Skip to main content
Browse Subject Areas

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Social determinants of COVID-19 incidence and outcomes: A rapid review

  • Tara L. Upshaw ,

    Contributed equally to this work with: Tara L. Upshaw, Chloe Brown

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Validation, Writing – original draft, Writing – review & editing

    Affiliations Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, Canada, Translational Research Program, Faculty of Medicine, University of Toronto, Toronto, Canada

  • Chloe Brown ,

    Contributed equally to this work with: Tara L. Upshaw, Chloe Brown

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Validation, Writing – review & editing

    Affiliations Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, Canada, Undergraduate Medical Education, Faculty of Medicine, University of Toronto, Toronto, Canada

  • Robert Smith,

    Roles Data curation, Formal analysis, Investigation, Methodology, Validation, Writing – review & editing

    Affiliations Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, Canada, Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, Toronto, Canada, Department of Family and Community Medicine, Faculty of Medicine, University of Toronto, Toronto, Canada

  • Melissa Perri,

    Roles Data curation, Validation, Writing – review & editing

    Affiliations Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, Canada, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada

  • Carolyn Ziegler,

    Roles Conceptualization, Investigation, Methodology, Writing – review & editing

    Affiliation Health Sciences Library, Unity Health Toronto, Toronto, Canada

  • Andrew D. Pinto

    Roles Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Software, Supervision, Writing – original draft, Writing – review & editing

    Affiliations Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, Canada, Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, Toronto, Canada, Department of Family and Community Medicine, Faculty of Medicine, University of Toronto, Toronto, Canada, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada, Department of Family and Community Medicine, St. Michael’s Hospital, Toronto, Canada


Early reports indicate that the social determinants of health are implicated in COVID-19 incidence and outcomes. To inform the ongoing response to the pandemic, we conducted a rapid review of peer-reviewed studies to examine the social determinants of COVID-19. We searched Ovid MEDLINE, Embase, PsycINFO, CINAHL and Cochrane Central Register of Controlled Trials from December 1, 2019 to April 27, 2020. We also searched the bibliographies of included studies, COVID-19 evidence repositories and living evidence maps, and consulted with expert colleagues internationally. We included studies identified through these supplementary sources up to June 25, 2020. We included English-language peer-reviewed quantitative studies that used primary data to describe the social determinants of COVID-19 incidence, clinical presentation, health service use and outcomes in adults with a confirmed or presumptive diagnosis of COVID-19. Two reviewers extracted data and conducted quality assessment, confirmed by a third reviewer. Forty-two studies met inclusion criteria. The strongest evidence was from three large observational studies that found associations between race or ethnicity and socioeconomic deprivation and increased likelihood of COVID-19 incidence and subsequent hospitalization. Limited evidence was available on other key determinants, including occupation, educational attainment, housing status and food security. Assessing associations between sociodemographic factors and COVID-19 was limited by small samples, descriptive study designs, and the timeframe of our search. Systematic reviews of literature published subsequently are required to fully understand the magnitude of any effects and predictive utility of sociodemographic factors related to COVID-19 incidence and outcomes. PROSPERO: CRD4202017813.


In the year since SARS-CoV-2 was identified in Wuhan, China the coronavirus disease 2019 (COVID-19) pandemic has resulted in more than 96 million cases and over 2 million related deaths [1]. Although COVID-19 was initially deemed a “great equalizer” given universal susceptibility to this novel virus [2], reports emerged in late March 2020 that COVID-19 morbidity and mortality disproportionately impacted groups made vulnerable by policies that create and reinforce health disparities [36].

Preliminary analyses from the United States, Canada and the United Kingdom reported high rates of COVID-19 infections, hospitalizations and mortality in geographic regions with high densities of low-income and crowded households, and in locations where a high proportion of individuals were racialized [710]. Early epidemiological studies from the United States found that African-Americans had the highest mortality rates of any group, with Native Americans close behind [1116]. The United Kingdom’s Office of National Statistics reported that Black males and females were respectively 4.2 and 4.3 times more likely to die from COVID-19, when compared to White individuals adjusting for age [17]. People in congregate settings, including prisons and homeless shelters, and long-term institutional care facilities also appeared to be at higher risk for infection and worse outcomes once infected [1820].

Infection rates and outcomes for infectious diseases are influenced by social factors [21]. For example, during the 2009 H1N1 pandemic, incidence was highest among those without access to paid sick leave, and racialized individuals and those experiencing high material deprivation were more likely to be admitted to intensive care units [2224]. While there is a growing recognition that social factors have similarly influenced COVID-19, the synthesis of relevant research is limited. Given that an empirical understanding of the broader social determinants of COVID-19 could inform ongoing pandemic response efforts, we conducted a rapid review of early reports on the social determinants of COVID-19 infection, health service use and health outcomes.


Data sources and searches

We designed this rapid review using interim guidance from the Cochrane Rapid Review Methods Group [25] and registered the review protocol with PROSPERO (CRD42020178131). We searched Ovid MEDLINE, Embase, PsycINFO, CINAHL and Cochrane Central Register of Controlled Trials bibliographic databases (S1 File) from December 1, 2019 to April 1, 2020, updating once on April 16, 2020, and again on April 27, 2020. We used a combination of search terms for SARS-CoV-2 and COVID-19. Database searches were supplemented with manual searches of bibliographies of included studies, COVID-19 evidence repositories and living evidence maps [2628], and report referrals solicited from research colleagues (Australia, Belgium, Canada, China, United States and United Kingdom) with expertise in health equity and population health (S2 File). Experts were contacted by email and asked to forward reports that fit within our inclusion criteria. They were welcomed to forward our request to others within their network. We included studies identified through supplementary sources up to June 25, 2020.

Study selection

We included English-language peer-reviewed quantitative studies that used primary data to describe the social determinants of COVID-19 infection, health service use or health outcomes in adults (18 years and older) with a confirmed or presumptive diagnosis of COVID-19. We included analyses of surveillance data published by public health agencies, and excluded modelling studies, secondary analyses, news items, opinions and editorials. Using emerging reports on how social factors impacted COVID-19 and the framework of the World Health Organization Commission on the Social Determinants of Health [29], we focused on studies that reported participant race or ethnicity, income, education, employment, housing status, food security, and social isolation (S1 Table). We excluded studies that only reported age and biological sex of participants, as these relationships are well-described elsewhere [30].

Data extraction and quality assessment

Data extraction was completed using a piloted form (S3 File) by two members of the study team (TLU and CB) and confirmed by another study team member (RS). We conducted quality assessment using the Mixed Methods Appraisal Tool (MMAT) [31]. The MMAT is a validated tool for appraising quality of quantitative randomized, quantitative non-randomized, quantitative descriptive, qualitative and mixed-methods studies included in mixed literature reviews [31, 32].

Data synthesis and analysis

Given the heterogeneity of study designs, we did not conduct a pooled analysis and instead conducted a narrative synthesis [33, 34]. We organized article findings by related social determinants of health, and then by study design within each determinant category. If studies addressed more than one determinant, we described them in multiple categories.


Of 7,376 records screened, (Fig 1), 42 articles met our inclusion criteria (Table 1), 12 of which were identified through supplementary sources. These studies were conducted in China (13) and high-income countries, including Australia (2), Singapore (2), Spain (1), the United Kingdom (2), the USA (21), and a group of European Union member countries. They included cross-sectional (n = 19), cohort (n = 11), case series (n = 8) and case-control (n = 4) designs. Of included studies, 23 reported participant race or ethnicity data, 16 on occupation, 5 on income, 2 each on education and social isolation, 1 on food security and 6 on housing status.

Table 1. Characteristics and summarized results of studies.

Race or ethnicity

Twenty-three studies (55%) reported participant race or ethnicity data. Three large studies found statistically significant differences in COVID-19 infection incidence and hospitalization outcomes by race or ethnicity [3537]. A prospective analysis of UK Biobank data (n = 348,598; 499 cases) found that compared to White individuals, Black and South Asian individuals were more likely to test positive for COVID-19 after adjustment for socioeconomic, lifestyle and health-related factors (Black: OR = 4.30, 95% CI: 2.92–6.31, p<0.001; South Asian: OR = 2.42, 95% CI = 1.50–3.93, p<0.001) [35]. Another UK study (n = 3,802; 587 cases) observed similar increases in the likelihood of testing COVID-19 positive among Black compared to White adults after adjustment for potential confounders (OR = 4.75, 95% CI = 2.65–8.51) [36]. In California, USA, a retrospective cohort study (n = 14,036 adults; 1,052 cases) found that non-Hispanic Black-identifying participants positive for COVID-19 were more likely to be admitted to hospital than non-Hispanic White-identifying participants after adjusting for age, sex, comorbidities and income (OR = 2.67, 95% CI: 1.30–5.47, p<0.01) [37].

Five studies involving bivariate analyses found no statistically significant differences in COVID-19 prevalence, clinical presentation, or outcomes across racial or ethnic groups [3842]. A cross-sectional study of 305 people with COVID-19 admitted to hospitals in Georgia, USA, found that 83% identified as non-Hispanic Black (n = 247); however, compared to patients grouped as “other” race or ethnicity (including White, Asian, Hispanic and Pacific Islander; 17%, n = 50) there were no statistically significant differences in the proportions who received mechanical ventilation or died [42]. In a NYC, USA retrospective cohort study of patients with COVID-19 (n = 338), Toussie et al. did not find statistically significant differences in primary health outcomes of COVID-19 patients according to race or ethnicity [41]. The authors suggested, however, that Hispanic ethnicity was an independent predictor of having more severe chest x-ray findings among admitted patients (n = 145, OR = not reported, 95% CI: not reported, p = 0.03) [41].

Fifteen studies reported relative frequencies of participant race or ethnicity without testing for statistically significant differences in study outcomes [11, 4356]; among these, most did not report comparisons with general population race or ethnicity demographics. Goyal et al studied the clinical characteristics of 393 COVID-19 patients from New York City, USA and found the majority of cases were non-White [52]. H. Sun et al described the race and ethnicity of 30 palliative COVID-19 patients in NYC, USA and found most were of Hispanic origin (66.7%) [50]. A case series from NYC found that, of 18 COVID-19 cases with cardiac events, 50% were Hispanic [55]. Notably, Laurencin and McClinton found that 17.2% of people infected with (n = 3141) and 14.4% of those dying from (n = 96) COVID-19 in Connecticut were Black. The authors remarked that these frequencies are higher than the proportion of the Connecticut population that identifies as Black (12%, n = NR), though tests of heterogeneity were not conducted [48].


Sixteen studies identified the occupations of participants. Ten studies were conducted in China [5766], five in the USA [6771] and one in Spain [72]. In China, labourers, retail staff, agricultural workers and healthcare workers were more commonly represented among those infected. Fan et al suggested that the first wave of infection in Gansu Province may have stemmed from migrant labor workers returning from Wuhan, as 29.1% of COVID-positive patients were migrant workers (7/24; p = 0.009) [58]. Another study examined 26 admitted COVID-19 positive cases in Liaochang and found that 16 (61.5%) were retail staff, 11 of whom worked at the same supermarket [65]. Agricultural workers or farmers were represented in six Chinese studies of COVID-19 patients, with relative frequencies ranging from 7.7% to 54.6% [61, 62, 6466, 73]. In bivariate analyses, Shi et al found differences in the proportion of people with mild and severe COVID-19 symptoms comparing agricultural, non-agricultural, retired and student occupational groups, with agricultural workers having the most severe cases (p<0.001) [73]. Among four studies examining COVID-19 clinical features and describing occupation of participants, two did not assess differences by occupation [61, 62] and two found no statistically significant differences in the severity of symptoms by occupation [64, 66]. In Wuhan, China, Chu et al reported that among 54 hospitalized medical staff with COVID-19, severe disease tended to be more common among those working in non-emergency clinical or non-clinical settings [57]. Wang X. et al found that 16 of 80 hospitalized frontline medical workers in Wuhan were “other” non-medical healthcare workers compared to doctors and nurses [63].

Blanco et al conducted a case series of five HIV positive patients in Spain and found that two patients were sex workers, one of whom was admitted to ICU [72]. In the USA, five CDC Morbidity and Mortality Weekly Reports present the prevalence of COVID-19 among employees of homeless shelters, correctional or detention facilities and meat processing facilities. For homeless shelter staff, 21.0% (8/38) of staff at three homeless shelters in King County, Washington were positive [70], and 11.0% (33/313) of staff at 19 homeless shelters in Boston, Seattle, San Francisco and Atlanta were positive [69]. For correctional or detention facility staff, a study by the CDC using data from 37 states reported 2,778 cases of COVID-19 among staff members, of whom 3% became hospitalized and 1.0% died [67]. Another CDC study on 46 correctional and detention facilities in Louisiana found 253 staff members were infected with COVID-19, 7.5% of whom were hospitalized and 1.6% died [68]. Dyal et al assessed the incidence of COVID-19 among workers in meat and poultry processing facilities in 19 states [71]. Of the 130,578 workers in 115 affected meat and poultry processing facilities, 3.0% tested positive (4,913 cases) and 0.4% died; the authors hypothesized that language barriers, overcrowded housing, overcrowded transportation and incentives to continue to work while ill limited effective infection control.

Income and socioeconomic status

Five studies reported on income or proxies for income. Two studies from the UK examined the association between socioeconomic status and positive COVID-19 case incidence. Hastie et al used the Townsend score to assess socioeconomic deprivation, which incorporates measures of unemployment, non-car ownership, non-home ownership and household overcrowding [35]. The authors found that higher socioeconomic deprivation predicted COVID-19 positive status in a multivariable logistic regression model (highest vs. lowest Townsend quintile OR = 1.89; 95% CI = 1.37–2.60; p <0.001). De Lusignan et al assessed socioeconomic deprivation using the English Index of Multiple Deprivation, which incorporates measures including income, employment, education, health, crime, barriers to housing and services and living environment [36]. In both univariate and multivariate analyses, people living in more deprived areas were more likely to test positive for COVID-19 (OR = 2.03, 95% CI: 1.51–2.71, p<0.0001).

Three studies reported associations between income factors and COVID-19 outcomes [3537]. Azar et al found that COVID-19 patients in a California healthcare system with Medicaid or who were uninsured were more likely to be admitted to hospital compared to those with commercial insurance (Medicaid: OR = 2.13; 95% CI: 1.24–3.68, p<0.01; Self-Pay/Unknown: OR = 2.19; 95% CI: 1.03–4.36, p<0.05) [37]. The same study found COVID-19 patients residing in higher income neighbourhoods were less likely than those residing in lower income neighbourhoods to be admitted to hospital (High income, fourth quartile: OR = 0.55, 95% CI: 0.33–0.91, p<0.05; High income, Third quartile: OR = 0.24, 95% CI: 0.12–0.46, p<0.001). Two studies only described the income or insurance status of participants with COVID-19 without testing for associations or drawing comparisons to general population income demographics [42, 74].

Social isolation

Two studies assessed factors related to social isolation. In a UK study of 3,802 adults tested for COVID-19, the odds of a positive test were lower in households with two to eight people, compared to single-person households in a univariate analysis (p<0.0001), but this was no longer statistically significant after adjusting for sociodemographic, lifestyle and health related factors [36]. One study examined social capital in relation to sleep quality and mental health outcomes among 170 adults in central China isolating at home following confirmed or suspected COVID-19, or a known exposure [74]. Social capital was measured using the Personal Social Capital Scale 16, scored according to number and professions of friends, relatives, coworkers; social trust; and civil society, recreational and political participation. After adjusting for potential confounders, higher social capital scores were significantly associated with lower anxiety and stress (structural equation model coefficients: anxiety, β = 0.619, p<0.001; stress, β = 0.327, p<0.001) [74].


Two descriptive studies conducted in China examined education level of participants. Zhang et al surveyed 205 individuals to study mental health outcomes of populations affected by COVID-19 in Zhongshan [75]. Of the 57 individuals who reported having COVID-19, 30.9% had a junior-middle school education or less, 27.3% had a senior-middle school education and 41.8% had a college education or more. No statistically significant differences were observed by education level between patients who reported having COVID-19, were put under quarantine, or were non-infected members of the general public. The second study described the education level of 170 participants without examining differences in study outcomes [74].

Food security

One study by Li et al examined the association between malnutrition and COVID-19 prevalence in elderly hospitalized patients with COVID-19 in Wuhan, China [76]. Of 182 study participants, 52.7% were malnourished, 27.5% were at risk of malnutrition and 19.8% were non-malnourished (p = 0.018). In their discussion, the authors reported that the level of malnourishment was higher in elderly COVID-19 patients than in elderly people with other health issues described by published literature.

Housing status

Six studies assessed housing-related factors among COVID-19 patient populations. Three descriptive studies and one cohort study from the USA examined COVID-19 incidence and outcomes among people experiencing homelessness. Tobolowsky et al studied a COVID-19 outbreak among three homeless services sites in King County, Washington and found a positive COVID-19 diagnosis in 35 of 195 residents (18%) tested [70]. Mosites et al assessed COVID-19 in 19 homeless shelters in Boston, Seattle, San Francisco and Atlanta [69]. Of the 1,292 shelter residents, 292 tested positive (25%). One shelter in San Francisco had 66% of 95 residents test positive. Baggett et al studied COVID-19 prevalence among homeless shelter residents in Boston and found that, of the 408 residents tested, 147 (36%) had a positive test result [77]. Among people testing positive for COVID-19 in California, Azar et al found no statistically significant association between homelessness and hospital admissions [37].

Two studies examined the incidence and outcomes of COVID-19 in correctional and detention facilities. We classified these as related to housing status because the authors describe the challenges of infection control within correctional facilities in relation to housing: crowded dormitories, shared bathrooms, limited medical resources, limited quarantine space and daily entry and exit of staff and visitors [67, 68]. Wallace, Hagan et al examined national incidence of COVID-19 in 37 US jurisdictions that reported outcomes on correctional and detention facilities. Across 32 jurisdictions, 420 facilities had at least one case of COVID-19 [67]. They found 4,893 COVID-19 cases among incarcerated or detained persons, of whom 491 (10%) were hospitalized and 88 (2%) died. In a separate study of 144 correctional and detention Louisiana, Wallace, Marlow et al identified 489 laboratory-confirmed COVID-19 cases among incarcerated or detained persons, of which 47 (7.6%) were hospitalized and 10 (2%) died [68].

Quality assessment

The overall quality of included studies was low. Among the studies that involved comparison between people with or without COVID-19, or compared health outcomes among people with COVID-19, risk of selection and confounding biases were most common (Table 2). This was most often due to the descriptive nature of analyses and small samples recruited over short periods of time, with limited information provided by authors to assist readers in evaluating the representativeness of samples. Eleven studies were at high risk of confounding (e.g. bivariate analyses), while seven had unclear risk of confounding (e.g. multivariable analyses accounting for sociodemographic, lifestyle and health-related confounders, but not other factors thought to be implicated in racial/ethnic differences in COVID-19 risk, such as employment in high-risk professions) [20, 38, 40, 54, 60, 6264, 66, 74, 75]. Many of the case series or cross-sectional studies also relied on small sample sizes and similarly had risk of selection bias (Table 3); eight of these either provided insufficient detail for measurement methods for sociodemographic variable or outcomes, or insufficient detail of handling of missing data, and were therefore at risk of measurement error [11, 44, 48, 52, 57, 67, 71].

Table 2. Mixed methods assessment tool quality assessment matrix for quantitative non-randomized studies.

Table 3. Mixed methods assessment tool quality assessment matrix for quantitative descriptive studies.


In this rapid review we identified 42 peer-reviewed studies that included sociodemographic factors in analyses of COVID-19 incidence, clinical presentation, and prognosis. Most studies involved descriptive analyses, however more recent studies involving larger samples and multivariable analyses found key social determinants of health to be associated with COVID-19 incidence and outcomes. The strongest evidence of associations stems from three observational studies from the USA and UK which found associations between race and ethnicity, health insurance status, neighbourhood-level socioeconomic deprivation, and likelihood of COVID-19 positive status and COVID-19 hospital admission [3537]. Limited evidence was available on other factors including occupation, educational attainment, housing status or food security.

While it remains possible that these associations could at least in part be explained by residual confounding and selection bias, the emergent findings are consistent with patterns observed during the H1N1 pandemic [2224]. Adverse social conditions at the individual and community level, reinforced by systemic issues such as racism [78, 79], may increase the likelihood of both COVID-19 infection and poor COVID-19 disease outcomes. Low-income earners are more likely to hold essential sales and service jobs and live in crowded housing conditions where ability to maintain physical distance from others is limited, increasing risk of virus exposure and transmission [10, 8082]. Across studies and settings, labourers, retail staff, agricultural workers, healthcare workers and people working in congregate settings (shelters, correctional facilities, meat processing facilities) were reported to be over-represented among those infected. Homeless shelters face similar challenges in preventing the spread of COVID-19, including overcrowding, limited access to facilities for maintaining basic hygiene, and high rates of underlying comorbidities among clients [69, 70, 77].

This rapid review had several limitations. As with many rapid reviews, the short review timeframe, combined with the emergent nature of COVID-19 literature, limited the breadth of our analysis [8386]. However, rapid reviews and full systematic reviews conducted on the same topic often produce similar conclusions [86]. Further, we screened all indexed English-language literature on COVID-19 published during the search period, ensuring we captured eligible studies. We did not address all social determinants of health, but focused on the ones that were likely most relevant to COVID-19 [87]. Our search extended only to April 27, 2020, with records identified through supplementary sources up until June 25, 2020. Small sample sizes, cohorts restricted to people testing positive for COVID-19, and the use of descriptive statistical methods limited the inferences that could be drawn from most of the early studies we reviewed. However, a number of studies published more recently have addressed these limitations.

Studies published since June 2020 tend to support our findings of disparities in COVID-19 infection, hospitalization, and mortality by race or ethnicity [8896], socioeconomic status and deprivation [8890, 92, 93, 97], and housing insecurity [95, 96, 98, 99]. At least two recent studies did not find associations between race and mortality outcomes among those able to access hospital care [100, 101], contrary to findings of most other research, including this review. More recent studies have also examined a wider range of sociodemographic factors in relation to COVID-19 infection such as primary spoken language [96], and additional studies have examined those factors less often assessed in early reports, such as educational attainment [90, 93, 97], occupation [97, 102, 103], and marital status [93]. Contrasting the early findings from one study included in our analysis, at least two studies indicate that cohabitation and larger households are associated with COVID-19 infection and mortality [103, 104]. Food insecurity appears to remain an understudied factor in relation to COVID-19 incidence and outcomes. At the time of publication, we identified only one systematic review examining COVID-19 outcomes by ethnicity [105].

Among early reports, few studies collected data on the social determinants of health. Those that did were at high risk of bias and frequently had missing data was common, with incomplete or missing data for race or ethnicity reported by nineteen studies, with missing data ranging from 2.6% to 61% [11, 3645, 4854, 56]. To enhance availability of high-quality evidence for policymakers, we recommend that further large-scale prospective studies are complemented by knowledge sources from community health, social service and advocacy organizations. Studies initiated at the outset of future pandemics should endeavor to collect and asses individual-level data on social risk factors using standard tools, ensuring data collection, interpretation and subsequent actions taken are led by the communities most impacted. The literature on COVID-19 continues to expand rapidly [106], and future systematic reviews with meta-analyses will be required to fully understand the magnitude of any effects and predictive utility of sociodemographic factors related to COVID-19 incidence and outcomes.

Supporting information

S2 File. Expert contacts.

A list of individuals contacted to refer additional articles on the social determinants of COVID-19 incidence and outcomes who consented to be named.


S1 Table. Review PICO framework.

A breakdown of the criteria applied to articles to determine inclusion eligibility.



We thank the following individuals who assisted with reviewing citations for this review: Anne Marie Tynan, Rose Wang, Ayu Hapsari, Amy Craig-Neil, Khysa Bishop, Shailesh Advani and Nada Dali. We thank Anne Rucchetto for helpful edits of the manuscript.


  1. 1. World Health Organization. WHO Coronovirus Disease (COVID-19) Dashboard. 2020 [cited 21 Jan 2021].
  2. 2. Jones BL, Jones JS. Gov. Cuomo is wrong, covid-19 is anything but an equalizer. The Washington Post. Apr 2020.
  3. 3. Roberts KC, Rao DP, Bennett TL, Loukine L, Jayaraman GC. Prevalence and patterns of chronic disease multimorbidity and associated determinants in Canada. Heal Promot Chronic Dis Prev Canada. 2015;35: 87–94. pmid:26302227
  4. 4. Paradies Y, Ben J, Denson N, Elias A, Priest N, Pieterse A, et al. Racism as a determinant of health: A systematic review and meta-analysis. PLoS One. 2015;10: 1–48. pmid:26398658
  5. 5. Shaw KM, Theis KA, Self-Brown S, Roblin DW, Barker L. Chronic disease disparities by county economic status and metropolitan classification, behavioral risk factor surveillance system, 2013. Prev Chronic Dis. 2016;13: 1–12. pmid:27584875
  6. 6. Block S, Dhunna S. COVID-19: It’s time to protect frontline workers. 2020 Mar. Available:
  7. 7. Chung H, Fung K, Ferreira-Legere L, Chen B, Ishiguro L, Kalappa G, et al. COVID-19 Laboratory Testing in Ontario: Patterns of Testing and Characteristics of Individuals Tested, as of April 30, 2020. Toronto; 2020.
  8. 8. Associated Press. Lower income people, new immigrants at higher COVID-19 risk in Toronto, data suggests. CBC News. May.
  9. 9. Caul S. Deaths involving COVID-19 by local area and socioeconomic deprivation. Off Natl Stat. 2020. Available:
  10. 10. Chen JT, Krieger N. Revealing the unequal burden of COVID-19 by income, race/ethnicity, and household crowding: US county vs. ZIP code analyses. Harvard Cent Popul Dev Stud Work Pap Ser. Boston; 2020. Report No.: 1.
  11. 11. Garg S, Kim L, Whitaker M, O’Halloran A, Cummings C, Holstein R, 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: 458–464. Available: pmid:32298251
  12. 12. Thebault R, Tran A, Williams V. The coronavirus is infecting and killing black Americans at an alarmingly high rate. In: The Washington Post [Internet]. Washington, DC; 7 Apr 2020 [cited 19 Apr 2020].
  13. 13. Chicago Department of Public Health. Chicago COVID-19 Update. Chicago; 2020 Jun.
  14. 14. Stafford K, Hoyer M, Morrison A. Outcry over racial data grows as virus slams black Americans. In: Associated Press. 2020.
  15. 15. New York State Department of Health. COVID-19 Tracker. 2020.
  16. 16. COVID Tracking Project, Antiracist Research & Policy Centre. The COVID Racial Data Tracker Racial Data Dashboard. 2020.
  17. 17. Office for National Statistics. Coronavirus (COVID-19) related deaths by ethnic group, England and Wales: 2 Mar 2020 to 10 April 2020. 2020 May.
  18. 18. Tsai J, Wilson M. COVID-19: a potential public health problem for homeless populations. Lancet Public Heal. 2020;5: e186–e187. pmid:32171054
  19. 19. Akiyama M, Spaulding A, Rich J. Flattening the Curve for Incarcerated Populations—Covid-19 in Jails and Prisons. N Engl J Med. 2020. pmid:32240582
  20. 20. Mosites E, Parker E, Clarke K, Gaeta J, Baggett T, Imbert E, et al. Assessment of SARS-CoV-2 infection prevalence in homeless shelters—four US cities. MMWR Morb Mortal Wkly Rep. 2020;69: 1–2.
  21. 21. Dean HD, Fenton KA. Addressing Social Determinants of Health in the Prevention and Control of HIV/AIDS, Viral Hepatitis, Sexually Transmitted Infections, and Tuberculois. Public Health Rep. 2010;125.
  22. 22. Placzek H, Madoff L. Effect of race/ethnicity and socioeconomic status on pandemic H1N1-related outcomes in Massachusetts. Am J Public Health. 2014;104. pmid:24228651
  23. 23. Lowcock EC, Rosella LC, Foisy J, McGeer A, Crowcroft N. The social determinants of health and pandemic h1n1 2009 influenza severity. Am J Public Health. 2012;102: 51–58. pmid:22698024
  24. 24. Kumar S, Quinn SC, Kim KH, Daniel LH, Freimuth VS. The impact of workplace policies and other social factors on self-reported influenza-like illness incidence during the 2009 H1N1 pandemic. Am J Public Health. 2012;102: 134–140. pmid:22095353
  25. 25. Garritty C, Gartlehner G, Kamel C, King V, Nussbaumer-Streit B, Stevens A, et al. Interim Guidance from the Cochrane Rapid Reviews Methods Group. Cochrane Rapid Rev. 2020.
  26. 26. Social Interventions Research and Evaluation Network. COVID-19 Resource Page. 2020.
  27. 27. Norwegian Institute of Public Health. Live map of COVID-19 evidence. 2020.
  28. 28. EPPI-Centre at the University of College London. COVID-19: a living systematic map of the evidence.
  29. 29. World Health Organization. Commission on Social Determinants of Health. Geneva; 2008.
  30. 30. Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, et al. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ. 2020;369. pmid:32265220
  31. 31. Hong Q, Pluye P, Fàbregues S, Bartlett G, Boardman F, Cargo M, et al. Mixed Methods Appraisal Tool (MMAT) Version 2018: User guide. McGill. 2018; 1–11.
  32. 32. Pace R, Pluye P, Bartlett G, Macaulay AC, Salsberg J, Jagosh J, et al. Testing the reliability and efficiency of the pilot Mixed Methods Appraisal Tool (MMAT) for systematic mixed studies review. Int J Nurs Stud. 2012;49: 47–53. pmid:21835406
  33. 33. Popay J, Roberts H, Sowden A, Petticrew M, Arai L, Rodgers M, et al. Narrative Synthesis in Systematic Reviews: A Product from the ESRC methods programme. Report. United Kingdom; 2006.
  34. 34. Rodgers M, Sowden A, Petticrew M, Arai L, Roberts H, Britten N, et al. Testing the guidance on the conduct of narrative synthesis in systematic reviews: Effectiveness of interventions to promote smoke alarm ownership and function. Evaluation. 2009;15: 47–71.
  35. 35. Hastie CE, Mackay DF, Ho F, Celis-Morales CA, Katikireddi S V, Niedzwiedz CL, et al. Vitamin D concentrations and COVID-19 infection in UK Biobank. Diabetes Metab Syndr. 2020;14: 561–65. pmid:32413819
  36. 36. de Lusignan S, Dorward J, Correa A, Jones N, Akinyemi O, Amirthalingam G, et al. Risk factors for SARS-CoV-2 among patients in the Oxford Royal College of General Practitioners Research and Surveillance Centre primary care network: a cross-sectional study. Lancet Infect Dis. 2020. pmid:32422204
  37. 37. Azar KMJ, Shen Z, Romanelli RJ, Lockhart SH, Smits K, Robinson S, et al. Disparities In Outcomes Among COVID-19 Patients In A Large Health Care System In California. Health Aff (Millwood). 2020;39. pmid:32437224
  38. 38. Sun Y, Koh V, Marimuthu K, Ng OT, Young B, Vasoo S, et al. Epidemiological and Clinical Predictors of COVID-19. Clin Infect Dis. 2020;. pmid:32211755
  39. 39. Nobel YR, Phipps M, Zucker J, Lebwohl B, Wang TC, Sobieszczyk ME, et al. Gastrointestinal Symptoms and COVID-19: Case-Control Study from the United States. Gastroenterology. 2020; 1–3. pmid:32294477
  40. 40. Yan CH, Faraji F, Prajapati DP, Ostrander BT, DeConde AS. Self-reported olfactory loss associates with outpatient clinical course in Covid-19. Int Forum Allergy Rhinol. 2020; 1–11. pmid:32329222
  41. 41. Toussie D, Voutsinas N, Finkelstein M, Cedillo MA, Manna S, Maron SZ, et al. Clinical and Chest Radiography Features Determine Patient Outcomes In Young and Middle Age Adults with COVID-19. Radiology. 2020. pmid:32407255
  42. 42. Gold JAW, Wong KK, Szablewski CM, Patel PR, Rossow J, Silva J da, et al. Characteristics and Clinical Outcomes of Adult Patients Hospitalized with COVID-19—Georgia, March 2020. MMWR Morb Mortal Wkly Rep. 2020;69. pmid:32379729
  43. 43. Hasan Z, Narasimhan M. “Preparing for the COVID-19 Pandemic: Our Experience in New York.” Chest. 2020;157: 1420–1422. pmid:32222587
  44. 44. Team C-NIRS. COVID-19, Australia: Epidemiology Report 9 (Reporting week to 23:59 AEDT 29 March 2020). Commun Dis Intell. 2020;44. 10.33321/cdi.2020.44.29
  45. 45. Team C-NIRS. COVID-19, Australia: Epidemiology Report 11 (Reporting week to 23:59 AEST 12 April 2020). Commun Dis Intell. 2020;44. 10.33321/cdi.2020.44.34
  46. 46. Pung R, Chiew CJ, Young BE, Chin S, Chen MI-C, Clapham HE, et al. Investigation of three clusters of COVID-19 in Singapore: implications for surveillance and response measures. Lancet. 2020;395: 1039–1046. pmid:32192580
  47. 47. Lechien JR, Chiesa-Estomba CM, De Siati DR, Horoi M, Le Bon SD, Rodriguez A, et al. Olfactory and gustatory dysfunctions as a clinical presentation of mild-to-moderate forms of the coronavirus disease (COVID-19): a multicenter European study. Eur Arch Oto-Rhino-Laryngology. 2020. pmid:32253535
  48. 48. Laurencin CT, McClinton A. The COVID-19 Pandemic: a Call to Action to Identify and Address Racial and Ethnic Disparities. J Racial Ethn Heal Disparities. 2020;7: 398–402. pmid:32306369
  49. 49. Tolia VM, Chan TC, Castillo EM. Preliminary Results of Initial Testing for Coronavirus (COVID-19) in the Emergency Department. West J Emerg Med. 2020;21: 503–606. pmid:32223871
  50. 50. Sun H, Lee J, Meyer BJ, Myers EL, Nishikawa MS, Tischler JL, et al. Characteristics and Palliative Care Needs of COVID-19 Patients Receiving Comfort Directed Care. J Am Geriatr Soc. 2020;68: 1162–1164. pmid:32329525
  51. 51. Burrer SL (CDC), de Perio MA (CDC), Hughes MM (CDC), Kuhar DT (CDC), Luckhaupt SE (CDC), McDaniel CJ (CDC), et al. Characteristics of Health Care Personnel with COVID-19—United States, February 12-April 9, 2020. MMWR Morb Mortal Wkly Rep. 2020;69: 477–481. pmid:32298247
  52. 52. Goyal P, Choi JJ, Pinheiro LC, Schenck EJ, Chen R, Jabri A, et al. Clinical Characteristics of Covid-19 in New York City. N Engl J Med. 2020;382: 2372–2374. pmid:32302078
  53. 53. Richardson S, Hirsch JS, Narasimhan M, Crawford JM, McGinn T, Davidson KW, et al. Presenting Characteristics, Comorbidities, and Outcomes among 5700 Patients Hospitalized with COVID-19 in the New York City Area. JAMA—J Am Med Assoc. 2020;323: E1–E8. pmid:32320003
  54. 54. Baggett TP, Keyes H, Sporn N, Gaeta JM. Prevalence of SARS-CoV-2 Infection in Residents of a Large Homeless Shelter in Boston. JAMA. 2020;323: 2191–2192. pmid:32338732
  55. 55. Bangalore S, Sharma A, Slotwiner A, Yatskar L, Harari R, Shah B, et al. ST-Segment Elevation in Patients with Covid-19—A Case Series. N Engl J Med. 2020;382: 2478–2480. pmid:32302081
  56. 56. Mehta N, Kalra A, Nowacki AS, Anjewierden S, Han Z, Bhat P, et al. Association of Use of Angiotensin-Converting Enzyme Inhibitors and Angiotensin II Receptor Blockers With Testing Positive for Coronavirus Disease 2019 (COVID-19). JAMA Cardiol. 2020. pmid:32936273
  57. 57. Chu J, Yang N, Wei Y, Yue H, Zhang F, Zhao J, et al. Clinical Characteristics of 54 medical staff with COVID-19: A retrospective study in a single center in Wuhan, China. J Med Virol. 2020;92: 807–813. pmid:32222986
  58. 58. Fan J, Liu X, Pan W, Douglas MW, Bao S. Epidemiology of 2019 Novel Coronavirus Disease-19 in Gansu Province, China, 2020. Emerg Infect Dis. 2020;26: 1257–1265. pmid:32168465
  59. 59. Xu XW, Wu XX, Jiang XG, Xu KJ, Ying LJ, Ma CL, et al. Clinical findings in a group of patients infected with the 2019 novel coronavirus (SARS-Cov-2) outside of Wuhan, China: Retrospective case series. BMJ. 2020;368: 1–7. pmid:32075786
  60. 60. Jia J, Hu X, Yang F, Song X, Dong L, Zhang J, et al. Epidemiological Characteristics on the Clustering Nature of COVID-19 in Qingdao City, 2020: A Descriptive Analysis. Disaster Med Public Heal Prep. 2020; 1–5. pmid:32228732
  61. 61. Wang R, Pan M, Zhang X, Fan X, Han M, Zhao F, et al. Epidemiological and clinical features of 125 Hospitalized Patients with COVID-19 in Fuyang, Anhui, China. Int J Infect Dis. 2020;95: 421–428. pmid:32289565
  62. 62. Dai H, Zhang X, Xia J, Zhang T, Shang Y, Huang R, et al. High-resolution Chest CT Features and Clinical Characteristics of Patients Infected with COVID-19 in Jiangsu, China. Int J Infect Dis. 2020;95: 106–112. pmid:32272262
  63. 63. Wang X, Liu W, Zhao J, Lu Y, Wang X, Yu C, et al. Clinical characteristics of 80 hospitalized frontline medical workers infected with COVID-19 in Wuhan, China. J Hosp Infect. 2020;S0195–6701. pmid:32302722
  64. 64. Ouyang Y, Yin J, Wang W, Shi H, Shi Y, Xu B, et al. Down-regulated gene expression spectrum and immune responses changed during the disease progression in COVID-19 patients. Clin Infect Dis. 2020;. pmid:32307550
  65. 65. Wang L, Duan Y, Zhang W, Liang J, Xu J, Zhang Y, et al. Epidemiologic and Clinical Characteristics of 26 Cases of COVID-19 Arising from Patient-to-Patient Transmission in Liaocheng, China. Clin Epidemiol. 2020;12: 387–391. pmid:32308494
  66. 66. Yu X, Sun S, Shi Y, Wang H, Zhao R, Sheng J. SARS-CoV-2 viral load in sputum correlates with risk of COVID-19 progression. Crit Care. 2020;24: 170. pmid:32326952
  67. 67. Wallace M, Hagan L, Curran KG, Williams SP, Handanagic S, Bjork A, et al. COVID-19 in Correctional and Detention Facilities—United States, February-April 2020. MMWR Morb Mortal Wkly Rep. 2020;69. pmid:32407300
  68. 68. Wallace M, Marlow M, Simonson S, Walker M, Christophe N, Dominguez O, et al. Public Health Response to COVID-19 Cases in Correctional and Detention Facilities—Louisiana, March-April 2020. MMWR Morb Mortal Wkly Rep. 2020;69. pmid:32407301
  69. 69. Mosites E, Parker EM, Clarke KEN, Gaeta JM, Baggett TP, Imbert E, et al. Assessment of SARS-CoV-2 Infection Prevalence in Homeless Shelters—Four U.S. Cities, March 27-April 15, 2020. MMWR Morb Mortal Wkly Rep. 2020;69. pmid:32352957
  70. 70. Tobolowsky FA, Gonzales E, Self JL, Rao CY, Keating R, Marx GE, et al. COVID-19 Outbreak Among Three Affiliated Homeless Service Sites—King County, Washington, 2020. MMWR Morb Mortal Wkly Rep. 2020;69. pmid:32352954
  71. 71. Dyal JW, Grant MP, Broadwater K, Bjork A, Waltenburg MA, Gibbins JD, et al. COVID-19 Among Workers in Meat and Poultry Processing Facilities—19 States, April 2020. MMWR Morb Mortal Wkly Rep. 2020;69: 557–561. pmid:32379731
  72. 72. Blanco JL, Ambrosioni J, Garcia F, Martinez E, Soriano A, Mallolas J, et al. COVID-19 in patients with HIV: clinical case series. Lancet HIV. 2020;7: E314–E316. pmid:32304642
  73. 73. Shi Y, Yu X, Zhao H, Wang H, Zhao R, Sheng J. Host susceptibility to severe COVID-19 and establishment of a host risk score: findings of 487 cases outside Wuhan. 2020;24: 108. pmid:32188484
  74. 74. Xiao H, Zhang Y, Kong D, Li S, Yang N. Social Capital and Sleep Quality in Individuals Who Self-Isolated for 14 Days During the Coronavirus Disease 2019 (COVID-19) Outbreak in January 2020 in China. Med Sci Monit. 2020;26: e923921. pmid:32194290
  75. 75. Zhang J, Lu H, Zeng H, Zhang S, Du Q, Jiang T, et al. The differential psychological distress of populations affected by the COVID-19 pandemic. Brain, Behav Immun. 2020;87: 49–50. pmid:32304883
  76. 76. Li T, Zhang Y, Gong C, Wang J, Liu B, Shi L, et al. Prevalence of malnutrition and analysis of related factors in elderly patients with COVID-19 in Wuhan, China. Eur J Clin Nutr. 2020;74: 871–875. pmid:32322046
  77. 77. Baggett Travis P, MD, MPH Keyes Harrison, MPAS, PA-C Sporn Nora, MA MG MJ. Prevalence of SARS-CoV-2 Infection in Residents of a Large Homeless Shelter in Boston. JAMA—J Am Med Assoc. 2020; E1–E2. pmid:32338732
  78. 78. Williams DR, Lawrence JA, Davis BA. Racism and Health: Evidence and Needed Research. Annu Rev Public Health. 2019;40: 105–125. pmid:30601726
  79. 79. Maness SB, Merrell L, Thompson EL, Griner SB, Kline N, Wheldon C. Social Determinants of Health and Health Disparities: COVID-19 Exposures and Mortality Among African American People in the United States. Public Health Reports. SAGE Publications Ltd; 2021. pp. 18–22. pmid:33176112
  80. 80. Amarasinghe U, Motha-Pollock A, Felder M, Oschinski M. COVID-19 and Ontario’s Sales and Service Workers: Who is most vulnerable? Toronto; 2020.
  81. 81. Gamio L. The Workers Who Face the Greatest Coronavirus Risk. In: The New York Times [Internet]. 2020 [cited 14 Jun 2020].
  82. 82. Shingler B, Stevenson V. COVID-19’s devastating toll on families in Montreal’s poorest neighbourhoods. CBC News. 15 May 2020.
  83. 83. Watt A, Cameron A, Sturm L, Lathlean T, Babidge W, Blamey S, et al. Rapid versus full systematic reviews: Validity in clinical practice? ANZ J Surg. 2008;78: 1037–1040. pmid:18959712
  84. 84. Ganann R, Ciliska D, Thomas H. Expediting systematic reviews: methods and implications of rapid reviews. Implement Sci. 2010;5: 1–10. pmid:20047652
  85. 85. Royle P, Waugh N. Literature searching for clinical and cost-effectiveness studies used in health technology assessment reports carried out for the National Institute for Clinical Excellence appraisal system. Health Technol Assess (Rockv). 2003;7. pmid:14609481
  86. 86. Watt A, Cameron A, Sturm L, Lathlean T, Babidge W, Blamey S, et al. Rapid reviews versus full systematic reviews: An inventory of current methods and practice in health technology assessment. Int J Technol Assess Health Care. 2008;24: 133–139. pmid:18400114
  87. 87. Semenza JC, Suk JE, Tsolova S. Social determinants of infectious diseases: a public health priority. Eur Commun Dis Bull. 2010;15: 2–4. pmid:20630148
  88. 88. Clift AK, Coupland CAC, Keogh RH, Diaz-Ordaz K, Williamson E, Harrison EM, et al. Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study. BMJ. 2020;371. pmid:33082154
  89. 89. Williamson EJ, Walker AJ, Bhaskaran K, Bacon S, Bates C, Morton CE, et al. Factors associated with COVID-19-related death using OpenSAFELY. Nature. 2020;584: 430–436. pmid:32640463
  90. 90. Seligman B, Ferranna M, Bloom DE. Social determinants of mortality from COVID-19: A simulation study using NHANES. Kretzschmar MEE, editor. PLOS Med. 2021;18: e1003490. pmid:33428624
  91. 91. Galvão MHR, Roncalli AG. Factors associated with increased risk of death from covid-19: a survival analysis based on confirmed cases. Rev Bras Epidemiol. 2021;23: e200106.
  92. 92. Lundon DJ, Mohamed N, Lantz A, Goltz HH, Kelly BD, Tewari AK. Social Determinants Predict Outcomes in Data From a Multi-Ethnic Cohort of 20,899 Patients Investigated for COVID-19. Front Public Heal. 2020;8: 571364. pmid:33324596
  93. 93. Drefahl S, Wallace M, Mussino E, Aradhya S, Kolk M, Brandén M, et al. A population-based cohort study of socio-demographic risk factors for COVID-19 deaths in Sweden. Nat Commun. 2020;11. pmid:33037218
  94. 94. Egede LE, Walker RJ, Garacci E, Raymond JR. Racial/ethnic differences in COVID-19 screening, hospitalization, and mortality in southeast Wisconsin. Health Aff. 2020;39: 1926–1934. pmid:33136498
  95. 95. Hsu HE, Ashe EM, Silverstein M, Hofman M, Lange SJ, Razzaghi H, et al. Race/Ethnicity, Underlying Medical Conditions, Homelessness, and Hospitalization Status of Adult Patients with COVID-19 at an Urban Safety-Net Medical Center—Boston, Massachusetts, 2020. MMWR Morb Mortal Wkly Rep. 2020;69: 864–869. pmid:32644981
  96. 96. Rozenfeld Y, Beam J, Maier H, Haggerson W, Boudreau K, Carlson J, et al. A model of disparities: Risk factors associated with COVID-19 infection. Int J Equity Health. 2020;19. pmid:32727486
  97. 97. Batty GD, Deary IJ, Luciano M, Altschul DM, Kivimäki M, Gale CR. Psychosocial factors and hospitalisations for COVID-19: Prospective cohort study based on a community sample. Brain Behav Immun. 2020;89: 569–578. pmid:32561221
  98. 98. Richard L, Booth R, Rayner J, Clemens KK, Forchuk C, Shariff SZ. Testing, infection and complication rates of COVID-19 among people with a recent history of homelessness in Ontario, Canada: a retrospective cohort study. C Open. 2021;9: E1–E9. pmid:33436450
  99. 99. Lewer D, Braithwaite I, Bullock M, Eyre MT, White PJ, Aldridge RW, et al. COVID-19 among people experiencing homelessness in England: a modelling study. Lancet Respir Med. 2020;8: 1181–1191. pmid:32979308
  100. 100. Yehia BR, Winegar A, Fogel R, Fakih M, Ottenbacher A, Jesser C, et al. Association of Race With Mortality Among Patients Hospitalized With Coronavirus Disease 2019 (COVID-19) at 92 US Hospitals. JAMA Netw open. 2020;3: e2018039. pmid:32809033
  101. 101. Price-Haywood EG, Burton J, Fort D, Seoane L. Hospitalization and Mortality among Black Patients and White Patients with Covid-19. N Engl J Med. 2020;382: 2534–2543. pmid:32459916
  102. 102. de Gier B, de Oliveira Bressane Lima P, van Gaalen RD, de Boer PT, Alblas J, Ruijten M, et al. Occupation- and age-associated risk of SARS-CoV-2 test positivity, the Netherlands, June to October 2020. Eurosurveillance. 2020;25: 2001884. pmid:33334396
  103. 103. Jing QL, Liu MJ, Bin Zhang Z, Fang LQ, Yuan J, Zhang AR, et al. Household secondary attack rate of COVID-19 and associated determinants in Guangzhou, China: a retrospective cohort study. Lancet Infect Dis. 2020;20: 1141–1150. pmid:32562601
  104. 104. Joy M, Richard Hobbs FD, Bernal JL, Sherlock J, Amirthalingam G, McGagh D, et al. Excess mortality in the first COVID pandemic peak: Cross-sectional analyses of the impact of age, sex, ethnicity, household size, and long-term conditions in people of known SARS-CoV-2 status in England. Br J Gen Pract. 2020;70: E890–E898. pmid:33077508
  105. 105. Sze S, Pan D, Nevill CR, Gray LJ, Martin CA, Nazareth J, et al. Ethnicity and clinical outcomes in COVID-19: A systematic review and meta-analysis. EClinicalMedicine. 2020;29–30: 100630. pmid:33200120
  106. 106. Nature Index. Coronovirus research publishing. 2020 [cited 7 Jul 2020].