Seroprevalence and risk factors of SARS-CoV-2 infection in an urban informal settlement in Nairobi, Kenya, December 2020

Study site and population

Kibera is a densely populated informal settlement within Nairobi, Kenya, characterised by overcrowding, poor sanitation, and poor infrastructure. This seroprevalence survey was embedded in an ongoing population-based infectious disease surveillance (PBIDS) within the informal settlement.^15,16 The Kibera PBIDS covers an area < 0.5 km² with an estimated population of about 23,103 individuals in 5,265 households (as of December 2020) living in two villages, Soweto and Gatwekera, that are under active surveillance. The PBIDS area is divided into 10 zones, referred to as residential areas (Figure 1). The platform is maintained by KEMRI-Centre for Global Health Research (CGHR) and Washington State University-Global Health in Kenya (WSU-GH) with technical and financial support from U.S. Centers for Disease Control and Prevention (CDC). The seroprevalence survey in Kibera was implemented at the same time as a wider seroprevalence survey across Nairobi County.¹⁷

Figure 1. Map showing the area under population-based infectious disease surveillance (PBIDS) in Kibera informal settlement, Nairobi, Kenya.

The subdivisions show the areas of residence i.e. PBIDS zones 1-10.

Sample size calculation

We selected a sample size of 684 persons from 171 households (assuming each household had an average of 4 individuals). This was based on an expected seroprevalence of 25% with a precision of 5.0%, a design effect of 2, and 20% attrition should a repeat seroprevalence survey be possible in the future. More households (n = 181) were eventually included to boost the number of participants enrolled.

Household selection and enrolment

We conducted a cross-sectional household-based survey aligned with World Health Organisation’s (WHO) UNITY seroepidemiological protocol.¹⁸ The study households were randomly selected from the most recent PBIDS dataset and household members were consented before enrolment. Efforts were made to recruit all household members, both adults and children, regardless of current or prior COVID-19 infection status. Individuals residing in the selected households who were not yet registered in the PBIDS platform were also approached for consenting if they were residents for a minimum of four months. We conducted a minimum of three study visits to a household before replacing it due to unavailability of household members. When a household was enrolled in the study, we conducted a minimum of three return visits for household members not available at the time of the initial study visit. The household enrolment and data collection were conducted from 27^th November to 5^th December 2020 by five trained field teams, each consisting of a field worker and a phlebotomist.

Data and specimen collection

All participants were interviewed for sociodemographic data such as age, sex, education level, and occupation. Data on current occupation and highest education level were collected from adult participants (≥18 years) only. Data were collected and managed using REDCap (Research Electronic Data Capture) electronic data tools hosted at Washington State University.^19,20 Venous blood samples (approximately 5 ml for persons aged >12; 2-3 ml for children 2-12 years and 1.5 ml for children <2 years) were collected from each participant and transported in a cool box at 2-8°C to the CDC-supported KEMRI laboratory in Nairobi the same day. Serum samples were extracted from the whole blood specimen and stored at −80°C before testing.

Serological testing

We tested for total immunoglobulins (IgM and IgG) antibodies to the SARS-CoV-2 spike protein using the Wantai SARS-CoV-2 two-step antigen sandwich enzyme immunoassay kit (Catalogue number, WS-1096; Wantai Biological Pharmacy Enterprise Ltd, Beijing, China). The assay was optimised by including 10 washes instead of five washes recommended by the manufacturer to reduce background cross-reactivity, as described elsewhere.¹⁷ The test results were considered positive when the ratio of antibody titer in the sample to a negative control exceeded 1.5 according to manufacturer’s instructions. The assay has a reported sensitivity and specificity of 94.4% and 100% respectively from prospective clinical evaluations in China (https://www.ystwt.cn/wp-content/uploads/2020/05/Brochure-Wantai-SARS-CoV-2-Ab-ELISA.pdf). All laboratory tests were performed in an ISO15189 certified and GCP-accredited KEMRI laboratory in Nairobi, Kenya.

Statistical analysis

Individual seroprevalence of SARS-CoV-2 antibodies was expressed as a percentage of the seropositive among the individuals tested. The disaggregated individual seropositivity estimates accounted for household clustering and weighted by the age and sex structure of the PBIDS population (Figure 2). We didn’t account for test performance due to lack sensitivity and specificity estimates from our or similar settings in sub-Saharan Africa. The standard errors for generating the 95% confidence intervals were computed using the Taylor linearized variance estimation method.²¹ Pearson’s chi-square test was used to assess the association of categorical variables with individual seropositivity. Household seroprevalence (defined as the percentage of households with at least one seropositive member) was estimated and stratified by household size (usual number of persons living in the household), number of persons enrolled in the serosurvey per household and location in the PBIDS area. Age, sex, area of residence, relationship to head of household, household size, and underlying medical conditions (known hypertensive, asthmatic or diabetic) were considered in the univariable logistic regression model for determining the factors associated with individual seropositivity. Age and sex were considered a priori for inclusion in the multivariable logistic regression. The final multivariable logistic regression model included statistically significant variables, accounting for sampling weights and clustering by household using the clustered sandwich estimator.^22,23 Adjusted odds ratio (aOR) and 95% confidence intervals (CI) were presented and two-sided p-values <0.05 were considered significant.

Figure 2. Figure showing age and sex distribution of (a) population-based infectious disease surveillance (PBIDS) population as of December 2020; (b) serosurvey participants; and (c) the probability of participant selection from the PBIDS population into the serosurvey.

The red dashed line shows the overall expected probability of selection with bars above the red line indicating overrepresentation while those below the line denoting underrepresentation of the age-sex groups in the sero serosurvey.

Stata 15.1 software [STATA Corp, Texas, USA] was used for random selection of households to be enrolled in the study, data cleaning, management, and analyses.

Ethical considerations

Individual written informed consent/assent was obtained from all the study participants and/or their parents/guardian. Ethical approval for the study was provided by the KEMRI Scientific and Ethical Review Committee in Kenya (#4098) and reliance approval provided by the Washington State University. This activity was also reviewed by CDC and was conducted consistent with applicable federal law and CDC policy as provided for in the Code of Federal Regulations (45 C.F.R part 46 and 21 C.F.R. part 56). The PBIDS platform is approved by KEMRI Scientifical and Ethical Review Committee in Kenya (#2761), Washington State University reliance agreement and CDC reliance approval (#6775).

Participant enrolment and baseline characteristics

Of the 252 randomly selected households, 175 (69.4%) agreed to participate in the survey (Figure 3). Of the 77 households that did not participate, 38 (49.4%) did not have a household head available for consenting, 26 (33.8%) had moved, and 13 (16.9%) declined participation. Consent was obtained from 523 individuals yielding 511 blood samples; field teams were unable to get a blood specimen from 12 participants. The median number of individuals with a specimen collected per household was 3 (interquartile range, IQR, 2-4; range, 1 – 10).

Figure 3. Flow chart showing recruitment process of study household (HHs) and individuals and specimen collection.

Of the 511 sampled individuals, 58.5% (299) were female, 23.5% (120) were below the age of 10 and 1.4% (7) were 60 years or older (Table 1). Males aged 5-9 years and females aged 30-59 years were overrepresented, while both sexes below 5 years and males aged 30 years and above were underrepresented in the surveyed participants relative to the PBIDS general population (Figure 2).

Prevalence of SARS-CoV-2 antibodies

Of the 511 tested individuals, 222 (43.4%) were seropositive. The overall weighted-seroprevalence was 43.3% (95% CI, 37.4 – 49.5%), with no difference detected between females and males (Table 1, Figure 4). Seroprevalence increased with age from 7.6% (95% CI, 2.4 – 21.3%) among young children (<5 years), 32.7% (95% CI, 22.9 – 44.4%) among children 5 – 9 years, 41.8% (95% CI, 33.0 – 51.1%) for those 10-19 years, and 54.9% (46.2 – 63.3%) for adults 20 years and above. The elderly (60 years and above) had a seroprevalence of 52.6% (95% CI, 13.2 – 89.0%). The age effect was also observed for seroprevalence estimates by relationships to household head with grandchildren (30.2%; 95% CI, 8.4 – 67.2%) and children (37.3%; 95% CI, 30.4 – 44.8%) registering lower estimates compared to the household head (53.1%; 95% CI, 41.6 – 64.2%) and other adults including spouses (48.4%; 95% CI, 35.4 – 61.7%) and other relatives (63.1%; 95% CI, 35.1 – 84.5%).

Figure 4. Weighted individual seropositivity of SARS-CoV-2 antibodies stratified by age-sex groups in Kibera urban informal settlement, Nairobi, Kenya.

The prevalence of SARS-CoV-2 antibodies by area of residence ranged from 25.8% in zone 7 to 69.7% in zone 4. However, the differences in prevalence by area of residence were not statistically significant [Pearson’s design-based F statistic = 1.5421, p-value = 0.144]. Participants (≥18 years) with primary, secondary, and post-secondary level of education had similar seroprevalence of 54.6%, 54.5% and 67.2%, respectively. Those with no formal education were few (n = 7) and had a seroprevalence of 21.9% (95% CI, 4.3 – 63.3%). All occupation groups (Table 1) had a seroprevalence of between 43.3% to 65.0%. Only six health care workers were included in the survey and their seroprevalence was 42.9% (95% CI, 16.3 – 74.3%).

Participants with any underlying medical condition (n = 38) had a seroprevalence of 48.8% (95% CI, 29.5 – 68.5%) which was not statistically different compared with those without, 42.9% (95% CI, 36.7 – 49.3%); (Pearson design-based F statistic = 0.3100, p-value = 0.5784).

Of the households enrolled, 122 (69.7%) had at least one individual with detectable SARS-CoV-2 antibodies (Table 2). The proportion of households with at least one seropositive individual varied by area of residence ranging from 50.0% in zone 3 and 9 to 82.1% in zone 10, but the differences were not statistically significant (Figure 5, Table 2). For 132 households with two or more members enrolled, 98 (74.2%) had at least one person seropositive. The median seropositivity within these households with at least one seropositive person (‘exposed’) and two or more participants enrolled was 50.0% (range, 16.7% to 100%). The vast majority (81/98, 82.7%) of these ‘exposed’ households also included one or more seronegative individual(s). The largest proportion of the seronegative household contacts (n = 72) were children of the household head (55, 76.4%), followed by grandchildren of household head (6, 8%), household head (6, 8.3%), spouse (3, 4.2%) and other relatives (2, 2.8%) (Figure 6).

Figure 5.

Household (HH) seropositivity (at least one person testing positive in the household) by household size, number of enrolled persons per household and location in the population-based infectious disease surveillance (PBIDS) area as of December 2020. PBIDS zones are areas of residence numbered 1-10.

Figure 6.

Distribution of 72 seronegative individuals by relationship to household (HH) head, age groups in years and sex from the 98 households with least one seropositive person in Kibera urban informal settlement, Nairobi, Kenya.

Risk factors for individual seropositivity

Sex, area of residence, relationship to head of household, and underlying medical conditions were not significantly associated with individual seropositivity (Tables 3 and 4). Individual’s age and household size were the independent predictors of seroconversion. Relative to adults aged 20-29 years, young age groups (<20 years) had reduced odds of being seropositive. The odds of being seropositive were similar for older adults (age groups ≥30 years) compared to the reference group, 20-29 years. The odds for being seropositive among the elderly groups (≥60 years) were not different from the referent 20-29 years age group (adjusted odds ratio, aOR, 0.83 (95% CI, 0.19 – 3.64).

Relative to those from medium-sized households (of three and four individuals), participants from large (≥5 persons) households had significantly increased odds of being seropositive, aOR, 1.98 (95% CI, 1.17 – 3.34), while those from small-sized households (≤2 individuals) had increased odds but not statistically significant, aOR 2.31 (95% CI, 0.93 – 5.74).

Disclaimer

The findings and conclusions in this study are those of the authors and do not necessarily represent the official position of the US National Institutes of Health, KEMRI, or U.S. Centers for Disease Control and Prevention.