Seroprevalence of SARS-CoV-2 infection and associated factors among Bangladeshi slum and non-slum dwellers in pre-COVID-19 vaccination era: October 2020 to February 2021.

BACKGROUND: Seroprevalence studies have been carried out in many developed and developing countries to evaluate ongoing and past infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Data on this infection in marginalized populations in urban slums are limited, which may offer crucial information to update prevention and mitigation policies and strategies. We aimed to determine the seroprevalence of SARS-CoV-2 infection and factors associated with seropositivity in slum and non-slum communities in two large cities in Bangladesh.
METHODS: A cross-sectional study was carried out among the target population in Dhaka and Chattogram cities between October 2020 and February 2021. Questionnaire-based data, anthropometric and blood pressure measurements and blood were obtained. SARS-CoV-2 serology was assessed by Roche Elecsys® Anti-SARS-CoV-2 immunoassay.
RESULTS: Among the 3220 participants (2444 adults, ≥18 years; 776 children, 10-17 years), the overall weighted seroprevalence was 67.3% (95% confidence intervals (CI) = 65.2, 69.3) with 71.0% in slum (95% CI = 68.7, 72.2) and 62.2% in non-slum (95% CI = 58.5, 65.8). The weighted seroprevalence was 72.9% in Dhaka and 54.2% in Chattogram. Seroprevalence was positively associated with limited years of formal education (adjusted odds ratio [aOR] = 1.61; 95% CI = 1.43, 1.82), lower income (aOR = 1.23; 95% CI = 1.03, 1.46), overweight (aOR = 1.2835; 95% CI = 1.26, 1.97), diabetes (aOR = 1.67; 95% CI = 1.21, 2.32) and heart disease (aOR = 1.38; 95% CI = 1.03, 1.86). Contrarily, negative associations were found between seropositivity and regular wearing of masks and washing hands, and prior BCG vaccination. About 63% of the population had asymptomatic infection; only 33% slum and 49% non-slum population showed symptomatic infection.
CONCLUSION: The estimated seroprevalence of SARS-CoV-2 was more prominent in impoverished informal settlements than in the adjacent middle-income non-slum areas. Additional factors associated with seropositivity included limited education, low income, overweight and pre-existing chronic conditions. Behavioral factors such as regular wearing of masks and washing hands were associated with lower probability of seropositivity.

Introduction

The coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) led to immense suffering and an unprecedented burden on health care systems globally. It was declared a ‘pandemic’ by the World Health Organization (WHO) in March 2020. Worldwide, numbers of confirmed COVID-19 cases are based on PCR-positivity of nasopharyngeal samples for SARS-CoV-2 among symptomatic subjects. However, reported cases and deaths especially in low and middle income countries (LMIC), are likely to be underestimates of the actual disease prevalence; diverse clinical presentations like asymptomatic infections, limited testing capacity, hesitation and inaccessisibility to test are key contributing factors. The WHO has recommended all countries to carry out population-based SARS-CoV-2 seroprevalence surveys for rapid screening of ongoing epidemics, determining the extent of spread and estimating the proportion of symptomatic and asymptomatic infected subpopulations [1-3]. Population-based serosurveys for SARS-CoV-2 conducted in LMICs have been either national surveys [4,5] or predominantly focused on frontline healthcare workers or industry workers [6-10] but few have studied marginalized populations in slums [11,12].

People living in urban slums are more susceptible to multiple health threats than non-slum populations because of their underprivileged living conditions. Consequently, the population becomes a major reservoir for a wide spectrum of diseases, which may accelerate the spread of infection [13,14]. Commonly perceived risk factors include population density, poverty, lack of educaton, lack of access to clean water and soap, poor hygiene and hand-washing practices, inability to maintain physical distance, and limited access to health care. Slums in the developing world e.g. Mumbai, Rio de Janeiro, Lagos as well as marginalized city population in resource-rich countries e.g. New York and London have witnessed rapid spread of COVID-19 infections with disastrous consequences [15-19]. Around 12.2 million impoverished people of Dhaka and Chattogram live in densely populated, low-income urban slums [20-22]. So far limited data about the COVID situation in these slum communities are available from Bangladesh [23]. Studies targeting RT-PCR-based detection of SARS-CoV-2 infection in Bangladesh have been mainly carried out in hospital settings [24-26]; such data from the community is lacking. COVID-19 seroprevalence surveys are helpful to estimate the percentage of people in the communities who have been previously infected with SARS-CoV-2 and may reflect the extent of spread of the infection.

Older age, male sex, overweight and underlying chronic diseases have been found to be associated with severity of COVID-19 [27,28]. It is important to find out whether different demographic characteristics and co-morbidities prevailing in the slum and non-slum urban areas are also associated with seroprevalence of SARS-CoV-2. The findings can help in determining targeted public health measures to address the specific requirements of the populations most in need.

We aimed to study the seroprevalence of SARS-CoV-2 to evaluate the magnitude of spread of infection in the poor slum and surrounding non-slum middle-class communities in Dhaka, and Chattogram, the two large cities of Bangladesh, and identify factors associated with seropositivity.

Methods

Study design and setting

The survey was conducted in purposively selected large slums and neighboring non-slum areas of Dhaka and Chattogram from October 2020 to February 2021. A slum is defined here as a cluster of compact settlements of 5 or more households, which generally grow unsystematically and haphazardly in an unhealthy condition and atmosphere, on government and private vacant land [29]. In 2015, the icddr,b established an urban Health and Demographic Surveillance System (UHDSS) in Dhaka over 125,000 population to include slums. The location, household head’s name, age, identification number, and GIS coordinates were available from the Dhaka UHDSS [30]. Four slums in and around Dhaka city (Korail, Mirpur, Dhalpur and Ershad Nagar) under UHDSS were selected. Slum Census Report 2014, Bangladesh Bureau of Statistics [29] was used for selecting 2 slums in Chattogram city (Shaheed Lane and Akbar Shah Kata Pahar). The non-slum area was selected to include middle-class households, defined as single or multi-stored buildings with wall and roof constructed with bricks, and having one or no security guard in the building.

For selecting the sample, cluster random sampling procedure was followed. After mapping the study area, households were divided into clusters (equal size) and clusters were selected randomly to get the required sample size (Dhaka: 15 clusters from slum, 12 clusters from non-slum; Chattogram: 12 clusters for slum, 8 clusters for non-slum). UHDSS sampling frame was used for selecting the household in Dhaka slums. For rest of the areas, household listing was done initially for the selected clusters, and data was collected according to the list. Please see S1 File for field data collection and study team.

Sampling frame

Data and samples were collected from 2118 slum inhabitants and 1102 from non-slums in a 2:1 ratio. After household selection, all the eligible members aged ≥10 years, were invited to participate. In the early phase of the pandemic, susceptibility to COVID-19 was rare in <10 years old children and existing reports on adolescents (10–19 years) were controversial [31]. Therefore, adolescents, who can give assent and donate blood were enrolled in this study to determine seroprevalence of SARS-CoV-2 in this age group along with the adults. The exclusion criteria included refusal to give time to respond to all questions and give blood. For sample size calculation please see S2 File.

Household questionnaire survey

Participants were enrolled in the study after obtaining informed consent/assent. The household questionnaire included data on (i) sociodemographic features, (ii) preventive behavior practiced within last 6 months, (iii) chronic health conditions, (iv) current morbidity or in the past 6 months, related to COVID-like symptoms, (v) presence of confirmed COVID-19 cases in the household and (vi) physical activity assessment using Global Physical Activity Questionnaire (GPAQ) version 2 [32]. The GPAQ consists of 3 parts: (a) work, (b) travel to and from places, and (c) recreational activities. However, only sections (a) and (b) were included in this study to reduce the total time of data collection. The physical activity was categorized into vigorous-, moderate- and light- intensity activities (walking or cycling to visit places in a typical day). For physical activity assessment, 2012 individuals were included as the GPAQ assessment tool was introduced few weeks later in the study. BCG vaccination status was determined by checking the immunization card or verifying the vaccination mark on the upper arm.

The sociodemographic data included age, sex, education, migration background, marital status, employment, and income. The family’s monthly income was considered as a proxy indicator of its economic status. The preventive behavior included wearing masks, washing hands with soap, wearing gloves, sneezing on hand or tissue paper, maintaining a distance of 6 feet while meeting others, avoiding crowds, avoiding putting fingers in the nose or on the face, repeated drinking of hot water. Chronic health conditions assessed included diabetes, asthma, lung disease, hypertension, heart disease, stroke and cancer. These were confirmed by checking physician’s prescription or medical report. The self-reported COVID-like symptoms included fever or chills, cough, sore throat, congestion or runny nose, shortness of breath or difficulty in breathing, fatigue, muscle or body aches, headache, loss of taste or smell, nausea or vomiting and diarrhea [33]. Confirmed COVID-19 cases included those who were identified as positive by PCR for SARS-CoV-2 in nasopharyngeal samples (by checking test report).

The survey data collected in Tablet/Android-based electronic questionnaire was synchronized with the server after each interview. The SQLite program was used to write data collection, SQLite browser, SQL and Visual Foxpro were used for data management and data cleaning.

Anthropometry, blood pressure, specimen collection

Height and weight were measured twice using the free-standing stadiometer (Seca 217, Hamburg, Germany) and digital weighing scale (Camry-EB9063, China) calibrated weekly, and body mass index (BMI) was calculated. Blood pressure (BP) was measured twice using a manual BP machine (ALPK2 V500, Japan) in a sitting position with a 5-minutes interval between the measurements, and the average was used. The study defined hypertension was, systolic blood pressure ≥140 mmHg, or diastolic blood pressure ≥90 mmHg. Single venous blood samples (7.5 ml) were collected in trace element-free heparinized tubes in the household and were delivered to the Laboratory within 2–3 hours. Plasma was separated and stored at -80°C until analysis.

Assessment of SARS-CoV-2 specific antibodies

The Elecsys® Anti-SARS-CoV-2 assay was used to determine the nucleocapsid (N) antigen-specific antibodies (IgM and IgG) against SARS-CoV-2 in plasma on Cobas-e601 immunoassay analyzer (Roche Diagnostics GmbH, Mannheim) indicating recent or prior infection. Based on the antibody cut-off index (COI), the serological response to SARS-CoV-2 is categorized as reactive (COI≥1.0, seropositive) and non-reactive (COI<1.0, seronegative). According to the manufacturer’s performance characteristics, the Elecsys assay has an overall specificity of 99.8% (95% CI = 99.69–99.88%) and an overall sensitivity of >99.5% (95% CI = 97.0–100%) for ≥14 days of post PCR confirmation. We carried out an internal validation to evaluate the kit’s performance. In this study, the assay showed an overall specificity of 100% (95% CI, 97.9%-100%) and an overall sensitivity of >93.3% using serum samples from SARS-CoV-2 specific RT-PCR positive cases (n = 30x3 from day 7, 14 and 21 days post diagnosis and n = 70 from day >21; total 160), pre-pandemic COVID-19 negative healthy cases (n = 100), non-bacterial pneumonia cases (n = 51) and other viruses (n = 13) (S1 Table).

Ethics approval

The study was approved by the institutional review board (PR-20070, dated 1st September 2020) of the icddr,b. Written informed consent was obtained from adult participants while assent was obtained from 10–17 years old children and consent from their parents.

Statistical analysis

The main outcome of interest SARS-CoV-2 serological data was categorized as reactive (seropositive) and non-reactive (seronegative) based on the antibody cut-off. Demographic data was expressed as number and percentages for categorical observation or mean with standard deviation for continuous data. The prevalence of seropositivity among the demographic features, body mass index (BMI), COVID-like symptoms, co-morbidities, preventive measures practiced and physical activities, stratified by locality (slums, non-slums) was expressed as prevalence with 95% CI. A population-based weighted seroprevalence of SARS-CoV-2 was calculated on the basis of sum of two probabilities i.e. between cluster probability (p1) and within cluster probability (p2). The sample was collected from 4 areas of Dhaka (Korail, Mirpur, Dhalpur and Ershad Nagar) and 2 areas of Chottagram (Shaheed Lane and Akbar Shah Kata Pahar) that included both slum and neighboring non-slum areas. For each selected area, a total probability score was calculated (p1+p2). The weight was determined as the inverse of total probability of each selected area (1/(p1+p2)). Thereafter, the calculated weight was distributed to all selected participants and the weighted prevalence was estimated.

Initially, univariate logistic regression was performed to estimate the relationship between several predictors (sociodemographic factors, BMI, Bacillus Calmette-Guérin (BCG) vaccination, symptoms, comorbidities, preventive measures practiced and physical activities) and seropositivity. Since the in-house validation of the antibody assay showed a sensitivity of about 93% and a specificity between 97.9%-100% (manufacturer reported sensitivity is 99% and specificity is 100%), to correct the test inaccuracy, we estimated the seroprevalence of SARS-CoV-2 associated risks (odds ratio) by Bayesian multivariate generalized linear mixed model (MGLMM). The Bayesian MGLMM was fitted with sociodemographic factors, BMI, Bacillus Calmette-Guérin (BCG) vaccination, symptoms, comorbidities, preventive measures practiced and physical activities as fixed effects and cluster effects (weighted score) were taken as a random effect. The data management and statistical analyses were performed with Stata 15 (StataCorp, LP, College Station, Texas, USA), and the graphs were prepared by GrapPad prism 8.3.0. The significance level was established at p ≤0.05.

Results

Demography

Data were collected from 3,220 inhabitants of 1337 households (1910 from Dhaka slum, 705 from Dhaka non-slum, 334 from Chattogram slum and 272 from Chattogram non-slum) with an average of 4 members per household. In Dhaka, around 64% of slum and 34% of non-slum populations agreed to provide data and blood samples, while in Chattogram the respondents were 69% for slum and 44% for non-slum. The majority of the middle-class families from non-slum areas did not allow study staffs to enter their households for fear of getting infected with SARS-CoV-2. The demographic features of the population in slums and non-slums are given in Table 1. The male, female distribution among the enrolled participants was 43% and 57%, respectively. Higher numbers of females were enrolled because of their availability at home during the visits; male members were mostly away for earning wages. The average age in slums and non-slum areas were 31 and 33.3 years, respectively with 24% being pre-/adolescents and 15% being above 50 years of age in the total study population. An average of 4.87 members were living in the same household in slums and 5.09 in nonslum areas. The percentage of inhabitants with longer years of formal education (11–15 years), monthly income of >40000 BDT (equivalent to USD $472) and BCG vaccine coverage were higher in non-slums than in slums. (Table 1). Around two-third of the seropositive participants (~63%) were asymptomatic. The proportion of asymptomatic infection was higher in seropositive adolescents (71%) than adults (60%). More non-slum (49.3%) than slum populations (33%) experienced COVID-19 like symptoms.

Table 1

Demographic characteristic of the study participants.

Variables	Overall (n = 3220)	Slum (n = 2118)	Non-slum (n = 1102)
Sex
Male	1392(43.2%)	953(44.8%)	439(40.2%)
Female	1828(56.8)	1175(55.2%)	653(59.8%)
Age, years		31.02±16.42	33.34±16.63
Age, category
10–17 years	776(24.1%)	555(26.1%)	221(20.2%)
18–30 years	945(29.4%)	623(29.3%)	322(29.4%)
31–50 years	1015(31.5%)	645(30.4%)	370(33.8%)
>50 years	484(15.0%)	302(14.2%)	182(16.6%)
House hold member, mean±SD	4.87±1.95	4.76±1.92	5.09±2.0
Education in years
No education	847(26.3%)	754(35.4%)	93(8.52%)
1–5 years	966(30.0%)	804(37.8%)	162(14.8%)
6–10 years	942(29.3%)	500(23.6%)	442(40.3%)
11–15 years	465(14.4%)	70(3.29%)	395(36.2%)
Occupation
Service	469(14.6%)	310(14.6%)	159(14.6%)
Self employed	336(10.4%)	294(13.9%)	42(3.83%)
Business	262(8.14%)	152(7.16%)	110(10.0%)
Homemakers	846(26.3%)	488(22.9%)	358(32.8%)
Unemployed	504(15.7%)	399(18.8%)	105(9.62%)
Student	803(24.9%)	485(22.8%)	318(29.1%)
Monthly income, taka
<20000	1280(39.8%)	1241(58.5%)	39(3.56%)
20000–40000	1019(31.7%)	767(36.15)	252(23.0%)
40000–70000	636(19.8%)	115(5.42%)	521(47.5%)
>70000	285(8.85%)	5(0.23%)	280(25.6%)
Presence of COVID-19 like symptoms	1144(35.5%)	673(31.7%)	471(42.9%)
BCG given	2717(84.4%)	1766(83.2%)	951(86.7%)
BMI	23.43±5.24	22.65±5.07	24.94±5.23
Normal	1405(43.6%)	971(45.6%)	434(39.7%)
Underweight	607(18.9%)	489(23.0%)	118(10.8%)
Overweight	1208(37.5%)	668(31.4%)	544(49.5%)

Note. BMI, Body mass index; BCG, Bacillus Calmette-Guérin. Data was presented as mean±SD or number (percent).

Seroprevalence of SARS-CoV-2 and sociodemographic features

The overall weighted seroprevalence of SARS-CoV-2 in the population was 67.3%, with a higher positivity rate among slum dwellers (71.0%) than the non-slum dwellers (62.2%) (Table 2). A higher weighted seroprevalence was observed in Dhaka city (72.9%) than seen in Chattogram (54.2%). Age-wise weighted seroprevalence rate among slum and non-slum dwellers of Dhaka and Chattogram cities is given in S2 Table.

Table 2

Weighted seroprevalence of SARS-CoV2 among the residents of slum and non-slum neighborhoods.

Variables	Overall (n = 3220)	Slum (95% CI) (n = 2123)	Non-slum (95% CI) (n = 1097)
Overall	67.3(65.2, 69.3)	71.0(68.7, 72.2)	62.2(58.5, 65.8)
Sex
Male	64.6(61.4, 67.6)	67.0(63.4, 70.5)	60.7(54.8, 66.3)
Female	69.3(66.7, 71.9)	74.5(71.4, 77.1)	63.2(58.4, 67.9)
Age category, years
10–17 years	62.8(58.5, 66.9)	65.9(61.1, 70.5)	56.9(48.5, 65.0)
18–30 years	68.0(64.2, 71.6)	72.4(68.2, 76.2)	62.2(55.2, 68.7)
31–50 years	69.7(66.1, 73.1)	74.1(70.1, 77.8)	64.3(57.8, 70.4)
> 50 years	68.4(62.9, 73.4)	71.3(64.9, 76.9)	65.5(56.4, 73.6)
Years of education
No education	68.0(64.0, 71.8)	71.6(67.8, 75.1)	52.6(39.9, 64.9)
1–5 years	70.3(66.6, 73.7)	71.3(67.3, 74.9)	66.8(57.1, 75.3)
6–10 years	66.2(62.3, 69.9)	69.2(64.3, 73.6)	63.8(57.8, 69.3)
11–15 years	63.0(57.3, 68.3)	75.2(61.9, 84.9)	61.3(55.1, 67.1)
Occupation
Service	72.5(67.3, 77.3)	78.7(73.3, 83.3)	64.4(54.5, 73.3)
Self employed	65.4(59.0, 71.4)	67.2(60.7, 73.1)	56.7(36.9, 74.6)
Business	66.3(58.9, 73.0)	73.0(64.8, 80.0)	59.4(47.1, 70.6)
Homemaker	70.8(66.8, 74.5)	77.1(72.6, 81.0)	65.1(58.5, 71.1)
Unemployed	65.0(59.7, 70.0)	67.6(62.1, 72.7)	59.6(47.4, 70.6)
Student	63.1(58.9, 67.2)	65.2(60.0, 70.2)	60.6(53.7, 67.2)
Monthly income, taka
<20000	67.5(64.4, 70.5)	67.3(64.2, 70.3)	70.8(53.4, 83.7)
20000–40000	71.6(68.1, 74.8)	76.3(72.7, 79.5)	62.5(55.0, 69.5)
40000–70000	64.3(59.3, 69.0)	76.9(67.6, 84.1)	62.6(57.1, 67.8)
>70000	58.4(50.7, 65.7)	-	58.9(51.1, 66.3)
BMI
Normal	66.1(62.9, 69.1)	71.7(68.3, 74.9)	57.8(53.8, 63.6)
Underweight	60.0(55.1, 64.7)	60.6(55.3, 65.6)	58.4(47.0, 68.9)
Overweight	72.7(69.5, 75.7)	79.0(75.4, 82.1)	67.3(62.1, 72.0)
BMI (Adult)
Normal	66.3(62.7, 69.8)	71.6(67.7, 75.1)	58.4(51.4, 65.0)
Underweight	62.0(53.8, 69.7)	61.1(52.4, 69.1)	64.3(45.6, 79.5)
Overweight	72.6(69.3, 75.7)	78.6(74.9, 81.9)	67.4(62.1, 72.4)
BMI (Adolescent)
Normal	65.1(58.2, 71.5)	72.6(64.5, 78.7)	56.0(43.6, 67.6)
Underweight	58.9(52.8, 64.7)	60.4(53.8, 66.6)	54.5(40.3, 68.0)
Overweight	73.8(61.3, 83.4)	82.8(67.0, 91.9)	65.4(45.7, 80.9)
BCG vaccination
Given	66.3(64.1, 68.6)	69.8(67.2, 72.2)	62.0(57.9, 65.9)
Not given	71.4(66.3, 76.0)	76.1(70.8, 80.7)	63.6(53.4, 72.7)

Note. BMI, Body mass index; BCG, Bacillus Calmette-Guérin. Results was presented as prevalence with 95% confidence interval.

Female participants of the study showed higher odds of seropositivity than males. The odds of seropositivity was higher among inhabitants who had no or <10 years of education compared to those with >11 years of education (Table 3). Households members having monthly family income of more than 70,000 BDT (equivalent to USD >$825) had lower seroprevalence compared to those with lower income (Table 2). Overweight individuals revealed higher SARS-CoV-2 seroprevalence 72.7% (95% CI 69.5%-75.7%) compared to those with normal BMI (range 18.5–24.9) (Table 2) and the odds was 1.35 (95% CI 1.26–1.97) folds higher in overweight individuals (Table 3). The seroprevalence was found to be lower in the BCG vaccinated 66.3% (95% CI 64%-68.6%) than non-vaccinated 71.4% (95% CI 66.3%-76%) participants

Table 3

Odds of seropositivity among the study participants residing in slum and non-slum areas.

	Overall	Slum	Non-slum
Variables	OR(95% CI)	OR (95% CI)	OR(95% CI)
Sex
Male	Ref.	Ref.	Ref.
Female	1.62(1.40, 1.86)	1.70(1.01, 2. 94)	1.25(0.87, 1. 73)
Years of education
11–15 years	Ref.	-	Ref.
6–10 years	1.47 (1.16, 1.88)	Ref.	1.32(1.02, 1. 75)
1–5 years	1. 45(1.05, 11.99)	120(0. 76, 1. 95)	1.39(0.96, 1.70)
No education	0.86 (0.62, 1.19)	0. 87(0.44, 1.70)	0.58 (0. 34, 1.03)
Monthly family income, taka
>70000	Ref.	-	Ref.
40000–70000	1.35 (1.08, 1.72)	Ref	1.39(1.09, 1.86)
20000–40000	1. 28(0. 97, 1.63)	1.47(0.70, 3.42)	1.21(0.96, 1.70)
<20000	1.13(0.85, 1. 48)	2.43(1.20, 5.21)	1.35(0.86, 2.23)
BMI
Normal	Ref.	Ref.	Ref.
Underweight	0.97(0.75, 1.27)	0.73(0.56, 0.94)	1.28 (0.81, 2.05)
Overweight	1.35(1.26, 1. 97)	1.28(1.01, 1.63)	1.39 (1.05, 1.79)
BCG vaccination
Not given	Ref.	Ref.	Ref.
Given	0.84(0.60, 0.96)	0.79(0.48, 1. 17)	0.80 (0.51, 1.35)

Data was presented as Odds ratio (OR) with 95% confidence interval. Bayesian multivariate generalized linear mixed model was applied to estimate seroprevalence-associated risks (odds ratio). The regression model was adjusted by sex, age, years of education, occupation, family income and body mass index (BMI).

Seroprevalence of SARS-CoV-2 and self-reported COVID-like symptoms/ chronic diseases

About 36% of the household members reported the presence of ongoing COVID-like symptoms or occurrence within past 6 months. Overall individuals reporting fever, dry cough or sore throat had higher prevalence of SARS-CoV-2 infection (S3 Table). Additionally higher odds of seropositivity were obtained in the overall population having fever, sore throat and diarrhoea compared to those without symptoms (). The odds of seropositivity was 1.55 (95% CI 1.30,1.86) folds higher for residents who exhibited presence of any 3 symptoms in the preceding 6 months compared to those without symptoms (). Only 4 slum dwellers and 18 non-slum participants reported that they were hospitalized with moderate to severe disease in the past 6 months and were seropositive for SARS-CoV-2.

Odds of SARS-CoV-2 sero-positivity among study participants with or without self-reported presence of COVID-19 like symptoms (33).

(A) represents data in overall study population, (B) on urban slum population and (C) on inhabitants of the neighboring non-slum areas. Data were presented as adjusted odds ratio (aOR) with 95% confidence interval. Bayesian multivariate generalized linear mixed model (MGLMM) was used to estimate the p-value. The Bayesian MGLMM was fitted with sociodemographic factors, BMI, Bacillus Calmette-Guérin (BCG) vaccination, and symptoms, as fixed effects and cluster effects were taken as a random effect.

Among the individuals reporting various co-morbid conditions, those with diabetes and heart diseases had higher seroprevalence of SARS-CoV-2 (77.5% (95% CI 71.%-82.9%) and 74.1% (95% CI 63.9%- 82.2%) respectively) (S4 Table). The individuals having diabetes and heart problem also had higher odds of seropositivity compared to those without the pre-existing chronic diseases (). Only in non-slum participants, the odds of seropositivity was higher among those who had a history of stroke (aOR = 1.84; 95% CI 1.19,2.83) (). SARS-CoV-2 seropositivity was higher in slums among those participants who had a history of hypertension than those who did not [78.9% (95% CI 72.8%-84.0%) vs 71.8% (95% CI 68.9%-74.6%)] but not in non-slums (S4 Table). No other co-morbidities were associated with seroprevalence of SARS-CoV-2 in this population.

Odds of SARS-CoV-2 sero-positivity among study participants with or without co-morbid conditions (diabetes, stroke, heart disease, hypertension, and asthma).

(A) represents data in overall study population, (B) on urban slum population and (C) on inhabitants of the neighboring non-slum areas. Data was presented as adjusted odds ratio (aOR) with 95% confidence interval. Bayesian multivariate generalized linear mixed model (MGLMM) was used to estimate the p-value. The Bayesian MGLMM was fitted with sociodemographic factors, BMI, Bacillus Calmette-Guérin (BCG) vaccination, and comorbidities, as fixed effects and cluster effects were taken as a random effect.

Seroprevalence of SARS-CoV-2 and behavioral aspects/physical activity

Individuals who wore face masks on a regular basis had lower odds of getting seropositive (aOR = 0.33; 95% CI = 0.22, 0.46), which was evident in both slum and non-slum populations (). Individuals who practiced washing hands with soap had lower odds of becoming seropositive (aOR = 0.40; 95% CI = 0.23, 0.73); this was more prominent in non-slum population (aOR = 0.19; 95% CI = 0.10, 0.35). No other personal beheviour of the participants were associated with the odds of seropositivity. Assessment of data on physical activities revealed that there was no impact of light-to-intense physical activity on SARS-CoV-2 seropositivity (data not shown).

Odds of SARS-CoV-2 sero-positivity among study participants who have taken or not taken preventive measures.

The preventive procedures include wearing masks, washing hands with soap, wearing gloves, avoid putting fingers in the nose and/ on the face. (A) represents data in overall study population, (B) on urban slum population and (C) on inhabitants of the neighboring non-slum areas. Data was presented as adjusted odds ratio (aOR) with 95% confidence interval. Bayesian multivariate generalized linear mixed model (MGLMM) was used to estimate the p-value. The Bayesian MGLMM was fitted with sociodemographic factors, BMI, Bacillus Calmette-Guérin (BCG) vaccination and preventive measures practiced as fixed effects and cluster effects were taken as a random effect.

Discussion

The study provides important insight into COVID-19 pandemic in the informal settlements and the urban neighborhood communities of two large cities in Bangladesh. This cross-sectional serosurvey involving more than 3,200 participants showed an overall weighted SARS-CoV-2 seroprevalence of 67.3%, with the seropositivity rate being higher in slums (71.0%) than in non-slum localities (62.2%). The significant factors associated with seroprevalence of SARS-CoV-2 among the study population included education, income, certain preventive behaviors, BCG vaccination status and pre-existing chronic conditions, such as diabetes, overweight, heart problems and stroke.

A large SARS-CoV-2 seroprevallence study carried out in India showed a seroprevalence of about 26% in 70 districts [8]. This study, included rural areas, in which one might anticipate lower prevalence because of the lower population density, and it was also conducted much earlier in the epidemic. Cross-sectional studies carried out in large Indian cities of states like Odisha, Madhya Pradesh, Karnataka and Maharashtra showed wide variation in seroprevalence between the cities, which increased with time in subsequent rounds (5% to 76.8%) [34-36]. The nation wide SARS-CoV-2 seroprevalence study in India carried out in August to September, 2020 showed a seroprevalence of 7%, while in June-July 2021, it increased to 67.6% [5,8]. The seroprevalence of SARS-CoV-2 we found towards the last stage of the first wave of COVID-19 in Bangladesh (October 2020-February 2021) before initiation of vaccination against COVID-19, was comparable to those seen in Karnataka and Maharashtra and in nation-wide serosurvey in India. A recent publication from Bangladesh on seroprevalence of SARS-CoV-2 in a subdistrict of Chattogram also found a similar level of seroprevalence (64%) [37] comparing urban and rural populations.

Densely populated slums showed higher seropositivity (71.0%) than the neighboring non-slum areas (62.2%) [23]. Even though the numbers of family members living in the same household in slums (4.87) were found to be fewer than those in non-slum households (5.09), populaton density in slum is much higher because of the the congested living quarters (82% of the households possess a one-room dwelling space with a mean area of 119 sq ft). Other facilities are commonly shared in slums (e.g. water sources: 92%, latrine: 90% and cooking places: 60%) [30]. Positive association of seroprevalence with lower income and limited years of formal education may also explain the higher SARS-CoV-2 seropositivity in slum than non-slum populations.

Studies among hospitalized patients or patients recovering from COVID-19 have observed that older age, male gender, overweight/obesity and underlying chronic diseases are all associated with worse disease outcomes [27,28]. Our study, based in urban community settings showed that a great proportion of the seropositive population have been asymptomatic (62.4%) with fewer having mild COVID-like symptoms. Nonetheless, the same risk factors that were identified elsewhere in moderately or severely ill patients were found to be linked with higher odds of SARS-CoV-2 seropositivity. We found that irrespective of the living conditions, seropositivity was positively associated with, overweight,self-reported diabetes, heart disease or a history of stroke.

Practice of preventive measures, such as regular wearing of masks and washing hands were associated with lower seropositivity Our finding is in accord with a recently conducted randomized controlled trial from Bangladesh which demonstrated that promotion of increased use of masks reduced symptomatic SARS-CoV-2 infections in the community [38].

We found that BCG vaccinated people had lower probability of getting infected with SARS-CoV-2. There is no solid evidence yet that the BCG vaccination protects against the infection. A number of studies have described broad cross-protective effects of the BCG vaccine toward diverse unrelated infections, which is believed to be mediated through induction of trained immunity or long-term maintenance of innate immune memory [39,40]. The WHO does not endorse BCG vaccination to protect against COVID-19; however, it plans to assess the findings of the ongoing clinical trials addressing this query.

A number of studies have indicated possible relationship of physical activity with SARS-CoV-2 seropositivity. A study carried out in 48,440 adult patients analyzing the association between severe COVID-19 outcomes and self-reported physical activity demonstrated that patients consistently meeting physical activity guidelines were at lower risk of severe disease outcomes [41]. An inpatient- and outpatient-based study showed that individuals with lower levels of routine physical activity were affected by more severe forms of COVID-19 [42]. Among athletes, the maximal exercise capacity prior to infection was inversely associated with the likelihood of hospitalization due to COVID-19 [43]. Another study reported that physical activity at the county level was negatively associated with both COVID-19 cases and deaths per 100,000 residents in USA [44]. However, we did not find any association of mild-to-intense physical activity with SARS-CoV-2 seropositivity.

Our study has a number of limitations. The study is based on a community sample where the seropositive cases were mostly asymptomatic or mildly symptomatic. Thus, there was no scope to study association of disease severity with various factors (biological/behavioral/social). Data and samples were collected purposively from two large cities in Bangladesh focusing on slums and adjacent non-slum areas. This sampling was not based on primary sampling unit, which would have been representative of the cities. The participant enrolment from slums and non-slums were not equal due to non-response, particularly from non-slum households during the COVID-19 pandemic. To overcome this bias due to non-response, a population-based weighted score was created and applied to estimate weighted seroprevalence of SARS-CoV-2 and also used as cluster effect in the logistic regression model [45]. The serosurvey was carried out over a long period (~5 months) during which the transmission levels could change, making it difficult to estimate the prevalence and understand its importance in relation to case-loads. However, during the period of December 2020 to February 2021, the rate of active infection (RT-PCR) was low [46], COVID-19 vaccination had not yet started and thus may have minimally affected the survey. Another limitation was that a shortened version of the physical activity questionnaire was applied that did not capture the full spectrum of activities. Establishing BCG vaccination status, in absence of vaccination cards required a visible vaccination scar on the upper arm, which may fade or disappear with time and thus vaccination status may be underrepresented. Self-reported COVID-like symptoms and preventive behaviors may suffer from recall bias.

In conclusion, the estimated SARS-CoV-2 antibody seroprevalence was higher in slum than in non-slum areas of two large cities of Bangladesh. Already identified risk factors for disease severity in clinical patients such as overweight, diabetes, heart disease, and stroke were also found to be associated with infection in the urban communities. The behavior of wearing masks and washing hands regularly seemed to have beneficial effect against SARS-CoV-2 infection. Future serosurveillance studies in the SARS-CoV-2 vaccine era should be planned to monitor exposure and side-by-side determine and differentiate between infection- and vaccine-induced humoral immunity. Such data will be crucial to inform public health decision-makers to improve vaccine distribution and allocation, and appraise booster dose requirements during the COVID-19 pandemic.

Internal validation of the Elecsys Anti-SARS-CoV-2 Immunoassay Kit.

(DOCX)

Click here for additional data file.

Weighted seroprevalence of SARS-CoV-2 antibodies among the residents of slum and non-slum neighborhoods of the Dhaka and Chattogram districts.

(DOCX)

Click here for additional data file.

Weighted seroprevalence of SARS-CoV-2 antibodies among the participants with self-reported occurrence of COVID-like symptoms in the past six months.

(DOCX)

Click here for additional data file.

Weighted seroprevalence of SARS-CoV-2 antibodies among the participants with co-morbidities.

(DOCX)

Click here for additional data file.

Field data collection and study team.

(DOCX)

Click here for additional data file.

Sample size.

(DOCX)

Click here for additional data file.

20 Jan 2022

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Reviewer #1: Partly

Reviewer #2: Yes

**********

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Reviewer #1: No

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: No

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Reviewer #2: Yes

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Reviewer #1: Rephrase line 91-92: The claims made in the sentence, that there have been limited surveys in LMICs aren't supported by the citations. In fact, the citations negate the point you are trying to make. Please rephrase the sentence or provide citations that corroborate your claim.

Line 102-103: Around 12.2 million people live in impoverished, densely populated urban centers of Dhaka and Chattogram areas (19-21)

Do the authors mean that the total population of Dhaka and Chittagong is only 12.2 million? If not please rephrase the sentence to say that 12.2 million people are impoverished in Dhaka and Chattogram (or something to that effect).

Line 104-105: "However, there are no data about the COVID situation in these communities."

It is unlikely there are not no data at all. There must have been RT-PCR tests done, even if inadequately. The authors probably mean there are limited data about the COVID-19 situation in these communities. And COVID-19 instead of COVID.

"SARS-CoV-2 serosurveys would estimate the prevalence of SARS CoV-2 infection in the communities."

A serosurvey will not estimate the prevalence of SARS-COV-2 infection. It will estimate the prevalence of individuals with a history of SARS-COV2 infection. Those two are not the same.

Line 107: "... associated with disease severity" do you mean COVID-19 severity? It is not clear from the sentence.

Line 108-109: "It would be important to find out whether different demographic characteristics and co-morbidities prevailing in the slums and non-slum urban areas are also associated with seroprevalence."

Line 104 and 107: Consider replacing "would be" with is.

Line 109: "... are also associated with seroprevalence."

What does this mean? Do you mean "are also associated with SARS-CoV-2 seropositivity? Same issue in some other instances (line 304, 306 for example).

Line 112: "... taking into account both the symptomatic and asymptomatic cases".

Please consider deleting this part. It will make the sentence more clear.

Line 214-215: "A weighted analysis was performed using the number of clusters from the eight selected areas and selected participants from the clusters to better reflect the current seroprevalence of SARS-CoV-2."

What does this mean? What was the weighting based on? Age? Sex? Other variables? It is not clear from the text. Please clarify explicitly.

Line 344-355: It is plausible that the lower probability of SARS-CoV-2 infection observed among the Bangladeshi participants, who carry out moderate physical activity may be linked to stronger immune status.

How is this plausible? Multiple jumps in logic. And there really is no such thing as a strong immune status; there is immunocompromised status, normal immune response and a hyper-immune response (which is a problem in its own, even in COVID-19).

Line 359-360:

".. physicians advise COVID patients to refrain from exercising when overt symptoms develop, and gradually return to "

Do they? Isn't this a casual blanket statement?

Other issues:

Was the test inaccuracy corrected for? The authors have focused on developing a regression model to evaluate predictors of seropositivity using a mixed effects model, however there was no effort in correcting test errors. Based on the authors' own validation studies, the sensitivity was around 93% and the specificity was between than 97.9%-100%. When these inaccuracies are corrected for, the final adjusted seroprevalence will be different from the one that has been presented in this study. Without them seroprevalence studies aren't really considered methodologically sound. Probabilistic models make it easy to correct these errors. Probabilistic models also make the weighting process easy. But such corrections can also be done without using a probabilistic model.

Please see here: https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssc.12435

here: https://ete-online.biomedcentral.com/articles/10.1186/1742-7622-9-9.

And here: https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)31304-0/fulltext

If the authors are not going to correct for test inaccuracy, there should at least be a discussion about why they chose not to do so.

Some of the conclusions drawn from the study findings have problems with face validity (physical activity and seropositivity for example). There also appear to be obvious issues with confounding and identification. Therefore it would be prudent to tread these findings with caution and not read too much into them. Many of the predictors that the authors have tried to study in this observational study have been studied in experimental trials (The Bangladesh mask study for example https://www.science.org/doi/10.1126/science.abi9069) and it might be useful to discuss those findings as well.

Reviewer #2: The authors used standard English with a very clear flow, they also presented clear tables, graphs and additional information for reference. I did not encounter any errors while reviewing the manuscript.

**********

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Submitted filename: Review_Seroprevalence of SARS-CoV-2 and associated factors.doc

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3 Apr 2022

Reviewers' comments:

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1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

________________________________________

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

________________________________________

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

________________________________________

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

________________________________________

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Rephrase line 91-92: The claims made in the sentence, that there have been limited surveys in LMICs aren't supported by the citations. In fact, the citations negate the point you are trying to make. Please rephrase the sentence or provide citations that corroborate your claim.

Response: As advised we have rephrased the sentence. The revised phrase now reads as-

“Population-based serosurveys for SARS-CoV-2 conducted in LMICs have been either national surveys (4, 5) or predominantly focused on frontline healthcare workers or industry workers (6-10) but few have studied marginalized populations in slums (11, 12).” (Lines 74-77).

Line 102-103: Around 12.2 million people live in impoverished, densely populated urban centers of Dhaka and Chattogram areas (19-21).

Do the authors mean that the total population of Dhaka and Chittagong is only 12.2 million? If not please rephrase the sentence to say that 12.2 million people are impoverished in Dhaka and Chattogram (or something to that effect).

Response: The revised sentence is as follows:

“Around 12.2 million impoverished people of Dhaka and Chattogram cities live in densely populated low-income urban slums.” Line: 86-87

Line 104-105: "However, there are no data about the COVID situation in these communities."

It is unlikely there are not no data at all. There must have been RT-PCR tests done, even if inadequately. The authors probably mean there are limited data about the COVID-19 situation in these communities. And COVID-19 instead of COVID.

Response: The time when the survey was carried out in Bangladesh, information about the COVID-19 situation in slums was largely missing, because majority of the slum dwellers are poor and they did not go to the hospital or clinic facilities for PCR testing because of high test price. Moreover, very long queues in public facilities and fear of wage loss/loss of income due to lockdowns imposed by city corporations over the slum areas or other neighborhoods made people reluctant to go for testing. We have revised the sentences as follows:

“So far limited data about the COVID situation in these slum communities are available from Bangladesh (22). Studies targeting RT-PCR-based detection of SARS-CoV-2 infection in Bangladesh have been mainly carried out in hospital settings (23-25); such data from the community is lacking.” Line: 87-90

"SARS-CoV-2 serosurveys would estimate the prevalence of SARS CoV-2 infection in the communities."

A serosurvey will not estimate the prevalence of SARS-COV-2 infection. It will estimate the prevalence of individuals with a history of SARS-COV2 infection. Those two are not the same.

Response: As correctly indicated by the Reviewer, a SARS-CoV-2 serosurvey will estimate the prevalence of cases/individuals with a history of SARS-CoV-2 infection in the communities. We have revised the sentence accordingly (Lines 90-93).

Line 107: "... associated with disease severity" do you mean COVID-19 severity? It is not clear from the sentence.

Response: We meant ‘severity of COVID-19’ (not severity of NCDs). The sentence has been revised accordingly (Line 95).

Line 108-109: "It would be important to find out whether different demographic characteristics and co-morbidities prevailing in the slums and non-slum urban areas are also associated with seroprevalence."

Line 104 and 107: Consider replacing "would be" with ‘is’.

Response: Corrected as advised (Line 95).

Line 109: "... are also associated with seroprevalence."

What does this mean? Do you mean "are also associated with SARS-CoV-2 seropositivity? Same issue in some other instances (line 304, 306 for example).

Response: Yes, as indicated by the reviewer the correct phrase is “SARS-CoV-2 seropositivity or Seroprevalence of SARS-CoV-2”. We have made the necessary changes in several places (Line 133, 211, 245, 252, 262, 274, 277, 285, 290, 293, 295, 307, 309, 313, 321, 354).

Line 112: "... taking into account both the symptomatic and asymptomatic cases".

Please consider deleting this part. It will make the sentence more clear.

Response: Done as suggested and sentence rephrased (Lines 101).

Line 214-215: "A weighted analysis was performed using the number of clusters from the eight selected areas and selected participants from the clusters to better reflect the current seroprevalence of SARS-CoV-2."

What does this mean? What was the weighting based on? Age? Sex? Other variables? It is not clear from the text. Please clarify explicitly.

Response: We have calculated population-based weighted prevalence on the basis of sum of two probabilities, between cluster probability (p1) and within cluster probability (p2). The sample was collected from 4 areas of Dhaka (Korail, Mirpur, Dhalpur and Ershad Nagar) and 2 areas of Chottagram (Shaheed Lane and Akbar Shah Kata Pahar,) that harbored both slum and surrounding non-slum areas. For each selected area, a total probability score was calculated as (p1+p2). The weight was determined as the inverse of total probability of each selected area (1/(p1+p2)). Thereafter, the calculated weight was distributed to all selected participants and the weighted prevalence was estimated. This description has been added in Statistical analysis section (Line 198-205).

The sum of probability scores of all selected areas was 1.00.

Line 344-355: It is plausible that the lower probability of SARS-CoV-2 infection observed among the Bangladeshi participants, who carry out moderate physical activity may be linked to stronger immune status.

How is this plausible? Multiple jumps in logic. And there really is no such thing as a strong immune status; there is immunocompromised status, normal immune response and a hyper-immune response (which is a problem in its own, even in COVID-19).

Response: According to published literature, “physical inactivity is associated with a higher risk for severe COVID-19 outcomes” or in other words “...meeting physical activity guidelines was strongly associated with a reduced risk for severe COVID-19 outcomes among infected adults” (Sallis R 2021). We found a positive association between seropositivity and moderate physical activity, but not with intense activity which was surprising.

However, based on the Reviewer’s suggestion below to correct for “test inaccuracy” a Bayesian multivariate generalized linear mixed model (MGLMM) was applied. This led to some changes in the results. The association of seropositivity with grades of physical activity no longer remained significant. Accordingly, revisions were made in the Results (Lines 301-302), Discussion (Lines 354-355 & 363-364). The figure on effect of physical activity on seroprevalence (previous Figure 3) has now been removed.

- Sallis R, Young DR, Tartof SY, Sallis JF, Sall J, Li Q, et al. Physical inactivity is associated with a higher risk for severe COVID-19 outcomes: a study in 48 440 adult patients. Br J Sports Med. 2021;55(19):1099-105.

Line 359-360:

".. physicians advise COVID patients to refrain from exercising when overt symptoms develop, and gradually return to "

Do they? Isn't this a casual blanket statement?

Response: We agree with the reviewer; this sentence has been deleted.

Other issues:

Was the test inaccuracy corrected for? The authors have focused on developing a regression model to evaluate predictors of seropositivity using a mixed effects model, however there was no effort in correcting test errors. Based on the authors' own validation studies, the sensitivity was around 93% and the specificity was between than 97.9%-100%. When these inaccuracies are corrected for, the final adjusted seroprevalence will be different from the one that has been presented in this study. Without them seroprevalence studies aren't really considered methodologically sound. Probabilistic models make it easy to correct these errors. Probabilistic models also make the weighting process easy. But such corrections can also be done without using a probabilistic model.

Please see here: https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssc.12435

here: https://ete-online.biomedcentral.com/articles/10.1186/1742-7622-9-9.

And here: https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)31304-0/fulltext

If the authors are not going to correct for test inaccuracy, there should at least be a discussion about why they chose not to do so.

Response: We very much appreciate the useful advice by the Reviewer. Accordingly, we have carried out relevant analysis in the manuscript and made necessary changes in the Tables, figures and texts. A paragraph has been added in the statistical description about the application of Bayesian theorem to fix the inaccuracy of the antibody test (Lines 212-216)

“Since the in-house validation of the antibody assay showed a sensitivity of about 93% and a specificity between 97.9%-100% (manufacturer reported sensitivity is 99% and specificity is 100%), to correct the test inaccuracy we estimated seroprevalence of SARS-CoV-2 associated risks (odds ratio) by Bayesian multivariate generalized linear mixed model (MGLMM).”

Some of the conclusions drawn from the study findings have problems with face validity (physical activity and seropositivity for example). There also appear to be obvious issues with confounding and identification. Therefore, it would be prudent to tread these findings with caution and not read too much into them. Many of the predictors that the authors have tried to study in this observational study have been studied in experimental trials (The Bangladesh mask study for example https://www.science.org/doi/10.1126/science.abi9069) and it might be useful to discuss those findings as well.

Response: We thank the reviewer for a useful and constructive comment. Based on the suggestion above to correct analysis for test inaccuracy, we applied Bayesian MGLMM. The revised analysis showed that individuals routinely wearing face masks and washing hands had lower odds of getting seropositive (Lines 296-300). This was in agreement with the findings of the Bangladesh Mask study and we referred to it (Abaluck J 2022). Accordingly, we have revised the findings in the Discussion (Lines 343-346). The Conclusion has also been revised (Lines 387-388).

Furthermore, as advised, we have substantially reduced the sections on BCG vaccination and physical activity.

- Abaluck J, Kwong LH, Styczynski A, Haque A, Kabir MA, Bates-Jefferys E, et al. Impact of community masking on COVID-19: A cluster-randomized trial in Bangladesh. Science. 2022;375(6577):eabi9069.

Reviewer #2: The authors used standard English with a very clear flow, they also presented clear tables, graphs and additional information for reference. I did not encounter any errors while reviewing the manuscript.

________________________________________

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Reviewer #1: No

Reviewer #2: No

Reviewer: Amos Hamukale

Manuscript:

Line 141-143: highlights that during lockdown accessibility to non-slum households was limited hence collected data from 2118 slum inhabitants and 1102 from non-slum in a 2:1 ratio. It is not clear how sample size was determined, it seems the sample size was adjusted to suit the non-response in households that could not be reached hence introducing systematic bias.

Response: We thank the reviewer for raising this issue. The study was conducted in the early stage of COVID-19 in Bangladesh. During that time no published data was found about the prevalence of COVID-19. Thus, we conducted a pilot study in icddr,b’s HDSS area and calculated the sample size based on this finding. The non-slum participants frequently did not allow study teams to enter their household and the rejection rate was high. Thus, we draw the samples in a 2:1 ratio. To reduce the systematic bias, a population-based weighted score was created, which was applied to estimate weighted seroprevalence as well as use as cluster effects in the logistic regression model (Gelman A). Please also see response to Reviewer#1’s query about weighted analysis.

- Gelman A, Carpenter B. Bayesian analysis of tests with unknown specificity and sensitivity. Journal of Royal statistical Society Applied Statistics 2020;69(5):1269-83.

Line 210-211: The mean with standard deviation are reported for continuous data but there is not indication that the data was checked for normality which would determine whether to use mean or median.

Response: Main exposure of the study was dichotomous and the statistical model was performed with mixed effect logistic regression model thus there is no need to check the normality.

Line 214-216 & 238: Authors indicated that they conducted a weighted analysis but did not provide adequate information on what kind of weights were used for example Design weights or post-Stratification weights for the different variables.

Response: We have calculated population-based weighted prevalence on the basis of sum of two probabilities, between cluster probability (p1) and within cluster probability (p2). The sample was collected from 4x2 areas of Dhaka (Korail, Mirpur, Dhalpur and Ershad Nagar) and 2x2 areas of Chottagram (Shaheed Lane and Akbar Shah Kata Pahar,) that harbored both slum and corresponding non-slum areas. For each selected area, a total probability score was calculated as (p1+p2). The weight was determined as the inverse of total probability of each selected area (1/(p1+p2)). Thereafter, the calculated weight was distributed to all selected participants and the weighted prevalence was estimated. This description has been added in Statistical analysis section (Line 198-205).

Table 1: Age in years should be categorized to better understand the distribution and how they affect the analysis. Having more children in one group can easily skew the data hence giving a false average.

Response: As suggested, we have included the number of participants in each age category in Table 1 showing the distribution in slum and non-slum areas. Additionally, for the reviewer, we have provided a table below showing age-wise distribution in seropositive and seronegative categories.

Age category, years Overall Number Seropositive Seronegative

10-17 years 776 508 268

18-30 years 945 649 296

31-50 years 1015 715 300

>50 years 484 337 147

Total 3220 2209 1011

Table 1: Authors have indicated p-value in line 248-249 but these are not reported in the table.

Response: We thank the reviewer for indicating the mistake. The sentence has been deleted from the footnote of Table 1.

Table 3: On years of education, the authors use 11-15 years as the reference group. This reference group does not adequately represent the Slum dwellers 3% hence reduces the precision.

Response: The Reviewer has correctly indicated the drawback. For slum dwellers, the reference group will be 6-10 years of education. This has been changed in the Table 3.

Submitted filename: Response to reviewer.docx

Click here for additional data file.

20 Apr 2022

21 Apr 2022

Comment on 29 March 2022

-We note you updated your Data Availability statement to the following:

- "Yes - all data are fully available without restriction"

- "All datasets used in this study are available."

While you have stated that the data is fully available without legal or ethical restrictions, you have not explained where the data can be found and how others may access the data.

Response: We have added the Data Availability statement describing the compliance with PLOS' data policy in the MS (in data availability section line number:405-409) as well as in the MS submission system. The revised manuscript was submitted on 3rd April 2022.

Comment on 13 April 2022

-We are requesting for an updated version of your manuscript including the ethical approval number provided by the institutional review board.

Response: As requested, we have added the ethical approval number (PR-20070, dated 1st September 2020) provided by the Ethical Review Committee (ERC) of icddr,b in the manuscript Lines 193-196. However, it was not possible to upload the revised manuscript in the submission system, as the system did not allow uploading. Therefore, we replied to the mail by attaching the revised manuscript.

Comment on 20th April 2022

-The editorial manager system can now allow you to upload your updated revised manuscript. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'

Response: There are no further comments from the reviewers after submission of the revised manuscript and response to Reviewers’ comments on 10th February 2022. The rebuttal letter that responds to point raised by the academic editor and Managerial Desk have been addressed in the file labeled as 'Response to Reviewers'.

As indicated by the Academic Editor, we have now uploaded a revised copy of the manuscript with revision marked in track changes, labeled as “Revised Manuscript with Track Changes” and a clean unmarked copy of the revised manuscript labelled as “Manuscript”.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

22 Apr 2022

Seroprevalence of SARS-CoV-2 infection and associated factors among Bangladeshi slum and non-slum dwellers in pre-COVID-19 vaccination era: October 2020 to February 2021

PONE-D-21-35521R2

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28 Apr 2022

PONE-D-21-35521R2

Seroprevalence of SARS-CoV-2 infection and associated factors among Bangladeshi slum and non-slum dwellers in pre-COVID-19 vaccination era: October 2020 to February 2021

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