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Characterization of COVID-19 cases in the early phase (March to July 2020) of the pandemic in Kenya

Philip Ngere1,2, Joyce Onsongo3, Daniel Langat1, Elizabeth Nzioka4, Faith Mudachi5, Samuel Kadivane1, Bernard Chege4, Elvis Kirui6, Ian Were7, Stephen Mutiso5, Amos Kibisu4, Josephine Ihahi4, Gladys Mutethya4, Trufosa Mochache3, Peter Lokamar6, Waqo Boru8, Lyndah Makayotto1, Emmanuel Okunga1, Nollascus Ganda3, Adam Haji3, Carolyne Gathenji3, Winfred Kariuki3, Eric Osoro2, Kadondi Kasera4, Francis Kuria9, Rashid Aman10, Juliet Nabyonga3, Patrick Amoth7

1 Department of Disease Surveillance and Epidemic Response, Ministry of Health, Kenya
2 Washington State University, Global Health, Kenya
3 World Health Organization, Nairobi Kenya
4 Public Health Emergency Operation Centre, Ministry of Health, Kenya
5 Department of Promotive and Preventive Health, Ministry of Health, Kenya
6 National Public Health Laboratory Services, Ministry of Health, Kenya
7 Office of the Director General, Ministry of Health, Kenya
8 Field Epidemiology and Laboratory Training Program, Ministry of Health, Kenya
9 Directorate of Public Health, Ministry of Health, Kenya
10 Cabinet Administrative Secretary, Ministry of Health, Kenya

DOI: 10.7189/jogh.12.15001

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Abstract

Background

Kenya detected the first case of COVID-19 on March 13, 2020, and as of July 30, 2020, 17 975 cases with 285 deaths (case fatality rate (CFR) = 1.6%) had been reported. This study described the cases during the early phase of the pandemic to provide information for monitoring and response planning in the local context.

Methods

We reviewed COVID-19 case records from isolation centres while considering national representation and the WHO sampling guideline for clinical characterization of the COVID-19 pandemic within a country. Socio-demographic, clinical, and exposure data were summarized using median and mean for continuous variables and proportions for categorical variables. We assigned exposure variables to socio-demographics, exposure, and contact data, while the clinical spectrum was assigned outcome variables and their associations were assessed.

Results

A total of 2796 case records were reviewed including 2049 (73.3%) male, 852 (30.5%) aged 30-39 years, 2730 (97.6%) Kenyans, 636 (22.7%) transporters, and 743 (26.6%) residents of Nairobi City County. Up to 609 (21.8%) cases had underlying medical conditions, including hypertension (n = 285 (46.8%)), diabetes (n = 211 (34.6%)), and multiple conditions (n = 129 (21.2%)). Out of 1893 (67.7%) cases with likely sources of exposure, 601 (31.8%) were due to international travel. There were 2340 contacts listed for 577 (20.6%) cases, with 632 contacts (27.0%) being traced. The odds of developing COVID-19 symptoms were higher among case who were aged above 60 years (odds ratio (OR) = 1.99, P = 0.007) or had underlying conditions (OR = 2.73, P < 0.001) and lower among transport sector employees (OR = 0.31, P < 0.001). The odds of developing severe COVID-19 disease were higher among cases who had underlying medical conditions (OR = 1.56, P < 0.001) and lower among cases exposed through community gatherings (OR = 0.27, P < 0.001). The odds of survival of cases from COVID-19 disease were higher among transport sector employees (OR = 3.35, P = 0.004); but lower among cases who were aged ≥60 years (OR = 0.58, P = 0.034) and those with underlying conditions (OR = 0.58, P = 0.025).

Conclusion

The early phase of the COVID-19 pandemic demonstrated a need to target the elderly and comorbid cases with prevention and control strategies while closely monitoring asymptomatic cases.

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Coronavirus disease 2019 (COVID-19), an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was first reported in December 2019 in Wuhan City, China, as clusters of pneumonia of unknown origin and spread rapidly across the globe [1]. The World Health Organization (WHO) declared COVID-19 a public health emergency of international concern (PHEIC) in January, 2020, and a pandemic in March 2020 [2]. As of July 27, 2020, a total of 16 114 449 cases and 646 641 deaths (case fatality rate (CFR) = 4.0%) globally had been reported to WHO, including 712 920 cases and 11 900 deaths (CFR = 1.7%) in Africa and 17 975 cases and 285 deaths (CFR = 1.6%) in Kenya [3,4]. This phase of the pandemic was driven by the importation of cases and sporadic isolated cluster outbreaks in Kenya. The prevention and control strategies implemented at that time were traveller screening at points of entry (POEs); movement restriction in and out of hotspots, targeted (cluster) mass testing, mandatory quarantine of travellers and all contacts, and institutional isolation of all cases [4].

The COVID-19 disease presents with a non-pathognomonic spectrum of symptoms, ranging from asymptomatic cases to mild and severe illnesses affecting several body organs and systems[5]. The incubation period is five to six days, with a range of one to 14 days; the commonly reported symptoms are fever, cough, difficulty in breathing, general malaise, sore throat, diarrhoea, loss of appetite, loss of smell (anosmia), loss of taste (ageusia), and headache [6,7]. The severe forms of the disease (including respiratory distress, respiratory failure, vital organ injuries, shock, and death) have been particularly documented among those with underlying conditions such as diabetes, chronic respiratory, cardiovascular, and gastrointestinal diseases, and hypertension; and the elderly [810].

As the pandemic evolved from sporadic clusters to widespread (community) transmissions, so did the need to urgently expand public health interventions to mitigate its adverse impact grow [11]. Kenya’s testing strategy focused on high-risk groups such as frontliners (including health care workers, disciplined forces, academic staff members, etc.), travellers, and close contacts of confirmed cases. This testing strategy yielded several asymptomatic cases, yet there was no data on their clinical progression, infectiousness, and duration of virus shading. In this study, we describe the characterization of the COVID-19 cases reported in Kenya in the pandemic’s early phase to understand the epidemiology and provide information necessary for monitoring the pandemic and developing prevention and control strategies.

METHODS

We analysed the data of COVID-19 cases in Kenya from March through to July 2020. Data was abstracted from records of COVID-19 cases admitted in isolation centres in Kenya (Figure 1). We used a purposive sampling that aligns with the WHO sampling guideline for clinical characterization of COVID-19 pandemic within a country to select the isolation sites [12] and considered all the eight administrative provinces during the selection of the isolation centres to ensure a national representation. We selected counties with high caseloads in each province (taking into consideration POEs) and included the main isolation centres for every selected county. All available case files at the selected isolation centres were reviewed.

Figure 1.  COVID-19 characterization study counties and isolation sites, Kenya, 2020.

A COVID-19 case was defined as any patient admitted into an isolation centre with a positive reverse transcriptase polymerase chain reaction (RT-PCR) test result for SARS-CoV-2 in Kenya. The cases were categorized as symptomatic or asymptomatic cases based on clinical syndromes at the time of admission. An asymptomatic COVID-19 case was defined any patient admitted at the isolation centre but without clinical symptoms or signs. A symptomatic COVID-19 case was defined as any patient who was admitted at an isolation centre with a record of any of the following clinical presentations on admission: fever, cough, difficulty in breathing, running nose, sore throat, headache, myalgia, anosmia, fatigue, diarrhoea, nausea, vomiting, irritability, confusion, pharyngeal exudate, conjunctival injection, seizures, or altered consciousness. We further classified the symptomatic cases into severe and non-severe cases based on their clinical course. A non-severe case was a COVID-19 symptomatic case that did not require oxygen supplementation while a severe case required oxygen supplementation. The cases were also grouped into survivors (those discharged alive from the isolation centre) and non-survivors (died while in isolation).

Data collection

Data collectors gathered the patients’ personal information, residence, occupation, comorbidities, symptoms, clinical profiles, likely exposures, and case contacts using an open data kit (ODK) application-based abstraction tool installed on tablets. Each data collector was issued with a tablet with the abstraction tool pre-installed, trained, and pre-tested on the data collection process. During the field activity, each team was assigned a field supervisor to provide support. Personal information abstracted included age, sex, nationality, and completed level of education; residence information abstracted from the patients included country, county, sub-county, ward, and village; occupation variables were health worker, veterinarian, transport, tourism, and hospitality sectors; co-morbidities considered were pregnancy, heart disease, hypertension, diabetes, liver disease, chronic lung disease, neurological disease, immunodeficiency conditions, renal disease, and drug use. The symptoms variables included symptoms on admission, date of onset, fever, general malaise, cough, sore throat, running nose, difficulty in breathing, diarrhoea, nausea, vomiting, loss of appetite, loss of smell (anosmia), loss of taste (ageusia), and headache. The clinical profiles were fever (°C) on admission, highest fever (°C) recorded, respiratory rate (/min), oxygen saturation (SpO2), upper respiratory tract infections (URTI), pneumonia, acute respiratory distress syndrome (ARDS), sepsis, cardiac disease, thromboembolic events, neurological conditions, ocular manifestations, renal disease, and survivorship. The likely exposures 14 days prior to illness included were international travel, local travel, health care setting, contact with a case, and gathering.

Data analysis

The data were downloaded and cleaned in Microsoft Excel spreadsheets and analysed using both Epi Info 7 (US CDC, 2020) and Stata statistical software 17 (StataCorp LLC. 2021). Socio-demographic data (personal information, residence, and occupation), co-morbidities, symptoms, clinical profile, exposures, and contacts were summarized using median and mean for continuous variables and proportions for categorical variables. The socio-demographics, co-morbidities, symptoms, exposures, and contacts were assigned exposure variables, while the clinical spectrums (symptoms and clinical profiles) assigned outcome variables, and bivariate analysis done to assess their associations. The exposure variables with P ≤ 0.20 from the bivariate analysis were included in an unconditional logistic regression model using the backward elimination method at a P = 0.05 level of significance [13]. The model was run and variables with P > 0.05 level of confidence were dropped. The process was repeated until only variables with P ≤ 0.05 remained in the final model. The variables retained in the model were considered independently associated with the outcome variables.

RESULTS

A total of 2796 case records were reviewed from health facility-based isolation centres (n = 2359 (84.4%)), standalone institutional isolation units (n = 389 (13.9%)), and home-based isolation and care (HBIC) (n = 48 (1.7%)). The cases included 2049 (73.3%) males, 852 (30.5%) persons aged 30-39 years, 2730 (97.6%) Kenyans, 532 (19.0%) with tertiary level education, 636 (34.8%) transport sector employees, and 743 (26.6%) Nairobi City County residents (Table 1). The data demonstrated an outbreak that began on the March 13, 2020, and steadily increased with a small spike in late May 2020 to early June 2020 and finally peaked in the last week of July 2020 with a daily average caseload of 60 cases (Figure 2).

Table 1.  Distribution of COVID-19 cases by sex, age, nationality, occupation, and residence during the early phase of the pandemic in Kenya, 2021

Variable Frequency (n) Proportion (%)
Sex
Male 2049 73,3
Female 747 26,7
Age group (years)
0-9 69 2,5
lis.19 77 2,8
20-29 603 21,6
30-39 852 30,5
40-49 578 20,7
50-59 362 12,9
>60 255 9,1
Nationality
Kenyan 2730 97,6
Non-Kenyans 66 2,4
Race
Blacks 2748 98,3
Asian 42 1,5
Caucasian 6 0,2
Level of education
None 508 18,2
Informal 59 2,1
Primary 144 5,2
Secondary 215 7,7
Tertiary 532 19
Not Indicated 1338 47,9
Occupation
Transport 636 34,8
Business 194 10,6
Health worker 174 9,5
Minor 135 7,4
Hospitality 107 5,9
Semi-skilled labour 81 4,4
Judiciary 76 4,2
Technician 72 3,9
Disciplined forces 64 3,5
Farmer 52 2,8
Finance officer 47 2,6
Education 42 2,3
House help 41 2,2
Civil servant 34 1,9
Manager 25 1,4
Elderly 23 1,3
Humanitarian 18 1
Religious leader 6 0,3
County of usual residence
Nairobi 743 26,6
Mombasa 311 11,1
Kajiado 259 9,3
Kiambu 228 8,2
Migori 228 8,2
Machakos 177 6,3
Uasin Gishu 105 3,8
Others* 745 26,6
Total 2796 100

*Includes Nakuru, Nyeri, Garissa, Busia, Laikipia, Muranga, Wajir, Kisumu, Meru, Kitui, Lamu, Makueni, Mandera, Kakamega, Kilifi, Kwale, Taita Taveta, Tana River, Siaya, Nyandarua, Kericho, Kisii, Trans Nzoia, Homa Bay, Nandi, Marsabit, Vihiga, Bungoma, Isiolo, Baringo, Embu, Narok, Bomet, Kirinyaga, Nyamira, Elgeyo Marakwet, and Tharaka-Nithi.

Figure 2.  Distribution of cases of COVID-19 by date of admission during the early phases of the pandemic in Kenya.

Up to 1200 (42.9%) cases had symptoms, including 443 (36.9%) cases on admission and 757 (63.1%) cases while at the isolation centres (Table 2). Among the 757 cases who developed symptoms while at the isolation centres, 258 (34.1%) had severe disease, while 50 (6.6%) did not survive it. The symptoms included cough (n = 572 (47.7%)), difficulty in breathing (n = 383 (31.9%)), fever (n = 332 (27.7%)), among others. Six hundred and nine (21.8%) cases had at least one underlying medical condition, out of which 129 (21.2%) had more than one (>1). The underlying medical conditions included hypertension (n = 260 (42.7%)) and diabetes (n = 211 (34.6%)), among others. Up to 1893 (67.7%) cases had reports on the possible sources of exposure; 1013 (53.5%) through travel, 498 (26.3%) through contact with a case, and 181 (9.6%) during community gatherings, among others. From the reviewed records, 2340 (83.7%) cases had their contacts traced, 2219 (98.4%) cases had no contacts, while the 121 (5.2%) cases with contacts had a median of three contacts (range = 1-66). A total of 760 contacts were listed, 632 (83.2%) of which were tested, with 118 (18.7%) testing positive. Among the 1200 symptomatic cases, 372 (31.0%) had severe outcomes. At the time of the records review, 1946 (69.6%) cases had been discharged from the isolation centres out of which 1866 (66.7%) were discharged alive (recoveries) while 80 cases had died (CFR = 2.8%).

Table 2.  Distribution of COVID-19 cases by symptoms, underlying conditions, possible exposures, contacts, and outcomes during the early phase of the pandemic in Kenya, 2021

Variables Frequency Proportion (%)
Symptomatology (n = 1200)
Symptomatic on isolation 443 36,9
Developed symptoms in isolations 757 63,1
Reported symptoms:
Cough 572 47,7
Difficulty in breathing 383 31,9
Fever (≥38.0°C) 332 27,7
General malaise 178 14,8
Headache 171 14,3
Sore throat 116 9,7
Loss of appetite 56 4,7
Running nose 48 4
Vomiting 47 3,9
Diarrhoea 39 3,3
Nausea 30 2,5
Anosmia 27 2,3
Ageusia 21 1,8
Chest pain 11 0,9
Epigastric pain 6 0,5
Urine retention 2 0,2
Convulsions 1 0,1
Underlying conditions (n = 609)
At least 1 underlying medical condition 609 21,8
>1 underlying medical condition 129 4,6
Type of underlying medical condition:
Hypertension 260 42,7
Diabetes 211 34,6
HIV 86 14,1
Chronic lung disease 56 9,2
Pregnancy 31 5,1
Drug/substance abuse 30 4,9
Neurological disease 28 4,6
Heart disease 25 4,1
Renal disease 22 3,6
Liver disease 13 2,1
Possible sources of exposure (n = 2769)
Indicated 1893 67,7
Not indicated 903 32,3
Possible sources of exposure:
Travel 1013 53,5
Contact with a case 498 26,3
Community gatherings 181 9,6
Healthcare setting 118 6,2
Workplace 49 2,6
Prison 34 1,8
Contacts (n = 2769)
Cases with contacts traced: 2340 83,7
Cases with contacts identified 121 5,2
Cases with no contacts identified 2219 94,2
Cases with no contacts traced 456 16,3
Outcomes
Symptomatology (n = 2796):
Symptomatic 1200 42,9
Asymptomatic 1596 57,1
Severity (n = 1200):
Severe cases 372 31
Non-severe cases 828 69
Survivorship (n = 2796):
Cases discharged from isolation units 1946 69,6
Cases discharged alive (survivor) 1866 95,9
Cases discharged dead (non-survivors) 80 4,1
Cases still admitted in isolation units 850 30,4

The odds of developing COVID-19 symptoms was higher among case aged above 60 years (odds ratio (OR) = 1.99, P = 0.007), males (OR = 1.47, P = 0.019), cases with at least one (OR = 2.73, P < 0.001) or more than one underlying medical condition (OR = 1.76, P = 0.023), or cases who had been exposed to the disease through the community gatherings (OR = 1.93, P = 0.003) compared to those who were not (Table 3). The odds of developing COVID-19 symptoms were lower among cases who worked in the disciplined forces (OR = 0.47, P = 0.009), transport sector employees (OR = 0.31, P = 0.000), drugs and substance users (OR = 0.11, P < 0.001), those with neurological disorders (OR = 0.20, P < 0.001), and those with heart diseases (OR = 0.16, P < 0.001) compared to those who were not. The COVID-19 cases who had at least one underlying medical condition (OR = 1.56, P < 0.001) or more than one underlying medical condition (OR = 1.66, P = 0.030) had higher odds of developing severe COVID-19 disease compared to those who were not (Table 4). The odds of developing severe COVID-19 disease were lower among cases who were male (OR = 0.51, P < 0.001), exposed through community gatherings (OR = 0.27, P < 0.001), or exposed as contacts of confirmed cases (OR = 0.41, P < 0.001) compared to those who were not. The odds of survival of cases from COVID-19 disease was higher among transport sector employees (OR = 3.35, P = 0.004) and those who were exposed through contacts with confirmed cases (OR = 3.98, P = 0.009) compared to those who are not (Table 5). However, cases who were aged ≥60 years (OR = 0.58, P = 0.034), had at least one underlying medical condition (OR = 0.58, P = 0.025), were pregnant (OR = 0.18, P = 0.016), had liver disease (OR = 0.27, P = 0.001), were exposed through travelling (OR = 0.18, P < 0.001), were symptomatic (OR = 0.05, P < 0.001), and had severe COVID-19 disease (OR = 0.01, P < 0.001) were at lower odds of survival compared to those who were not.

Table 3.  Factors associated with the development of COVID-19 symptoms during early phases of the pandemic in Kenya

Variables Yes No Bivariate analysis Multivariate analysis
Symptomatic Asymptomatic Odds Symptomatic Asymptomatic Odds OR 95% CI P-value OR 95% CI P-value
Socio-demographic
Aged ≥60 y 179 76 2,36 1021 1520 0,67 3,51 2.65-4.64 <0.001 1,99 1.21-3.29 0,007
Male sex 348 399 0,87 852 1197 0,71 1,23 1.04-1.45 0,018 1,47 1.06-2.03 0,019
Black race 1166 1582 0,74 34 14 2,43 0,3 0.16-0.57 <0.001 0,56 0.21-1.50 0,252
Kenyan national 1559 1171 1,33 37 29 1,28 1,04 0.64-1.71 0,865 - - -
Occupation
Disciplined forces 38 94 0,4 656 1039 0,63 0,64 0.43-0.94 0,024 0,47 0.26-0.82 0,009
Health worker 91 91 1 603 1042 0,58 1,73 1.27-2.35 <0.001 1,29 0.83-1.93 0,265
Hospitality 45 62 0,73 649 1071 0,61 1,2 0.81-1.78 0,371 - - -
Public service 83 103 0,81 611 1030 0,59 1,36 1.00-1.84 0,049 1,07 0.70-1.63 0,769
Transport 139 497 0,28 555 636 0,87 0,32 0.26-0.40 <0.001 0,31 0.23-0.42 <0.001
Underlying medical conditions
Having at condition 400 209 1,91 800 1387 0,58 3,32 2.75-4.01 <0.001 2,73 2.01-3.71 <0.001
Multiple (>1) condition 92 37 2,49 308 172 1,79 1,39 0.91-2.12 0,129 1,76 1.08-2.87 0,023
Type of medical condition
Hypertension 178 82 2,17 222 127 1,75 1,24 0.88-1.75 0,212 - - -
Diabetes mellitus 154 57 2,7 246 152 1,62 1,67 1.16-2.40 0,006 1,17 0.77-1.79 0,463
Immunosuppression 61 25 2,44 339 184 1,84 1,32 0.80-2.18 0,269 - - -
Chronic lung disease 38 18 2,11 362 191 1,9 1,11 0.62-2.00 0,719 - - -
Pregnancy 24 6 4 376 203 1,85 2,16 0.84-6.56 0,114 1,73 0.68-4.40 0,25
Drug abuse 6 24 0,25 394 185 2,13 0,18 0.04-0.30 <0.001 0,11 0.04-0.27 <0.001
Neurological disorders 9 19 0,47 391 190 2,06 0,23 0.10-0.52 <0.001 0,2 0.09-0.45 <0.001
Heart disease 9 16 0,56 391 193 2,03 0,28 0.12-0.64 0,001 0,16 0.07-0.40 <0.001
Renal disease 15 7 2,14 385 202 1,91 1,12 0.42-3.31 1 - - -
Liver disease 11 2 5,5 389 207 1,88 2,93 0.63-27.38 0,236 - - -
Exposures
Travel 373 646 0,58 388 485 0,8 0,72 0.60-0.87 <0.001 0,81 0.62-1.06 0,131
Community 103 77 1,34 658 1054 0,62 2,14 1.57-2.92 <0.001 1,93 1.26-2.95 0,003
Contact 211 288 0,73 550 844 0,65 1,12 0.91-1.38 0,269 - - -

OR – odds ratio, CI – confidence interval

Table 4.  Factors associated with severity of COVID-19 during early phases of the pandemic in Kenya, May-July 2020

Variables Yes No Bivariate analysis Multivariate analysis
Severe Non-severe Odds Severe Non-severe Odds OR 95% CI P-value OR 95% CI P-value
Socio-demographic
Aged ≥60 y 44 134 0,33 328 693 0,47 0,69 0.48-0.99 0,044 0,53 0.26-1.10 0,086
Black race 362 804 0,45 10 24 0,42 1,08 0.51-2.28 0,839 - - -
Kenyan national 362 809 0,45 10 19 0,53 0,85 0.39-1.85 0,681 - - -
Occupation
Disciplined forces 16 22 0,73 199 457 0,44 1,67 0.86-3.25 0,127 0,65 0.30-1.42 0,281
Health worker 25 66 0,38 190 413 0,46 0,82 0.50-1.35 0,438 - - -
Hospitality 15 30 0,5 200 449 0,45 1,12 0.59-2.13 0,724 - - -
Public service 19 64 0,3 196 415 0,47 0,63 0.37-1.08 0,089 0,8 0.28-2.25 0,687
Transport 41 98 0,42 174 381 0,46 0,92 0.61-1.38 0,672 - - -
Underlying medical conditions
Having at condition 148 252 0,59 224 576 0,39 1,51 1.17-1.95 0,001 1,56 1.20-2.01 <0.001
Multiple (>1) condition 44 48 0,92 104 204 0,51 1,8 1.12-2.88 0,014 1,66 1.05-2.64 0,03
Type of medical condition
Hypertension 70 108 0,65 78 144 0,54 1,2 0.80-1.80 0,388 - - -
Diabetes mellitus 54 100 0,54 94 152 0,62 0,87 0.57-1.33 0,526 - - -
Immunosuppression 27 34 0,79 121 218 0,56 1,43 0.82-2.48 0,202 - - -
Chronic lung disease 13 25 0,52 135 227 0,59 0,87 0.43-1.77 0,708 - - -
Pregnancy 14 10 1,4 134 242 0,55 2,53 1.10-5.85 0,026 1,4 0.62-3.15 0,416
Drug abuse 3 3 1 145 249 0,58 1,72 0.23-12.97 0,674 - - -
Neurological disorders 2 7 0,29 146 245 0,6 0,48 0.05-2.57 0,495 - - -
Heart disease 2 7 0,29 146 245 0,6 0,48 0.05-2.57 0,495 - - -
Renal disease 7 8 0,88 141 244 0,58 1,51 0.46-4.89 0,428 - - -
Liver disease 5 6 0,83 143 246 0,58 1,43 0.33-5.74 0,544 - - -
Exposures
Travel 118 255 0,46 67 321 0,21 2,22 1.58-3.12 <0.001 0,79 0.58-1.06 0,115
Community 13 90 0,14 172 486 0,35 0,41 0.22-0.75 0,003 0,27 0.15-0.49 <0.001
Contact 37 174 0,21 148 402 0,37 0,58 0.39-0.86 0,007 0,41 0.28-0.60 <0.001

OR – odds ratio, CI – confidence interval

Table 5.  Factors associated with survivorship from COVID-19 during early phases of the pandemic in Kenya

Variables Yes No Bivariate analysis Multivariate analysis
Survivor Non-survivor Odds Survivor Non-survivor Odds OR 95% CI P-value OR 95% CI P-value
Socio-demographic
Aged ≥60 y 190 17 11,18 1676 63 26,6 0,42 0.24-0.73 0,002 0,58 0.30-0.95 0,034
Black race 1827 78 23,42 39 2 19,5 1,2 0.14-4.79 0,684 - - -
Kenyan national 1824 78 23,38 42 2 21 1,11 0.13-4.42 0,702 - - -
Occupation
Disciplined forces 87 4 21,75 1143 33 34,64 0,63 0.22-2.50 0,333 - - -
Health worker 125 4 31,25 1105 33 33,48 0,93 0.32-3.69 0,785 - - -
Hospitality 73 3 24,33 1157 34 34,03 0,72 0.22-3.73 0,483 - - -
Public service 128 3 42,67 1102 34 32,41 1,32 0.40-6.79 1 - - -
Transport 410 7 58,57 820 30 27,33 2,14 0.93-4.92 0,066 3,35 1.47-7.62 0,004
Underlying medical conditions
Having at condition 436 27 16,15 1430 53 26,98 0,6 0.37-0.96 0,033 0,58 0.36-0.93 0,025
Multiple (>1) condition 93 5 18,6 343 22 15,59 1,19 0.43-4.14 1 - - -
Type of medical condition
Hypertension 193 8 24,13 243 19 12,79 1,89 0.81-4.40 0,136 1,9 0.81-4.44 0,138
Diabetes mellitus 162 9 18 274 18 15,22 1,18 0.52-2.69 0,69 - - -
Immunosuppression 62 5 12,4 374 22 17 0,73 0.26-2.56 0,571 - - -
Chronic lung disease 42 2 21 394 25 15,76 1,33 0.31-12.00 1 - - -
Pregnancy 21 3 7 415 24 17,29 0,4 0.11-2.27 0,157 0,18 0.04-0.75 0,016
Drug abuse 5 1 5 431 26 16,58 0,3 0.03-14.81 0,305 - - -
Neurological disorders 20 1 20 416 26 16 1,25 0,81-53-78 1 - - -
Heart disease 16 2 8 420 25 16,8 0,48 0.10-4.51 0,283 - - -
Renal disease 14 0 0 422 27 15,63 - - - - - -
Liver disease 9 2 4,5 427 25 17,08 0,26 0.05-2.65 0,13 0,27 0.06-1.33 0,001
Exposures
Travel 732 40 18,3 623 8 77,88 0,23 0.11-0.51 0 0,18 0.08-0.39 <0.001
Community 153 4 38,25 1202 44 27,32 1,4 0.50-5.44 0,278 0 0 0
Contact 348 4 87 1007 44 22,89 3,8 1.37-14.66 0,006 3,98 1.41-11.25 0,009
Clinical profiles
Symptomatic 896 77 11,64 970 3 323,33 0,04 0.01-0.11 <0.001 0,05 0.02-0.17 <0.001
Severe case 192 76 2,53 704 1 704 0,01 0.00-0.02 <0.001 0,01 0.00-0.04 <0.001

OR – odds ratio, CI – confidence interval

DISCUSSION

The early phase of COVID-19 pandemic in Kenya demonstrated heavy caseloads among the male, middle aged, transporters, and residents of Nairobi and Mombasa counties. The transmission of SARS-CoV-2 during the early phase was driven by travellers and sporadic isolated cases at the main POE in Nairobi and Mombasa counties. Men had a higher risk of contracting the infection, which was not surprising, as they constitute most of the travellers and spend most of the time outdoors due to social or livelihood activities. They also have a higher prevalence of smoking and alcohol consumption[1416]. Kenya’s strategy of testing international travellers at the POEs, implementing movement restrictions from Nairobi and Mombasa Counties (which were considered hotspots), targeting the hotspots for mass testing, and suspending international passenger flights could explain the larger caseloads being Kenyans, travellers, and residents of the major POE [4,17]. The trend of cases in the study coincided with the national picture of COVID-19 cases, reaffirming the representativeness of the study group [4].

The profile of symptoms noted during this study cut across several systems, including respiratory, cardiovascular, gastrointestinal, hepatobiliary, musculoskeletal, neurological, urinary, and reproductive systems similar to observations in other settings [1820]. This picture of symptoms involving multiple systems renders the basic syndromic screening less specific [21,22]. Few cases in this study were symptomatic on admission, consistent with findings from other countries [23,24]. However, many asymptomatic cases developed symptoms while in isolation and progressed to severe states of the disease or death. This phenomenon was documented in other settings and necessitates close monitoring of patients on HBIC [25,26]. The high yield of asymptomatic cases of COVID-19 is indicative of effective surveillance, screening, and testing strategies, which is important considering that asymptomatic cases have been linked to resurgence [26,27]. This poses a big challenge to the low to middle income countries, which may not be able to implement robust screening strategies.

This study also described chronic medical conditions and infections which, if comorbid with COVID-19, could result in unfavourable outcomes, consistent with studies within and outside the African settings [28,29]. This requires that those living with these conditions obtain early screening, close monitoring, and prompt management to minimize chances of unfavourable outcomes, should they contract the disease. During the early phase of the pandemic in Kenya, transmission was largely driven by importation of cases. Contact tracing is a key measure for containing infectious diseases such as COVID-19 [30]. We observed good contact tracing during the early phases, but with very poor yields, considering that travellers, transporters, and community gatherings make many contacts. This poor yield could be attributed to the public health and social measures, such as evacuation of contacts using ambulances with sirens and backed up by security personnel, mandatory quarantine at designated sites at the cost of contacts, and associated stigma which were deterrent to disclosure of contacts [4,31,32]. Due to the high positivity rates observed among the contacts, the poor yields occasioned by the deterrence could have worsened the transmission within the populations.

The presence of disease symptoms plays a fundamental surveillance role in identifying illness or clusters early before confirmation, which supports rapid response, thereby reducing associated morbidity and mortality [33,34]. Older adults were more likely to develop clinical manifestations of COVID-19 compared to younger age groups due to their immune response possibly being blunted and underlying chronic illnesses possibly also augmenting the signs of infection [35]. Male cases were shown to be more likely to become symptomatic than female cases. This has been attributed to several possible factors, such as the higher expression of angiotensin-converting enzyme-2 (the receptor for SARS-CoV-2) in males than females, the sex-based immunological differences driven by sex hormones and the X-chromosome [36,37], and possibly even lifestyle differences (higher levels of smoking, drinking, and drug abuse among men compared to women) [36,38]. This study found that many asymptomatic COVID-19 cases upon diagnosis could be attributed to the testing strategy targeting high risk groups such as travellers, transporters, close contacts, etc. This testing strategy should be effective in stopping transmission if coupled with mandatory isolation, since both the asymptomatic and pre-symptomatic are capable of transmitting the disease [39,40]. Due to the high number of cases converting from asymptomatic to symptomatic, severe, and even dying within the isolation centres, cases on HBIC should be monitored closely to minimize the occurrence of adverse outcomes.

Having at least one underlying medical condition was associated with severe disease outcomes. These findings align with other studies that have demonstrated that cases with comorbidities are at a higher risk of severe COVID-19 outcomes (including death), as they impair the body’s ability to fight off disease [41,42]. The higher the number of comorbidities, the worse the outcomes [43]. The pandemic happening in settings with a high prevalence of chronic diseases can overwhelm health care systems. The strategy of mandatory testing and isolation of positive cases within health care settings meant early diagnosis and close monitoring reducing the risk of severe COVID-19 disease among the travellers, transporters, and contacts as well.

The high CFR among cases in isolation centres when compared to the general population may be attributed to the mandatory isolation and its attendant psychological, social, and economic distress (which worsens health outcomes) [4,44,45]. Work stress among the health care workers and pressure on limited essential supplies needed to manage cases could also have contributed to this high CFR [46,47]. Advanced age, underlying medical conditions (including pregnancy), and severe cases were less likely to survive the disease consistent with previous studies [48,49]. Similarly, early recognition (as in the case of mandatory screening of risk groups such as travellers or contacts)and prompt intervention have been shown to reduce mortality associated with COVID-19 [50].

Limitations

This study was based on record reviews from different COVID-19 isolation centres, so there may be variations in the way data was gathered and recorded, limiting the extraction and interpretation of the variables. Some records were incomplete or lost in the course of time, leading to missing data.

CONCLUSIONS

This characterization of COVID-19 cases in Kenya in the early phase of the pandemic observed that mandatory testing of risk groups and isolation of positive cases was an effective control strategy, as many cases were potential asymptomatic spreaders. Public health and social measures in response to the COVID-19 pandemic need to be carefully implemented, without jeopardizing other critical response functions such as contact tracing. Many cases initially classified as asymptomatic later developed symptoms with severe and non-survivor outcomes which has implications on the selection and close monitoring of cases put under HBIC. The elderly and those with underlying medical conditions (comorbidities) are at higher risk of COVID-19 morbidities and mortalities, hence there is a need to target these groups with prevention and control measures.

Acknowledgements

Our gratitude goes to the World Health Organization, Kenya for the technical support during this study. This study was done at the height of COVID-19 pandemic to generate information that was urgently required to inform the development of control and prevention strategies, as such, could not go through the ethical review process due to time constraints. However, we are grateful for the permission to use the case data in this study by the Ministry of Health, County Governments, and isolation center managers.

[1] Funding: This study was supported by the Ministry of Health, Kenya and the World Health Organization, Kenya.

[2] Author contributions: PN, JO, DL, EN, FM, BC, IW, SM, AK, JI, GM, TM, PL, WB, LM, EO, NG, AH, CG, WK, EO2, KK, FK, RA, JN, and PA developed the concept, designed, and revised the final manuscript. PN, JO, DL, EN, FM, BC, AK, JI, GM, WB, LM, CG, WK, and KK collected the epidemiological and clinical data. PN, JO, DL, EN, FM, SK, BC, EK, IW, SM, AK, JI, GM, TM, LM, EO2, KK, FK, RA, and PA processed the data. PN, JO, DL, EO2 and EN drafted the manuscript. All authors agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

[3] Disclosure of interest: The authors completed the ICMJE Disclosure of Interest Form (available upon request from the corresponding author) and disclose no relevant interests.

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Corresponding author:
Philip Ngere
Ministry of Health
30016-00100 Nairobi
Kenya
[email protected]