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Article

Disease Severity and Comorbidities among Healthcare Worker COVID-19 Admissions in South Africa: A Retrospective Analysis

by
Edith Ratshikhopha
1,2,
Munyadziwa Muvhali
1,
Nisha Naicker
3,
Nonhlanhla Tlotleng
1,
Waasila Jassat
4 and
Tanusha Singh
1,2,3,*
1
National Health Laboratory Service, National Institute for Occupational Health, Johannesburg 2000, South Africa
2
Department of Clinical Microbiology and Infectious Diseases, School of Pathology, University of the Witwatersrand, Johannesburg 2000, South Africa
3
Department of Environmental Health, University of Johannesburg, Doornfontein 2028, South Africa
4
National Health Laboratory Service, National Institute for Communicable Diseases, Sandringham 2192, South Africa
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2022, 19(9), 5519; https://doi.org/10.3390/ijerph19095519
Submission received: 25 February 2022 / Revised: 22 April 2022 / Accepted: 24 April 2022 / Published: 2 May 2022
(This article belongs to the Section Occupational Safety and Health)
Review Reports Versions Notes

Abstract

:
Healthcare workers (HCWs) are among the most vulnerable in regard to contracting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Comorbidities are reported to increase the risk for more severe COVID-19 outcomes, often requiring hospitalization. However, the evidence on disease severity and comorbidities among South African HCWs is lacking. This retrospective study analyzed the prevalence of comorbidities among HCW hospitalized with COVID-19 and its association with the severity of outcomes. Data from public and private hospitals in nine provinces of South Africa were extracted from the national hospital surveillance database for COVID-19 admissions. A total of 10,149 COVID-19 HCWs admissions were reported from 5 March 2020 to 31 December 2021. The risk of disease severity among HCWs increased with age, with those older (≥60 years) having seven times the odds of disease severity (aOR 7.0; 95% CI 4.2–11.8) compared to HCWs in the younger age (20–29 years) group. The most commonly reported comorbidity was hypertension (36.3%), followed by diabetes (23.3%) and obesity (16.7%). Hypertension (aOR 1.3; 95% CI 1.0–1.6), diabetes (aOR 1.6; 95% CI 1.3–2.0), and HIV (aOR 1.6; 95% CI 1.2–2.1) were significantly associated with disease severity. In conclusion, age, gender, and existing comorbidities were strong predictors of the prognosis of severe COVID-19 among HCWs in South Africa. The information is important in the development of occupational health policies and vulnerability risk assessments for HCWs in light of future COVID-19 waves or similar outbreaks.

1. Introduction

Healthcare workers (HCWs) are a high-risk group for infections, including tuberculosis (TB) [1,2], and are among the most vulnerable to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection [3]. Healthcare workers are in the frontline of the coronavirus disease of 2019 (COVID-19) outbreak response and are the most at risk of acquiring the infection, as they are exposed to infectious material [4,5,6]. A recent rapid review reported a lack of and/or inadequate personal protection equipment (PPE), exposure to infected patients, work overload, poor infection control, and pre-existing medical conditions as important risk factors for COVID-19 infection among HCWs [7]. As with many the HCWs in other countries, South African HCWs have not been spared from the coronavirus disease of 2019 (COVID-19); and they are reported to be at higher risk of getting infected [8,9,10]. In Europe, the overall positivity amongst HCWs was 8.8% in Milan, Italy, with a positivity rate of 24% in symptomatic HCWs alone [11]. However, a much higher prevalence of 42.4% of SARS-CoV-2 infection among symptomatic HCWs was reported in a large university tertiary hospital in São Paulo, Brazil [12]. In a cross-sectional study in Egypt, a 14.3% PCR positivity was reported among asymptomatic HCWs [13]. Similar rates (14%) were reported among primary health HCWs of HIV service providers in five districts of South Africa by the end of September 2020 [14]. In Cape Town, South Africa, 10.4% seroprevalence among HCWs from pediatric facilities enrolled between May and July 2020 were reported [15]. In another South African study, 34.6% of HCWs in a cohort from a large tertiary hospital had PCR-confirmed SARS-CoV-2. Among PCR-confirmed cases, 59.3% were symptomatic, 30.4% were pre-symptomatic, and 10.4% remained asymptomatic [6].
Studies, including a systematic review and meta-analysis, showed that severe COVID-19 and mortality were associated with gender, age, smoking status, and comorbidities. The reported comorbidities included obesity, hypertension, cardiovascular diseases, diabetes, chronic obstructive pulmonary disease (COPD), asthma, and cancer [16,17,18,19,20,21,22,23]. Additionally, the age of the patients has also been a contributing factor [17,19,20,21,22,23,24,25]. Most of these studies reporting an association of COVID-19 and comorbidities were from Asia, Europe, and America [25,26,27,28,29,30,31,32,33] and three representing Africa. A South African study investigated the role of comorbidities and health outcome among all COVID-19 hospital admissions [34]. In addition, the Critical Care Outcomes Study (ACCCOS), a multicenter cohort study in 64 hospitals from ten African countries, including South Africa (SA), reported an association between increased mortality and HIV/AIDS, diabetes, chronic liver disease, kidney disease, and severity of organ dysfunction at admission of adults with suspected or confirmed COVID-19, as well as insufficient critical care resources [35]. However, no significant association was found between COVID-19 infection with chronic lung disease and active TB. The third African study conducted between March and June 2020 in South African district hospitals in the Western Cape province also did not show an association between increased mortality and TB, asthma, chronic obstructive pulmonary disease, and post-TB structural lung disease among COVID-19 positive patients. Mortality was significantly associated with increasing age, male sex, obesity, type 2 diabetes, HIV, and chronic kidney disease [36]. A total of 126,226 COVID-19 infections (2.1% of all cases) among health workers in the region were reported, with South Africa accounting for about 45.0% (56,180) of the total infections [37]. Other African countries that recorded high numbers of health worker infections included Algeria (11,936, 9.5%), Kenya (7542, 6.0%), Zimbabwe (5366, 4.3%), and Mozambique (4779, 3.8%). Only Eritrea has not reported any healthcare worker infections [37].
Identifying and understanding coexisting diseases associated with COVID-19 severity can help identify the potential risk factors and predictors of the disease severity, manage patients at risk, and develop policies and guidelines to effectively allocate healthcare resources that will reduce future risk of severe COVID-19 disease [6,38]. Estimating the prevalence of risk factors in this population group is important to guide occupational health measures to protect HCWs and maintain a functioning healthcare system. Mitigating the risk of COVID-19 infection among HCWs is a high priority; however, data to inform such initiatives are limited. Currently, studies from developing countries on COVID-19 and comorbidities among HCWs are limited to those from China. This has been mainly due to insufficient data collection during the pandemic in other developing countries [39]. Other studies in SA have reported risk factors for mortality, whilst this study examines risk factors for severe disease. A national COVID-19 hospital surveillance system has been locally established which captures HCW status. In this manuscript, we report on the prevalence of comorbidities among HCW COVID-19 admissions in South Africa and its association with disease severity (i.e., severe disease requiring intensive care or ventilation and possibly death). This study will provide information to guide recommendations for effective preventive measures to reduce transmission of the virus to other workers and patients, thus reducing secondary transmission to communities, as well.

2. Materials and Methods

2.1. Data Sources

The DATCOV national hospital surveillance system was established in March 2020 to monitor COVID-19 hospital admissions in South Africa. DATCOV contains data on HCWs across the nine provinces in SA that had a positive test for SARS-CoV-2, using real-time reverse-transcription polymerase chain reaction (rRT-PCR) assay or a positive SARS-CoV-2 antigen test, with a confirmed duration of stay in hospital of one full day or longer, regardless of age or reason for admission.

2.2. Study Population

This retrospective study design included HCWs who tested positive for SARS-CoV-2 who were admitted to public and private health facilities in South Africa from 5 March 2020 to 31 December 2021. HCW status included administrators and porters, doctors, nurses, allied healthcare workers, laboratory staff, and paramedics.

2.3. Data Analysis

Frequencies and percentages were used to summarize categorical variables. In the analysis, variables that reported on disease severity of the patients, such as HCWs admitted to intensive care unit (ICU) or high care unit (HCU), those who reported having acute respiratory distress syndrome (Ever ARDS), those who received supplemental oxygen and or were on ventilation, and those with mortality as an outcome status, were combined to create the dependent variable, disease severity. All patients who died of non-COVID causes were excluded from the analysis. Pearson chi-squared and Fishers exact tests were used to assess the association between study risk factors stratified by disease severity. A multivariable logistic regression model was implemented to assess the association of disease severity and existing comorbidities (hypertension, diabetes, chronic renal diseases, chronic pulmonary asthma, chronic cardiovascular diseases, current and past tuberculosis (TB), and HIV status), adjusting for age, sex, gender, and sector. A maximum likelihood test was used to include variables at the 5% level to generate a final model. The results were presented as unadjusted odds ratios (ORs) and adjusted odds ratios (aORs) and 95% confidence intervals. To investigate the effect of missing observation on the final model, a model with the variable containing more than 50% missing was fitted, which was compared to the model without the variable. Obesity was therefore excluded in the regression analysis, as it contained more than 50% missing data. The Hosmer–Lemeshow test for logistic regression was used for goodness of fit. A 5% significance level representing a p < 0.05 to reject the null hypothesis of no association was used. A data analysis was conducted by using STATA 15 (Stata Corp®, College Station, Texas, USA). We followed STROBE guideline recommendations.

3. Results

A cumulative total of 10,149 HCW admissions related to COVID-19 were reported across 666 hospitals, 407 from the public sector and 259 from the private sector, from 5 March 2020 to 31 December 2021 (Table 1). Table 1 shows the clinical and demographic characteristics of admitted HCWs reported with and without disease severity. This represented 3.8% (10,149/266,516) of all COVID-19 admissions. The majority of HCW admissions (83.6%) was recorded in four of the nine provinces, with the highest proportion reported in Gauteng (34.5%), followed by 23.6% in KwaZulu-Natal, 12.9% in Eastern Cape, and 12.6% in Western Cape provinces (Table 1). There were 2941 (29.0%) admissions in HCWs 50–59 years and 2088 (20.6%) in 60 years and older, comprising 50% of all admissions in this group (p < 0.001). A majority of COVID-19 admissions in HCWs were female (67.2%), and 2.5% were pregnant at the time. The most commonly reported comorbidity among HCWs admitted with disease severity was hypertension (42.7%)(p < 0.001), followed by diabetes (27.6%) (p < 0.001) and obesity (22.8%)(p < 0.001). Only 6.7% of HCWs reported having asthma (p = 0.811), HIV (5.1%) (p = 0.416), cardiac disease (2.5%), and TB (1.4%) (p = 0.117) as comorbidities.
The risk of disease severity among HCWs increased with age, with HCWs between 50 and 59 years having four times the risk of disease severity (aOR 4.3; 95% CI 3.4–5.4) and those older (≥60 years) with seven times the risk of disease severity (aOR 6.1; 95% CI 4.8–7.7) compared to HCWs in the younger age group (20–29 years) (Table 2). Male HCWs had a higher risk (aOR 1.5, 95% CI 1.4–1.7) of disease severity compared to female HCWs. Having the comorbidities hypertension (aOR 1.3; 95% CI 1.2–1.5), diabetes (aOR 1.3; 95% CI 1.2–1.5), and HIV (aOR 1.4; 95% CI 1.1–1.7) was significantly associated with increased risk of disease severity among HCWs. In the adjusted model, cardiac disease, asthma, TB (current/past), chronic kidney disease, and malignancy were not significantly associated with COVID-19 disease severity (Table 2). The risk of disease severity significantly increased among HCW admissions in Gauteng, Kwa-Zulu Natal, Mpumalanga, North West, Eastern Cape, Free State, and Limpopo provinces in South Africa compared to the Western Cape province (Table 2).

4. Discussion

This study is one of the largest studies in developing countries investigating 10,149 HCW admissions related to COVID-19 from 5 March 2020 to 31 December 2021. Although many countries in the WHO African region had seen a decline in COVID-19 cases for three consecutive months since May 2021, South Africa still ranked the highest [37]. Collectively, the country reported the highest number of COVID-19 cases in the region, with 3,559,230 cases (46.4%), followed by Ethiopia, with 457,322 (6.0%) cases [37]. The highest number of deaths in the region (93,364 deaths, 58.0% of all deaths) was also reported by South Africa [37]. With an increased number of HCW infections and an overwhelmed health system, determining the prevalence of risk factors among HCWs is critical to guide occupational health measures to protect frontline workers and stabilize the healthcare system’s human resources.
In this study, the risk of disease severity among HCWs increased with age, with HCWs in the older age group (≥60 years) having seven times the odds of disease severity compared to HCWs in the younger age group (20–29 years). The older age in most studies was associated with severe COVID-19 patients compared to COVID-19 in younger patients [29,30,32,40]. This poses a significant challenge, as it points to the loss of older HCWs who are more experienced and possess the much-needed skills to sustain the health system during a pandemic. Male HCWs were at an increased risk of severe disease compared to female HCWs. This is consistent with what has been reported from other studies [30,32,37]. Understanding the gender risk helps us to strategize resources, especially among male-dominated HCW teams. The risk of disease severity was significantly increased among HCW admissions in Gauteng, Kwa-Zulu Natal, Mpumalanga, and Limpopo provinces compared to the Western Cape. Incidentally, Gauteng and Kwa-Zulu Natal account for approximately 50% of the total number of confirmed COVID-19 cases, putting enormous strain on the healthcare system; this also accounts for a majority HCWs who are meant to care for the public requiring advanced medical care themselves.
These findings are important, as most previous COVID-19 studies have not reported the prevalence of comorbidities as a risk of developing severity of COVID-19 disease among HCWs. However, these studies primarily focused on COVID-19 and risk factors among the general public [6,41,42,43]. Only one study by Gibson and Greene (2020) investigated the risk of severe COVID-19 among HCWs who directly work with patients; the authors found that a quarter of the HCWs were deemed at higher risk of COVID-19 due to their multiple high-risk comorbidities. One of the noted comorbidities within the study was chronic lung disease/asthma attack in the past year [44]. However, the study by Gibson and Greene (2020) was not based on data acquired during the pandemic but on retrospective data regarding comorbidities among HCWs [44]. It does, however, highlight the importance of identifying vulnerable work populations. It is evident that HCW risk of occupational exposure to SARS-CoV-2 may vary from very high to high, medium, or low risk depending on the work activities they perform. In terms of the COVID-19 Direction on Health and Safety in the Workplace issued by the South African Ministry of Employment and Labour, employers must ensure that preventive measures are consistent with the overall national strategies and policies to minimize the spread of COVID-19 in the workplace [45]. The latter includes identifying vulnerable employees, that is, those with known or disclosed health issues or comorbidities or any other condition that may place the employee at a higher risk of complications or death than other employees if infected with COVID-19, or those above the age of 60 years, who are at a higher risk of complications or death if infected.
Furthermore, employers must take special measures to mitigate the risk of COVID-19 for vulnerable employees. However, there have been differing reports on the risk factors for COVID-19-related severity and mortality, with considerable variation in the prevalence estimates of comorbidities and their effects on COVID-19 severity. Risk stratification and effective control strategies for COVID-19 hinge on the risk factors, which may differ by geographical location and worker population. Numerous studies have identified comorbidities, including obesity, hypertension, diabetes, and HIV, with the severe infections and sometimes death of COVID-19 [29,31,32,40]. Asthma, for example, although a respiratory tract disease, was not associated with COVID-19 [46]. However, these focus on the general population and did not include HCWs from developing countries. In our study, adjusted analyses indicate that HCW comorbidities were significantly associated with disease severity and increased the risk of disease severity. These were hypertension (aOR 1.3; 95% CI 1.2–1.5), diabetes (aOR 1.3; 95% CI 1.2–1.5), and HIV (aOR 1.4; 95% CI 1.1–1.7). The HIV results were similar to those reported for patients with COVID-19 infection admitted to African high-care or intensive-care units (ACCCOS) with HIV/AIDS (1.91; 1.31–2.79). However, our risk ratio findings were marginally higher for hypertension and marginally higher for diabetes reported in the ACCCOS study. It is well-known that SARS-CoV-2 has overwhelmed healthcare systems globally by causing high rates of severe illness and more so if the rates of critical illness are high among the HCWs meant to provide care for others. Given that South Africa has a high burden of non-communicable disease, obesity, HIV, and TB [34], identifying worker vulnerabilities to mitigate the risk of developing severe COVID-19 and the loss of skilled HCWs is important. Furthermore, South Africa’s large untreated HIV population poses a risk to the generation of new viral variants [47], undermining prevention strategies to contain the pandemic to an endemic state. New variants evade pre-existing immunity and favor viral survival through enhanced transmissibility, resulting in acute surges with inherent hospitalization and subsequent severity of disease [48]. In addition, chronic diseases share several features with infectious disorders, including the pro-inflammatory state, as well as the attenuation of the innate immune response. For example, metabolic disorders may result in reduced immune function impairing macrophage and lymphocyte function, leading to diseases susceptibility [43]. Therefore, understanding the HCW exposure and risk for SARS-CoV-2 infection is important for characterizing the virus transmission patterns, risks factors for infection, and severity of disease and informing the effectiveness of infection prevention and control practices. Lastly, despite being the last alternative in the hierarchy of controls, wearing appropriate and well-fitted PPE is still one of the most effective preventative measures, particularly in poorly controlled areas and in settings where exposure is unknown or unpredictable, such as healthcare facilities [49].

5. Limitations

Data submitted to DATCOV are dependent on information submitted by healthcare institutions; thus, completeness of the data is a limitation in this study. The system has not yet been linked to other data sources, such as laboratory records, mortality records, and other national hospital records, to verify and confirm occupation and comorbid disease status; this may result in the under-reporting of such fields. Thus, we could not further classify HCWs according to occupation and job type. The DATCOV surveillance system also does not include vaccination status, and the effect of vaccination on severe disease was, therefore, not analyzed for this paper. Since HCW admissions are recorded retrospectively, it is possible that not all admissions from 5 March 2020 to 31 December 2021 were recorded. In addition, there may also be misclassification of HCW status, resulting in undercounting. The results of this study cannot be generalizable to HCW population in South Africa, as the analysis included admitted HCWs diagnosed with COVID-19 infection.

6. Conclusions

Age, gender, hypertension, diabetes, and HIV are strong predictors of the prognosis of COVID-19 in HCWs in SA. These findings can help in identifying the HCWs with the potential of developing severe COVID-19 disease. The information is important in the development of occupational health policies and vulnerability risk assessments for HCWs in light of future COVID-19 waves or similar outbreaks.

Author Contributions

The authors have contributed equally to this work, including manuscript preparation, editing, and final review, and share senior authorship. All authors have read and agreed to the published version of the manuscript.

Funding

The labor contribution of the authors was funded by the National Health Laboratory Service.

Institutional Review Board Statement

The Human Research Ethics Committee (Medical), University of the Witwatersrand, approved the study as part of a national surveillance program (M160667).

Informed Consent Statement

This was a retrospective study of COVID-19 hospital surveillance data in South Africa.

Data Availability Statement

Data are available upon reasonable request and within the prescripts of the Protection of Personal Information Act (POPIAct).

Acknowledgments

We thank the provinces and hospital management for providing data. We also thank the NICD, NIOH, and NHLS for supporting the project.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Malotle, M.M.; Spiegel, J.M.; Yassi, A.; Ngubeni, D.; O’Hara, L.M.; Adu, P.; Bryce, E.A.; Mlangeni, N.; Gemell, G.S.M.; Zungu, M. Occupational tuberculosis in South Africa: Are health care workers adequately protected? Public Health Action 2017, 7, 258–267. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. WHO. Global Tuberculosis Report 2020; Licence: CC BY-NC-SA 3.0 IGO; World Health Organization: Geneva, Switzerland, 2020. [Google Scholar]
  3. Singh, T.S.; Matuka, D.O.; Muvhali, M.; Duba, T. COVID-19 pandemic: Workplace readiness to control and contain the spread of coronavirus. Occup. Health S. Afr. 2020, 26, 42–45. [Google Scholar]
  4. Dzinamarira, T.; Mhango, M.; Dzobo, M.; Ngara, B.; Chitungo, I.; Makanda, P.; Atwine, J.; Nkambule, S.J.; Musuka, G. Risk factors for COVID-19 among healthcare workers. A protocol for a systematic review and meta-analysis. PLoS ONE 2021, 16, e0250958. [Google Scholar] [CrossRef] [PubMed]
  5. Mehta, S.; Machado, F.; Kwizera, A.; Papazian, L.; Moss, M.; Azoulay, É.; Herridge, M. COVID-19: A heavy toll on health-care workers. Lancet Respir. Med. 2021, 9, 226–228. [Google Scholar] [CrossRef]
  6. Nunes, M.C.; Baillie, V.L.; Kwatra, G.; Bhikha, S.; Verwey, C.; Menezes, C.; Cutland, C.L.; Moore, D.P.; Dangor, Z.; Adam, Y.; et al. SARS-CoV-2 infection among healthcare workers in South Africa: A longitudinal cohort study. Clin. Infect. Dis. 2021, 73, 1896–1900. [Google Scholar] [CrossRef]
  7. Mhango, M.; Dzobo, M.; Chitungo, I.; Dzinamarira, T. COVID-19 risk factors among health workers: A rapid review. Saf. Health Work 2020, 11, 262–265. [Google Scholar] [CrossRef]
  8. Leeds, C. COVID 19: Health care workers, risks, protection and transmission. Lancet Reg. Health Eur. 2021, 1, 100022. [Google Scholar] [CrossRef]
  9. Hussen, H.; Alemu, Z.A. Risk of COVID-19 Infection and Associated Factors Among Healthcare Workers: A Cross-Sectional Study at Eka Kotebe Treatment Center in Ethiopia. Int. J. Gen. Med. 2021, 14, 1763–1772. [Google Scholar] [CrossRef]
  10. Karlsson, U.; Fraenkel, C.-J. COVID-19: Risks to healthcare workers and their families. BMJ 2020, 371, m3944. [Google Scholar] [CrossRef]
  11. Lombardi, A.; Consonni, D.; Carugno, M.; Bozzi, G.; Mangioni, D.; Muscatello, A.; Castelli, V.; Palomba, E.; Cantù, A.P.; Ceriotti, F. Characteristics of 1573 healthcare workers who underwent nasopharyngeal swab testing for SARS-CoV-2 in Milan, Lombardy, Italy. Clin. Microbiol. Infect. 2020, 26, 1413.e9–1413.e13. [Google Scholar] [CrossRef]
  12. Buonafine, C.P.; Paiatto, B.N.M.; Leal, F.B.; de Matos, S.F.; de Morais, C.O.; Guerra, G.G.; Martuchelli, M.V.V.; Oliveira, D.B.L.; Durigon, E.L.; Soares, C.P.; et al. High prevalence of SARS-CoV-2 infection among symptomatic healthcare workers in a large university tertiary hospital in São Paulo, Brazil. BMC Infect. Dis. 2020, 20, 917. [Google Scholar] [CrossRef] [PubMed]
  13. Abdelmoniem, R.; Fouad, R.; Shawky, S.; Amer, K.; Elnagdy, T.; Hassan, W.A.; Tantawi, O.; Musa, S.; Talaat, H.; Kassem, A.M.; et al. SARS-CoV-2 infection among asymptomatic healthcare workers of the emergency department in a tertiary care facility. J. Clin. Virol. 2021, 134, 104710. [Google Scholar] [CrossRef] [PubMed]
  14. Rees, K.; Dunlop, J.L.; Patel-Abrahams, S.; Struthers, H.; McIntyre, J.A. Primary healthcare workers at risk during COVID-19: An analysis of infections in HIV service providers in five districts of South Africa. South Afr. Med. J. 2021, 111, 309–314. [Google Scholar] [CrossRef] [PubMed]
  15. Goldblatt, D.; Johnson, M.; Falup-Pecurariu, O.; Ivaskeviciene, I.; Spoulou, V.; Tamm, E.; Lischka, J.; Franckling-Smith, Z.; Isarova, D.; Grandjean, L.; et al. Cross-sectional prevalence of SARS-CoV-2 antibodies in healthcare workers in paediatric facilities in eight countries. J. Hosp. Infect. 2021, 110, 60–66. [Google Scholar] [CrossRef] [PubMed]
  16. CDC COVID-19 Response Team; Chow, N.; Fleming-Dutra, K.; Gierke, R.; Hall, A.; Hughes, M.; Pilishvili, T.; Ritchey, M.; Roguski, K.; Skoff, T.; et al. Preliminary Estimates of the Prevalence of Selected Underlying Health Conditions Among Patients with Coronavirus Disease 2019–United States, 12 February–28 March 2020. MMWR Morb. Mortal. Wkly. Rep. 2020, 69, 382–386. [Google Scholar]
  17. Guan, W.-J.; Liang, W.-H.; Zhao, Y.; Liang, H.-R.; Chen, Z.-S.; Li, Y.-M.; Liu, X.-Q.; Chen, R.-C.; Tang, C.-L.; Wang, T.; et al. Comorbidity and its impact on 1590 patients with COVID-19 in China: A nationwide analysis. Eur. Respir. J. 2020, 55, 2000547. [Google Scholar] [CrossRef] [Green Version]
  18. Huang, C.; Wang, Y.; Li, X.; Ren, L.; Zhao, J.; Hu, Y.; Yu, T.; Xia, J.; Wei, Y.; Wu, W.; et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020, 395, 497–506. [Google Scholar] [CrossRef] [Green Version]
  19. Jain, V.; Yuan, J.-M. Systematic review and meta-analysis of predictive symptoms and comorbidities for severe COVID-19 infection. Int. J. Public Health 2020, 65, 533–546. [Google Scholar] [CrossRef]
  20. WHO. Coronavirus Disease (COVID-19) Advice for the Public: Mythbusters. Available online: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/advice-for-public/ (accessed on 28 January 2022).
  21. Xu, X.W.; Wu, X.X.; Jiang, X.G.; Xu, K.J.; Ying, L.J.; Ma, C.L.; Li, S.B.; Wang, H.Y.; Zhang, S.; Gao, H.N.; et al. Clinical findings in a group of patients infected with the 2019 novel coronavirus (SARS-CoV-2) outside of Wuhan, China: Retrospective case series. BMJ 2020, 19, m606. [Google Scholar] [CrossRef] [Green Version]
  22. Yang, X.; Yu, Y.; Xu, J.; Shu, H.; Xia, J.; Liu, H.; Wu, Y.; Zhang, L.; Yu, Z.; Fang, M.; et al. Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: A single-centered, retrospective, observational study. Lancet Respir. Med. 2020, 8, 475–481. [Google Scholar] [CrossRef] [Green Version]
  23. Zhou, F.; Yu, T.; Du, R.; Fan, G.; Liu, Y.; Liu, Z.; Xiang, J.; Wang, Y.; Song, B.; Gu, X. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study. Lancet 2020, 395, 1054–1062. [Google Scholar] [CrossRef]
  24. Huang, C.-G.; Lee, K.-M.; Hsiao, M.-J.; Yang, S.-L.; Huang, P.-N.; Gong, Y.-N.; Hsieh, T.-H.; Huang, P.-W.; Lin, Y.-J.; Liu, Y.-C.; et al. Culture-Based Virus Isolation to Evaluate Potential Infectivity of Clinical Specimens Tested for COVID-19. J. Clin. Microbiol. 2020, 58, e01068-20. [Google Scholar] [CrossRef] [PubMed]
  25. Brurberg, K.G.; Fretheim, A. COVID-19: The Relationship between Age, Comorbidity and Disease Severity—A Rapid Review, 1st Update; Norwegian Institute of Public Health: Trondheim, Norway, 2020. [Google Scholar]
  26. Monárrez-Espino, J.; Zubía-Nevárez, C.I.; Reyes-Silva, L.; Castillo-Palencia, J.P.; Castañeda-Delgado, J.E.; Herrera van-Oostdam, A.S.; López-Hernández, Y. Clinical Factors Associated with COVID-19 Severity in Mexican Patients: Cross-Sectional Analysis from a Multicentric Hospital Study. Healthcare 2021, 9, 895. [Google Scholar] [CrossRef] [PubMed]
  27. Shaikh, F.S.; Aldhafferi, N.; Buker, A.; Alqahtani, A.; Dey, S.; Abdulhamid, S.; AlBuhairi, D.A.M.; Alkabour, R.S.A.; O Atiyah, W.S.; Chrouf, S.B.; et al. Comorbidities and Risk Factors for Severe Outcomes in COVID-19 Patients in Saudi Arabia: A Retrospective Cohort Study. J. Multidiscip. Healthc. 2021, 14, 2169–2183. [Google Scholar] [CrossRef]
  28. Li, J.; He, X.; Yuan, Y.; Zhang, W.; Li, X.; Zhang, Y.; Li, S.; Guan, C.; Gao, Z.; Dong, G. Meta-analysis investigating the relationship between clinical features, outcomes, and severity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia. Am. J. Infect. Control 2021, 49, 82–89. [Google Scholar] [CrossRef]
  29. Cheng, S.; Zhao, Y.; Wang, F.; Chen, Y.; Kaminga, A.C.; Xu, H. Comorbidities’ potential impacts on severe and non-severe patients with COVID-19: A systematic review and meta-analysis. Medicine 2021, 100, e24971. [Google Scholar] [CrossRef]
  30. Ou, M.; Zhu, J.; Ji, P.; Li, H.; Zhong, Z.; Li, B.; Pang, J.; Zhang, J.; Zheng, X. Risk factors of severe cases with COVID-19: A meta-analysis. Epidemiol. Infect. 2020, 148, e175. [Google Scholar] [CrossRef]
  31. Thakur, B.; Dubey, P.; Benitez, J.; Torres, J.P.; Reddy, S.; Shokar, N.; Aung, K.; Mukherjee, D.; Dwivedi, A.K. A systematic review and meta-analysis of geographic differences in comorbidities and associated severity and mortality among individuals with COVID-19. Sci. Rep. 2021, 11, 8562. [Google Scholar] [CrossRef]
  32. Dessie, Z.G.; Zewotir, T. Mortality-related risk factors of COVID-19: A systematic review and meta-analysis of 42 studies and 423,117 patients. BMC Infect. Dis. 2021, 21, 855. [Google Scholar] [CrossRef]
  33. Wang, Z.; Deng, H.; Ou, C.; Liang, J.; Wang, Y.; Jiang, M.; Li, S. Clinical symptoms, comorbidities and complications in severe and non-severe patients with COVID-19. A systematic review and meta-analysis without cases duplication. Medicine 2020, 99, e23327. [Google Scholar] [CrossRef]
  34. Jassat, W.; Mudara, C.; Ozougwu, L.; Tempia, S.; Blumberg, L.; Davies, M.-A.; Pillay, Y.; Carter, T.; Morewane, R.; Wolmarans, M.; et al. Difference in mortality among individuals admitted to hospital with COVID-19 during the first and second waves in South Africa: A cohort study. Lancet 2021, 9, E1216–E1225. [Google Scholar] [CrossRef]
  35. The African COVID-19 Critical Care Outcomes Study (ACCCOS) Investigators. Patient care and clinical outcomes for patients with COVID-19 infection admitted to African high-care or intensive care units (ACCCOS): A multicentre, prospective, observational cohort study. Lancet 2021, 397, 1885–1894. [Google Scholar] [CrossRef]
  36. Mash, R.J.; Presence-Vollenhoven, M.; Adeniji, A.; Christoffels, R.; Doubell, K.; Eksteen, L.; Hendrikse, A.; Hutton, L.; Jenkins, L.; Kapp, P.; et al. Evaluation of patient characteristics, management and outcomes for COVID-19 at district hospitals in the Western Cape, South Africa: Descriptive observational study. BMJ Open 2021, 11, e047016. [Google Scholar] [CrossRef] [PubMed]
  37. WHO. WHO Regional Office for Africa. In Weekly Bulletin on Outbreaks and Other Emergencies, Week 41: 4–10 October 2021; WHO Health Emergencies Programme: Brazzaville, Congo, 2021. [Google Scholar]
  38. Gold, M.S.; Sehayek, D.; Gabrielli, S.; Zhang, X.; McCusker, C.; Ben-Shoshan, M. COVID-19 and comorbidities: A systematic review and meta-analysis. Postgr. Med. 2020, 132, 749–755. [Google Scholar] [CrossRef]
  39. Dahmm, H. In Low-Income Countries Fundamental Data Issues Remain for COVID-19 Response: Thematic Research Network on Data and Statistics. 17 April 2020. Available online: https://www.sdsntrends.org/blog/covid19andlowincome-countries (accessed on 14 February 2022).
  40. Gandhi, R.T.; Lynch, J.B.; Rio, C.D. Mild or Moderate COVID-19. N. Engl. J. Med. 2020, 383, 1757–1766. [Google Scholar] [CrossRef]
  41. Zhang, J.J.; Dong, X.; Cao, Y.Y.; Yuan, Y.D.; Yang, Y.B.; Yan, Y.Q.; Akdis, C.A.; Gao, Y.D. Clinical characteristics of 140 patients infected with SARS-CoV-2 in Wuhan, China. Allergy 2020, 75, 1730–1741. [Google Scholar] [CrossRef]
  42. Jordan, R.E.; Adab, P.; Cheng, K.K. COVID-19: Risk factors for severe disease and death. BMJ 2020, 368, m1198. [Google Scholar] [CrossRef] [Green Version]
  43. Yang, J.; Zheng, Y.A.; Gou, X.; Pu, K.; Chen, Z.; Guo, Q.; Ji, R.; Wang, H.; Wang, Y.; Zhou, Y. Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: A systematic review and meta-analysis. Int. J. Infect. Dis. 2020, 94, 91–95. [Google Scholar] [CrossRef]
  44. Gibson, D.M.; Greene, J. Risk for Severe COVID-19 Illness Among Health Care Workers Who Work Directly with Patients. J. Gen. Intern. Med. 2020, 35, 2804–2806. [Google Scholar] [CrossRef]
  45. Regulation Gazette. Consolidated Direction on Occupational Health and Safety Measures in Certain Workplaces; No. 44700; Government Gazette: Pretoria, South Africa, 2021; Volume 672, ISSN 1682-5845. [Google Scholar]
  46. Sunjaya, A.P.; Allida, S.M.; Tanna, G.L.D.; Jenkins, C.R. Asthma and Coronavirus Disease 2019 Risk: A systematic review and meta-analysis. Eur. Respir. J. 2021, 59, 2101209. [Google Scholar] [CrossRef]
  47. Dyer, O. COVID-19: South Africa’s surge in cases deepens alarm over omicron variant. BMJ 2021, 375, n3013. [Google Scholar] [CrossRef] [PubMed]
  48. Bekker, L.-G.; Ntusi, N.A.B. Lessons from two SARS-CoV-2 waves in Sotuh Africa. Lancet 2021, 9, e1177. [Google Scholar]
  49. Tian, Z.; Stedman, M.; Whyte, M.; Anderson, S.G.; Thomson, G.; Heald, A. Personal protective equipment (PPE) and infection among healthcare workers—What is the evidence? Int. J. Clin. Pract. 2020, 74, e13617. [Google Scholar] [CrossRef] [PubMed]
Table
Table 1. Characteristics of admitted HCWs with severe and non-severe COVID-19 disease outcome, 5 March 2020 to 31 December 2021 (N = 10,149).
Table 1. Characteristics of admitted HCWs with severe and non-severe COVID-19 disease outcome, 5 March 2020 to 31 December 2021 (N = 10,149).
CharacteristicHCWs (N = 10,149)Total (N, %)Chi, p-Value
Disease Severity (YES) (n = 5300; 52.2%)Disease Severity (NO) (n = 4849; 47.8%)
Age Group (Years)<0.001
20–29158 (3.0)550 (11.3)708 (7.0)
30–39692 (13.1)1230 (25.4)1922 (18.9)
40–491202 (22.7)1288 (26.6)2490 (24.5)
50–591769 (33.4)1172 (24.2)2941 (29.0)
≥601479 (27.9)609 (12.6)2088 (20.6)
Sex<0.001
Female3241 (61.2)3583 (73.9)6824 (67.2)
Male2059 (38.9)1265 (26.1)3324 (32.8)
Pregnancy (n/N, %)<0.001
No5264 (99.3)4630 (95.5)9894 (97.5)
Yes36 (0.7)219 (4.5)255 (2.5)
Chronic Diseases (n/N, %)
Hypertension<0.001
No2740/4781 (57.3)2634/3651 (72.1)5374/8432 (63.7)
Yes2041/4781 (42.7)1017/3651 (27.9)3058/8432 (36.3)
Diabetes<0.001
No3404/4703 (72.4)2962/3600 (82.3)6366/8303 (76.7)
Yes1299/4703 (27.6)638/3600 (17.7)1937/8303 (23.3)
Cardiac Disease<0.001
No4482/4596 (97.5)3516/3559 (98.8)7998/8155 (98.1)
Yes114/4596 (2.5)43/3559 (1.2)157/8155 (1.9)
Asthma0.811
No3365/3607 (93.3)4306/4630 (93.0)7671/8237 (93.1)
Yes242/3607 (6.7)324/4630 (7.0)566/8237 (6.9)
Obesity<0.001
No1005/1302 (77.2)1076/1195 (90.0)2081/2497 (83.3)
Yes297/1302 (22.8)119/1195 (10.0)416/2497 (16.7)
HIV0.416
No4336/4570 (94.9)3366/3564 (94.4)7702/8134 (94.7)
Yes234/4570 (5.1)198/3564 (5.6)432/8134 (5.3)
TB (Current/Past)0.117
No4530/4596 (98.6)3520/3582 (98.3)8050/8178 (98.4)
Yes66/4596 (1.4)62/3582 (1.7)128/8178 (1.6)
Chronic Kidney Disease0.837
No4552/4582 (99.4)3532/3554 (99.4)8084/8136 (99.4)
Yes30/4528 (0.7)22/3554 (0.6)52/8136 (0.6)
Malignancy0.929
No4554/4574 (99.6)3515/3533 (99.5)8069/8107 (99.5)
Yes20/4574 (0.4)18/3533 (0.5)38/8107 (0.5)
Province<0.001
Gauteng1739 (32.8)1763 (36.4)3502 (34.5)
Kwa-Zulu Natal1250 (23.6)1149 (23.7)2399 (23.6)
Eastern Cape793 (15.0)512 (10.6)1305 (12.9)
Western Cape547 (10.3)736 (15.2)1283 (12.6)
North West407 (7.7)286 (5.9)639 (6.8)
Free State159 (3.0)192 (4.0)351 (3.5)
Mpumalanga186 (3.5)94 (1.9)280 (2.8)
Limpopo163 (3.1)69 (1.9)232 (2.3)
Northern Cape56 (1.1)48 (1.0)104 (1.0)
Table
Table 2. Factors associated with disease severity among HCWs admitted with COVID-19 (N = 10 149), 5 March 2020–31 December 2021.
Table 2. Factors associated with disease severity among HCWs admitted with COVID-19 (N = 10 149), 5 March 2020–31 December 2021.
CharacteristicDisease Severity HCWsUnadjusted OR (95% CI)pAdjusted OR (95% CI)p
Age Group (Years)
20–29158 (3.0)ref (1.00) ref
30–39692 (13.1)2.0 (1.6–2.4)<0.0011.9 (1.5–2.3)<0.001
40–491202 (22.7)3.2 (2.7–3.9)<0.0013.1 (2.5–4.0)<0.001
50–591769 (33.4)5.3 (4.3–6.4)<0.0014.3 (3.4–5.4)<0.001
≥601479 (27.9)8.4 (6.9–10.3)<0.0016.1 (4.8–7.7)<0.001
Sex
Female3241 (61.2)ref ref
Male2059 (38.9)1.8 (1.7–2.0)<0.0011.5 (1.4–1.7)0.003
Chronic Diseases
Hypertension
No2740/4781 (57.3)ref ref
Yes2041/4781 (42.7)1.9 (1.8–2.1)<0.0011.3 (1.2–1.5)0.027
Diabetes
No3404/4703 (72.4)ref ref
Yes1299/4703 (27.6)1.8 (1.6–2.0)<0.0011.3 (1.2–1.5)<0.001
Cardiac Disease
No4482/4596 (97.5)ref
Yes114/4596 (2.5)2.1 (1.5–3.0)<0.0011.4 (0.9–2.0)0.133
Asthma
No3365/3607 (93.3)ref ref
Yes242/3607 (6.7)1.0 (0.9–1.2)0.607
HIV
No4336/4570 (94.9)ref ref
Yes234/4570 (5.1)0.9 (0.8–1.1)0.3851.4(1.1–1.7)0.008
TB (Current/Past)
No4530/4596 (98.6)ref
Yes66/4596 (1.4)0.8 (0.6–1.2)0.287
Chronic Kidney Disease
No4552/4582 (99.4)ref
Yes30/4528 (0.7)1.1 (0.6–1.8)0.841
Malignancy
No4554/4574 (99.6)ref
Yes20/4574 (0.4)0.9 (0.4–1.6)0.929
Province
Western Cape1739 (32.8)ref ref
Gauteng1250 (23.6)1.3 (1.2–1.5)<0.0012.2 (1.8–2.5)<0.001
Kwa-Zulu natal793 (15.0)1.5 (1.3–1.7)<0.0011.6 (1.4–1.9)<0.001
Eastern Cape547 (10.3)2.1 (1.8–2.4)<0.0012.2 (1.8–2.6)<0.001
North West407 (7.7)1.9 (1.6–2.3)<0.0012.3 (1.8–2.8)<0.001
Free State159 (3.0)1.1 (0.9–1.4)0.3721.4 (1.0–1.8)0.030
Mpumalanga186 (3.5)2.7 (2.0–3.5)<0.0012.3 (1.8–2.8)<0.001
Limpopo163 (3.1)3.2 (2.3–4.3)<0.0012.8 (1.8–4.4)<0.001
Northern Cape56 (1.1)1.6 (1.1–2.3)0.0281.3 (0.8–2.2)0.304
Hosmer–Lemeshow chi2 (8) Prob > chi2 = 0.0665
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Ratshikhopha, E.; Muvhali, M.; Naicker, N.; Tlotleng, N.; Jassat, W.; Singh, T. Disease Severity and Comorbidities among Healthcare Worker COVID-19 Admissions in South Africa: A Retrospective Analysis. Int. J. Environ. Res. Public Health 2022, 19, 5519. https://doi.org/10.3390/ijerph19095519

AMA Style

Ratshikhopha E, Muvhali M, Naicker N, Tlotleng N, Jassat W, Singh T. Disease Severity and Comorbidities among Healthcare Worker COVID-19 Admissions in South Africa: A Retrospective Analysis. International Journal of Environmental Research and Public Health. 2022; 19(9):5519. https://doi.org/10.3390/ijerph19095519

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Ratshikhopha, Edith, Munyadziwa Muvhali, Nisha Naicker, Nonhlanhla Tlotleng, Waasila Jassat, and Tanusha Singh. 2022. "Disease Severity and Comorbidities among Healthcare Worker COVID-19 Admissions in South Africa: A Retrospective Analysis" International Journal of Environmental Research and Public Health 19, no. 9: 5519. https://doi.org/10.3390/ijerph19095519

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