Characterization of the first few COVID-19 mortalities in Nairobi and Mombasa, Kenya 2020

Charles Mulwa Muendo^{1, 2, &}, Maryanne Gachari¹, Waqo Gufu Boru¹, Ahmed Abade¹, Dorothy Njeru³, David Otieno⁴, Adam Haji⁴, Elvis Oyugi¹, Penina Munyua⁵, Kadondi Kasera², Linda Makayotto⁶

 

¹Kenya Field Epidemiology and Laboratory Training Program, Ministry of Health, PO Box 225-00202, Nairobi, ²Public Health Emergency Operation Centre (PHEOC), Ministry of Health 30016-00100, Nairobi, ³Department of Pathology, Kenyatta National Hospital, PO Box 20723-00202, Nairobi, ⁴World Health Organization- Kenya Country office, 45335 Nairobi, Kenya, ⁵Centers for Disease Control and Prevention- Kenya, KEMRI Complex, Mbagathi Road off Mbagathi Way PO Box 606-00621, Village Market, Nairobi, Kenya, ⁶Division of Disease Surveillance and Response (DDSR), Ministry of Health 30016-00100, Nairobi

 

 

^&Corresponding author
Charles Mulwa Muendo, Kenya Field Epidemiology and Laboratory Training Program, Ministry of Health, Nairobi, Kenya

 

 

 

 

World Health Organization (WHO) defines Severe Acute Respiratory Syndrome (SARS) as an acute respiratory illness, which is severe, with a history of fever or measured fever ≥38⁰C, cough, with onset within 10 days, and requires hospitalization [1]. Coronavirus, a major cause of SARS, is a group of viruses with a crown shape and are single-stranded Ribonucleic Acid (RNAs) [2]. SARS-COV2 is the new coronavirus that causes COVID-19 disease [3]. This was first reported in Wuhan China in December 2019 [4] and has since evolved to become a global pandemic, declared by the WHO on March 11, 2020 [4]. Severe and critical SARS-COV2 disease often presents with respiratory distress, requiring hospitalization, and mostly resulting in organ failure and death [5, 6].

 

Clinical progression of patients on admission has been shown that the majority go into Acute Respiratory Distress Syndrome (ARDS), septic shock, refractory metabolic acidosis, coagulation disorder, Multi-Organ Dysfunction (MODs), and death [7]. Viral sepsis has been shown to have similar clinical progression and outcome as sepsis by other pathogens [8]. The survival rate of hospitalized patients in this study was 80% while that of those admitted to ICU was 60% [9]. Intensive Care Unit (ICU) is, therefore, key in the management of severe COVID-19 patients with three out of every 15 patients with severe COVID-19 having been found to develop viral sepsis and eventually MODs if not well taken care of [9].

 

Several factors have been associated with the rise in COVID-19 morbidity and mortality [10]. Mortality has been described to be high in old age and especially in those with underlying conditions [11]. However, factors associated with mortality have still not been well understood and described. Differences in association from one factor to another are still not clear. Much is still unknown about Covid-19, from its transmission and spread, treatment, prevention, risk factors, and risk groups. Morbidity and mortality are on the rise. There also seems to be some speculated differences in peoples´ groups in terms of COVID-19 morbidity and mortality [12].

 

By June 27, 2020, globally there were 9,653,048 confirmed cases with mortality at 491,128 and Case Fatality Rate (CFR) of 5.1% [13]. Africa had reported 268,102 cases with 5,673 deaths and 2.1% CFR. Kenya, by then, had 5,533 cases with 137 deaths, CFR- 2.5% [14]. At the onset of the pandemic, the government of Kenya put in place several measures to help in the control of COVID-19 spread. These included total lockdown of areas with high community spread, partial lockdown of Nairobi and Mombasa counties which had reported a high number of cases, and with high community spread. In addition, there was a dusk to dawn curfew and the regular personal precautionary measures of keeping social and physical distance, washing and sanitizing hands, cough etiquette, and use of masks [14].

 

At the start of the study (18 June 2020), Nairobi and Mombasa accounted for 3,202 (75.2%) of the cases and 101/117 (86.3%) of the deaths. The two counties had the highest attack rates: Nairobi at 47/100,000 and Mombasa at 103/100,000. By this time, no study had been done in Kenya to characterize COVID-19 mortalities and identify common factors among these mortalities. The study was also aimed at informing early identification and management for severe cases without even awaiting confirmatory tests. It was also important to underscore the need to build more capacity for Intensive Care Services in Kenya health facilities. 

 

 

Study site

 

The study was conducted in health facilities within Nairobi and Mombasa counties of Kenya (Figure 1). By the time this study was carried out (between 18 June 2020 and 27 June 2020), Nairobi and Mombasa accounted for 3,202 (75.2%) of all cases and 101/117 (86.3%) of all deaths in Kenya. Out of the 1135 health facilities in Nairobi county, only seven were reporting COVID-19 admissions, and out of the 340 health facilities in Mombasa county, seven were reporting COVID-19 admissions [15]. The health facilities reporting cases incuded: Kenyatta national, Aga Khan University, Nairobi, Caost general, Mombasa, Al Farouk, Jocham, Kenyatta University Teaching Research and referral, Ganjoni, Potreiz, Care, Alliance Medical Centre, Gur Nanak, and Mbangathi hospital. The health facilities included in the study were both public and private in both Nairobi and Mombasa. The health facilities were both government and private owned; with facility tiers ranging from national referral, regional referral, to sub-county referrals. All the above 14 health facilities were included in the study. The first mortality in Nairobi County was reported on 26 March 2020, while the first in Mombasa county was on 28 March 2020. The study also included deaths reported in the course of the data collection up to 27 June 2020.

 

Study population

 

The study population was all confirmed COVID-19 patients who died in the course of hospitalization or community deaths confirmed for COVID-19 post-humous.

 

Study design

 

This was a cross-sectional study involving a review of health records and inpatient files. Health facilities reporting COVID-19 deaths were first identified from the national COVID-19 line-list. These health facilities were all visited between 18 June 2020 and 27 June 2020. Data were abstracted from hospital records in each hospital for all deaths reported by the hospital. The period under review was 13 March 2020, when the first case was reported in Kenya to 27 June 2020. Data on community deaths were accessed either from the public health office at the sub-county or from the health facility mortuary where the body was admitted. A structured data collection tool with variables on demographics, signs, and symptoms, and the presence of comorbidities was used by the sub-county public health officers for data collection on community deaths.

 

Key definitions

 

A case of confirmed COVID-19 disease was defined as any patient with laboratory confirmation of the SARS COV2 through a Real-Time Polymerase Chain Reaction (RT-PCR) test from any authorized laboratories in the country.

 

Fever was defined as such symptom reported by the patient on admission or recorded temperature equal to or more than 38⁰C.

 

Comorbidity was the presence of any known or newly diagnosed underlying condition at the point of admission or during the hospital stay.

 

A post-humous test is a test carried out or conducted after a person has died.

 

Laboratory tests were defined as either normal or out of the normal ranges. The laboratory parameters reviewed and their normal ranges included: Hemoglobin- 12─18g/dl, White Blood Cell count (WBCs) - 4000─11000 per microliter (mcL), Neutrophils- 1500─7000/mcL, Lymphocyte- 1000─4000/mcL, C-Reactive Protein- <10 mg/L, Urea- 2─8 mmol/L, Creatinine- 60─130 micromol/L, Alanine and aminotransferase- 0-40 units/L, Sodium levels- 135─145 mEq/L and Potassium- 3.3─5.4mmol/L. Lympocytopenia was defined as the level of lymphocytes below the lower limit, that is, less than 1000/mcL.

 

Clinical complications were recorded as outlined in the clinical notes by the clinician.

 

Data collection instruments

 

An excel data extraction tool was used to extract data from hospital records. The data extracted ranged from identifying information, presenting signs and symptoms, laboratory and radiological findings during hospitalization, and case management.

 

Data analysis plan

 

Data were analyzed using Microsoft Excel® (Seattle, USA) and Epi info® 7.2.4 software (CDC, Atlanta, Georgia, USA). Means and medians were calculated for continuous variables and frequencies and proportions for categorical variables. Maps were generated using QGIS® version 2.8.6 (QGIS Int., Los Angeles, USA).

 

Ethical considerations

 

This being a public health event, the study was commissioned by the Kenya Ministry of Health and permission granted by the participating County Departments of Health. Unique identifiers were used for the line-listed deaths and all the data collection sheets were password protected.

 

 

Demographic characteristics of COVID-19 deaths

 

By the end (undertaken for 2 weeks) of this review, 126 deaths had been reported from both Nairobi and Mombasa counties. Mortality case records reviewed in the two counties were 94/126 (75%), 48 (51%) in Mombasa, and 46 (49%) in Nairobi health facilities. Twenty-five percent (32/126) of mortalities reported in the national line-list could not be reviewed. This was due to such patients having been referred before their death, or their records could not be traced in the hospital. The median age of the 94 cases was 61 years (interquartile range 50─70 years). There were 67 (71%) males and 39 (41%) patients were aged >65 years (Table 1). Kenyan citizens accounted for 97% (91/94) of the deaths and 3% (3/94) were other nationalities. Of the 94 deaths, 74 (79%) occurred in health facilities and 20 (21%) in the community. Among the 20 community deaths, 18 (90%) occurred in Mombasa County. In Mombasa county, out of the 29 health facility deaths, 12 (41.4%) were in Coast General Hospital, while in Nairobi county 21/44 (48%) were in Kenyatta National Hospital.

 

Spatial distribution of COVID-19 deaths

 

Mortalities were reported in patients from 11 (65%) of the 17 sub-counties in Nairobi county. Of the 46 deaths in Nairobi County, the majority were from Kamukunji 12 (26%) and in Langata sub-counties 5 (11%) (Figure 2). COVID-19 death rates by sub-county per 100,000 population in the Sub counties were: Kamukunji -4.5, Starehe - 2.9, Mathare - 2.9, Lang´ata -2.5, Makadara-1.6, and Kibra -1.6, Dagoretti (North and South) -0.7, Embakasi - 1, Kasarani - 0.1.

 

In Mombasa County, deaths were reported from patients residing in 5 (71%) of 7 sub-counties. Of the 48 deaths from Mombasa, 24 (50%) were residents of the Mvita sub-county and 10 (21%) from the Kisauni sub-county (Figure 3). COVID-19 death rates by sub-county per 100,000 population were as follows: Mvita- 16.8, Kisauni- 5.2, Jomvu- 4.9, and Changamwe- 3.4.

 

Distribution of COVID-19 Mortalities according to Date of Laboratory Confirmation

 

The first COVID-19 death was a case admitted at the Aga Khan University Hospital in Nairobi on 23 March 2020 and died three days later on 26 March 2020 (Figure 4). In Mombasa, the first death was a case confirmed on 28 March 2020 at the Coast General Hospital who died the same day. The first community death in Mombasa was a case whose sample was shipped to KEMRI Welcome Trust laboratory in Kilifi County on 30 April 2020; the case died two days later. There was an increase in COVID-19 deaths on 5 May 2020, with four community deaths. Community deaths increased gradually in Mombasa County between 5 May 2020 and 14 May 2020. The total COVID-19 deaths in facilities increased from 9 May 2020 until the beginning of this investigation on the 8 June 2020 with a second peak observed on June 11 June 2020. The median time from onset of symptoms to seeking health care was 2 days (IQR 0-5). The median hospital stay from admission to death was 2 days (IQR 0-6) (Figure 5).

 

Clinical and exposure characteristics of COVID-19 deaths

 

Of the 94 deaths reviewed, 62 (66%) presented with difficulty in breathing, 54 (57%) with general body weakness, 44 (47%) with cough, and 29 (31%) with fever (Table 2). Those with a recorded existing underlying illness were 65/94 (69%), with hypertension and diabetes were reported in 43 (66%) and 32 (49%) patients respectively. Five (7%) reported contact with a confirmed or probable COVID-19 case 14 days before symptom onset while out of the 48 cases who had information on travel, 3 (6%) reported international travel, and 3 (6%) reported local travel 14 days before symptom onset.

 

Clinical presentation of hospitalized COVID-19 deaths

 

Seventy nine percent (74 ) of the 94 deaths were among patients admitted at a hospital (Table 1). The clinical signs that were reported at admission were respiratory distress in 36 (51%) and altered consciousness in 17 (24%) (Table 3). On admission, the median temperature recorded was 36.6°C, (IQR: 36.2-37.4) Patients who had fever with a recorded temperature of ≥38°C were 9 (14%) and those with tachypnea were 36 (80%), 33 (70%) had an oxygen saturation of <90% on admission.

 

Laboratory and radiological investigations

 

On admission, 39/70 (56%) patients had full heamogram done, while 33% (18/55) had reduced hemoglobin levels in blood (Table 4). Thirty eight percent (20/52) had leukocytosis, lymphocytopenia 47% (25/53) and neutrophilia 58% (31/53). Elevated C-reactive protein levels (CRP) and elevated creatinine levels were recorded in 97% (32/33) and 52% (26/50) of the deaths respectively. Only 42 patients had data on liver function tests, and the elevated alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels were recorded in 18 (43%) and 27(64%) patients respectively. Computed tomography (CT) scan was done in 24% (17/70) of patients, and the most common abnormality was ground-glass opacification in 9/17 (53%) and atypical viral pneumonia features in 9/17 (53%) CT scans. Chest x-ray was done for 37% (26/70) of the patients and the commonest abnormality was patchy opacities 65% (17/26) followed by pneumonia features 31% (8/26), ground-glass opacification was seen only 2/26 (8%) of the X-rays.

 

Treatment and complications

 

Intravenous antibiotics were administered to 77% (54/70) of patients and glucocorticoids to 19% (13/70). Ninety four percent (60/64) of the patients were on supportive oxygen therapy and those on mechanical ventilation were 33% (23/70). The most common complications recorded were acute respiratory distress syndrome (ARDS) in 54% (36/67) and pneumonia in 49% (33/67) patients (Table 5).

 

 

This study followed other studies that had described COVID-19 deaths [3, 4] but few have been done on the Kenyan population [16]. The most affected demographic groups were males and those above 65 years old. Community deaths were high in Mombasa county, specifically in the Mvita sub-county. Mvita in Mombasa and Kamukunji in Nairobi had the highest death rates from COVID-19. Majority of these deaths presented in the hospital with difficulty in breathing and having an underlying condition, many with more than one. Laboratory test results were deranged in majority of the patients, ground-glass opacification being the common finding in radiological tests. Almost all admitted cases were treated with intravenous antibiotics and acute respiratory distress syndrome was the most common clinical complication leading to death.

 

In this study, the most affected were those aged over 65 years contributing almost half of the deaths. Looking at gender, males were most affected. Other studies have recorded almost similar findings regarding the most affected demographic groups, with one showing that the most affected age was those over 70 years of age and majorly males [4]. Being male and a smoker (though smoking was not assessed in this study) was found to be a risk factor for COVID-19 severe disease and death [10]. It has not been yet explained why males are the most affected. However, we do speculate that it could be due to the nature of jobs they do and that they could be less keen on following public health measures/rules than females.

 

A majority of the community deaths were seen in Mvita, Mombasa County, and Kamukunji Nairobi County. Mvita was characterized by resistance from the beginning of the outbreak, with residents clashing with government officers enforcing the government regulations to contain COVID-19. This could be attributed to an increase in community transmission due to the flouting of COVID-19 containment regulations by members of the community at the start of the outbreak. First, this study looked at the initial cases in Kenya, when there was no community immunity to the disease. With such naïve immune systems, it is possible to see such high levels of patients admitted with severe disease and death. Further research and data review are important to look at the proportions of severe disease later in the pandemic in comparison with the findings of this study. Secondly, there was a high stigma associated with the disease at the beginning of the pandemic. Such stigma was related to low health-seeking behaviors even for other ailments. From a quick survey done by WHO between January and May 2020 (194 nations responded), 122 (63%) countries indicated that non-communicable disease (NCD) services were either partially disrupted or completely disrupted [17]. Since COVID-19 testing became mandatory for those seeking healthcare services, visiting a hospital became necessary only when a condition became severe. These two theories may partly explain why the proportion of community deaths for Mvita, Mombasa was high in this study.

 

A study done in Shenzhen City, China showed that a reduction in mobility was effective in the reduction of transmission but must be combined with other transmission containment measures [18]. The level of transmission could be different also dependent on the degree of mobility and restriction. In this regard, the Kenyan government through the ministry of health was compelled to go a step further to institute total lockdown in the two areas. Similarly, these two communities are known for communal living. Kamukunji in particular has the largest assorted goods market in Nairobi, Kenya, and is a supplier of such goods to many small business traders in the country. This market has shopping malls selling goods on both retail and wholesale. Traders come from all over the country to buy supplies in this market. This was a point from which the disease could easily spread to the whole country.

 

Most of the patients who died at the point of presentation to health facilities had difficulty in breathing and generalized body malaise while a few had fever and cough. This contrasted with findings from a meta-analysis done in March 2020 that indicated that the common signs and symptoms seen at the time of presentation were fever, cough, loss of smell, loss of taste, diarrhea, difficulty in breathing [19]. Similarly, a review conducted in May 2020 found fever, cough, and diarrhea to be common presenting features pointing to severity [10]. However, a different study showed features pointing to the severity and even death were dyspnea and hypoxemia setting in especially seven days after the onset of symptoms [7]. These symptoms, therefore, seem to follow a sequence, from symptoms of a mild disease to those signifying severe disease in some cases. In some cases, the disease onset could be with symptoms pointing to severe disease and death. Respiratory distress, tachypnea, and oxygen concentration <90% were found to be the most reported signs by clinicians. A study on COVID-19 mortality found shortness of breath in 98.8% of all such cases at admission [4].

 

The majority of these cases had an underlying condition before the COVID-19 infection. The most common underlying conditions being hypertension and diabetes. More than half of the mortalities with comorbidities had more than one comorbidity. Other studies have demonstrated hypertension and diabetes to be the most common underlying conditions in patients who died from the disease [4, 20] . Diabetes has been significantly associated with severe illness and mortality [20]. Diabetics have shown a two-fold risk of severe disease and subsequently ICU care (OR- 2.10 95% CI 1.71-2.57) and a three-fold risk of mortality (OR- 2.68 95% CI 2.09-3.44) [20]. Hypertension has also been associated with a low survival rate in patients with severe disease admitted to ICU [21]. We propose that further studies be done to look at the associations between diabetes and/or hypertension and COVID-19 severe disease and mortality.

 

Underlying pneumonia (bacterial) infection and its diagnosis in COVID-19 patients may be difficult to prove and hence be missed. In a study by Emmanuel Dudoignon et al. [22], the prevalence of bacterial pneumonia especially in severely ill COVID-19 patients was 37%. These were patients under mechanical ventilation, with 75% having hospital-acquired pneumonia. It is likely then that most coinfections with pneumonia are nosocomial pneumonia. Other viral causes of pneumonia are a possibility too. Emmanuel Dudoignon et al. felt that Rawson et al. [23] overestimated the prevalence of bacterial pneumonia in COVID-19 patients, cautioning clinicians from using broad-spectrum antibiotics. Testing for pathogenic causes of pneumonia must be encouraged, especially for patients with severe COVID-19. Where such laboratory capacities are unavailable, broad-spectrum treatment of bacterial pneumonia may be encouraged, especially with severe COVID-19 patients.

 

Laboratory investigations and radiological imaging have proved to be key over decades in disease diagnosis. Similarly, the tools have been utilized with the emergence of COVID-19 disease. In this study, the most outstanding laboratory findings seen were lymphocytopenia (decreased lymphocytes), neutrophilia (increased neutrophils), increased C-Reactive Protein, increased creatinine, and increased AST and ALT. A review on severe COVID-19 conducted in April 2020 showed that a majority of patients presented with lymphocytopenia and elevated inflammatory factors like C-reactive protein (CRP) [7]. The deranged markers point to end stages of organ damage and eventually multi-organ failure [7]. Neutrophil-Lymphocyte Ratio (NLR) ≥3.13 is a pointer to the severity and poor outcome [7, 24]. Our study did not focus on such calculation but the picture was clear with lymphocytopenia and neutrophilia. The most common radiological finding on the chest CT scan was ground-glass opacification. The variance between findings in chest X-ray and CT scan can be due to high yield for CT scan. A study by Jin An (etal) showed that abnormal findings were common (37.3%) in CT scans in rather normal X-ray [25]. This is consistent with findings in other studies [26]. In combination with routine laboratory findings, a chest CT scan can be used for COVID-19 diagnosis in absence of RT-PCR for COVID-19.

 

The mainstay of treatment for COVID-19 is supportive management [6]. As of October 2020, remdesivir was the only approved antiviral for COVID-19 management [7, 27]. Since then, many other drugs have been added to the list including chloroquine, lopinavir, etc. [28]. However, this study found that most patients were on intravenous antibiotics. This could have been to cover for any underlying bacterial infection. Viral sepsis has been shown to have similar clinical progression and outcome as sepsis by other pathogens [8]. Viral sepsis seems to be highly ignored compared to other bacterial causes of sepsis. This could also partly explain why antibiotics were used for almost all the cases in this study. This may point to the need for proper guidelines on the management of viral sepsis. ICU admissions were too low compared to the clinical presentation of the patients described in this study. This could be due to the low ICU capacity in Kenya that necessitates the need to build ICU capacities in the country.

 

Most of the patients died from clinical complications; the most common being acute respiratory distress (ARDS) [29, 30], pneumonia, and sepsis in order of frequency. ARDS has been reported as one of the most common complications for COVID-19 patients [4, 7, 9]. Other common complications described include septic shock, refractory metabolic acidosis, coagulation disorders, and Multi-Organ Dysfunction (MODs) [7].

 

Limitations of the study

 

This review would have sought to interview proxies of the deaths but that was not done due to the high stigma associated with the disease at the start of the pandemic which was the study period. Hospital records had missing data in some variables. With no clear guideline for COVID-19 management at the start, different health facilities had different practices. Community deaths had only identification information, signs and symptoms, and comorbidities.

 

 

From this study, difficulty in breathing was a common finding. More than half of the patients presented with difficulty in breathing with a smaller proportion having fever. Laboratory findings of lymphocytopenia (decreased lymphocytes), and neutrophilia (increased neutrophils), and/or ground glass opacification in imaging points to COVID-19 infections. Such patients should be commenced on treatment for COVID-19 without waiting for a confirmatory test. The common presentation of difficulty in breathing, and the clinical sequelae of ARDS and organ malfunction underscores the need for ICU facilities to better manage these patients. We hope this study will be an important tool both to clinicians and decision-makers.

 

Recommendations

 

Mortality was highest in older patients with co-morbidities, which underscores the importance of emphasizing prevention and control measures in these groups. Clinicians should have a high index of suspicion for COVID-19 in older patients who present with difficulty breathing and malaise, even in the absence of fever. Laboratory findings suggestive of COVID-19 such as neutrophilia, lymphocytopenia, elevated CRP and ground-glass opacities on CT scan may be used as an indicator of infection before laboratory confirmation. Expansion of critical care units in health facilities for patients who require mechanical ventilation since ARDS was a common complication.

 

 

 

 

The authors declare that they have no competing interests.

 

Funding

 

The costs for carrying out this study were provided by the Kenya Field Epidemiology and Laboratory Training Program (KFELTP)

 

Declarations

 

Ethics approval and consent to participate

 

This being a public health event, the study was commissioned by the Kenya Ministry of Health and permission granted by the participating County Departments of Health.

 

Consent for publication

 

We hereby give consent to JIEPH to publish this study

 

Availability of data and materials

 

The data that support the findings of this study are available from Public Health Emergency Operation Center (PHEOC)- Ministry of Health Kenya but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Public Health Emergency Operation Center (PHEOC)- Ministry of Health Kenya.

 

 

CMM and MG was involved in the conception, design, data collection, data analysis and writing of the manuscript WGB, AA, DN, DO, AH, PM, KK, and LM were involved in the conception, and design EO was involved in the conception, design, and data collection All authors read and approved the final manuscript.

 

 

We would like to acknowledge the great technical support offered by the Kenya Field Epidemiology and Laboratory Training Program (KFELTP) and Centers for Disease Control and prevention during the development of this manuscript.

 

 

Table 1: Demographic characteristics of laboratory confirmed COVID-19 Deaths in Nairobi and Mombasa Counties, Kenya, 2020 (n=94)

Table 2: Clinical and exposure characteristics of patients who died from COVID-19 in Nairobi and Mombasa Counties, Kenya, 2020 (n=94)

Table 3: Clinical presentation of COVID-19 patients who died in health facilities within Nairobi and Mombasa Counties, Kenya, 2020 (n=70)

Table 4: Laboratory and radiological findings of COVID-19 patients who died in health facilities in Nairobi and Mombasa counties, Kenya, 2020 (n=70)

Table 5: Treatment and complications of COVID-19 patients who died in health facilities within Nairobi and Mombasa Counties, Kenya, 2020 (n=70)

Figure 1: Map of Kenya showing the Nairobi and Mombasa counties and their respective sub-counties

Figure 2: COVID-19 deaths by Sub-County, Nairobi, Kenya, 2020

Figure 3: COVID-19 deaths by Sub-County, Mombasa, Kenya, 2020

Figure 4: COVID-19 cases who died by the date of laboratory confirmation in Nairobi and Mombasa Counties, Kenya, 2020 (N=94)

Figure 5: Nairobi and Mombasa County COVID-19 death cases, time of onset to admission to death, March to June 2020

 

 

1. World Health Organization. Severe Acute Respiratory Syndrome (SARS) [Internet]. Geneva (Switzerland): World Health Organization; c2024 [cited 2024 Jul 22]. Google Scholar


2. Perlman S, Netland J.Coronaviruses post-SARS: update on replication and pathogenesis . Nat Rev Microbiol [Internet]. 2009 May 11 [cited 2024 Jul 22];7(6):439-50. https://doi.org/10.1038/nrmicro2147 PubMed | Google Scholar


3. Yang X, Yu Y, Xu J, Shu H, Xia J, Liu H, Wu Y, Zhang L, Yu Z, Fang M, Yu T, Wang Y, Pan S, Zou X, Yuan S, Shang Y.Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study . The Lancet Respiratory Medicine [Internet]. 2020 Feb 24 [cited 2024 Jul 22];8(5):475-81. https://doi.org/10.1016/s2213-2600(20)30079-5 PubMed | Google Scholar


4. Yao T, Gao Y, Cui Q, Peng B, Chen Y, Li J, Huang C, He C, Pu J, Wei J, Zhan Y, Yan J, Tian J, Zhang Z, Liu Z.Clinical characteristics of a group of deaths with COVID-19 pneumonia in Wuhan, China: a retrospective case series . BMC Infect Dis [Internet]. 2020 Sep 22 [cited 2024 Jul 22];20(1):695. https://doi.org/10.1186/s12879-020-05423-7 PubMed | Google Scholar


5. Li H, Liu L, Zhang D, Xu J, Dai H, Tang N, Su X, Cao B.SARS-CoV-2 and viral sepsis: observations and hypotheses . The Lancet [Internet]. 2020 Apr 17 [cited 2024 Jul 22];395(10235):1517-20. https://doi.org/10.1016/s0140-6736(20)30920-x PubMed | Google Scholar


6. Word Health Organization.COVID-19 clinical management: living guidance, 25 January 2021 [Internet]. Geneva (Switzerland): World Health Organization; 2021 Jan 25[ cited 2024 Jul 22]. 81 p. WHO Document No.: WHO/2019-nCoV/clinical/2021.1 Download WHO-2019-nCoV-clinical-2021.1-eng. Google Scholar


7. Xie P, Ma W, Tang H, Liu D.Severe COVID-19: A Review of Recent Progress With a Look Toward the Future . Front Public Health [Internet]. 2020 May 13 [cited 2024 Jul 22];8:189. https://doi.org/10.3389/fpubh.2020.00189 PubMed | Google Scholar


8. Iwashyna TJ, Ely EW, Smith DM, Langa KM.Long-term cognitive impairment and functional disability among survivors of severe sepsis . JAMA [Internet]. 2010 Oct 27 [cited 2024 Jul 22];304(16): 1787-1794. https://doi.org/10.1001/jama.2010.1553 PubMed | Google Scholar


9. Liu D, Wang Q, Zhang H, Cui L, Shen F, Chen Y, Sun J, Gan L, Sun J, Wang J, Zhang J, Cai Q, Deng J, Jiang J, Zeng L.Viral sepsis is a complication in patients with Novel Corona Virus Disease (COVID-19) . Med Drug Discov [Internet]. 2020 Jul 24[cited 2024 Jul 22];8:100057. https://doi.org/10.1016/j.medidd.2020.100057 PubMed | Google Scholar


10. Rahman A, Sathi NJ.Risk factors of the severity of COVID-19: A meta-analysis. Int J Clin Pract [Internet]. 2020 Dec 7 [cited 2024 Jul 22];75(7): e13916. https://doi.org/1111/ijcp.13916 Subscription or payment required to access full text. PubMed | Google Scholar


11. Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, Xiang J, Wang Y, Song B, Gu X, Guan L, Wei Y, Li H, Wu X, Xu J, Tu S, Zhang Y, Chen H, Cao B.Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study . The Lancet [Internet]. 2020 Mar 11[cited 2024 Jul 22];395(10229):1054-62. https://doi.org/10.1016/s0140-6736(20)30566-3 PubMed | Google Scholar


12. Holmes L, Enwere M, Williams J, Ogundele B, Chavan P, Piccoli T, Chinaka C, Comeaux C, Pelaez L, Okundaye O, Stalnaker L, Kalle F, Deepika K, Philipcien G, Poleon M, Ogungbade G, Elmi H, John V, Dabney KW. Black-White Risk Differentials in COVID-19 (SARS-COV2) Transmission, Mortality and Case Fatality in the United States: Translational Epidemiologic Perspective and Challenges . IJERPH [Internet]. 2020 Jun 17 [cited 2024 Jul 22];17(12):4322. https://doi.org/10.3390/ijerph17124322 Download ijerph-17-04322-v2.pdf. PubMed | Google Scholar


13. World Health Organization. Coronavirus disease (COVID-19) Situation Report - 159 [Internet]. Geneva (Switzerland): World Health Organization; 2020 Jun 27[cited 2024 Jul 22]; 16 p. Select Situation Report -159 from Situation reports June 2020. Download 20200627-covid-19-sitrep-159.pdf. Google Scholar


14. Ministry of Health (KE). COVID-19 outbreak in KENYA daily situation report - 117. Nairobi (Kenya): Ministry of Health (KE); 2020 July 12. 14 p. Google Scholar


15. Ministry of Health (Kenya).Kenya Master Health Facility Registry [Internet]. Nairobi (Kenya); [cited 2024 Jul 22]. Google Scholar


16. Ombajo LA, Mutono N, Sudi P, Mutua M, Sood M, Loo AM, Juma P, Odhiambo J, Shah R, Wangai F, Maritim M, Anzala O, Amoth P, Kamuri E, Munyu W, Thumbi SM.Epidemiological and clinical characteristics of patients hospitalised with COVID-19 in Kenya: a multicentre cohort study . BMJ Open [Internet]. 2022 May 19[cited 2024 Jul 22];12(5):e049949. https://doi.org/10.1136/bmjopen-2021-049949 PubMed | Google Scholar


17. Zhou Y, Xu R, Hu D, Yue Y, Li Q, Xia J. Effects of human mobility restrictions on the spread of COVID-19 in Shenzhen, China: a modelling study using mobile phone data . The Lancet Digital Health [Internet]. 2020 Aug [cited 2024 Jul 19];2(8):e417-24. https://doi.org/10.1016/s2589-7500(20)30165-5 PubMed | Google Scholar


18. Sun P, Qie S, Liu Z, Ren J, Li K, Xi J.Clinical characteristics of hospitalized patients with SARS-CoV-2 infection: A single arm meta-analysis . Journal of Medical Virology [Internet]. 2020 Feb 28 [cited 2024 Jul 19];92(6):612-7. https://doi.org/10.1002/jmv.25735 PubMed | Google Scholar


19. Mantovani A, Byrne CD, Zheng MH, Targher G.Diabetes as a risk factor for greater COVID-19 severity and in-hospital death: A meta-analysis of observational studies . Nutrition, Metabolism and Cardiovascular Diseases [Internet]. 2020 May 29 [cited 2024 Jul 22];30(8):1236-48. https://doi.org/10.1016/j.numecd.2020.05.014 PubMed | Google Scholar


20. Grasselli G, Greco M, Zanella A, Albano G, Antonelli M, Bellani G, Bonanomi E, Cabrini L, Carlesso E, Castelli G, Cattaneo S, Cereda D, Colombo S, Coluccello A, Crescini G, Forastieri Molinari A, Foti G, Fumagalli R, Iotti GA, Langer T, Latronico N, Lorini FL, Mojoli F, Natalini G, Pessina CM, Ranieri VM, Rech R, Scudeller L, Rosano A, Storti E, Thompson BT, Tirani M, Villani PG, Pesenti A, Cecconi M, COVID-19 Lombardy ICU Network, Agosteo E, Albano G, Albertin A, Alborghetti A, Aldegheri G, Antonini B, Barbara E, Bardelloni G, Basilico S, Belgiorno N, Bellani G, Beretta E, Berselli A, Bianciardi L, Bonanomi E, Bonazzi S, Borelli M, Bottino N, Bronzini N, Brusatori S, Cabrini L, Capra C, Carnevale L, Castelli G, Catena E, Cattaneo S, Cecconi M, Celotti S, Cerutti S, Chiumello D, Cirri S, Citerio G, Colombo S, Coluccello A, Coppini D, Corona A, Cortellazzi P, Costantini E, Covello RD, Crescini G, De Filippi G, Dei Poli M, Dughi P, Fieni F, Florio G, Forastieri Molinari A, Foti G, Fumagalli R, Galletti M, Gallioli GA, Gay H, Gemma M, Gnesin P, Grasselli G, Greco S, Greco M, Grosso P, Guatteri L, Guzzon D, Iotti GA, Keim R, Langer T, Latronico N, Lombardo A, Lorini FL, Mamprin F, Marino G, Marino F, Merli G, Micucci A, Militano CR, Mojoli F, Monti G, Muttini S, Nadalin S, Natalini G, Perazzo P, Perego GB, Perotti L, Pesenti A, Pessina CM, Petrucci N, Pezzi A, Piva S, Portella G, Protti A, Racagni M, Radrizzani D, Raimondi M, Ranucci M, Rech R, Riccio M, Rosano A, Ruggeri P, Sala G, Salvi L, Sebastiano P, Severgnini P, Sigurtà D, Stocchetti N, Storti E, Subert M, Tavola M, Todaro S, Torriglia F, Tubiolo D, Valsecchi R, Villani PG, Viola U, Vitale G, Zambon M, Zanella A, Zoia E.Risk Factors Associated With Mortality Among Patients With COVID-19 in Intensive Care Units in Lombardy, Italy . JAMA Intern Med [Internet]. 2020 Jul 15 [cited 2024 Jul 19];180(10): 1345-1355. https://doi.org/10.1001/jamainternmed.2020.3539 PubMed | Google Scholar


21. Mandal A.The connection between severe COVID–19 and white blood cell counts [Internet]. News Medical Life Sciences; 2020 Oct 19[cited 2024 Jul 19]. PubMed | Google Scholar


22. Li W, Cui H, Li K, Fang Y, Li S.Chest computed tomography in children with COVID-19 respiratory infection . Pediatr Radiol [Internet]. 2020 Mar 11 [cited 2024 Jul 19];50(6):796-9. https://doi.org/10.1007/s00247-020-04656-7 PubMed | Google Scholar


23. Cennimo J D. Coronavirus Disease 2019 (COVID-19) Management [Internet]. Michael Stuart Bronze, editor. Newark (NJ): WebMD LLC; c1994-2024 [updated 2020 Dec 7; cited 2024 Jul 19].147 p. PubMed | Google Scholar


24. Wu R, Wang L, Kuo HCD, Shannar A, Peter R, Chou PJ, Li S, Hudlikar R, Liu X, Liu Z, Poiani GJ, Amorosa L, Brunetti L, Kong AN.An Update on Current Therapeutic Drugs Treating COVID-19 . Curr Pharmacol Rep [Internet]. 2020 May 11[cited 2024 Jul 18];6(3):56-70. https://doi.org/10.1007/s40495-020-00216-7 PubMed | Google Scholar


25. Prescott HC, Girard TD.Recovery From Severe COVID-19: Leveraging the Lessons of Survival From Sepsis. JAMA [Internet]. 2020 Aug 5 [cited 2024 Jul 18];324(8): 739-740. https://doi.org/10.1001/jama.2020.14103 PubMed | Google Scholar


26. Wiersinga WJ, Rhodes A, Cheng AC, Peacock SJ, Prescott HC.Pathophysiology, Transmission, Diagnosis, and Treatment of Coronavirus Disease 2019 (COVID-19): A Review. JAMA [Internet]. 2020 Jul 10 [cited 2024 Jul 18];324(8): 782-793. https://doi.org/10.1001/jama.2020.12839 PubMed | Google Scholar


27. Lowenstein Charles J, Solomon SD. Severe COVID-19 Is a Microvascular Disease. Circulation [Internet]. 2020 Sep 2[cited 2024 Jul 18];142(17):1609-11. https://doi.org/10.1161/circulationaha.120.050354 PubMed | Google Scholar


28. Levitan R.The infection that´s silently killing coronavirus patients . New York Times [Internet]. New York (United States of America): 2020 Apr 20 [cited 2024 Jul 18]. [about 2 p.]. PubMed | Google Scholar


29. Pickett KE, Wilkinson RG.Income inequality and health: A causal review. Social Science & Medicine [Internet]. 2014 Dec 30 [cited 2024 Jul 18];128:316-26. https://doi.org/10.1016/j.socscimed.2014.12.031 PubMed | Google Scholar


30. An J, Lee JH, Yoo Y, Kwon HS, Lee JS, Lee SW, Kim TB, Oh YM, Cho YS, Lee SD, Song WJ.Chest computed tomography scan utilization and diagnostic outcomes in chronic cough patients with normal chest X-rays: analysis of routinely collected data of a tertiary academic hospital . J Thorac Dis [Internet]. 2023 Apr 6 [cited 2024 Jul 18];15(4):2324-32. https://doi.org/10.21037/jtd-22-1404 PubMed | Google Scholar