Reduction of SARS-CoV-2 intra-household child-to-parent transmission associated with ventilation: results from a case–control study

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Abstract

Purpose

Our objective was to describe circumstances of SARS-CoV-2 household transmission and to identify factors associated with a lower risk of transmission in a nationwide case–control study in France.

Methods

In a descriptive analysis, we analysed cases reporting transmission from someone in the household (source case). Index cases could invite a non-infected household member to participate as a related control. In such situations, we compared the exposures of the index case and related control to the source case by conditional logistic regression matched for household, restricted to households in which the source case was a child, and the index case and related control were the infected child’s parents.

Results

From October 27, 2020 to May 16, 2022, we included 104 373 cases for the descriptive analysis with a documented infection from another household member. The source case was mostly the index case’s child (46.9%) or partner (45.7%). In total, 1026 index cases invited a related control to participate in the study. In the case–control analysis, we included 611 parental pairs of cases and controls exposed to the same infected child. COVID-19 vaccination with 3 + doses versus no vaccination (OR 0.1, 95%CI: 0.04–0.4), isolation from the source case (OR 0.6, 95%CI: 0.4–0.97) and the ventilation of indoor areas (OR 0.6, 95%CI: 0.4–0.9) were associated with lower risk of infection.

Conclusion

Household transmission was common during the SARS-CoV-2 pandemic in France. Mitigation strategies, including isolation and ventilation, decreased the risk of secondary transmission within the household.

Trial registration

ClinicalTrials.gov registration number: NCT04607941.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-023-16144-2.

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Most cited references 22

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Sensitivity Analysis in Observational Research: Introducing the E-Value.

Peng Ding, Tyler J. VanderWeele (2017)

Sensitivity analysis is useful in assessing how robust an association is to potential unmeasured or uncontrolled confounding. This article introduces a new measure called the "E-value," which is related to the evidence for causality in observational studies that are potentially subject to confounding. The E-value is defined as the minimum strength of association, on the risk ratio scale, that an unmeasured confounder would need to have with both the treatment and the outcome to fully explain away a specific treatment-outcome association, conditional on the measured covariates. A large E-value implies that considerable unmeasured confounding would be needed to explain away an effect estimate. A small E-value implies little unmeasured confounding would be needed to explain away an effect estimate. The authors propose that in all observational studies intended to produce evidence for causality, the E-value be reported or some other sensitivity analysis be used. They suggest calculating the E-value for both the observed association estimate (after adjustments for measured confounders) and the limit of the confidence interval closest to the null. If this were to become standard practice, the ability of the scientific community to assess evidence from observational studies would improve considerably, and ultimately, science would be strengthened.

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Purposeful selection of variables in logistic regression

Zoran Bursac, C Gauss, David Williams … (2008)

Background The main problem in many model-building situations is to choose from a large set of covariates those that should be included in the "best" model. A decision to keep a variable in the model might be based on the clinical or statistical significance. There are several variable selection algorithms in existence. Those methods are mechanical and as such carry some limitations. Hosmer and Lemeshow describe a purposeful selection of covariates within which an analyst makes a variable selection decision at each step of the modeling process. Methods In this paper we introduce an algorithm which automates that process. We conduct a simulation study to compare the performance of this algorithm with three well documented variable selection procedures in SAS PROC LOGISTIC: FORWARD, BACKWARD, and STEPWISE. Results We show that the advantage of this approach is when the analyst is interested in risk factor modeling and not just prediction. In addition to significant covariates, this variable selection procedure has the capability of retaining important confounding variables, resulting potentially in a slightly richer model. Application of the macro is further illustrated with the Hosmer and Lemeshow Worchester Heart Attack Study (WHAS) data. Conclusion If an analyst is in need of an algorithm that will help guide the retention of significant covariates as well as confounding ones they should consider this macro as an alternative tool.

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Variable selection – A review and recommendations for the practicing statistician

Georg Heinze, Christine Wallisch, Daniela Dunkler (2018)

Abstract Statistical models support medical research by facilitating individualized outcome prognostication conditional on independent variables or by estimating effects of risk factors adjusted for covariates. Theory of statistical models is well‐established if the set of independent variables to consider is fixed and small. Hence, we can assume that effect estimates are unbiased and the usual methods for confidence interval estimation are valid. In routine work, however, it is not known a priori which covariates should be included in a model, and often we are confronted with the number of candidate variables in the range 10–30. This number is often too large to be considered in a statistical model. We provide an overview of various available variable selection methods that are based on significance or information criteria, penalized likelihood, the change‐in‐estimate criterion, background knowledge, or combinations thereof. These methods were usually developed in the context of a linear regression model and then transferred to more generalized linear models or models for censored survival data. Variable selection, in particular if used in explanatory modeling where effect estimates are of central interest, can compromise stability of a final model, unbiasedness of regression coefficients, and validity of p‐values or confidence intervals. Therefore, we give pragmatic recommendations for the practicing statistician on application of variable selection methods in general (low‐dimensional) modeling problems and on performing stability investigations and inference. We also propose some quantities based on resampling the entire variable selection process to be routinely reported by software packages offering automated variable selection algorithms.

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Author and article information

Contributors

Arnaud Fontanet: fontanet@pasteur.fr

Journal

Journal ID (nlm-ta): BMC Public Health

Journal ID (iso-abbrev): BMC Public Health

Title: BMC Public Health

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2458

Publication date (Electronic): 26 June 2023

Publication date PMC-release: 26 June 2023

Publication date Collection: 2023

Volume: 23

Electronic Location Identifier: 1240

Affiliations

[1 ]Emerging Diseases Epidemiology Unit, Institut Pasteur, Université Paris Cité, 25 rue du Docteur Roux, Paris, 75015 France

[2 ]GRID grid.462844.8, ISNI 0000 0001 2308 1657, Sorbonne Université, Ecole Doctorale Pierre Louis de Santé Publique, ; Paris, France

[3 ]Institut Pasteur, Université Paris Cité, Centre for Translational Research, Paris, France

[4 ]Institut Ipsos, Paris, France

[5 ]GRID grid.484005.d, ISNI 0000 0001 1091 8892, Caisse Nationale de L’Assurance Maladie, ; Paris, France

[6 ]GRID grid.493975.5, ISNI 0000 0004 5948 8741, Santé Publique France, ; Saint-Maurice, France

[7 ]Sorbonne Université, Inserm, IPLESP, Hôpital Saint-Antoine, AP-HP, Paris, France

[8 ]GRID grid.36823.3c, ISNI 0000 0001 2185 090X, Conservatoire National Des Arts Et Métiers, , Unité PACRI, ; Paris, France

Article

Publisher ID: 16144

DOI: 10.1186/s12889-023-16144-2

PMC ID: 10294317

PubMed ID: 37365557

SO-VID: 5d8ccd33-138a-4111-b9db-10641c453c87

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 23 February 2023

Date accepted : 18 June 2023

Funding

Funded by: Agence Nationale de la Recherche

Award ID: PIA/ANR-16-CONV-0005

Award ID: ANR-10-LABX-62-IBEID

Award Recipient : Simon Galmiche Arnaud Fontanet

Funded by: Fondation de France

Award ID: Alliance “Tous unis contre le virus”

Award Recipient : Tiffany Charmet

Custom metadata

ScienceOpen disciplines: Public health

Keywords: sars-cov-2,home environment,family,patient isolation,ventilation

Data availability:

ScienceOpen disciplines: Public health

Keywords: sars-cov-2, home environment, family, patient isolation, ventilation

Reduction of SARS-CoV-2 intra-household child-to-parent transmission associated with ventilation: results from a case–control study

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Novel Coronavirus Disease COVID-19

Most cited references 22

Sensitivity Analysis in Observational Research: Introducing the E-Value.

Purposeful selection of variables in logistic regression

Variable selection – A review and recommendations for the practicing statistician

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