The association between non-communicable diseases and COVID-19 severity and mortality among infected hospitalized healthcare workers in 29 countries: a cohort study

Yusuf Sheku Tejan; Jacklyne Ashubwe; Mher Beglaryan; Shermarke Hassan; Sartie Kenneh; Francis Moses; Abdulai Tejan Jalloh; Fassou Mathias Grovogui; Ibrahima Kaba; Sia Morenike Tengbe; Mustapha Kabba; Mamud Idriss Kamara; Santigie Sesay; Jonta Kamara; Jerry-Jonas Mbasha; Pryanka Relan; Innocent Nuwagira; Ibrahim Franklyn Kamara

doi:10.12688/f1000research.150838.1

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Research Article

The association between non-communicable diseases and COVID-19 severity and mortality among infected hospitalized healthcare workers in 29 countries: a cohort study

[version 1; peer review: 1 approved with reservations]

Yusuf Sheku Tejan ¹, Jacklyne Ashubwe², Mher Beglaryan³, [...] Shermarke Hassan^4,5, Sartie Kenneh¹, Francis Moses¹, Abdulai Tejan Jalloh¹, Fassou Mathias Grovogui⁶, Ibrahima Kaba⁶, Sia Morenike Tengbe¹, Mustapha Kabba¹, Mamud Idriss Kamara¹, Santigie Sesay¹, Jonta Kamara⁷, Jerry-Jonas Mbasha⁸, Pryanka Relan⁹, Innocent Nuwagira¹⁰, Ibrahim Franklyn Kamara¹⁰

Yusuf Sheku Tejan ¹, Jacklyne Ashubwe², [...] Mher Beglaryan³, Shermarke Hassan^4,5, Sartie Kenneh¹, Francis Moses¹, Abdulai Tejan Jalloh¹, Fassou Mathias Grovogui⁶, Ibrahima Kaba⁶, Sia Morenike Tengbe¹, Mustapha Kabba¹, Mamud Idriss Kamara¹, Santigie Sesay¹, Jonta Kamara⁷, Jerry-Jonas Mbasha⁸, Pryanka Relan⁹, Innocent Nuwagira¹⁰, Ibrahim Franklyn Kamara¹⁰

PUBLISHED 12 Jun 2024

Author details Author details

¹ Ministry of Health, 4th Floor, Youyi Building, Freetown, Sierra Leone
² Medwise Solutions Consultancy, Nairobi, P.O. Box 2356 00202, Kenya
³ Tuberculosis Research and Prevention Centre, Yerevan, 0014, Armenia
⁴ Infectious Diseases Data Observatory, University of Oxford, Oxford, UK
⁵ Nuffield Department of Medicine, Center for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
⁶ Africa Center of Excellence for the Prevention and Control of Communicable Diseases, University Gamal Abdel Nasser, Conakry, Guinea
⁷ Multidisciplinary Adolescent and Youth Review Board, Second Lancet Commission on Adolescent Health and Wellbeing, Toronto, Ontario, Canada
⁸ World Health Organization, Africa Regional Office, Brazzaville, Congo
⁹ World Health Organization, Headquarters, Geneva, Switzerland
¹⁰ World Health Organization Country Office, 21 A-B Kingharman Road, Freetown, Sierra Leone

Yusuf Sheku Tejan
Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Jacklyne Ashubwe
Roles: Conceptualization, Investigation, Methodology, Resources, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Mher Beglaryan
Roles: Data Curation, Formal Analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Shermarke Hassan
Roles: Data Curation, Formal Analysis, Resources, Software, Validation, Writing – Review & Editing

Sartie Kenneh
Roles: Validation, Writing – Review & Editing

Francis Moses
Roles: Validation, Writing – Review & Editing

Abdulai Tejan Jalloh
Roles: Validation, Writing – Review & Editing

Fassou Mathias Grovogui
Roles: Validation, Writing – Review & Editing

Ibrahima Kaba
Roles: Validation, Writing – Review & Editing

Sia Morenike Tengbe
Roles: Validation, Writing – Review & Editing

Mustapha Kabba
Roles: Validation, Writing – Review & Editing

Mamud Idriss Kamara
Roles: Validation, Writing – Review & Editing

Santigie Sesay
Roles: Validation, Writing – Review & Editing

Jonta Kamara
Roles: Validation, Writing – Review & Editing

Jerry-Jonas Mbasha
Roles: Validation, Writing – Review & Editing

Pryanka Relan
Roles: Validation, Writing – Review & Editing

Innocent Nuwagira
Roles: Validation, Writing – Review & Editing

Ibrahim Franklyn Kamara
Roles: Conceptualization, Funding Acquisition, Investigation, Methodology, Resources, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

OPEN PEER REVIEW

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This article is included in the TDR: Ebola and Emerging Infections in West and Central Africa collection.

Abstract

Background

Due to occupational exposure, healthcare workers (HCWs) have a higher risk of Coronavirus Disease 2019(COVID-19) infection than the general population. Non-communicable diseases (NCDs) may increase the risk of COVID-19-related morbidity and mortality among HCWs, potentially reducing the available health workforce. We examined the association between NCDs and COVID-19 disease severity and mortality among infected HCWs.

Methods

This cohort study used data from the International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC) database. HCWs hospitalized between January 2020 and January 2023 due to clinically suspected or laboratory-confirmed COVID-19 were eligible for inclusion. Variables collected included demographic data, comorbidities, and hospitalization outcomes. Descriptive statistics were reported using mean/standard deviation (SD), median/interquartile range (IQR), or frequencies and proportions. For each NCD, the relative risk of death, adjusted for age and sex, was calculated using log-binomial regression as well as the population-attributable fraction.

Results

There were 17,502 HCWs, 95.7% of whom had a confirmed COVID-19 diagnosis. The majority were female (66.5%) and the mean age (SD) was 49.8 (14.3) years. Roughly, half (51.42%) of HCWs had no comorbidities, 29.28% had one comorbidity, 14.68% had 2 comorbidities and <5% had ≥3 comorbidities. The most common comorbidities were diabetes mellitus (49.40%) and cardiovascular disease (36.90%). Approximately one-fifth of the HCWs had severe COVID-19 (16.95%) and 10.68% of the HCWs with COVID-19 died. Being ≥45 years old, male gender, smoking, obesity, and certain NCDs increased the risk of COVID-19 severity and mortality. Obesity and diabetes mellitus were the leading risk factors in terms of the population-attributable risk for COVID-19 severity (6.89%) and mortality (36.00%) respectively.

Conclusions

Many HCWs with COVID-19 had one or more NCDs. Obesity and diabetes mellitus increased COVID-19 severity and mortality risk. Reducing the prevalence of obesity and diabetes mellitus would yield the biggest reduction in COVID-19-related morbidity and mortality among HCWs.

Keywords

COVID-19, Healthcare workers, Noncommunicable diseases, Mortality, Disease severity, SORT IT

Corresponding author: Yusuf Sheku Tejan

Competing interests: No competing interests were disclosed.

Grant information: This SORT IT Programme was funded by the Special Programme for Research and Training in Tropical Diseases (TDR), Geneva, Switzerland (Grant Number HQTDR 2422924-4.1-72863. The APC was also funded by TDR. TDR is able to conduct its work thanks to the commitment and support from a variety of funders. A full list of TDR donors is available at: https://tdr.who.int/about-us/our-donors.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2024 World Health Organisation. This is an open access article distributed under the terms of the Creative Commons Attribution IGO License, which permits copying, adaptation and distribution in any medium or format for any purpose, provided the original work is properly cited, a link is provided to the license, and any changes made are indicated. Any such copying, adaptation and distribution must not in any way suggest that World Health Organisation endorses you or your use.

How to cite: Tejan YS, Ashubwe J, Beglaryan M et al. The association between non-communicable diseases and COVID-19 severity and mortality among infected hospitalized healthcare workers in 29 countries: a cohort study [version 1; peer review: 1 approved with reservations]. F1000Research 2024, 13:624 (https://doi.org/10.12688/f1000research.150838.1) First published: 12 Jun 2024, 13:624 (https://doi.org/10.12688/f1000research.150838.1) Latest published: 11 Jun 2025, 13:624 (https://doi.org/10.12688/f1000research.150838.2)

Introduction

The world was grappling with the triple burden of communicable and non-communicable diseases (NCDs) and injuries, amplified by climate change effects, when it was struck by the Coronavirus Disease (COVID-19) pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus- 2 (SARS-CoV-2) virus.¹^–⁵ In December 2019, the first case of COVID-19 was detected in Wuhan China. Within a short period the disease had spread to all regions of the world which compelled the World Health Organization (WHO) to declare it a public health emergency of international concern (PHEIC) in January 2020 and characterize it as a pandemic in March 2020 (WHO - COVID-19). This novel virus continued to evolve and spread with emerging strains accounting for increased risk of transmission and different degrees of disease severity within the community and in healthcare settings.⁶

Healthcare workers (HCWs) bore the brunt of the pandemic through occupational exposure as they cared for COVID-19 patients.⁷ There is a growing body of evidence showing that HCWs were at higher risk of contracting the virus compared to the general population, as they cared for COVID-19 patients.⁸^,⁹ The resultant negative physical and mental impacts of the disease on HCWs are well-documented.¹⁰^,¹¹

HCWs serving in low-and-middle-income countries (LMICs), whose health systems are plagued by resource constraints, were more likely to be adversely affected due to gaps in infection prevention and control (IPC) mechanisms.¹¹^–¹³ A recent study conducted in Sierra Leone documented a high (29%) COVID-19 secondary infection rate among HCWs at three regional hospitals.¹⁴ Additionally, two other studies reported that HCWs experienced increased psychological stress including anxiety, isolation, fear, and being overwhelmed particularly in the first three months of the pandemic.¹⁵^,¹⁶

Furthermore, a recent systematic review documented geographical variations in COVID-19-related case fatality rates among HCWs.¹⁷ The highest mortality was seen in the Eastern Mediterranean (5.7%) followed by Southeast Asia (3.1%), Africa (1.2%), and Europe (0.6%).¹⁷ These differences in mortality rates might have been due to several factors including age, sex, comorbidities, and health system structures.¹⁸

A country-based study in Egypt indicated that chronic diseases and home-based management were predictors of COVID-19 severity among HCWs.¹⁹ Evidence from a systematic review concurred with these findings that NCDs potentiated the severity of COVID-19 manifestations in the general population.²⁰ These studies highlight the relationship between highly transmissible communicable diseases like COVID-19 and NCDs.

Globally, WHO reports that NCDs cause 41 million deaths annually.²¹ The most common NCDs are cardiovascular diseases including hypertension, diabetes mellitus, chronic obstructive pulmonary diseases, and cancers.²¹ LMICs, previously plagued primarily by infectious diseases, are now seeing an upward trend in NCDs and their attendant disabilities. A recent systematic review indicated that sub-Saharan African countries had seen an increase of 67% in disability-adjusted life years (DALYs) attributable to NCDs between 1990 and 2017.²²

These trends have been associated with an increase in modifiable behavioural factors such as tobacco and harmful alcohol use, low consumption of fruits and vegetables, high intake of salts and sugar, low-fibre diets, obesity, and low physical activity. These behavioural factors are observed both in the general population and among HCWs.²³ Several country-specific studies have documented evidence of risk factors for the development of NCDs among HCWs. A study conducted in Brazil estimated a high overall NCD prevalence (30%) among nurses.²⁴ Another study conducted across several provinces in Zimbabwe documented that half (50%) of HCWs had at least one NCD with hypertension (36%) being the most common.²⁵

NCDs, when present as comorbidities, increase the risk of poor treatment outcomes and mortality. Evidence has shown that patients with pre-existing NCDs who were infected with COVID-19 had an increased risk of severe disease and mortality.⁸^,²⁶ A systematic review documented a two-fold increase in severity and a three-fold mortality risk in COVID-19 patients with underlying diabetes mellitus.²⁶

NCDs may increase the risk of COVID-19-related morbidity and mortality among HCWs, potentially reducing the available health workforce. Few studies have been conducted on the effects of NCDs on COVID-19 among HCWs, who have a higher exposure to the disease. Furthermore, no analysis has been conducted on the association between COVID-19 severity and mortality and risk posed by NCDs such as hypertension, diabetes mellitus, or other NCDs among HCWs from an multinational dataset.

To address this knowledge gap, we conducted a multi-country study among HCWs hospitalized for COVID-19 to (i) describe their demographic attributes and prevalence of NCDs and other risk factors, (ii) examine the association between NCDs and other risk factors and COVID-19 severity and mortality, and (iii) estimate the population-attributable fraction (PAF) of COVID-19 severity and death due to NCDs and other risk factors.

The findings and recommendations from this study may inform policies and strategies for reducing the vulnerability of HCWs in future public health emergencies and promote the resilience of health systems in the face of such shocks.

Methods

This was a cohort study that used secondary data collected in 29 countries spread across different geographical regions. The study period ran from January 2020 to January 2023. Patients’ records were followed up from hospital admission to exit.

General setting

Globally, 60 countries distributed across Africa, Europe, North and South America, Australia, and the Arab and South-East Asian regions reported COVID-19 cases in healthcare facilities as part of the International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC) study. Data collection, aggregation, curation, and harmonisation processes of the ISARIC study have been described previously.²⁷

Specific setting

Twenty-nine countries out of the 60 reporting COVID-19 data into the ISARIC database had reported information on HCWs hospitalized for COVID-19. The reporting countries were Egypt, Gambia, Ghana, Guinea, Libya, Malawi, South Africa, Estonia, Germany, Italy, Ireland, Luxembourg, Poland, Spain, United Kingdom, Argentina, Brazil, Canada, Peru, United States of America, Jordan, Kuwait, Palestine, United Arab Emirates, Australia, Hong Kong, Indonesia, Malaysia, and the Philippines.

Study population

HCWs who were hospitalized with clinically suspected or laboratory-confirmed COVID-19 from the 29 countries that reported information on HCWs to the ISARIC database from January 2020 to January 2023 were eligible for inclusion.

Data collection and variables

Participating sites used the ISARIC-WHO case report form to enter data into a Research Electronic Data Capture (REDCap version 8.11.11, Vanderbilt University, Nashville, TN) database or the local databases before uploading to the Infectious Diseases Data Observatory (IDDO) database (Open Data Kit is a suitable open access alternative). Centrally collated data were wrangled and mapped to the structure and controlled terminologies of the Study Data Tabulation Model version 1.7, Clinical Data Interchange Standards Consortium, Austin, TX) using TrifactaVR software (OpenRefine is a suitable open access alternative). The independent variables were patient demographic information and the presence of NCD comorbidities; disease severity and patient outcomes were the dependent variables.

Operational definitions

Severe COVID-19 was defined as one or more of the following: admission to an intensive care unit (ICU), treatment with invasive mechanical ventilation (IMV), non-invasive ventilation (NIV), high-flow nasal cannulas (HFNC), extra-corporeal membrane oxygenation (ECMO), administration of inotropes and/or vasopressors. Severity was calculated for cases where all the components were not missing. The following comorbidities were assessed; diabetes mellitus (any type), asthma, cardiovascular disease, chronic pulmonary disease (not asthma), rheumatological disorder, chronic kidney disease, malignant neoplasm, chronic neurological disorder, chronic haematological disease, dementia, malnutrition, smoking status and obesity. The data on comorbidities (except obesity) were self-reported. Obesity was defined by the clinicians attending to the patients based on the Body Mass Index of ≥ 30.

Data analysis

Data was extracted from the IDDO database.²⁷ R code was used to create a dataset for analysis (R version 4.3.2; R Core Team (2023). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria). We used Stata v18.0 [StataCorp. 2023. Stata Statistical Software: Release 18. College Station, TX: StataCorp LLC] to prepare the data for analysis.

To estimate the relative risks (RR) of severity and death, we fitted log-binomial regression models (generalized linear models for the binomial family with the log link) for both severity and death outcomes using glm function of R. We omitted predictors with less than 15 outcome events in their exposed category. We adjusted comorbidities’ RR estimates for age and sex. To that end, we fitted a separate model for each comorbidity where predictors were the comorbidity, age, and sex. A P-value of 0.05 was used as the cut-off for statistical significance.

To estimate the proportion of deaths among HCWs that could be attributed to a specific NCD, we calculated the population attributable fraction (PAF). The PAF (and 95%CI) was calculated for comorbidities that were significant in the adjusted analysis and had RR estimates greater than 1. The R package ‘graphPAF’ R package version 2.0.0) was used to calculate age-and sex adjusted PAF estimates.²⁸

Ethics considerations

Ethical approval was obtained via the global IDDO approval from different countries. The WHO Ethics Review Committee (RPC571 and RPC572, Apr 25, 2013) and the local or national Ethics Committees for participating sites approved the Execution of the ISARIC-WHO Clinical Characterization Protocol. Approvals included the South Central—Oxford C Research Ethics Committee for England (Ref. 13/SC/0149), the Scotland A Research Ethics Committee (Ref. 20/SS/0028) for Scotland, and the Human Research Ethics Committee (Medical) at the University of the Witwatersrand in South Africa as part of a national surveillance programme (M160667), which collectively represent most of the data. For patient data collected and used in research, patient consent was waived according to local norms determined by the responsible Ethics Committee. The IDDO governance processes covered the arrangements surrounding the pooling, storage, curation, and sharing of these data.

Results

Sociodemographic characteristics of healthcare workers admitted for COVID-19

A total of 841,640 records of patients admitted with COVID-19 were stored in the IDDO platform. Patients were hospitalized between January 2020 and January 2023. Of these, 17,502 (2.08%) were HCWs. Among these HCWs, a COVID-19 diagnosis was confirmed for 16,750 (95.70%). Of the 16,750 HCWs hospitalized with confirmed COVID-19, the majority (11,130, 66.45%) were females, and the mean age (SD) was 49.78 (14.33). Nearly all HCWs in this study were hospitalized in either South Africa or the United Kingdom (11,229, 67.04% and 4,782, 28.55% respectively) (Table 1, Figure 1).

Table 1. Demographic characteristics and comorbidities and other risk factors among healthcare workers hospitalized for COVID-19 from 2020 to 2023 in 29 countries.

Variables	N	%
Total number of COVID-19 patients	841,640
Total number of HCWs with COVID-19	16750	1.99
Age in years
15-44	6,032	36.01
45-64	8,295	49.52
≥65	2,275	13.58
Unknown	148	0.88
Sex
Female	11,130	66.45
Male	5,609	33.49
Not specified/Unknown	11	0.07
Countries
South Africa	11,229	67.04
United Kingdom	4,782	28.55
United States of America	222	1.33
Ghana	99	0.59
Brazil	77	0.46
Guinea	76	0.45
Canada	56	0.33
Philippines	31	0.19
Egypt	25	0.15
Ireland	25	0.15
Malaysia	23	0.14
Kuwait	20	0.12
Italy	12	0.07
Spain	11	0.07
Malawi	10	0.06
Libya	8	0.05
Gambia	7	0.04
Jordan	7	0.04
Luxembourg	7	0.04
Argentina	5	0.03
Poland	4	0.02
United Arab Emirates	4	0.02
Estonia	2	0.01
Palestine	2	0.01
Peru	2	0.01
Australia	1	0.01
Germany	1	0.01
Hong Kong	1	0.01
Indonesia	1	0.01
Number of comorbidities
0 Comorbidities	8,613	51.42
1 comorbidity	4,905	29.28
2 comorbidities	2,459	14.68
≥3 comorbidities	773	4.61
Types of comorbidities*
Cardiovascular disease	5,197	36.90
Diabetes mellitus	3,308	49.40
Asthma	1,379	9.80
Chronic pulmonary disease (not asthma)	346	2.50
Rheumatological disorder	293	6.50
Chronic kidney disease	248	1.80
Malignant neoplasm	209	1.50
Chronic neurological disorder	150	3.10
Chronic haematological disease	78	1.70
Dementia	62	1.40
Malnutrition	25	0.50
Current smoker
No	3,815	22.78
Yes	1,056	6.30
Unknown	11,879	70.92
Obesity
No	5,340	31.88
Yes	1,478	8.82
Unknown	9,932	59.30

* Percentages of comorbidities are calculated among non-missing entries.

Figure 1. Distribution of healthcare workers hospitalized for COVID-19 from 2020 to 2023 across countries.

Healthcare workers with different comorbidities

At the time of admission, half (8,613, 51.42%) of the HCWs had no comorbidities, more than a quarter (4,905, 29.28%) had one comorbidity, 2,459 (14.68%) had 2 comorbidities, and a small proportion had more than 3 comorbidities (773, 4.61%). The most common comorbidities were diabetes mellitus (3,308, 49.40%), cardiovascular disease (5,197, 36.9%), asthma (1,379, 9.80%), and chronic pulmonary disease (not asthma) (346, 2.50%). About a tenth of the HCWs were obese (1,478, 8.82%) (Table 1). Percentages of comorbidities were calculated among entries that had no missing data.

COVID-19 severity and hospitalization outcomes of healthcare workers

2,839 (16.95%) of the HCWs suffered from severe COVID-19 during their period of hospitalization. Less than a quarter (2800, 16.72%) were admitted to the ICU, nearly one quarter (4,012, 23.95%) of the HCWs received oxygen via HFNC and less than one-tenth (1,293, 7.72%) received oxygen via IMV. The proportion of HCWs who died from COVID-19 was 10.68% (1,789) (Table 2).

Table 2. COVID-19 severity and hospitalization outcomes of healthcare workers.

Variables	N	(%)
Severe COVID-19
No	5,414	32.32
Yes	2,839	16.95
Unknown	8,497	50.73
ICU admission
No	13,730	81.97
Yes	2,800	16.72
Unknown	220	1.31
High-flow oxygen nasal cannulas
No	12,120	72.36
Yes	4,012	23.95
Unknown	618	3.69
Non-invasive ventilation
No	7,328	43.75
Yes	1,183	7.06
Unknown	8,239	49.19
Invasive mechanical ventilation
No	15,226	90.90
Yes	1,293	7.72
Unknown	231	1.38
Extracorporeal membrane oxygenation
No	16,421	98.04
Yes	77	0.46
Unknown	252	1.50
Inotropes, vasopressors
No	7,807	46.61
Yes	499	2.98
Unknown	8,444	50.41
Patient outcomes
Survived	14,885	88.87
Death	1,789	10.68
Unknown	76	0.45

Factors associated with COVID-19 severity and deaths

After adjustment for age and sex, the following factors were significantly associated with severe COVID-19. Age range 45-64 years (RR, 1.91; 95% CI, 1.76-2.07), age ≥ 65 years (RR, 2.02; 95% CI, 1.78-2.27), male gender (RR, 1.52; 95% CI, 1.42-1.62), obesity (aRR, 1.31; 95% CI, 1.21-1.41), smoking (aRR, 1.11; 95% CI, 1.01-1.22), asthma (aRR, 1.24; 95% CI, 1.14-1.35), cardiovascular disease (aRR, 1.14; 95% CI, 1.06-1.23), diabetes mellitus (aRR, 1.00; 95% CI, 0.93-1.08) and chronic pulmonary disease (not asthma) (aRR, 1.22; 95% CI, 1.04-1.41) (Table 3).

Table 3. Relative risks and population attributable fractions (as percents) of COVID-19 severity due to non-communicable diseases among hospitalized health care workers in 29 countries from 2020 to 2023.

		Total (N =7858)		Severe		Adjusted RR^† (95%CI)		PAF %^†† (95%CI)
		n	(%)	n	(%)	Adjusted RR^† (95%CI)		PAF %^†† (95%CI)
Age in years
	15-44	3055	(38.88)	607	(19.87)	Reference
	45-64	4084	(51.97)	1547	(37.88)	1.91***	(1.76-2.07)	NA
	≥65	602	(7.66)	241	(40.03)	2.02***	(1.78-2.27)
Sex
	Female	5585	(71.07)	1511	(27.05)	Reference
	Male	2264	(28.81)	931	(41.12)	1.52***	(1.42-1.62)	NA
Obesity
	No	4222	(53.73)	1348	(31.93)	Reference
	Yes	1371	(17.45)	560	(40.85)	1.31***	(1.21-1.41)	6.89	(4.65-9.22)
Current smoker
	No	3199	(40.71)	1025	(32.04)	Reference
	Yes	985	(12.53)	380	(38.58)	1.11*	(1.01-1.22)	2.61	(-0.13-5.09)
Comorbidities
Asthma	No	5426	(69.05)	1756	(32.36)	Reference
	Yes	862	(10.97)	343	(39.79)	1.24***	(1.14-1.35)	3.21	(1.75-4.55)
Cardiovascular disease	No	4170	(53.07)	1236	(29.64)	Reference
	Yes	2275	(28.95)	914	(40.18)	1.14***	(1.06-1.23)	5.29	(2.07-8.12)
Diabetes mellitus	No	3220	(40.98)	1240	(38.51)	Reference
	Yes	1392	(17.71)	583	(41.88)	1.00	(0.93-1.08)	0.08	(-2.59-2.91)
Chronic haematological disease	No	4032	(51.31)	1641	(40.70)	Reference
	Yes	71	(0.90)	28	(39.44)	0.94	(0.67-1.22)	NA
Chronic kidney disease	No	6015	(76.55)	2019	(33.57)	Reference
	Yes	184	(2.34)	60	(32.61)	0.82	(0.65-1.01)	NA
Malignant neoplasm	No	6011	(76.50)	2022	(33.64)	Reference
	Yes	160	(2.04)	52	(32.50)	0.84	(0.66-1.04)	NA
Chronic neurological disorder	No	3975	(50.59)	1626	(40.91)	Reference
	Yes	132	(1.68)	45	(34.09)	0.81	(0.63-1.01)	NA
Chronic pulmonary disease (not asthma)	No	5982	(76.13)	1984	(33.17)	Reference
	Yes	212	(2.70)	97	(45.75)	1.22**	(1.04-1.41)	0.84	(0.01-1.51)
Rheumatological disorder	No	3826	(48.69)	1546	(40.41)	Reference
	Yes	281	(3.58)	123	(43.77)	1.02	(0.89-1.17)	0.17	(-0.84-1.1)

† RR for age and sex are not adjusted.

†† Population attributable fraction as percent.

* p-value < 0.05.

** p-value < 0.01.

*** p-value < 0.001.

After adjustment for age and sex, the following factors were significantly associated with mortality from COVID-19. Age range 45-64 years (RR, 3.42; 95% CI, 2.95-3.97), age ≥ 65 years (RR, 8.45; 95% CI, 7.28-9.85), male gender (RR, 1.73; 95% CI, 1.59-1.89), obesity (aRR, 1.34; 95% CI, 1.13-1.57), smoking (aRR, 1.09; 95% CI, 0.88-1.36), dementia (aRR, 1.33; 95% CI, 0.87-1.89), diabetes mellitus (aRR, 2.00; 95% CI, 1.73-2.33), chronic haematological disease (aRR, 1.48; 95% CI, 0.80-2.29), cardiovascular disease (aRR, 1.32; 95% CI, 1.20-1.44), chronic kidney disease (CKD) (aRR, 1.25; 95% CI, 0.98-1.54), malignant neoplasm (aRR, 1.34; 95% CI, 1.04-1.68), chronic neurological disorder (aRR, 1.07; 95% CI, 0.66-1.58), chronic pulmonary disease (not asthma) (aRR, 1.21; 95% CI, 0.97-1.46) and rheumatological disorders (aRR, 1.39; 95% CI, 1.01-1.84) (Table 4).

Table 4. Relative risks and population attributable fractions (as percents) of COVID-19 death due to non-communicable diseases among hospitalized health care workers in 29 countries from 2020 to 2023.

Risks		Total (N =16674)		Death		Adjusted RR^† (95%CI)		PAF %^†† (95%CI)
		n	(%)	n	(%)	Adjusted RR^† (95%CI)		PAF %^†† (95%CI)
Age in years
	15-44	6002	(36.00)	201	(3.35)	Reference
	45-64	8271	(49.60)	946	(11.44)	3.42***	(2.95-3.97)
	>=65	2258	(13.54)	639	(28.30)	8.45***	(7.28-9.85)
Sex
	Female	11080	(66.45)	956	(8.63)	Reference
	Male	5583	(33.48)	833	(14.92)	1.73***	(1.59-1.89)
Obesity
	No	5306	(31.82)	458	(8.63)	Reference
	Yes	1473	(8.83)	160	(10.86)	1.34***	(1.13-1.57)	6.41	(2.74-10.5)
Current smoker
	No	3797	(22.77)	244	(6.43)	Reference
	Yes	1043	(6.26)	100	(9.59)	1.09	(0.88-1.36)	2.53	(-4-9.44)
Comorbidities
Asthma	No	12578	(75.43)	1509	(12.00)	Reference
	Yes	1371	(8.22)	114	(8.32)	0.78**	(0.65-0.93)
Cardiovascular disease	No	8856	(53.11)	775	(8.75)	Reference
	Yes	5173	(31.02)	884	(17.09)	1.32***	(1.20-1.44)	12.70	(8.68-16.5)
Dementia	No	4425	(26.54)	324	(7.32)	Reference
	Yes	61	(0.37)	19	(31.15)	1.33	(0.87-1.89)	1.32	(-0.60-2.92)
Diabetes mellitus	No	3366	(20.19)	224	(6.65)	Reference
	Yes	3292	(19.74)	587	(17.83)	2.00***	(1.73-2.33)	36.00	(28.8-44.5)
Chronic kidney disease	No	13608	(81.61)	1564	(11.49)	Reference
	Yes	246	(1.48)	55	(22.36)	1.25	(0.98-1.54)	0.67	(0.18-1.49)
Malignant neoplasm	No	13617	(81.67)	1574	(11.56)	Reference
	Yes	209	(1.25)	47	(22.49)	1.34*	(1.04-1.68)	0.71	(0.06-1.28)
Chronic neurological disorder	No	4602	(27.60)	338	(7.34)	Reference
	Yes	149	(0.89)	17	(11.41)	1.07	(0.66-1.58)	0.30	(-1.81-2.27)
Chronic pulmonary disease (not asthma)	No	13509	(81.02)	1553	(11.50)	Reference
	Yes	342	(2.05)	69	(20.18)	1.21	(0.97-1.46)	0.72	(-0.24-1.48)
Rheumatological disorder	No	4172	(25.02)	304	(7.29)	Reference
	Yes	290	(1.74)	38	(13.10)	1.39*	(1.01-1.84)	3.23	(0.19-6.3)

† RR for age and sex are not adjusted.

†† Population attributable fraction as percent.

* p-value < 0.05.

** p-value < 0.01.

*** p-value < 0.001.

Population attributable fraction (PAF)

Obesity (PAF, 6.89%; 95% CI, 4.65-9.22) and cardiovascular disease (PAF,5.29%; 95% CI, 2.07-8.12) were found to have a relatively higher population-attributable risk for COVID-19 severity compared to the other factors (Table 3). Moreover, diabetes mellitus had the highest population-attributable risk for the mortality associated with COVID-19 (PAF, 36.00%; 95% CI, 28.8-44.5) (Table 4). The PAF for COVID-19 mortality associated with cardiovascular disease was also high, calculated at 12.70% (95% CI, 8.68-16.5).

Under the assumption of a causal relationship between the exposure and, severity and mortality outcomes, we estimated that approximately 6.89% of severe COVID-19 cases and 36.00% of COVID-19-related deaths could potentially be averted if obesity and diabetes mellitus respectively were eliminated as risk factors.

Discussion

Our study is the most comprehensive multi-country study to evaluate the association between NCDs and COVID-19 severity and deaths. The findings add to the global body of evidence on the interaction between NCDs and COVID-19. About half of the HCWs had comorbidities at the time of admission, with the most common comorbidities being hypertension, diabetes mellitus, asthma, cardiac disease, and chronic pulmonary diseases (not asthma). A little over 50% of the HCWs suffered from severe COVID-19 during the period of hospitalization and roughly 11% died. Under the assumption of a causal relationship between the exposure and severity and mortality outcomes, we estimated that approximately 6.89% (95% CI, 4.65-9.22) of severe COVID-19 cases and 36.00% (95% CI, 28.8-44.5) of COVID-19-related deaths could potentially be averted if obesity and diabetes mellitus respectively were eliminated as risk factors.

Evidence from our study showed that COVID-19 affected more female HCWs than males in terms of hospitalization. Other studies have documented similar findings to ours by demonstrating female predominance of HCWs admitted for COVID-19.¹⁷^,²⁹ A study conducted in Iran recorded a higher COVID-19 prevalence among female HCWs (53.5%) compared to males (46.5%), despite having a higher infection prevalence among males (56.4%) in the general population.²⁹ The reasons for more COVID-19 infection among female HCWs might be due to women being overrepresented in patient-facing healthcare roles such as nursing. Furthermore, women tend to have better health-seeking behaviour than men.³⁰ However, our finding has highlighted the need to explore more robust gender-focused IPC strategies to reduce this HCW vulnerability during infectious disease outbreaks.

Our study identified that about half (49.58%) of HCWs had at least one comorbidity at the time of admission. In keeping with our findings, other studies have also demonstrated the presence of at least one comorbidity and associated risk factors in half of the HCWs.²⁵^,³¹ Furthermore, a 2021 systematic review examining the risk factors for SARS-CoV-2 infection among HCWs found an overall NCD prevalence of 18.4%; hypertension contributed the largest proportion (2.5%), followed by cardiovascular diseases (2.4%), chronic obstructive pulmonary disease (2.4%), and diabetes mellitus (1.4%).³² However, this review did not examine an association between the NCDs and COVID-19 severity and mortality. It is clear that the burden of NCDs is high among HCWs as documented by the present available evidence including our study. NCD prevention and treatment should be prioritized among HCWs.

Evidence from our study indicated that there was an increased likelihood of the HCWs suffering from severe COVID-19 with older age (≥ 45 years), smoking, obesity, and being male. Additionally, we observed that asthma, cardiovascular diseases, and diabetes mellitus were positive predictors of COVID-19 severity. A 2021 study by Joo et al. documented an increased risk for severe COVID-19 in Malaysian HCWs with underlying comorbidities.³³

When HCWs are vulnerable to adverse health outcomes during an infectious disease outbreak it weakens the health systems’ response to and recovery from public health emergencies. This significant threat to the resilience of health systems highlights the imperative need for strategies to mitigate against the vulnerability of HCWs.³⁴

Specific risk factors were identified in our study that increased the predisposition for mortality from COVID-19 among the HCWs with COVID-19. These included being older than 45 years, male gender, obesity, and smoking. A WHO study aimed at estimating the impact of COVID-19 on HCWs also highlighted higher mortality (60%) among male HCWs.³⁵ Moreover, from a 2021 systematic review older age, male gender, and obesity heightened the risk of COVID-19 severity and mortality in the general population.³⁶ The study on all the COVID-19 patient data in the ISARIC database documented that obesity led to a 24% increased risk for mortality.³⁷ Looking at the increasing global burden of obesity, HCWs should embark on lifestyle modification activities that will avert the development of obesity.

Our study also found that cardiovascular disease, dementia, diabetes mellitus, chronic haematological disease, chronic kidney disease, chronic pulmonary diseases (not asthma), malignant neoplasm, chronic neurological disorder, and rheumatological disorders significantly increased the risk of COVID-19 deaths. This finding concurred with other studies which showed a higher mortality risk for COVID-19 patients with concurrent cardiac disease and diabetes mellitus.²⁶^,³⁸

In our analysis in terms of the PAF, obesity emerged as the leading risk factor for disease severity and diabetes mellitus as the leading risk factor for mortality associated with COVID-19. The PAF of 6.89% and 36.00% for severity and mortality respectively underscores the substantial impact that such modifiable factors could have on reducing COVID-19 severity and mortality rates. Similarly, a systematic review conducted in 2020 highlighted that the PAF for COVID-19 severity for obesity was 7.1% and that for diabetes mellitus within the general population was 6.5%.²⁰ By focusing on interventions aimed at reducing the incidence of obesity and diabetes mellitus, public health authorities can potentially achieve a dual benefit of mitigating the immediate impact of COVID-19 and enhancing the overall resilience of the HCWs against future infectious disease outbreaks and other health threats.

The findings from this study have several important implications. Due to the risks posed by NCDs to HCWs, we recommend the establishment of effective occupational health and NCD prevention, screening, and treatment programmes for HCWs. Investing in lifestyle interventions for the prevention of NCDs and associated risk factors particularly obesity and diabetes mellitus amongst HCWs to reduce NCD burden will generate dividends in reducing their vulnerability during infectious disease outbreaks. Additionally, based on the increased gender-related risk of COVID-19 among HCWs, health stakeholders need to explore gender-focused IPC advocacy among HCWs.

Our study had several strengths. First, we utilized a dataset collected from the largest cohort of hospitalized COVID-19 patients from 29 countries across 7 regions. This large sample size and the broad distribution of the HCW population give credence to the emerging evidence. This large and geographically broad sample also reduces biases that might be related to country-level data reporting which might underreport cases³⁵^,³⁹ and thus weaken the validity of the evidence. Second, we adhered to Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for reporting study findings. Third, we conducted a robust statistical analysis to generate the population-attributable fraction which will support the extrapolation of our study findings beyond our study population.

Despite the strengths, our study had some limitations. The majority of the patient information in the ISARIC database was from South Africa and the United Kingdom, whereas the other countries reported limited data. Since reporting to the ISARIC database was voluntary, the reasons for limited data from the other countries remain unknown. However, this underlying reason needs further exploration to develop solutions that will improve data sharing by countries and increase the robustness and geographic generalisability of the database. Based on this limitation, our findings should be generalized with caution. Second, the high number of missing values for some of the comorbidities limited the statistical power concerning some of the comorbidities.

Conclusions

Our study revealed that many of the HCWs at the frontline of managing COVID-19 had NCDs which increased their vulnerability in the face of infectious disease outbreaks. One fifth of the admitted HCWs suffered from severe COVID-19 and one-tenth of HCWs died due to COVID-19. Additionally, HCWs who were ≥45 years old, male, smokers, and obese had a higher risk of increased severity and/or dying from COVID-19 disease. We recommend the implementation of health education and promotion activities to reduce the NCD burden among HCWs to reduce their vulnerability during infectious disease outbreaks.

Data availability

Underlying data

The data that underpin this analysis are available via a governed data access mechanism following a review of a data access committee. Data can be requested via the IDDO COVID-19 Data Sharing Platform (http://www.iddo.org/covid-19). The Data Access Application, Terms of Access and details of the Data Access Committee are available on the website. Briefly, the requirements for access are a request from a qualified researcher working with a legal entity who have a health and/or research remit; a scientifically valid reason for data access which adheres to appropriate ethical principles, and has plans to promote equity in the use of data. The full terms are at: https://www.iddo.org/ebola/data-access-guidelines. These data are a part of https://doi.org/10.48688/cpwp-ft84.

Reporting guidelines

This article complied with the STROBE guidelines for reporting observational studies.

Acknowledgments

This research was conducted through the Structured Operational Research and Training Initiative (SORT IT), a global partnership led by TDR, the Special Programme for Research and Training in Tropical Diseases hosted at the World Health Organization. The specific SORT IT program that led to this publication is a SORT IT partnership with the WHO Emergency Medical Teams (Geneva), WHO-AFRO (Brazzaville), WHO Country Offices and Ministries of Health of Guinea, Liberia, Sierra Leone, and the Democratic Republic of the Congo, the Infectious Diseases Data Repository (IDDO); The International Union Against Tuberculosis and Lung Diseases, Paris, France and South East Asia offices, Delhi, India; The Tuberculosis Research and Prevention Center Non-Governmental Organization, Yerevan, Armenia; I-Tech, Lilongwe, Malawi; Medwise solutions, Nairobi, Kenya; All India Institute of Medical Sciences, Hyderabad, India; and the National Training and Research Centre in Rural Health, Maferinyah, Guinea.

There should be no suggestion that the WHO endorses any specific organization, products or services. The views expressed in this article are those of the authors and do not necessarily reflect those of their affiliated institutions. The use of the WHO logo is not permitted.

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Version 2

VERSION 2 PUBLISHED 12 Jun 2024

Author details Author details

Jacklyne Ashubwe
Roles: Conceptualization, Investigation, Methodology, Resources, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Mher Beglaryan
Roles: Data Curation, Formal Analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Shermarke Hassan
Roles: Data Curation, Formal Analysis, Resources, Software, Validation, Writing – Review & Editing

Sartie Kenneh
Roles: Validation, Writing – Review & Editing

Francis Moses
Roles: Validation, Writing – Review & Editing

Abdulai Tejan Jalloh
Roles: Validation, Writing – Review & Editing

Fassou Mathias Grovogui
Roles: Validation, Writing – Review & Editing

Ibrahima Kaba
Roles: Validation, Writing – Review & Editing

Sia Morenike Tengbe
Roles: Validation, Writing – Review & Editing

Mustapha Kabba
Roles: Validation, Writing – Review & Editing

Mamud Idriss Kamara
Roles: Validation, Writing – Review & Editing

Santigie Sesay
Roles: Validation, Writing – Review & Editing

Jonta Kamara
Roles: Validation, Writing – Review & Editing

Jerry-Jonas Mbasha
Roles: Validation, Writing – Review & Editing

Pryanka Relan
Roles: Validation, Writing – Review & Editing

Innocent Nuwagira
Roles: Validation, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

This SORT IT Programme was funded by the Special Programme for Research and Training in Tropical Diseases (TDR), Geneva, Switzerland (Grant Number HQTDR 2422924-4.1-72863. The APC was also funded by TDR. TDR is able to conduct its work thanks to the commitment and support from a variety of funders. A full list of TDR donors is available at: https://tdr.who.int/about-us/our-donors.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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https://doi.org/10.12688/f1000research.150838.2

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Tejan YS, Ashubwe J, Beglaryan M et al. The association between non-communicable diseases and COVID-19 severity and mortality among infected hospitalized healthcare workers in 29 countries: a cohort study [version 1; peer review: 1 approved with reservations]. F1000Research 2024, 13:624 (https://doi.org/10.12688/f1000research.150838.1)

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Reviewer Report 25 Sep 2024

Max Carlos Ramírez-Soto, Centro Nacional de Salud Puública, Universidad de San Martín de Porres, Lima, Peru

Approved with Reservations

https://doi.org/10.5256/f1000research.165440.r317489

In this study they evaluate risk factors for COVID-19 in health care workers. Their findings reveal that age, sex, diabetes and cardiovascular disease are risk factors for severity and death. These risk factors have been studied extensively, in the general population and in different risk groups. Although the study has interesting data, further analysis is needed to prove the association described above.

Major comments:
Methods.
To check for compliance with the STROBE guideline items.
Statistical analysis. What assumptions were used to calculate the RR.
Regarding the number of comorbidities, 2 or more should define multi-morbidity.

Results
They should include a flow chart with information on included and excluded cases with their respective reasons.
Table 1 and 2. There are many unknowns, e.g., Current smoker, obesity, severity, Non-invasive ventilation, Inotropes, vasopressors, and High-flow oxygen nasal cannulas.
Table 3. The authors should revise their analyses. This table includes data from 7858 patients, however in the comparisons of variables, none of the sums of yes and no variables match.
Table 4. They do not include ratios because they include the population 16674.

The authors included the population between January 2020 and January 2023. Here, my main concern is that from 2021 onwards health workers were the first at-risk group to be vaccinated, but the authors did not include vaccination as an adjustment variable. Also, the study does not assess, nor does it compare variables before vaccination and after vaccination. It is likely that the highest number of cases occurred before vaccination (during 2020). For this year alone, a pre-vaccination analysis should be conducted to determine the association with risk factors, and another analysis in the vaccinated population (2021 to 2023), to assess whether the risk of age, sex, cardiovascular disease and diabetes persist in the vaccinated population compared to the unvaccinated.

Discussion and conclusions should be modified based on further analysis.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

No
Are sufficient details of methods and analysis provided to allow replication by others?

No
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

No

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Infectious diseases

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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Report a concern

Author Response 10 Sep 2025

Yusuf Tejan, Ministry of Health, 4th Floor, Youyi Building, Freetown, Sierra Leone

10 Sep 2025

Author Response
Thank you for the good summary of our study aim and findings. We take note of your recommendation for further analysis to strengthen the association that our study demonstrated.

... Continue reading
Thank you for the good summary of our study aim and findings. We take note of your recommendation for further analysis to strengthen the association that our study demonstrated.

A. Methods.
1. To check for compliance with the STROBE guideline items.
Response:
Thank you for this comment. We have ensured alignment with the STROBE guidelines as outlined below:

We described this to be a “cohort study design”

We have described the general setting as “the International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC) study database, whereas the specific setting was the 29 specific countries (Egypt, Gambia, Ghana, Guinea, Libya, Malawi, South Africa, Estonia, Germany, Italy, Ireland, Luxembourg, Poland, Spain, United Kingdom, Argentina, Brazil, Canada, Peru, United States of America, Jordan, Kuwait, Palestine, United Arab Emirates, Australia, Hong Kong, Indonesia, Malaysia, and the Philippines)” whose data was extracted from this database.

Additionally, we have stated the study data collection period was from “January 2020 to January 2023; and the follow-up period defined as from hospital admission to exit”.

We have described the study participants and the inclusion criteria as healthcare workers (HCWs) who were hospitalized with clinically suspected or laboratory-confirmed COVID-19 from the 29 countries; the source from which their data was extracted, as “the ISARIC database from January 2020 to January 2023”.

We defined the variables as follows: “The independent variables were patient demographic information and the presence of noncommunicable disease (NCD) comorbidities; while disease severity and patient outcomes were the dependent variables.”

Operational definitions have been provided as follows:

Severe COVID-19 was defined as one or more of the following: admission to an intensive care unit (ICU), treatment with invasive mechanical ventilation (IMV), non-invasive ventilation (NIV), high-flow nasal cannulas (HFNC), extra-corporeal membrane oxygenation (ECMO), administration of inotropes and/or vasopressors

The comorbidities that were assessed; diabetes mellitus (any type), asthma, cardiovascular disease, chronic pulmonary disease (not asthma), rheumatological disorder, chronic kidney disease, malignant neoplasm, chronic neurological disorder, chronic haematological disease, dementia, malnutrition, smoking status and obesity. The data on comorbidities (except obesity) were self-reported.

Obesity was defined by the clinicians attending to the patients based on the Body Mass Index of ≥ 30.

The data source was the Infectious Diseases Data Observatory (IDDO) database and R code was used to create the dataset for analysis. Data was collected using the standardized ISARIC-WHO case report form.

To address potential bias we took the following steps:

We clearly defined the inclusion criteria for the participants to be recruited into the study

We included all the participants who met the inclusion criteria to eliminate selection bias

We clearly defined the participants who were to quality as severe COVID-19 disease to avoid misclassification. The standardized ISARIC-WHO case report form was used for data collection

We clearly defined the period of follow-up (from hospital admission to exit: discharge/ death) so there was no loss to follow-up

R code was used to create the dataset for analysis. Stata version 18 was then used to prepare the data for analysis

For analysis:

To estimate the relative risks (RR) of severity and death, we fitted log-binomial regression models (generalized linear models for the binomial family with the log link) for both severity and death outcomes using glm function of R. We omitted predictors with less than 15 outcome events in their exposed category. We adjusted comorbidities’ RR estimates for age and sex. To that end, we fitted a separate model for each comorbidity where predictors were the comorbidity, age, and sex. A P-value of 0.05 was used as the cut-off for statistical significance.

To estimate the proportion of deaths among HCWs that could be attributed to a specific NCD, we calculated the population attributable fraction (PAF). The PAF (and 95%CI) was calculated for comorbidities that were significant in the adjusted analysis and had RR estimates greater than 1. The R package ‘ graphPAF’ R package version 2.0.0) was used to calculate age-and sex adjusted PAF estimates.

2. Statistical analysis. What assumptions were used to calculate the RR.

Author's response:
Thank you for this comment. We estimated the risk ratio directly from the log binomial regression (as opposed to using a logistic regression and making rare outcome assumption to interpret the odds ratio as approximate risk ratios). We kindly request further clarification/suggestion if we have overlooked any specific assumptions.

3. Regarding the number of comorbidities, 2 or more should define multi-morbidity.
Author’s response:
Thank you for this comment. Table 1 the distribution of co-morbidities is made explicit.

B. Results
1. They should include a flow chart with information on included and excluded cases with their respective reasons.
Author’s response:
Thank you for this. We had a relatively simple criteria adopted. Inclusion required to satisfy: “HCWs who were hospitalized with clinically suspected or laboratory-confirmed COVID-19 from the 29 countries between January 2020 to January 2023” (as reported in the study population section). Based on this criterion, there were a total of 841,640 records of patients admitted with COVID-19 were stored in the IDDO platform from these 29 countries between January 2020 and January 2023. Of these, only 17,502 (2.08%) were HCWs that formed the analysis database. These are presented in the main results in the text.

2. Table 1 and 2. There are many unknowns, e.g., Current smoker, obesity, severity, Non-invasive ventilation, Inotropes, vasopressors, and High-flow oxygen nasal cannulas.

Author’s response:
Thank you for this comment. We have transparently reported all the unknowns and have presented the completed of the information for all the variables included in the analysis across the tables presented in this manuscript. Many unknowns and missingness/completeness of records are major issue and we have clearly acknowledged it as a major limitation in the discussion section:
“Second, the high number of missing values for some of the comorbidities limited the statistical
power concerning some of the comorbidity”

3. Table 3. The authors should revise their analyses. This table includes data from 7858 patients, however in the comparisons of variables, none of the sums of yes and no variables match.

Author’s response:
Thank you for pointing this out. The analysis is subject to completeness of the records and outcomes, a major limitation of the analysis that has been acknowledged in the discussion section. The completeness of the records differed across the variables used and hence they won’t sum to 7858. Since each analysis has slightly different total (other than 7858), we are removing this from the main table to avoid the confusion (see version 2).

4. Table 4. They do not include ratios because they include the population 16674.

Author’s response:
Thank you for this comment. We have presented the adjusted risk ratios (Adjusted RR) in table 4. We kindly request further clarification of this comment.

5. The authors included the population between January 2020 and January 2023. Here, my main concern is that from 2021 onwards health workers were the first at-risk group to be vaccinated, but the authors did not include vaccination as an adjustment variable. Also, the study does not assess, nor does it compare variables before vaccination and after vaccination. It is likely that the highest number of cases occurred before vaccination (during 2020). For this year alone, a pre-vaccination analysis should be conducted to determine the association with risk factors, and another analysis in the vaccinated population (2021 to 2023), to assess whether the risk of age, sex, cardiovascular disease and diabetes persist in the vaccinated population compared to the unvaccinated.

Author’s response:
Thank you very much for raising this important aspect. We did consider this aspect in the initial planning but were not able to rigorously address this for several reasons. First, there was a lack of reliable vaccination information captured in the database. Second, we didn’t have access to the vaccination roll-out period for each of the countries included in the analysis, the availability of vaccination likely differed across the countries. For these reasons, thoroughly assessing the impact of the vaccination in the current analysis was not possible. However, when we present the mortality by calendar year, we do find higher mortality during 2020 and 2021 and much lower in 2022. As the reviewer suspects, this may be due to the vaccination status, however, we cannot attribute this with certainty due to a lack of information regarding the vaccination status

We have acknowledged this as a major limitation in the discussion and have added the following in the limitations section.
“Another limitation is that we were unable to adjust for vaccination status into the analysis as this information was not reliably captured in the database. The case fatality was found to higher during 2020 and 2021 and much lower in 2022 (data not shown), but we weren’t able to attribute this to vaccination status due to lack of information regarding this variable”

Please see new version for further review. Thank you for your review.
Thank you for the good summary of our study aim and findings. We take note of your recommendation for further analysis to strengthen the association that our study demonstrated.

A. Methods.
1. To check for compliance with the STROBE guideline items.
Response:
Thank you for this comment. We have ensured alignment with the STROBE guidelines as outlined below:

We described this to be a “cohort study design”

We have described the general setting as “the International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC) study database, whereas the specific setting was the 29 specific countries (Egypt, Gambia, Ghana, Guinea, Libya, Malawi, South Africa, Estonia, Germany, Italy, Ireland, Luxembourg, Poland, Spain, United Kingdom, Argentina, Brazil, Canada, Peru, United States of America, Jordan, Kuwait, Palestine, United Arab Emirates, Australia, Hong Kong, Indonesia, Malaysia, and the Philippines)” whose data was extracted from this database.

Additionally, we have stated the study data collection period was from “January 2020 to January 2023; and the follow-up period defined as from hospital admission to exit”.

We have described the study participants and the inclusion criteria as healthcare workers (HCWs) who were hospitalized with clinically suspected or laboratory-confirmed COVID-19 from the 29 countries; the source from which their data was extracted, as “the ISARIC database from January 2020 to January 2023”.

We defined the variables as follows: “The independent variables were patient demographic information and the presence of noncommunicable disease (NCD) comorbidities; while disease severity and patient outcomes were the dependent variables.”

Operational definitions have been provided as follows:

Severe COVID-19 was defined as one or more of the following: admission to an intensive care unit (ICU), treatment with invasive mechanical ventilation (IMV), non-invasive ventilation (NIV), high-flow nasal cannulas (HFNC), extra-corporeal membrane oxygenation (ECMO), administration of inotropes and/or vasopressors

The comorbidities that were assessed; diabetes mellitus (any type), asthma, cardiovascular disease, chronic pulmonary disease (not asthma), rheumatological disorder, chronic kidney disease, malignant neoplasm, chronic neurological disorder, chronic haematological disease, dementia, malnutrition, smoking status and obesity. The data on comorbidities (except obesity) were self-reported.

Obesity was defined by the clinicians attending to the patients based on the Body Mass Index of ≥ 30.

The data source was the Infectious Diseases Data Observatory (IDDO) database and R code was used to create the dataset for analysis. Data was collected using the standardized ISARIC-WHO case report form.

To address potential bias we took the following steps:

We clearly defined the inclusion criteria for the participants to be recruited into the study

We included all the participants who met the inclusion criteria to eliminate selection bias

We clearly defined the participants who were to quality as severe COVID-19 disease to avoid misclassification. The standardized ISARIC-WHO case report form was used for data collection

We clearly defined the period of follow-up (from hospital admission to exit: discharge/ death) so there was no loss to follow-up

R code was used to create the dataset for analysis. Stata version 18 was then used to prepare the data for analysis

For analysis:

To estimate the relative risks (RR) of severity and death, we fitted log-binomial regression models (generalized linear models for the binomial family with the log link) for both severity and death outcomes using glm function of R. We omitted predictors with less than 15 outcome events in their exposed category. We adjusted comorbidities’ RR estimates for age and sex. To that end, we fitted a separate model for each comorbidity where predictors were the comorbidity, age, and sex. A P-value of 0.05 was used as the cut-off for statistical significance.

To estimate the proportion of deaths among HCWs that could be attributed to a specific NCD, we calculated the population attributable fraction (PAF). The PAF (and 95%CI) was calculated for comorbidities that were significant in the adjusted analysis and had RR estimates greater than 1. The R package ‘ graphPAF’ R package version 2.0.0) was used to calculate age-and sex adjusted PAF estimates.

2. Statistical analysis. What assumptions were used to calculate the RR.

Author's response:
Thank you for this comment. We estimated the risk ratio directly from the log binomial regression (as opposed to using a logistic regression and making rare outcome assumption to interpret the odds ratio as approximate risk ratios). We kindly request further clarification/suggestion if we have overlooked any specific assumptions.

3. Regarding the number of comorbidities, 2 or more should define multi-morbidity.
Author’s response:
Thank you for this comment. Table 1 the distribution of co-morbidities is made explicit.

B. Results
1. They should include a flow chart with information on included and excluded cases with their respective reasons.
Author’s response:
Thank you for this. We had a relatively simple criteria adopted. Inclusion required to satisfy: “HCWs who were hospitalized with clinically suspected or laboratory-confirmed COVID-19 from the 29 countries between January 2020 to January 2023” (as reported in the study population section). Based on this criterion, there were a total of 841,640 records of patients admitted with COVID-19 were stored in the IDDO platform from these 29 countries between January 2020 and January 2023. Of these, only 17,502 (2.08%) were HCWs that formed the analysis database. These are presented in the main results in the text.

2. Table 1 and 2. There are many unknowns, e.g., Current smoker, obesity, severity, Non-invasive ventilation, Inotropes, vasopressors, and High-flow oxygen nasal cannulas.

Author’s response:
Thank you for this comment. We have transparently reported all the unknowns and have presented the completed of the information for all the variables included in the analysis across the tables presented in this manuscript. Many unknowns and missingness/completeness of records are major issue and we have clearly acknowledged it as a major limitation in the discussion section:
“Second, the high number of missing values for some of the comorbidities limited the statistical
power concerning some of the comorbidity”

3. Table 3. The authors should revise their analyses. This table includes data from 7858 patients, however in the comparisons of variables, none of the sums of yes and no variables match.

Author’s response:
Thank you for pointing this out. The analysis is subject to completeness of the records and outcomes, a major limitation of the analysis that has been acknowledged in the discussion section. The completeness of the records differed across the variables used and hence they won’t sum to 7858. Since each analysis has slightly different total (other than 7858), we are removing this from the main table to avoid the confusion (see version 2).

4. Table 4. They do not include ratios because they include the population 16674.

Author’s response:
Thank you for this comment. We have presented the adjusted risk ratios (Adjusted RR) in table 4. We kindly request further clarification of this comment.

5. The authors included the population between January 2020 and January 2023. Here, my main concern is that from 2021 onwards health workers were the first at-risk group to be vaccinated, but the authors did not include vaccination as an adjustment variable. Also, the study does not assess, nor does it compare variables before vaccination and after vaccination. It is likely that the highest number of cases occurred before vaccination (during 2020). For this year alone, a pre-vaccination analysis should be conducted to determine the association with risk factors, and another analysis in the vaccinated population (2021 to 2023), to assess whether the risk of age, sex, cardiovascular disease and diabetes persist in the vaccinated population compared to the unvaccinated.

Author’s response:
Thank you very much for raising this important aspect. We did consider this aspect in the initial planning but were not able to rigorously address this for several reasons. First, there was a lack of reliable vaccination information captured in the database. Second, we didn’t have access to the vaccination roll-out period for each of the countries included in the analysis, the availability of vaccination likely differed across the countries. For these reasons, thoroughly assessing the impact of the vaccination in the current analysis was not possible. However, when we present the mortality by calendar year, we do find higher mortality during 2020 and 2021 and much lower in 2022. As the reviewer suspects, this may be due to the vaccination status, however, we cannot attribute this with certainty due to a lack of information regarding the vaccination status

We have acknowledged this as a major limitation in the discussion and have added the following in the limitations section.
“Another limitation is that we were unable to adjust for vaccination status into the analysis as this information was not reliably captured in the database. The case fatality was found to higher during 2020 and 2021 and much lower in 2022 (data not shown), but we weren’t able to attribute this to vaccination status due to lack of information regarding this variable”

Please see new version for further review. Thank you for your review.
Competing Interests: I am the Author. Close
Report a concern
Respond or Comment

COMMENTS ON THIS REPORT

Author Response 10 Sep 2025

Yusuf Tejan, Ministry of Health, 4th Floor, Youyi Building, Freetown, Sierra Leone

10 Sep 2025

Author Response
Thank you for the good summary of our study aim and findings. We take note of your recommendation for further analysis to strengthen the association that our study demonstrated.

... Continue reading
Thank you for the good summary of our study aim and findings. We take note of your recommendation for further analysis to strengthen the association that our study demonstrated.

A. Methods.
1. To check for compliance with the STROBE guideline items.
Response:
Thank you for this comment. We have ensured alignment with the STROBE guidelines as outlined below:

We described this to be a “cohort study design”

We have described the general setting as “the International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC) study database, whereas the specific setting was the 29 specific countries (Egypt, Gambia, Ghana, Guinea, Libya, Malawi, South Africa, Estonia, Germany, Italy, Ireland, Luxembourg, Poland, Spain, United Kingdom, Argentina, Brazil, Canada, Peru, United States of America, Jordan, Kuwait, Palestine, United Arab Emirates, Australia, Hong Kong, Indonesia, Malaysia, and the Philippines)” whose data was extracted from this database.

Additionally, we have stated the study data collection period was from “January 2020 to January 2023; and the follow-up period defined as from hospital admission to exit”.

We have described the study participants and the inclusion criteria as healthcare workers (HCWs) who were hospitalized with clinically suspected or laboratory-confirmed COVID-19 from the 29 countries; the source from which their data was extracted, as “the ISARIC database from January 2020 to January 2023”.

We defined the variables as follows: “The independent variables were patient demographic information and the presence of noncommunicable disease (NCD) comorbidities; while disease severity and patient outcomes were the dependent variables.”

Operational definitions have been provided as follows:

Severe COVID-19 was defined as one or more of the following: admission to an intensive care unit (ICU), treatment with invasive mechanical ventilation (IMV), non-invasive ventilation (NIV), high-flow nasal cannulas (HFNC), extra-corporeal membrane oxygenation (ECMO), administration of inotropes and/or vasopressors

The comorbidities that were assessed; diabetes mellitus (any type), asthma, cardiovascular disease, chronic pulmonary disease (not asthma), rheumatological disorder, chronic kidney disease, malignant neoplasm, chronic neurological disorder, chronic haematological disease, dementia, malnutrition, smoking status and obesity. The data on comorbidities (except obesity) were self-reported.

Obesity was defined by the clinicians attending to the patients based on the Body Mass Index of ≥ 30.

The data source was the Infectious Diseases Data Observatory (IDDO) database and R code was used to create the dataset for analysis. Data was collected using the standardized ISARIC-WHO case report form.

To address potential bias we took the following steps:

We clearly defined the inclusion criteria for the participants to be recruited into the study

We included all the participants who met the inclusion criteria to eliminate selection bias

We clearly defined the participants who were to quality as severe COVID-19 disease to avoid misclassification. The standardized ISARIC-WHO case report form was used for data collection

We clearly defined the period of follow-up (from hospital admission to exit: discharge/ death) so there was no loss to follow-up

R code was used to create the dataset for analysis. Stata version 18 was then used to prepare the data for analysis

For analysis:

To estimate the relative risks (RR) of severity and death, we fitted log-binomial regression models (generalized linear models for the binomial family with the log link) for both severity and death outcomes using glm function of R. We omitted predictors with less than 15 outcome events in their exposed category. We adjusted comorbidities’ RR estimates for age and sex. To that end, we fitted a separate model for each comorbidity where predictors were the comorbidity, age, and sex. A P-value of 0.05 was used as the cut-off for statistical significance.

To estimate the proportion of deaths among HCWs that could be attributed to a specific NCD, we calculated the population attributable fraction (PAF). The PAF (and 95%CI) was calculated for comorbidities that were significant in the adjusted analysis and had RR estimates greater than 1. The R package ‘ graphPAF’ R package version 2.0.0) was used to calculate age-and sex adjusted PAF estimates.

2. Statistical analysis. What assumptions were used to calculate the RR.

Author's response:
Thank you for this comment. We estimated the risk ratio directly from the log binomial regression (as opposed to using a logistic regression and making rare outcome assumption to interpret the odds ratio as approximate risk ratios). We kindly request further clarification/suggestion if we have overlooked any specific assumptions.

3. Regarding the number of comorbidities, 2 or more should define multi-morbidity.
Author’s response:
Thank you for this comment. Table 1 the distribution of co-morbidities is made explicit.

B. Results
1. They should include a flow chart with information on included and excluded cases with their respective reasons.
Author’s response:
Thank you for this. We had a relatively simple criteria adopted. Inclusion required to satisfy: “HCWs who were hospitalized with clinically suspected or laboratory-confirmed COVID-19 from the 29 countries between January 2020 to January 2023” (as reported in the study population section). Based on this criterion, there were a total of 841,640 records of patients admitted with COVID-19 were stored in the IDDO platform from these 29 countries between January 2020 and January 2023. Of these, only 17,502 (2.08%) were HCWs that formed the analysis database. These are presented in the main results in the text.

2. Table 1 and 2. There are many unknowns, e.g., Current smoker, obesity, severity, Non-invasive ventilation, Inotropes, vasopressors, and High-flow oxygen nasal cannulas.

Author’s response:
Thank you for this comment. We have transparently reported all the unknowns and have presented the completed of the information for all the variables included in the analysis across the tables presented in this manuscript. Many unknowns and missingness/completeness of records are major issue and we have clearly acknowledged it as a major limitation in the discussion section:
“Second, the high number of missing values for some of the comorbidities limited the statistical
power concerning some of the comorbidity”

3. Table 3. The authors should revise their analyses. This table includes data from 7858 patients, however in the comparisons of variables, none of the sums of yes and no variables match.

Author’s response:
Thank you for pointing this out. The analysis is subject to completeness of the records and outcomes, a major limitation of the analysis that has been acknowledged in the discussion section. The completeness of the records differed across the variables used and hence they won’t sum to 7858. Since each analysis has slightly different total (other than 7858), we are removing this from the main table to avoid the confusion (see version 2).

4. Table 4. They do not include ratios because they include the population 16674.

Author’s response:
Thank you for this comment. We have presented the adjusted risk ratios (Adjusted RR) in table 4. We kindly request further clarification of this comment.

5. The authors included the population between January 2020 and January 2023. Here, my main concern is that from 2021 onwards health workers were the first at-risk group to be vaccinated, but the authors did not include vaccination as an adjustment variable. Also, the study does not assess, nor does it compare variables before vaccination and after vaccination. It is likely that the highest number of cases occurred before vaccination (during 2020). For this year alone, a pre-vaccination analysis should be conducted to determine the association with risk factors, and another analysis in the vaccinated population (2021 to 2023), to assess whether the risk of age, sex, cardiovascular disease and diabetes persist in the vaccinated population compared to the unvaccinated.

Author’s response:
Thank you very much for raising this important aspect. We did consider this aspect in the initial planning but were not able to rigorously address this for several reasons. First, there was a lack of reliable vaccination information captured in the database. Second, we didn’t have access to the vaccination roll-out period for each of the countries included in the analysis, the availability of vaccination likely differed across the countries. For these reasons, thoroughly assessing the impact of the vaccination in the current analysis was not possible. However, when we present the mortality by calendar year, we do find higher mortality during 2020 and 2021 and much lower in 2022. As the reviewer suspects, this may be due to the vaccination status, however, we cannot attribute this with certainty due to a lack of information regarding the vaccination status

We have acknowledged this as a major limitation in the discussion and have added the following in the limitations section.
“Another limitation is that we were unable to adjust for vaccination status into the analysis as this information was not reliably captured in the database. The case fatality was found to higher during 2020 and 2021 and much lower in 2022 (data not shown), but we weren’t able to attribute this to vaccination status due to lack of information regarding this variable”

Please see new version for further review. Thank you for your review.
Thank you for the good summary of our study aim and findings. We take note of your recommendation for further analysis to strengthen the association that our study demonstrated.

A. Methods.
1. To check for compliance with the STROBE guideline items.
Response:
Thank you for this comment. We have ensured alignment with the STROBE guidelines as outlined below:

We described this to be a “cohort study design”

We have described the general setting as “the International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC) study database, whereas the specific setting was the 29 specific countries (Egypt, Gambia, Ghana, Guinea, Libya, Malawi, South Africa, Estonia, Germany, Italy, Ireland, Luxembourg, Poland, Spain, United Kingdom, Argentina, Brazil, Canada, Peru, United States of America, Jordan, Kuwait, Palestine, United Arab Emirates, Australia, Hong Kong, Indonesia, Malaysia, and the Philippines)” whose data was extracted from this database.

Additionally, we have stated the study data collection period was from “January 2020 to January 2023; and the follow-up period defined as from hospital admission to exit”.

We have described the study participants and the inclusion criteria as healthcare workers (HCWs) who were hospitalized with clinically suspected or laboratory-confirmed COVID-19 from the 29 countries; the source from which their data was extracted, as “the ISARIC database from January 2020 to January 2023”.

We defined the variables as follows: “The independent variables were patient demographic information and the presence of noncommunicable disease (NCD) comorbidities; while disease severity and patient outcomes were the dependent variables.”

Operational definitions have been provided as follows:

Severe COVID-19 was defined as one or more of the following: admission to an intensive care unit (ICU), treatment with invasive mechanical ventilation (IMV), non-invasive ventilation (NIV), high-flow nasal cannulas (HFNC), extra-corporeal membrane oxygenation (ECMO), administration of inotropes and/or vasopressors

The comorbidities that were assessed; diabetes mellitus (any type), asthma, cardiovascular disease, chronic pulmonary disease (not asthma), rheumatological disorder, chronic kidney disease, malignant neoplasm, chronic neurological disorder, chronic haematological disease, dementia, malnutrition, smoking status and obesity. The data on comorbidities (except obesity) were self-reported.

Obesity was defined by the clinicians attending to the patients based on the Body Mass Index of ≥ 30.

The data source was the Infectious Diseases Data Observatory (IDDO) database and R code was used to create the dataset for analysis. Data was collected using the standardized ISARIC-WHO case report form.

To address potential bias we took the following steps:

We clearly defined the inclusion criteria for the participants to be recruited into the study

We included all the participants who met the inclusion criteria to eliminate selection bias

We clearly defined the participants who were to quality as severe COVID-19 disease to avoid misclassification. The standardized ISARIC-WHO case report form was used for data collection

We clearly defined the period of follow-up (from hospital admission to exit: discharge/ death) so there was no loss to follow-up

R code was used to create the dataset for analysis. Stata version 18 was then used to prepare the data for analysis

For analysis:

To estimate the relative risks (RR) of severity and death, we fitted log-binomial regression models (generalized linear models for the binomial family with the log link) for both severity and death outcomes using glm function of R. We omitted predictors with less than 15 outcome events in their exposed category. We adjusted comorbidities’ RR estimates for age and sex. To that end, we fitted a separate model for each comorbidity where predictors were the comorbidity, age, and sex. A P-value of 0.05 was used as the cut-off for statistical significance.

To estimate the proportion of deaths among HCWs that could be attributed to a specific NCD, we calculated the population attributable fraction (PAF). The PAF (and 95%CI) was calculated for comorbidities that were significant in the adjusted analysis and had RR estimates greater than 1. The R package ‘ graphPAF’ R package version 2.0.0) was used to calculate age-and sex adjusted PAF estimates.

2. Statistical analysis. What assumptions were used to calculate the RR.

Author's response:
Thank you for this comment. We estimated the risk ratio directly from the log binomial regression (as opposed to using a logistic regression and making rare outcome assumption to interpret the odds ratio as approximate risk ratios). We kindly request further clarification/suggestion if we have overlooked any specific assumptions.

3. Regarding the number of comorbidities, 2 or more should define multi-morbidity.
Author’s response:
Thank you for this comment. Table 1 the distribution of co-morbidities is made explicit.

B. Results
1. They should include a flow chart with information on included and excluded cases with their respective reasons.
Author’s response:
Thank you for this. We had a relatively simple criteria adopted. Inclusion required to satisfy: “HCWs who were hospitalized with clinically suspected or laboratory-confirmed COVID-19 from the 29 countries between January 2020 to January 2023” (as reported in the study population section). Based on this criterion, there were a total of 841,640 records of patients admitted with COVID-19 were stored in the IDDO platform from these 29 countries between January 2020 and January 2023. Of these, only 17,502 (2.08%) were HCWs that formed the analysis database. These are presented in the main results in the text.

2. Table 1 and 2. There are many unknowns, e.g., Current smoker, obesity, severity, Non-invasive ventilation, Inotropes, vasopressors, and High-flow oxygen nasal cannulas.

Author’s response:
Thank you for this comment. We have transparently reported all the unknowns and have presented the completed of the information for all the variables included in the analysis across the tables presented in this manuscript. Many unknowns and missingness/completeness of records are major issue and we have clearly acknowledged it as a major limitation in the discussion section:
“Second, the high number of missing values for some of the comorbidities limited the statistical
power concerning some of the comorbidity”

3. Table 3. The authors should revise their analyses. This table includes data from 7858 patients, however in the comparisons of variables, none of the sums of yes and no variables match.

Author’s response:
Thank you for pointing this out. The analysis is subject to completeness of the records and outcomes, a major limitation of the analysis that has been acknowledged in the discussion section. The completeness of the records differed across the variables used and hence they won’t sum to 7858. Since each analysis has slightly different total (other than 7858), we are removing this from the main table to avoid the confusion (see version 2).

4. Table 4. They do not include ratios because they include the population 16674.

Author’s response:
Thank you for this comment. We have presented the adjusted risk ratios (Adjusted RR) in table 4. We kindly request further clarification of this comment.

5. The authors included the population between January 2020 and January 2023. Here, my main concern is that from 2021 onwards health workers were the first at-risk group to be vaccinated, but the authors did not include vaccination as an adjustment variable. Also, the study does not assess, nor does it compare variables before vaccination and after vaccination. It is likely that the highest number of cases occurred before vaccination (during 2020). For this year alone, a pre-vaccination analysis should be conducted to determine the association with risk factors, and another analysis in the vaccinated population (2021 to 2023), to assess whether the risk of age, sex, cardiovascular disease and diabetes persist in the vaccinated population compared to the unvaccinated.

Author’s response:
Thank you very much for raising this important aspect. We did consider this aspect in the initial planning but were not able to rigorously address this for several reasons. First, there was a lack of reliable vaccination information captured in the database. Second, we didn’t have access to the vaccination roll-out period for each of the countries included in the analysis, the availability of vaccination likely differed across the countries. For these reasons, thoroughly assessing the impact of the vaccination in the current analysis was not possible. However, when we present the mortality by calendar year, we do find higher mortality during 2020 and 2021 and much lower in 2022. As the reviewer suspects, this may be due to the vaccination status, however, we cannot attribute this with certainty due to a lack of information regarding the vaccination status

We have acknowledged this as a major limitation in the discussion and have added the following in the limitations section.
“Another limitation is that we were unable to adjust for vaccination status into the analysis as this information was not reliably captured in the database. The case fatality was found to higher during 2020 and 2021 and much lower in 2022 (data not shown), but we weren’t able to attribute this to vaccination status due to lack of information regarding this variable”

Please see new version for further review. Thank you for your review.
Competing Interests: I am the Author. Close
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Version 2

VERSION 2 PUBLISHED 12 Jun 2024

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Version 2 (revision) 11 Jun 25		read	read
Version 1 12 Jun 24	read

Max Carlos Ramírez-Soto, Centro Nacional de Salud Puública, Universidad de San Martín de Porres, Lima, Peru
Recai Ergün, University of Selcuk Faculty of Medicine, Konya, Turkey
Bashir Bello, Federal University, Gusau, Nigeria

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18 Jul 2025 | for Version 2

Bashir Bello, Department of Sociology, Federal University, Gusau, Nigeria

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Approved

The research is okay and indexable. The introduction was precise and concise and in it was a the statement of the problems. methodology is appropriate and detailed. The data collected were properly analyzed and well presented. The subject matter was properly addressed.. The references also captured all what was presented. The research is okay just a brief grammatic editing

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

No
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests

No competing interests were disclosed.

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SOCIOLOGY, SOCIAL PROBLEM , INDUSTRIAL AND WORKS AND THEORY

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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08 Jul 2025 | for Version 2

Recai Ergün, University of Selcuk Faculty of Medicine, Konya, Konya, Turkey

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Approved

The manuscript titled “Association between noncommunicable diseases and COVID-19 severity and mortality among infected hospitalized HCWs in 29 countries: a cohort study” presents an investigation into the role of noncommunicable diseases (NCDs) in COVID-19 outcomes among HCWs.

This study includes a large multinational cohort of over 17,000 HCWs in 29 countries.

The methodology is rigorous and uses appropriate statistical models (e.g., log-binomial regression) and population attributable fraction analysis to quantify the impact of various comorbidities.

To enhance scientific transparency and rigor, the study follows STROBE reporting guidelines.

The findings aim to contribute to public health by providing recommendations for occupational health strategies designed to protect HCWs during infectious disease outbreaks.

The manuscript is well written, the data are clearly presented, and the conclusions are strongly supported by the results. I believe this study makes a valuable contribution to the literature on COVID-19 and HCW safety.
I recommend that you accept the article in its current form.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Pulmonary vascular diseases and pulmonary hypertension; Occupational and environmental lung diseases; Interstitial lung diseases and fibrosis; Respiratory failure and mechanical ventilation; Critical care in respiratory medicine; Bronchoscopy and pleural procedures; COPD, asthma and obstructive lung diseases; Tuberculosis and post-tuberculosis lung disorders; Pulmonary infections and sepsis in ICU; Lung cancer and pulmonary nodules; COVID-19

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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Reviewer Report

16 Views

25 Sep 2024 | for Version 1

Max Carlos Ramírez-Soto, Centro Nacional de Salud Puública, Universidad de San Martín de Porres, Lima, Peru

16 Views Cite this report Responses(1)

Approved With Reservations

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

No
Are sufficient details of methods and analysis provided to allow replication by others?

No
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

No

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Infectious diseases

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Responses (1)

Author Response

10 Sep 2025

Yusuf Tejan, Ministry of Health, 4th Floor, Youyi Building, Freetown, Sierra Leone

Thank you for the good summary of our study aim and findings. We take note of your recommendation for further analysis to strengthen the association that our study demonstrated.

A. Methods.
1. To check for compliance with the STROBE guideline items.
Response:
Thank you for this comment. We have ensured alignment with the STROBE guidelines as outlined below:

We described this to be a “cohort study design”
We have described the general setting as “the International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC) study database, whereas the specific setting was the 29 specific countries (Egypt, Gambia, Ghana, Guinea, Libya, Malawi, South Africa, Estonia, Germany, Italy, Ireland, Luxembourg, Poland, Spain, United Kingdom, Argentina, Brazil, Canada, Peru, United States of America, Jordan, Kuwait, Palestine, United Arab Emirates, Australia, Hong Kong, Indonesia, Malaysia, and the Philippines)” whose data was extracted from this database.

Additionally, we have stated the study data collection period was from “January 2020 to January 2023; and the follow-up period defined as from hospital admission to exit”.

We have described the study participants and the inclusion criteria as healthcare workers (HCWs) who were hospitalized with clinically suspected or laboratory-confirmed COVID-19 from the 29 countries; the source from which their data was extracted, as “the ISARIC database from January 2020 to January 2023”.
We defined the variables as follows: “The independent variables were patient demographic information and the presence of noncommunicable disease (NCD) comorbidities; while disease severity and patient outcomes were the dependent variables.”
Operational definitions have been provided as follows:
- Severe COVID-19 was defined as one or more of the following: admission to an intensive care unit (ICU), treatment with invasive mechanical ventilation (IMV), non-invasive ventilation (NIV), high-flow nasal cannulas (HFNC), extra-corporeal membrane oxygenation (ECMO), administration of inotropes and/or vasopressors
- The comorbidities that were assessed; diabetes mellitus (any type), asthma, cardiovascular disease, chronic pulmonary disease (not asthma), rheumatological disorder, chronic kidney disease, malignant neoplasm, chronic neurological disorder, chronic haematological disease, dementia, malnutrition, smoking status and obesity. The data on comorbidities (except obesity) were self-reported.
- Obesity was defined by the clinicians attending to the patients based on the Body Mass Index of ≥ 30.
The data source was the Infectious Diseases Data Observatory (IDDO) database and R code was used to create the dataset for analysis. Data was collected using the standardized ISARIC-WHO case report form.
To address potential bias we took the following steps:
- We clearly defined the inclusion criteria for the participants to be recruited into the study
- We included all the participants who met the inclusion criteria to eliminate selection bias
- We clearly defined the participants who were to quality as severe COVID-19 disease to avoid misclassification. The standardized ISARIC-WHO case report form was used for data collection
- We clearly defined the period of follow-up (from hospital admission to exit: discharge/ death) so there was no loss to follow-up
R code was used to create the dataset for analysis. Stata version 18 was then used to prepare the data for analysis
For analysis:
- To estimate the relative risks (RR) of severity and death, we fitted log-binomial regression models (generalized linear models for the binomial family with the log link) for both severity and death outcomes using glm function of R. We omitted predictors with less than 15 outcome events in their exposed category. We adjusted comorbidities’ RR estimates for age and sex. To that end, we fitted a separate model for each comorbidity where predictors were the comorbidity, age, and sex. A P-value of 0.05 was used as the cut-off for statistical significance.
- To estimate the proportion of deaths among HCWs that could be attributed to a specific NCD, we calculated the population attributable fraction (PAF). The PAF (and 95%CI) was calculated for comorbidities that were significant in the adjusted analysis and had RR estimates greater than 1. The R package ‘ graphPAF’ R package version 2.0.0) was used to calculate age-and sex adjusted PAF estimates.

2. Statistical analysis. What assumptions were used to calculate the RR.

Author's response:
Thank you for this comment. We estimated the risk ratio directly from the log binomial regression (as opposed to using a logistic regression and making rare outcome assumption to interpret the odds ratio as approximate risk ratios). We kindly request further clarification/suggestion if we have overlooked any specific assumptions.

3. Regarding the number of comorbidities, 2 or more should define multi-morbidity.
Author’s response:
Thank you for this comment. Table 1 the distribution of co-morbidities is made explicit.

B. Results
1. They should include a flow chart with information on included and excluded cases with their respective reasons.
Author’s response:
Thank you for this. We had a relatively simple criteria adopted. Inclusion required to satisfy: “HCWs who were hospitalized with clinically suspected or laboratory-confirmed COVID-19 from the 29 countries between January 2020 to January 2023” (as reported in the study population section). Based on this criterion, there were a total of 841,640 records of patients admitted with COVID-19 were stored in the IDDO platform from these 29 countries between January 2020 and January 2023. Of these, only 17,502 (2.08%) were HCWs that formed the analysis database. These are presented in the main results in the text.

2. Table 1 and 2. There are many unknowns, e.g., Current smoker, obesity, severity, Non-invasive ventilation, Inotropes, vasopressors, and High-flow oxygen nasal cannulas.

Author’s response:
Thank you for this comment. We have transparently reported all the unknowns and have presented the completed of the information for all the variables included in the analysis across the tables presented in this manuscript. Many unknowns and missingness/completeness of records are major issue and we have clearly acknowledged it as a major limitation in the discussion section:
“Second, the high number of missing values for some of the comorbidities limited the statistical
power concerning some of the comorbidity”

3. Table 3. The authors should revise their analyses. This table includes data from 7858 patients, however in the comparisons of variables, none of the sums of yes and no variables match.

Author’s response:
Thank you for pointing this out. The analysis is subject to completeness of the records and outcomes, a major limitation of the analysis that has been acknowledged in the discussion section. The completeness of the records differed across the variables used and hence they won’t sum to 7858. Since each analysis has slightly different total (other than 7858), we are removing this from the main table to avoid the confusion (see version 2).

4. Table 4. They do not include ratios because they include the population 16674.

Author’s response:
Thank you for this comment. We have presented the adjusted risk ratios (Adjusted RR) in table 4. We kindly request further clarification of this comment.

5. The authors included the population between January 2020 and January 2023. Here, my main concern is that from 2021 onwards health workers were the first at-risk group to be vaccinated, but the authors did not include vaccination as an adjustment variable. Also, the study does not assess, nor does it compare variables before vaccination and after vaccination. It is likely that the highest number of cases occurred before vaccination (during 2020). For this year alone, a pre-vaccination analysis should be conducted to determine the association with risk factors, and another analysis in the vaccinated population (2021 to 2023), to assess whether the risk of age, sex, cardiovascular disease and diabetes persist in the vaccinated population compared to the unvaccinated.

Author’s response:
Thank you very much for raising this important aspect. We did consider this aspect in the initial planning but were not able to rigorously address this for several reasons. First, there was a lack of reliable vaccination information captured in the database. Second, we didn’t have access to the vaccination roll-out period for each of the countries included in the analysis, the availability of vaccination likely differed across the countries. For these reasons, thoroughly assessing the impact of the vaccination in the current analysis was not possible. However, when we present the mortality by calendar year, we do find higher mortality during 2020 and 2021 and much lower in 2022. As the reviewer suspects, this may be due to the vaccination status, however, we cannot attribute this with certainty due to a lack of information regarding the vaccination status

We have acknowledged this as a major limitation in the discussion and have added the following in the limitations section.
“Another limitation is that we were unable to adjust for vaccination status into the analysis as this information was not reliably captured in the database. The case fatality was found to higher during 2020 and 2021 and much lower in 2022 (data not shown), but we weren’t able to attribute this to vaccination status due to lack of information regarding this variable”

Please see new version for further review. Thank you for your review.

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Competing Interests

I am the Author.

Alongside their report, reviewers assign a status to the article:

Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested

Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.

Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions

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The association between non-communicable diseases and COVID-19 severity and mortality among infected hospitalized healthcare workers in 29 countries: a cohort study

Abstract

Background

Methods

Results

Conclusions

Keywords

Introduction

Methods

General setting

Specific setting

Study population

Data collection and variables

Operational definitions

Data analysis

Ethics considerations

Results

Sociodemographic characteristics of healthcare workers admitted for COVID-19

Table 1. Demographic characteristics and comorbidities and other risk factors among healthcare workers hospitalized for COVID-19 from 2020 to 2023 in 29 countries.

Figure 1. Distribution of healthcare workers hospitalized for COVID-19 from 2020 to 2023 across countries.

Healthcare workers with different comorbidities

COVID-19 severity and hospitalization outcomes of healthcare workers

Table 2. COVID-19 severity and hospitalization outcomes of healthcare workers.

Factors associated with COVID-19 severity and deaths

Table 3. Relative risks and population attributable fractions (as percents) of COVID-19 severity due to non-communicable diseases among hospitalized health care workers in 29 countries from 2020 to 2023.

Table 4. Relative risks and population attributable fractions (as percents) of COVID-19 death due to non-communicable diseases among hospitalized health care workers in 29 countries from 2020 to 2023.

Population attributable fraction (PAF)

Discussion

Conclusions

Data availability

Underlying data

Reporting guidelines

Acknowledgments

References

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