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In addition to these psychological and behavioral factors, differences in occupational risk exist between men and women. In the United States, a larger number of women than men are deemed essential workers primarily because of the large share of women employed as social workers and in health care Nevertheless, the low-skilled or low-paid occupations that are considered essential workers eg, food processing, transportation, delivery, warehousing, construction, manufacturing , where men outnumber women, seem to be associated with a greater risk of mortality In summary, a range of biological, psychological, and behavioral factors can explain why men have higher rates of COVID—associated morbidity and mortality than women.

Although it is critical to identify the factors associated with increased risk for men of COVID mortality, it is equally important to determine how to reduce the risk of men dying of COVID 1,4.

Educational efforts to increase compliance with public health recommendations may be more effective in changing the behavior of men if these efforts incorporate some of the principles from health communications research that consider how health behavior is gendered 33, Building on principles of the self-determination theory, we suggest that messages to engage men seek ways to motivate them to consciously choose to engage in healthier behaviors, not because of shame, pressure, or coercion but because they are intrinsically motivated to do so For example, some men may be motivated to engage in behaviors to reduce their risk of contracting or potentially transmitting COVID not by focusing on their risk but by focusing on the high rates of morbidity or mortality of their racial or ethnic group, communities, neighborhood, or family.

As a result, a federally qualified health center in Baton Rouge, Louisiana, for example, is conducting outreach to men with underlying conditions and their partners to ensure that they are aware of their susceptibility to COVID Increasing access and eliminating barriers to community-wide testing are additional ways to improve COVID outcomes.

Testing or screening use may be influenced by exposure to decision education and the influence of screening-related primary care practice factors Federally qualified health centers offering primary care services are key community institutions that have increased COVID testing — with no out-of-pocket costs to patients in many areas.

These kinds of programs allow men to have access to testing without cost barriers that may otherwise deter them from accessing testing. The community-wide testing also offers an opportunity for men to be tested before returning to work as states begin to reopen and more services barber shops, gyms, restaurants are offered in communities. These initiatives help to normalize testing and reduce the stigma of getting tested, although they may not reduce the stigma of receiving a positive test result.

Given the rates of cardiometabolic risk factors and underlying or preexisting conditions such as obesity or comorbid chronic diseases eg, diabetes, heart disease, cancer among men, a focus on men with underlying conditions that increase their risk of COVID mortality is critical 34, Although the greater severity of complications attributable to COVID among men is not well understood, preliminary findings of a higher incidence of mortality attributable to underlying comorbid conditions suggest that clinicians tailor current treatment options with this in mind.

The study, which used data from 9 high-volume cardiac catheterization laboratories, showed that total STEMI activations decreased from more than per month mean, We need to reassure patients that although routine and elective care might be curtailed by the pandemic, new symptoms of myocardial infarction and stroke still need to be immediately addressed.

For men who are at increased risk because of a history of a chronic condition or disease, clinicians should actively assess risks; optimize antihypertensive and statin therapies where indicated; provide behavioral and pharmacotherapy for tobacco use cessation cigarettes and vaping ; educate on healthy diets rich in vegetables, legumes, grains, fruits and nuts; and make exercise recommendations In addition to providing information, clinicians should encourage men to participate in behavioral interventions that target psychosocial factors eg, self-efficacy, motivation that can facilitate lifestyle change and maintenance of behavior changes over time These important interventions should continue during a pandemic through virtual visits and telemedicine platforms.

Several professional organizations have made COVID—specific clinical and operational guidelines in their specialties; these include patient education information on occupational risk mitigations and recognizing signs and symptoms of COVID infection, hand hygiene and surface decontamination, and protecting family members 40, While designing clinical trials to address COVID—related conditions, clinicians and researchers need to consistently consider sex as a biological variable and the behaviors and social stressors associated with gender that might affect drug efficacy, treatment options, and adverse outcomes 3, There is a long history of not analyzing and reporting sex differences and underrepresenting women in cardiovascular clinical trials and in the treatment of infectious diseases 10 , and COVID is proving no different in many countries 4, Results from the randomized, controlled Adaptive COVID Treatment Trial, which tested remdesivir as a therapeutic agent for the treatment of COVID, showed a 4-day difference in time to recovery between the treatment group and the control group, but the study did not provide explicit information on sex-based efficacy or adverse reactions Only by investigating sex differences consistently, critically, and reflectively can we fulfill the requirements of scientific rigor, excellence, and maximum impact.

Strategies aimed at preventing complications associated with COVID are essential for safe and effective return to personal, professional, and societal obligations. Urgent needs also exist to provide post—acute care rehabilitation services for patients recovering from COVID and to train a new workforce to care for these patients Strong evidence suggests that interventions engaging community health workers improve health outcomes for patients, including men, across multiple chronic conditions.

As care extenders, community health workers provide a culturally and linguistically appropriate clinical—community linkage for difficult-to-reach patients, such as men. They can provide direct outreach to men with comorbidities that make them more susceptible to COVID and its complications.

Given the high rates of pre-existing chronic conditions among men 1 , the Center for Medicare and Medicaid Services may need to expand access to telehealth services for men to receive care where they are to allow them to remain in isolation and prevent spread of the virus; however, most assisted living and long-term care facilities do not have computer access for residents for this purpose. This patient-centered care delivery model could be a particularly useful strategy to increase access to preventive medicine for men who are from medically underrepresented groups or groups with lower socioeconomic status In addition to various practice initiatives to reduce virus transmission and mortality, we must also consider the potential policy efforts to address the COVID epidemic in the United States.

Because men are dying of COVID disproportionately, policy makers need to explicitly consider gender but not conflate gender with women 1. To do so, local, state, and national policy makers should ensure that legislation includes language that promotes data collection, disaggregation, and dissemination by race, ethnicity, and sex 1,4, Finally, it is essential for policy makers to adopt an equity-based approach that considers the heterogeneity among men 1, Men who are marginalized or disadvantaged because of their race, ethnicity, sexual orientation, incarceration, homelessness, or other factor are particularly vulnerable to COVID and policies should explore which groups of men are overrepresented among essential workers, at risk because of preexisting health conditions, or most in need because of other socioeconomic factors.

All data analyzed were accessed on June 12, There was substantial variability between states in the difference between official COVID deaths and the estimated burden of excess deaths. Conclusions and Relevance Excess deaths provide an estimate of the full COVID burden and indicate that official tallies likely undercount deaths due to the virus. The mortality burden and the completeness of the tallies vary markedly between states. Some officials have raised concerns that deaths not caused by the virus were improperly attributed to COVID, inflating the reported tolls.

However, given the limited availability of viral testing and the imperfect sensitivity of the tests, 3 , 4 there have likely been a number of deaths caused by the virus that were not counted. Furthermore, if patients with chronic conditions turn away from the health care system because of concerns about potential COVID infection, there could be increases in certain categories of deaths unrelated to COVID In the midst of a large outbreak, there is also an unavoidable delay in the compilation of death certificates and ascertainment of causes of death.

Overall, the degree of testing, criteria for attributing deaths to COVID, and the length of reporting delays are expected to vary between states, further complicating efforts to obtain an accurate count of deaths related to the pandemic.

To estimate the mortality burden of a new infectious agent when there is a lack of comprehensive testing, it is common to assess increases in rates of death beyond what would be expected if the pathogen had not circulated. The excess deaths methodology has been used to quantify official undercounting of deaths for many pathogens, including pandemic influenza viruses and HIV.

We compare these estimates of excess deaths with the reported numbers of deaths due to COVID in different states and evaluate the timing of these increases in relation to testing and pandemic intensity. These analyses provide insights into the burden of COVID in the early months of the outbreak in the United States and serve as a surveillance platform that can be updated as new data accrue.

The NCHS data are based on the state where the death occurred rather than the state of residence. The NCHS reports deaths as they are received from the states and processed; counts of deaths from recent weeks are highly incomplete, reflecting delays in reporting. All data were accessed June 12, Connecticut and North Carolina were missing mortality data for recent months and were therefore excluded from the analyses and from the baseline numbers.

We also compiled data on COVID—related morbidity to gauge the timing and intensity of the pandemic in different locations. We used CDC data on influenza-like illness, 11 a long-standing indicator of morbidity due to acute respiratory infections, which has been used to monitor COVID We also obtained information on influenza virus circulation to adjust baseline estimates. These analyses use publicly available aggregate data and were deemed exempt from human subjects review by the Yale institutional review board protocol To calculate the number of excess deaths, we first needed to estimate the baseline number of deaths in the absence of COVID We then subtracted the expected number of deaths in each week from the observed number of deaths for the period March 1, , to May 30, Each of the 48 states excluding North Carolina and Connecticut and the District of Columbia were analyzed individually.

We fit Poisson regression models to the weekly state-level death counts from January 5, , to January 25, see the eAppendix in the Supplement for details. The baseline was then projected forward until May 30, , to generate baseline deaths; excess mortality was defined as the observed mortality minus the baseline for the pandemic period March 1, , to May 30, The baseline model was adjusted for seasonality, year-to-year baseline variation, influenza epidemics, and reporting delays.

To obtain national-level estimates, the observed count and predicted counts median estimate from the model for each state were summed for each week and compared. Reporting delays make it challenging to estimate excess deaths for recent weeks. To adjust for incomplete data in recent weeks, we adjusted the baseline based on an estimate for data completeness in that week. The estimate of completeness is based on the number of weeks that passed between the week in which the data set was obtained and the week in which the death occurred.

We used a modified version of the NobBS package in R to estimate the proportion of deaths that were reported for each date and incorporated that as an adjustment in the main analysis 16 eAppendix in the Supplement.

These reporting delays were estimated using provisional data for deaths that occurred since March 29, , and thus reflect changes in reporting that might have occurred during the pandemic. The completeness of the data varied markedly between states eFigure 1 in the Supplement.

A study by Woolf et al 17 of excess deaths in the US used the same database and a related harmonic regression method. The main differences in methodology are that Woolf et al did not adjust for reporting delays, the study period ended on April 25, , and that study controlled for time trends using an adjustment for calendar year rather than epidemiological year. The analyses were run using R version 3. More details about the data and methods are in the eAppendix in the Supplement.

The changes in mortality that occurred during the pandemic varied by state and region. There were notable per capita increases in rates of death due to any cause in many other states, including New Jersey, Massachusetts, Louisiana, Illinois, and Michigan, where the number of deaths greatly exceeded the expected levels Table , Figure 2 , and Figure 3 ; eFigure 2 in the Supplement for additional states.

Other states, particularly smaller states in the central United States and northern New England, had some COVID deaths reported in official tallies but small or no detectable increases in all-cause deaths above expected levels Table. Some of the discrepancy between reported COVID deaths and excess deaths could be related to the intensity and timing of increases in testing. In some states eg, Texas, California , excess all-cause mortality preceded the widespread adoption of testing for SARS-CoV-2 by several weeks Figure 4 ; eFigure 4 in the Supplement for additional states.

In other states eg, Massachusetts, Minnesota , testing intensity increased prior to or with the increase in excess deaths, and the gap between COVID deaths and excess deaths was smaller Figure 4. The increase in excess deaths in many states trailed an increase in outpatient visits due to influenza-like illness by several weeks eFigure 5 in the Supplement. We performed several sensitivity analyses.

We refit the seasonal baseline without adjusting for influenza activity eTable in the Supplement. Excluding influenza pulled the baseline upward and led to smaller excess estimates in some states. Furthermore, we created an empirical baseline by averaging the number of deaths in corresponding weeks of the previous years. This yielded weekly estimates of excess death that aligned closely with estimates from our model in April The estimates of excess deaths based on the empirical baseline were slightly higher than those calculated with the modeled baseline in March and much lower estimates for May eFigure 6 in the Supplement.

The difference in the estimates for May is driven by reporting delays, which are adjusted for in the modeling approach but not in the empirical baseline. This suggests that our modeling approach provides robust estimates of excess mortality while allowing for formal quantification of uncertainty and more timely estimates than other empirical approaches.

Finally, we explored the accuracy of our adjustment for reporting lags eFigure 8 in the Supplement. Therefore, our excess mortality estimates for the most recent week are modestly conservative. Monitoring excess deaths has been used as a method for tracking influenza mortality for more than a century. Herein, we used a similar strategy to capture COVID deaths that had not been attributed specifically to the pandemic coronavirus.

Given the variability in testing intensity between states and over time, this type of monitoring provides key information on the severity of the pandemic and the degree to which viral testing might be missing deaths caused by COVID These findings demonstrate that estimates of the death toll of COVID based on excess all-cause mortality may be more reliable than those relying only on reported deaths, particularly in places that lack widespread testing. However, in the absence of widespread and systematic testing for COVID, they provide a useful measure of pandemic progression and the impact of interventions.

The gap between reported COVID deaths and excess deaths can be influenced by several factors, including the intensity of testing; guidelines on the recording of deaths that are suspected to be related to COVID but do not have a laboratory confirmation; and the location of death eg, hospital, nursing home, or unattended death at home. For instance, deaths that occur in nursing homes might be more likely to be recognized as part of an epidemic and correctly recorded as due to COVID As the pandemic has progressed, official statistics have become better aligned with excess mortality estimates, perhaps due to enhanced testing and increased recognition of the clinical features of COVID Many European countries have experienced sharp increases in all-cause deaths associated with the pandemic.

These gaps are more pronounced in countries that were affected more and earlier by the pandemic and had weak testing. Very limited excess mortality information is available from Asia, Africa, the Middle East, and South America thus far; these data will be important to fully capture the heterogeneity of death rates related to the COVID pandemic across the world. Prior work on the and pandemics has shown substantial heterogeneity in mortality burden between countries, in part related to health care.

These analyses are all based on provisional data, which are incomplete for recent weeks in some states because of reporting delays. We have attempted to correct for these reporting delays in the analysis.

Moreover, we anticipate to study how Hougaard et al. We ended our paper with a hope that the results presented would contribute to discussions around the use of biological samples for research purposes. We view this correspondence as the first of hopefully many discussions on this topic. We are now even more optimistic that biobanks like the Danish Newborn Screening Biobank acknowledge the value of transparency in their work and an open debate with the research environment as well as with the general public.

Eu J Hum Genet. Download references. You can also search for this author in PubMed Google Scholar. Reprints and Permissions. Nordfalk, F. Reply to DM Hougaard et al.. Eur J Hum Genet 27, — Download citation. Received : 15 May Accepted : 15 May Published : 28 June Issue Date : November Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative.