Skip to main content
  • Research article
  • Open access
  • Published:

Informing the public health response to COVID-19: a systematic review of risk factors for disease, severity, and mortality



Severe Acute Respiratory Syndrome coronavirus-2 (SARS-CoV-2) has challenged public health agencies globally. In order to effectively target government responses, it is critical to identify the individuals most at risk of coronavirus disease-19 (COVID-19), developing severe clinical signs, and mortality. We undertook a systematic review of the literature to present the current status of scientific knowledge in these areas and describe the need for unified global approaches, moving forwards, as well as lessons learnt for future pandemics.


Medline, Embase and Global Health were searched to the end of April 2020, as well as the Web of Science. Search terms were specific to the SARS-CoV-2 virus and COVID-19. Comparative studies of risk factors from any setting, population group and in any language were included. Titles, abstracts and full texts were screened by two reviewers and extracted in duplicate into a standardised form. Data were extracted on risk factors for COVID-19 disease, severe disease, or death and were narratively and descriptively synthesised.


One thousand two hundred and thirty-eight papers were identified post-deduplication. Thirty-three met our inclusion criteria, of which 26 were from China. Six assessed the risk of contracting the disease, 20 the risk of having severe disease and ten the risk of dying. Age, gender and co-morbidities were commonly assessed as risk factors. The weight of evidence showed increasing age to be associated with severe disease and mortality, and general comorbidities with mortality. Only seven studies presented multivariable analyses and power was generally limited. A wide range of definitions were used for disease severity.


The volume of literature generated in the short time since the appearance of SARS-CoV-2 has been considerable. Many studies have sought to document the risk factors for COVID-19 disease, disease severity and mortality; age was the only risk factor based on robust studies and with a consistent body of evidence. Mechanistic studies are required to understand why age is such an important risk factor. At the start of pandemics, large, standardised, studies that use multivariable analyses are urgently needed so that the populations most at risk can be rapidly protected.


This review was registered on PROSPERO as CRD42020177714.

Peer Review reports


The world is currently experiencing a pandemic of coronavirus disease (COVID-19) caused by the Severe Acute Respiratory Syndrome coronavirus-2 (SARS-CoV-2) [1]. The risk of morbidity and mortality from the virus is strongly stratified, with poor clinical outcomes considered more likely in certain vulnerable groups. For example, studies from different countries have established that older age groups are at increased risk of death [2, 3].

The ability to identity the population groups most at risk from the virus has manifold public health purposes. Using such data, stratified vaccination policies for governmental delivery can be designed, similar to those for influenza [4]. It may also be possible to prioritise more active monitoring of groups more at risk of clinical deterioration, and facilitate access to healthcare facilities by early identification of the individuals most likely to progress to severe disease who would thus be in need of intensive care and ventilation. Official advice can be issued to vulnerable groups to let them know that they are more at risk from SARS-CoV-2 virus, to promote behaviour modification [5, 6]. Such population groups can also be the target of more formalised ‘segment and shield’ approaches: having divided the population into groups that present with similar health care concerns and needs (segmenting) it is possible to determine which groups require extra protection by reducing interaction with other groups (shielding), whilst relaxing restrictions for the rest of the population [7]. Potential public health policies along this route have been critiqued, however, on an inclusivity basis, particularly due to the unintended harmful consequences to already marginalised groups [8].

In the UK, vulnerable people were stratified into two tiers early on- 30th March 2020 (Table 1); those at risk of severe illness, who were advised to be particularly stringent with social distancing measures, and those within that group at further risk – described as ‘shielded’ individuals – who were advised to self-isolate and were provided with additional advice [9,10,11,12]. The former categorisation was based on the groups targeted for National Health Service programmes on influenza vaccination and the latter on clinical consensus. These strata were deliberately broad, to maximise the number of individuals protected. As the evidence evolves – e.g. regarding whether the development of lesions in the cardiovascular system contributes meaningfully to disease pathogenesis in patients with and without pre-existing cardiovascular conditions [13] – there is the opportunity for the categorisation of risk of COVID-19 and serious outcomes from COVID-19 to become more evidence-based.

Table 1 UK risk groupings for COVID-19 disease on 30th March 2020

During epidemics and pandemics of emerging infectious diseases, it is critical to rapidly and accurately identify the populations most at risk. In the case of COVID-19, we undertook a systematic review and quality assessment of the rapidly-evolving global literature in this area, looking at three key outcomes: COVID-19 disease, disease severity, and mortality from the condition. Any potential risk factors, populations, and study designs were included. Arising from our findings, we highlight key knowledge gaps in the current literature and the need for unified global approaches moving forwards, particularly for the next pandemic.

Materials and methods

Literature search

We systematically searched Medline, Embase, and Global Health (all via the Ovid platform), in addition to the Web of Science, for published literature between 1st November 2019 and 26th March 2020; then subsequently updated this search for a later period to 29th April. In order to avoid missing publications on risk factors, only terms specific to the virus and the disease were used, which were combined with ‘or’:

  • ‘coronavirus’

  • ‘covid-19’

  • ‘severe acute respiratory syndrome coronavirus 2’

  • ‘2019-nCoV-2’

  • ‘SARS-CoV-2’

  • ‘acute respiratory syndrome’

No limits or filters were applied to the search. The same search terms were used across all databases.

Reference lists of included papers and review articles were also searched, as was the grey literature of public health reports for the 26 countries with the highest numbers of reported patients with COVID-19 at the end of April 2020, for other countries it was assumed there would be insufficient numbers of cases to yield relevant data.

Eligibility criteria and study selection

The following inclusion and exclusion criteria were applied to the search results.

Inclusion criteria:

  • Studies had to provide comparative data on risk factors of any kind for disease (versus no disease), severe disease (versus milder disease) or mortality (versus survival),

  • Studies were eligible if they presented data on patients with polymerase chain reaction (PCR)-confirmed SARS-CoV-2 infections. There was considerable variation in case definitions between studies, but PCR testing was the gold standard test for active disease at the start of the pandemic [14], and other testing methods such as Loop-Mediated Isothermal Amplification or serological tests were not included,

  • Any study design,

  • Any population group,

  • Any language of publication.

Exclusion criteria:

  • No comparator group included in the study,

  • Publication concerned other viruses and diseases,

  • Work conducted in animals or in vitro,

  • Study population was less than 20 individuals.

Two reviewers independently screened all titles, abstracts and full texts for both literature searches. Discrepancies were resolved by consensus. In all cases where studies were published in any language other than English, with no translations available, these were screened by at least one additional reviewer, with further quality control by another member of the reviewing team.

Data extraction

Three reviewers independently double-extracted the studies into a pre-designed spreadsheet that collected:

  • First author,

  • Paper title,

  • Journal,

  • Type of study,

  • Country,

  • Study population,

  • Overall number in study,

  • Number with PCR confirmed SARS-CoV-2,

  • Median age of participants/age range,

  • Sex ratio,

  • Analytical method used,

  • Factors adjusted for during the analysis,

  • Whether disease, disease severity, or death (or a combination of these) was the outcome of interest,

  • The definition of disease severity used, if applicable,

  • The risk factors analysed and the direction of effect.

Results were compared and discrepancies resolved by discussion. Data from studies published in languages other than English, at this stage only the Chinese language, were extracted by two additional reviewers, with further quality control by another member of the reviewing team.

Quality assessment

Two reviewers independently assessed the quality of included studies. Studies published in languages other than English were quality assessed by two additional reviewers, with further quality control by another member of the reviewing team. Assessments were undertaken from the perspective of the objectives of this review, which were not necessarily identical to the objectives of the underlying studies. The quality of included studies was assessed using a checklist adapted from Downs and Black [15], as per the guidance issued by Deeks et al. [16] When assessing the power of studies, the minimum sample size required to detect a relative increase in risk of 10% from a statistically conservative baseline of 50% among the unexposed was calculated at different powers using the Kelsey method within Epi Info, software made available by the United States Center for Disease Control [17]. This 10% value was based on governmental discussions taking place in the UK at the time the review took place. An alpha of 5% was set as the standard. Pragmatically, we assumed only two strata and a ratio of 1:1 between exposure strata. Different thresholds were used for case-control studies and for cohort or cross-sectional studies. These criteria were scored from 0 (< 70% power) to 5 (> 99% power). We considered results sufficient adjusted for confounding if they adjusted for at least the minimal variable set of age, sex, ethnicity and any measure of comorbidities. For ethnically homogenous populations, the need for adjustment for ethnicity was discounted. If two analyses were presented within a single paper with different quality scores, the most conservative score was retained. Studies were not excluded on the basis of the quality assessment.

Analysis and synthesis

Studies were grouped on the basis of the outcome examined (disease, disease severity, mortality) and then the risk factors examined. Results were classified on the basis of whether they presented evidence as to the exposure under study being a risk factor, taking into account the number of individuals exposed. Where studies focussed on a single risk factor of interest with adjustment for confounding, we extracted all data on potential risks in order to maximise the value of our dataset (whilst accepting that such mutually adjusted estimates for covariates may remain confounded even if that for the primary exposure does not) [18]. As there was substantial heterogeneity in study design, reporting, and the risk factors examined, we present a detailed descriptive summary and narrative synthesis of our findings, rather than a meta-analysis.

Registration and reporting

This review was registered on PROSPERO as CRD42020177714 and is reported according to the PRISMA guidelines.


Two thousand eight hundred and sixty-eight hits were obtained by the searches across the two dates (Fig. 1). After de-duplication across the different databases, this was reduced to 1238. Thirty studies were included at the extraction stage; the main reasons for exclusion were small numbers of participants and studies not having a comparator population. From the grey literature an additional report was included and two studies were identified from reference lists.

Fig. 1
figure 1

PRISMA flow chart of selection

Included studies are presented in Table 2. Twenty-nine of the 33 studies were conducted in China, with one each from France, Italy, Singapore and a combined study from England, Wales and Northern Ireland. Six were studies with COVID-19 disease as the outcome, 20 of disease severity and ten of mortality. One additional study looked at a combined outcome of disease severity and mortality.

Table 2 Included studies

Quality assessment

Included studies were generally too small to detect a 10% increase in risk of disease, disease severity, or mortality (Table 3). One study among the 33 was assessed to have 95% power and two others 99%; all were large, national, investigations. As 26 studies were purely descriptive or presented univariable analysis only, there was no adjustment for confounding. Remaining studies with a regression component did not adjust for our minimal confounder set. Only nine studies provided estimates of the random variability of effect estimates. The majority of studies ascertained exposure information from clinical records, which would have collected data prospectively and thus with limited recall bias. Blinding of outcome and exposure recording by investigators was not documented. In the case of certain disease severity outcomes, such as admittance to intensive care units (ICU), variability in thresholds for reaching these outcomes is likely to exist between settings and clinicians.

Table 3 Quality assessment

Risk factors for disease

Six studies compared the likelihood of having COVID-19 to other infectious conditions (Table 4). Of note, as testing strategies were largely focussed on hospitalised individuals i.e. those displaying noticeable symptoms, studies were of the likelihood of COVID-19 disease, rather than more broadly of SARS-CoV-2 infection (and particularly of severe disease, although patients with mild and symptomatic infection were also reported to be hospitalised in some studies for the purposes of isolation or observation). Age and sex were key foci as potential risk factors, comparing patients with COVID-19 to either: a) SARS-CoV or Middle Eastern Respiratory Syndrome (MERS), or b) other forms of pneumonia. Generally, sex ratios were skewed such that men were over-represented among those with disease. In England, Northern Ireland, and Wales, Asian and Black individuals were found to be at increased risk of COVID-19 in descriptive analyses, with 15.4 and 10.7% of patients falling into these groupings, respectively, versus 5.8 and 2.8% of individuals with other viral pneumonia [30]. Higher body mass index (BMI) was also suggested to be a risk factor with two descriptive analyses, for example in the Intensive Care National Audit and Research Centre (ICNARC) report 31.2% of COVID-19 patients had a BMI of 30- < 40, versus 23.5% of people with other viral pneumonia [30, 37]. Given the large, national, scope of the ICNARC dataset, results from it are particularly likely to be reliable.

Table 4 Potential risk factors for disease

Risk factors for severe disease

Among the 20 studies of risk factors for severe versus milder disease and one of a mixed outcome (severe disease and death), a wide array of definitions of severity were used, such as ICU admission, the need for mechanical ventilation, and various measures of respiration and oxygenation (Table 2). Many risk factors were examined (Table 5). As well as potential demographic risks (age, sex, ethnicity), behavioural traits (smoking) and broad clinical factors (BMI, infectious diseases) were analysed. Large numbers of papers sought to explore the implications of different comorbidities on the risk of severe COVID-19, particularly respiratory and cardiovascular conditions.

Table 5 Potential risk factors for disease severity

The least equivocal evidence was presented for age as a risk factor, including four studies where it was an independent risk in a multivariable regression model [19, 20, 31, 36]. The clearest analysis to present age data (i.e. which used different comparison groups) was a univariable regression model where individuals 65 years and over had 3.26 times the hazard rate of ARDS than those under 65 [44]. Eight studies suggested that diabetes could be a risk factor [19, 31, 36, 39, 41, 43, 44, 50], six hypertension [31, 36, 41, 43, 44, 50], and four the presence of unspecified comorbidities) [39, 41, 48, 50], but the balance of evidence for these co-morbidities being risk factors was generally inconclusive. Many other factors were examined by one study, often with small numbers of individuals with the condition. None of the included studies for disease severity were assessed to have been powered to detect a 10% increase in effect size.

Risk factors for mortality

Ten studies examined risk factors for mortality, often by nesting case-control studies within prospective or retrospective cohorts (Table 6). Among these studies, many included statistical testing, but none presented an adjusted regression model for the risk factors considered.

Table 6 Potential risk factors for mortality

Eight studies examined age and all provided evidence for it being a risk factor for mortality [21, 25, 27, 35, 44,45,46,47], although none adjusted for other factors, such as comorbidities. Age groups from 50 upwards were considered particularly at risk. In the single regression analysis, the hazard rate for death in those 65 years or over was estimated to be six times that of individuals under 65 [44]. The evidence was similarly consistent for general comorbidities (albeit all the studies were descriptive); among individuals who died, comorbidities were 1.5 to 2.8 times more common than among those who survived [21, 35, 46, 47, 51]. Specific comorbidities were discussed in several studies, generally under overarching classifications such as ‘cardiovascular disease’ or ‘diabetes’, with more specific definitions not provided. Evidence was more equivocal, but still in favour, of hypertension [3, 21, 25, 27, 47, 51], cardiovascular disease [21, 25, 35, 45, 47, 51], diabetes [21, 25, 45,46,47, 51], and chronic respiratory/lung diseases being risk factors (references presented for studies in support only) [21, 45, 51]. Of these studies, data from two well-powered, national-level studies from China supported cardiovascular disease and diabetes as risk factors for mortality from COVID-19 [25, 45].


In this systematic review of risk factors for COVID-19 disease, disease severity and mortality, we document 33 comparative studies examining sociodemographic, behavioural and clinical exposures. Age and sex were very commonly examined; a wide array of comorbidities have also been considered.

Within the synthesised evidence, risk factors for mortality were the clearest, plausibly partly because this outcome is easy to define. Increasing age (different studies presented different thresholds, but being over 50 years of age was common) was an uncontested risk factor. Five studies also presented evidence for the presence of any comorbidities being a risk factor [21, 35, 46, 47, 51], with none demonstrating evidence against. Given the increasing prevalence of comorbidities with age, the lack of adjustment for confounding in these studies likely over-emphasises the effect size of each risk factor. We note that work subsequent to our literature search documents an independent effect of age on COVID-19 mortality from overall comorbidities, as measured by the Charlson Comorbidity Index Score, but not vice-versa [52]. Another study published outside of the time range of our search found both age and an array of comorbidities, each analysed separately (chronic cardiac disease, chronic pulmonary disease, chronic kidney disease, chronic neurological disease, dementia, malignancy, moderate/severe liver disease; and obesity), to be independent risk factors (as well as sex) [53].

Risk factors for severe disease were more complex to synthesise, likely due to the mixed array of outcome measures that can also be prone to observer bias. The impact of age was very commonly assessed, generally showing evidence in favour of this being a risk factor (with a similar age spectrum to the mortality data). Ethnicity was studied in two publications internationally [26, 30], with mixed results. We note that such findings are likely to be highly context-specific, given that ethnicity acts as a proxy for a series of sociodemographic factors that are highly relevant to the spread of an infectious condition (as well as, perhaps, some biological traits).

Studies of risk factors for COVID-19 disease have been complicated by testing strategies globally, which have largely been concentrated on severe disease. As our knowledge of the full symptom spectrum of the disease moves forward, it will be possible to have a broader case definition that does not solely focus on viral testing, and thus the ability for more generalised complementary studies. Additionally, serological surveys assessing the history of infection with SARS-CoV-2 in different population groups will allow the identification of risk factors for infection, whether symptomatic or not. Both ethnicity (Black and Asian individuals at higher risk; from a single study in England, Northern Ireland and Wales) [30] and higher BMI were found to be associated with disease severity within the included literature [30, 37], again from descriptive studies only. While these studies were not eligible for our review, we note a series of reports from non-comparative studies documenting the potential influence of ethnicity on the likelihood of getting COVID-19 e.g. the work of Price-Haywood from the US [52]. Male sex was reasonably consistently shown to be a risk factor for presence of COVID-19 but not with severity of disease or mortality [24, 30, 40]. As with ethnicity, socioeconomic and behavioural factors make this association likely to vary between settings.

In considering the role of comorbidities in COVID-19, it is important to consider the underlying pathology of the virus. Respiratory coronaviruses associated with the common cold in immunocompetent people generally affect only cells in the upper respiratory tract (URT), whereas the previously discovered highly pathogenic coronaviruses SARS-CoV and MERS-CoV affect cells in the URT and lower respiratory tract (LRT). SARS-CoV-2 has been shown to do the same [54], and one of the host cell receptors it targets is Angiotensin-Converting Enzyme 2 (ACE2), with a second major receptor being Transmembrane Serine Protease 2 (TMPRSS2) [55]. SARS-CoV-2 can infect all the major cell types in the respiratory tract – type I and type II pneumocytes, alveolar macrophages and endothelial cells [56, 57]. This infection leads to cell death, with significant leaking of fluid into the alveolar spaces (pulmonary oedema), which compromises gas exchange [58], eventually leading to ARDS. The inflammatory response adds aggregation of repair proteins such as fibrin, which can lead to creation of hyaline membranes which further reduces the surface available for gas exchange [58]. Subsequently, inflammatory cells are activated, recruited by release or exposure of cytokines such as the interleukins (IL) 1β and 6, monocyte chemoattractant protein-1 [56], and proteins of the extracellular matrix, as well as upregulation of the complement system. Inflammatory cells release cytokines which have systemic effects, eventually leading to disseminated intravascular coagulation (DIC), hypotensive shock and metabolic disturbances if not checked [58].

This pathogenesis therefore offers several points where co-morbidities may exacerbate the process. The target receptor TMPRSS2 is modulated in response to air pollution and in autoimmune conditions such as asthma [55], which may affect the number of receptors available for SARS-CoV-2 to target, and ACE2 is involved in the renin-angiotensin system (RAS) which controls blood pressure. Viral interference causes dysfunction, which leads to a pro-inflammatory state and increased vascular permeability in response to changes in vascular contraction and sodium homeostasis – exacerbating the effect from the physical damage to the affected cells [58]. Conditions causing hypertension – both primary and secondary to renal disease, endocrine dysfunctions such as hypothyroidism, cardiovascular dysfunction such as arteriosclerosis, or neurological dysfunctions such as acute stress – also affect the RAS [58], meaning that these conditions might be expected to exacerbate pathology caused by SARS-CoV-2. Any condition creating a pro-inflammatory state, such as type II diabetes or pre-existing infection, or involving autoimmunity, such as type I diabetes, might also be expected to contribute to increased pathology. There is also the direct effect of cell damage – if the target tissues are already damaged this reduces ‘spare’ capacity and therefore the leeway for adaptation to allow the host to continue to maintain homeostasis whilst still being able to eliminate the pathogen and repair the damage. The need for inflammatory cells to clear the infection is also a potential area of interface with comorbidities e.g. conditions such as unsuppressed HIV infection, or congenital deficiencies, or cancer malignancies; or the administration of immunosuppressant drugs such as chemotherapy for cancer or steroids.

The effect of ageing was particularly strong within our review, both in terms of the magnitude of effect estimates and the number of studies presenting evidence. As well as the above impact of comorbidities, we note that the host’s age may influence pathogenesis, both in terms of the likelihood of having various comorbidities, and also due to its effect on the immune system. Indeed, the immune system becomes less effective over time (immunosenescence), which affects the quality and number of immune system cells generated [59]. Given the scale of the impact of age documented within this review, it seems unlikely that its effect can be explained by a single or a small number of comorbidities which are yet to be detected. This opens up the need to explore biological markers, for example ACE2 [60], and markers of immunosenescence.

The strengths of our review include its systematic approach and broad use of search terms to avoid missing studies. We additionally present a quality assessment to aid the interpretation of the strength of the evidence. In some instances, included publications may have focussed on one specific outcome, whereas our quality assessment took the perspective of the outcomes extracted for this review. We were unable to detect instances where two publications used the same patient populations for their analyses, potentially over-emphasising certain findings. Given the global nature of the pandemic, our review includes studies from around the world, albeit with a large preponderance from China, including studies conducted early after the emergence of SARS-CoV-2 when the at-risk population was predominantly those who had contact with Huanan seafood market and their contacts, and not necessarily representative of the general population. We note a particular lack of studies from the African continent and the Americas, which may have implications for generalisability. Given the rapidly evolving literature on COVID-19, we also note our exclusion of studies published online after April 2020 (and the time period in which the surrounding text was written), for example the Dai report on cancer as a risk factor [61] and our exclusion of preprints (which was undertaken to ensure that all included studies had undergone an external quality assessment prior to inclusion).

Across the included publications, variability in study design, exposure and outcome measurement, and analyses made exact syntheses of effect sizes across different risk factors very difficult. Measures of disease severity varied, e.g. admission to ICUs or clinical parameters such as percentage oxygen saturation of the blood. Even measures such as admission to ICU can be subjective and may be time-, clinician-, and health systems-dependent. If severity is recorded at admission, risk factors may reflect issues associated with delayed access to healthcare, which may differ between settings and healthcare systems. It is also important to note that, in some studies of disease severity, mild disease included both people who were hospitalised with symptoms and asymptomatic individuals identified through contact tracing. Generally, analyses were descriptive or univariable and thus did not control for confounding. As documented above, this may be particularly problematic when it comes to separating the impact of age and the presence of comorbidities, as well as for identifying which comorbidities truly increase risk, given that many patients may have multi-morbidity.

The implications of our findings are two-fold for COVID-19, firstly for current public health practice and secondly for the design of future studies. We flag a number of factors of interest that should be considered by governments and public health agencies when designing shielding strategies and the targeting of future vaccines, as well as in mathematical modelling projecting the likely impact of the pandemic over time. We note, however, the need for sensitive handling of population groups deemed to be at higher risk, and how such labelling does not devolve responsibility from public bodies to these individuals for their own welfare [8]. Some public health agencies are now including reporting of potential risk factors in their routine outputs, including ICNARC (included in this review) [30] and the newer European Centre for Disease Prevention and Control reports, which were released after this review was conducted [62].

Our review demonstrates both the volume of literature that can be published within only a few months since the appearance of an emerging infectious disease, and the need for co-ordinated approaches to such pathogens. Global efforts using national datasets are hugely valuable in systematically determining the aetiology of a disease, particularly to detect smaller effect sizes. Determination of the exact threshold of important risk depends on public perceptions of the disease [63], as well as policy needs. Data collection should be standardised where possible, e.g. by using consistent definitions of outcomes and the treatment of exposures (for example for hypertension, given that blood pressure is continuous). (For COVID-19 we note both the valuable World Health Organization interim guidelines on its management in providing consistent approaches for testing and the definition of ARDS [14], and that platforms such as the International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC) have aimed to facilitate such standardisation [64].) The choice of comparison groups should also merit careful consideration; comparison to other forms of the same condition (e.g. SARS and MERS for COVID-19), although interesting, provide little information about risk groups to be currently acted upon. Where key potential risk factors of interest, such as deprivation, are linked to both the disease of interest and the comparator condition, this limits the inferences possible. Saying this, studies of COVID-19 with the comparator group of other forms of viral pneumonia are a useful complement to studies using a general population comparator, as they show whether people with particular risk factors are at risk over and above what they might experience from ‘normal’ respiratory viruses, which might inform the level of additional precautions they could consider taking.

Finally, appropriately adjusted multivariable analyses should be prioritised, in order to separate the implications of different risk factors and to infer true causal relationships, for example exploring specific markers of comorbidity severity and control, such as the use of specific medications. We can then make the recommendations for shielding criteria more targeted, meaning that the public can be made more aware of the risk factors that are likely to have clinical significance and adapt their behaviour accordingly. Early clinical studies during pandemics are critically important and published rapidly under extremely difficult circumstances, but we would argue that high-quality epidemiological studies should also be seen as a priority, and that emergency response plans should include provision of appropriate epidemiological and statistical expertise.


The volume of literature generated in the short time since the appearance of SARS-CoV-2 has been considerable. Many studies have sought to document the risk factors for COVID-19 disease, disease severity and mortality. Age was the only risk factor based on robust studies and with a consistent body of evidence. Mechanistic studies are required to understand why age is such an important risk factor. At the start of pandemics, large, standardised, studies using multivariable analyses – e.g. using national surveillance data – are urgently needed in order to inform stratified approaches to rapidly protecting the population groups most at risk.

Availability of data and materials

All data generated or analysed during this study are included in this published article and the underlying papers.



Angiotensin-converting enzyme 2


Acute respiratory distress syndrome


Body mass index


Center for Disease Control and Prevention


Chronic obstructive pulmonary disease


Coronavirus disease-19


Computed tomography


Disseminated intravascular coagulation

FiO2 :

Inspired oxygen fraction


Intensive care national audit and research centre


Intensive care unit




International severe acute respiratory and emerging infections consortium


Lower respiratory tract


Middle Eastern Respiratory Syndrome


Multiple sclerosis


Not applicable

PaO2 :

Arterial partial pressure of oxygen


Polymerase chain reaction


Renin-angiotensin system


Respiratory rate


Severe acute respiratory syndrome


Severe acute respiratory syndrome coronavirus-2

SpO2 :

Oxygen saturation


Transmembrane serine protease 2


Upper respiratory tract


  1. World Health Organization. WHO Director-General's opening remarks at the media briefing on COVID-19 2020 [updated 11th March 2020. Available from: Accessed 20 Mar 2020.

  2. CDC COVID-19 Response Team. Severe Outcomes Among Patients with Coronavirus Disease 2019 (COVID-19)- United States, February 12–March 16, 2020. Morb Mortal Wkly Rep. 2020;69(12):343–6.

  3. Wu JT, Leung K, Bushman M, Kishore N, Niehus R, de Salazar PM, et al. Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China. Nat Med. 2020;26:506–10.

  4. Ortiz JR, Perut M, Dumolard L, Wijesinghe PR, Jorgensen P, Ropero AM, Danovaro-Holliday MC, Heffelfinger JD, Tevi-Benissan C, Teleb NA, Lambach P, Hombach J. A global review of national influenza immunization policies: analysis of the 2014 WHO/UNICEF joint reporting form on immunization. Vaccine. 2016;34(45):5400–5.

    Article  PubMed  PubMed Central  Google Scholar 

  5. NHS Digital. Coronavirus (COVID-19): Shielded patients list 2020. Available from: Accessed 27 May 2020.

  6. Health Protection Surveillance Centre. Guidance for Vulnerable Groups 2020. Available from: Accessed 3 June 2020.

  7. van Bunnik BAD, Morgan ALK, Bessell P, Calder-Gerver G, Zhang F, Haynes S, et al. Segmentation and shielding of the most vulnerable members of the population as elements of an exit strategy from COVID-19 lockdown. MedRxiv. 2020.

  8. Ganguli-Mitra A, Young I, Engelmann L, Harper I, McCormack D, Marsland R, et al. Segmenting communities as public health strategy: a view from the social sciences and humanities. Wellcome Open Res. 2020;5:104.

  9. NHS Digital. Statement for people who think they have inaccurately been sent communication about being vulnerable to coronavirus 2020 [updated 24th March 2020. Available from: Accessed 26 Mar 2020.

  10. Public Health England. Guidance on social distancing for everyone in the UK and protecting older people and vulnerable adults 2020 [updated 30th March 2020. Available from: Accessed 27 Apr 2020.

  11. Cabinet Office. Staying alert and safe (social distancing) 2020 [Available from: Accessed 28 May 2020.

  12. Public Health Scotland. Search criteria for highest risk patients for shielding. Version 4.0 2020 [Available from: Accessed 3 June 2020.

  13. Zheng YY, Ma YT, Zhang JY, Xie X. COVID-19 and the cardiovascular system. Nat Rev Cardiol. 2020;17(5):259–60.

    Article  CAS  PubMed  Google Scholar 

  14. World Health Organization. Clinical management of severe acute respiratory infection (SARI) when COVID-19 disease is suspected Interim guidance 2020 [updated 13th March 2020. Available from: Accessed 27 May 2020. (ncov)-infection-is-suspected.

  15. Downs SH, Black N. The feasibility of creating a checklist for the assessment of the methodological quality both of randomised and non-randomised studies of health care interventions. J Epidemiol Community Health. 1998;52(6):377–84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Deeks JJ, Dinnes J, D'Amico R, Sowden AJ, Sakarovitch C, Song F, et al. Evaluating non-randomised intervention studies. Health Technol Assess. 2003;7(27):iii-x, 1–173.

    Article  Google Scholar 

  17. Arner TGS, G.G.; Friedman, R.; Lantinga, M.; Sangam, S.; Zubieta, J.C.; Sullivan, K.M.; Brendel, K.A.; Gao, Z.; Fontaine, N.; Shu, M.; Fuller, G.; Smith, D.C.; Nitschke, D.A.; Fagan, R.F.;. Epi info™, a database and statistics program for public health professionals. GA, USA: Centers for Disease Control and Prevention; 2011.

  18. Westreich D, Greenland S. The table 2 fallacy: presenting and interpreting confounder and modifier coefficients. Am J Epidemiol. 2013;177(4):292–8.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Cai Q, Huang D, Ou P, Yu H, Zhu Z, Xia Z, et al. COVID-19 in a designated infectious diseases hospital outside Hubei Province, China. Allergy. 2020;75(7):1742–52.

  20. Chen J, Qi T, Liu L, Ling Y, Qian Z, Li T, Li F, Xu Q, Zhang Y, Xu S, Song Z, Zeng Y, Shen Y, Shi Y, Zhu T, Lu H. Clinical progression of patients with COVID-19 in Shanghai, China. J Infect. 2020;80(5):e1–6.

  21. Chen T, Wu D, Chen H, Yan W, Yang D, Chen G, et al. Clinical characteristics of 113 deceased patients with coronavirus disease 2019: Retrospective study. The BMJ. 2020;368(no pagination).

  22. Chen X, Yang Y, Huang M, Liu L, Zhang X, Xu J, et al. Differences between COVID-19 and suspected then confirmed SARS-CoV-2-negative pneumonia: a retrospective study from a single center. J Med Virol. 2020;92(9):1572–79.

  23. Chen G, Wu D, Guo W, Cao Y, Huang D, Wang H, Wang T, Zhang X, Chen H, Yu H, Zhang X, Zhang M, Wu S, Song J, Chen T, Han M, Li S, Luo X, Zhao J, Ning Q. Clinical and immunological features of severe and moderate coronavirus disease 2019. J Clin Invest. 2020;130(5):2620–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Cheng Z, Lu Y, Cao Q, Qin L, Pan Z, Yan F, Yang W. Clinical features and chest CT manifestations of coronavirus disease 2019 (COVID-19) in a single-center study in Shanghai, China. AJR Am J Roentgenol. 2020;215(1):121–6.

    Article  PubMed  Google Scholar 

  25. Epidemiology Working Group for Ncip Epidemic Response CCfDC, Prevention. The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China. Zhonghua Liu Xing Bing Xue Za Zhi. 2020;41(2):145–51.

    Google Scholar 

  26. Fan BE, Chong VCL, Chan SSW, Lim GH, Lim KGE, Tan GB, et al. Hematologic parameters in patients with COVID-19 infection. Am J Hematol. 2020;95(6):E131–E134.

  27. Grasselli G, Zangrillo A, Zanella A, Antonelli M, Cabrini L, Castelli A, Cereda D, Coluccello A, Foti G, Fumagalli R, Iotti G, Latronico N, Lorini L, Merler S, Natalini G, Piatti A, Ranieri MV, Scandroglio AM, Storti E, Cecconi M, Pesenti A, for the COVID-19 Lombardy ICU Network. Baseline characteristics and outcomes of 1591 patients infected with SARS-CoV-2 admitted to ICUs of the Lombardy region, Italy. JAMA. 2020;323(16):1574–81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Han H, Xie L, Liu R, Yang J, Liu F, Wu K, et al. Analysis of heart injury laboratory parameters in 273 COVID-19 patients in one hospital in Wuhan, China. J Med Virol. 2020;92(7):819–23.

  29. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X, Cheng Z, Yu T, Xia J, Wei Y, Wu W, Xie X, Yin W, Li H, Liu M, Xiao Y, Gao H, Guo L, Xie J, Wang G, Jiang R, Gao Z, Jin Q, Wang J, Cao B. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395(10223):497–506.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Intensive Care National Audit and Research Centre. ICNARC report on COVID-19 in critical care 2020 [Available from: Accessed 21 May 2020.

  31. Liang W, Guan W, Chen R, Wang W, Li J, Xu K, Li C, Ai Q, Lu W, Liang H, Li S, He J. Cancer patients in SARS-CoV-2 infection: a nationwide analysis in China. Lancet Oncol. 2020;21(3):335–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Liu R, Ma Q, Han H, Su H, Liu F, Wu K, et al. The value of urine biochemical parameters in the prediction of the severity of coronavirus disease 2019. Clin Chem Lab Med. 2020;58(7):1121–24.

  33. Liu R, Han H, Liu F, Lv Z, Wu K, Liu Y, Feng Y, Zhu C. Positive rate of RT-PCR detection of SARS-CoV-2 infection in 4880 cases from one hospital in Wuhan, China, from Jan to Feb 2020. Clin Chim Acta. 2020;505:172–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Qiu H, Wu J, Hong L, Luo Y, Song Q, Chen D. Clinical and epidemiological features of 36 children with coronavirus disease 2019 (COVID-19) in Zhejiang, China: an observational cohort study. Lancet Infect Dis. 2020;20(6):689–96.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Ruan Q, Yang K, Wang W, Jiang L, Song J. Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China. Intensive Care Med. 2020;46(5):846–48.

  36. 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. Crit Care. 2020;24(1):108.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Simonnet A, Chetboun M, Poissy J, Raverdy V, Noulette J, Duhamel A, et al. High prevalence of obesity in severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) requiring invasive mechanical ventilation. Obesity. 2020;28(7):1195–99.

  38. Tian S, Hu N, Lou J, Chen K, Kang X, Xiang Z, Chen H, Wang D, Liu N, Liu D, Chen G, Zhang Y, Li D, Li J, Lian H, Niu S, Zhang L, Zhang J. Characteristics of COVID-19 infection in Beijing. J Infect. 2020;80(4):401–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Wan S, Xiang Y, Fang W, Zheng Y, Li B, Hu Y, Lang C, Huang D, Sun Q, Xiong Y, Huang X, Lv J, Luo Y, Shen L, Yang H, Huang G, Yang R. Clinical features and treatment of COVID-19 patients in Northeast Chongqing. J Med Virol. 2020;92(7):797–806.

    Article  CAS  PubMed  Google Scholar 

  40. Wang C, Horby PW, Hayden FG, Gao GF. A novel coronavirus outbreak of global health concern. Lancet. 2020;395(10223):470–3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, et al. Clinical Characteristics of 138 Hospitalized Patients with 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. JAMA. 1061;323(11):1061–9.

    Article  Google Scholar 

  42. Wang L, Li X, Chen H, Yan S, Li D, Li Y, Gong Z. Coronavirus disease 19 infection does not result in acute kidney injury: an analysis of 116 hospitalized patients from Wuhan, China. Am J Nephrol. 2020;51(5):343–8.

    Article  CAS  PubMed  Google Scholar 

  43. Wang Z, Yang B, Li Q, Wen L, Zhang R. Clinical features of 69 cases with coronavirus disease 2019 in Wuhan, China. Clin Infect Dis. 2020;71(15):769–77.

  44. Wu C, Chen X, Cai Y, Xia J, Zhou X, Xu S, et al. Risk factors associated with acute respiratory distress syndrome and death in patients with coronavirus disease 2019 pneumonia in Wuhan, China. JAMA Intern Med. 2020;180(7):934–43.

  45. Wu Z, JM MG. Characteristics of and Important Lessons from the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72314 Cases from the Chinese Center for Disease Control and Prevention. JAMA. 1239;323(13):1239–42.

    Article  Google Scholar 

  46. Yang X, Yu Y, Xu J, Shu H, Xia J, Liu H, et al. Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study. Lancet Respir Med. 2020;8(5):475–81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Yuan M, Yin W, Tao Z, Tan W, Hu Y. Association of radiologic findings with mortality of patients infected with 2019 novel coronavirus in Wuhan, China. Plos One. 2020;15(3):e0230548.

  48. Zhang JJ, Dong X, Cao YY, Yuan YD, Yang YB, Yan YQ, et al. Clinical characteristics of 140 patients infected with SARS-CoV-2 in Wuhan, China. Allergy: Eur J Allergy Clin Immunol. 2020;75(7):1730–41.

  49. Zhang G, Zhang J, Wang B, Zhu X, Wang Q, Qiu S. Analysis of clinical characteristics and laboratory findings of 95 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a retrospective analysis. Respir Res. 2020;21(1):74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Zhang R, Ouyang H, Fu L, Wang S, Han J, Huang K, et al. CT features of SARS-CoV-2 pneumonia according to clinical presentation: a retrospective analysis of 120 consecutive patients from Wuhan city. Eur Radiol. 2020. epub ahead of print.

  51. Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 1054;395(10229):1054–62.

    Article  Google Scholar 

  52. 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(26):2534–43.

    Article  CAS  PubMed  Google Scholar 

  53. Docherty AB, Harrison EM, Green CA, Hardwick HE, Pius R, Norman L, et al. Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO clinical characterisation protocol: prospective observational cohort study. BMJ. 2020;369:m1985.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Chen J. Pathogenicity and transmissibility of 2019-nCoV-A quick overview and comparison with other emerging viruses. Microbes Infect. 2020;22(2):69–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Gengler IW, J.C.; Speth, M.M.; Sedaghat A.R.;. Sinonasal pathophysiology of SARS-CoV-2 and COVID-19: a systematic review of the current evidence. Laryngoscope Invest Otolaryngol. 2020;5(3):354–9.

  56. Chu H, Chan JF, Wang Y, Yuen TT, Chai Y, Hou Y, et al. Comparative replication and immune activation profiles of SARS-CoV-2 and SARS-CoV in human lungs: an ex vivo study with implications for the pathogenesis of COVID-19. Clin Infect Dis. 2020;71(6):1400–9.

    Article  CAS  PubMed  Google Scholar 

  57. Fu Y, Cheng Y, Wu Y. Understanding SARS-CoV-2-mediated inflammatory responses: from mechanisms to potential therapeutic tools. Virol Sin. 2020;35(3):266–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Kumar VA, A.K.; Aster, J.C.; . Robbins & Cotran pathologic basis of disease, 9e (Robbins pathology) 2014. 1408 p.

  59. Pangrazzi L, Meryk A, Naismith E, Koziel R, Lair J, Krismer M, Trieb K, Grubeck-Loebenstein B. “Inflamm-aging” influences immune cell survival factors in human bone marrow. Eur J Immunol. 2017;47(3):481–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Bunyavanich S, Do A, Vicencio A. Nasal gene expression of angiotensin-converting enzyme 2 in children and adults. JAMA. 2020;323(23):2427–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Dai M, Liu D, Liu M, Zhou F, Li G, Chen Z, Zhang Z, You H, Wu M, Zheng Q, Xiong Y, Xiong H, Wang C, Chen C, Xiong F, Zhang Y, Peng Y, Ge S, Zhen B, Yu T, Wang L, Wang H, Liu Y, Chen Y, Mei J, Gao X, Li Z, Gan L, He C, Li Z, Shi Y, Qi Y, Yang J, Tenen DG, Chai L, Mucci LA, Santillana M, Cai H. Patients with Cancer appear more vulnerable to SARS-CoV-2: a multicenter study during the COVID-19 outbreak. Cancer Discov. 2020;10(6):783–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. European Centre for Disease Prevention and Control. ECDC launches new weekly COVID-19 surveillance report 2020 [Available from: Accessed 27 May 2020.

  63. McGlothlin AE, Lewis RJ. Minimal clinically important difference: defining what really matters to patients. JAMA. 2014;312(13):1342–3.

    Article  CAS  PubMed  Google Scholar 

  64. International Severe Acute Respiratory and Emerging Infection Consortium. COVID-19 Clinical Research Resources 2020 [Available from: Accessed 30 May 2020.

Download references


Not applicable.


HRS and MF are supported by the Medical Research Council [MR/R008345/1]. CJ’s salary came through MRC core funding MC_UU_12023/26. SJS is funded by the Wellcome Trust [WT 209560/Z/17/Z]. CRS has received funding from the Medical Research Council [MR/R008345/1], the National Institute for Health Research [11/46/23] and the New Zealand Health Research Council [20/1018] and Ministry for Business, Innovation and Employment. EV is funded by the Medical Research Council [MR/R008345/1] through the EAVE II grant and supported by the Scottish Government. We also acknowledge the support of HDR UK. The views and opinions expressed here are those of the authors and do not necessarily reflect those of the Health Technology Assessment programme, NIHR, NHS, or the UK Department of Health. Our funders had no role in the design of the study and collection, analysis, and interpretation of data, and in writing the manuscript.

Author information

Authors and Affiliations



HRS, MF and AS conceived of the work. HRS, MF, CJ, EV and CS designed the work. MF, CJ, HRS, EV, MDM, YJ and XW acquired and interpreted the data. CRS, UA, CM, JLKM, LDR, CR, SJS, MEJW and AS also interpreted the data. HRS and MF drafted the work. CJ, EV, CRS, MDM, UA, CM, YJ, JLKM, LDR, CR, SJS, XW, MEJW and AS revised it critically for important intellectual content. All authors give final approval of the version to be published. All authors agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Corresponding author

Correspondence to H. R. Stagg.

Ethics declarations

Ethics approval and consent to participate

This is a systematic review and therefore presents secondary data. No ethical approvals were required.

Consent for publication

Not applicable.

Competing interests

CR is a member of the Scottish Government’s Chief Medical Officer’s COVID-19 Advisory Group, a member of the UK SPI-M committee and the Commission Human Medicines COVID-19 Vaccine Safety Benefits and Risks Working Group. JLKM is Incident Director for COVID-19 at Public Health Scotland and reports no conflicts of interest. LDR serves on a number of Scottish Government Advisory Groups, including COVID-19. MEJW is a member of the SPI-M advisory committee for the UK Government and the Covid-19 Advisory Group for the Scottish Government. AS is a member of the Scottish Government’s Chief Medical Officer’s COVID-19 Advisory Group and the New and Emerging Respiratory Virus Threats (NERVTAG) Risk Stratification Subgroup. HRS is an advisor to the Scottish Parliament’s COVID committee. The views represented in this article do not represent the views of the UK or Scottish Government. All other authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Flook, M., Jackson, C., Vasileiou, E. et al. Informing the public health response to COVID-19: a systematic review of risk factors for disease, severity, and mortality. BMC Infect Dis 21, 342 (2021).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: