Coronavirus disease 2019 (COVID-19) has already raised serious concern globally as the number of confirmed or suspected cases have increased rapidly. Epidemiological studies reported that obesity is associated with a higher rate of mortality in patients with COVID-19. Yet, to our knowledge, there is no comprehensive systematic review and meta-analysis to assess the effects of obesity and mortality among patients with COVID-19. We, therefore, aimed to evaluate the effect of obesity, associated comorbidities, and other factors on the risk of death due to COVID-19. We did a systematic search on PubMed, EMBASE, Google Scholar, Web of Science, and Scopus between January 1, 2020, and August 30, 2020. We followed Cochrane Guidelines to find relevant articles, and two reviewers extracted data from retrieved articles. Disagreement during those stages was resolved by discussion with the main investigator. The random-effects model was used to calculate effect sizes. We included 17 articles with a total of 543,399 patients. Obesity was significantly associated with an increased risk of mortality among patients with COVID-19 (RRadjust: 1.42 (95%CI: 1.24–1.63, p < 0.001). The pooled risk ratio for class I, class II, and class III obesity were 1.27 (95%CI: 1.05–1.54, p = 0.01), 1.56 (95%CI: 1.11–2.19, p < 0.01), and 1.92 (95%CI: 1.50–2.47, p < 0.001), respectively). In subgroup analysis, the pooled risk ratio for the patients with stroke, CPOD, CKD, and diabetes were 1.80 (95%CI: 0.89–3.64, p = 0.10), 1.57 (95%CI: 1.57–1.91, p < 0.001), 1.34 (95%CI: 1.18–1.52, p < 0.001), and 1.19 (1.07–1.32, p = 0.001), respectively. However, patients with obesity who were more than 65 years had a higher risk of mortality (RR: 2.54; 95%CI: 1.62–3.67, p < 0.001). Our study showed that obesity was associated with an increased risk of death from COVID-19, particularly in patients aged more than 65 years. Physicians should aware of these risk factors when dealing with patients with COVID-19 and take early treatment intervention to reduce the mortality of COVID-19 patients.

Data at the Local Authority (UK) level, for Covid-19 mortality rates and potential risk factors - median IMD score, population density, ethnicity, overweight/obesity, PM2.5 pollution. All data are in the public domain and references are given in the paper.

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.

Background: Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) Delta variant (B.1.617.2) has been responsible for the current increase in Coronavirus disease 2019 (COVID-19) infectivity rate worldwide. We compared the impact of the Delta variant and non-Delta variant on the COVID-19 outcomes in patients from Yogyakarta and Central Java provinces, Indonesia.Methods: In this cross-sectional study, we ascertained 161 patients, 69 with the Delta variant and 92 with the non-Delta variant. The Illumina MiSeq next-generation sequencer was used to perform the whole-genome sequences of SARS-CoV-2.Results: The mean age of patients with the Delta variant and the non-Delta variant was 27.3 ± 20.0 and 43.0 ± 20.9 (p = 3 × 10−6). The patients with Delta variant consisted of 23 males and 46 females, while the patients with the non-Delta variant involved 56 males and 36 females (p = 0.001). The Ct value of the Delta variant (18.4 ± 2.9) was significantly lower than that of the non-Delta variant (19.5 ± 3.8) (p = 0.043). There was no significant difference in the hospitalization and mortality of patients with Delta and non-Delta variants (p = 0.80 and 0.29, respectively). None of the prognostic factors were associated with the hospitalization, except diabetes with an OR of 3.6 (95% CI = 1.02–12.5; p = 0.036). Moreover, the patients with the following factors have been associated with higher mortality rate than the patients without the factors: age ≥65 years, obesity, diabetes, hypertension, and cardiovascular disease with the OR of 11 (95% CI = 3.4–36; p = 8 × 10−5), 27 (95% CI = 6.1–118; p = 1 × 10−5), 15.6 (95% CI = 5.3–46; p = 6 × 10−7), 12 (95% CI = 4–35.3; p = 1.2 × 10−5), and 6.8 (95% CI = 2.1–22.1; p = 0.003), respectively. Multivariate analysis showed that age ≥65 years, obesity, diabetes, and hypertension were the strong prognostic factors for the mortality of COVID-19 patients with the OR of 3.6 (95% CI = 0.58–21.9; p = 0.028), 16.6 (95% CI = 2.5–107.1; p = 0.003), 5.5 (95% CI = 1.3–23.7; p = 0.021), and 5.8 (95% CI = 1.02–32.8; p = 0.047), respectively.Conclusions: We show that the patients infected by the SARS-CoV-2 Delta variant have a lower Ct value than the patients infected by the non-Delta variant, implying that the Delta variant has a higher viral load, which might cause a more transmissible virus among humans. However, the Delta variant does not affect the COVID-19 outcomes in our patients. Our study also confirms that older age and comorbidity increase the mortality rate of patients with COVID-19.

Additional inpatient cases/hospitalizations, ICU admissions and deaths (per 10,000 total population in country and as percentage of total COVID-19 outcomes) related to overweight and obesity in 2020 and 2021.

Key pointsAs of January 2022, the COVID-19 pandemic was on-going, affecting populations worldwide. The potential risks of the Omicron variant (and future variants) still remain an area of active investigation. Thus, the ultimate human toll of SARS-CoV-2, and, by extension, the variations in that toll among diverse populations, remain unresolved. Nonetheless, an extensive literature on causal factors in the observed patterns of COVID-19 morbidity and cause-specific mortality has emerged—particularly at the aggregate level of analysis. This article explores potential pitfalls in the attribution of COVID outcomes to specific factors in isolation by examining a diverse set of potential factors and their interactions.MethodsWe sourced published data to establish a global database of COVID-19 outcomes for 68 countries and augmented these with an array of potential explanatory covariates from a diverse set of sources. We sought population-level aggregate factors from both health- and (traditionally) non-health domains, including: (a) Population biomarkers (b) Demographics and infrastructure (c) Socioeconomics (d) Policy responses at the country-level. We analyzed these data using (OLS) regression and more flexible non-parametric methods such as recursive partitioning, that are useful in examining both potential joint factor contributions to variations in pandemic outcomes, and the identification of possible interactions among covariates across these domains.ResultsUsing the national obesity rates of 68 countries as an illustrative predictor covariate of COVID-19 outcomes, we observed marked inconsistencies in apparent outcomes by population. Importantly, we also documented important variations in outcomes, based on interactions of health factors with covariates in other domains that are traditionally not related to biomarkers. Finally, our results suggest that single-factor explanations of population-level COVID-19 outcomes (e.g., obesity vs. cause-specific mortality) appear to be confounded substantially by other factors.Conclusions/implicationsOur methods and findings suggest that a full understanding of the toll of the COVID-19 pandemic, as would be central to preparing for similar future events, requires analysis within and among diverse variable domains, and within and among diverse populations. While this may seem apparent, the bulk of the recent literature on the pandemic has focused on one or a few of these drivers in isolation. Hypothesis generation and testing related to pandemic outcomes will benefit from accommodating the nuance of covariate interactions, in an epidemiologic context. Finally, our results add to the literature on the ecological fallacy: the attempt to infer individual drivers and outcomes from the study of population-level aggregates.

BackgroundThe COVID-19 pandemic caused significant global mortality. Obesity is associated with worse COVID-19 outcomes. This study examined the relationship between BMI, clinical interventions, and outcomes in hospitalized COVID-19 patients using pre-vaccine national data.MethodsWe conducted a retrospective cohort study using de-identified electronic health records from the HCA Healthcare database, comprising 149 hospitals across 18 U.S. states. Adults (≥18 years) hospitalized with confirmed SARS-CoV-2 infection between March 1 and December 31, 2022, were included. The primary outcome was a composite of in-hospital mortality or discharge to hospice, analyzed by BMI category. Secondary outcomes included inpatient mortality, need for mechanical ventilation or tracheostomy, duration of mechanical ventilation, and ICU (Intensive Care Unit) length of stay.ResultsOut of 38,321 hospital encounters, 21,996 met the inclusion criteria. Unadjusted analyses showed no significant differences in rates of all-cause mortality or hospice discharge across BMI categories. However, obese patients had higher rates of mechanical ventilation (7.8% vs. 4.6%, p 

This repository accompanies the doctoral thesis titled: "The Effect of Health System, Health Risk Factors and Health Service Coverage on Fertility, Morbidity and Mortality in HDI Countries: An Econometric Analysis." Given the complexity of the data and methodological procedures, key supplementary materials detailing the data sources, processing techniques, analytical scripts, and extended results are provided in the present Mendeley Data Repository. These materials are intended to promote transparency, reproducibility, and further research. The repository includes the following supplementary files: Supplementary File 1: Contains detailed information on all indicators used in the study, including those from the Global Reference List (GRL) and control variables. It specifies the definition, unit of measurement, data source, missing data proportions, inclusion status, and whether the indicator is positively or negatively associated with the outcome. Supplementary File 2: Provides the R script used to perform Multiple Imputation by Chained Equations (MICE) to handle missing data across indicators. Supplementary File 3: Describes the imputed dataset generated using the MICE method for both pre-COVID (2015–2019) and post-COVID (2020–2021) periods. Supplementary File 4: Contains the R script used to construct the composite and sub-indices for Health System, Health Risk Factors, Service Coverage, and Health Status. Supplementary File 5: Provides the R script used to compute Compound Annual Growth Rates (CAGR) for all indices and component indicators. Supplementary File 6: Includes the Stata Do-file used to run panel data regression models, estimating the impact of Health System, Health Risk Factors, and Service Coverage on fertility, morbidity, and mortality. Supplementary File 7: Contains the Stata Do-file used for conducting the Phillips and Sul Convergence Analysis to assess convergence/divergence trends among countries toward selected health-related SDG targets. Supplementary File 8: Provides descriptive statistics—including mean, standard deviation, and coefficient of variation—for selected health indicators across 100 HDI countries during the study period (2015–2021). Supplementary File 9: Presents the CAGR estimates of all constructed indices, separately reported for pre-COVID (2015–2019) and post-COVID (2020–2021) phases. Supplementary File 10: Provides the forecasted values for 57 indicators across 100 countries up to the year 2025, supporting the study’s predictive analysis.

Background. Chronic obstructive pulmonary disease (COPD) is a debilitating lung condition characterised by progressive lung function limitation. COPD is an umbrella term and encompasses a spectrum of pathophysiologies including chronic bronchitis, small airways disease and emphysema. COPD caused an estimated 3 million deaths worldwide in 2016, and is estimated to be the third leading cause of death worldwide. The British Lung Foundation (BLF) estimates that the disease costs the NHS around £1.9 billion per year. COPD is therefore a significant public health challenge. This dataset explores the impact of hospitalisation in patients with COPD during the COVID pandemic.

PIONEER geography The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK. There are particularly high rates of physical inactivity, obesity, smoking & diabetes. The West Midlands has a high prevalence of COPD, reflecting the high rates of smoking and industrial exposure. Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS.

EHR. University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & 100 ITU beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Scope: All hospitalised patients admitted to UHB during the COVID-19 pandemic first wave, curated to focus on COPD. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes ICD-10 & SNOMED-CT codes pertaining to COPD and COPD exacerbations, as well as all co-morbid conditions. Serial, structured data pertaining to process of care (timings, staff grades, specialty review, wards), presenting complaint, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, nebulisers, antibiotics, inotropes, vasopressors, organ support), all outcomes. Linked images available (radiographs, CT).

Available supplementary data: More extensive data including wave 2 patients in non-OMOP form. Ambulance, 111, 999 data, synthetic data.

Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

Mortality rates for hospitalized COVID–19 patients during the first three and final three months of data collection.

BackgroundPublished data regarding the impact of obesity on COVID-19 outcomes are inconsistent. However, in most studies, body composition was assessed using body mass index (BMI) alone, thus neglecting the presence and distribution of adipose tissue. Therefore, we aimed to investigate the impact of body and visceral fat on COVID-19 outcomes.MethodsObservational, prospective cohort study included 216 consecutive COVID-19 patients hospitalized at University Clinical Center Kragujevac (Serbia) from October to December 2021. Body composition was assessed using the BMI, body fat percentage (�), and visceral fat (VF) via bioelectrical impedance analysis (BIA). In addition to anthropometric measurements, variables in the research were socio-demographic and medical history data, as well as admission inflammatory biomarkers. Primary end-points were fatal outcomes and intensive care unit (ICU) admission.ResultsThe overall prevalence of obesity was 39.3% according to BMI and 50.9% according to % BF, while 38.4% of patients had very high VF levels. After adjusting odds ratio values for cofounding variables and obesity-related conditions, all three anthropometric parameters were significant predictors of primary end-points. However, we note that % BF and VF, compared to BMI, were stronger predictors of both mortality (aOR 3.353, aOR 3.05, and aOR 2.387, respectively) and ICU admission [adjusted odds ratio (aOR) 7.141, aOR 3.424, and aOR 3.133, respectively].ConclusionObesity is linked with COVID-19 mortality and ICU admission, with BIA measurements being stronger predictors of outcome compared to BMI use alone.

IntroductionPatients with multiple myeloma (MM) face heightened infection susceptibility, particularly severe risks from COVID-19. This study, the first systematic review in its domain, seeks to assess the impacts of COVID-19 on MM patients.MethodAdhering to PRISMA guidelines and PROSPERO registration (ID: CRD42023407784), this study conducted an exhaustive literature search from January 1, 2020, to April 12, 2024, using specified search terms in major databases (PubMed, EMBASE, and Web of Science). Quality assessment utilized the JBI Critical checklist, while publication bias was assessed using Egger’s test and funnel plot. The leave-one-out sensitivity analyses were performed to assess the robustness of the results by excluding one study at a time to identify studies with a high risk of bias or those that significantly influenced the overall effect size. Data synthesis involved fitting a random-effects model and estimating meta-regression coefficients.ResultsA total of 14 studies, encompassing a sample size of 3214 yielded pooled estimates indicating a hospitalization rate of 53% (95% CI: 40.81, 65.93) with considerable heterogeneity across studies (I2 = 99%). The ICU admission rate was 17% (95% CI: 11.74, 21.37), also with significant heterogeneity (I2 = 94%). The pooled mortality rate was 22% (95% CI: 15.33, 28.93), showing high heterogeneity (I2 = 97%). The pooled survival rate stood at 78% (95% CI: 71.07, 84.67), again exhibiting substantial heterogeneity (I2 = 97%). Subgroup analysis and meta-regression highlighted that study types, demographic factors, and patient comorbidities significantly contributed to the observed outcome heterogeneity, revealing distinct patterns. Mortality rates increased by 15% for participants with a median age above 67 years. ICU admission rates were positively correlated with obesity, with a 20% increase for groups with at least 19% obesity. Mortality rates rose by 33% for the group of patients with at least 19% obesity, while survival rates decreased by 33% in the same group.ConclusionOur meta-analysis sheds light on diverse COVID-19 outcomes in multiple myeloma. Heterogeneity underscores complexities, and study types, demographics, and co-morbidities significantly influence results, emphasizing the nuanced interplay of factors.

Previous studies have evaluated comorbidities and sociodemographic factors individually or by type but not comprehensively. This study aims to analyze the influence of a wide variety of factors in a single study to better understand the big picture of their effects on case-fatalities. This cross-sectional study used county-level comorbidities, social determinants of health such as income and race, measures of preventive healthcare, age, education level, average household size, population density, and political voting patterns were all evaluated on a national and regional basis. Analysis was performed through Generalized Additive Models and adjusted by the COVID-19 Community Vulnerability Index (CCVI). Effect estimates of COVID-19 fatality rates for risk factors such as comorbidities, sociodemographic factors and determinant of health. Factors associated with reducing COVID-19 fatality rates were mostly sociodemographic factors such as age, education and income, and preventive health measures. Obesity, minimal leisurely activity, binge drinking, and higher rates of individuals taking high blood pressure medication were associated with increased case fatality rate in a county. Political leaning influenced case case-fatality rates. Regional trends showed contrasting effects where larger household size was protective in the Midwest, yet harmful in Northeast. Notably, higher rates of respiratory comorbidities such as asthma and chronic obstructive pulmonary disease (COPD) diagnosis were associated with reduced case-fatality rates in the Northeast. Increased rates of chronic kidney disease (CKD) within counties were often the strongest predictor of increased case-fatality rates for several regions. Our findings highlight the importance of considering the full context when evaluating contributing factors to case-fatality rates. The spectrum of factors identified in this study must be analyzed in the context of one another and not in isolation.

Independent predictors of inpatient mortality in patients with COVID-19 across the entire study period.

Predictors associated with in-hospital mortality of hospitalized patients with COVID-19.

Resource utilization and outcomes of COVID-19 patients admitted to Inova Health System hospitals.

Demographic, clinical, and therapeutic characteristics of hospitalized COVID-19 patients by outcome.

Vital signs, symptoms and laboratory parameters on admission.

Initial laboratory characteristics of hospitalized COVID-19 patients by outcome.