Abstract
Among 513 adults aged 18–49 years without underlying medical conditions hospitalized with coronavirus disease 2019 (COVID-19) during March 2020–August 2020, 22% were admitted to an intensive care unit, 10% required mechanical ventilation, and 3 patients died (0.6%). These data demonstrate that healthy younger adults can develop severe COVID-19.
Keywords: SARS-CoV-2, COVID-19, COVID-NET, hospitalization, young adults
Existing evidence indicates that the majority of people infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (COVID-19), experience mild symptoms, but some people may experience severe illness that requires hospitalization and intensive care [1]. The majority of COVID-19–associated hospitalizations occur in adults aged ≥65 years and/or those with underlying conditions [2, 3]. Adults aged <50 years can also be hospitalized with COVID-19 [3]; yet, data about this group are limited.
The US Centers for Disease Control and Prevention’s (CDC) COVID-19–Associated Hospitalization Surveillance Network (COVID-NET) conducts population-based surveillance for laboratory-confirmed COVID-19–associated hospitalizations in all ages. COVID-NET data show that weekly hospitalization rates among younger adults aged 18–49 years increased during the summer months and peaked during the week ending 18 July 2020 [4]. Using COVID-NET data from 1 March 2020–1 August 2020, we describe the epidemiology, characteristics, and outcomes of nonpregnant adults aged 18–49 years without underlying conditions hospitalized with COVID-19 in the United States.
METHODS
COVID-NET surveillance is conducted in 99 counties in 14 US states and covers approximately 10% of the US population [3]. This analysis used data from 13 states (California, Colorado, Connecticut, Georgia, Maryland, Michigan, Minnesota, New Mexico, New York, Ohio, Oregon, Tennessee, and Utah). Eligible patients were COVID-NET catchment area residents with a positive SARS-CoV-2 test by real-time reverse-transcription polymerase chain reaction within 14 days prior to or during hospitalization. SARS-CoV-2 testing is requested at the discretion of treating healthcare providers or based on facility testing policies. Trained personnel identified patients who met the case definition through active review of hospital, laboratory, and reportable diseases databases and used a standard case report form to conduct medical chart abstractions for each case. This analysis was limited to patients with completed medical chart abstractions and a discharge disposition (ie, discharged or died in-hospital).
We described epidemiologic and clinical characteristics for a convenience sample of hospitalized nonpregnant adults aged 18–49 years with no underlying conditions (see Supplementary Table 1 for list of underlying conditions). Obesity was defined as body mass index ≥30 kg/m2 or by International Classification of Diseases discharge diagnosis codes (E66.0–E66.9). Any medication prescribed solely for treatment of SARS-CoV-2 was classified as a COVID-19–associated treatment. We collected information on whether or not patients were documented to be healthcare personnel (yes/no). Invasive mechanical ventilation, bilevel positive airway pressure, continuous positive airway pressure, and high-flow nasal cannula were defined based on the highest level of respiratory support received; data were not collected on oxygen delivered via standard nasal cannula.
This activity was determined by the CDC to meet the requirements of public health surveillance as defined in 45 CFR 46.102(l)(2). Participating sites received approval from their respective state and local institutional review boards, as required.
We calculated frequencies and percentages for categorical variables and median and interquartile range (IQR) for continuous variables. Data analysis was completed in SAS statistical software version 9.4 (SAS Institute Inc., Cary, NC, USA).
RESULTS
During 1 March 2020–1 August 2020, 44 865 patients hospitalized with COVID-19 were identified through COVID-NET. Adults aged 18–49 years represented 29.3% (n = 13 167) of all hospitalized patients. Of the 13 167 patients aged 18–49 years, 3720 (28.3%) had medical chart abstractions completed (Supplementary Figure 1). Among these 3720 patients, 2.7% (n = 101) did not have information on underlying conditions. Of the remaining 3619 patients, 14.2% (n = 513) had no underlying conditions and were included in the analysis.
Among nonpregnant adults aged 18–49 years (median age, 38 years; IQR, 30–44) without underlying conditions, 378 (73.7%) were men, 216 (42.1%) were Hispanic or Latino, 46 (9.0%) were healthcare personnel, and 70 (13.7%) were current tobacco smokers (Table 1). The most common symptoms present at admission were cough (67.1%), fever/chills (64.7%), and shortness of breath (63.4%). In total, 34.9% of patients (n = 179) were prescribed treatment for COVID-19, and 22.0% (113) were admitted to the intensive care unit (ICU). The median number of days from symptom onset to admission was 7 (IQR, 4–9). The median hospital length of stay was 4 days (IQR, 2–7), and the median length of ICU stay (among those admitted to an ICU) was 5 days (IQR, 1–10). The highest respiratory support received was as follows: invasive mechanical ventilation (9.9%), bilevel positive airway pressure or continuous positive airway pressure (1.2%), and high-flow nasal cannula (6.2%). Based on discharge summaries, 51.1% of patients had pneumonia, 37.4% developed acute respiratory failure, and 16.6% developed sepsis during hospitalization.
Table 1.
Characteristics and Outcomes of US Adults Aged 18–49 Years Without Underlying Medical Conditions Hospitalized With Laboratory-Confirmed Coronavirus Disease 2019: COVID-NET, 1 March 2020–1 August 2020
| Characteristic | Total | Frequency (%)/ Median (IQR) |
|---|---|---|
| Total number of patients | 513 | |
| Age, n (%), y | ||
| 18–29 | 513 | 109 (21.3) |
| 30–39 | 513 | 172 (33.5) |
| 40–49 | 513 | 232 (45.2) |
| Sex, n (%) | ||
| Men | 513 | 378 (73.7) |
| Women | 513 | 135 (26.3) |
| Race, n (%) | ||
| Hispanic or Latino | 513 | 216 (42.1) |
| Non-Hispanic White | 513 | 103 (20.1) |
| Non-Hispanic Black | 513 | 100 (19.5) |
| Non-Hispanic Asian or Pacific Islander | 513 | 45 (8.8) |
| Non-Hispanic American Indian or Alaska Native | 513 | 13 (2.5) |
| Non-Hispanic Multiracial | 513 | 2 (0.4) |
| Unknown | 513 | 34 (6.6) |
| Type of residence at the time of hospitalization, n (%) | ||
| Private residence | 513 | 467 (91.0) |
| Homeless/shelter | 513 | 32 (6.2) |
| Facility a | 513 | 5 (1.0) |
| Corrections facility | 513 | 2 (0.4) |
| Other/Unknown | 513 | 7 (1.4) |
| Healthcare personnel, b n (%) | 513 | 46 (9.0) |
| Smoking status, n (%) | ||
| Current | 513 | 70 (13.7) |
| Former | 513 | 46 (9.0) |
| Never/Unknown | 513 | 397 (77.4) |
| Symptoms at admission, n (%) | ||
| Cough | 513 | 344 (67.1) |
| Fever/chills | 513 | 332 (64.7) |
| Shortness of breath | 513 | 325 (63.4) |
| Myalgia | 513 | 168 (32.8) |
| Nausea/vomiting | 513 | 132 (25.7) |
| Headache | 513 | 124 (24.2) |
| Chest pain | 513 | 119 (23.2) |
| Diarrhea | 513 | 111 (21.6) |
| Sore throat | 513 | 69 (13.5) |
| Abdominal pain | 513 | 73 (14.2) |
| Nasal congestion/rhinorrhea | 513 | 42 (8.2) |
| Loss of taste | 513 | 37 (7.2) |
| Loss of smell | 513 | 35 (6.8) |
| Altered mental status/confusion | 513 | 13 (2.5) |
| Wheezing | 513 | 9 (1.8) |
| Vital signs on admission and initial laboratory results, median (IQR) | ||
| Heart rate (beats/min) | 511 | 102 (88–113) |
| Respiratory rate (breaths/min) | 508 | 20 (18–24) |
| Systolic blood pressure (mm Hg) | 513 | 125 (114–136) |
| Temperature (Celsius) | 508 | 37.3 (36.8–38.3) |
| Oxygen saturation among those on room air (%) | 437 | 96 (92–98) |
| White blood cell count (per mm3) | 497 | 7.4 (5.4–10.2) |
| Hematocrit (%) | 499 | 42.4 (39.4–45.1) |
| Platelets (per mm3) | 498 | 215.5 (175.0–281.0) |
| Aspartate transaminase (U/L) | 437 | 43 (30–75) |
| Alanine aminotransferase (U/L) | 436 | 42 (25–73) |
| COVID-19 treatment,c n (%) | ||
| Any COVID-19 treatment | 513 | 179 (34.9) |
| Hydroxychloroquine | 513 | 97 (18.9) |
| Azithromycind | 513 | 89 (17.4) |
| Remdesivir | 513 | 61 (11.9) |
| Convalescent plasma | 513 | 29 (5.7) |
| Tocilizumab | 513 | 25 (4.9) |
| Atazanavire | 513 | 3 (0.6) |
| Number of days from symptom onset to admission, median (IQR) | 377 | 7 (4–9) |
| Hospitalization length of stay (days), median (IQR) | 513 | 4 (2–7) |
| Number of days in ICU,f median (IQR) | 107 | 5 (1–10) |
| Hospital course and outcomes, n (%) | ||
| ICU admission | 513 | 113 (22.0) |
| Vasopressor use | 513 | 42 (8.2) |
| Invasive mechanical ventilationg | 513 | 51 (9.9) |
| Bilevel positive airway pressure or continuous positive airway pressureg | 513 | 6 (1.2) |
| High-flow nasal cannulag | 513 | 32 (6.2) |
| Extracorporeal membrane oxygenation | 513 | 2 (0.4) |
| Systemic steroids | 513 | 70 (13.7) |
| Renal replacement therapy | 513 | 3 (0.6) |
| Discharge diagnosis,h n (%) | ||
| Pneumonia | 495 | 253 (51.1) |
| Acute respiratory failure | 495 | 185 (37.4) |
| Sepsis | 495 | 82 (16.6) |
| Acute respiratory distress syndrome | 495 | 25 (5.1) |
| Acute kidney injury | 495 | 30 (6.1) |
| Acute encephalopathy | 495 | 17 (3.4) |
| Diabetic ketoacidosis | 495 | 4 (0.8) |
| Congestive heart failure | 495 | 2 (0.4) |
| Acute myocardial infarction | 495 | 1 (0.2) |
| Died in the hospital, n (%) | 513 | 3 (0.6) |
Abbreviations: COVID-19, coronavirus disease 2019; ICU, intensive care unit; IQR, interquartile range.
aFacility includes nursing home/skilled nursing facility, alcohol/drug abuse treatment, rehabilitation facility, assisted living/residential care, long-term acute care hospitals, group/retirement home, transitional care facility, psychiatric facility, other long-term care facility.
bHealthcare personnel include all paid and unpaid persons serving in healthcare settings who have the potential for direct or indirect exposure to patients or infectious materials. They include physicians, nurses, students, respiratory therapists, phlebotomists, laboratory staff, as well as transport, food service, housekeeping, volunteers, and maintenance personnel.
cThe treatments listed are not mutually exclusive.
dLimited to azithromycin prescribed in combination with other antivirals.
eNot used for treatment of human immunodeficiency virus.
fAmong patients who were admitted to the ICU.
gInvasive mechanical ventilation, bilevel positive airway pressure/continuous positive airway pressure, and high-flow nasal cannula were defined based on the highest level of respiratory support received.
hDischarge diagnoses associated with coronavirus disease 2019-associated hospitalization. Discharge diagnoses are not mutually exclusive.
Three patients (0.6%) died during hospitalization. For these 3 patients, the number of days from symptom onset to admission ranged from 6 to 15, number of days in the hospital ranged from 13 to 36 , and all were admitted to the ICU and required invasive mechanical ventilation and vasopressors.
Discussion
Within a large, geographically diverse surveillance network of laboratory-confirmed COVID-19–associated hospitalizations during 1 March 2020–1 August 2020, adults aged 18–49 years accounted for about one-third of all hospitalizations. In contrast, adults aged 18–49 years have made up 13%–23% of patients hospitalized with influenza over the past 5 seasons [5]. In this convenience sample, approximately 14% of adults aged 18–49 years had no underlying medical conditions. Although in-hospital deaths were rare, 22% of these healthy, younger adults were admitted to the ICU, and 17% required invasive or noninvasive respiratory support, excluding oxygen administered by nasal cannula. These findings suggest that adults aged 18–49 years without underlying medical conditions can experience severe COVID-19–related illness.
While evidence shows that older age and certain underlying conditions are risk factors for severe COVID-19 [2, 3], the mechanisms behind severe COVID-19 in younger adults without underlying conditions is not well understood. Emerging evidence suggests that biological factors may explain why some individuals develop severe COVID-19 and others do not. One biological factor that may explain individual differences in COVID-19 severity is angiotensin-converting enzyme 2 (ACE2), a known receptor of coronavirus that facilitates entry of the virus into host cells [6] such that individuals with higher levels of ACE2 may be more vulnerable to SARS-CoV-2 infection. Evidence suggests that ACE2 levels may be influenced by sex hormones, with males showing increased ACE2 expression compared with females [7]. These sex differences in ACE2 and the role of sex hormones have been offered as potential biological explanations for the increased risk of severe COVID-19 in males [7, 8]. In our analysis, we found that approximately 74% of adults aged 18–49 years without underlying conditions who were hospitalized with COVID-19 were males. These findings suggest that, in the absence of underlying conditions, male sex may increase the risk of severe COVID-19, leading to hospitalization and the need for intensive care in adults aged <50 years [8, 9].
In addition to male sex, racial and ethnic disparities in severe COVID-19 have been reported. Data from Baltimore, Maryland–Washington, D.C. [10], and our earlier report [3] indicate that COVID-19 disproportionately affects minority populations including Hispanic or Latino and non-Hispanic Black or African American persons. In the present study, we found that although Hispanic or Latino persons represent only 16% of the 18- to 49-year-old population under surveillance within the COVID-NET catchment area, 42% of hospitalized adults aged 18–49 years without underlying conditions in the COVID-NET study sample were Hispanic or Latino. To determine whether our convenience sampling over-selected for Hispanic or Latino persons, we compared the proportions of patients aged 18-49 years who were Hispanic or Latino among those with and without completed medical chart abstractions, and found the proportions to be 35% and 42%, respectively, indicating that we did not over-select Hispanic or Latino persons. (Supplementary Table 2). One possible explanation for the observed disparities is that younger Hispanic or Latino adults are overrepresented in occupations with limited opportunities for social distancing [11, 12], which may place them at increased risk for SARS-CoV-2 infection. Further studies are needed to better explain why Hispanic or Latino persons may be overrepresented in COVID-19–related hospitalizations in otherwise healthy adults aged 18–49 years.
There are several limitations to this analysis. Since SARS-CoV-2 testing was conducted at the discretion of healthcare providers, COVID-NET may not capture all COVID-19–associated hospitalizations. We included a convenience sample of hospitalized patients with a discharge disposition and for whom completed medical chart abstractions were available; therefore, our findings may not be representative of all patients aged 18–49 years who reside within the COVID-NET catchment area or hospitalized with COVID-19. Although COVID-NET covers about 10% of the US population, its limited geographic coverage means that the findings may not be generalizable to the US population. While detailed chart abstractions were conducted to ascertain the presence of underlying conditions at admission, it is possible that persons from certain racial and ethnic minority groups may have had undiagnosed underlying medical conditions due to limited healthcare access, leading to possible misclassification and inclusion of some patients in our analysis with undiagnosed medical conditions.
Our study showed that among younger adults hospitalized with COVID-19, more than 1 in 5 of those without underlying medical conditions experienced severe illness that required ICU-level care and other interventions. Our findings reinforce the need for social distancing, rigorous hand hygiene, and use of masks to prevent infection, even among younger adults considered to be at relatively lower risk for severe COVID-19.
Supplementary Data
Supplementary materials are available at Clinical Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.
Notes
COVID-NET Surveillance Team. Charisse N. Cummings (Centers for Disease Control and Prevention [CDC] COVID-NET, Chickasaw Nation Industries); Rachel Holstein (CDC COVID-NET, Oak Ridge Institute for Science and Education); Anita Kambhampati (CDC COVID-NET); Seth Meador (CDC COVID-NET); Jonathan M. Wortham (CDC COVID-NET, US Public Health Service, Rockville, MD); Shua J. Chai (California Emerging Infections Program, Oakland; Career Epidemiology Field Officer, CDC Division Of State And Local Readiness); Breanna Kawasaki (Colorado Department of Public Health and Environment, Denver); Kimberly Yousey-Hindes (Connecticut Emerging Infections Program, Yale School of Public Health, New Haven); Kyle P. Openo (Emerging Infections Program, Georgia Department of Health, Atlanta; Veterans Affairs Medical Center, Atlanta; Foundation for Atlanta Veterans Education and Research, Decatur); Maya L. Monroe (Maryland Department of Health, Baltimore); Libby Reeg (Michigan Department of Health and Human Services, Lansing); Ruth Lynfield (Minnesota Department of Health, St. Paul); Nancy Eisenberg (New Mexico Emerging Infections Program, Santa Fe); Grant R. Barney (New York State Department of Health, Albany); Christina B. Felsen (University of Rochester School of Medicine and Dentistry, Rochester, NY); Jessica Shiltz (Ohio Department of Health, Columbus); Nicole West (Oregon Health Authority, Portland); William Schaffner (Vanderbilt University School of Medicine, Nashville, TN); Andrea Price (Salt Lake County Health Department, Salt Lake City, UT).
Acknowledgments. The authors thank Pamela Daily Kirley, Kareena Hundal, Jeremy Roland, Alison Ryan (California Emerging Infections Program); Mathew Cartter (Connecticut Department of Public Health); Gaggan Brar, Paula Clogher, Hazal Kayalioglu, Carol Lyons, James Meek, Adam Misiorski, Linda Niccolai, Danyel Olson, Christina Parisi (Connecticut Emerging Infections Program, Yale School of Public Health); Emily Fawcett, Siyeh Gretzinger, Katelyn Lengacher, Jeremiah Williams (Emerging Infections Program, Georgia Department of Health, Atlanta Veterans Affairs Medical Center, Foundation for Atlanta Veterans Education and Research); Andy Weigel (Iowa Department of Public Health); David Blythe, Alicia Brooks, Elisabeth Vaeth, Cindy Zerrlaut (Maryland Department of Health); Rachel Park, Michelle Wilson (Maryland Emerging Infections Program—Johns Hopkins Bloomberg School of Public Health); Jim Collins, Sam Hawkins, Justin Henderson, Shannon Johnson, Val Tellez Nunez (Michigan Department of Health and Human Services); Austin Bell, Kayla Bilski, Erica Bye, Emma Contestabile, Richard Danila, Kristen Ehresmann, Claire Henrichsen, Emily Holodick, Lisa Nguyen, Katherine Schleiss, Samantha Siebman (Minnesota Department of Health); Elizabeth Dufort, Adam Rowe, Nancy Spina (New York State Department of Health); Sophrena Bushey, Maria Gaitan, RaeAnne Kurtz, Savanah Russ (Rochester Emerging Infections Program, University of Rochester Medical Center); Ama Owusu-Dommey (Public Health Division, Oregon Health Authority); Kathy Billings, Katie Dyer, Anise Elie, Karen Leib, Tiffanie Markus, Terri McMinn, Danielle Ndi, Manideepthi Pemmaraju, John Ujwok (Vanderbilt University Medical Center); Ryan Chatelain, Andrea George, Mary Hill, Laine McCullough, Jake Ortega, Ilene Risk, Melanie Spencer, Ashley Swain (Salt Lake County Health Department); Keegan McCaffrey (Utah Department of Health); Mimi Huynh (Council of State and Territorial Epidemiologists); Rainy Henry, Sonja Mali Nti-Berko, Robert W. Pinner, Alvin Shultz (Emerging Infections Program); Anna Acosta (CDC); Elif Alyanak (CDC, Battelle Memorial Institute); Abirami, Balajee (CDC, Maximus); Adria Mathis (CDC, Strategic Innovative Solutions); David Yankey (CDC).
Disclaimer. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the CDC and the US Public Health Service.
Financial support. This work was supported by grant CK17-1701 from the CDC through an Emerging Infections Program cooperative agreement and by cooperative agreement NU38OT000297-02-00 awarded to the Council of State and Territorial Epidemiologists from the CDC.
Potential conflicts of interest. N. B. A., L. M. B., R. L., P. A. R., W. S., M. S., J. S., N. W., and K. Y.-H. are recipients of grants from Centers for Disease Control and Prevention, outside the submitted work. S. K., L. R., J. S., and L. M. Billing are recipients of grants from Council of State and Territorial Epidemiologists (CSTE). E. J. A. reports personal fees from AbbVie, personal fees from Pfizer, personal fees from Sanofi Pasteur, grants from MedImmune, grants from Regeneron, grants from PaxVax, grants from Pfizer, grants from GSK, grants from Merck, grants from Novavax, grants from Sanofi-Pasteur, grants from Micron, grants from Janssen, personal fees from Kentucky BioProcessing, Inc. W. S. reports personal fees from VBI Vaccines. J. S. is recipient of an Epidemiology and Laboratory Capacity (ELC) and Immunizations and Vaccines for Children (VFC) grant funding from the CDC to support vaccine preventable disease epidemiology staffing. All remaining authors: No reported conflicts of interest.All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.
Contributor Information
COVID-NET Surveillance Teama:
Charisse N Cummings, Rachel Holstein, Anita Kambhampati, Seth Meador, Jonathan M Wortham, Shua J Chai, Breanna Kawasaki, Kimberly Yousey-Hindes, Kyle P Openo, Maya L Monroe, Libby Reeg, Ruth Lynfield, Nancy Eisenberg, Grant R Barney, Christina B Felsen, Jessica Shiltz, Nicole West, William Schaffner, and Andrea Price
References
- 1. Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention. JAMA 2020; 323:1239–42. [DOI] [PubMed] [Google Scholar]
- 2. Petrilli CM, Jones SA, Yang J, et al. Factors associated with hospital admission and critical illness among 5279 people with coronavirus disease 2019 in New York City: prospective cohort study. BMJ 2020; 369:m1966. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Garg S, Kim L, Whitaker M, et al. Hospitalization rates and characteristics of patients hospitalized with laboratory-confirmed coronavirus disease 2019–COVID-NET, 14 states, March 1-30, 2020. MMWR Morb Mortal Wkly Rep 2020; 69:458–64. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. CDC. COVID-NET: Laboratory-confirmed COVID-19-associated hospitalizations [Internet]. 2020. Available at: https://gis.cdc.gov/grasp/COVIDNet/COVID19_3.html. Accessed 7 October 2020.
- 5. CDC. FluView interactive: laboratory-confirmed influenza hospitalizations [Internet]. 2020. Available at: https://gis.cdc.gov/GRASP/Fluview/FluHospRates.html. Accessed 8 October 2020.
- 6. Li W, Moore MJ, Vasilieva N, et al. Angiotensin-converting enzyme 2 is a functional receptor for the SARS coronavirus. Nature 2003; 426:450–4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. La Vignera S, Cannarella R, Condorelli RA, Torre F, Aversa A, Calogero AE. Sex-specific SARS-CoV-2 mortality: among hormone-modulated ACE2 expression, risk of venous thromboembolism and hypovitaminosis D. Int J Mol Sci 2020; 21:2948. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Spagnolo PA, Manson JE, Joffe H. Sex and gender differences in health: what the COVID-19 pandemic can teach us. Ann Intern Med 2020; 173:385–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Klein SL, Dhakal S, Ursin RL, Deshpande S, Sandberg K, Mauvais-Jarvis F. Biological sex impacts COVID-19 outcomes. PLoS Pathog 2020; 16:e1008570. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Martinez DA, Hinson JS, Klein EY, et al. SARS-CoV-2 positivity rate for Latinos in the Baltimore-Washington, DC Region. JAMA 2020; 324:392–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. US Department of Health and Human Services. Profile: Hispanic/Latino Americans [Internet]. 2019. Available at: https://minorityhealth.hhs.gov/omh/browse.aspx?lvl=3&lvlid=64. Accessed 21 September 2020.
- 12. US Bureau of Labor Statistics. Job flexibilities and work schedules summary [Internet]. 2019. Available at: https://www.bls.gov/news.release/flex2.nr0.htm. 21 September 2020.
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