Higher Frequency of Healthcare Professionals is Associated With a Low Incidence of COVID-19-Related Death

Abstract

The COVID-19 pandemic has affected over 200 countries with varying levels of infection and mortality rates. To understand the impact of healthcare resources and cultural factors, a cross-sectional study was conducted on 76 countries. The study used K-means clustering to identify 2 distinct clusters and performed a Welch’s test to compare different parameters. The countries were then plotted on the Inglehart-Welzel global cultural map. By incorporating this framework, researchers can systematically scrutinize the intricate interplay of cultural factors. This will provide valuable context for understanding individuals’ behaviors, preferences, and decision-making as they pertain to the challenges posed by COVID-19 and its mitigation strategies. The results showed that countries with higher levels of healthcare professionals had a lower death rate, even with a relatively high rate of infection. These countries also had higher levels of individual self-expression. The study highlights the importance of adhering to recommended protocols, as neglect can result from a lack of self-expression, leading to an increase in the spread of communicable diseases. It also emphasizes crucial role of healthcare professionals in managing crisis related to the pandemic.

Keywords

COVID-19 SARS-CoV-2 infection death healthcare professionals K-means clustering

What do we already know about this topic?

COVID-19 pandemic has affected over 200 countries with varying levels of infection and mortality rates.

It is important to adhere to the recommended protocols; else it will lead to an increase in the spread of communicable diseases.

How does your research contribute to the field?

The countries having higher levels of individual self-expression as per Inglehart-Welzel global cultural map had a relatively high rate of infection.

Even with a relatively high rate of infection, the research shows that countries with higher levels of healthcare professionals had a lower death rate.

What are your research’s implications toward theory, practice, or policy?

Trained healthcare professionals’ efforts are crucial in slowing the spread of the virus and preventing a higher death toll.

Traditional, secular-rational, survival and self-expression values have a significant impact on the social behavior of an individual and contribute to the degree of spread based of an infectious disease

Introduction

Human coronaviruses (CoV) have been kindred with 3 major outbreaks, namely severe acute respiratory syndrome (SARS) in 2002, middle east respiratory syndrome (MERS) in 2012 and COVID-19 in 2019 with a mortality rate of 9.6%, 40%, and 1.21%, independently.^1
-4 COVID-19 emerged in Wuhan city of Central China in mid-November 2019 and gradually spread to more than 200 countries with a death toll of 5 771 355 as of May 2022. In March 2020 World Health Organization (WHO) specified COVID-19, as pandemic and triggered a global public health emergency.^5,6 Physical distancing and proper hand hygiene are the most effective ways to avoid the spread of this virus.^7,8 Different organizations like WHO and Centers for Disease Control and Prevention (CDC) have published and revised their guidelines continuously to prevent the spread and disease associated complications.⁹ The adoption of recommended safety measures primarily hinges on individual factors like social tolerance, life fulfillment, public attitudes, and the importance of personal freedom. By taking these elements into consideration, we can see a change from a society driven by individualism and capitalism to one that places greater emphasis on individual liberties and self-expression.¹⁰ Given that virus-induced pneumonia is the leading cause of hospitalization and mortality, the pivotal role of frontline healthcare professionals is paramount in the battle against the pandemic.¹¹ More over primary targets for any communicable disease are frontline healthcare professionals.¹² Massive work load and high menace of getting infected during this pandemic situation¹³ inquires a significant number of these trained doctors and nurses.

In our study, we utilized an unsupervised learning algorithm, K-means, to classify countries based on the accessibility of healthcare professionals and the impact of the pandemic. We analyzed the density of doctors and nurses, as well as the infection and death rates, to determine the relationship between these factors. Our results indicated that a significant portion of countries with high pandemic severity also had a shortage of healthcare workers. These findings emphasize the crucial role that healthcare professionals play in mitigating the spread of the disease and reducing the number of fatalities. In the face of a pandemic, these front-line workers are instrumental in providing medical care, treatment, and support to those affected. Their efforts are crucial in slowing the spread of the virus and preventing a higher death toll. It’s important to note that having an adequate number of healthcare workers and a strong healthcare infrastructure can make a significant impact in the response to a pandemic.

Methods

Study Design and Data Collection

Our global qualitative study was aimed at understanding the role of medical infrastructure in the Covid-19 pandemic, the subtle significance of culture, and traditional reflections in shaping the consequences of the pandemic. Population data was collected from Worldometer (https://www.worldometers.info/coronavirus/), a reference website that usually provides real-time statistics. We started data collection from 22^nd January, 2020 and set 8th May, 2022 as the cutoff date. We collected the density of medical doctors (per 100 000 people) and nursing and midwifery personnel (per 10 000 population) for 192 countries from World Health Statistics 2020 (Monitoring Health for Sustainable Development Goals, ANNEX 2, Part-3, Page 58) published by WHO.

Inglehart-Welzel Cultural Map, also known as the World Values Survey Cultural Map, is a widely recognized framework used to assess and understand cultural and societal values across different countries and regions. Firstly, it offers a comprehensive and empirically grounded method for mapping and comparing cultural values, attitudes, and beliefs on a global scale. By incorporating this framework, researchers can systematically examine how cultural factors shape individuals’ behaviors, preferences, and decision-making processes in diverse socio-cultural contexts. The classification of countries into the 4 quadrants (Q1, Q2, Q3, and Q4) on the Inglehart-Welzel Cultural Map is based on dynamic cultural values and may vary with time and datasets. Historically, Q1 (Traditional/Survival) has included countries such as Saudi Arabia and Pakistan, while Q2 (Traditional/Self-Expression) encompassed nations like Russia and Mexico. Q3 (Secular/Survival) often featured Germany and Japan, and Q4 (Secular/Self-Expression) included Sweden and Canada. Our research utilizes the Inglehart-Welzel Cultural Map as of 2020 to categorize societies according to traditional, secular-rational, survival, and self-expression values. We specifically investigate how these cultural dimensions relate to the global distribution of COVID-19 infection and mortality rates.

Heat Map Generation

Total positive cases and deaths were found to be complete for 218 countries. Infection rate was defined as (total cases/total population) × 100 and death rate was calculated as (total deaths/total cases) × 100. Heat map was generated for these data using excel.

Screening

Of the 218 countries, only 175 countries had available data on the density of physicians and nurses. We applied 3 filters namely population filter (P_f), significance filter (S_f) and cultural filter (C_f) on this data set to further narrow down our study. First, we applied a population threshold where we chose countries with more than 1 million population. Thus, 33 entries were removed, selecting 142 countries. As we focus more on COVID-19 global infection and deaths, countries that have a significant fraction of infection, death, and recovered/under treatment values will justify the study statistically. We consider 3 components, namely Sfc, S_fd, and Sfr, respectively, to calculate S_f [S_fc is the percentage of detected cases, S_fd indicates the percentage of death and Sfris is the percentage of recovered/under treatment per country]. Next, we added the individual components S_fc, Sfd, and S_fr for 142 entries, and the resulting data (S_f) were subjected to a threshold cut-off of 0.1 unit. Entries with S_f < 0.1 were removed to adopt 102 countries for the next level. Any predicament on humanity always engenders an elemental response in most human beings. Otherwise operating from the background, cultural inclination becomes instigated in crisis period. COVID-19 was a major crisis worldwide, and hence we chose to shortlist the countries that have been studied earlier based on their cultural spectrum. We chose Inglehart-Welzel global cultural map (2020) and removed countries from our study that are not present in the map. Thus, 76 countries were selected for the rest of our study.

K-means Clustering

We used K-means clustering, an unsupervised machine learning approach to get divergent clusters for our selected countries based on different parameters like infection/death rate or doctor/nurse frequency. To identify better clusters, we first used principal component analysis (PCA) first on the data and then applied the K-means algorithm. When running this iterative algorithm using numpy, pandas, matplotlib, and scikit-learn Python (3.7.3) packages, we first checked the optimal number of clusters (K). In our study we applied 3 methods, namely Elbow, Silhouette and Gap Statistic to determine “K.” While Elbow method uses WCSS (Within Cluster Sum of Squares) value,¹⁴ Silhouette score (ranging from −1 to 1) ensures goodness of the clustering technique¹⁵ and Gap Statistic compares change in within cluster dispersion.¹⁶

Statistics

Most of our clusters have unequal sample sizes. We used Welch’s t-test,¹⁷ or unequal variances t-test for significance testing. 5% significance level was used. Statistical significance was marked by “***” with a “P” value < .001.

Results

Global Picture of SARS-CoV-2 Infection

Since its inception COVID-19 has created a huge impact on global health. The total number of fatal cases has increased from 1 million on November 2020 to more than6 million in April 2022 (Figure 1C). We prepared a heat map with an infection rate of 218 countries and 40 countries were found to have an infection rate below 0.5. Although 22 countries had an infection rate above 40. A total of 156 countries were found to have infection rates between 0.5 and 40 (Figure 1A and Supplemental Table 1). We also followed a similar methodology for varied death rates. Fifty-one countries showed a death rate below 0.5. Identical count of 57 countries each, attributed to the death rate range of 0.5 to 1 and 1 to 2. While 46 countries witnessed death rate of 2 to 5, only 5 and 2 countries reported death rate between 5 to 10 and 10 to 20 respectively (Figure 1B and Supplemental Table 2).

Figure 1.

Heat map showing global COVID-19 infection and death rate data. SARS-CoV-2 infection rate of different countries. Color code indicates higher or lower values (A). Diverged death rates of individual countries with mentioned color code (B). Line diagram showing global death toll (C).

Infection and Death Rate Data Clustered the Selected Countries Into 2 Distinct Groups

We selected 76 countries applying divergent criteria mentioned earlier (Figure 2) and performed K-means clustering. Initially, Elbow, Silhouette, and Gap Statistics were performed to determine optimal cluster numbers, and it was found to be 2 (Figure 3A, Supplemental Figure 1A-C). Next, we applied K-means clustering, an unsupervised clustering approach, to classify these countries. Two distinct clusters were obtained, namely cluster 1 and cluster 2 (Figure 3B). In these 2 clusters (cluster 1and cluster 2) 36 and 40 countries were listed respectively (Table 1). We found cluster 2 countries are having significantly lower infection rate while cluster 1 countries show relatively higher values (Figure 3C and Supplemental Table 3). Interestingly, cluster 1 countries depict a significant reduction in death rate, while cluster 2 countries have higher death rate (Figure 3D and Supplemental Table 3).

Figure 2.

Country selection and study design. Countries were selected based on population size, data availability and different parameters like infection/death rate, doctor and nurse density. Here significance filter is a conglomeration of total reported case, death rate and recovery rate of individual countries. Cultural filter actually uses countries in the Inglehart-Welzel recommended countries to further dissect the list specified by the previous selection criteria.

Figure 3.

K-means clustering of screened countries based on death and infection rates. Elbow statistics showing possible number of clusters (A). Different clusters with indicated color coding using K-means clustering algorithm (B). Box plot depicting infection and death rates of clustered countries (C and D).

Table 1.

List of Different Countries in 2 Distinct Clusters Designated According to COVID-19 Infection and Death Rates.

Cluster 1	Cluster 2
Argentina	Albania
Australia	Algeria
Austria	Armenia
Belarus	Azerbaijan
Belgium	Bangladesh
Croatia	Bolivia
Czechia	Bosnia and Herzegovina
Denmark	Brazil
Estonia	Bulgaria
Finland	Chile
France	China
Georgia	Colombia
Germany	Egypt
Greece	Ethiopia
Israel	Guatemala
Italy	Hungary
Japan	India
Jordan	Indonesia
Lebanon	Iran
Lithuania	Iraq
Malaysia	Kazakhstan
Netherlands	Kyrgyzstan
New Zealand	Libya
Qatar	Mexico
Serbia	Moldova
Singapore	Morocco
Slovakia	Myanmar
Slovenia	North Macedonia
Spain	Pakistan
Sweden	Philippines
Switzerland	Poland
Turkey	Romania
UK	Russia
Uruguay	Saudi Arabia
USA	South Africa
Vietnam	Thailand
	Trinidad and Tobago
	Uganda
	Uzbekistan
	Zimbabwe

Data on Doctor and Nurse Density Classified the Countries Into 2 Separate Clusters

We follow a similar approach namely Elbow, Silhouette, and Gap Statistics for healthcare professional data as well. Congruously all the statistical methods suggested 2 as the most suitable cluster number (Figure 4A, Supplemental Figure 2A-C). When performing K-means clustering with doctor and nurse density data, we found 2 distinct clusters (Figure 4B). Forty-two and 34 countries were congregated in cluster 1 and cluster 2 respectively (Table 2). We found that cluster 1 countries have significantly higher healthcare professionals (both doctor and nurse density) compared to cluster 2 countries (Figure 4C and D and Supplemental Table 3).

Figure 4.

K-means clustering of selected countries based on their doctor and nurse density data. Elbow statistics suggesting plausible cluster numbers (A). Stipulated clusters with distinct color coding were obtained applying K-means clustering algorithm (B). Box plot showing cluster wise doctor and nurse density data (C and D).

Table 2.

Selected Countries in 2 Different Clusters as per Their Doctor and Nurse Density.

Cluster 1	Cluster 2
Argentina	Jordan
Australia	Lebanon
Austria	Malaysia
Belarus	Singapore
Belgium	Slovakia
Croatia	Turkey
Czechia	Vietnam
Denmark	Albania
Estonia	Algeria
Finland	Bangladesh
France	Bolivia
Georgia	Bosnia and Herzegovina
Germany	China
Greece	Colombia
Israel	Egypt
Italy	Ethiopia
Japan	Guatemala
Lithuania	India
Netherlands	Indonesia
New Zealand	Iran
Qatar	Iraq
Serbia	Kyrgyzstan
Slovenia	Libya
Spain	Mexico
Sweden	Morocco
Switzerland	Myanmar
UK	North Macedonia
Uruguay	Pakistan
USA	Philippines
Armenia	Saudi Arabia
Azerbaijan	South Africa
Brazil	Thailand
Bulgaria	Uganda
Chile	Zimbabwe
Hungary
Kazakhstan
Moldova
Poland
Romania
Russia
Trinidad and Tobago
Uzbekistan

Countries With Higher Healthcare Professionals Showed Low Death Rate

As discussed earlier we found 2 clusters (cluster 1 and cluster 2) depending upon infection and death rate data. Cluster 1 countries have a higher infection rate (Figure 3C) but surprisingly relatively lower death rate (Figure 4C and Supplemental Table 3). Now one possible reason for this may be better healthcare facilities. Interestingly, we found that these cluster 1 countries have higher healthcare professionals (both doctor and nurse density) for the period of 2010 to 2018 (Figure 5A and B and Supplemental Table 3). Considering reasonable contribution of traditional, secular-rational, survival and self-expression values toward the outspread of the disease we plotted Infection-Death rate wise clustered countries on Inglehart-Welzel cultural map. Countries with a higher infection rate were found to be congregated in Q1 (Figure 5C). As per Inglehart-Welzel cultural plotting, Q1 countries are mostly having higher secular-rational and self-expression values. Whereas Q3 countries are exhibiting more traditional and conservative approach.¹⁰

Figure 5.

Doctor and Nurse density data and Infection-Death rate clustered countries were plotted on cultural map. Selected countries were clustered based on their infection and death rate data. Cluster wise doctor (A) and nurse (B) density distributions were plotted. Infection-Death rate wise clustered countries were plotted on Inglehart-Welzel cultural map (C).

Discussion

COVID-19 is one of the major health concerns of the 21st century due to its rapid spread and deadly outcome.¹⁸ In a very short period of time, SARS-CoV-2 has been transmitted throughout the world.¹⁹ This virus is decidedly contagious and is transmitted by direct contact and/or droplet nuclei.²⁰ For centuries communicable diseases and unrestrained epidemics have impeded life expectancy significantly.²¹ Social distancing and self-hygiene have been the most effective measures to avoid this aggressive infection.^7,8 This study focuses on dissecting the global distribution of COVID-19 infection and the death rate. Traditional, secular-rational, survival and self-expression values have a significant impact on the social behavior of an individual.¹⁰ Our study employs the Inglehart-Welzel Cultural Map from 2020 to categorize societies based on traditional, secular-rational, survival, and self-expression values, with a specific focus on their influence on the global distribution of COVID-19 infection and death rates. This approach acknowledges the significant impact of cultural values on an individual’s social behavior, which, in the context of the COVID-19 pandemic, can lead to varying responses and outcomes. For instance, societies with traditional values may prioritize collective responsibility and adherence to established norms, potentially influencing their approach to disease prevention and control, while societies emphasizing self-expression values may prioritize personal freedom, potentially affecting compliance with public health measures. Here the cultural map provides a valuable lens for understanding the interplay between cultural factors and pandemic responses, offering insights that can inform public health strategies and policy planning. Being an infectious disease, COVID-19 can have differential degree of spread based on disparate social behavior. Furthermore, aggression and probable outcome of any pandemic partly depend on how stringently people are acting in accordance with the guideline prescribed by the appropriate authority. We found that countries that have higher self-expression values depict a higher infection rate (Figure 5C). Carelessness or laxity toward recommended protocol could be the plausible reason for this occurrence. Progression of this disease may develop certain physiological complications leading to hospitalization and death.²² Healthcare workers bore a substantial burden during the COVID-19 pandemic as they were on the frontlines of managing this medical crisis. Their significant exposure to the virus, coupled with the demands of providing care in a rapidly evolving and high-stress environment, resulted in profound challenges and consequences for their physical and mental well-being.^23
-25 Frontline medical professionals are the prevailing role player in avoiding COVID-19 related death. Thus, the frequency of these professionals could be one deciding factor to the aftermath of SARS-CoV-2 infection. Based on the infection and death rate data, cluster 1 and cluster 2 countries have shown a contrasting pattern. Countries with higher infection rate surprisingly depicted lower death rate (Figure 3C and D). Concomitantly these countries are also possessing higher number of doctors and nurses. According to this countries with lower frequency of healthcare professionals have exhibited relatively higher case fatality (Figure 5A and B). This study stands as the inaugural inquiry into elucidating the association between an elevated presence of healthcare professionals and diminished COVID-19 related mortality rates.

Conclusion

In summary, our study reveals key insights into the global COVID-19 landscape. Countries with higher self-expression values tend to have higher infection rates, possibly due to non-compliance with health guidelines. A well-equipped healthcare workforce significantly reduces COVID-19-related deaths. Two country clusters emerged: high infection rate/low death rate with more healthcare professionals, and low infection rate/high death rate with fewer healthcare workers. This underscores the importance of healthcare infrastructure in pandemic response. In conclusion, social behavior, healthcare capacity, and adherence to guidelines are crucial factors in COVID-19 outcomes. Policymakers should focus on these aspects to improve pandemic management. Further research should explore these dynamics in greater depth.

Limitations

While the study identifies associations between societal values, healthcare infrastructure, and COVID-19 outcomes, it does not establish causation, leaving room for the influence of other unexplored factors. Measuring societal values is complex and may not fully capture cultural nuances. Temporal changes in behavior and healthcare infrastructure are not comprehensively considered, and regional variations within countries may be overlooked. Additionally, other influential variables, such as government policies and demographics, are not fully analyzed. The assumption that the quantity of healthcare professionals directly correlates with healthcare quality oversimplifies the healthcare system. Further studies are warranted to delve deeper into these limitations and their potential impact on the observed trends and conclusions.

Supplemental Material

sj-docx-1-inq-10.1177_00469580231221290 – Supplemental material for Higher Frequency of Healthcare Professionals is Associated With a Low Incidence of COVID-19-Related Death

Supplemental material, sj-docx-1-inq-10.1177_00469580231221290 for Higher Frequency of Healthcare Professionals is Associated With a Low Incidence of COVID-19-Related Death by Soumya Kanti Guha and Sougata Niyogi in INQUIRY: The Journal of Health Care Organization, Provision, and Financing

Footnotes

Author Contributions

SN conceptualized the idea and designed the study. SKG performed the experiments. SN and SKG analyzed the data and wrote the manuscript. SN emended it and approved the final version.

Data Availability Statement

Data related to the study are available from the corresponding author on reasonable request.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Ethical Approval

Not applicable.

ORCID iD

Sougata Niyogi

Supplemental Material

Supplemental material for this article is available online.

References

Kahn

McIntosh

History and recent advances in coronavirus discovery. Pediatr Infect Dis J. 2005;24:S223-S227. doi:10.1097/01.inf.0000188166.17324.60

Zhong

Zheng

, et al. Epidemiology and cause of severe acute respiratory syndrome (SARS) in Guangdong, People’s Republic of China, in February, 2003. Lancet. 2003;362:1353-1358. doi:10.1016/S0140-6736(03)14630-2

Guan

, et al. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med. 2020;382:1708-1720. doi:10.1056/NEJMoa2002032

Zhao

, et al. Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. Lancet. 2020;395:565-574. doi:10.1016/S0140-6736(20)30251-8

Sohrabi

Alsafi

O'Neill

, et al. World Health Organization declares global emergency: a review of the 2019 novel coronavirus (COVID-19). Int J Surg. 2020;76:71-76. doi:10.1016/j.ijsu.2020.02.034

Whitworth

COVID-19: a fast evolving pandemic. Trans R Soc Trop Med Hyg. 2020;114:241-248.

Tang

KHD

. Movement control as an effective measure against Covid-19 spread in Malaysia: an overview. J Public Health. 2022;30:583-586. doi:10.1007/s10389-020-01316-w

Abboah-Offei

Salifu

Adewale

, et al. A rapid review of the use of face mask in preventing the spread of COVID-19. Int J Adv Nurs Stud. 2021;3:1–30. doi:10.1016/j.ijnsa.2020.100013

Maguire

Guérin

PJ.

A living systematic review protocol for COVID-19 clinical trial registrations. Wellcome Open Res. 2020;5:60. doi:10.12688/wellcomeopenres.15821.1

10.

Lin

Mancik

AM.

National culture on the cross-national variation of homicide: an empirical application of the Inglehart–Welzel Cultural Map. Sociol Forum. 2020;35:1114-1134. doi:10.1111/socf.12640

11.

Wang

Jiang

, et al. A retrospective cohort study of methylprednisolone therapy in severe patients with COVID-19 pneumonia. Signal Transduct Target Ther. 2020;5:57-63. doi:10.1038/s41392-020-0158-2

12.

Iyengar

Ish

Upadhyaya

, et al. COVID-19 and mortality in doctors. Diabetes Metab Syndr Clin Res Rev. 2020;14:1743-1746. doi:10.1016/j.dsx.2020.09.003

13.

Ing

Salimi

Torun

Physician deaths from corona virus (COVID-19) disease. Occup Med. 2020;70:kqaa088-NaN4. doi:10.1093/occmed/kqaa088

14.

Bholowalia

Kumar

EBK-means: A clustering technique based on elbow method and k-means in WSN. Int J Comput Appl. 2014;105:17-24.

15.

Luck

Small

Little

. Real-time tracking of articulated human models using a 3d shape-from-silhouette method. In: Robot Vision: International Workshop RobVis 2001 Auckland, New Zealand, February 16–18, 2001 Proceedings. Springer; 2001:19-26.

16.

Tibshirani

Walther

Hastie

Estimating the number of clusters in a data set via the gap statistic. J R Stat Soc. 2001;63:411-423. doi:10.1111/1467-9868.00293

17.

Zimmerman

Zumbo

Rank transformations and the power of the Student t test and Welch t'test for non-normal populations with unequal variances. Can J Exp Psychol. 1993;47:523. doi:10.1037/h0078850

18.

Sam

Redefining vulnerability in the era of COVID-19. Lancet. 2020;395:1089. doi:10.1016/S0140-6736(20)30757-1

19.

Ioannidis

JPA

. Reconciling estimates of global spread and infection fatality rates of COVID-19: an overview of systematic evaluations. Eur J Clin Invest. 2021;51:1–13. doi:10.1111/eci.13554

20.

Gesesew

Koye

Fetene

, et al. Risk factors for COVID-19 infection, disease severity and related deaths in Africa: a systematic review. BMJ Open. 2021;11:1–10. doi:10.1136/bmjopen-2020-044618

21.

Bloom

Canning

Fink

Disease and development revisited. J Polit Econ. 2014;122:1355-1366.

22.

Wadhera

Gaba

, et al. Variation in COVID-19 hospitalizations and deaths across New York City boroughs. JAMA. 2020;323:2192-2195. doi:10.1001/jama.2020.7197

23.

Spoorthy

Pratapa

Mahant

Mental health problems faced by healthcare workers due to the COVID-19 pandemic–a review. Asian J Psychiatr. 2020;51:1–5. doi:10.1016/j.ajp.2020.102119

24.

Vindrola-Padros

Andrews

Dowrick

, et al. Perceptions and experiences of healthcare workers during the COVID-19 pandemic in the UK. BMJ Open. 2020;10:1–8. doi:10.1136/bmjopen-2020-040503

25.

Bandyopadhyay

Baticulon

Kadhum

, et al. Infection and mortality of healthcare workers worldwide from COVID-19: a systematic review. BMJ Glob Health. 2020;5:1–11. doi:10.1136/bmjgh-2020-003097

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

2.30 MB