Search

ImportanceAmid efforts in the United States to promote health equity, there is a need to assess progress in reducing excess deaths and years of potential life lost (YPLL) among Black people compared with White people.ObjectiveTo evaluate trends in excess mortality and YPLL among Black people compared with White people.DesignSerial cross-sectional.SettingNational data from the Centers for Disease Control and Prevention, 1999-2020ParticipantsNon-Hispanic White and non-Hispanic Black peopleExposuresRace as documented in the death certificates.Main outcomes and measuresExcess age-adjusted all-cause and disease-specific mortality rate (per 100,000 individuals) and YPLL among Black people compared with White people.ResultsFrom 1999 to 2020, the total number of excess deaths was 658,356 and 1,154,108 among Black females and males, representing 34,938,070 and 47,005,048 excess YPLL among Black females and males. The excess deaths and YPLL were largest among infants and non-elderly adults. Heart disease had the most excess deaths. From 1999, the age-adjusted excess mortality rate declined at an annual average of -9.0 (95% CI: -10.0, -8.0; PConclusions and relevanceOver a recent 22-year period, Black people in the US lost more than 80 million years of life when compared with White people. After a period of progress, improvements stalled, and most gains were eliminated in 2020.KEY POINTSQuestionHow many excess deaths and years of potential life lost (YPLL) for Black people, compared with White people, occurred in the United States from 1999 through 2020?FindingsBased on Centers for Disease Control and Prevention data, excess deaths and YPLL persisted throughout the period, with initial progress followed by little improvement, and then worsening in 2020 to about 1999 levels. Black people had 1.8 million excess deaths and over 80 million YPLL over the study period.MeaningAfter initial progress, excess mortality and YPLL among Black people stagnated and then worsened, indicating a need for new approaches.

The COVID-19 pandemic has disproportionately affected Hispanic or Latino, non-Hispanic Black (Black), non-Hispanic American Indian or Alaska Native (AI/AN), and non-Hispanic Native Hawaiian or Other Pacific Islander (NH/PI) populations in the United States. These populations have experienced higher rates of infection and mortality compared with the non-Hispanic White (White) population (1-5) and greater excess mortality (i.e., the percentage increase in the number of persons who have died relative to the expected number of deaths for a given place and time) (6). A limitation of existing research on excess mortality among racial/ethnic minority groups has been the lack of adjustment for age and population change over time. This study assessed excess mortality incidence rates (IRs) (e.g., the number of excess deaths per 100,000 person-years) in the United States during December 29, 2019-January 2, 2021, by race/ethnicity and age group using data from the National Vital Statistics System. Among all assessed racial/ethnic groups (non-Hispanic Asian [Asian], AI/AN, Black, Hispanic, NH/PI, and White populations), excess mortality IRs were higher among persons aged ≥65 years (426.4 to 1033.5 excess deaths per 100,000 person-years) than among those aged 25-64 years (30.2 to 221.1) and those aged25 years (-2.9 to 14.1). Among persons aged65 years, Black and AI/AN populations had the highest excess mortality IRs. Among adults aged ≥65 years, Black and Hispanic persons experienced the highest excess mortality IRs of1,000 excess deaths per 100,000 person-years. These findings could help guide more tailored public health messaging and mitigation efforts to reduce disparities in mortality associated with the COVID-19 pandemic in the United States,* by identifying the racial/ethnic groups and age groups with the highest excess mortality rates.

Made available in DSpace on 2022-04-28T19:40:08Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-07-01 Background: The COVID-19 pandemic is having profound mental health consequences for many people. Concerns have been expressed that, at their most extreme, these consequences could manifest as increased suicide rates. We aimed to assess the early effect of the COVID-19 pandemic on suicide rates around the world. Methods: We sourced real-time suicide data from countries or areas within countries through a systematic internet search and recourse to our networks and the published literature. Between Sept 1 and Nov 1, 2020, we searched the official websites of these countries’ ministries of health, police agencies, and government-run statistics agencies or equivalents, using the translated search terms “suicide” and “cause of death”, before broadening the search in an attempt to identify data through other public sources. Data were included from a given country or area if they came from an official government source and were available at a monthly level from at least Jan 1, 2019, to July 31, 2020. Our internet searches were restricted to countries with more than 3 million residents for pragmatic reasons, but we relaxed this rule for countries identified through the literature and our networks. Areas within countries could also be included with populations of less than 3 million. We used an interrupted time-series analysis to model the trend in monthly suicides before COVID-19 (from at least Jan 1, 2019, to March 31, 2020) in each country or area within a country, comparing the expected number of suicides derived from the model with the observed number of suicides in the early months of the pandemic (from April 1 to July 31, 2020, in the primary analysis). Findings: We sourced data from 21 countries (16 high-income and five upper-middle-income countries), including whole-country data in ten countries and data for various areas in 11 countries). Rate ratios (RRs) and 95% CIs based on the observed versus expected numbers of suicides showed no evidence of a significant increase in risk of suicide since the pandemic began in any country or area. There was statistical evidence of a decrease in suicide compared with the expected number in 12 countries or areas: New South Wales, Australia (RR 0·81 [95% CI 0·72–0·91]); Alberta, Canada (0·80 [0·68–0·93]); British Columbia, Canada (0·76 [0·66–0·87]); Chile (0·85 [0·78–0·94]); Leipzig, Germany (0·49 [0·32–0·74]); Japan (0·94 [0·91–0·96]); New Zealand (0·79 [0·68–0·91]); South Korea (0·94 [0·92–0·97]); California, USA (0·90 [0·85–0·95]); Illinois (Cook County), USA (0·79 [0·67–0·93]); Texas (four counties), USA (0·82 [0·68–0·98]); and Ecuador (0·74 [0·67–0·82]). Interpretation: This is the first study to examine suicides occurring in the context of the COVID-19 pandemic in multiple countries. In high-income and upper-middle-income countries, suicide numbers have remained largely unchanged or declined in the early months of the pandemic compared with the expected levels based on the pre-pandemic period. We need to remain vigilant and be poised to respond if the situation changes as the longer-term mental health and economic effects of the pandemic unfold. Funding: None. Centre for Mental Health Melbourne School of Population and Global Health University of Melbourne Swansea University Medical School Translational Health Research Institute Western Sydney University Ministry of Public Health Department of Health Promotion National Confidential Inquiry into Suicide and Safety in Mental Health University of Manchester Centre for Mental Health and Safety and National Institute for Health Research Patient Safety Translational Research Centre University of Manchester School of Public Health National Suicide Research Foundation University College Cork Australian Institute for Suicide Research and Prevention School of Applied Psychology Griffith University Institute for Life Course Health Research Department of Global Health Stellenbosch University Working Group on Prevention of Suicide and Depression at Public Health Council Department of Psychiatry Blekinge Hospital Botucatu Medical School Universidade Estadual Paulista Instituto Nacional de Psiquiatría Ramon de la Fuente Muñiz University of Rochester Medical Center Region Friuli Venezia Giulia Central Health Directorate Outpatient and Inpatient Care Service Department of Medicine University of Udine Institute of Health Behaviors and Community Sciences College of Public Health National Taiwan University Thames Valley Local Criminal Justice Board Department of Psychiatry Psychotherapy and Psychosomatics Medical University of Innsbruck Center for Outcomes Research and Evaluation Yale School of Medicine Coroners Court of Victoria Danish Research Institute for Suicide Prevention Department of Mental Health Johns Hopkins School of Public Health Centre for Mental Health Research Australian National University Brigham and Women's Hospital Department of Emergency Medicine School of Population Health University of Auckland Magistrates Court of Tasmania (Coronial Division) Bureau of Vital Records and Statistics Louisiana Office of Public Health Ministry of Public Health Undersecretary of Health Services Research Department, 113 Suicide Prevention Departments of Psychiatry and Epidemiology Columbia University Medical Center/New York State Psychiatric Institute Centre for Suicide Research University of Oxford Louisiana Department of Health Greater Manchester Mental Health NHS Foundation Trust Department of Psychiatry Aga Khan University KU Leuven Center for Contextual Psychiatry Population Health Sciences Bristol Medical School University of Bristol South Asian Clinical Toxicology Research Collaboration Faculty of Medicine University of Peradeniya World Health Organization Country Office for Nepal Karolinska Institutet Bekhterev National Medical Research Center of Psychiatry and Neurology Pavlov First Saint Petersburg State Medical University Unit Suicide Research and Mental Health Promotion Department of Social and Preventive Medicine Center for Public Health Medical University of Vienna Department of Child and Adolescent Psychiatry Medical University of Vienna School of Psychology University of Nottingham Self-Harm Research Group School of Psychology University of Nottingham Mental Health Centre Copenhagen Department of Psychiatry and Psychotherapy Klinikum Klagenfurt am Wörthersee Suicidal Behaviour Research Lab University of Glasgow Preventing Deaths from Poisoning Research Group University of Edinburgh Usher Institute University of Edinburgh Suicide Research and Prevention Center Shanghai Mental Health Center Shanghai Jiao Tong University School of Medicine Departments of Psychiatry and Epidemiology Columbia University Department of Child and Adolescent Psychiatry and Psychotherapy University of Ulm Psychosocial Services in Vienna National Centre for Suicide Research and Prevention Institute of Clinical Medicine University of Oslo Department of Child and Adolescent Psychiatry Psychotherapy and Psychosomatics University Hospital Leipzig Department of Psychiatry and Psychotherapeutic Medicine Landeskrankenhaus Villach Department of Psychiatry Psychosomatic Medicine and Psychotherapy University Hospital Frankfurt Department of Borderline Disorders and Psychotherapy Bekhterev National Medical Research Center of Psychiatry and Neurology Saint Petersburg State University Department of Psychiatry Health Authority Frankfurt am Main Department of Addictive Disorders Psychiatry and Psychotherapy LVR-Klinik Köln Department of Psychiatry Psychosomatic Medicine and Psychotherapy Goethe-University Organizational-Scientific Department Bekhterev National Medical Research Center of Psychiatry and Neurology Department of Psychiatry University of Toronto Department of Psychiatry Sunnybrook Health Sciences Centre Waseda University Faculty of Political Science and Economics Sneha—Suicide Prevention Centre Voluntary Health Services Department of Community Medicine Faculty of Medicine and Allied Sciences Rajarata University of Sri Lanka Department of Psychiatry Sackler School of Medicine Tel Aviv University and Geha Mental Health Center Division of Molecular Imaging and Neuropathology New York State Psychiatric Institute and Department of Psychiatry Columbia University National Institute of Health Research Biomedical Research Centre University Hospitals Bristol and Weston NHS Foundation Trust University of Bristol Department for Psychiatry University Psychiatric Hospital Vrapče School of Medicine University of Zagreb Botucatu Medical School Universidade Estadual Paulista

This cohort study examines mortality data from Texas, a racially and ethnically diverse state, to better understand excess mortality among adults aged 25 to 44 years during early months of the COVID-19 pandemic.

BackgroundAll-cause excess mortality (the number of deaths that exceed projections in any period) has been widely reported during the Covid-19 pandemic. Whether excess mortality has occurred during the Delta wave is less well understood.MethodsWe performed an observational study using data from the Massachusetts Department of Health. Five years of US Census population data and CDC mortality statistics were applied to a seasonal autoregressive integrated moving average (sARIMA) model to project the number of expected deaths for each week of the pandemic period, including the Delta period (starting in June 2021, extending through August 28th 2021, for which mortality data are >99% complete). Weekly Covid-19 cases, Covid-19-attributed deaths, and all-cause deaths are reported. County-level excess mortality during the vaccine campaign are also reported, with weekly rates of vaccination in each county that reported 100 or more all-cause deaths during any week included in the study period.ResultsAll-cause mortality was not observed after March 2021, by which time over 75% of persons over 65 years of age in Massachusetts had received a vaccination. Fewer deaths than expected (which we term ‘deficit mortality’) occurred both during the summer of 2020, the spring of 2021 and during the Delta wave (beginning June 13, 2021 when Delta isolates represented >10% of sequenced cases). After the initial wave in the spring of 2020, more Covid-19-attributed deaths were recorded that all-cause excess deaths, implying that Covid-19 was misattributed as the underlying cause, rather than a contributing cause of death in some cases.ConclusionIn a state with high vaccination rates, excess mortality has not been recorded during the Delta period. Deficit mortality has been recorded during this period.

BackgroundExcess death for Black people compared with White people is a measure of health equity. We sought to determine the excess deaths under the age of 65 (MethodsWe used the Center of Disease Control (CDC) WONDER’s Multiple Cause of Death 1999-2019 dataset to report age-adjusted mortality rates among non-Hispanic Black (Black) and non-Hispanic White (White) people and to calculate annual age-adjusted ResultsFrom 1999 to 2019, the age-adjusted mortality rate for Black men was 1,186 per 100,000 and for White men was 921 per 100,000, for a difference of 265 per 100,000. The age-adjusted mortality rate for Black women was 802 per 100,000 and for White women was 664 per 100,000, for a difference of 138 per 100,000. While the gap for men and women is less than it was in 1999, it has been increasing among men since 2014. These differences have led to many Black people dying before age 65. In 1999, there were 22,945 age-adjusted excess deaths among Black women CommentIn the US, over the recent 20-year period, disparities in mortality rates resulted in between 61,827 excess deaths in 1999 and 40,294 excess deaths in 2019 among

This study uses national death certificate data to characterize trends in death and excess mortality from drug overdoses, homicides, unintentional injuries, motor vehicle crashes, and suicide during the first 6 months of the pandemic in the US.

Introduction The COVID-19 pandemic has been associated with substantial rates of all-cause excess mortality. The contribution of external causes of death to excess mortality including drug overdose, homicide, suicide, and unintentional injuries during the initial outbreak in the United States is less well documented. Methods Using public data published by the National Center for Health Statistics on February 10, 2021, we measured monthly excess mortality (the gap between observed and expected deaths) from five external causes using national-level data published by National Center for Health Statistics; assault (homicide); intentional self-harm (suicide); accidents (unintentional injuries); and motor vehicle accidents. We used seasonal autoregressive integrated moving average (sARIMA) models developed with cause-specific monthly mortality counts and US population data from 2015-2019 and estimated the contribution of individual cause-specific mortality to all-cause excess mortality from March-July 2020. Results From March-July, 2020, 212,825 (95% CI 136,236-290,776) all-cause excess deaths occurred in the US). There were 8,540 excess drug overdoses (all intents) (95% CI 5,106 to 11,975), accounting for 4% of all excess mortality; 1,455 excess homicide deaths (95% CI 708 to 2202, accounting for 0.7% of excess mortality; 5,492 excess deaths due to unintentional accidents occurred (95% CI 85 to 10,899, accounting for 2.6% of excess mortality. Though a non-significantly 135 (95% CI -1361 to 1,630) more MVA deaths were recorded during the study period, a significant decrease in April (525; 95% CI -817 to -233) and significant increases in June-July (965; 95% CI 348 to 1,587) were observed. Suicide deaths were statistically lower than projected by 2,067 (95% CI 941-3,193 fewer deaths). Meaning Excess deaths from drug overdoses, homicide, and addicents occurred during the pandemic but represented a small fraction of all-cause excess mortality. The excess external causes of death, however, still represent thousands of lives lost. Notably, deaths from suicide were lower than expected and therefore did not contribute to excess mortality.

ObjectivesWe identify a correction for excess mortality that takes the sudden unexpected changes in the size of the United States population into account.DesignThis is a weekly cross-sectional analysis of all-cause mortality since week 5, 2020. We describe and apply a simple correction that takes population changes into account in order to provide corrected weekly estimates of expected deaths for 2020 and 2021.SettingThe United States.ParticipantsAll United States residents.InterventionsThe covid-19 pandemic.Main outcome measuresExpected and excess mortality for the United States during the covid-19 period.ResultsAs of week 53, 2020 (ending January 2, 2021), approximately >10,200 more excess deaths have occurred in the United States than could be detected if expected deaths projections were not amended to reflect population decreases during 2020. The figure is projected to rise to >12,600 (>600 weekly) by week 5, 2021. Assuming recent excess mortality and pandemic-associated visa reductions continue until the earliest time herd immunity could be approached resulting from a combination of infections and vaccinations (week 17, 2021), if point estimates of expected deaths are not corrected, expected deaths will be overestimated (and therefore potential excess mortality underestimated) by ∼43,000 during 2021, or >53,300 since the outbreak of the pandemic measurement period (beginning week 5, 2020). By late December 2021, weekly expected death differences are projected to approach 1,000 per week.ConclusionsCurrent models measuring excess mortality should be revised immediately so that public health officials do not lose the ability to detect ongoing excess mortality as the population changes continue to compound, lowering the number of weekly expected deaths. A similar approach should be used in the middle and late phases of all future pandemics.

ImportanceCOVID-19 case fatality and hospitalization rates, calculated using the number of confirmed cases of COVID-19, have been described widely in the literature. However, the number of infections confirmed by testing underestimates the total infections as it is biased based on the availability of testing and because asymptomatic individuals may remain untested. The infection fatality rate (IFR) and infection hospitalization rate (IHR), calculated using the estimated total infections based on a representative sample of a population, is a better metric to assess the actual toll of the disease.ObjectiveTo determine the IHR and IFR for COVID-19 using the statewide SARS-CoV-2 seroprevalence estimates for the non-congregate population in Connecticut.DesignCross-sectional.SettingAdults residing in a non-congregate setting in Connecticut between March 1 and June 1, 2020.ParticipantsIndividuals aged 18 years or above.ExposureEstimated number of adults with SARS-CoV-2 antibodies.Main Outcome and MeasuresCOVID-19-related hospitalizations and deaths among adults residing in a non-congregate setting in Connecticut between March 1 and June 1, 2020.ResultsOf the 2.8 million individuals residing in the non-congregate settings in Connecticut through June 2020, 113,515 (90% CI 56,758–170,273) individuals had SARS-CoV-2 antibodies. There were a total of 9425 COVID-19-related hospitalizations and 4071 COVID-19-related deaths in Connecticut between March 1 and June 1, 2020, of which 7792 hospitalizations and 1079 deaths occurred among the non-congregate population. The overall COVID-19 IHR and IFR was 6.86% (90% CI, 4.58%–13.72%) and 0.95% (90% CI, 0.63%–1.90%) among the non-congregate population. Older individuals, men, non-Hispanic Black individuals and those belonging to New Haven and Litchfield counties had a higher burden of hospitalization and deaths, compared with younger individuals, women, non-Hispanic White or Hispanic individuals, and those belonging to New London county, respectively.Conclusion and RelevanceUsing representative seroprevalence estimates, the overall COVID-19 IHR and IFR were estimated to be 6.86% and 0.95% among the non-congregate population in Connecticut. Accurate estimation of IHR and IFR among community residents is important to guide public health strategies during an infectious disease outbreak.

IntroductionCoronavirus disease-19 (COVID-19) has caused a marked increase in all-cause deaths in the United States, mostly among adults aged 65 and older. Because younger adults have far lower infection fatality rates, less attention has been focused on the mortality burden of COVID-19 in this demographic.MethodsWe performed an observational cohort study using public data from the National Center for Health Statistics at the United States Centers for Disease Control and Prevention, and CDC Wonder. We analyzed all-cause mortality among adults ages 25-44 during the COVID-19 pandemic in the United States. Further, we compared COVID-19-related deaths in this age group during the pandemic period to all drug overdose deaths and opioid-specific overdose deaths in each of the ten Health and Human Services (HHS) regions during the corresponding period of 2018, the most recent year for which data are available.ResultsAs of September 6, 2020, 74,027 all-cause deaths occurred among persons ages 25-44 years during the period from March 1st to July 31st, 2020, 14,155 more than during the same period of 2019, a 23% relative increase (incident rate ratio 1.23; 95% CI 1.21–1.24), with a peak of 30% occurring in May (IRR 1.30; 95% CI 1.27-1.33). In HHS Region 2 (New York, New Jersey), HHS Region 6 (Arkansas, Louisiana, New Mexico, Oklahoma, Texas), and HHS Region 9 (Arizona, California, Hawaii, Nevada), COVID-19 deaths exceeded 2018 unintentional opioid overdose deaths during at least one month. Combined, 2,450 COVID-19 deaths were recorded in these three regions during the pandemic period, compared to 2,445 opioid deaths during the same period of 2018.MeaningWe find that COVID-19 has likely become the leading cause of death—surpassing unintentional overdoses—among young adults aged 25-44 in some areas of the United States during substantial COVID-19 outbreaks.NoteThe data presented here have since been updated. As a result, an additional 1,902 all-cause deaths occurring among US adults ages 25-44 during the period of interest are not accounted for in this manuscript.

ImportanceThe coronavirus disease 2019 (COVID-19) outbreak has been associated with decreases in acute myocardial infarction diagnoses (AMI) and admissions in the United States. Whether this affected heart disease deaths is unknown.ObjectiveTo determine whether changes in heart disease deaths occurred during the early pandemic period in the US, we analyzed areas without large COVID-19 outbreaks. This isolated the effect of decreased healthcare-seeking behavior during the early outbreak.Design, Setting, and ParticipantsWe performed an observational study of heart disease-specific mortality using National Center for Health Statistics data (NCHS). Weekly provisional counts were disaggregated by jurisdiction of occurrence during 2019 and 2020 for all-cause deaths, COVID-19 deaths, and heart disease deaths. For the primary analysis, jurisdictions were included if; 1) There was no all-cause excess mortality during the early pandemic period (weeks 14–17, 2020); 2) The completeness of that data was estimated by NCHS to be >97% as of July 22, 2020, and; 3) Decreases in emergency department (ED) visits occurred during the study period. We compared heart disease death rates during the early pandemic period with corresponding weeks in 2019 and a pre-pandemic control period of 2020 as a sensitivity analysis. Incident rate and rate ratios were calculated.ExposureThe US COVID-19 outbreak.Main Outcomes and MeasuresIncidence of heart disease deaths.ResultsTwelve states met the primary inclusion criteria, capturing 747,375,188 person-weeks for the early pandemic period and 740,987,984 person-weeks for the 2019 control period. The mean incidence rate (per 100,000 person-weeks) for heart disease in states without excess deaths during the early pandemic period was 3.95 (95% CI 3.83 to 4.06) versus 4.19 (95% CI 4.14 to 4.23) during the corresponding period in 2019. The incident rate ratio (2020/2019) was 0.91 (95% CI 0.87 to 0.97). No state recorded an increase from either the corresponding period in 2019 or the 2020 prepandemic control period. Two states recorded fewer heart disease deaths.Conclusions and RelevanceThis observational study found a decrease in heart disease deaths during the early US outbreak in regions without significant COVID-19 burdens, despite decreases in ED utilization. Long term follow-up data are needed.

During the 1918 H1N1 influenza pandemic, there were approximately 50 million influenza-related deaths worldwide, including 675 000 in the US. Few persons in the US have a frame of reference for the historic levels of excess mortality currently being observed during the coronavirus disease 2019 (COVID-19) pandemic.1 In this study, excess deaths in New York City during the peak of the 1918 H1N1 influenza pandemic were compared with those during the initial period of the COVID-19 outbreak.

Physicians, nurses, and other health care providers initiated the #GetMePPE movement on Twitter to spread awareness of the shortage of personal protective equipment (PPE) during the coronavirus disease (COVID-19) pandemic. Dwindling supplies, such as face masks, gowns and goggles, and inadequate production to meet increasing demand have placed health care workers and patients at risk. The momentum of the #GetMePPE Twitter hashtag resulted in the creation of a petition to urge public officials to address the PPE shortage through increased funding and production. Simultaneously, the GetUsPPE.org website was launched through the collaboration of physicians and software engineers to develop a digital platform for the donation, request, and distribution of multi-modal sources of PPE. GetUsPPE.org and #GetMePPE were merged in an attempt to combine public engagement and advocacy on social media with the coordination of PPE donation and distribution. Within 10 days, over 1800 hospitals and PPE suppliers were registered in a database that enabled the rapid coordination and distribution of scarce and in-demand materials. One month after its launch, the organization had distributed hundreds of thousands of PPE items and had built a database of over 6000 PPE requesters. The call for action on social media and the rapid development of this digital tool created a productive channel for the public to contribute to the health care response to COVID-19 in meaningful ways. #GetMePPE and GetUsPPE.org were able to mobilize individuals and organizations outside of the health care system to address the unmet needs of the medical community. The success of GetUsPPE.org demonstrates the potential of digital tools as a platform for larger health care institutions to rapidly address urgent issues in health care. In this paper, we outline this process and discuss key factors determining success.

The SARS-CoV-2 pandemic is associated with a reduction in hospitalization for an acute cardiovascular conditions. In a major health system in Massachusetts, there was a 43% reduction in these types of hospitalizations in March 2020 compared with March 2019.4 Whether mortality rates from heart disease have changed over this period is unknown.We assembled information from the National Center for Health Statistics (Centers for Disease Control and Prevention) for 118,356,533 person-weeks from Week 1 (ending January 4) through Week 17 (ending April 25) of 2020 for the state of Massachusetts. We found that heart disease deaths are unchanged during the Covid-19 pandemic period as compared to the corresponding period of 2019. This is despite reports that admissions for acute myocardial infarction have fallen during this time.

UNSTRUCTURED Physicians, nurses, and other healthcare providers initiated the #GetMePPE movement on Twitter to spread awareness of the shortage of personal protective equipment (PPE) during the COVID-19 pandemic. Dwindling supplies, such as face masks, gowns and goggles, and inadequate production to meet increasing demands, has left healthcare workers and patients at risk. The momentum of this Twitter hashtag resulted in a petition to urge public officials to address the PPE shortage through increased funding and production. Simultaneously, GetUsPPE.org was launched by a collaborative of physicians and software engineers to develop a digital platform for the donation, request, and distribution of multi-modal sources of PPE. GetUsPPE.org and #GetMePPE merged in an attempt to combine public engagement and advocacy on social media with the coordination of PPE donation and distribution. Within ten days, over 1800 hospitals and PPE suppliers were registered in a database that allowed for the rapid coordination and distribution of scarce and in-demand materials. One month after its launch, the organization has distributed hundreds of thousands of items of PPE and built a database of over 6,000 PPE requesters[1] The call for action on social media and the rapid development of this digital tool created a productive channel for the public to contribute to the healthcare response to COVID-19 in meaningful ways. #GetMePPE and GetUsPPE.org were able to mobilize individuals and organizations outside of the healthcare system to address the unmet needs of the medical community. The success of GetUsPPE.org demonstrates the potential of digital tools as a platform for larger healthcare institutions (table 1) to rapidly address urgent issues in healthcare. In this manuscript, we outline this process and discuss key factors determining success.

Coronavirus disease 2019 (COVID-19) is a novel human respiratory disease caused by the SARS-CoV-2 virus. Asymptomatic carriers of the COVID-19 virus display no clinical symptoms but are known to be contagious. Recent evidence reveals that this subpopulation, as well as persons with mild disease, are a major contributor in the propagation of the disease. The rapid spread of COVID-19 forced governments around the world to establish and enforce generalized risk mitigation strategies, from lockdowns to guidelines for social distancing, in an effort to minimize community transmission. This created an unprecedented epidemiological situation not properly characterized by existing mathematical models of isolation and quarantine. In this manuscript, we present a mathematical model for community transmission of COVID-19 taking into account asymptomatic carriers and varying degrees of risk mitigation. The main results consist of an exact calculation of the effective reproduction number , and a modeling framework that enables the quantification of the effect of risk mitigation and asymptomatism on community transmission. A computation of is provided using mean parameters. The point estimate of the basic reproduction number is .

The final case fatality rate (CFR) from SARS-CoV-2, the virus that causes covid-19, will likely be lower than those initially reported.1 Previous reviews of H1N1 and SARS show the systematic inflation of early mortality estimates.23 Early estimates of H1N1’s mortality were susceptible to uncertainty about asymptomatic and subclinical infections, heterogeneity in approaches to diagnostic testing, and biases in confounding, selection, detection, reporting, …

SEP-1, the new national quality measure on sepsis, resulted from an undertaking to standardize care for severe sepsis and septic shock regardless of the size of the emergency department where the patient is being treated. SEP-1 does not necessarily follow the best current evidence available. Nevertheless, a thorough understanding of SEP-1 is crucial because all hospitals and emergency providers will be accountable for meeting the requirements of this measure. SEP-1 is the first national quality measure on early management of sepsis care. This article provides a review of SEP-1 and all its potential implications on sepsis care in the United States.

Faust et al discuss the study which compares all-cause excess mortality and COVID-19-related mortality during the early pandemic period (March-July 2020) with unintentional drug overdoses, the usual leading cause of death in young adults, during the same period in 2018 among adults aged 25 to 44 years COVID-19 has caused a marked increase in all-cause deaths in the US, mostly among older adults Although the burden of COVID-19 among hospitalized younger adults has been described, fewer data focus on mortality in this demographic, owing to lower case-fatality rates

Over the past two decades, the Internet has emerged as a powerful tool for the dissemination of medical knowledge. The routine online publication of peer-reviewed journals and the standardisation of the portable document format (.pdf) marked a coming of age for Internet technology allowing readers, authors and editors to quickly access articles old and new and share them efficiently. Although the most prestigious medical journals still view the paper versions of their journals to be the pinnacle product, it is now standard for articles to be e-published online ‘ahead of print’, weeks to months before the paper passes through the press, and arrives on the doorstep. Thus, for most web-savvy and voracious readers, by the time physical journals appear in mailboxes, important or controversial new research has often already been read, discussed and debated at length in online and ‘real life’ forums. Emails have been written and responses have been sent to the editors. Opinions have been formed. However, because of print delays, some of the most thought-out and qualified opinions (i.e. experts’ Letters to the Editor) have been embargoed from general consumption, waiting instead until future issues can be published. Meanwhile, print errors remain uncorrected. As the goals of peer-reviewed medical journals are to foster medical progress and improve research as a whole, this obsolete model must be phased out. The window between the acquisition of research knowledge and clinical translation can often gape longer than a decade. This was perhaps an acceptable although suboptimal reality before the advent of Internet-based technologies, but the recent social media revolution has made this untenable. If peer-reviewed journals adapt to a new reality, not only will such delays be shortened, but also the journals will find themselves to be more relevant than ever. On the other hand, those publications that fail to adapt will cease to be potent in the most meaningful ways. In this editorial, we suggest three key ways that peer-reviewed journals can maintain and increase their relevance in today’s milieu: first, by embracing real-time interactivity; second, by embracing new tools to measure clinical impact; and third, by finally eliminating print publication. Social media medical education might be the inspiration for many of these changes. However, medical social media is still in its nascent stages; it must evolve as well. It requires adoption and adaptation of systems for peer review and information verification to allow trust by clinicians. Furthermore, the time demands of product creation will not be sustainable unless there is a path for academic advancement to support these activities.

SEP-1, the new national quality measure on sepsis, resulted from an undertaking to standardize care for severe sepsis and septic shock regardless of the size of the emergency department where the patient is being treated. SEP-1 does not necessarily follow the best current evidence available. Nevertheless, a thorough understanding of SEP-1 is crucial because all hospitals and emergency providers will be accountable for meeting the requirements of this measure. SEP-1 is the first national quality measure on early management of sepsis care. This article provides a review of SEP-1 and all its potential implications on sepsis care in the United States.

Objective The objective was to determine the testing threshold for lumbar puncture (LP) in the evaluation of aneurysmal subarachnoid hemorrhage (SAH) after a negative head computed tomography (CT). As a secondary aim we sought to identify clinical variables that have the greatest impact on this threshold. Methods A decision analytic model was developed to estimate the testing threshold for patients with normal neurologic findings, being evaluated for SAH, after a negative CT of the head. The testing threshold was calculated as the pretest probability of disease where the two strategies (LP or no LP) are balanced in terms of quality-adjusted life-years. Two-way and probabilistic sensitivity analyses (PSAs) were performed. Results For the base-case scenario the testing threshold for performing an LP after negative head CT was 4.3%. Results for the two-way sensitivity analyses demonstrated that the test threshold ranged from 1.9% to 15.6%, dominated by the uncertainty in the probability of death from initial missed SAH. In the PSA the mean testing threshold was 4.3% (95% confidence interval = 1.4% to 9.3%). Other significant variables in the model included probability of aneurysmal versus nonaneurysmal SAH after negative head CT, probability of long-term morbidity from initial missed SAH, and probability of renal failure from contrast-induced nephropathy. Conclusions Our decision analysis results suggest a testing threshold for LP after negative CT to be approximately 4.3%, with a range of 1.4% to 9.3% on robust PSA. In light of these data, and considering the low probability of aneurysmal SAH after a negative CT, classical teaching and current guidelines addressing testing for SAH should be revisited.