Factors associated with mortality due to COVID-19 in hospitalised health workers and non-health workers in South Africa, 2020-2022

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However evidence comparing their in-hospital mortality with that of non-health workers (non-HWs) in South Africa remains limited. This study compares in-hospital COVID-19 mortality and associated factors between hospitalized HW and non-HW groups from 2020 to 2022. Methods Cross-sectional analysis of national COVID-19 hospitalization data from 408 public and 262 private hospitals in South Africa was conducted. Our dataset included individuals aged 20–69 years with confirmed SARS-CoV-2 infection admitted between March 2020 and October 2022. Mortality outcomes were analysed using descriptive statistics, chi-square tests, and logistic regression to identify demographic, clinical, and contextual factors associated with death due to SARS-COV-2 infection. Results Overall in-hospital mortality was lower among HWs than non-HWs (16.3% vs 21.7%, p < 0.001). For both groups, older age, admission to intensive care, and treatment in public sector hospitals were associated with increased mortality. Among HWs, the strongest comorbidity predictors of death were chronic renal failure (aOR = 3.43), malignancy (aOR = 3.25), and active tuberculosis (aOR = 2.34). In non-HWs, mortality risk was more broadly elevated across active tuberculosis (aOR = 1.67), malignancy (aOR = 1.64), chronic renal failure (aOR = 1.60), diabetes (aOR = 1.41), HIV (aOR = 1.35), obesity (aOR = 1.18), and cardiac disease (aOR = 1.16). COVID-19 vaccination was strongly protective in both groups (aOR; HWs = 0.5 vs non-HWs = 0.72), with significantly reduced odds of death compared to unvaccinated individuals. Conclusion HWs had lower in-hospital mortality than non-HWs, likely reflecting earlier access to vaccination, better healthcare access, and proactive disease management. However, HWs with serious comorbidities—particularly renal failure, malignancy, and tuberculosis—remained at high risk. These findings highlight the importance of sustained vaccination, targeted protection for high-risk groups, and addressing systemic inequities in healthcare delivery to strengthen resilience for future pandemics. SARS-CoV-2 Hospital Surveillance Health Workers Hospital Admissions In Hospital Mortality Figures Figure 1 Introduction The emergence and rapid global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) resulted in an unprecedented public health and economic crisis worldwide. 1 Early global estimates placed the infection fatality rate (IFR) for COVID-19 near 1%. 2 However, this average masked disparities, with risk of mortality soaring with advanced age and the presence of comorbidities like cardiovascular disease and diabetes. 3 The adoption of vaccination and the emergence of the Omicron variant drastically reduced the overall IFR, with later estimates falling to approximately 0.1% or lower in highly immunised populations. 4, 5 Despite this overall reduction, mortality remained disproportionately high among older adults, the unvaccinated, and immunocompromised groups, highlighting critical vulnerabilities. 6, 7 Concerns about the emergence of new SARS-CoV-2 variants with potential to evade immunity are on the rise globally, prompting renewed emphasis on genomic surveillance, booster vaccination strategies, and preparedness. 8 A global meta-analysis of 42 studies and 400,000 patients found a 17.6% pooled mortality rate among hospitalized COVID-19 cases, with higher risk linked to older age (pOR=2.61), male gender (pOR=1.45), and comorbidities such as cardiovascular disease (pOR=1.83), diabetes (pOR=1.52), hypertension (pOR=1.57), COPD (pOR=1.58), obesity (pOR=1.34), cancer (pOR=1.43), and kidney disease (pOR=1.87). 3 In another systematic review hospital mortality was 11.5% in general patients and 40.5% in the critically ill 9 . Black ethnic groups faced disproportionately high mortality, largely due to systemic inequalities in socioeconomic and healthcare domains 10 . South Africa was one of the hardest hit countries in Africa, with over 4 million cases and over 114,000 deaths at the end of 2022. 9 Moreover, According to authoritative analyses conducted jointly by the South African Medical Research Council and Statistics South Africa, which leveraged civil registration data from the National Population Register, the country experienced an estimated 591,000 excess deaths attributable to the pandemic through December 2022. This figure is not derived from reported laboratory-confirmed cases or medically certified COVID-19 deaths, metrics known to be severely undercounted due to limited testing capacity, inconsistent death certification practices, and fragmented health information systems, but rather from a robust time-series analysis of all-cause mortality deviations against a statistically modeled baseline. 10, 11 National data showed that case load was a significant predictor of mortality, with specific weeks of high COVID-19 admissions associated with a 50% increase in the odds of death (aOR = 1.5). 12 Care setting also played a role as public hospitals reported higher adjusted mortality odds (aOR=1.39) compared to private facilities. 13 Literature shows that health workers (HWs) faced elevated risks of contracting SARS-CoV-2 infection and poor outcomes from COVID-19 due to repeated high-risk occupational exposures while managing patients during the pandemic response. 14 However, evaluation of COVID-19 mortality outcomes among hospital-admitted HWs in comparison to hospital-admitted general population has been limited, especially from low and middle-income countries. 12 HWs admitted to public hospitals exhibited 40% lower odds of death than those treated in private health facilities. 12 Additionally, geographical disparities within the country were notable, with mortality rates among HWs in the Limpopo and Eastern Cape provinces being 1.9 and 2.5 times higher, respectively, than in the Western Cape. 12 COVID-19 vaccination efforts showed a substantial reduction in mortality rates across various populations since their rollout. 4 While physicians in high-exposure specialties, such as those working with oronasal secretions, faced increased risks, the pandemic underscored that all healthcare specialities encounter significant risks, and none should be overlooked. 15 This study builds upon the findings of a previous publication by Tlotleng et al. (2022), which analysed COVID-19 hospital admissions and mortality among HWs in South Africa during the period of March 2020 – April 2021. 12 The current study extends the analysis to include data until November 2022 when surveillance was discontinued, focusing on in-hospital mortality outcomes among both HWs and non-HWs. By examining the evolution of the pandemic's impact over a longer period, this study aims to provide an understanding of the factors influencing mortality and the effectiveness of interventions such as the COVID-19 vaccination. Methods Study design This is a repeated secondary data cross-sectional analysis of mortality rate and factors associated with COVID-19-related mortality among hospitalised HWs and non-HWs. 12 Data source The records accessed for this analysis were obtained from a merged dataset comprising the COVID-19 Daily Hospital Surveillance records (DATCOV) and the Electronic Vaccination Data System (EVDS), both maintained under the custodian of the National Department of Health. The EVDS was developed by the South African National Department of Health to support the COVID-19 vaccination roll out in South Africa. 16 The merged data-set contained data on all individuals, who had a positive test for SARS-CoV-2 (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 reason for admission. 12 Study population and case definition All SARS-CoV-2 patients who were admitted to 408 public and 262 private hospitals across South Africa from 5 March 2020 to 31 October 2022, whose data was submitted to the DATCOV national hospital surveillance system were included. The primary outcome of interest was the discharge status of the patients, classified as either "lived" or "died," with death defined as occurring during the hospital stay due to COVID-19. To facilitate comparisons, patients were divided into two categories: HWs and non-HWs. HW status was determined through clinician reports and encompassed a wide range of roles, including medical doctors, nurses, allied health professionals, laboratory personnel, paramedics, administrators, and porters. For individuals whose precise occupational roles were unknown, the category “other” was used. The analysis included adult participants aged 20 to 69 years. This sample was restricted to this age range because the dataset of HW only contained individuals aged 20 and older. To ensure a valid comparison, the same age criteria were applied to the non-HW worker group. Data Management The study examined mortality across five distinct COVID-19 waves in South Africa. We defined a ‘wave’ as a period where the COVID-19 weekly incidence was ≥ 30 cases per 100,000 persons until the weekly cases dropped to ≤ 30 cases per 100,000 persons. 17 The first wave spanned from 5 March to 21 August 2020, followed by the second wave from 22 August 2020 to 6 February 2021. The third wave occurred between 7 February and 18 September 2021, the fourth wave lasted from 19 September 2021 to 5 February 2022, and the fifth wave extended from 6 February to 31 October 2022. The study sought to account for the burden of hospital admissions by using weekly national case numbers as a proxy for hospital case load. Case load was divided into three categories: low (fewer than 3,500 national cases per week), moderate (3,500 to 7,999 cases per week), and high (more than 8,000 cases per week). 12 Key variables collected included vaccination status, age group (divided into 20–29, 30–39, 40–49, 50–59, and 60–69 years), and sex (male or female). Ethnicity was also considered, with patients identified as Black, Coloured, Indian, White, or Other. Additionally, the study took into account the province where death occurred and the healthcare sector (public or private) where treatment was provided. The highest level of care received by each patient was categorized as general ward, high care unit, and intensive care unit (ICU). The presence of comorbid conditions—such as hypertension, diabetes, chronic renal disease, chronic pulmonary asthma, chronic cardiovascular disease, tuberculosis (both current and past), HIV and obesity—was included as a key component in the dataset. Data Analysis The analyses were conducted using Stata version 18 (StataCorp®, College Station, Texas, USA). Descriptive statistics were conducted, with frequencies and percentages reported as all variables were categorical. The COVID-19-related in-hospital mortality ratio (CFRs) was calculated as the number of deaths divided by the total number of hospital admissions within each covariate group, multiplied by 100. Chi-square tests were used to assess statistically significant differences between HWs and non-HWs across covariates. To assess changes in hospitalisations over time and differences between HWs and non-HWs, a negative binomial regression model was fitted, with hospitalisation counts as the dependent variable and time (wave index) and HW status as independent variables. An interaction term ( HW × time ) was included to evaluate whether trends differed between the two groups. Overdispersion was assessed using the Pearson chi-squared statistic, and robust standard errors were applied to account for variance inflation. Univariable and multivariable logistic regression analyses were performed to identify factors associated with in-hospital mortality separately among HWs and non-HWs. A manual forward selection approach was used to select candidate predictor variables, including those with a significance level of p < 0.20 in univariable analysis. Variables were included in the final multivariable model based on significance or established epidemiological evidence and relevance in the literature. All hypothesis testing was conducted at a significance level of 0.05. Results There were 492,118 hospital admissions in South Africa between March 2020 and October 2022 with positive COVID-19 results. The majority 364,377/429,118 (84.9%) of these admissions were aged between 20 to 69 years. A total of 2.8% (10,197/364,377) of the hospital admissions were HWs. Over the span of nearly three years, a sharp rise in hospital admissions during the five defined waves was seen. The months in between the waves showed a baseline level in hospitalizations for both HWs and non-HWs (Figure 1). Trends reveal that HWs were disproportionately hospitalised during the first wave, accounting 36.9% of HW hospitalisations compared to 17.2% among non-HWs (Table 1). However, hospitalisations among HWs declined markedly in subsequent waves, reaching only 3.5% in the fifth wave, compared to 6.0% in non-HWs. There was no statistically significant difference in the rate of change over time between HWs and non-HWs (p = 0.193), though the overall decline over months was significant (p = 0.047). Among admitted HWs, females represented the majority (68.7%) of the cases, compared to 55.3% among non-HWs. The age group most affected among HWs was 50-59 years, accounting for 30.5% of cases, a pattern similarly observed in non-HWs, where this age group comprised 26.3% of cases. In terms of hospitalisation ethnic distribution, 54.8% were Black African, followed by 13.5% White, 6.9% Indian and 5.5% coloured, while 19.3% were classified as unknown. Among non-HWs, Black Africans also accounted for the majority (50%) of admissions. However, the proportion of cases with unclassified ethnicity was nearly double (36.2%) that observed in HWs. The occupational categories of HWs revealed that 17.2% were nurses, while a substantial 68.5% were classified as "Other". The hospitalisation of HWs was largely concentrated in the private healthcare sector, accounting for 69.1% of HW admissions compared to 46.9% of non-HWs. Geographically, Gauteng (30.3%), Western Cape (19.0%), and KwaZulu-Natal (17.3%) collectively represented nearly two-thirds of all hospitalisations among both HWs and non-HWs. The Eastern Cape had a higher proportion of HW admissions (12.4%) compared to non-HWs (9.2%). The remaining provinces (Free State, Limpopo, Mpumalanga, North West, and Northern Cape) each accounted for less than 7% of total admissions. In terms of severity, HWs demonstrated a higher proportion of high-care (7.7%) and ICU admissions (15.5%) compared to non-HWs (5.5% and 13.2% respectively). HWs had significantly better survival rates, with 89.1% of HWs surviving compared to 81.1% of non-HWs (X 2 = < 0.001). Vaccination data indicated that HWs had a higher vaccination rate (11.8%) relative to non-HWs (9.0%). Comorbidities were significantly more prevalent among HWs, with 44.3% reporting at least one comorbidity, compared to 40.1% of non-HWs (p = < 0.01). Obesity (17.2%), asthma (6.3%) and hypertension (34.7%) were significantly more common among HWs with comorbidities. In contrast, non-HWs exhibited higher proportions of HIV (11.73%), active tuberculosis (2.6%) and past tuberculosis (4.1%) compared to HWs, who reported lower rates of HIV (5.47%), active (0.8%) and past tuberculosis (1.0%). Non-HWs also suffered significantly more chronic renal disease and chronic pulmonary disease than HWs. There were no significant differences in the presence of diabetes, cardiac disease, and malignancies between the two groups (p = 0.055, 0.199 and 0.140 respectively). Table 1: Characteristics of hospitalised HWs and non-HWs with COVID–19 in South Africa, 5 th March 2020 to 31 st October 2022 (n=364 377). Non-HWs HWs Total (N = 354180) (N = 10197) (N = 364377) X 2 (p-value) Wave <0.001 1 60783 (17.2%) 3764 (36.9%) 64547 (17.7%) 2 98489 (27.8%) 3123 (30.6%) 101612 (27.9%) 3 127442 (36.0%) 2067 (20.3%) 129509 (35.5%) 4 46073 (13.0%) 882 (8.6%) 46955 (12.9%) 5 21393 (6.0%) 361 (3.5%) 21754 (6.0%) Birth Sex <0.001 Female 195665 (55.3%) 7007 (68.7%) 202672 (55.6%) Male 158371 (44.7%) 3190 (31.3%) 161561 (44.4%) Age Group <0.001 20-29 38629 (10.9%) 806 (7.9%) 39435 (10.8%) 30-39 66930 (18.9%) 2109 (20.7%) 69039 (18.9%) 40-49 73801 (20.8%) 2657 (26.1%) 76458 (21.0%) 50-59 93079 (26.3%) 3115 (30.5%) 96194 (26.4%) 60-69 81741 (23.1%) 1510 (14.8%) 83251 (22.8%) Ethnic Group <0.001 Black 176617 (49.9%) 5593 (54.8%) 182210 (50.0%) Coloured 15805 (4.5%) 560 (5.5%) 16365 (4.5%) Indian 11725 (3.3%) 700 (6.9%) 12425 (3.4%) Unknown 128264 (36.2%) 1966 (19.3%) 130230 (35.7%) White 21769 (6.1%) 1378 (13.5%) 23147 (6.4%) Health Worker Type . Administrators/porters 0 (0.%) 852 (8.4%) Allied Health 0 (0.%) 389 (3.8%) Doctor 0 (0.%) 216 (2.1%) Nurse 0 (0.%) 1750 (17.2%) Other 0 (0.%) 6990 (68.5%) Public Sector <0.001 Private 166234 (46.9%) 7044 (69.1%) 173278 (47.6%) Public 187946 (53.1%) 3153 (30.9%) 191099 (52.4%) Province <0.001 Eastern Cape 32717 (9.2%) 1262 (12.4%) 33979 (9.3%) Free State 21971 (6.2%) 351 (3.4%) 22322 (6.1%) Gauteng 108894 (30.7%) 3669 (36.0%) 112563 (30.9%) KwaZulu-Natal 60499 (17.1%) 2396 (23.5%) 62895 (17.3%) Limpopo 14500 (4.1%) 246 (2.4%) 14746 (4.0%) Mpumalanga 15979 (4.5%) 272 (2.7%) 16251 (4.5%) North West 23349 (6.6%) 693 (6.8%) 24042 (6.6%) Northern Cape 8148 (2.3%) 104 (1.0%) 8252 (2.3%) Western Cape 68123 (19.2%) 1204 (11.8%) 69327 (19.0%) Highest Level of Care <0.001 General Ward 287919 (81.3%) 7830 (76.8%) 295749 (81.2%) High Care 19602 (5.5%) 782 (7.7%) 20384 (5.6%) Intensive Care Unit 46659 (13.2%) 1585 (15.5%) 48244 (13.2%) Vaccination Status Outcome <0.001 No 322134 (91.0%) 8993 (88.2%) 331127 (90.9%) Yes 32046 (9.0%) 1204 (11.8%) 33250 (9.1%) Discharge Status <0.001 Lived 287089 (81.1%) 9089 (89.1%) 296178 (81.3%) Died 67091 (18.9%) 1108 (10.9%) 68199 (18.7%) Comorbidity <0.001 None 212217 (59.9%) 5682 (55.7%) 217899 (59.8%) ≥ 1 141963 (40.1%) 4515 (44.3%) 146478 (40.2%) Hypertension 0.021 No 169535 (66.5%) 5452 (65.3%) 174987 (66.5%) Yes 85320 (33.5%) 2896 (34.7%) 88216 (33.5%) Diabetes 0.055 No 188414 (76.3%) 6345 (77.2%) 194759 (76.3%) Yes 58580 (23.7%) 1874 (22.8%) 60454 (23.7%) Cardiac Disease 0.199 No 230909 (98.0%) 7971 (98.2%) 238880 (98.0%) Yes 4716 (2.0%) 146 (1.8%) 4862 (2.0%) Chronic Pulmonary Disease <0.001 No 225501 (96.1%) 7973 (98.6%) 233474 (96.2%) Yes 9189 (3.9%) 114 (1.4%) 9303 (3.8%) Asthma 0.046 No 226187 (94.2%) 7664 (93.7%) 233851 (94.2%) Yes 13800 (5.8%) 513 (6.3%) 14313 (5.8%) Chronic Renal Failure <0.001 No 229566 (97.9%) 8048 (99.4%) 237614 (98.0%) Yes 4888 (2.1%) 51 (0.6%) 4939 (2.0%) Malignancy 0.140 No 232654 (99.4%) 8036 (99.6%) 240690 (99.4%) Yes 1337 (0.6%) 36 (0.4%) 1373 (0.6%) HIV <0.001 No 213264 (88.27) 7653 (94.53) 220917 (88.47) Yes 28341 (11.73) 443 (5.47) 28784 (11.53) Tuberculosis Active <0.001 No 230328 (97.4%) 8050 (99.2%) 238378 (97.5%) Yes 6096 (2.6%) 68 (0.8%) 6164 (2.5%) Tuberculosis Past <0.001 No 219119 (95.9%) 7910 (99.0%) 227029 (96.1%) Yes 9249 (4.1%) 76 (1.0%) 9325 (3.9%) Obesity <0.001 No 63111 (88.2%) 2060 (82.8%) 65171 (88.0%) Yes 8421 (11.8%) 427 (17.2%) 8848 (12.0%) Case Fatality Ratios Among all hospitalised COVID-19 patients in South Africa, 18.7% (68,199/364,377) died between 2020 and 2022. Mortality among HWs was notably lower at 10.9% (1,108/10,197) compared with 18.9% (67,091/354,180) in non-HWs (Table 1). The adjusted odds of mortality for each group, taking comorbidities and other risk factors into consideration, are presented in Table 2. Both HWs and non-HWs suffered their highest CFRs during the third wave, with HWs at 14.6% and non-HWs at 25.3% mortality. Vaccination provided substantial protection for both groups with a decrease in CFR for vaccinated compared to unvaccinated cases. The effect was higher among HWs (45.8%) than non-HWs (39.8%). During weeks of high caseload, CFRs increased to 17.5% for HWs and 25.3% for non-HWs. For non-HWs, the CFR in public hospitals (22.4%) was considerably higher than in private facilities (15%). For HWs, however, this relationship was reversed, with a higher CFR in the private sector (12.31%) than in the public sector (7.6%). The mortality ratios for those requiring intensive care, CFRs rose to 42.8% for HWs and 47.6% for non-HWs admitted to the ICU. Among HWs, the CFR rose steadily with age, from 1.2% in the 20–29 age group to 24.5% in those aged 60–69 years . In comparison, non-HWs also experienced a higher mortality ratio at every age bracket compared to HWs, with their CFR starting at 5.1% in the 20–29 age group and climbing to 32.8% in those aged 60–69 years . Among HWs, the Eastern Cape Province recorded the highest CFR at 15.1% , followed by Mpumalanga (14.3%) and KwaZulu-Natal (11.2%) . The Western Cape , Gauteng , and Free State had lower CFRs, ranging between 7.7% and 10.6%. For non-HWs, the pattern was similar, the Eastern Cape again had the highest CFR at 26.2% , followed by Limpopo (24.5%) , Mpumalanga (21.0%) , and KwaZulu-Natal (19.0%) .The Western Cape (17.3%) , Gauteng (17.8%) , and North West (14.3%) reported comparatively lower CFRs similar to HWs. Among HWs, mortality ratio was highest for those with chronic renal failure (29.4%) and malignancy (27.8%) . In contrast, asthma (11.7%) and chronic pulmonary disease (10.5%) were associated with more modest CFRs, closer to the overall average for HWs. For non-HWs, the influence of comorbidities on mortality was also increased. The highest CFRs were also seen with chronic renal failure (38.6%) and malignancy (32.9%) . Factors associated with COVID-19 mortality in hospitalised cases Among HWs , the factor most strongly associated with mortality was admission to intensive care, which conferred more than a fifteen-fold increase in the odds of death (aOR = 15.34; 95% CI: 12.78–18.41). Age was the second strongest predictor, with those aged 60–69 years having nearly eleven times the odds of death compared to the 20–29 age group (aOR = 10.93; 95% CI: 5.21–22.91). Hospitalisation during weeks of high national caseload also significantly elevated mortality risk (aOR = 2.10; 95% CI: 1.63–2.71), as did receiving care in the public sector (aOR = 1.49; 95% CI: 1.10–2.02). Important comorbidities independently associated with mortality among HWs included chronic renal failure (aOR = 3.43; 95% CI: 1.64–7.19), malignancy (aOR = 3.25; 95% CI: 1.21–8.74), and active tuberculosis (aOR = 2.34; 95% CI: 1.09–5.02). Diabetes was also a significant risk factor, albeit more modest in effect (aOR = 1.52; 95% CI: 1.23–1.88). Vaccination was protective, reducing the odds of death by half (aOR = 0.50; 95% CI: 0.35–0.73). Compared to doctors, several occupational groups showed significantly lower mortality risk. Administrators/Porters (aOR 0.45, p=0.019), Nurses (aOR 0.49, p=0.010), and other health workers (aOR 0.48, p=0.003) had approximately half the adjusted odds of death. Among non-HWs , admission to ICU was also the strongest predictor of mortality, with a more than seven-fold increase in the odds of death (aOR = 7.18; 95% CI: 6.69–7.71). Age again showed a powerful gradient effect, with individuals aged 60–69 years experiencing more than eight times the odds of death compared to those aged 20–29 years (aOR = 8.23; 95% CI: 7.26–9.34). High case-load periods significantly increased risk (aOR = 1.54; 95% CI: 1.42–1.66), and receiving care in the public sector nearly tripled the odds of death compared to the private sector (aOR = 2.95; 95% CI: 2.74–3.17). A broader range of comorbidities were associated with mortality in this group: chronic renal failure (aOR = 1.60; 95% CI: 1.41–1.81), malignancy (aOR = 1.64; 95% CI: 1.33–2.02), active tuberculosis (aOR = 1.67; 95% CI: 1.45–1.94), history of tuberculosis (aOR = 1.20; 95% CI: 1.05–1.36), HIV (aOR = 1.35; 95% CI: 1.25–1.45), diabetes (aOR = 1.41; 95% CI: 1.33–1.50), cardiac disease (aOR = 1.16; 95% CI: 1.03–1.29), and obesity (aOR = 1.18; 95% CI: 1.09–1.27). Vaccination was again strongly protective, reducing the odds of mortality by 28% (aOR = 0.72; 95% CI: 0.63–0.83). Table 2: Factors associated with COVID–19 hospital mortality among South African hospitalisations, 5 th March 2020 to 31 st October 2022 (N=364 377). HWs Non-HWs Variable CFR (%) aOR (95% CI) P-value (aOR) CFR (%) aOR (95% CI) P-value (aOR) Pandemic Wave Wave 1 (Ref.) 8.87 — — 18.32 — — Wave 2 13.8 1.58 (1.26 to 1.98) 0.000 22.36 1.15 (1.06 to 1.24) 0.000 Wave 3 14.56 1.99 (1.54 to 2.56) 0.000 21.54 1.32 (1.22 to 1.42) 0.000 Wave 4 3.4 0.84 (0.51 to 1.4) 0.506 10.16 0.86 (0.77 to 0.96) 0.007 Wave 5 3.32 1.12 (0.53 to 2.37) 0.766 8.38 1 (0.85 to 1.17) 0.987 Weekly Admissions Low, 8 000 cases 17.51 2.1 (1.63 to 2.71) 0.000 25.25 1.54 (1.42 to 1.66) 0.000 Sex Female (Ref.) 9.52 — — 17.15 — — Male 13.82 0.94 (0.79 to 1.13) 0.510 21.15 1.2 (1.15 to 1.26) 0.000 Unknown n/a n/a n/a 18.75 Age Group 20–29 (Ref.) 1.24 — — 5.11 — — 30-39 3.79 2.25 (1.04 to 4.83) 0.038 9.08 1.85 (1.61 to 2.11) 0.000 40-49 9.07 4.15 (1.98 to 8.7) 0.000 15.12 2.86 (2.52 to 3.26) 0.000 50-59 13.07 4.92 (2.36 to 10.27) 0.000 22.68 4.85 (4.28 to 5.5) 0.000 60–69 24.50 10.93 (5.21 to 22.91) 0.000 32.75 8.23 (7.26 to 9.34) 0.000 Ethnic Group Black African (Ref.) 10.64 — — 20.62 — — Coloured 10.18 0.76 (0.52 to 1.12) 0.161 21.2 0.73 (0.66 to 0.81) 0.000 Indian 15.43 1.15 (0.85 to 1.57) 0.369 19.56 1.02 (0.91 to 1.16) 0.697 White 13.28 0.79 (0.62 to 1.02) 0.069 16.9 0.69 (0.61 to 0.77) 0.000 Unknown n/a 1.17 (0.88 to 1.55) 0.279 16.64 0.88 (0.79 to 0.98) 0.018 HW Category Doctor 20.37 — — n/a Administrators / porters 6.34 0.45 (0.23 to 0.88) 0.019 Allied Health 11.05 0.56 (0.28 to 1.1) 0.091 Nurse 10.34 0.49 (0.28 to 0.84) 0.010 Other 11.24 0.48 (0.29 to 0.78) 0.003 Healthcare Sector Private (Ref.) 12.31 — — 15.04 — — Public 7.64 1.49 (1.1 to 2.02) 0.011 22.40 2.95 (2.74 to 3.17) 0.000 Province Western Cape (Ref.) 10.63 — — 17.34 — — Eastern Cape 15.06 2.17 (1.53 to 3.08) 0.000 26.15 1.29 (1.16 to 1.43) 0.000 Free State 7.69 1.22 (0.7 to 2.15) 0.483 18.19 0.9 (0.81 to 1.01) 0.071 Gauteng 9.54 1.21 (0.88 to 1.67) 0.238 17.78 0.86 (0.77 to 0.96) 0.010 KwaZulu-Natal 11.23 1.36 (0.95 to 1.93) 0.091 18.95 0.94 (0.84 to 1.05) 0.254 Limpopo 10.98 3.4 (1.74 to 6.65) 0.000 24.54 1.04 (0.89 to 1.22) 0.582 Mpumalanga 14.34 1.39 (0.79 to 2.44) 0.254 20.98 1.12 (0.94 to 1.34) 0.197 North West 9.52 1.24 (0.79 to 1.94) 0.351 14.33 0.69 (0.6 to 0.8) 0.000 Northern Cape 11.54 1.54 (0.58 to 4.13) 0.388 20.09 0.71 (0.57 to 0.9) 0.004 Admission Severity General Ward 4.53 — — 14.17 — — High Care 9.59 2.1 (1.53 to 2.88) 0.000 20.82 2.16 (1.96 to 2.37) 0.000 ICU 42.78 15.34 (12.78 to 18.41) 0.000 47.6 7.18 (6.69 to 7.71) 0.000 Vaccination Status Unvaccinated (Ref.) 11.49 — — 19.65 — — Vaccinated 6.23 0.5 (0.35 to 0.73) 0.000 11.83 0.72 (0.63 to 0.83) 0.000 Comorbidities None (Ref.) 7.22 — — 15.29 — — ≥1 Comorbidity 15.46 1.06 (0.86 to 1.3) 0.593 24.41 1.12 (1.03 to 1.22) 0.006 Hypertension No (Ref.) 9.32 — — 15.47 — — Yes 17.4 26.08 0.98 (0.92 to 1.05) 0.596 Diabetes No (Ref.) 9.6 — — 15.62 — — Yes 20.22 1.52 (1.23 to 1.88) 0.000 29.08 1.41 (1.33 to 1.5) 0.000 Cardiac Disease No (Ref.) 11.92 — — 17.8 — — Yes 15.75 29.41 1.16 (1.03 to 1.29) 0.011 Chronic Pulmonary Disease No (Ref.) 12.04 — — 17.83 — — Yes 10.53 22.03 0.76 (0.66 to 0.88) 0.000 Asthma No (Ref.) 11.94 — — 17.85 — — Yes 11.7 19.01 0.94 (0.83 to 1.06) 0.292 Chronic Renal Failure No (Ref.) 11.84 — — 17.6 — — Yes 29.41 3.43 (1.64 to 7.19) 0.001 38.6 1.6 (1.41 to 1.81) 0.000 Malignancy No (Ref.) 11.95 — — 17.87 — — Yes 27.78 3.25 (1.21 to 8.74) 0.019 32.91 1.64 (1.33 to 2.02) 0.000 HIV No (Ref.) 12.14 — — 17.45 — — Yes 10.84 1.24 (0.81 to 1.88) 0.320 22.91 1.35 (1.25 to 1.45) 0.000 Active TB No (Ref.) 11.93 — — 17.77 — — Yes 17.65 2.34 (1.09 to 5.02) 0.028 23.23 1.67 (1.45 to 1.94) 0.000 TB History No (Ref.) 12 — — 17.66 — — Yes 11.84 21.56 1.2 (1.05 to 1.36) 0.006 Obesity No (Ref.) 9.42 — — 19.19 — — Yes 15.69 30.61 1.18 (1.09 to 1.27) 0.000 Pseudo R²: HW = 0.2932; Non-HW = 0.1645 Discussion This national study analysed over 480,000 hospitalisations for COVID-19 in South Africa between March 2020 and October 2022, providing the most comprehensive comparison to date of mortality outcomes and associated factors among HWs and non-HWs for South Africa. 2.8% of the hospital admissions were HWs which is above the 0.5% of the working age population public HWs makeup. 18 The mortality rate in our study (18.72%) was consistent with pooled international estimates for hospitalized COVID-19 patients (17.62%). 3 However, HWs experienced lower mortality rates compared to non-HWs (10.9% vs 18.9%). This likely reflects HWs better health literacy, early health seeking behaviour, greater vaccine access and uptake . 19 However, the mortality rate among HWs was still significant at about one in ten, highlighting the risks they faced, especially early in the pandemic. Differences in mortality were evident across pandemic waves, with both groups experiencing peak fatality rates during the third wave (aOR; 1.99 in HWs and 1.32 in non-HWs). This period corresponded with dominance of the Delta variant in South Africa, a strain known for its heightened transmissibility and severity. The substantial rise in mortality during this wave reflects not only the virulence of the Delta variant but also intense pressure it placed on an already strained health system. 20 Among HWs, crude fatality rates fell sharply from 14.6% in Wave 3 to 3.4% in Wave 4 and 3.3% in Wave 5. A similar pattern was seen in non-HWs, with fatality rates decreasing from 25.3% in Wave 3 to 10.2% in Wave 4 and 8.4% in Wave 5, highlighting the substantial mitigation of disease severity over time. The declining crude fatality rates in subsequent waves reflect the combined effects of vaccination, growing natural immunity, improved clinical management, and the emergence of the less virulent Omicron variant. 20 The pattern of admissions underscores the pressure faced by healthcare systems and workers, especially during peak periods (high caseloads), and highlights the critical need for continuous monitoring and adaptive responses to manage impact in any epidemic or pandemic. 21 Vaccination made a significant impact in our study similar to other studies where it lowered the odds of death for both HWs and non-HWs. 22 Non-HWs vaccination was associated with a 45.3% reduction in odds of dying whereas HWs vaccination was associated with a 49.2% reduction in odds of dying (p = 0.000). HWs showed a slightly stronger benefit which may be due to access to vaccines before the more virulent third wave compared to public access during the third wave. 23 This reinforces the protection vaccines provided and suggests benefits for HWs through greater access or possibly higher uptake of vaccines. 23 Eventhough the Sisonke program prioritised HWs be vaccinated first before the general population, only 11.81% of HWs were vaccinated before admission compared to 9.05% of non-HWs, this highlights the need to sustain high coverage of vaccines in healthcare staff who face elevated occupational exposure. 24 , 25 The risk of mortality from COVID-19 decreased in subsequent pandemic waves after the third wave, likely due to rising natural immunity, greater access to therapies, and expansion of vaccines. 26 Wave 3 saw peak fatality rates for HWs at 14.6%, showing challenges especially amidst the Delta surge. 27 During this wave, unvaccinated HWs had extremely high mortality at 15 530 per 100 000 admissions, before declining as vaccine access and uptake widened. This spotlights how essential timely vaccine rollouts are to protect healthcare capacity. 22 , 28 Moreover, the categorisation of weekly national admissions into low, moderate, and high loads underscored the direct impact of healthcare system strain on fatality ratios. 29 Advanced age and several comorbidities remained powerful predictors of in-hospital mortality in both HWs and non-HWs. These vulnerability factors have been well documented throughout the pandemic. 6 Mortality risk significantly increased with age. Men experienced significantly higher mortality odds (aOR = 1.2) than women among non-HWs, a finding consistent with global trends showing greater vulnerability in males. 3 This disparity may reflect differences in health-seeking behaviour, with men often delaying healthcare access or presenting at more advanced stages of illness compared to women. 30 In contrast, among HWs, sex was not a significant factor influencing mortality, possibly due to more equitable uptake of healthcare services, earlier testing, and prompt treatment interventions available within the occupational setting. 3 The significantly higher mortality odds observed among doctors compared to nurses, administrators, porters and other HW categories is possibly due to a combination of higher exposure to severe COVID-19 cases, higher viral loads in clinical settings and specific tasks and increased occupational stress. Allied Health Workers, however, did not show significantly different mortality odds compared to other HW categories. This may reflect their diverse roles many of which involve intermittent or indirect patient contact as well as generally lower exposure intensity and shorter cumulative time in high-risk clinical zones. 31 These differences highlight the need for risk assessments to account for the heterogeneity of exposure across occupational subgroups to better target prevention and protection strategies for higher-risk categories of health workers. 32 Admission to the ICU conferred dramatically elevated odds ratios for mortality, reflecting the critical illness of such patients. However, over 50% of HWs and 50% non-HWs admitted to the ICU survived, due to access to this life-saving care. This highlights the importance of preserving critical care capacity during surges. 33 Patients admitted to High Care also showed increased odd of mortality compared to those admitted to medical wards. Whites and coloureds among non-HWs had significantly reduced adjusted odds of COVID-19 related deaths compared to Black Africans, which remained after adjusting for private vs public healthcare. The differences may be attributed to racial disparities in COVID-19 outcomes in South Africa, structural and systemic inequalities that may increase not only susceptibility to infection but also barriers to appropriate medical care. 7 While no significant differences were seen in HWs between race groups. 34 The mortality aORs were consistently higher for public sector admissions than for the private sector in both HWs and non-HWs despite differences in CFR. There is a survival advantage in private healthcare, perhaps due to better staffing ratios, better access to treatment and advanced therapies, and more consistent quality of care. 35 The higher aOR for public sector admissions is likely driven by both broader socioeconomic factors and healthcare quality. Patients admitted to public hospitals are more often from lower-income backgrounds, which are associated with poorer baseline health, higher prevalence of undiagnosed comorbidities, and delays in accessing care. Furthermore, public sector facilities serve the majority of the population and are more vulnerable to overwhelming patient loads during pandemic surges, conditions that can compromise both timeliness and quality of care. Together, these factors explain why mortality risks were more pronounced the public sector compared with their private sector counterparts. 36 These differences underscore the need for targeted interventions to address the challenges faced by public healthcare, ensuring equitable access to healthcare resources, improving workplace safety, and strengthening the overall healthcare system. 34 , 37 Provincial variation in mortality aORs among non-HWs may reflect the uneven distribution of health system capacity and resources across South Africa. The Eastern Cape and Limpopo HWS faced a two to three-fold greater mortality risk compared to the Western Cape, underscoring severe geographic inequities in occupational protection and health system support during the pandemic. For non-HWs, the Eastern Cape also remained a vulnerability hotspot. Non-HW patients in Gauteng, North West, and the Northern Cape experienced significantly lower mortality odds than those in the Western Cape. These findings highlight that a patient's province of treatment was a major independent determinant of survival, reflecting underlying disparities in healthcare capacity, resource allocation, and possibly the baseline burden of disease, which compromised outcomes in certain regions like the Eastern Cape and Limpopo throughout the health crisis. 38 The overall impact of having any reported comorbidity was not statistically significant among HWs, whereas it was significant in non-HWs, albeit with only a small effect size. This weaker influence of comorbidities in HWs may be due to differences in age distribution, occupational exposures, stress levels, or overall health status between the two groups. Better access and uptake of healthcare among HWs may also contribute, as earlier diagnosis and treatment of chronic conditions could reduce the effect of these comorbidities on mortality outcomes. When individual conditions were examined, clear differences emerged in the ranking of risk factors between HWs and non-HWs. Among HWs, the strongest predictors of mortality were chronic renal failure, followed by malignancy, and active tuberculosis. Diabetes also increased the risk significantly, though to a lesser extent. These findings suggest that conditions that compromise immunity or organ function particularly renal failure and cancer were most consequential for HWs, while active TB further highlighted the vulnerability of those with pre-existing pulmonary inflammation and damage. The risk profile among non-HWs was broader, with more conditions independently associated with mortality, albeit with smaller effect sizes. The highest odds ratios were observed for the same comorbidities as HWs, active tuberculosis, malignancy, and chronic renal failure. These were followed by diabetes and HIV infection, while a history of tuberculosis, obesity, and cardiac disease were associated with more modest but still significant increases in mortality risk. These patterns may possibly be related to better classification of comorbidity status in HWs than the general population. The absence of significant associations for some conditions in HWs may reflect smaller sample sizes, or more consistent access to healthcare. Nonetheless, the elevated risks linked to renal disease, malignancy, and TB in both groups remain biologically credible and align with global evidence on the interplay between chronic illness and COVID-19 outcomes. 39 Overall, the findings from over 190,000 hospitalized cases after vaccine introduction provide evidence around the beneficial association of vaccination with mortality risk from COVID-19. The COVID-19 pandemic has provided valuable lessons for South Africa, emphasizing the need for a resilient and equitable healthcare system, effective public health communication, and strong socio-economic support mechanisms. By addressing these areas, South Africa can better prepare for future health emergencies and improve overall public health outcomes. 40 , 41 Conclusion This large national surveillance study of COVID-19 hospitalisations provides robust evidence on the factors shaping in-hospital COVID-19 mortality in South Africa during the pandemic. The findings reaffirm the pivotal role of vaccination in reducing deaths among both HWs and non-HWs, underscoring the need to sustain high vaccine coverage of vaccine controlled infectious disease, particularly for frontline staff. Mortality risks were highest among older adults, patients with severe comorbidities, and those requiring intensive care, while sectoral and provincial disparities highlighted persistent inequities in healthcare access and capacity. For HWs, mortality was affected by a smaller set of severe conditions most notably chronic renal failure, malignancy, active tuberculosis and diabetes while non-HWs experienced broader vulnerabilities including diabetes, HIV, obesity, and cardiovascular disease. These differences suggest that while HWs may benefit from earlier diagnosis and care, they remain at high risk when burdened with serious comorbidities. Importantly, survival advantages in private hospitals contrasted with poorer outcomes in the public sector, especially among non-HWs, reflecting systemic inequalities in resources and care delivery. Timely vaccination and targeted protection of high-risk groups were central to reducing mortality during this health crisis. Moreover, strengthening healthcare system resilience through investment in critical care, equitable resource allocation, and systematic surveillance is essential to safeguard both frontline workers and the broader population. Future work should extend beyond in-hospital outcomes to track long-term impacts among survivors, generating evidence that can inform preparedness for future pandemics and advance equity in health outcomes. Limitations This study has several limitations that should be considered when interpreting the findings. First, the analysis relied on secondary data sources, which may be subject to incomplete or inconsistent reporting, particularly for variables such as comorbidities, occupation, and disease severity. Moreover, reporting also relied on data submitted through DATCOV on whether an admitted patient was a HW and the categorization. We did not link to any external source or verify HW status. Missing data could introduce information bias, potentially underestimating or overestimating associations. Second, because the dataset reflects hospitalised individuals only, it may not capture those who died outside the hospital or who were never admitted, limiting representativeness and potentially introducing selection bias. Third, the comparatively small sample size of HWs relative to non-HWs may result in reduced statistical power and wider confidence intervals, increasing the likelihood of imprecision or unstable estimates. This difference in sample size warrants cautious interpretation of effect size differences between the two groups. Finally, given that hospital capacity, treatment protocols, and testing strategies evolved over the course of the pandemic, temporal changes may have influenced mortality risk in ways not fully accounted for in the analysis. These factors may limit the generalisability of the findings beyond the study population and period. Declarations Ethics The authors confirm that all relevant ethical guidelines were followed, and any necessary institutional research body (IRB) and ethics committee approvals for the study were obtained. The Human Research Ethics Committee (Medical) of the University of the Witwatersrand approved the study as part of a national surveillance program (ethics reference no: M160667). All methods were carried out in accordance with the accepted national and international guidelines and standards. Conflicts of interest The authors declare that they have no competing interests. Funding declaration DATCOV is funded by the National Institute for Communicable Diseases (NICD) and the South African National Government. No additional funding was obtained towards the completion of this analysis and the development of this manuscript. Clinical trial Clinical number trial is not applicable in this study as it used secondary data sources (DATCOV and EVDS). Consent to participate Given the use of anonymized, routinely collected surveillance data and the approval from the Human Research Ethics Committee (Medical) of the University of the Witwatersrand (ethics reference no: M160667), the requirement for individual informed consent was waived. All data were de-identified prior to analysis. Consent to publish Not applicable. This study reports on anonymized surveillance data, and no individual person’s data is presented in a form that could lead to identification. Availability of data and materials The datasets analyzed during this current study are available in the repository of the National Institute of Communicable Diseases. The data can be made available on request, which may be directed to [email protected] . Those requesting data will need to sign a data access agreement. The request will require approval by the National Department of Health. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8987304","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":627132990,"identity":"0699a964-42b3-4926-8786-2d274b8c37f5","order_by":0,"name":"Jonathan Ramodike","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYBACxgYYwd7AcADMIErLQRDBc4BILWAA1iKRADWAEGBu7z34+eMOuzzzmW8PHvi4g0Gev4H94Qe8Dus5lyxx8ExyscztvISDM88wGM44wJAsgVfLjBwDiYNtzIkzpHMMDvO2MTBuYGA4QEiL8Y+DbfWJMyTPgLXYb2BgbP5BQIsZ0JbDiTMkeMBaEjcwMLPht6XnjJnF2bbjiTN4cgwOzmyTSJ5xmI3NAp8Ww/Ye4xuVbdWJM9jPGH/42GZj29/e/vgGXi0NqHygk5jxqQcCeQLyo2AUjIJRMAoYGAAe9U4Nl69nUAAAAABJRU5ErkJggg==","orcid":"","institution":"National Institute for Occupational Health, National Health Laboratory Services","correspondingAuthor":true,"prefix":"","firstName":"Jonathan","middleName":"","lastName":"Ramodike","suffix":""},{"id":627132991,"identity":"f0a87547-6a5b-4b3f-9a2a-081f6ceecde2","order_by":1,"name":"Hlologelo Ramatsoma","email":"","orcid":"","institution":"National Institute for Occupational Health, National Health Laboratory Services","correspondingAuthor":false,"prefix":"","firstName":"Hlologelo","middleName":"","lastName":"Ramatsoma","suffix":""},{"id":627132992,"identity":"e83e8cee-2c50-4306-9956-222c5bfd4cd2","order_by":2,"name":"Asanda Mkulisi","email":"","orcid":"","institution":"National Institute for Occupational Health, National Health Laboratory Services","correspondingAuthor":false,"prefix":"","firstName":"Asanda","middleName":"","lastName":"Mkulisi","suffix":""},{"id":627132994,"identity":"5e27f253-7b2c-40ec-b414-9f06932a5f84","order_by":3,"name":"Waasila Jassat","email":"","orcid":"","institution":"National Institute for Communicable Diseases (NICD","correspondingAuthor":false,"prefix":"","firstName":"Waasila","middleName":"","lastName":"Jassat","suffix":""},{"id":627132995,"identity":"32afe962-fbe7-4677-b595-4e8aece77b6d","order_by":4,"name":"Lucille Blumberg","email":"","orcid":"","institution":"National Institute for Communicable Diseases (NICD","correspondingAuthor":false,"prefix":"","firstName":"Lucille","middleName":"","lastName":"Blumberg","suffix":""},{"id":627132996,"identity":"8c0472c1-78bd-4b4e-88d7-61f424486039","order_by":5,"name":"Nisha Naicker","email":"","orcid":"","institution":"National Institute for Occupational Health, National Health Laboratory Services","correspondingAuthor":false,"prefix":"","firstName":"Nisha","middleName":"","lastName":"Naicker","suffix":""},{"id":627132999,"identity":"1e5e6b25-3724-4a43-a063-ef2840ab9e56","order_by":6,"name":"Kerry Wilson","email":"","orcid":"","institution":"National Institute for Occupational Health, National Health Laboratory Services","correspondingAuthor":false,"prefix":"","firstName":"Kerry","middleName":"","lastName":"Wilson","suffix":""}],"badges":[],"createdAt":"2026-02-27 11:10:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8987304/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8987304/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107575788,"identity":"6ab9633f-6032-4dee-ae8d-758ab6b9aa30","added_by":"auto","created_at":"2026-04-22 20:17:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":68737,"visible":true,"origin":"","legend":"\u003cp\u003eCOVID-19 hospital admissions among HWs and non-HWs by month and year, 5\u003csup\u003eth\u003c/sup\u003e March 2020 to 31\u003csup\u003est\u003c/sup\u003e October 2022 (n=364,377). Blue epi curve is non-HWs and the orange curve is for HWs.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-8987304/v1/46428dfc3867eb2f2280be85.png"},{"id":107706296,"identity":"f0cc02b9-45df-4fee-85fe-6748c0ecd03b","added_by":"auto","created_at":"2026-04-24 09:17:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":911790,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8987304/v1/1f766e6c-28b8-4a4b-86e2-d7c91ebece53.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Factors associated with mortality due to COVID-19 in hospitalised health workers and non-health workers in South Africa, 2020-2022","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe emergence and rapid global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) resulted in an unprecedented public health and economic crisis worldwide.\u003csup\u003e1\u003c/sup\u003e\u0026nbsp; Early global estimates placed the infection fatality rate (IFR) for COVID-19 near 1%.\u003csup\u003e2\u003c/sup\u003e However, this average masked disparities, with risk of mortality soaring with advanced age and the presence of comorbidities like cardiovascular disease and diabetes.\u003csup\u003e3\u003c/sup\u003e The adoption of vaccination and the emergence of the Omicron variant drastically reduced the overall IFR, with later estimates falling to approximately 0.1% or lower in highly immunised populations.\u003csup\u003e4, 5\u003c/sup\u003e Despite this overall reduction, mortality remained disproportionately high among older adults, the unvaccinated, and immunocompromised groups, highlighting critical vulnerabilities.\u003csup\u003e6, 7\u003c/sup\u003e Concerns about the emergence of new SARS-CoV-2 variants with potential to evade immunity are on the rise globally, prompting renewed emphasis on genomic surveillance, booster vaccination strategies, and preparedness.\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eA global meta-analysis of 42 studies and 400,000 patients found a 17.6% pooled mortality rate among hospitalized COVID-19 cases, with higher risk linked to older age (pOR=2.61), male gender (pOR=1.45), and comorbidities such as cardiovascular disease (pOR=1.83), diabetes (pOR=1.52), hypertension (pOR=1.57), COPD (pOR=1.58), obesity (pOR=1.34), cancer (pOR=1.43), and kidney disease (pOR=1.87).\u003csup\u003e3\u003c/sup\u003e In another systematic review hospital mortality was 11.5% in general patients and 40.5% in the critically ill\u003csup\u003e\u0026nbsp;9\u003c/sup\u003e. Black ethnic groups faced disproportionately high mortality, largely due to systemic inequalities in socioeconomic and healthcare domains\u003csup\u003e10\u003c/sup\u003e. South Africa was one of the hardest hit countries in Africa, with over 4 million cases and over 114,000 deaths at the end of 2022.\u003csup\u003e9\u003c/sup\u003e \u0026nbsp;Moreover, According to authoritative analyses conducted jointly by the South African Medical Research Council and Statistics South Africa, which leveraged civil registration data from the National Population Register, the country experienced an estimated \u003cstrong\u003e591,000 excess deaths\u003c/strong\u003e attributable to the pandemic through December 2022. This figure is not derived from reported laboratory-confirmed cases or medically certified COVID-19 deaths, metrics known to be severely undercounted due to limited testing capacity, inconsistent death certification practices, and fragmented health information systems, but rather from a robust time-series analysis of all-cause mortality deviations against a statistically modeled baseline.\u003csup\u003e10, 11\u003c/sup\u003e National data showed that case load was a significant predictor of mortality, with specific weeks of high COVID-19 admissions associated with a 50% increase in the odds of death (aOR = 1.5).\u003csup\u003e12\u003c/sup\u003e Care setting also played a role as public hospitals reported higher adjusted mortality odds (aOR=1.39) compared to private facilities.\u003csup\u003e13\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLiterature shows that health workers (HWs) faced elevated risks of contracting SARS-CoV-2 infection and poor outcomes from COVID-19 due to repeated high-risk occupational exposures while managing patients during the pandemic response.\u003csup\u003e14\u003c/sup\u003e However, evaluation of COVID-19 mortality outcomes among hospital-admitted HWs in comparison to hospital-admitted general population has been limited, especially from low and middle-income countries.\u003csup\u003e12\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eHWs admitted to public hospitals exhibited 40% lower odds of death than those treated in private health facilities.\u003csup\u003e12\u003c/sup\u003e Additionally, geographical disparities within the country were notable, with mortality rates among HWs in the Limpopo and Eastern Cape provinces being 1.9 and 2.5 times higher, respectively, than in the Western Cape.\u003csup\u003e12\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eCOVID-19 vaccination efforts showed a substantial reduction in mortality rates across various populations since their rollout.\u003csup\u003e4\u003c/sup\u003e While physicians in high-exposure specialties, such as those working with oronasal secretions, faced increased risks, the pandemic underscored that all healthcare specialities encounter significant risks, and none should be overlooked.\u003csup\u003e15\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study builds upon the findings of a previous publication by Tlotleng et al. (2022), which analysed COVID-19 hospital admissions and mortality among HWs in South Africa during the period of March 2020 \u0026ndash; April 2021.\u003csup\u003e12\u003c/sup\u003e The current study extends the analysis to include data until November 2022 when surveillance was discontinued, focusing on in-hospital mortality outcomes among both HWs and non-HWs. By examining the evolution of the pandemic\u0026apos;s impact over a longer period, this study aims to provide an understanding of the factors influencing mortality and the effectiveness of interventions such as the COVID-19 vaccination.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eStudy design\u003c/h2\u003e\n\u003cp\u003eThis \u0026nbsp;is a repeated secondary data cross-sectional analysis of mortality rate and \u0026nbsp;factors associated with COVID-19-related mortality among hospitalised HWs and non-HWs.\u003csup\u003e12\u003c/sup\u003e\u003c/p\u003e\n\u003ch2\u003eData source\u003c/h2\u003e\n\u003cp\u003eThe records accessed for this analysis were obtained from a merged dataset comprising the COVID-19 Daily Hospital Surveillance records (DATCOV) and the Electronic Vaccination Data System (EVDS), both maintained under the custodian of the National Department of Health. The EVDS was developed by the South African National Department of Health to support the COVID-19 vaccination roll out in South Africa.\u003csup\u003e16\u003c/sup\u003e The merged data-set contained data on all individuals, who had a positive test for SARS-CoV-2 (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 reason for admission.\u003csup\u003e12\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eStudy population and case definition\u003c/h2\u003e\n\u003cp\u003eAll SARS-CoV-2 patients who were admitted to 408 public and 262 private hospitals across South Africa from 5 March 2020 to 31 October 2022, whose data was submitted to the DATCOV national hospital surveillance system were included. The primary outcome of interest was the discharge status of the patients, classified as either \u0026quot;lived\u0026quot; or \u0026quot;died,\u0026quot; with death defined as occurring during the hospital stay due to COVID-19. To facilitate comparisons, patients were divided into two categories: HWs and non-HWs. HW status was determined through clinician reports and encompassed a wide range of roles, including medical doctors, nurses, allied health professionals, laboratory personnel, paramedics, administrators, and porters. For individuals whose precise occupational roles were unknown, the category \u0026ldquo;other\u0026rdquo; was used. The analysis included adult participants aged 20 to 69 years. This sample was restricted to this age range because the dataset of HW only contained individuals aged 20 and older. To ensure a valid comparison, the same age criteria were applied to the non-HW worker group.\u003c/p\u003e\n\u003ch2\u003eData Management\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe study examined mortality across five distinct COVID-19 waves in South Africa. We defined a \u0026lsquo;wave\u0026rsquo; as a period where the COVID-19 weekly incidence was \u0026ge; 30 cases per 100,000 persons until the weekly cases dropped to \u0026le; 30 cases per 100,000 persons.\u003csup\u003e17\u003c/sup\u003e The first wave spanned from 5 March to 21 August 2020, followed by the second wave from 22 August 2020 to 6 February 2021. The third wave occurred between 7 February and 18 September 2021, the fourth wave lasted from 19 September 2021 to 5 February 2022, and the fifth wave extended from 6 February to 31 October 2022. The study sought to account for the burden of hospital admissions by using weekly national case numbers as a proxy for hospital case load. Case load was divided into three categories: low (fewer than 3,500 national cases per week), moderate (3,500 to 7,999 cases per week), and high (more than 8,000 cases per week).\u003csup\u003e12\u003c/sup\u003e Key variables collected included vaccination status, age group (divided into 20\u0026ndash;29, 30\u0026ndash;39, 40\u0026ndash;49, 50\u0026ndash;59, and 60\u0026ndash;69 years), and sex (male or female). Ethnicity was also considered, with patients identified as Black, Coloured, Indian, White, or Other. Additionally, the study took into account the province where death occurred and the healthcare sector (public or private) where treatment was provided. The highest level of care received by each patient was categorized as general ward, high care unit, and intensive care unit (ICU). The presence of comorbid conditions\u0026mdash;such as hypertension, diabetes, chronic renal disease, chronic pulmonary asthma, chronic cardiovascular disease, tuberculosis (both current and past), HIV and obesity\u0026mdash;was included as a key component in the dataset.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eData Analysis\u003c/h2\u003e\n\u003cp\u003eThe analyses were conducted using Stata version 18 (StataCorp\u0026reg;, College Station, Texas, USA). Descriptive statistics were conducted, with frequencies and percentages reported as all variables were categorical. The COVID-19-related in-hospital mortality ratio (CFRs) was calculated as the number of deaths divided by the total number of hospital admissions within each covariate group, multiplied by 100. Chi-square tests were used to assess statistically significant differences between HWs and non-HWs across covariates. To assess changes in hospitalisations over time and differences between HWs and non-HWs, a\u0026nbsp;\u003cstrong\u003enegative binomial regression model\u003c/strong\u003e was fitted, with hospitalisation counts as the dependent variable and time (wave index) and HW status as independent variables. An interaction term (\u003cstrong\u003eHW \u0026times; time\u003c/strong\u003e) was included to evaluate whether trends differed between the two groups. Overdispersion was assessed using the Pearson chi-squared statistic, and robust standard errors were applied to account for variance inflation. Univariable and multivariable logistic regression analyses were performed to identify factors associated with in-hospital mortality separately among HWs and non-HWs. A manual forward selection approach was used to select candidate predictor variables, including those with a significance level of p \u0026lt; 0.20 in univariable analysis. Variables were included in the final multivariable model based on significance or established epidemiological evidence and relevance in the literature. All hypothesis testing was conducted at a significance level of 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThere were 492,118 hospital admissions in South Africa between March 2020 and October 2022 with positive COVID-19 results. The majority 364,377/429,118 (84.9%) of these admissions were aged between 20 to 69 years. A total of 2.8% (10,197/364,377) of the hospital admissions were HWs.\u003c/p\u003e\n\u003cp\u003eOver the span of nearly three years, a sharp rise in hospital admissions during the five defined waves was seen. The months in between the waves showed a baseline level in hospitalizations for both HWs and non-HWs (Figure 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTrends reveal that HWs were disproportionately hospitalised during the first wave, accounting 36.9% of HW hospitalisations compared to 17.2% among non-HWs (Table 1). However, hospitalisations among HWs declined markedly in subsequent waves, reaching only 3.5% in the fifth wave, compared to 6.0% in non-HWs. There was no statistically significant difference in the rate of change over time between HWs and non-HWs (p = 0.193), though the overall decline over months was significant (p = 0.047).\u003c/p\u003e\n\u003cp\u003eAmong admitted HWs, females represented the majority (68.7%) of the cases, compared to 55.3% among non-HWs. The age group most affected among HWs was 50-59 years, accounting for 30.5% of cases, a pattern similarly observed in non-HWs, where this age group comprised 26.3% of cases. In terms of hospitalisation ethnic distribution, 54.8% were Black African, followed by 13.5% White, 6.9% Indian and 5.5% coloured, while 19.3% were classified as unknown. Among non-HWs, Black Africans also accounted for the majority (50%) of admissions. However, the proportion of cases with unclassified ethnicity was nearly double (36.2%) that observed in HWs.\u003c/p\u003e\n\u003cp\u003eThe occupational categories of HWs revealed that 17.2% were nurses, while a substantial 68.5% were classified as \u0026quot;Other\u0026quot;. The hospitalisation of HWs was largely concentrated in the private healthcare sector, accounting for 69.1% of HW admissions compared to 46.9% \u0026nbsp;of non-HWs. Geographically, Gauteng (30.3%), Western Cape (19.0%), and KwaZulu-Natal (17.3%) collectively represented nearly two-thirds of all hospitalisations among both HWs and non-HWs. The Eastern Cape had a higher proportion of HW admissions (12.4%) compared to non-HWs (9.2%). The remaining provinces (Free State, Limpopo, Mpumalanga, North West, and Northern Cape) each accounted for less than 7% of total admissions.\u003c/p\u003e\n\u003cp\u003eIn terms of severity, HWs demonstrated a higher proportion of high-care (7.7%) and ICU admissions (15.5%) compared to non-HWs (5.5% and 13.2% respectively). HWs had significantly better survival rates, with 89.1% of HWs surviving compared to 81.1% of non-HWs (X\u003csup\u003e2\u003c/sup\u003e = \u0026lt; 0.001). Vaccination data indicated that HWs had a higher vaccination rate (11.8%) relative to non-HWs (9.0%).\u003c/p\u003e\n\u003cp\u003eComorbidities were significantly more prevalent among HWs, with 44.3% reporting at least one comorbidity, compared to 40.1% of non-HWs (p = \u0026lt; 0.01). Obesity (17.2%), asthma (6.3%) and hypertension (34.7%) were significantly more common among HWs with comorbidities. In contrast, non-HWs exhibited higher proportions of HIV (11.73%), \u0026nbsp;active tuberculosis (2.6%) and past tuberculosis (4.1%) compared to HWs, who reported lower rates of HIV (5.47%), active (0.8%) and past tuberculosis (1.0%). Non-HWs also suffered significantly more chronic renal disease and chronic pulmonary disease than HWs. There were no significant differences in the presence of diabetes, cardiac disease, and malignancies between the two groups (p = 0.055, 0.199 and 0.140 respectively).\u003c/p\u003e\n\u003cp\u003eTable 1: Characteristics of hospitalised HWs and non-HWs with COVID\u0026ndash;19 in South Africa, 5\u003csup\u003eth\u003c/sup\u003e March 2020 to 31\u003csup\u003est\u003c/sup\u003e October 2022 (n=364 377).\u0026nbsp;\u003c/p\u003e\n\u003ctable style=\"width: 100%;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNon-HWs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHWs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(N = 354180)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(N = 10197)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(N = 364377)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eX\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e(p-value)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eWave\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60783 (17.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3764 (36.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e64547 (17.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e98489 (27.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3123 (30.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e101612 (27.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e127442 (36.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2067 (20.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e129509 (35.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e46073 (13.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e882 (8.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e46955 (12.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21393 (6.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e361 (3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21754 (6.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBirth Sex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e195665 (55.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7007 (68.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e202672 (55.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e158371 (44.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3190 (31.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e161561 (44.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAge Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 20-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38629 (10.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e806 (7.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39435 (10.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 30-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e66930 (18.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2109 (20.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e69039 (18.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 40-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e73801 (20.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2657 (26.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e76458 (21.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 50-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e93079 (26.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3115 (30.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e96194 (26.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 60-69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81741 (23.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1510 (14.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e83251 (22.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEthnic Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Black\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e176617 (49.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5593 (54.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e182210 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Coloured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15805 (4.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e560 (5.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16365 (4.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Indian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11725 (3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e700 (6.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12425 (3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e128264 (36.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1966 (19.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e130230 (35.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21769 (6.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1378 (13.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23147 (6.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHealth Worker Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Administrators/porters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0.%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e852 (8.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Allied Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0.%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e389 (3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Doctor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0.%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e216 (2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Nurse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0.%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1750 (17.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0.%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6990 (68.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePublic Sector\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Private\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e166234 (46.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7044 (69.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e173278 (47.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Public\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e187946 (53.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3153 (30.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e191099 (52.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eProvince\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Eastern Cape\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32717 (9.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1262 (12.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33979 (9.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Free State\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21971 (6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e351 (3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22322 (6.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Gauteng\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e108894 (30.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3669 (36.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e112563 (30.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; KwaZulu-Natal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60499 (17.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2396 (23.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e62895 (17.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Limpopo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14500 (4.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e246 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14746 (4.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Mpumalanga\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15979 (4.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e272 (2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16251 (4.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; North West\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23349 (6.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e693 (6.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24042 (6.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Northern Cape\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8148 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e104 (1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8252 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Western Cape\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e68123 (19.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1204 (11.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e69327 (19.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHighest Level of Care\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; General Ward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e287919 (81.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7830 (76.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e295749 (81.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; High Care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19602 (5.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e782 (7.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20384 (5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Intensive Care Unit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e46659 (13.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1585 (15.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e48244 (13.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVaccination Status Outcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e322134 (91.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8993 (88.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e331127 (90.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32046 (9.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1204 (11.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33250 (9.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDischarge Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Lived\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e287089 (81.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9089 (89.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e296178 (81.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Died\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e67091 (18.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1108 (10.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e68199 (18.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; None\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e212217 (59.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5682 (55.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e217899 (59.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026ge; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e141963 (40.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4515 (44.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e146478 (40.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e169535 (66.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5452 (65.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e174987 (66.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e85320 (33.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2896 (34.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e88216 (33.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e188414 (76.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6345 (77.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e194759 (76.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58580 (23.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1874 (22.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60454 (23.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCardiac Disease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e230909 (98.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7971 (98.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e238880 (98.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4716 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e146 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4862 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eChronic Pulmonary Disease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e225501 (96.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7973 (98.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e233474 (96.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9189 (3.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e114 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9303 (3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAsthma\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e226187 (94.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7664 (93.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e233851 (94.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13800 (5.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e513 (6.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14313 (5.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eChronic Renal Failure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e229566 (97.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8048 (99.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e237614 (98.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4888 (2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4939 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMalignancy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e232654 (99.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8036 (99.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e240690 (99.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1337 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1373 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHIV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e213264 (88.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7653 (94.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e220917 (88.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28341 (11.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e443 (5.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28784 (11.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTuberculosis Active\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e230328 (97.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8050 (99.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e238378 (97.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6096 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e68 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6164 (2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTuberculosis Past\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e219119 (95.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7910 (99.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e227029 (96.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9249 (4.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e76 (1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9325 (3.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eObesity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e63111 (88.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2060 (82.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e65171 (88.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8421 (11.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e427 (17.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8848 (12.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCase Fatality Ratios\u003c/p\u003e\n\u003cp\u003eAmong all hospitalised COVID-19 patients in South Africa, \u003cstrong\u003e18.7% (68,199/364,377)\u003c/strong\u003e died between 2020 and 2022. Mortality among HWs was notably lower at \u003cstrong\u003e10.9% (1,108/10,197)\u003c/strong\u003e compared with \u003cstrong\u003e18.9% (67,091/354,180)\u003c/strong\u003e in non-HWs (Table 1). The adjusted odds of mortality for each group, taking comorbidities and other risk factors into consideration, are presented in Table 2.\u003c/p\u003e\n\u003cp\u003eBoth HWs and non-HWs suffered their highest CFRs during the third wave, with HWs at 14.6% and non-HWs at 25.3% mortality. Vaccination provided substantial protection for both groups with a decrease in CFR for vaccinated compared to unvaccinated cases. The effect was higher among HWs (45.8%) than non-HWs (39.8%). During weeks of high caseload, CFRs increased to 17.5% for HWs and 25.3% for non-HWs. For non-HWs, the CFR in public hospitals (22.4%) was considerably higher than in private facilities (15%). For HWs, however, this relationship was reversed, with a higher CFR in the private sector (12.31%) than in the public sector (7.6%). The mortality ratios for those requiring intensive care, CFRs rose to 42.8% for HWs and 47.6% for non-HWs admitted to the ICU. Among HWs, the CFR rose steadily with age, from \u003cstrong\u003e1.2% in the 20\u0026ndash;29 age group\u003c/strong\u003e to \u003cstrong\u003e24.5% in those aged 60\u0026ndash;69 years\u003c/strong\u003e. In comparison, non-HWs also experienced a higher mortality ratio at every age bracket compared to HWs, with their CFR starting at \u003cstrong\u003e5.1% in the 20\u0026ndash;29 age group\u003c/strong\u003e and climbing to \u003cstrong\u003e32.8% in those aged 60\u0026ndash;69 years\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmong HWs, the \u003cstrong\u003eEastern Cape\u003c/strong\u003e Province recorded the highest CFR at \u003cstrong\u003e15.1%\u003c/strong\u003e, followed by \u003cstrong\u003eMpumalanga (14.3%)\u003c/strong\u003e and \u003cstrong\u003eKwaZulu-Natal (11.2%)\u003c/strong\u003e. The \u003cstrong\u003eWestern Cape\u003c/strong\u003e, \u003cstrong\u003eGauteng\u003c/strong\u003e, and \u003cstrong\u003eFree State\u003c/strong\u003e had lower CFRs, ranging between 7.7% and 10.6%. For non-HWs, the pattern was similar, the \u003cstrong\u003eEastern Cape again had the highest CFR at 26.2%\u003c/strong\u003e, followed by \u003cstrong\u003eLimpopo (24.5%)\u003c/strong\u003e, \u003cstrong\u003eMpumalanga (21.0%)\u003c/strong\u003e, and \u003cstrong\u003eKwaZulu-Natal (19.0%)\u003c/strong\u003e.The \u003cstrong\u003eWestern Cape (17.3%)\u003c/strong\u003e, \u003cstrong\u003eGauteng (17.8%)\u003c/strong\u003e, and \u003cstrong\u003eNorth West (14.3%)\u003c/strong\u003e reported comparatively lower CFRs similar to HWs. Among HWs, mortality ratio was highest for those with \u003cstrong\u003echronic renal failure (29.4%)\u003c/strong\u003e and \u003cstrong\u003emalignancy (27.8%)\u003c/strong\u003e. In contrast, asthma (11.7%) and chronic pulmonary disease (10.5%) were associated with more modest CFRs, closer to the overall average for HWs. For non-HWs, the influence of comorbidities on mortality was also increased. The highest CFRs were also seen with \u003cstrong\u003echronic renal failure (38.6%)\u003c/strong\u003e and \u003cstrong\u003emalignancy\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(32.9%)\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eFactors associated with COVID-19 mortality in hospitalised cases\u003c/h2\u003e\n\u003cp\u003eAmong \u003cstrong\u003eHWs\u003c/strong\u003e, the factor most strongly associated with mortality was admission to intensive care, which conferred more than a fifteen-fold increase in the odds of death (aOR = 15.34; 95% CI: 12.78\u0026ndash;18.41). Age was the second strongest predictor, with those aged 60\u0026ndash;69 years having nearly eleven times the odds of death compared to the 20\u0026ndash;29 age group (aOR = 10.93; 95% CI: 5.21\u0026ndash;22.91). Hospitalisation during weeks of high national caseload also significantly elevated mortality risk (aOR = 2.10; 95% CI: 1.63\u0026ndash;2.71), as did receiving care in the public sector (aOR = 1.49; 95% CI: 1.10\u0026ndash;2.02). Important comorbidities independently associated with mortality among HWs included chronic renal failure (aOR = 3.43; 95% CI: 1.64\u0026ndash;7.19), malignancy (aOR = 3.25; 95% CI: 1.21\u0026ndash;8.74), and active tuberculosis (aOR = 2.34; 95% CI: 1.09\u0026ndash;5.02). Diabetes was also a significant risk factor, albeit more modest in effect (aOR = 1.52; 95% CI: 1.23\u0026ndash;1.88). Vaccination was protective, reducing the odds of death by half (aOR = 0.50; 95% CI: 0.35\u0026ndash;0.73). Compared to doctors, several occupational groups showed significantly lower mortality risk. Administrators/Porters (aOR 0.45, p=0.019), Nurses (aOR 0.49, p=0.010), and other health workers (aOR 0.48, p=0.003) had approximately half the adjusted odds of death.\u003c/p\u003e\n\u003cp\u003eAmong \u003cstrong\u003enon-HWs\u003c/strong\u003e, admission to ICU was also the strongest predictor of mortality, with a more than seven-fold increase in the odds of death (aOR = 7.18; 95% CI: 6.69\u0026ndash;7.71). Age again showed a powerful gradient effect, with individuals aged 60\u0026ndash;69 years experiencing more than eight times the odds of death compared to those aged 20\u0026ndash;29 years (aOR = 8.23; 95% CI: 7.26\u0026ndash;9.34). High case-load periods significantly increased risk (aOR = 1.54; 95% CI: 1.42\u0026ndash;1.66), and receiving care in the public sector nearly tripled the odds of death compared to the private sector (aOR = 2.95; 95% CI: 2.74\u0026ndash;3.17). A broader range of comorbidities were associated with mortality in this group: chronic renal failure (aOR = 1.60; 95% CI: 1.41\u0026ndash;1.81), malignancy (aOR = 1.64; 95% CI: 1.33\u0026ndash;2.02), active tuberculosis (aOR = 1.67; 95% CI: 1.45\u0026ndash;1.94), history of tuberculosis (aOR = 1.20; 95% CI: 1.05\u0026ndash;1.36), HIV (aOR = 1.35; 95% CI: 1.25\u0026ndash;1.45), diabetes (aOR = 1.41; 95% CI: 1.33\u0026ndash;1.50), cardiac disease (aOR = 1.16; 95% CI: 1.03\u0026ndash;1.29), and obesity (aOR = 1.18; 95% CI: 1.09\u0026ndash;1.27). Vaccination was again strongly protective, reducing the odds of mortality by 28% (aOR = 0.72; 95% CI: 0.63\u0026ndash;0.83).\u003c/p\u003e\n\u003cp\u003eTable 2: Factors associated with COVID\u0026ndash;19 hospital mortality among South African hospitalisations, 5\u003csup\u003eth\u003c/sup\u003e March 2020 to 31\u003csup\u003est\u003c/sup\u003e October 2022 (N=364 377).\u003c/p\u003e\n\u003ctable style=\"width: 5.1e+2pt;border: none;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eHWs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-HWs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCFR (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eaOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eP-value (aOR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCFR (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eaOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eP-value (aOR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePandemic Wave\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eWave 1 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e8.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eWave 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.58 (1.26 to 1.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.15 (1.06 to 1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eWave 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e14.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.99 (1.54 to 2.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.32 (1.22 to 1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eWave 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.84 (0.51 to 1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.86 (0.77 to 0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eWave 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e3.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.12 (0.53 to 2.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1 (0.85 to 1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.987\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eWeekly Admissions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eLow, \u0026lt; 3 500 cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e8.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eMedium, 3500 to 7 999 cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.49 (1.21 to 1.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.22 (1.15 to 1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eHigh, \u0026gt; 8\u0026nbsp;000 cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e17.51\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2.1 (1.63 to 2.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.54 (1.42 to 1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFemale (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.94 (0.79 to 1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.2 (1.15 to 1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAge Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e20\u0026ndash;29 (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e30-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e3.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2.25 (1.04 to 4.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.85 (1.61 to 2.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e40-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e9.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e4.15 (1.98 to 8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2.86 (2.52 to 3.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e50-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e13.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e4.92 (2.36 to 10.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e4.85 (4.28 to 5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e60\u0026ndash;69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e10.93 (5.21 to 22.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e8.23 (7.26 to 9.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEthnic Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eBlack African (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e10.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eColoured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e10.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.76 (0.52 to 1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.73 (0.66 to 0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eIndian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e15.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.15 (0.85 to 1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.02 (0.91 to 1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.697\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e13.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.79 (0.62 to 1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.69 (0.61 to 0.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.17 (0.88 to 1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.88 (0.79 to 0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHW Category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eDoctor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e20.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"3\" rowspan=\"5\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eAdministrators / porters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e6.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.45 (0.23 to 0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eAllied Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e11.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.56 (0.28 to 1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eNurse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e10.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.49 (0.28 to 0.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e11.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.48 (0.29 to 0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHealthcare Sector\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrivate (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e15.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePublic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.49 (1.1 to 2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e22.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2.95 (2.74 to 3.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eProvince\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eWestern Cape (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e10.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e17.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eEastern Cape\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e15.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2.17 (1.53 to 3.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e26.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.29 (1.16 to 1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eFree State\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e7.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.22 (0.7 to 2.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e18.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.9 (0.81 to 1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eGauteng\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e9.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.21 (0.88 to 1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e17.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.86 (0.77 to 0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eKwaZulu-Natal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e11.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.36 (0.95 to 1.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e18.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.94 (0.84 to 1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eLimpopo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e10.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e3.4 (1.74 to 6.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e24.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.04 (0.89 to 1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eMpumalanga\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e14.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.39 (0.79 to 2.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e20.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.12 (0.94 to 1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eNorth West\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e9.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.24 (0.79 to 1.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e14.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.69 (0.6 to 0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eNorthern Cape\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e11.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.54 (0.58 to 4.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e20.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.71 (0.57 to 0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAdmission Severity\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eGeneral Ward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e4.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e14.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eHigh Care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e9.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2.1 (1.53 to 2.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e20.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e2.16 (1.96 to 2.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eICU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e42.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e15.34 (12.78 to 18.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e47.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e7.18 (6.69 to 7.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVaccination Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUnvaccinated (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e19.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVaccinated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.5 (0.35 to 0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e11.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.72 (0.63 to 0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNone (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e15.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge;1 Comorbidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.06 (0.86 to 1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e24.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.12 (1.03 to 1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e9.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e15.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e17.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e26.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.98 (0.92 to 1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.596\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e15.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e20.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.52 (1.23 to 1.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e29.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.41 (1.33 to 1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eCardiac Disease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e11.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e17.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e15.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e29.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.16 (1.03 to 1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eChronic Pulmonary Disease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e12.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e17.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e10.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e22.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.76 (0.66 to 0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eAsthma\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e11.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e17.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e11.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e19.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.94 (0.83 to 1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eChronic Renal Failure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e11.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e17.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e29.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.43 (1.64 to 7.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e38.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.6 (1.41 to 1.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eMalignancy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e11.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e17.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e27.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.25 (1.21 to 8.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e32.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.64 (1.33 to 2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eHIV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e12.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e17.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e10.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.24 (0.81 to 1.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e22.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.35 (1.25 to 1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eActive TB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e11.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e17.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e17.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.34 (1.09 to 5.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e23.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.67 (1.45 to 1.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eTB History\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e17.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e11.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e21.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.2 (1.05 to 1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u003cstrong\u003eObesity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo (Ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e9.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e19.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e15.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e30.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e1.18 (1.09 to 1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePseudo R\u0026sup2;: HW = 0.2932; Non-HW = 0.1645\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis national study analysed over 480,000 hospitalisations for COVID-19 in South Africa between March 2020 and October 2022, providing the most comprehensive comparison to date of mortality outcomes and associated factors among HWs and non-HWs for South Africa. 2.8% of the hospital admissions were HWs which is above the 0.5% of the working age population public HWs makeup.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e The mortality rate in our study (18.72%) was consistent with pooled international estimates for hospitalized COVID-19 patients (17.62%).\u003csup\u003e3\u003c/sup\u003e However, HWs experienced lower mortality rates compared to non-HWs (10.9% vs 18.9%). This likely reflects HWs better health literacy, early health seeking behaviour, greater vaccine access and uptake .\u003csup\u003e19\u003c/sup\u003e However, the mortality rate among HWs was still significant at about one in ten, highlighting the risks they faced, especially early in the pandemic.\u003c/p\u003e \u003cp\u003eDifferences in mortality were evident across pandemic waves, with both groups experiencing peak fatality rates during the third wave (aOR; 1.99 in HWs and 1.32 in non-HWs). This period corresponded with dominance of the Delta variant in South Africa, a strain known for its heightened transmissibility and severity. The substantial rise in mortality during this wave reflects not only the virulence of the Delta variant but also intense pressure it placed on an already strained health system.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Among HWs, crude fatality rates fell sharply from 14.6% in Wave 3 to 3.4% in Wave 4 and 3.3% in Wave 5. A similar pattern was seen in non-HWs, with fatality rates decreasing from 25.3% in Wave 3 to 10.2% in Wave 4 and 8.4% in Wave 5, highlighting the substantial mitigation of disease severity over time. The declining crude fatality rates in subsequent waves reflect the combined effects of vaccination, growing natural immunity, improved clinical management, and the emergence of the less virulent Omicron variant.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e The pattern of admissions underscores the pressure faced by healthcare systems and workers, especially during peak periods (high caseloads), and highlights the critical need for continuous monitoring and adaptive responses to manage impact in any epidemic or pandemic.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eVaccination made a significant impact in our study similar to other studies where it lowered the odds of death for both HWs and non-HWs.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Non-HWs vaccination was associated with a 45.3% reduction in odds of dying whereas HWs vaccination was associated with a 49.2% reduction in odds of dying (p\u0026thinsp;=\u0026thinsp;0.000). HWs showed a slightly stronger benefit which may be due to access to vaccines before the more virulent third wave compared to public access during the third wave.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e This reinforces the protection vaccines provided and suggests benefits for HWs through greater access or possibly higher uptake of vaccines.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e Eventhough the Sisonke program prioritised HWs be vaccinated first before the general population, only 11.81% of HWs were vaccinated before admission compared to 9.05% of non-HWs, this highlights the need to sustain high coverage of vaccines in healthcare staff who face elevated occupational exposure.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e The risk of mortality from COVID-19 decreased in subsequent pandemic waves after the third wave, likely due to rising natural immunity, greater access to therapies, and expansion of vaccines.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e Wave 3 saw peak fatality rates for HWs at 14.6%, showing challenges especially amidst the Delta surge.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e During this wave, unvaccinated HWs had extremely high mortality at 15 530 per 100 000 admissions, before declining as vaccine access and uptake widened. This spotlights how essential timely vaccine rollouts are to protect healthcare capacity.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e Moreover, the categorisation of weekly national admissions into low, moderate, and high loads underscored the direct impact of healthcare system strain on fatality ratios.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAdvanced age and several comorbidities remained powerful predictors of in-hospital mortality in both HWs and non-HWs. These vulnerability factors have been well documented throughout the pandemic.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Mortality risk significantly increased with age. Men experienced significantly higher mortality odds (aOR\u0026thinsp;=\u0026thinsp;1.2) than women among non-HWs, a finding consistent with global trends showing greater vulnerability in males.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e This disparity may reflect differences in health-seeking behaviour, with men often delaying healthcare access or presenting at more advanced stages of illness compared to women.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e In contrast, among HWs, sex was not a significant factor influencing mortality, possibly due to more equitable uptake of healthcare services, earlier testing, and prompt treatment interventions available within the occupational setting.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe significantly higher mortality odds observed among doctors compared to nurses, administrators, porters and other HW categories is possibly due to a combination of higher exposure to severe COVID-19 cases, higher viral loads in clinical settings and specific tasks and increased occupational stress. Allied Health Workers, however, did not show significantly different mortality odds compared to other HW categories. This may reflect their diverse roles many of which involve intermittent or indirect patient contact as well as generally lower exposure intensity and shorter cumulative time in high-risk clinical zones.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e These differences highlight the need for risk assessments to account for the heterogeneity of exposure across occupational subgroups to better target prevention and protection strategies for higher-risk categories of health workers.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAdmission to the ICU conferred dramatically elevated odds ratios for mortality, reflecting the critical illness of such patients. However, over 50% of HWs and 50% non-HWs admitted to the ICU survived, due to access to this life-saving care. This highlights the importance of preserving critical care capacity during surges.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e Patients admitted to High Care also showed increased odd of mortality compared to those admitted to medical wards.\u003c/p\u003e \u003cp\u003eWhites and coloureds among non-HWs had significantly reduced adjusted odds of COVID-19 related deaths compared to Black Africans, which remained after adjusting for private vs public healthcare. The differences may be attributed to racial disparities in COVID-19 outcomes in South Africa, structural and systemic inequalities that may increase not only susceptibility to infection but also barriers to appropriate medical care.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e While no significant differences were seen in HWs between race groups.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe mortality aORs were consistently higher for public sector admissions than for the private sector in both HWs and non-HWs despite differences in CFR. There is a survival advantage in private healthcare, perhaps due to better staffing ratios, better access to treatment and advanced therapies, and more consistent quality of care.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e The higher aOR for public sector admissions is likely driven by both broader socioeconomic factors and healthcare quality. Patients admitted to public hospitals are more often from lower-income backgrounds, which are associated with poorer baseline health, higher prevalence of undiagnosed comorbidities, and delays in accessing care. Furthermore, public sector facilities serve the majority of the population and are more vulnerable to overwhelming patient loads during pandemic surges, conditions that can compromise both timeliness and quality of care. Together, these factors explain why mortality risks were more pronounced the public sector compared with their private sector counterparts.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e These differences underscore the need for targeted interventions to address the challenges faced by public healthcare, ensuring equitable access to healthcare resources, improving workplace safety, and strengthening the overall healthcare system.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eProvincial variation in mortality aORs among non-HWs may reflect the uneven distribution of health system capacity and resources across South Africa. The Eastern Cape and Limpopo HWS faced a two to three-fold greater mortality risk compared to the Western Cape, underscoring severe geographic inequities in occupational protection and health system support during the pandemic. For non-HWs, the Eastern Cape also remained a vulnerability hotspot. Non-HW patients in Gauteng, North West, and the Northern Cape experienced significantly lower mortality odds than those in the Western Cape. These findings highlight that a patient's province of treatment was a major independent determinant of survival, reflecting underlying disparities in healthcare capacity, resource allocation, and possibly the baseline burden of disease, which compromised outcomes in certain regions like the Eastern Cape and Limpopo throughout the health crisis.\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe overall impact of having any reported comorbidity was not statistically significant among HWs, whereas it was significant in non-HWs, albeit with only a small effect size. This weaker influence of comorbidities in HWs may be due to differences in age distribution, occupational exposures, stress levels, or overall health status between the two groups. Better access and uptake of healthcare among HWs may also contribute, as earlier diagnosis and treatment of chronic conditions could reduce the effect of these comorbidities on mortality outcomes. When individual conditions were examined, clear differences emerged in the ranking of risk factors between HWs and non-HWs. Among HWs, the strongest predictors of mortality were chronic renal failure, followed by malignancy, and active tuberculosis. Diabetes also increased the risk significantly, though to a lesser extent. These findings suggest that conditions that compromise immunity or organ function particularly renal failure and cancer were most consequential for HWs, while active TB further highlighted the vulnerability of those with pre-existing pulmonary inflammation and damage. The risk profile among non-HWs was broader, with more conditions independently associated with mortality, albeit with smaller effect sizes. The highest odds ratios were observed for the same comorbidities as HWs, active tuberculosis, malignancy, and chronic renal failure. These were followed by diabetes and HIV infection, while a history of tuberculosis, obesity, and cardiac disease were associated with more modest but still significant increases in mortality risk. These patterns may possibly be related to better classification of comorbidity status in HWs than the general population. The absence of significant associations for some conditions in HWs may reflect smaller sample sizes, or more consistent access to healthcare. Nonetheless, the elevated risks linked to renal disease, malignancy, and TB in both groups remain biologically credible and align with global evidence on the interplay between chronic illness and COVID-19 outcomes.\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOverall, the findings from over 190,000 hospitalized cases after vaccine introduction provide evidence around the beneficial association of vaccination with mortality risk from COVID-19. The COVID-19 pandemic has provided valuable lessons for South Africa, emphasizing the need for a resilient and equitable healthcare system, effective public health communication, and strong socio-economic support mechanisms. By addressing these areas, South Africa can better prepare for future health emergencies and improve overall public health outcomes.\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis large national surveillance study of COVID-19 hospitalisations provides robust evidence on the factors shaping in-hospital COVID-19 mortality in South Africa during the pandemic. The findings reaffirm the pivotal role of vaccination in reducing deaths among both HWs and non-HWs, underscoring the need to sustain high vaccine coverage of vaccine controlled infectious disease, particularly for frontline staff. Mortality risks were highest among older adults, patients with severe comorbidities, and those requiring intensive care, while sectoral and provincial disparities highlighted persistent inequities in healthcare access and capacity. For HWs, mortality was affected by a smaller set of severe conditions most notably chronic renal failure, malignancy, active tuberculosis and diabetes while non-HWs experienced broader vulnerabilities including diabetes, HIV, obesity, and cardiovascular disease. These differences suggest that while HWs may benefit from earlier diagnosis and care, they remain at high risk when burdened with serious comorbidities. Importantly, survival advantages in private hospitals contrasted with poorer outcomes in the public sector, especially among non-HWs, reflecting systemic inequalities in resources and care delivery. Timely vaccination and targeted protection of high-risk groups were central to reducing mortality during this health crisis. Moreover, strengthening healthcare system resilience through investment in critical care, equitable resource allocation, and systematic surveillance is essential to safeguard both frontline workers and the broader population. Future work should extend beyond in-hospital outcomes to track long-term impacts among survivors, generating evidence that can inform preparedness for future pandemics and advance equity in health outcomes.\u003c/p\u003e \u003cp\u003eLimitations\u003c/p\u003e \u003cp\u003eThis study has several limitations that should be considered when interpreting the findings. First, the analysis relied on secondary data sources, which may be subject to incomplete or inconsistent reporting, particularly for variables such as comorbidities, occupation, and disease severity. Moreover, reporting also relied on data submitted through DATCOV on whether an admitted patient was a HW and the categorization. We did not link to any external source or verify HW status. Missing data could introduce information bias, potentially underestimating or overestimating associations. Second, because the dataset reflects hospitalised individuals only, it may not capture those who died outside the hospital or who were never admitted, limiting representativeness and potentially introducing selection bias. Third, the comparatively small sample size of HWs relative to non-HWs may result in reduced statistical power and wider confidence intervals, increasing the likelihood of imprecision or unstable estimates. This difference in sample size warrants cautious interpretation of effect size differences between the two groups. Finally, given that hospital capacity, treatment protocols, and testing strategies evolved over the course of the pandemic, temporal changes may have influenced mortality risk in ways not fully accounted for in the analysis. These factors may limit the generalisability of the findings beyond the study population and period.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003eEthics\u003c/p\u003e\n\u003cp\u003eThe authors confirm that all relevant ethical guidelines were followed, and any necessary institutional research body (IRB) and ethics committee approvals for the study were obtained. The Human Research Ethics Committee (Medical) of the University of the Witwatersrand approved the study as part of a national surveillance program (ethics reference no: M160667). All methods were carried out in accordance with the accepted national and international guidelines and standards.\u003c/p\u003e\n\u003cp\u003eConflicts of interest\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding declaration\u003c/p\u003e\n\u003cp\u003eDATCOV is funded by the National Institute for Communicable Diseases (NICD) and the South African National Government. No additional funding was obtained towards the completion of this analysis and the development of this manuscript.\u003c/p\u003e\n\u003cp\u003eClinical trial\u003c/p\u003e\n\u003cp\u003eClinical number trial is not applicable in this study as it used secondary data sources (DATCOV and EVDS).\u003c/p\u003e\n\u003cp\u003eConsent to participate\u003c/p\u003e\n\u003cp\u003eGiven the use of anonymized, routinely collected surveillance data and the approval from the Human Research Ethics Committee (Medical) of the University of the Witwatersrand (ethics reference no: M160667), the requirement for individual informed consent was waived. All data were de-identified prior to analysis.\u003c/p\u003e\n\u003cp\u003eConsent to publish\u003c/p\u003e\n\u003cp\u003eNot applicable. This study reports on anonymized surveillance data, and no individual person\u0026rsquo;s data is presented in a form that could lead to identification.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during this current study are available in the repository of the National Institute of Communicable Diseases. The data can be made available on request, which may be directed to [email protected]. Those requesting data will need to sign a data access agreement. The request will require approval by the National Department of Health.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe authors wish to acknowledge the DATCOV team at the NICD, the National Department of Health, the nine provincial departments of health, the Hospital Association of Southern Africa, private hospital groups, and public-sector hospitals who submitted data to DATCOV.\u003cbr clear=\"all\"\u003e \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSharma A, Tiwari S, Deb MK, Marty JL. Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2): a global pandemic and treatment strategies. International journal of antimicrobial agents. 2020; 56:106054.\u003c/li\u003e\n\u003cli\u003eIoannidis JP. Global perspective of COVID‐19 epidemiology for a full‐cycle pandemic. European journal of clinical investigation. 2020; 50:e13423.\u003c/li\u003e\n\u003cli\u003eDessie ZG, Zewotir T. Mortality-related risk factors of COVID-19: a systematic review and meta-analysis of 42 studies and 423,117 patients. BMC infectious diseases. 2021; 21:855.\u003c/li\u003e\n\u003cli\u003eMohammed I, Nauman A, Paul P, Ganesan S, Chen K-H, Jalil SMS, et al. The efficacy and effectiveness of the COVID-19 vaccines in reducing infection, severity, hospitalization, and mortality: a systematic review. Human vaccines \u0026amp; immunotherapeutics. 2022; 18:2027160.\u003c/li\u003e\n\u003cli\u003eHirahata T, ul Quraish R, ul Quraish A, ul Quraish S. A review of SARS-CoV-2 virology, vaccines, variants and their impact on the COVID-19 pandemic. Reviews and Research in Medical Microbiology. 2024:10.1097.\u003c/li\u003e\n\u003cli\u003eBiswas M, Rahaman S, Biswas TK, Haque Z, Ibrahim B. Association of sex, age, and comorbidities with mortality in COVID-19 patients: a systematic review and meta-analysis. Intervirology. 2021; 64:36-47.\u003c/li\u003e\n\u003cli\u003eMackey K, Ayers CK, Kondo KK, Saha S, Advani SM, Young S, et al. Racial and ethnic disparities in COVID-19\u0026ndash;related infections, hospitalizations, and deaths: a systematic review. Annals of internal medicine. 2021; 174:362-73.\u003c/li\u003e\n\u003cli\u003eNaveed Siddiqui A, Musharaf I, Gulumbe BH. The JN. 1 variant of COVID-19: immune evasion, transmissibility, and implications for global health. Therapeutic Advances in Infectious Disease. 2025; 12:20499361251314763.\u003c/li\u003e\n\u003cli\u003eDiseases NIfC. DAILY HOSPITAL SURVEILLANCE (DATCOV) REPORT. 2023.\u003c/li\u003e\n\u003cli\u003eBradshaw D, Dorrington R, Laubscher R, Groenewald P, Moultrie T. COVID-19 and all-cause mortality in South Africa-the hidden deaths in the first four waves. South African Journal of Science. 2022; 118:1-7.\u003c/li\u003e\n\u003cli\u003eNepomuceno MR, Klimkin I, Jdanov DA, Alustiza‐Galarza A, Shkolnikov VM. Sensitivity analysis of excess mortality due to the COVID‐19 pandemic. Population and development review. 2022; 48:279-302.\u003c/li\u003e\n\u003cli\u003eTlotleng N, Cohen C, Made F, Kootbodien T, Masha M, Naicker N, et al. COVID-19 hospital admissions and mortality among healthcare workers in South Africa, 2020\u0026ndash;2021. IJID regions. 2022; 5:54-61.\u003c/li\u003e\n\u003cli\u003eJassat W, Mudara C, Ozougwu L, Tempia S, Blumberg L, Davies M-A, 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. The Lancet Global Health. 2021; 9:e1216-e25.\u003c/li\u003e\n\u003cli\u003eDzinamarira T, Nkambule SJ, Hlongwa M, Mhango M, Iradukunda PG, Chitungo I, et al. Risk factors for COVID-19 infection among healthcare workers. A first report from a living systematic review and meta-analysis. Safety and Health at Work. 2022; 13:263-8.\u003c/li\u003e\n\u003cli\u003eBandyopadhyay S, Baticulon RE, Kadhum M, Alser M, Ojuka DK, Badereddin Y, et al. Infection and mortality of healthcare workers worldwide from COVID-19: a systematic review. BMJ global health. 2020; 5:e003097.\u003c/li\u003e\n\u003cli\u003eJassat W, Mudara C, Ozougwu L, Welch R, Arendse T, Masha M, et al. Trends in COVID-19 Admissions and Deaths Among People Living with HIV in South Africa. 2023.\u003c/li\u003e\n\u003cli\u003eDiseases NIfC. Proposed definition of COVID-19 wave in South Africa. Communicable Diseases Communiqu\u0026eacute;. 2021; 20:3-4.\u003c/li\u003e\n\u003cli\u003eMatseke MG. Taking stock of the Healthcare Workforce in the Public Health Sector of South Africa during Covid-19: implications for future pandemics. Africa Journal of Public Sector Development and Governance. 2023; 6:59-76.\u003c/li\u003e\n\u003cli\u003eLahner E, Dilaghi E, Prestigiacomo C, Alessio G, Marcellini L, Simmaco M, et al. Prevalence of Sars-Cov-2 infection in health workers (HWs) and diagnostic test performance: the experience of a teaching hospital in central Italy. International journal of environmental research and public health. 2020; 17:4417.\u003c/li\u003e\n\u003cli\u003eEl-Shabasy RM, Nayel MA, Taher MM, Abdelmonem R, Shoueir KR, Kenawy ER. Three waves changes, new variant strains, and vaccination effect against COVID-19 pandemic. International Journal of Biological Macromolecules. 2022; 204:161-8.\u003c/li\u003e\n\u003cli\u003eFilip R, Gheorghita Puscaselu R, Anchidin-Norocel L, Dimian M, Savage WK. Global challenges to public health care systems during the COVID-19 pandemic: a review of pandemic measures and problems. Journal of personalized medicine. 2022; 12:1295.\u003c/li\u003e\n\u003cli\u003eJeebhay MF, Naidoo RN, Naidoo S, Adams S, Zungu M, Kgalomono S, et al. Strengthening Social Compact and Innovative Health Sector Collaborations in Addressing COVID-19 in South African Workplaces. NEW SOLUTIONS: A Journal of Environmental and Occupational Health Policy. 2023; 32:288-303.\u003c/li\u003e\n\u003cli\u003eOrganization WH. Implementation guide for vaccination of health workers: World Health Organization; 2022.\u003c/li\u003e\n\u003cli\u003eCarlsten C, Gulati M, Hines S, Rose C, Scott K, Tarlo SM, et al. COVID‐19 as an occupational disease. American journal of industrial medicine. 2021; 64:227-37.\u003c/li\u003e\n\u003cli\u003eAdams JG, Walls RM. Supporting the health care workforce during the COVID-19 global epidemic. Jama. 2020; 323:1439-40.\u003c/li\u003e\n\u003cli\u003eIoannidis JP. The end of the COVID‐19 pandemic. European journal of clinical investigation. 2022; 52:e13782.\u003c/li\u003e\n\u003cli\u003eEl-Shabasy RM, Nayel MA, Taher MM, Abdelmonem R, Shoueir KR. Three waves changes, new variant strains, and vaccination effect against COVID-19 pandemic. 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Nature communications. 2020; 11:6317.\u003c/li\u003e\n\u003cli\u003eJin H, Chen Y, Fu Q, Qu Q. Occupational risk factors of contracting COVID-19 among health workers: A systematic review. Work. 2021; 69:721-34.\u003c/li\u003e\n\u003cli\u003eBiswas M, Rahaman S, Biswas TK, Haque Z, Ibrahim B. Effects of sex, age and comorbidities on the risk of infection and death associated with COVID-19: a meta-analysis of 47807 confirmed cases. Age and Comorbidities on the Risk of Infection and Death Associated with COVID-19: A Meta-Analysis of. 2020; 47807.\u003c/li\u003e\n\u003cli\u003eRodriguez-Llanes JM, Delgado RC, Pedersen MG, Meneghini M, Gonz\u0026aacute;lez PA. Surging critical care capacity for COVID-19: Key now and in the future. Progress in Disaster Science. 2020; 8:100136.\u003c/li\u003e\n\u003cli\u003eJassat W, Ozougwu L, Munshi S, Mudara C, Vika C, Arendse T, et al. The intersection of age, sex, race and socio-economic status in COVID-19 hospital admissions and deaths in South Africa. South African Journal of Science. 2022; 118.\u003c/li\u003e\n\u003cli\u003eJassat W, Cohen C, Tempia S, Masha M, Goldstein S, Kufa T, et al. Risk factors for COVID-19-related in-hospital mortality in a high HIV and tuberculosis prevalence setting in South Africa: a cohort study. The Lancet HIV. 2021; 8:e554-e67.\u003c/li\u003e\n\u003cli\u003eGrimm CA. Hospitals reported that the COVID-19 pandemic has significantly strained health care delivery. Office of Inspector General Report in Brief. 2021.\u003c/li\u003e\n\u003cli\u003eBalqis-Ali NZ, Fun WH, Ismail M, Ng RJ, Jaaffar FSA, Low LL. Addressing gaps for health systems strengthening: a public perspective on health systems\u0026rsquo; response towards COVID-19. International journal of environmental research and public health. 2021; 18:9047.\u003c/li\u003e\n\u003cli\u003eMokhele T, Sewpaul R, Sifunda S, Weir-Smith G, Dlamini S, Manyaapelo T, et al. Spatial analysis of perceived health system capability and actual health system capacity for COVID-19 in South Africa. The Open Public Health Journal. 2021; 14.\u003c/li\u003e\n\u003cli\u003eRatshikhopha 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; 19:5519.\u003c/li\u003e\n\u003cli\u003eZungu M, Voyi K, Mlangeni N, Moodley SV, Ramodike J, Claassen N, et al. Organizational factors associated with health worker protection during the COVID-19 pandemic in four provinces of South Africa. BMC Health Services Research. 2021; 21:1-15.\u003c/li\u003e\n\u003cli\u003eBurgess T, Rennie S, Moodley K. Key ethical issues encountered during COVID-19 research: a thematic analysis of perspectives from South African research ethics committees. BMC Medical Ethics. 2023; 24:11.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"SARS-CoV-2, Hospital Surveillance, Health Workers, Hospital Admissions, In Hospital Mortality","lastPublishedDoi":"10.21203/rs.3.rs-8987304/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8987304/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHealth workers (HWs) faced substantial risks during the COVID-19 pandemic. However evidence comparing their in-hospital mortality with that of non-health workers (non-HWs) in South Africa remains limited. This study compares in-hospital COVID-19 mortality and associated factors between hospitalized HW and non-HW groups from 2020 to 2022.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eCross-sectional analysis of national COVID-19 hospitalization data from 408 public and 262 private hospitals in South Africa was conducted. Our dataset included individuals aged 20\u0026ndash;69 years with confirmed SARS-CoV-2 infection admitted between March 2020 and October 2022. Mortality outcomes were analysed using descriptive statistics, chi-square tests, and logistic regression to identify demographic, clinical, and contextual factors associated with death due to SARS-COV-2 infection.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOverall in-hospital mortality was lower among HWs than non-HWs (16.3% vs 21.7%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For both groups, older age, admission to intensive care, and treatment in public sector hospitals were associated with increased mortality. Among HWs, the strongest comorbidity predictors of death were chronic renal failure (aOR\u0026thinsp;=\u0026thinsp;3.43), malignancy (aOR\u0026thinsp;=\u0026thinsp;3.25), and active tuberculosis (aOR\u0026thinsp;=\u0026thinsp;2.34). In non-HWs, mortality risk was more broadly elevated across active tuberculosis (aOR\u0026thinsp;=\u0026thinsp;1.67), malignancy (aOR\u0026thinsp;=\u0026thinsp;1.64), chronic renal failure (aOR\u0026thinsp;=\u0026thinsp;1.60), diabetes (aOR\u0026thinsp;=\u0026thinsp;1.41), HIV (aOR\u0026thinsp;=\u0026thinsp;1.35), obesity (aOR\u0026thinsp;=\u0026thinsp;1.18), and cardiac disease (aOR\u0026thinsp;=\u0026thinsp;1.16). COVID-19 vaccination was strongly protective in both groups (aOR; HWs\u0026thinsp;=\u0026thinsp;0.5 vs non-HWs\u0026thinsp;=\u0026thinsp;0.72), with significantly reduced odds of death compared to unvaccinated individuals.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eHWs had lower in-hospital mortality than non-HWs, likely reflecting earlier access to vaccination, better healthcare access, and proactive disease management. However, HWs with serious comorbidities\u0026mdash;particularly renal failure, malignancy, and tuberculosis\u0026mdash;remained at high risk. These findings highlight the importance of sustained vaccination, targeted protection for high-risk groups, and addressing systemic inequities in healthcare delivery to strengthen resilience for future pandemics.\u003c/p\u003e","manuscriptTitle":"Factors associated with mortality due to COVID-19 in hospitalised health workers and non-health workers in South Africa, 2020-2022","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-22 20:17:18","doi":"10.21203/rs.3.rs-8987304/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-04T06:54:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T01:04:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-27T14:50:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"235689406025188871606681811772484797136","date":"2026-04-22T13:59:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"263027736186934953808121091705593671205","date":"2026-04-21T10:29:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-17T19:55:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"140646827633578626879701480059339029331","date":"2026-04-17T13:35:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-15T09:38:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-27T03:15:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-26T10:35:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-23T08:54:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2026-03-23T08:18:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e9744502-570c-4440-8ba7-0a31f254adb3","owner":[],"postedDate":"April 22nd, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-04T06:54:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T01:04:19+00:00","index":78,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T04:38:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-22 20:17:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8987304","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8987304","identity":"rs-8987304","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}