. However, in more severe cases, the infection can cause breathing difficulties and even pneumonia. Those at higher risk include older persons and people with pre-existing medical conditions, including diabetes, heart disease, and lung disease. Those aged 85 years and older have accounted for around 27 percent of all COVID deaths in the United States, although this age group makes up just two percent of the total population