Maternal mental health and coping during the COVID‐19 lockdown in the UK: Data from the COVID‐19 New Mum Study

  1. Sarah Dib
  2. Emeline Rougeaux
  3. Adriana Vázquez‐Vázquez
  4. Jonathan C. K. Wells
  5. Mary Fewtrell

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Abstract

Objective

To assess how mothers are feeling and coping during lockdown, and to identify the potential pathways that can assist them.

Methods

A descriptive analysis of maternal mental health, coping, support, activities, lockdown consequences was conducted. Women living in the UK with an infant aged ≤12 months completed an online survey. Linear regression was used to identify predictors of maternal mental health and coping.

Results

A majority of the 1329 participants reported feeling down (56%), lonely (59%), irritable (62%), and worried (71%) to some extent since lockdown began, but 70% felt able to cope. Support with her own health (95% confidence interval [CI] 0.004–0.235), contacting infant support groups (95% CI −0.003 to 0.252), and higher gestational age of the infant (95% CI 0.000–0.063) predicted better mental health. Travelling for work (95% CI −0.680 to −0.121), the impact of lockdown on the ability to afford food (95% CI −1.202 to −0.177), and having an income <£30 000 (95% CI −0.475 to −0.042) predicted poorer mental health. Support with her own health and more equal division of household chores were associated with better coping.

Conclusion

There is a need to assess maternal mental health and identify prevention strategies for mothers during lockdown.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.08.04.20168039: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementIRB: For this analysis, we collected data regarding: Ethics and Consent: Ethical approval was obtained from the UCL Research Ethics committee (0326/017).
    Consent: The first page of the survey provides information about the study and, having read this, participants were asked to provide consent to participate before proceeding.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical Analysis: Data from the survey was exported from RedCap, the software collecting survey responses, and analysed in SPSS (IBM SPSS statistics v. 26).
    RedCap
    suggested: (REDCap, RRID:SCR_003445)
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    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: We detected the following sentences addressing limitations in the study:
    The main limitations of this study are the cross-sectional design and that the current population is not representative of all new mothers in the UK. Compared to the national data, our participants have higher educational attainment, higher income, are more likely to be married or cohabitating, and are more likely to be of white ethnic background. Further effort is being made to include the experiences of women of BAME backgrounds and from disadvantaged groups to increase the representativeness of our study sample. Another limitation is that we did not formally assess or diagnose depression or anxiety, due to the anonymous nature of the survey which made it not possible to identify participants for follow up. Consequently, we were not able to compare the rates of depression or anxiety in new mothers during lockdown with the rates before the pandemic. However, a larger proportion of the general population do not fit the diagnoses of depression or anxiety, but are rather at risk, which makes our results more generalizable. We will also monitor changes in mental health ‘symptoms’ and coping over the different phases of lockdown.

    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.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1002/ijgo.13397
  3. Version published to 10.1101/2020.08.04.20168039v1 on medRxiv