The epidemiology of COVID-19 in Tshwane District, South Africa, March 2020-June 2020: A cross-sectional study

Mmatswele Hellen Kgatla^1,2,3, Alex De Voux¹, Makgomo Rebecca Mphaka³, Hetani Mdose^1,2, Alfred Musekiwa², Emelda Ramutshila¹, Mpho Moshime³, Lekwetji Komane³, Lazarus Kuonza ^1,2

 

¹South African Field Epidemiology Training Programme, National Institute for Communicable Diseases, Johannesburg, South Africa, ²School of Health Systems and Public Health, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa, ³Expanded Programme on Immunisation, Communicable Disease Control, and Outbreak Response, Tshwane District, Department of Health, Pretoria, South Africa

 

 

^&Corresponding author
Mmatswele Hellen Kgatla, South African Field Epidemiology Training Programme, National Institute for Communicable Diseases, Johannesburg, South Africa. kgatlahellen@yahoo.com

 

 

 

 

A novel strain of coronavirus was identified in Wuhan City, China in December 2019 [1]. The respiratory disease caused by the novel coronavirus was named Coronavirus Disease 2019 (COVID-19). It continued to spread rapidly and was declared a Public Health Emergency of International Concern by the World Health Organization (WHO) on 30 January 2020 [2]. By 30 June 2020, the global number of infections had surpassed 10 million with over 500,000 deaths [3].

 

Outbreaks of coronaviruses were reported in the past. In 2003, an outbreak of a novel coronavirus named Severe Acute Respiratory Syndrome (SARS-CoV) was reported [4]. The outbreak affected 26 countries with 8,096 laboratory-confirmed cases and 774 fatalities. In 2012 another new strain of coronaviruses, now known as Middle East respiratory syndrome coronavirus (MERS–CoV), caused an outbreak that spread across 26 countries [5]. Patients in these two outbreaks presented with fever, headache, malaise, myalgia, shivering and diarrhoea. These symptoms are similar to COVID-19 symptoms.

 

South Africa (SA) reported its first case of COVID-19 on 5 March 2020 in a group of travellers returning from Italy [6]. Rapid spread occurred with 144,264 cases and 2,529 fatalities reported by 30 June 2020 [3]. SA had the highest number of COVID-19 cases in Africa and was in the top 5 countries with the highest infections globally by 30 June 2020 [3].

 

Covid-19 is a highly contagious disease [7]. According to literature, COVID-19 is transmitted from person to person through droplets, aerosols, sneezing, coughing and fomites2. The COVID-19 droplets can survive on surfaces for many hours. Poor hand hygiene can result in the transmission of these droplets from surfaces to susceptible host [8]. Although everyone is at risk of COVID-19 infection, some individuals have an increased risk of developing severe disease after infection. Severe COVID-19 infection can result in hospitalisation and mortality [9]. The elderly, males, smokers and people with a pre-existing co-morbidity were reported to have an increased risk of severe COVID-19 infection, resulting in hospitalisation and death [10]. In Italy, a case fatality rate of 20.7% was observed in case-patients aged 80 years and above, increasing to 22.7% in those aged 90 years and above [11]. Similar age-related trends were observed in China, where the median age of deceased cases was 68 years [10]. In both studies more males died compared to females [10-11] A high proportion of cases with severe COVID-19 infection reported in global studies had a pre-existing co-morbidity [12-13] , commonly hypertension, cardiovascular disease, diabetes mellitus and obesity [12, 14-15],. Smoking was identified as a risk factor for severe COVID-19 disease [13,16]. The odds of smoking were 2 times higher in hospitalised, compared to non-hospitalised cases, in Atlanta [12]. An increase in mortality and hospitalisation of COVID-19 cases resulted in some countries introducing lockdown measures to contain the spread of infection and to relieve pressure on the health systems [17].

 

There is a paucity of published studies on COVID-19 in the Sub-Saharan African region and South African context. Tshwane District in South Africa was among the districts with the highest number of COVID-19 cases in the country in June 2020, experiencing an increase in hospitalizations and deaths. With COVID-19 being new, no study has been conducted to determine the predictors of COVID-19 hospitalisation and mortality in Tshwane District, South Africa. Our study aimed to provide evidence on the factors associated with severe COVID-19 infections in Tshwane District, thus contributing to the body of knowledge on this phenomenon.

 

 

Study setting

 

The study was conducted in Tshwane District, Gauteng Province, which is one of eight metropolitan municipalities in South Africa [18]. The district is divided into seven regions or sub-districts. It has 72 clinics and 9 hospitals served by the Department of Health, 25 facilities served by the Local Authority, and 62 private health care providers.

 

Study design and period

 

We conducted a cross sectional study of COVID-19 laboratory-confirmed cases from March-June 2020.

 

Study population and selection

 

Our study population included laboratory-confirmed COVID-19 cases reported to the Tshwane District surveillance system by health care providers and cases received from the National Institute for Communicable Diseases (NICD). We included all the laboratory-confirmed cases, but excluded cases listed as untraceable during contact tracing due to incorrect contact numbers and residential addresses. Criteria for a confirmed case was any person, irrespective of clinical signs and symptoms, with a positive laboratory-conducted polymerase chain reaction (PCR) test result for COVID-19 in Tshwane District, March 2020-June 2020.

 

Data sources

 

The data was received from the NICD and those reported through the Tshwane District surveillance system by health care providers. NICD receives data on confirmed COVID-19 cases from both private and public laboratories, allocates the cases into respective provinces. Provincial office then allocates the cases into respective district. Additionally, the district surveillance officer receives clinical notifications of cases from health care providers. We used data from both surveillance systems to ensure that all reported cases were traced.

 

Data collection

 

A secondary analysis of surveillance data was conducted. During the study period, any person with COVID-19 defining symptoms or history of contact with a laboratory-confirmed COVID-19 case was considered for testing. Information of any suspected case was reported by health facilities using a standard Person Under Investigation (PUI) form. The district also received a line list of laboratory-confirmed COVID-19 cases from the NICD (which mostly contained the age, gender, residential address and contact details). Due to missing data from the line list provided by the NICD, cases were contacted telephonically to obtain additional information on comorbidities, symptoms and names of people they were in contact with, and to verify their residential address. This information was collated into a line list of only laboratory-confirmed COVID-19 cases.

 

Study variables

 

COVID-19 confirmed case

 

A confirmed case was any person with a positive polymerase chain reaction (PCR) COVID-19 test result irrespective of clinical signs and symptoms.

 

Asymptomatic case

 

A COVID-19 laboratory confirmed case without COVID-19 defining symptoms 14 days prior or on the day of testing

 

Comorbidity

 

Comorbidity was defined as the presence of one or more medical conditions in cases before COVID-19 infection reported by the cases.

 

Obesity

 

Obesity was defined as a body mass index (BMI) of over 30kg/m2

 

Data analysis

 

Data were received in a Microsoft Excel 2016 spreadsheet. Duplicates were removed through probabilistic matching using name, surname, age, gender, date of specimen collection, and testing laboratory. An observation was removed if all variables matched. Our data were exported to Stata (Version 15. StataCorp LLC, College Station, TX, United States of America, 2017) for analysis. Data were summarized using descriptive statistics. The mean and standard deviation were used to describe the age of cases. We used graphs and tables to display demographic and clinical presentation of cases. We used univariate and multivariable logistic regression models to identify factors associated with a severe form of infection that required hospitalisation; as well as factors associated with mortality. We collapsed our age groups in the logistic regression model due to the low frequencies seen in hospitalisation and deaths. Factors with a p-value ≤ 0.2 in the univariate analysis were considered for inclusion in the multivariable model using a manual backward elimination procedure. A p-value < 0.05 was considered statistically significant.

 

Ethical considerations

 

Ethical approval with waiver of informed consent was granted by the Faculty of Health Sciences Research Ethics Committee of the University of Pretoria (Ethics no: 553/2020) and the Tshwane District Ethics Committee (Ethics no: GP_202010_011).

 

 

The epidemiologic characteristics of cases

 

A total of 5,124 cases were extracted for the period March to June 2020 and 104 were excluded due to an unverified residential address at the time of tracing. The total number included in the study was 5,020 and the mean age was 39.7 (standard deviation (SD) ±16.1) years. The epidemic curve depicts that fewer than 100 cases were diagnosed in the first 11 weeks. An increase in cases was observed from week 12 (end of May 2020) Figure 1.

 

The highest number of cases was among those aged 31-40 years (26.2%, 1,315/5,020) with children 10 years and younger having the lowest number of cases (3.5%, 174/5,020). There were more female cases compared to male cases in all the age groups except for those aged 6-10 years Table 1.

 

Region 1 (26.5%, 1,328/5,020) and region 3 (25.8%=1,296/5,020) had the highest number of cases. The least number of cases were reported in region 7 (1.3%, 64/5,020) and 5 (1.2%, 61/5,020) Figure 2.

 

The clinical presentation of COVID-19 cases

 

Of the 5,020 cases, 2,192 cases were symptomatic. The most common symptoms were cough (47.1%, 1,033/2,192, sore throat (30.7%, 674/2,192), fever (26.1%, 573/2,192) and headache (26.0%, 571/2,192). Loss of taste and smell were also reported in 13.0%( 286/2,192) and 11.6% (255/2,192) cases respectively.

 

A total of 567 (11.3%, 567/5,020) cases presented with one or more pre-existing comorbidities. The majority presented with diabetes mellitus (64.5%, 366/567), hypertension (61%, 346/567) and HIV (26.8%, 152/567) Figure 3. The least commonly reported comorbidity was TB (1%, 5/567).

 

Death and hospitalisation of severe COVID-19 cases

 

The highest proportion of cases were hospitalised in April (12.0%, 14/117). Although month of June had the lowest hospitalisation proportion (4.3%, 194/4,535), the proportion of deaths were the highest (2.2%, 98/4,535). The fatality proportion increased monthly after March 2020 Figure 4.

 

Death and hospitalisation in COVID-19 cases with a pre-existing comorbidities

 

The highest proportion of deaths (50%, 6/12) occurred in cases who were obese. Half of these cases were hospitalised and they all subsequently died. The second -highest proportion of cases hospitalised (53.3%, 8/15) was reported among cases with a kidney problem, with a fatality proportion of 46.7% (7/15). Three out of five TB cases with COVID-19 were hospitalised, but no death was reported among these cases Figure 3.

 

Clinical characteristics of hospitalised cases

 

A total of 246 (4.9%) cases were hospitalised. The number of hospitalised cases increased with age and more than half were males aged 60 years and above (52.4%,129/246). Most of these cases were over the age of 50 years (65.9%, 162/246) and 47.2% (n=116/246) had no pre-existing comorbidity Table 2.

 

Factors associated with severe COVID-19 infection requiring hospitalisation

 

In our univariate analysis, we included age, gender and having pre-existing comorbidities as predictors for severe infection that resulted in hospitalisation. Males had almost 1.5 times higher odds of hospital admission compared to females (OR:1.47, 95%CI:1.14-1.90, p=0.003). Cases aged over 60 years had 23 times increased odds of hospitalisation than those aged 40 years and younger (OR 23.09, 95%CI: 15.08-35.13, p<0.001). The odds of hospital admission were higher among those with a pre-existing comorbidity (OR 4.32, 95%CI: 3.22-5.79, p<0.001).

 

When these variables were included in a multivariable logistic regression model older age, being male and having a pre-existing comorbidity were independent predictors of COVID-19 hospitalization. The odds of having severe COVID-19 infection increased with an increase age. The highest odds were observed in those aged 60 years and older (aOR: 18.70, 95%CI:11.83-29.60, p<0.001). The odds of hospital admission were 1.4 times higher in males than in females (aOR 1.38, 95%CI:1.10-1.87, p=0.035) The odds of admission were two times higher in those with pre-existing comorbidities than those without (aOR 2.90, 95%CI: 2.10-3.93, p<0.001) Table 3.

 

Factors associated with COVID-19 mortality in Tshwane District, March- June 2020

 

We included age, gender and pre-existing comorbidity as predictors for death due to COVID-19 in the univariate analysis. Those aged > 60 years had 71 times increased odds of dying from COVID-19 than those aged 40 years and younger (OR 71.3, 95%CI: 28.33-179.46, p<0.001). Males had increased odds of dying from COVID-19 compared to females (OR 1.77, 95%CI: 1.20-2.64, p=0.004). Having pre-existing comorbidities increased the odds of death (OR 9.00, 95%CI:5.80-13.92, p<0.001) compared to COVID-19 cases without any pre-existing comorbidities. Risk factors associated with COVID-19 mortality were age, gender and pre-existing co-morbidity. The odds of dying were 47 times higher in cases aged over 60 years compared to those aged 40 years and younger (aOR 47.13, 95%CI: 13.32-121.20, p <0.001). This wide confidence interval might be due to a small number of cases among the elderly. The odds of mortality were 1.72 times higher in males compared to females (aOR 1.72, 95%CI:1.10-2.72, p=0.020). Having pre-existing comorbidities increased the odds of COVID-19 mortality (aOR 5.58, 95%CI: 3.52-8.85, p<0.001). The goodness of fit of the multivariate model was adequate (p=0.1891) Figure 4.

 

 

Our study aimed to describe the epidemiology of COVID-19 cases in Tshwane District, South Africa, during the period from March to June 2020. We observed an increase in cases from end of May to end of June 2020. We also aimed to determine factors that were associated with a severe form of infection that resulted in hospitalisation and deaths. Our study found that older age, male gender, and having a pre-existing comorbidity was associated with a severe form of infection resulting in hospitalisation. Risk factors associated with mortality were older age, male gender and having a pre-existing comorbidity.

 

The increase in the number of COVID-19 cases in June 2020 is comparable with the increase reported in Gauteng Province, during which period the province was the epicentre of the pandemic in the country. The national testing capacity was increased between March and June 2020 [19]. In the same period, South Africa´s positivity rate increased from 2.2% at the beginning of March to 25.8% towards the end of June 2020. Gauteng conducted the highest number of tests compared to other provinces [19]. South Africa relaxed its lockdown measures from 1 June 2020, increasing economic activities, reopening of schools and allowing the sale of alcohol. This could have contributed to the increase in COVID-19 cases [20]. Alcohol consumption increased the spread of the virus through the sharing of beverage glasses and loss of ability to adhere to non-pharmaceutical prevention interventions such as physical distancing, regular handwashing and wearing of masks [21].

 

The demographic characteristics of COVID-19 cases have been studied globally, with age, gender and pre-existing comorbidity as predictors of mortality. The elderly has been found to be at an increased risk of a severe form of infection that required hospitalisation [9,22]. Age was found to be a predictor of SARS and MERS mortality in past outbreaks [23]. The elderly people have a deteriorating immune system, which can affect their ability to fight infections. Men were at a higher risk of death as compared to women. Our findings were similar to studies conducted in New York City, USA and Wuhan City, China [9,24]. Differences in lifestyle, genetics and health seeking behaviour between males and females may contribute to the significant difference in susceptibility [25]. Although not collected in this study, it is known that males are more likely to smoke, which could predispose them to severe infection [16,22].

 

COVID-19 cases can present as symptomatic or asymptomatic. Cough, sore throat fever and headache were the most common COVID-19 symptoms recorded in our study. Headache was a less common symptom in most studies [26-27]. Studies have reported loss of smell and taste as predictors of COVID-19 infection [28-29]. Our study also identified an increasing number of cases presenting with these symptoms. Loss of smell and taste often occur in 2.3% of influenza cases [30].

 

The presence of a pre-existing comorbidity has been identified as a risk factor for COVID-19 hospitalisation and death [9,14,22] Comorbidities impair the immune system, affecting an individual´s ability to fight infections [31]. Similar trends were observed in the MERS and influenza A virus (H1N1) outbreaks [30-33]. Obesity and kidney disease were common in hospitalised and deceased cases in our study. This is in contrast to the findings in Italy where chronic obstructive pulmonary disease and hypercholesterolemia were common in cases with a severe form of COVID-19 infection [14] An increase in the form of infection resulting in hospitalisation and death of obese cases was reported in studies in China, the USA and by WHO [9,32]. People with obesity are at a higher risk of chronic diseases such as diabetes mellitus, asthma, cardiovascular disease and hypertension [22]. In the twelve cases who were recorded as obese in our study, nine had a co-morbidity such as diabetes, heart conditions and hypertension. This might have contributed to the increased risk of hospitalisation and death in obese COVID-19 cases [22]. Obesity leads to difficulty in breathing even in the absence of COVID-19; increasing airway resistance, decreasing expiratory reserve volume, functional capacity and pulmonary compliance. Although HIV was among the top reported comorbidities, the proportion of hospitalisations and deaths among HIV-positive individuals was less than 10%. The low proportion in hospitalisation and deaths could be due to the lower prevalence of HIV in older age groups. Of the 152 COVID-19 cases with HIV, only 4 were aged over 60 years. The same trend was reported in the MERS outbreak with less than 5% of HIV positive cases developing severe infection [31].

 

The district´s mortality proportion was similar to South Africa´s national COVID-19 mortality proportion of 2%; but lower than the global mortality rate of 4.9% reported on 30 June 2020 [3]. The African region also reported a 2% mortality proportion [3]. In contrast, Wuhan and Italy reported mortality rates of 3.6% and 7.2%, respectively [11,34]. The difference in COVID-19 mortality between countries may be due to variation in the demographic characteristics of their populations. South Africa has a higher proportion of younger generation inhabitants, compared to Italy that has 23% of its population over the age of 65 years [11].

 

Our study used data reported from all facilities in the Tshwane District, therefore the data were representative of cases in the district. It however had a number of limitations. Firstly, we had missing information on cases, resulting in exclusion of some. Secondly, information of the oxygen status, treatment and length of hospitalisation, which were identified as predictors of mortality in other studies, were missing [10-11]. Therefore, we could not conduct an in-depth analysis of the hospitalised cases. Thirdly, our deaths were only facility-based deaths and those identified during contact tracing, so any death that could have occurred outside of the health facility, or occurred after contact tracing would have been missed. We acknowledge that their clinical characteristics could be different from the deaths reported in our study. It is also possible that there was misclassification bias for symptoms as some of the cases might have developed symptoms after testing was done. We also acknowledge potential misclassification and underreporting of comorbidities as it was self-reported.

 

 

The epidemiological characteristics of cases in our study were consistent with global studies. Age, gender and pre-existing comorbidities were important predictors of COVD-19 severity that resulted in hospitalisation and death. These findings are important in focusing strategies in the prevention of severe or complicated COVID-19 infections and in policy formulations. We recommend that the elderly, males and people with a pre-existing comorbidity should be at the centre of prevention strategies and prioritized when vaccines are developed and rolled-out. There is a need for a follow-up study to provide a more comprehensive analysis of severe COVID-19 cases looking at clinical records of these cases.

 

 

No conflict of interest was declared.

 

Funding

 

The study was funded by the South African Field Epidemiology Training Programme (SAFETP).

 

 

KH, DA, MH MM, ER and MR conceived the study. KH carried out the ethical application, analysis, and drafted the first manuscript. MA guided the analysis. KH, DA, MA, MM, MR, KL, RE and KL revised the manuscript and gave authorisation for publication.

 

 

We acknowledge Tshwane District COVID-19 tracers team for their contribution in collecting the data used for the study and the management for permission to use the data. We acknowledge the supervisors from Tshwane District Department of Health and the South African Field Epidemiology Programme for their constant availability in reviewing the paper.

 

 

Table 1: Clinical characteristics of hospitalised COVID-19 cases, Tshwane District, South Africa, March-June 2020

Table 2: Factors associated with hospitalisation in COVID-19 cases, Tshwane District, South Africa, March-June 2020

Table 3: Factors associated with mortality in COVID-19 cases, Tshwane District, South Africa, March-June 2020

Figure 1: Epidemiological curve of COVID-19 cases in Tshwane District, March-June 2020

Figure 2: The distribution of COVID-19 cases in Tshwane District by regions, March-June 2020

Figure 3: Number of confirmed COVID-19 cases by pre-existing comorbidities, March-June 2020

Figure 4: Proportion of COVID-19 case hospitalisation and deaths by month in Tshwane District, March-June 2020

 

 

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