Seroprevalence of SARS-CoV-2 in slums versus non-slums in Mumbai, India
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Abstract
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Our take
This cross-sectional study, available as a preprint and thus not yet peer reviewed, found that participants randomly sampled in slum communities in Mumbai, India had a 54.1% seroprevalence of IgG antibodies to SARS-CoV-2 infection compared to 16.1% in non-slum communities. These findings suggest that those in slum communities have had greater exposure to the virus than those in non-slum communities, potentially due, in part, to higher population density in slum areas. However, those in non-slum areas were less likely to participate, and thus results may be subject to sampling bias.
Study design
cross-sectional
Study population and setting
Investigators sampled 6,904 individuals 12 years or older from three wards in Mumbai, India (Matunga, Chembur West, and Dahisar) to test the positivity rate …
Our take
This cross-sectional study, available as a preprint and thus not yet peer reviewed, found that participants randomly sampled in slum communities in Mumbai, India had a 54.1% seroprevalence of IgG antibodies to SARS-CoV-2 infection compared to 16.1% in non-slum communities. These findings suggest that those in slum communities have had greater exposure to the virus than those in non-slum communities, potentially due, in part, to higher population density in slum areas. However, those in non-slum areas were less likely to participate, and thus results may be subject to sampling bias.
Study design
cross-sectional
Study population and setting
Investigators sampled 6,904 individuals 12 years or older from three wards in Mumbai, India (Matunga, Chembur West, and Dahisar) to test the positivity rate of IgG antibodies to SARS-CoV-2 infection which would indicate prior infection. Investigators sampled individuals by randomly sampling households and testing one member of a sampled household in order to ensure a representative sample of the surveyed areas. They aimed to compare the seroprevalence of antibodies between slums (i.e. communities of people living on land for which they do not have legal rights) and non-slums. There were 4,202 participants in slums and 2,702 participants in non-slums. Investigators estimated the average prevalence over a two-week period (June 29 to July 14, 2020 in slums and July 3 to July 19, 2020 in non-slums).
Summary of main findings
The positive test rate of IgG antibodies in those sampled from slums was 54.1% compared to 16.1% among those in non-slums, for a prevalence difference of 38%. When adjusting for potential test imperfections (i.e. having a sensitivity of 0.90 and a specificity of 1.00) the positive test rate was 58.4% in slums and 17.3% in non-slums.
Study strengths
This study is one of few studies to provide seroprevalence estimates in from Mumbai, India and to compare results between slum and non-slum areas.
Limitations
The denominator for the seroprevalence was estimated through using 2011 Census data, which may not accurately reflect the current population estimates. Additionally, these data may not be representative to the general Mumbai population if the specific wards are not reflective of Mumbai demographics. Thus, the figures can be an over or underestimation of the prevalence of IgG antibodies among slums or non-slums. The research team couldn’t gain access to all buildings in non-slum areas and therefore some of the participant sampling was out of their control and may not have been random. Additionally, the authors note that participants in non-slums were less willing to participate due to fear of being exposed to the virus when getting tested.
Value added
This study shows a significantly higher burden of SARS-CoV-2 exposure among slums compared to non-slums, and thus highlights underpinning issues (e.g. high population density and poor infrastructure) that may contribute to higher infection rates. This has the potential to lead to higher morbidity and mortality in these areas as it would be harder to control transmission.
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SciScore for 10.1101/2020.08.27.20182741: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement Consent: ) Individuals were eligible if they were age 12 years or older and excluded if they refused informed consent or had a contraindication to venipuncture. Randomization not detected. Blinding not detected. Power Analysis To balance statistical power and bias, we stopped sampling either when we hit sample-size targets or the sampling period lapsed. Sex as a biological variable not detected. Table 2: Resources
Antibodies Sentences Resources At Kasturba Hospital in Mumbai, plasma was separated and used to test for IgG antibodies via chemiluminescence (CLIA) using Abbott Diagnostics Architect™ N-protein based test. IgGsuggested: NoneSoftware and Algorithms Sentences Resources SciScore for 10.1101/2020.08.27.20182741: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement Consent: ) Individuals were eligible if they were age 12 years or older and excluded if they refused informed consent or had a contraindication to venipuncture. Randomization not detected. Blinding not detected. Power Analysis To balance statistical power and bias, we stopped sampling either when we hit sample-size targets or the sampling period lapsed. Sex as a biological variable not detected. Table 2: Resources
Antibodies Sentences Resources At Kasturba Hospital in Mumbai, plasma was separated and used to test for IgG antibodies via chemiluminescence (CLIA) using Abbott Diagnostics Architect™ N-protein based test. IgGsuggested: NoneSoftware and Algorithms Sentences Resources At Kasturba Hospital in Mumbai, plasma was separated and used to test for IgG antibodies via chemiluminescence (CLIA) using Abbott Diagnostics Architect™ N-protein based test. Abbott Diagnostics Architect™suggested: NoneAbbott recommends a 1.4 cutoff for IgG score to label a test result as positive. Abbottsuggested: (Abbott, RRID:SCR_010477)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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