Factors associated with COVID-19 infection in southern province Rwanda, June 2020-January 2021

Noella Benemariya^1,2,3,&, Frederic Ntirenganya¹, Edouard Ruseesa¹, Emmanuel Ntawuyirusha⁴, Jared Omolo², Edson Rwagasore¹

 

¹Rwanda Biomedical Centre, Kigali, Rwanda, ²Rwanda Field Epidemiology and Laboratory Training Program³Department of Epidemiology and Biostatistics, University of Rwanda, Kigali, Rwanda, ⁴Rwanda Ministry of Health, Kigali, Rwanda

 

 

^&Corresponding author
Noella Benemariya, Rwanda Biomedical Centre, Kigali, Rwanda.

 

 

 

 

The global outbreak of coronavirus disease (COVID-19) is caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). During the last 20 years, 2 other coronavirus epidemics, SARS-CoV and Middle East respiratory syndrome (MERS)-CoV, have resulted in a considerable burden of cases across multiple countries [1]. On 30 January 2020, the World Health Organization (WHO) declared the COVID-19 outbreak a Public Health Emergency of International Concern, and on 11 March 2020, the epidemic was upgraded to pandemic. Since the beginning of the pandemic, COVID-19 has affected many lives [2]. Globally by 18 September 2020; 101,916,053 cases had been reported including 2,197,124 deaths. In Africa, 3,529,076 cases were recorded including 88,899 deaths [3].

 

The evidence showed that in the absence of a vaccine or effective prophylaxis, the containment of SARS-CoV-2 is contingent on interrupting transmission through rapid identification and isolation of all infected individuals. Symptomatic contacts had to be isolated early, while close contacts of cases who may be incubating infection needed to be quarantined and monitored [4].

 

On 14 March 2020, Rwanda reported the country´s first case of COVID-19. From March 15, 2020 to January 28, 2021 there were 14,529 cases including 186 deaths [5].

 

People from various sectors were involved in implementing various public health interventions including surveillance, contact tracing, laboratory testing, Infection prevention and control (IPC), case management and psychological support. Response structures were established in all 30 Districts in Rwanda under the leadership of Mayors. Rapid Response Teams (RRTs) were activated at District levels with the main responsibility to report all information regarding COVID-19 on a daily basis to the central level. In addition, a multi-sectoral COVID-19 Joint Task Force was established, composed of Public health professionals, local authorities, security organs, civil society and development partners. Different technical support teams were deployed in all provinces and Kigali city to facilitate the response teams and implement preventive actions to deter the spread of the pandemic. The COVID-19 positivity rate in south province of Rwanda was 4.8%[6]. Hence, we assessed possible factors associated with COVID-19 positivity in southern province, Rwanda.

 

 

Study setting

 

Southern province is one of Rwanda's five provinces. It is composed of 8 administrative districts: Nyamagabe, Nyaruguru, Huye, Gisagara, Nyanza, Ruhango, Muhanga and Kamonyi. Southern province has a total population of 2,589,975 on an area of 5963 km², the population density is 434 habitant /km² slightly over the national population density of 415/km². The testing strategy in the southern province involved collecting samples from various communal settings such as markets, churches, car parks, schools, and correctional centers for surveillance purposes. A specific number of samples according to available testing kits were obtained from each location, and the individuals whose samples were taken were identified and registered into the Health Management Information System (HMIS). Qualified laboratory scientists from district hospitals ensured the safe transportation of samples to the laboratory and conducted the testing.

 

Study design and population

 

This cross-sectional study sought to identify the factors associated with COVID-19 positivity in Southern Province, Rwanda. The study used COVID-19 laboratory testing data collected over 8 months from June 2020 to January 2021 extracted from the Health Management Information System (HMIS). Any person who was a suspect, probable or confirmed case of COVID-19, whose sample was collected for laboratory testing in southern province from June 2020 up to January, 2021 residing in Southern province was included. Those who tested positive and come back for re-testing (control tests) to see if they recovered from COVID-19 disease were not included in the study.

 

Sample size and sampling technique

 

All individuals registered from June 1, 2020 up to January 15, 2021 for COVID-19 laboratory testing were considered for data analysis.

 

Data collection

 

The data were collected by laboratory technicians and recorded in Health Management Information System (HMIS) by data managers. The data comprised of socio-demographic information such as sex, age, occupation, place of residence; and clinical characteristics which include the presence of signs and symptoms (like fever, headache, cough, shortness of breath), with or without comorbidities and COVID-19 results status (Positive, presumptive positive and negative). Because the presumptive positive was taken as inconclusive result, during our analysis, it was omitted. Only positive and negative results were considered.

 

Data analysis

 

Data were extracted from the electronic system into MS Excel and then imported into Stata SE 13 for analysis. Demographic and clinical characteristics were summarized using descriptive statistics - frequencies, percentages and ratios. The few missing sociodemographic information were omitted during data analysis.

 

Bivariate analysis was also performed to identify the factors associated with COVID-19 infection in southern province, Rwanda using the logistic regression model. The binary outcome variable was COVID-19 status positive or negative. Odds ratio (OR) at 95% confidence level and p-value were computed. After performing bivariate analysis, the characteristics which showed marked association (P-value < 0.05) with the outcome (COVID-19) were selected for multivariate analysis using logistic regression.

 

Ethical considerations

 

This study analyzed secondary data from the health information management system, the permission to analyze data was obtained from the division manager of epidemic surveillance and response. Patient privacy and confidentiality were considered. The data were anonymous which ensured confidentiality.

 

 

Up to January 15, 2021, a total of 23737 samples were collected and 1114 were COVID-19 positive, 22,537 were negative and 86 were presumptive positive (Table 1). Further analysis was limited to 23,651 samples excluding the 86 presumptive positive cases. Majority were male, 70%(16522/23651). The age range was from 3 months to 104 years. The most predominant age group was 18-34 years (51%, 12171/23651). Most of the participants did not have clinical symptoms (Table 2).

 

The positivity rate in the Southern province was 4.7 % (1114 / 23737). The study shows that each district in the southern province had positive COVID-19 cases (Figure 1). The highest positivity rate was observed in Muhanga district (9.8%, 292/2964) and the lowest COVID-19 positivity rate was observed in Kamonyi district (1.4%, 13/895) (Table 1 ).

 

The study shows that the highest COVID-19 cases in south province were recorded in November 2020 (346 COVID-19 cases) while the lowest were recorded in June 2020 (10 COVID-19 cases). It also shows a decrease of cases from November 2020 (346 covid-19 cases ) to January 2021 (183 COVID-19 cases) (Figure 2).

 

The study shows that males (85.6%, 954/1114) are the most affected by the COVID-19 compared to women (14.4%, 160/1114) of the participants. On the hand, among the negative cases (69.1% , 15,568/22,537) were males compared to (30.9%, 6,969/22,537) females. The mean age for negative cases was 33 (3 months-90 years) compared to 30 years (1 year-90 years) for positive cases. The 18-34 years age group had the most positive cases (61%, 681/114) and negative cases (51.0%,11,490/22537) .

 

At bivariate analysis, males were 2.5 (95%CI: 2.1-3.0) times more likely to be COVID-19 positive cases compared to females. Only the 18-34 years age group was significantly associated with being a COVID-19 postive case compared to 0-17 years age group (cOR: 1.30, 95% CI: 1.04-1.61). The following occupations were significantly associated with COVID-19 positivity compared to being a public servant/government official; Refugees (cOR: 0.4, 95% CI: 0.2-0.8), frontline worker (cOR: 2.4, 95%CI: 1.5-3.0), student (cOR: 3.5, 95%CI: 2.3-5.0), wanderers (cOR: 9, 95%CI: 6.1-14.5). Notably being a refugee appears to be a protective factor.

 

These clinical features were significantly associated with being a COVID-19 case: Fever (cOR: 4.1, 95%CI: 2.2-4.5), headache (cOR: 4.9, 95%CI: 3.8-6.2), and cough (cOR: 2.7, 95%CI: 2.1-3.4). Conversly, there was no statistically significant association between having hypertension (cOR: 1.1, 95%CI:0.4-2.8) or diabetes (cOR:0.7, 95%CI: 0.17-2.9) with being a positive COVID-19 case (Table 3).

 

In the final logistic regression model sex and age were not statistically significantly associated with COVID-19 (Table 4). The following occupation types were significantly associated with COVID-19 compared to government staff: frontline (aOR:7.1, 95% CI: 3.1-9.7), student (aOR:3.5, 95%CI: 1.6-7.9), wanderers (OR:6.7, 95%CO:3.1-14). Fever (aOR: 2.7, 95% CI:1.4 - 4.8) and headache (aOR= 2.3, 95% CI: 1.6 - 3.3) were the only clinical features that were significantly associated with COVID-19 test positivity.

 

 

This study aimed at identifying demographic and clinical factors associated with COVID-19 infection in the southern province of Rwanda. The only independent demographic factor in this study was the occupation. COVID-19 cases were more likely to have been working as Frontline workers, wanderers and students. Compared with other government staff, the frontliners were 7 times more likely to get infected with Covid_19 in the Southern province. This may be due to the nature of the work they do of dealing with the population and may also be exposed as they may not stay home to stay safe. This is similar to a study conducted in the US where people who could not work at home were more exposed to Covid-19 infections compared with their colleagues working online[7]. In this study, we also found that students were 3.5 times more likely to get Covid_19 than government staff. This may be due to the social life of students that they don´t practice social distancing. This is similar to the study conducted in UK where people who were used to closed spaces , did not put much effort in social distancing for self-protection[8]. The wanderers were 6.7 times more likely to be infected than government staff. This may be due to their behavior that impedes them to properly wear masks, and practice social distancing for self-protection. This is similar to a study in the US and UK where people lacking comfortable housing complied less with preventive norms [8, 9]. Among clinical factors, participants having fever were 2.7 times more likely to be infected with Covid-19 than those without those symptoms. Similarly those with headache were 2.3 times more likely to be infected than those without symptoms. This is proven in many studies where the positivity and severity of Covid-19 are associated with having symptoms.[1, 4]. Many of our participants were not asymptomatic as in the study in China where asymptomatic patients did not develop severity but were still infecting others[10].

 

No difference was found to be among those with diabetes or high blood pressure about the positivity of the tests. It is also similar with what a study in Brazil found where there was not yet a proof about the influence of diabetes about the positivity but influences the severity among those infected[11]

 

Although the positivity rate in southern province was not higher compared with the East and West it was slightly higher than the overall positivity rate in the country at this time. Rwanda´s overall response to the pandemic has been commendable, with proactive measures such as widespread testing, contact tracing, and strict adherence to preventive guidelines[6].

 

Muhanga district reported the highest rate of infection, while its neighboring district, Kamonyi, had the lowest rate within the province. This suggests differing degrees of COVID-19 spread among people living together especially wanderers in rehabilitation centers which likely increased the risk of COVID-19 transmission compared to Kamonyi, where such centers are absent. At this time there was no community transmission, it was found in clusters of people instead.

 

Limitations

 

Due to low internet connectivity on field, and workload for data collectors, some sociodemographic information for tested persons was not entered in the system causing some missing information in dataset.

 

 

The research concluded that certain types of occupation and having symptoms of fever and headache were significantly associated with Covid-19 positivity in Southern Province, Rwanda. We recommend strengthening of the surveillance and response activities especially in these groups. Frontline workers should be protected and people living together such as students and wanderers in rehabilitation centers should be educated about preventive measures.

 

 

The authors declare no competing interests.

 

 

All authors have significantly contributed to the work, and have read and approved the manuscript. BN,ERU,ERWA and JO conceptualized, designed the methodology, and facilitated the data curation. BN, EN and FN cleaned and analysed data; BN wrote the original draft of the manuscript, JO supervised the work.

 

 

The authors are grateful to the Rwanda biomedical centre,the management of University of Rwanda and the Southern province for having granted the permission to conduct this study.

 

 

Table 1: Distribution of COVID-19 cases in Southern Province of Rwanda by district, June 2020-January 2021

Table 2: Demographic and clinical characteristics of COVID -19 laboratory testing samples, Southern Province Rwanda, June 2020-January 2021

Table 3: Bivariate analysis of factors associated with COVID-19 infection among residents of Southern Province Rwanda, June 2020-January 2021

Table 4: Multivariate analysis of factors associated with COVID-19 infection in Southern Province of Rwanda, June 2020 - January 2021

Figure 1: Distribution of COVID-19 Positivity Rate by District

Figure 2: Distribution of Covid-19 cases for the period of 8 months from June 2020 to January 2021

 

 

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