Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 279
Filter
1.
MMWR Morb Mortal Wkly Rep ; 70(14): 519-522, 2021 04 09.
Article in English | MEDLINE | ID: covidwho-1384037

ABSTRACT

CDC's National Vital Statistics System (NVSS) collects and reports annual mortality statistics using data from U.S. death certificates. Because of the time needed to investigate certain causes of death and to process and review data, final annual mortality data for a given year are typically released 11 months after the end of the calendar year. Daily totals reported by CDC COVID-19 case surveillance are timely but can underestimate numbers of deaths because of incomplete or delayed reporting. As a result of improvements in timeliness and the pressing need for updated, quality data during the global COVID-19 pandemic, NVSS expanded provisional data releases to produce near real-time U.S. mortality data.* This report presents an overview of provisional U.S. mortality data for 2020, including the first ranking of leading causes of death. In 2020, approximately 3,358,814 deaths† occurred in the United States. From 2019 to 2020, the estimated age-adjusted death rate increased by 15.9%, from 715.2 to 828.7 deaths per 100,000 population. COVID-19 was reported as the underlying cause of death or a contributing cause of death for an estimated 377,883 (11.3%) of those deaths (91.5 deaths per 100,000). The highest age-adjusted death rates by age, race/ethnicity, and sex occurred among adults aged ≥85 years, non-Hispanic Black or African American (Black) and non-Hispanic American Indian or Alaska Native (AI/AN) persons, and males. COVID-19 death rates were highest among adults aged ≥85 years, AI/AN and Hispanic persons, and males. COVID-19 was the third leading cause of death in 2020, after heart disease and cancer. Provisional death estimates provide an early indication of shifts in mortality trends and can guide public health policies and interventions aimed at reducing numbers of deaths that are directly or indirectly associated with the COVID-19 pandemic.


Subject(s)
COVID-19/mortality , Mortality/trends , Adolescent , Adult , Aged , Aged, 80 and over , COVID-19/ethnology , Cause of Death/trends , Child , Child, Preschool , Female , Health Status Disparities , Humans , Infant , Male , Middle Aged , Mortality/ethnology , United States/epidemiology , Vital Statistics , Young Adult
3.
Nature ; 605(7910): 410-413, 2022 05.
Article in English | MEDLINE | ID: covidwho-1860316
4.
Crit Care ; 25(1): 260, 2021 07 23.
Article in English | MEDLINE | ID: covidwho-1854842

ABSTRACT

BACKGROUND: The optimal protein dose in critical illness is unknown. We aim to conduct a systematic review of randomized controlled trials (RCTs) to compare the effect of higher versus lower protein delivery (with similar energy delivery between groups) on clinical and patient-centered outcomes in critically ill patients. METHODS: We searched MEDLINE, EMBASE, CENTRAL and CINAHL from database inception through April 1, 2021.We included RCTs of (1) adult (age ≥ 18) critically ill patients that (2) compared higher vs lower protein with (3) similar energy intake between groups, and (4) reported clinical and/or patient-centered outcomes. We excluded studies on immunonutrition. Two authors screened and conducted quality assessment independently and in duplicate. Random-effect meta-analyses were conducted to estimate the pooled risk ratio (dichotomized outcomes) or mean difference (continuous outcomes). RESULTS: Nineteen RCTs were included (n = 1731). Sixteen studies used primarily the enteral route to deliver protein. Intervention was started within 72 h of ICU admission in sixteen studies. The intervention lasted between 3 and 28 days. In 11 studies that reported weight-based nutrition delivery, the pooled mean protein and energy received in higher and lower protein groups were 1.31 ± 0.48 vs 0.90 ± 0.30 g/kg and 19.9 ± 6.9 versus 20.1 ± 7.1 kcal/kg, respectively. Higher vs lower protein did not significantly affect overall mortality [risk ratio 0.91, 95% confidence interval (CI) 0.75-1.10, p = 0.34] or other clinical or patient-centered outcomes. In 5 small studies, higher protein significantly attenuated muscle loss (MD -3.44% per week, 95% CI -4.99 to -1.90; p < 0.0001). CONCLUSION: In critically ill patients, a higher daily protein delivery was not associated with any improvement in clinical or patient-centered outcomes. Larger, and more definitive RCTs are needed to confirm the effect of muscle loss attenuation associated with higher protein delivery. PROSPERO registration number: CRD42021237530.


Subject(s)
Dietary Proteins/administration & dosage , Energy Intake/physiology , Critical Illness/therapy , Dietary Proteins/therapeutic use , Enteral Nutrition/methods , Enteral Nutrition/standards , Humans , Mortality/trends , Randomized Controlled Trials as Topic/statistics & numerical data
5.
JAMA ; 327(17): 1641, 2022 05 03.
Article in English | MEDLINE | ID: covidwho-1843808
6.
Lancet ; 398(10301): 685-697, 2021 08 21.
Article in English | MEDLINE | ID: covidwho-1815297

ABSTRACT

BACKGROUND: Associations between high and low temperatures and increases in mortality and morbidity have been previously reported, yet no comprehensive assessment of disease burden has been done. Therefore, we aimed to estimate the global and regional burden due to non-optimal temperature exposure. METHODS: In part 1 of this study, we linked deaths to daily temperature estimates from the ERA5 reanalysis dataset. We modelled the cause-specific relative risks for 176 individual causes of death along daily temperature and 23 mean temperature zones using a two-dimensional spline within a Bayesian meta-regression framework. We then calculated the cause-specific and total temperature-attributable burden for the countries for which daily mortality data were available. In part 2, we applied cause-specific relative risks from part 1 to all locations globally. We combined exposure-response curves with daily gridded temperature and calculated the cause-specific burden based on the underlying burden of disease from the Global Burden of Diseases, Injuries, and Risk Factors Study, for the years 1990-2019. Uncertainty from all components of the modelling chain, including risks, temperature exposure, and theoretical minimum risk exposure levels, defined as the temperature of minimum mortality across all included causes, was propagated using posterior simulation of 1000 draws. FINDINGS: We included 64·9 million individual International Classification of Diseases-coded deaths from nine different countries, occurring between Jan 1, 1980, and Dec 31, 2016. 17 causes of death met the inclusion criteria. Ischaemic heart disease, stroke, cardiomyopathy and myocarditis, hypertensive heart disease, diabetes, chronic kidney disease, lower respiratory infection, and chronic obstructive pulmonary disease showed J-shaped relationships with daily temperature, whereas the risk of external causes (eg, homicide, suicide, drowning, and related to disasters, mechanical, transport, and other unintentional injuries) increased monotonically with temperature. The theoretical minimum risk exposure levels varied by location and year as a function of the underlying cause of death composition. Estimates for non-optimal temperature ranged from 7·98 deaths (95% uncertainty interval 7·10-8·85) per 100 000 and a population attributable fraction (PAF) of 1·2% (1·1-1·4) in Brazil to 35·1 deaths (29·9-40·3) per 100 000 and a PAF of 4·7% (4·3-5·1) in China. In 2019, the average cold-attributable mortality exceeded heat-attributable mortality in all countries for which data were available. Cold effects were most pronounced in China with PAFs of 4·3% (3·9-4·7) and attributable rates of 32·0 deaths (27·2-36·8) per 100 000 and in New Zealand with 3·4% (2·9-3·9) and 26·4 deaths (22·1-30·2). Heat effects were most pronounced in China with PAFs of 0·4% (0·3-0·6) and attributable rates of 3·25 deaths (2·39-4·24) per 100 000 and in Brazil with 0·4% (0·3-0·5) and 2·71 deaths (2·15-3·37). When applying our framework to all countries globally, we estimated that 1·69 million (1·52-1·83) deaths were attributable to non-optimal temperature globally in 2019. The highest heat-attributable burdens were observed in south and southeast Asia, sub-Saharan Africa, and North Africa and the Middle East, and the highest cold-attributable burdens in eastern and central Europe, and central Asia. INTERPRETATION: Acute heat and cold exposure can increase or decrease the risk of mortality for a diverse set of causes of death. Although in most regions cold effects dominate, locations with high prevailing temperatures can exhibit substantial heat effects far exceeding cold-attributable burden. Particularly, a high burden of external causes of death contributed to strong heat impacts, but cardiorespiratory diseases and metabolic diseases could also be substantial contributors. Changes in both exposures and the composition of causes of death drove changes in risk over time. Steady increases in exposure to the risk of high temperature are of increasing concern for health. FUNDING: Bill & Melinda Gates Foundation.


Subject(s)
Cause of Death/trends , Cold Temperature/adverse effects , Global Burden of Disease/statistics & numerical data , Global Health/statistics & numerical data , Hot Temperature/adverse effects , Mortality/trends , Bayes Theorem , Heart Diseases/epidemiology , Humans , Metabolic Diseases/epidemiology
7.
Sci Total Environ ; 755(Pt 1): 142523, 2021 Feb 10.
Article in English | MEDLINE | ID: covidwho-1778441

ABSTRACT

The Italian government has been one of the most responsive to COVID-2019 emergency, through the adoption of quick and increasingly stringent measures to contain the outbreak. Despite this, Italy has suffered a huge human and social cost, especially in Lombardy. The aim of this paper is dual: i) first, to investigate the reasons of the case fatality rate (CFR) differences across Italian 20 regions and 107 provinces, using a multivariate OLS regression approach; and ii) second, to build a "taxonomy" of provinces with similar mortality risk of COVID-19, by using the Ward's hierarchical agglomerative clustering method. I considered health system metrics, environmental pollution, climatic conditions, demographic variables, and three ad hoc indexes that represent the health system saturation. The results showed that overall health care efficiency, physician density, and average temperature helped to reduce the CFR. By the contrary, population aged 70 and above, car and firm density, air pollutants concentrations (NO2, O3, PM10, and PM2.5), relative average humidity, COVID-19 prevalence, and all three indexes of health system saturation were positively associated with the CFR. Population density, social vertical integration, and altitude were not statistically significant. In particular, the risk of dying increases with age, as 90 years old and above had a three-fold greater risk than the 80-to-89 years old and four-fold greater risk than 70-to-79 years old. Moreover, the cluster analysis showed that the highest mortality risk was concentrated in the north of the country, while the lowest risk was associated with southern provinces. Finally, since prevalence and health system saturation indexes played the most important role in explaining the CFR variability, a significant part of the latter may have been caused by the massive stress of the Italian health system.


Subject(s)
Air Pollution , COVID-19 , Aged , Aged, 80 and over , COVID-19/mortality , Delivery of Health Care , Humans , Italy/epidemiology , Mortality/trends , SARS-CoV-2
9.
JAMA Netw Open ; 5(3): e221870, 2022 03 01.
Article in English | MEDLINE | ID: covidwho-1733815

ABSTRACT

Importance: There has been recent media attention on the risk of excess mortality among homeless individuals during the COVID-19 pandemic, yet data on these deaths are limited. Objectives: To quantify and describe deaths among people experiencing homelessness in San Francisco during the COVID-19 pandemic and to compare the characteristics of these deaths with those in prior years. Design, Setting, and Participants: A cross-sectional study tracking mortality among people experiencing homelessness from 2016 to 2021 in San Francisco, California. All deceased individuals who were homeless in San Francisco at the time of death and whose deaths were processed by the San Francisco Office of the Chief Medical Examiner were included. Data analysis was performed from August to October 2021. Exposure: Homelessness, based on homeless living status in an administrative database. Main Outcomes and Measures: Descriptive statistics were used to understand annual trends in demographic characteristics, cause and manner of death (based on autopsy), substances present in toxicology reports, geographic distribution of deaths, and use of health and social services prior to death. Total estimated numbers of people experiencing homelessness in San Francisco were assessed through semiannual point-in-time counts. The 2021 point-in-time count was postponed owing to the COVID-19 pandemic. Results: In San Francisco, there were 331 deaths among people experiencing homelessness in the first year of the COVID-19 pandemic (from March 17, 2020, to March 16, 2021). This number was more than double any number in previous years (eg, 128 deaths in 2016, 128 deaths in 2017, 135 deaths in 2018, and 147 deaths in 2019). Most individuals who died were male (268 of 331 [81%]). Acute drug toxicity was the most common cause of death in each year, followed by traumatic injury. COVID-19 was not listed as the primary cause of any deaths. The proportion of deaths involving fentanyl increased each year (present in 52% of toxicology reports in 2019 and 68% during the pandemic). Fewer decedents had contacts with health services in the year prior to their death during the pandemic than in prior years (13% used substance use disorder services compared with 20% in 2019). Conclusions and Relevance: In this cross-sectional study, the number of deaths among people experiencing homelessness in San Francisco increased markedly during the first year of the COVID-19 pandemic. These findings may guide future interventions to reduce mortality among individuals experiencing homelessness.


Subject(s)
Homeless Persons/statistics & numerical data , Adult , Aged , Aged, 80 and over , COVID-19/epidemiology , Cause of Death/trends , Cross-Sectional Studies , Female , Humans , Male , Middle Aged , Mortality/trends , SARS-CoV-2 , San Francisco
10.
JAMA Netw Open ; 5(3): e221754, 2022 03 01.
Article in English | MEDLINE | ID: covidwho-1733813

ABSTRACT

Importance: The increased hospital mortality rates from non-SARS-CoV-2 causes during the SARS-CoV-2 pandemic are incompletely characterized. Objective: To describe changes in mortality rates after hospitalization for non-SARS-CoV-2 conditions during the COVID-19 pandemic and how mortality varies by characteristics of the admission and hospital. Design, Setting, and Participants: Retrospective cohort study from January 2019 through September 2021 using 100% of national Medicare claims, including 4626 US hospitals. Participants included 8 448 758 individuals with non-COVID-19 medical admissions with fee-for-service Medicare insurance. Main Outcomes and Measures: Outcome was mortality in the 30 days after admission with adjusted odds generated from a 3-level (admission, hospital, and county) logistic regression model that included diagnosis, demographic variables, comorbidities, hospital characteristics, and hospital prevalence of SARS-CoV-2. Results: There were 8 448 758 non-SARS-CoV-2 medical admissions in 2019 and from April 2020 to September 2021 (mean [SD] age, 73.66 [12.88] years; 52.82% women; 821 569 [11.87%] Black, 438 453 [6.34%] Hispanic, 5 351 956 [77.35%] White, and 307 218 [4.44%] categorized as other). Mortality in the 30 days after admission increased from 9.43% in 2019 to 11.48% from April 1, 2020, to March 31, 2021 (odds ratio [OR], 1.20; 95% CI, 1.19-1.21) in multilevel logistic regression analyses including admission and hospital characteristics. The increase in mortality was maintained throughout the first 18 months of the pandemic and varied by race and ethnicity (OR, 1.27; 95% CI, 1.23-1.30 for Black enrollees; OR, 1.25; 95% CI, 1.23-1.27 for Hispanic enrollees; and OR, 1.18; 95% CI, 1.17-1.19 for White enrollees); Medicaid eligibility (OR, 1.25; 95% CI, 1.24-1.27 for Medicaid eligible vs OR, 1.18; 95% CI, 1.16-1.18 for noneligible); and hospital quality score, measured on a scale of 1 to 5 stars with 1 being the worst and 5 being the best (OR, 1.27; 95% CI, 1.22-1.31 for 1 star vs OR, 1.11; 95% CI, 1.08-1.15 for 5 stars). Greater hospital prevalence of SARS-CoV-2 was associated with greater increases in odds of death from the prepandemic period to the pandemic period; for example, comparing mortality in October through December 2020 with October through December 2019, the OR was 1.44 (95% CI, 1.39-1.49) for hospitals in the top quartile of SARS-CoV-2 admissions vs an OR of 1.19 (95% CI, 1.16-1.22) for admissions to hospitals in the lowest quartile. This association was mostly limited to admissions with high-severity diagnoses. Conclusions and Relevance: The prolonged elevation in mortality rates after hospital admission in 2020 and 2021 for non-SARS-CoV-2 diagnoses contrasts with reports of improvement in hospital mortality during 2020 for SARS-CoV-2. The results of this cohort study suggest that, with the continued impact of SARS-CoV-2, it is important to implement interventions to improve access to high-quality hospital care for those with non-SARS-CoV-2 diseases.


Subject(s)
COVID-19/mortality , Hospitalization/trends , Medicare/statistics & numerical data , Mortality/trends , Pandemics , SARS-CoV-2 , Aged , COVID-19/ethnology , Cohort Studies , Female , Humans , Insurance Claim Review , Male , Socioeconomic Factors , United States/epidemiology
11.
Eur J Cancer ; 160: 261-272, 2022 01.
Article in English | MEDLINE | ID: covidwho-1719649

ABSTRACT

AIM OF THE STUDY: The coronavirus disease 2019 (COVID-19) pandemic significantly impacted cancer care. In this study, clinical patient characteristics related to COVID-19 outcomes and advanced care planning, in terms of non-oncological treatment restrictions (e.g. do-not-resuscitate codes), were studied in patients with cancer and COVID-19. METHODS: The Dutch Oncology COVID-19 Consortium registry was launched in March 2020 in 45 hospitals in the Netherlands, primarily to identify risk factors of a severe COVID-19 outcome in patients with cancer. Here, an updated analysis of the registry was performed, and treatment restrictions (e.g. do-not-intubate codes) were studied in relation to COVID-19 outcomes in patients with cancer. Oncological treatment restrictions were not taken into account. RESULTS: Between 27th March 2020 and 4th February 2021, 1360 patients with cancer and COVID-19 were registered. Follow-up data of 830 patients could be validated for this analysis. Overall, 230 of 830 (27.7%) patients died of COVID-19, and 60% of the remaining 600 patients with resolved COVID-19 were admitted to the hospital. Patients with haematological malignancies or lung cancer had a higher risk of a fatal outcome than other solid tumours. No correlation between anticancer therapies and the risk of a fatal COVID-19 outcome was found. In terms of end-of-life communication, 50% of all patients had restrictions regarding life-prolonging treatment (e.g. do-not-intubate codes). Most identified patients with treatment restrictions had risk factors associated with fatal COVID-19 outcome. CONCLUSION: There was no evidence of a negative impact of anticancer therapies on COVID-19 outcomes. Timely end-of-life communication as part of advanced care planning could save patients from prolonged suffering and decrease burden in intensive care units. Early discussion of treatment restrictions should therefore be part of routine oncological care, especially during the COVID-19 pandemic.


Subject(s)
COVID-19/mortality , Hospitalization/statistics & numerical data , Life Support Care/statistics & numerical data , Mortality/trends , Neoplasms/mortality , SARS-CoV-2/isolation & purification , Withholding Treatment/statistics & numerical data , Aged , Aged, 80 and over , COVID-19/epidemiology , COVID-19/therapy , COVID-19/virology , Female , Humans , Male , Middle Aged , Neoplasms/epidemiology , Neoplasms/therapy , Neoplasms/virology , Netherlands/epidemiology , Prognosis , Risk Factors , Survival Rate
12.
J Med Virol ; 94(4): 1592-1605, 2022 Apr.
Article in English | MEDLINE | ID: covidwho-1718405

ABSTRACT

The COVID-19 pandemic has appeared as the predominant disease of the 21st century at the end of 2019 and was a drastic start with thousands of casualties and the COVID-19 victims in 2020. Due to the drastic effect, COVID-19 scientists are trying to work on pandemic diseases and Governments are interested in the development of methodologies that will minimize the losses and speed up the process of cure by providing vaccines and treatment for such pandemics. The development of a new vaccine for any pandemic requires long in vitro and in vivo trials to use. Thus the strategies require understanding how the pandemic is spreading in terms of affected cases and casualties occurring from this disease, here we developed a forecasting model that can predict the no of cases and deaths due to pandemic and that can help the researcher, government, and other stakeholders to devise their strategies so that the damages can be minimized. This model can also be used for the judicial distribution of resources as it provides the estimates of the number of casualties and number of deaths with high accuracy, Government and policymakers on the basis of forecasted value can plan in a better way. The model efficiency is discussed on the basis of the available dataset of John Hopkins University repository in the period when the disease was first reported in the six countries till the mid of May 2020, the model was developed on the basis of this data, and then it is tested by forecasting the no of deaths and cases for next 7 days, where the proposed strategy provided excellent forecasting. The forecast models are developed for six countries including Pakistan, India, Afghanistan, Iran, Italy, and China using polynomial regression of degrees 3-5. But the models are analyzed up to the 6th-degree and the suitable models are selected based on higher adjusted R-square (R2 ) and lower root-mean-square error and the mean absolute percentage error (MAPE). The values of R2 are greater than 99% for all countries other than China whereas for China this R2 was 97%. The high values of R2 and Low value of MAPE statistics increase the validity of proposed models to forecast the total no cases and total no of deaths in all countries. Iran, Italy, and Afghanistan also show a mild decreasing trend but the number of cases is far higher than the decrease percentage. Although India is expected to have a consistent result, more or less it depicts some other biasing factors which should be figured out in separate research.


Subject(s)
Epidemiological Models , Forecasting/methods , Pandemics , Algorithms , COVID-19/epidemiology , COVID-19/mortality , COVID-19/prevention & control , Humans , Models, Statistical , Mortality/trends , Pandemics/prevention & control , Pandemics/statistics & numerical data , Prevalence , SARS-CoV-2
13.
Anesth Analg ; 134(3): 524-531, 2022 03 01.
Article in English | MEDLINE | ID: covidwho-1709740

ABSTRACT

BACKGROUND: Coronavirus disease 2019 (COVID-19) cases continue to surge in the United States with the emergence of new variants. Statewide variability and inconsistency in implementing risk mitigation strategies are widespread, particularly in regards to enforcing mask mandates and encouraging the public to become fully vaccinated. METHODS: This is a cross-sectional study conducted on July 31, 2021, utilizing publicly available data from the Wisconsin Department of Health Services. The authors abstracted data on total COVID-19-related cases, hospitalizations, and deaths in the state of Wisconsin. The primary objective was comparison of total COVID-19-related cases, hospitalizations, and deaths in vaccinated versus unvaccinated people in the state of Wisconsin over a 31-day period (July 2021). Furthermore, we also performed a narrative review of the literature on COVID-19-related outcomes based on mask use and vaccination status. RESULTS: In the state of Wisconsin during July 2021, total COVID-19 cases was 125.4 per 100,000 fully vaccinated people versus 369.2 per 100,000 not fully vaccinated people (odds ratio [OR] = 0.34, 95% confidence interval [CI], 0.33-0.35; P < .001). Total COVID-19 hospitalizations was 4.9 per 100,000 fully vaccinated people versus 18.2 per 100,000 not fully vaccinated people (OR = 0.27, 98% CI, 0.22-0.32; P < .001). Total COVID-19 deaths was 0.1 per 100,000 fully vaccinated people versus 1.1 per 100,000 not fully vaccinated people (OR = 0.09, 95% CI, 0.03-0.29; P < .001). Narrative review of the literature demonstrated high vaccine effectiveness against COVID-19 infection prevention (79%-100% among fully vaccinated people), COVID-19-related hospitalization (87%-98% among fully vaccinated people), and COVID-19-related death (96.7%-98% among fully vaccinated people). Studies have also generally reported that mask use was associated with increased effectiveness in preventing COVID-19 infection ≤70%. CONCLUSIONS: Strict adherence to public mask use and fully vaccinated status are associated with improved COVID-19-related outcomes and can mitigate the spread, morbidity, and mortality of COVID-19. Anesthesiologists and intensivists should adhere to evidence-based guidelines in their approach and management of patients to help mitigate spread.


Subject(s)
COVID-19/mortality , Cost of Illness , Hospitalization/trends , Mandatory Programs/trends , Masks/trends , Vaccination/trends , COVID-19/prevention & control , Cross-Sectional Studies , Data Interpretation, Statistical , Hospitalization/statistics & numerical data , Humans , Mandatory Programs/statistics & numerical data , Masks/statistics & numerical data , Mortality/trends , Vaccination/statistics & numerical data , Wisconsin/epidemiology
14.
BMJ ; 376: o262, 2022 02 03.
Article in English | MEDLINE | ID: covidwho-1673392
16.
J Med Virol ; 94(5): 2126-2132, 2022 05.
Article in English | MEDLINE | ID: covidwho-1653289

ABSTRACT

We aimed to investigate COVID-19 case fatality rate (CFR), mortality, and screening in the older population of East Azerbaijan Province. We conducted a population-based registry study from Death Registration System in the elderly population (N = 433 445) from the outbreak that emerged up to May 30, 2021 (before vaccination). We analyzed CFR and mortality rates due to COVID-19 as well as the case findings and characteristics in the elderly population. Logistic regression analysis was carried out for the association between COVID-19 mortality and effective factors. During the study, the province had 18 079 confirmed cases and 4390 deaths. The male to female CFR risk ratio was 3.2. The overall CFR and mortality rates were 24% and 1%, respectively. CFR and mortality ranged from 9.56% to 0.37% in the 60-64 age group to 70% and 2.6% in the age group ≥85 years, respectively. We found a significant trend in CFR and mortality of COVID-19 with advanced age. Male sex, advanced age, marital status, and living alone were associated with an increased risk of COVID-19 fatality. COVID-19 mortality measures were higher in the older population of this province. Advanced treatment supports and interventions are needed to reduce mortality rates of COVID-19 in the elderly population.


Subject(s)
COVID-19 , Aged , Aged, 80 and over , COVID-19/mortality , Female , Humans , Iran/epidemiology , Male , Middle Aged , Mortality/trends , Registries , Risk Factors , Sociodemographic Factors
17.
Public Health Rep ; 137(2): 234-238, 2022.
Article in English | MEDLINE | ID: covidwho-1643029

ABSTRACT

Sickle cell disease (SCD) is associated with increased risk of poor health outcomes from respiratory infections, including COVID-19 illness. We used US death data to investigate changes in SCD-related mortality before and during the COVID-19 pandemic. We estimated annual age- and quarter-adjusted SCD-related mortality rates for 2014-2020. We estimated the number of excess deaths in 2020 compared with 2019 using the standardized mortality ratio (SMR). We found 1023 SCD-related deaths reported in the United States during 2020, of which 86 (8.4%) were associated with COVID-19. SCD-related deaths, both associated and not associated with COVID-19, occurred most frequently among adults aged 25-59 years. The SCD-related mortality rate changed <5% year to year from 2014 to 2019 but increased 12% in 2020; the sharpest increase was among adults aged ≥60 years. The SMR comparing 2020 with 2019 was 1.12 (95% CI, 1.06-1.19). Overall, 113 (95% CI, 54-166) excess SCD-related deaths occurred in 2020.


Subject(s)
Anemia, Sickle Cell/mortality , COVID-19/epidemiology , Adolescent , Adult , Age Distribution , Anemia, Sickle Cell/complications , COVID-19/complications , Child , Child, Preschool , Ethnicity , Humans , Infant , Middle Aged , Mortality/trends , Race Factors , SARS-CoV-2 , Time Factors , United States/epidemiology
18.
JAMA Intern Med ; 182(3): 291-300, 2022 Mar 01.
Article in English | MEDLINE | ID: covidwho-1637435

ABSTRACT

IMPORTANCE: Telomeres protect DNA from damage. Because they shorten with each mitotic cycle, leukocyte telomere length (LTL) serves as a mitotic clock. Reduced LTL has been associated with multiple human disorders. OBJECTIVE: To determine the association between LTL and overall as well as disease-specific mortality and morbidity. DESIGN, SETTING, AND PARTICIPANTS: This multicenter, community-based cohort study conducted from March 2006 to December 2010 included longitudinal follow-up (mean [SD], 12 [2] years) for 472 432 English participants from the United Kingdom Biobank (UK Biobank) and analyzed morbidity and mortality. The data were analyzed in 2021. MAIN OUTCOMES AND MEASURES: Hazard ratios (HRs) and odds ratios for mortality and morbidity associated with a standard deviation change in LTL, adjusted for age, sex, body mass index (calculated as weight in kilograms divided by height in meters squared), and ethnicity. RESULTS: This study included a total of 472 432 English participants, of whom 54% were women (mean age, 57 years). Reduced LTL was associated with increased overall (HR, 1.08; 95% CI, 1.07-1.09), cardiovascular (HR, 1.09; 95% CI, 1.06-1.12), respiratory (HR, 1.40; 95% CI, 1.34-1.45), digestive (HR, 1.26; 95% CI, 1.19-1.33), musculoskeletal (HR, 1.51; 95% CI, 1.35-1.92), and COVID-19 (HR, 1.15; 95% CI, 1.07-1.23) mortality, but not cancer-related mortality. A total of 214 disorders were significantly overrepresented and 37 underrepresented in participants with shorter LTL. Respiratory (11%), digestive/liver-related (14%), circulatory (18%), and musculoskeletal conditions (6%), together with infections (5%), accounted for most positive associations, whereas (benign) neoplasms and endocrinologic/metabolic disorders were the most underrepresented entities. Malignant tumors, esophageal cancer, and lymphoid and myeloid leukemia were significantly more common in participants with shorter LTL, whereas brain cancer and melanoma were less prevalent. While smoking and alcohol consumption were associated with shorter LTL, additional adjustment for both factors, as well as cognitive function/major comorbid conditions, did not significantly alter the results. CONCLUSIONS AND RELEVANCE: This cohort study found that shorter LTL was associated with a small risk increase of overall mortality, but a higher risk of mortality was associated with specific organs and diseases.


Subject(s)
Leukocytes/physiology , Mortality/trends , Telomere/physiology , Adult , Aged , Female , Follow-Up Studies , Humans , Longitudinal Studies , Male , Middle Aged , Risk , United Kingdom
20.
PLoS Med ; 19(1): e1003870, 2022 01.
Article in English | MEDLINE | ID: covidwho-1608093

ABSTRACT

BACKGROUND: Excess mortality captures the total effect of the Coronavirus Disease 2019 (COVID-19) pandemic on mortality and is not affected by misspecification of cause of death. We aimed to describe how health and demographic factors were associated with excess mortality during, compared to before, the pandemic. METHODS AND FINDINGS: We analysed a time series dataset including 9,635,613 adults (≥40 years old) registered at United Kingdom general practices contributing to the Clinical Practice Research Datalink. We extracted weekly numbers of deaths and numbers at risk between March 2015 and July 2020, stratified by individual-level factors. Excess mortality during Wave 1 of the UK pandemic (5 March to 27 May 2020) compared to the prepandemic period was estimated using seasonally adjusted negative binomial regression models. Relative rates (RRs) of death for a range of factors were estimated before and during Wave 1 by including interaction terms. We found that all-cause mortality increased by 43% (95% CI 40% to 47%) during Wave 1 compared with prepandemic. Changes to the RR of death associated with most sociodemographic and clinical characteristics were small during Wave 1 compared with prepandemic. However, the mortality RR associated with dementia markedly increased (RR for dementia versus no dementia prepandemic: 3.5, 95% CI 3.4 to 3.5; RR during Wave 1: 5.1, 4.9 to 5.3); a similar pattern was seen for learning disabilities (RR prepandemic: 3.6, 3.4 to 3.5; during Wave 1: 4.8, 4.4 to 5.3), for black or South Asian ethnicity compared to white, and for London compared to other regions. Relative risks for morbidities were stable in multiple sensitivity analyses. However, a limitation of the study is that we cannot assume that the risks observed during Wave 1 would apply to other waves due to changes in population behaviour, virus transmission, and risk perception. CONCLUSIONS: The first wave of the UK COVID-19 pandemic appeared to amplify baseline mortality risk to approximately the same relative degree for most population subgroups. However, disproportionate increases in mortality were seen for those with dementia, learning disabilities, non-white ethnicity, or living in London.


Subject(s)
COVID-19/epidemiology , COVID-19/mortality , Mortality/trends , Adult , Aged , Female , Humans , Male , Middle Aged , Models, Statistical , Pandemics , Risk Factors , SARS-CoV-2/pathogenicity , Time Factors , United Kingdom/epidemiology
SELECTION OF CITATIONS
SEARCH DETAIL