Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Open Access

Study Protocol

How do perceptions of Covid-19 risk impact pregnancy-related health decisions? A convergent parallel mixed-methods study protocol

  • Meredith Vanstone ,

    Roles Conceptualization, Funding acquisition, Methodology, Writing – original draft, Writing – review & editing

    vanstomg@mcmaster.ca

    Affiliation Department of Family Medicine, McMaster University, Hamilton, Canada

    ⨯
  • Rebecca H. Correia,

    Roles Methodology, Writing – original draft, Writing – review & editing

    Affiliation Health Research Methodology Graduate Program, McMaster University, Hamilton, Canada

    ⨯
  • Michelle Howard,

    Roles Conceptualization, Funding acquisition, Methodology, Writing – review & editing

    Affiliation Department of Family Medicine, McMaster University, Hamilton, Canada

    ⨯
  • Elizabeth Darling,

    Roles Conceptualization, Funding acquisition, Methodology, Writing – review & editing

    Affiliation Department of Obstetrics and Gynecology, McMaster University, Hamilton, Canada

    ⨯
  • Hamideh Bayrampour,

    Roles Conceptualization, Funding acquisition, Methodology, Writing – review & editing

    Affiliation Department of Family Practice, Midwifery Program, University of British Columbia, University Endowment Lands, Canada

    ⨯
  • Andrea Carruthers,

    Roles Methodology, Writing – review & editing

    Affiliation Department of Family Medicine, McMaster University, Hamilton, Canada

    ⨯
  • Amie Davis,

    Roles Conceptualization, Funding acquisition, Methodology, Writing – review & editing

    Affiliation Department of Family Medicine, McMaster University, Hamilton, Canada

    ⨯
  • Dima Hadid,

    Roles Methodology, Writing – review & editing

    Affiliation Health Policy PhD Program, McMaster University, Hamilton, Canada

    ⨯
  • Erin Hetherington,

    Roles Methodology, Writing – review & editing

    Affiliation Department of Epidemiology, Biostatistics & Occupational Health, McGill University, Montreal, Canada

    ⨯
  • Aaron Jones,

    Roles Methodology, Writing – review & editing

    Affiliation Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada

    ⨯
  • Sujane Kandasamy,

    Roles Funding acquisition, Methodology, Writing – review & editing

    Affiliations Department of Child and Youth Health, Brock University, St. Catharines, Canada, Department of Medicine, McMaster University, Hamilton, Canada

    ⨯
  • Cassandra Kuyvenhoven,

    Roles Methodology, Writing – review & editing

    Affiliation Department of Family Medicine, McMaster University, Hamilton, Canada

    ⨯
  • Jessica Liauw,

    Roles Conceptualization, Funding acquisition, Methodology, Writing – review & editing

    Affiliation Department of Obstetrics and Gynecology, University of British Columbia, University Endowment Lands, Canada

    ⨯
  • Sarah D. McDonald,

    Roles Conceptualization, Funding acquisition, Methodology, Writing – review & editing

    Affiliations Department of Obstetrics and Gynecology, McMaster University, Hamilton, Canada, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada, Department of Radiology, McMaster University, Hamilton, Canada, Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, McMaster University, Hamilton, Canada

    ⨯
  • Caroline Mniszak,

    Roles Methodology, Writing – review & editing

    Affiliation School of Population and Public Health, University of British Columbia, University Endowment Lands, Canada

    ⨯
  • Monica L. Molinaro,

    Roles Conceptualization, Funding acquisition, Methodology, Writing – review & editing

    Affiliation Department of Family Medicine, McMaster University, Hamilton, Canada

    ⨯
  • Manisha Pahwa,

    Roles Funding acquisition, Methodology, Writing – review & editing

    Affiliation Health Policy PhD Program, McMaster University, Hamilton, Canada

    ⨯
  • Tejal Patel,

    Roles Conceptualization, Funding acquisition, Methodology, Writing – review & editing

    Affiliation Department of Family Medicine, McMaster University, Hamilton, Canada

    ⨯
  • Marina Sadik,

    Roles Methodology, Writing – review & editing

    Affiliation Department of Family Medicine, McMaster University, Hamilton, Canada

    ⨯
  • Njideka Sanya,

    Roles Methodology, Writing – review & editing

    Affiliation Department of Family Medicine, McMaster University, Hamilton, Canada

    ⨯
  • Katrina Shen,

    Roles Methodology, Writing – review & editing

    Affiliation Department of Family Medicine, McMaster University, Hamilton, Canada

    ⨯
  •  [ ... ],
  • Devon Greyson

    Roles Conceptualization, Funding acquisition, Methodology, Writing – review & editing

    Affiliation School of Population and Public Health, University of British Columbia, University Endowment Lands, Canada

    ⨯
  • [ view all ]
  • [ view less ]

Abstract

Introduction

Pregnant people have a higher risk of severe COVID-19 disease. They have been disproportionately impacted by COVID-19 infection control policies, which exacerbated conditions resulting in intimate partner violence, healthcare access, and mental health distress. This project examines the impact of accumulated individual health decisions and describes how perinatal care and health outcomes changed during the COVID-19 pandemic.

Objectives

  1. Quantitative strand: Describe differences between 2019, 2021, and 2022 birth groups related to maternal vaccination, perinatal care, and mental health care. Examine the differential impacts on racialized and low-income pregnant people.
  2. Qualitative strand: Understand how pregnant people’s perceptions of COVID-19 risk influenced their decision-making about vaccination, perinatal care, social support, and mental health.

Methods and analysis

This is a Canadian convergent parallel mixed-methods study. The quantitative strand uses a retrospective cohort design to assess birth group differences in rates of Tdap and COVID-19 vaccination, gestational diabetes screening, length of post-partum hospital stay, and onset of depression, anxiety, and adjustment disorder, using administrative data from ICES, formerly the Institute for Clinical Evaluative Sciences (Ontario) and PopulationData BC (PopData) (British Columbia). Differences by socioeconomic and ethnocultural status will also be examined. The qualitative strand employs qualitative description to interview people who gave birth between May 2020- December 2021 about their COVID-19 risk perception and health decision-making process. Data integration will occur during design and interpretation.

Ethics and dissemination

This study received ethical approval from McMaster University and the University of British Columbia. Findings will be disseminated via manuscripts, presentations, and patient-facing infographics.

Trial registration

Registration: Clinicaltrials.gov registration number: NCT05663762.

Citation: Vanstone M, Correia RH, Howard M, Darling E, Bayrampour H, Carruthers A, et al. (2023) How do perceptions of Covid-19 risk impact pregnancy-related health decisions? A convergent parallel mixed-methods study protocol. PLoS ONE 18(8): e0288952. https://doi.org/10.1371/journal.pone.0288952

Editor: Steve Zimmerman, Public Library of Science, UNITED KINGDOM

Received: July 15, 2023; Accepted: July 24, 2023; Published: August 10, 2023

Copyright: © 2023 Vanstone et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Qualitative data are not available for sharing; when participants consented to participate in the study, they did not consent to access transcripts or data beyond what is reported within this report. The dataset for the quantitative portion of this study is held securely in coded form at ICES and PopData BC. While legal data sharing agreements between these data stewards and data providers prohibit ICES and PopData BC from making the dataset publicly available, access may be granted to those who meet pre-specified criteria for confidential access. Readers are welcome to contact the research team for further information. Data queries to ICES can be directed to Data or Analytic Services at das@ices.on.ca. Data queries to PopData BC can be directed to the Data Access Unit, dataaccess@popdata.bc.ca.

Funding: This work was supported by Canadian Institutes of Health Research grant number 179921. SDM and MV are supported by a Tier 2 Canada Research Chairs. MM is supported by a Banting Postdoctoral Fellowship.

Competing interests: The authors have no conflicts of interest to declare.

Introduction

Pregnant people experience higher rates of COVID-19 hospitalization, intubation, and death compared to similar age and sex-matched peers who are not pregnant [1–3]. As in the general population, pregnant people who are racialized, low-income, or who live in high-density neighbourhoods have elevated COVID-19 infection rates [3, 4].

While there is little evidence specific to pregnant people, evidence about health care access and well-being in women, trans, and gender non-binary people helps us understand the circumstances faced in pandemic pregnancies. Most pregnant people identify as women, and women have been disproportionately impacted by the COVID-19 pandemic [5, 6]. Overall well-being of pregnant people has also been jeopardized by COVID-19 social isolation policies resulting in increased intimate partner violence [7–9] and mental health distress [10, 11]. During the pandemic, women experienced unemployment and income loss partly attributed to precarious employment and increased childcare responsibilities [12, 13]. Women were also overrepresented in designated essential service jobs with high levels of COVID-19 exposure (e.g., nursing and patient service occupations) [14]. Trans and non-binary people faced disproportionate pandemic-related challenges to mental health, violence, income loss, and access to healthcare compared to people of other genders [15, 16].

This project examines pregnant people’s health decisions within workplace, home, and community environments, describing their accumulated impact on key pregnancy outcomes and care indicators related to three themes: 1) vaccination, 2) perinatal care, 3) social supports and mental health. The decisions made during pregnancy have longitudinal impacts on the life of the pregnant person, future child, and family [17]. Given the likely unique and elevated health, social, and economic challenges faced by pregnant people during the pandemic, and the importance of health decisions during pregnancy, it is necessary to understand how pandemic circumstances have shaped decisions about accessing pregnancy-related healthcare. Understanding how and why pregnant people make health decisions allows for targeted clinical and social support as the pandemic endures and will inform future policy planning.

Theme 1: Vaccination

Although pregnant people were not deliberately included in pre-market clinical trials, based on international immunization registry data [18], in May 2021 the Canadian National Advisory Committee on Immunization (NACI) recommended COVID-19 vaccination for pregnant people, with a preference for mRNA vaccines. Accordingly, pregnant populations were prioritized for access in late April (Ontario) and early May (British Columbia) 2021 [19, 20]. COVID-19 mRNA vaccines have shown a good safety profile during pregnancy [21], and protect against severe disease in the pregnant person as well as negative neonatal outcomes [22] including stillbirth [23] and infant hospitalization for COVID-19 [24].

Since 2018, Canadian guidelines have recommended that all pregnant people be offered pertussis vaccination (available via combination tetanus toxoid, diphtheria, and acellular pertussis, or Tdap, vaccine) [25, 26]. Uptake of routine (Tdap, influenza) vaccination during pregnancy in Canada has lagged behind that seen in comparator countries, but increased during the COVID-19 pandemic [27]. However, this overall increase obscures growing disparities (e.g., by ethnicity, income) and access barriers that may have been exacerbated by the pandemic [27]. Further, there have been indications of greater vaccination delay or refusal during pregnancy than in the general population, largely due to safety concerns [28, 29]. Factors commonly associated with low uptake of vaccination during pregnancy include socioeconomic status, demographic and geographic characteristics, access to prenatal care, and health literacy [30–32]. While a recommendation from a trusted prenatal care provider is typically understood to be one of the strongest influences for vaccine uptake [32–36], limited in-person appointments may have weakened that influence during the pandemic. We do not yet know how vaccine uptake has changed, including how decreased access to in-person prenatal care may have influenced decisions regarding vaccines recommended in pregnancy.

Theme 2: Perinatal care

Perinatal care includes medical care delivered during the prenatal, intrapartum and postpartum periods. Routine prenatal care consists of a series of regular interactions with a health care provider–typically a family physician, midwife, and/or obstetrician (OB)–for education, screening, and treatment interventions [37]. Inadequate prenatal care has been associated with a number of negative health outcomes, including stillbirth, pre-term birth, low birth weight, NICU admission, and postpartum depression and anxiety [38]. The COVID-19 pandemic instigated many changes to the delivery of routine prenatal care related to the incorporation of telephone or virtual visits [39–41], and changes to prenatal care schedules to adapt to re-allocation of healthcare resources, personal protective equipment limitations, and lack of continuity of care from the same prenatal care provider [42]. Perinatal care also changed in relation to policies that reduced support persons and visitors, and exacerbated other access issues related to childcare, precarious work, and transportation [43–45]. There are little data about the outcomes associated with these changes to prenatal care delivery, with some studies finding no difference in maternal and infant health outcomes [46, 47] and others in a global meta-analysis demonstrating shorter in-hospital stays after delivery, less prenatal care attendance, lower participation in screening for infection, anemia and fetal anomaly [47]. We do not know how these changes to the delivery of prenatal care influenced features such as uptake of gestational diabetes screening, or the duration of postpartum hospitalization in Canada. Further, it is unknown whether any changes in healthcare access that may have occurred reflected patient choice or influenced patient experience.

Theme 3: Mental health and social support

Untreated antenatal depression and anxiety are associated with pre-term birth, low birth weight, and increased risk of infant hospitalization and death within the first year of life [48, 49]. A systematic review and meta-analysis of global data indicate high rates of prenatal depression (30.5%) and anxiety (25.6%) during the COVID-19 pandemic [50]. Pregnant people in Canada may experience even higher rates of depression and anxiety [51], perhaps in response to concerns about threats of COVID-19 to themselves or their fetus, insufficient prenatal care, strained relationships, and social isolation [52]. Mental health concerns may be greater for those who experienced extra stress or safety fears due to income disruptions, difficulties balancing childcare and work responsibilities, are single parents, or had difficulty obtaining childcare [11, 53, 54]. The stress and anxiety experienced during the pandemic may have contributed to lower birth weights, younger gestational age at birth, and other birth-related issues [42, 52]. So far, there are little data about how perinatal mental health diagnosis rates have changed throughout the pandemic. We do not know whether any observed changes in mental healthcare use reflect barriers to accessing care. We do not know how pregnant people experienced their feelings of depression and anxiety, and how they chose to cope with these experiences.

Objectives, questions and hypotheses

This research study has two objectives:

  1. Evaluate potential associations between exposure(s) and outcome(s) for births occurring in 2019 (Jan 1 2019 –Mar 31 2019 births), 2021 (Jan 1 2021 –Mar 31 2021 births), and 2022 (Jan 1 2022- Mar 31 2022 births) birth groups in Ontario and BC related to vaccination, perinatal care, and mental health, overall, by race/ethnicity and by income. (Quantitative strand)
  2. Understand how people who gave birth in Ontario or BC in 2020 or 2021 perceived COVID-19 risk and how pandemic circumstances influenced their decision-making about key elements of pregnancy, including vaccination, perinatal care, social support and mental health. (Qualitative strand)

Research questions and hypotheses have been operationalized according to our three themes:

Theme 1: Vaccination

  • Quantitative Research Question (RQ): Were rates of vaccination different between 2019, 2021, and 2022 birth groups? What factors are associated with vaccination rates? Outcomes: Tdap (ON) and COVID-19 (ON and BC) vaccination rates
    1. ○ Hypothesis 1.1: 2019 group will have a higher rate of Tdap vaccination than the 2021 and 2022 groups.
    2. ○ Hypothesis 1.2: COVID-19 vaccination rates in the 2022 group will be lower than those of the comparable overall female population in each province.
    3. ○ Hypothesis 1.3: In the 2022 group, pregnant persons who receive Tdap vaccination will be more likely to receive COVID-19 vaccination than those who do not receive Tdap vaccination. (Ontario only due to data availability)
  • Qualitative RQ: How have pregnant people’s choices about Tdap and COVID-19 vaccination been influenced by perceptions of COVID-19 risk, COVID-19 policies and personal socioeconomic circumstances during the pandemic (“pandemic circumstances”)?
    1. ○ How do personal identities and circumstances (e.g., race, education, age, income, presence of other children in the home) affect these decision-making processes?

Theme 2: Perinatal care

  • Quantitative RQ: Were rates of in-person perinatal care different between 2019, 2021, and 2022 pandemic groups? What factors are associated with in-person perinatal care? Outcomes: Gestational diabetes screening, maternal post-partum length-of-stay (BC and ON)
    1. ○ Hypothesis 2.1: 2021 group will have lower rates of gestational diabetes screening than the 2019 or 2022 groups.
    2. ○ Hypothesis 2.2: 2019 group will have longer length of hospital stay after delivery than 2021 or 2022 groups.
  • Qualitative RQ: How have pregnant people’s decisions about seeking prenatal care and planning for birth been influenced by perceptions of COVID-19 risk?
    1. ○ How do personal identities and circumstances (e.g., race, education, partnered status, presence of other children in the home) affect risk perception and decision-making processes?

Theme 3: Mental health and social support

  • Quantitative RQ: Were the rates of clinical diagnosis for new depression, anxiety, and adjustment disorders during pregnancy and up to six months postpartum different between 2019, 2021, and 2022 birth groups? What factors are associated with this clinical diagnosis? Outcomes: New onset of depression, anxiety and/or adjustment disorder diagnosis during pregnancy and six months postpartum (ON and BC) validated by Frey and colleagues [55].
    1. ○ Quantitative hypothesis 3.1: 2019 group will have lower rates of new clinical diagnosis for depression, anxiety, or adjustment disorder than 2021 and 2022 groups.
  • Qualitative RQ: How do pregnant people describe the relationship between their perceptions of COVID-19 risk, experiences of mental health, and decisions about seeking social support during pregnancy?
  • Qualitative RQ: How do pregnant people describe their decisions to balance COVID-19 protocols (e.g., masking, limiting social contact) with their physical, mental, and social needs? How do personal identities and circumstances (e.g. race, education, household composition) affect these decision-making processes?

Methods and analysis

This project is a parallel mixed-methods study of two Canadian provinces, with thematic data integration at the design and interpretation stages. Ontario and BC were chosen as the two provinces of study because they are populous provinces in Canada with large numbers of live births each year, widespread COVID-19 throughout the pandemic, and access to comprehensive administrative health data relevant for the quantitative arm of this study.

This study was funded in late February 2022. The quantitative cohort creation plans and data access requests were finalized in late Fall 2022. Qualitative data collection was piloted in Summer 2022, and preliminary data from a small sample of Ontario participants was collected in Fall 2022. Qualitative data collection in both provinces is currently ongoing. Study completion is anticipated for February 2024.

Patient partnership

Our team includes multiple individuals who have lived experience of pandemic pregnancy. Three co-investigators with lived experience helped to design the study, secure funding, and determine priorities. They contributed to the design of the interview guide and acted as pilot interview participants for interview refinement. Two additional researchers with lived experience conducted interviews and will participate in analysis.

Quantitative design and methods

Study design.

Using a population-based retrospective cohort design, we will assess differences in rates of clinical care and health outcomes for key pregnancy-related outcomes across three birth groups. A cohort study is appropriate given our interests in making inferences about individuals’ health decision-making and examining individual-level social and clinical exposures. This observational design will enable us to examine the real-world effect of the COVID-19 pandemic on pregnancy-related outcomes within entire jurisdictions [56].

Data sources.

Administrative health data from two provinces will be obtained from ICES in Ontario and PopData in British Columbia. These databases are comprehensive sources of health administrative data for healthcare encounters using provincial health insurance in each province. We will link multiple maternal perinatal care datasets at ICES and PopData BC concerning using unique encoded identifiers (person-level deterministic linkage) (S1 Table).

Cohort creation.

Our cohort will be defined using an ICES derived cohort (MOMBABY) in Ontario and the BC Perinatal Data Registry, both of which contain medical records for delivering mothers and newborns. S1 Table provides details.

Study population.

Our cohort for the quantitative portion will be defined by three groups according to the date they gave birth: January 1 to March 31, 2019; January 1 to March 31, 2021; and January 1 to March 31, 2022 (Fig 1). These groups represent gestation before the pandemic, gestation during the pandemic but before widespread vaccine availability, and gestation after widespread vaccine availability. These timeframes reflect an assumption that in the experience of pregnant people in Ontario and BC, the pandemic “started” with the enactment of public health measures in mid-March 2020. While the three-month birthing window excludes large portions of the year, this approach is necessary to construct groups within each time period that have no overlap in gestational periods. Overlapping gestational periods would result in cohort members being exposed to multiple time periods, leading to bias in our results. These groups are standardized by birth month to reflect evidence of seasonality in birth rates and health outcomes [57, 58].

thumbnail
Download:
Fig 1. Study population for quantitative portion by birth group.

https://doi.org/10.1371/journal.pone.0288952.g001

To be eligible for inclusion in our cohort, the individual must have had a live, in-hospital birth during our timeframes of interest. Stillbirths are excluded due to confounding factors around length of stay and mental health. Additional criteria are that the birthing person and newborn must have a valid ICES Key Number (IKN), delivery and birth date, the birthing person must be of female sex, and have been eligible for the provincial health insurance plans in Ontario or BC for the entirety of their perinatal period.

We will also describe pertinent characteristics of linked newborns to mothers in our cohort within MOMBABY and the BC Perinatal Data Registry. In addition, we will create a separate matched cohort of females using the Registered Persons Database (RPDB) (Ontario) and the Consolidation File (BC) who were not pregnant during our timeframes of interest for Hypothesis 1.2.

Outcomes.

We will use COVaxON in ICES and COVID-19 Immunization Data in PopData to define our COVID-19 vaccination outcome. Tdap vaccination status will be derived from the Ontario Laboratory Information System (OLIS); Tdap records are not available in PopData. OLIS and the MSP dataset in BC will define gestational diabetes screening outcomes. The Discharge Abstract Data (DAD) will provide data for our postpartum length-of-stay outcome. New onset of depression, anxiety or adjustment disorder will be ascertained using Ontario Health Insurance Plan (OHIP) diagnostic codes from physician billing claims, the DAD, and the Ontario Mental Health Reporting System (OMHRS) in Ontario; the BCPDR and physician billing claims report cases of depression, mood and anxiety. The timeframes of interest for our outcomes are illustrated in Fig 2.

thumbnail
Download:
Fig 2. Timeframes of interest for quantitative analyses.

https://doi.org/10.1371/journal.pone.0288952.g002

Data from the OHIP claims database and Medical Services Plan (MSP) dataset in BC will provide information about publicly funded perinatal services. The Registered Persons Database (RPDB) in Ontario, the BC Vital Events and Statistics Births dataset, the DAD, and the National Ambulatory Care Reporting System (NACRS) will provide information on baseline characteristics, covariates, and confounders of interest. The Canadian Index of Multiple Deprivation (CIMD) provides data on area-level socioeconomic and ethnocultural markers of marginalization.

Tdap vaccine record: vaccination can occur between 0–40 weeks. Preferentially received between 27–36 weeks.

Covid-19 vaccine record: 2022 birth cohort was eligible for vaccination during gestation. Vaccine could have been received during or after pregnancy.

Gestational diabetes screening: screening could occur between 0–40 weeks. Typically occurs between 24–28 weeks (or 0–13 weeks for people with obesity).

Postpartum length-of-stay: Interval (hours) between delivery time and discharge time.

New onset of depression, anxiety, or adjustment disorder: A) Gestation: Diagnosed between conception date and index event. B) Postpartum: Diagnosed between index event and 6 months postpartum.

Sample size.

Each year, there are approximately 140,000 live births in Ontario and 42,000 in BC [59]. Our total population of January 1 to March 31 births over the three-year study period is, therefore, approximately 105,000 in Ontario and 31,500 in BC. Given our use of population-based health administrative data sources, we anticipate achieving large sample sizes to detect differences in outcome rates across groups of interest. S2 Table contains sample size calculations, based on anticipated incidence rates. When we report the findings from this study, we will compute post-hoc power calculations to demonstrate the adequacy of our sample in detecting differences. Guidelines for regression suggest assessing no more than 1 potential candidate predictor per 10–20 events [60, 61].

Quantitative analyses

Descriptive.

The primary goal of the quantitative analysis is to describe and compare rates of our outcomes across birth groups to help explain the qualitative findings. Pregnancy-related factors will be reported using measures of general frequency (i.e., counts and proportions), central tendency (i.e., means and medians), and dispersion (i.e., standard derivations and interquartile range). We will examine maternal age, rurality, CIMD measures (i.e., ethnocultural composition, economic dependency, and residential instability), medical practice characteristics of the mother’s perinatal care provider, parity, mental health history (e.g., previous diagnoses or emergency department visits for mental health), the use of assistive fertility, delivery characteristics (e.g., gestation length and delivery type), and newborn outcomes (e.g., infant birth weight and neonatal intensive care unit admission). These factors were identified a priori based on a review of the literature and consultations with theme group experts.

Data will be examined for completeness, the presence of outliers, and the nature of the variable distribution for continuous variables (e.g., skew). We will report the completeness range for each exposure variable. Cases with missing data will be deleted within each analysis. Where data are incomplete for key variables, we will consider using multiple imputation.

Inferential.

The secondary analysis is to test for associations between our outcomes, primary exposure (birth group), and other covariates using multivariable logistic and linear regression. Covariates will be selected based on review of the literature, affirmed by topic and clinical experts. We will utilize logistic regression for our binary outcomes (i.e., vaccination status, gestational diabetes screening, and new onset of depression, anxiety, or adjustment disorder) and linear regression for our continuous outcome (i.e., postpartum length-of-stay). The best-subset approach will be used to identify the subset of covariates that best define the relationship with each outcome. This approach was selected to avoid suppressor effects commonly found with stepwise methods and statistically pre-determined predictors (e.g., p < 0.25). The Akaike information criterion (AIC) will be used to evaluate fit of the multivariable logistic regression models; R2 will be used to assess goodness of model fit for linear regression. We will verify model assumptions and employ methods such as robust variance estimation to address violations. We will also stratify these models by area-level information on socioeconomic status and ethnocultural composition to examine differences for low-income and racialized sub-groups.

All analyses will be performed using Statistical Analysis Software (SAS), version 9.4 (SAS Institute, Inc., Cary, North Carolina).

Qualitative design and methods

This strand uses a qualitative descriptive approach [62–64] which is most suitable for integrating with quantative analytic outputs, since both rely on post-positivist epistemology. Qualitative description is a low-inference applied health research methodology which instructs analysts to stay close to the explicit meaning of participants, rather than progressing to abstraction or interpretation.

Participants.

Eligible participants are people aged 18 or over who gave birth to a live baby in Ontario or BC between May 2020 and December 2021. Participants must be comfortable completing an interview in English.

Sampling and recruitment.

We will sample those who gave birth from May 1, 2020-Dec 1, 2021 to balance concerns of recall with information about different pandemic contexts. Guidance from one co-investigator with lived experience suggests that the complexity of health decision-making during the pandemic enhanced memory of this experience. We will use purposive sampling strategies, beginning with maximum variation to enroll an initial sample that represents diversity in age, SES, parity, race, geography, and occupational exposure to COVID-19. Preliminary analysis will suggest sampling strategies for future participants likely to yield analytically relevant findings. We will use a multi-pronged recruitment strategy, including posting advertisements online, in clinics and public parenting spaces. These advertisements will direct potential participants to an online or paper consent form which will explain the study and ask for socio-demographic information to be used for purposive sampling [65].

Sample size.

We anticipate requiring 60–75 interview participants to reach sufficient information power that ensures our findings are credible and trustworthy [66]. This sample will be split between Ontario (~2/3) and BC (~1/3) to reflect the population differences between the provinces. This determination was made based on the heterogeneity of the sample, the three-pronged aim of the study, the use of established theory and the anticipated medium quality of dialogue [66].

Interview guide development.

The interview guide was developed iteratively, with input from co-investigators who brought clinical, research, and lived experience of pandemic pregnancy. It was piloted and refined with three individuals who gave birth during the pandemic. The interview guide starts with a discussion of pandemic circumstances (family, housing, employment) and then elicits self-perceived risk of COVID-19 infection, complications, and long-term ramifications. Finally, the interviewer will inquire about decision-making processes relevant to the three topics of focus for this investigation (vaccination, prenatal care, mental health and social support).

Data collection.

Each enrolled participant will participate in one individual, semi-structured interview. The interviews will be conducted by telephone, Zoom, or in-person as participant preferences and geographical restrictions allow. Pilot data collection indicates that the interviews will last between 30–60 minutes. Interviews will be audio-recorded and transcribed verbatim.

Qualitative analyses.

Multiple analysts, including those with lived experience, will participate in conventional (inductive) content analysis [67, 68], using a staged process of coding adapted from Grounded Theory [69]. Analysis will focus on each theme separately, in conjunction with initial interview data about circumstances and risk perception. A multi-site qualitative analytic approach will maintain the site-specific context in the interviews while still offering comparative opportunity by theme [70]. An audit trail will be maintained, comprising of memos from each stage of data collection and analysis. Findings will be triangulated between analysts, and participants. Resonance will be ensured by presenting emerging finding to a focus group of pregnant people.

Data integration

Data integration will occur at two points: during design and during interpretation. The project was designed with an objective framework-based mixing approach to develop complementary approaches for each of the three prenatal care themes [71]. After independent data collection and analysis, we will integrate findings at the point of interpretation, using a joint display technique, to compare changes in health behaviours with qualitative data explaining those changes [72].

Strengths and limitations

This study has several strengths. The large administrative datasets offer significant statistical power to investigate changes in health care use, stratified by markers of socioeconomic and ethnocultural status. The cross-provincial nature of our analysis allows the observation of consistency or change based on different healthcare policies, vaccine availability, medical care provision, and COVID-19 burden. The integration with qualitative data offers the potential for explaining observed differences. Our study team incorporates multiple individuals with lived experience of a pandemic pregnancy, and frontline clinicians who cared for pregnant people in both provinces during the pandemic.

This study has some limitations. Relying on administrative data hinders the ability to gather some data points of interest. For example, neither ICES or PopData have patient-level demographic data about race or socioeconomic status. Instead, we will rely on area-level data abstracted from the 2016 Canadian Census, operationalized through the CIMD. The administrative datasets exclude some births, such as those which took place outside of a hospital setting. Qualitative data will be gathered from people who gave birth between May 1, 2020 to Dec 1, 2021, which may introduce some challenges with recall and social desirability bias. However, our co-investigators with lived experience assure us that due to the unique and challenging nature of these decisions, they are able to recall their decision-making very clearly.

Ethics & dissemination

Ethics approval was received from McMaster University via the Hamilton Integrated Research Ethics Board for the qualitative strand (14632) and for the BC portion of the quantitative strand (15100C). Approval for the project was also received from the University of British Columbia Behavioural Research Ethics Board (H22-01905). Ethics approval was waived by McMaster University for the portion of the quantitative strand using ICES data (September 22, 2022) because the use of ICES data in this project is authorized under section 45 of Ontario’s Personal Health Information Protection Act (PHIPA) and does not require review by a Research Ethics Board.

This research will yield understanding about how a global pandemic and self-perceived risk influences health decision-making, which may inform health promotion initiatives through clinical counselling, public health communication and resource allocation, and policy development. Findings will be disseminated through peer-review manuscripts, at academic conferences, through clinical briefing notes and patient-facing materials.

Supporting information

S1 Table. Description of health administrative data sources.

https://doi.org/10.1371/journal.pone.0288952.s001

(DOCX)

S2 Table. Sample size calculations.

https://doi.org/10.1371/journal.pone.0288952.s002

(DOCX)

References

  1. 1. Woodworth KR, Olsen EOM, Neelam V, Lewis EL, Galang RR, Oduyebo T, et al. Birth and infant outcomes following laboratory-confirmed SARS-CoV-2 infection in pregnancy—SET-NET, 16 jurisdictions, March 29–October 14, 2020. Morbidity and Mortality Weekly Report. 2020;69(44):1635. pmid:33151917
  2. 2. Wei SQ, Bilodeau-Bertrand M, Liu S, Auger N. The impact of COVID-19 on pregnancy outcomes: a systematic review and meta-analysis. Cmaj. 2021;193(16):E540–E8. pmid:33741725
  3. 3. Zambrano LD, Ellington S, Strid P, Galang RR, Oduyebo T, Tong VT, et al. Update: characteristics of symptomatic women of reproductive age with laboratory-confirmed SARS-CoV-2 infection by pregnancy status—United States, January 22–October 3, 2020. Morbidity and Mortality Weekly Report. 2020;69(44):1641. pmid:33151921
  4. 4. Joseph NT, Stanhope KK, Badell ML, Horton JP, Boulet SL, Jamieson DJ. Sociodemographic predictors of SARS-CoV-2 infection in obstetric patients, Georgia, USA. Emerging infectious diseases. 2020;26(11):2786. pmid:33050982
  5. 5. Burki T. The indirect impact of COVID-19 on women. The Lancet Infectious Diseases. 2020;20(8):904–5. pmid:32738239
  6. 6. Wenham C, Smith J, Davies SE, Feng H, Grépin KA, Harman S, et al. Women are most affected by pandemics—lessons from past outbreaks. Nature Publishing Group; 2020. pmid:32641809
  7. 7. Van Gelder N, Peterman A, Potts A, O’Donnell M, Thompson K, Shah N, et al. COVID-19: Reducing the risk of infection might increase the risk of intimate partner violence. EClinicalMedicine. 2020;21. pmid:32292900
  8. 8. Moreira DN, da Costa MP. The impact of the Covid-19 pandemic in the precipitation of intimate partner violence. International journal of law and psychiatry. 2020;71:101606. pmid:32768122
  9. 9. Piquero AR, Jennings WG, Jemison E, Kaukinen C, Knaul FM. Domestic violence during the COVID-19 pandemic-Evidence from a systematic review and meta-analysis. Journal of criminal justice. 2021;74(C). pmid:36281275
  10. 10. Santomauro DF, Herrera AMM, Shadid J, Zheng P, Ashbaugh C, Pigott DM, et al. Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. The Lancet. 2021. pmid:34634250
  11. 11. Racine N, Hetherington E, McArthur BA, McDonald S, Edwards S, Tough S, et al. Maternal depressive and anxiety symptoms before and during the COVID-19 pandemic in Canada: a longitudinal analysis. The Lancet Psychiatry. 2021;8(5):405–15. pmid:33773109
  12. 12. Qian Y, Fuller S. COVID-19 and the gender employment gap among parents of young children. Canadian Public Policy. 2020;46(S2):S89–S101.
  13. 13. Petts RJ, Carlson DL, Pepin JR. A gendered pandemic: Childcare, homeschooling, and parents’ employment during COVID‐19. Gender, Work & Organization. 2021;28:515–34.
  14. 14. Côté D, Durant S., MacEachen E., Majowicz S., Meyer S., Huynh A., et al. A rapid scoping review of COVID‐19 and vulnerable workers: Intersecting occupational and public health issues. American Journal of Industrial Medicine. 2021;64(7):551–66. pmid:34003502
  15. 15. Radusky PD, Cardozo N, Duarte M, Fabian S, Frontini E, Sued O, et al. Mental health, substance use, experiences of violence, and access to health care among transgender and non-binary people during the COVID-19 lockdown in Argentina. International Journal of Transgender Health. 2021:1–14.
  16. 16. Restar AJ, Jin H, Jarrett B, Adamson T, Baral SD, Howell S, et al. Characterising the impact of COVID-19 environment on mental health, gender affirming services and socioeconomic loss in a global sample of transgender and non-binary people: a structural equation modelling. BMJ Global Health. 2021;6(3):e004424. pmid:33753401
  17. 17. Rockliffe L, Peters S, Heazell AE, Smith DM. Factors influencing health behaviour change during pregnancy: A systematic review and meta-synthesis. Health psychology review. 2021;15(4):613–32. pmid:34092185
  18. 18. Canada Go. COVID-19 vaccine: Canadian Immunization Guide 2023 [cited 2023 January 16, 2023]. Available from: https://www.canada.ca/en/public-health/services/publications/healthy-living/canadian-immunization-guide-part-4-active-vaccines/page-26-covid-19-vaccine.html#a6.1.
    • 19. Abbasi J. Widespread misinformation about infertility continues to create COVID-19 vaccine hesitancy. JAMA. 2022;327(11):1013–5. pmid:35191947
    • 20. Hsu AL, Johnson T, Phillips L, Nelson TB, editors. Sources of vaccine hesitancy: pregnancy, infertility, minority concerns, and general skepticism. Open forum infectious diseases; 2022: Oxford University Press US.
      • 21. Sadarangani M, Soe P, Shulha H, Valiquette L, Vanderkooi O, Kellner J, et al. Safety of COVID-19 vaccines in pregnancy: a Canadian National Vaccine Safety (CANVAS) network cohort study. The Lancet Infectious Diseases. 2022;22(11):1553–64. pmid:35964614
      • 22. Watanabe A, Yasuhara J, Iwagami M, Miyamoto Y, Yamada Y, Suzuki Y, et al. Peripartum Outcomes Associated With COVID-19 Vaccination During Pregnancy: A Systematic Review and Meta-analysis. JAMA Pediatrics. 2022;176(11):1098–106. pmid:36190704
      • 23. Prasad S, Kalafat E, Blakeway H, Townsend R, O’Brien P, Morris E, et al. Systematic review and meta-analysis of the effectiveness and perinatal outcomes of COVID-19 vaccination in pregnancy. Nature Communications. 2022;13(2414).
      • 24. Halasa NB, Olson SM, Staat MA, Newhams MM, Price AM, Pannaraj PS, et al. Maternal Vaccination and Risk of Hospitalization for Covid-19 among Infants. New England Journal of Medicine. 2022;387(2):109–19. pmid:35731908.
      • 25. Castillo E, Poliquin V. No. 357-Immunization in Pregnancy. Journal of Obstetrics and Gynaecology Canada. 2018;40(4):478–89. pmid:29503251
      • 26. Advisory Committee Statement. Update on Immunization in Pregnancy with Tetanus Toxoid, Reduced Diptheria Toxoid and Reduced Acellular Pertussis (Tdap) Vaccine. Ottawa: Public Health Agency of Canada, 2018 February 2018. Report No.: Contract No.: 170472.
        • 27. Statistics Canada. Recommended vaccines received during pregnancy, 2021 2022 [cited 2023 January 16, 2023]. Available from: https://www150.statcan.gc.ca/n1/daily-quotidien/221213/dq221213a-eng.htm.
          • 28. BORN Ontario. COVID-19 Vaccination During Pregnancy in Ontario: December 14, 2020 to September 30, 2022 Ottawa, ON: BORN Ontario: Centre for Practice-Changing Research Building; 2022 [cited 2022 December 15, 2022]. Available from: https://www.bornontario.ca/en/whats-happening/resources/Documents/Report-6-_Accessible.pdf.
            • 29. Reifferscheid L, Marfo E, Assi A, Dubé E, MacDonald NE, Meyer SB, et al. COVID-19 vaccine uptake and intention during pregnancy in Canada. Canadian Journal of Public Health. 2022;113(4):547–58.
            • 30. Bhattacharya O, Siddiquea BN, Shetty A, Afroz A, Billah B. COVID-19 vaccine hesitancy among pregnant women: A systematic review and meta-analysis. BMJ open. 2022;12(8):e061477. pmid:35981769
            • 31. Kiefer M, Mehl R, Constantine M, Johnson A, Cohen J, Summerfield T, et al. Characteristics and perceptions associated with COVID-19 vaccination hesitancy among pregnant and postpartum individuals: A cross-sectional study. BJOG—An International Journal of Obstetrics and Gynaecology. 2022;129:1342–51. pmid:35104382
            • 32. Poliquin V, Greyson D, Castillo E. A Systematic Review of Barriers to Vaccination During Pregnancy in the Canadian Context. Journal of Obstetrics and Gynaecology Canada. 2019;41(9):1344–55. pmid:30361161
            • 33. Charlebois S, Music J. Covid-19 Well-being. Agri-Food Analytics Lab. Halifax, NS: Dalhousie University, 2021 April 27, 2021. https://www.dal.ca/sites/agri-food/research/covid-19-well-being.html. Accessed 2023-07-28.
              • 34. Dubé E, Gagnon D, Ouakki M, Bettinger JA, Witteman HO, MacDonald S, et al. Measuring vaccine acceptance among Canadian parents: A survey of the Canadian Immunization Research Network. Vaccine. 2018;36(4):545–52. pmid:29233605
              • 35. Mijović H, Greyson D, Gemmell E, Trottier M-È, Vivion M, Graham JE, et al. Perinatal health care providers’ approaches to recommending and providing pertussis vaccination in pregnancy: a qualitative study. CMAJ open. 2020;8(2):E377. pmid:32414884
              • 36. DesJardin M, Raff E, Baranco N, Mastrogiannis D. Cross-Sectional Survey of High-Risk Pregnant Women’s Opinions on COVID-19 Vaccination. Women’s Health Reports. 2022;3(1):608–16. pmid:35814609
              • 37. Rowe S, Karkhaneh Z, MacDonald I, Chambers T, Amjad S, Osornio-Vargas A, et al. Systematic review of the measurement properties of indices of prenatal care utilization. BMC pregnancy and childbirth. 2020;20(1):1–9. pmid:32183724
              • 38. Heaman MI, Martens PJ, Brownell MD, Chartier MJ, Derksen SA, Helewa ME. The association of inadequate and intensive prenatal care with maternal, fetal, and infant outcomes: A population-based study in Manitoba, Canada. Journal of Obstetrics and Gynaecology Canada. 2019;41(7):947–59.
              • 39. Zork NM, Aubey J, Yates H, editors. Conversion and optimization of telehealth in obstetric care during the COVID-19 pandemic. Seminars in Perinatology; 2020: Elsevier.
                • 40. Palmer KR, Tanner M, Davies-Tuck M, Rindt A, Papacostas K, Giles ML, et al. Widespread implementation of a low-cost telehealth service in the delivery of antenatal care during the COVID-19 pandemic: an interrupted time-series analysis. The Lancet. 2021;398(10294):41–52. pmid:34217399
                • 41. Bogler T, Bogler O. Interim schedule for pregnant women and children during the COVID-19 pandemic. Canadian Family Physician. 2020;66(5):e155–e61. pmid:32404468
                • 42. Khoury JE, Atkinson L., Bennett T., Jack S. M., & Gonzalez A. Prenatal distress, access to services, and birth outcomes during the COVID-19 pandemic: Findings from a longitudinal study. Early Human Development. 2022;170:105606. pmid:35728399
                • 43. Altman MR, Eagen‐Torkko MK, Mohammed SA, Kantrowitz‐Gordon I, Khosa RM, Gavin AR. The impact of COVID‐19 visitor policy restrictions on birthing communities of colour. Journal of Advanced Nursing. 2021;77(12):4827–35. pmid:34331371
                • 44. Brown J, Moore K, Keer E, Kane Low L. A Qualitative Study Focused on Maternity Care Professionals’ Perspectives on the Challenges of Providing Care During the COVID-19 Pandemic. The Journal of perinatal & neonatal nursing. 2022;36(1):46–54. pmid:35089177
                • 45. Kolker S, Biringer A, Bytautas J, Blumenfeld H, Kukan S, Carroll JC. Pregnant during the COVID-19 pandemic: an exploration of patients’ lived experiences. BMC Pregnancy and Childbirth. 2021;21(1):1–13.
                • 46. Duryea EL, Adhikari EH, Ambia A, Spong C, McIntire D, Nelson DB. Comparison between in-person and audio-only virtual prenatal visits and perinatal outcomes. JAMA Network Open. 2021;4(4):e215854–e. pmid:33852002
                • 47. Townsend R, Chmielewska B, Barratt I, Kalafat E, van der Meulen J, Gurol-Urganci I, et al. Global changes in maternity care provision during the COVID-19 pandemic: A systematic review and meta-analysis. EClinicalMedicine. 2021;37:100947. pmid:34195576
                • 48. Jarde A, Morais M, Kingston D, Giallo R, MacQueen GM, Giglia L, et al. Neonatal outcomes in women with untreated antenatal depression compared with women without depression: a systematic review and meta-analysis. JAMA psychiatry. 2016;73(8):826–37. pmid:27276520
                • 49. Jacques N, de Mola CL, Joseph G, Mesenburg MA, da Silveira MF. Prenatal and postnatal maternal depression and infant hospitalization and mortality in the first year of life: a systematic review and meta-analysis. Journal of affective disorders. 2019;243:201–8. pmid:30245252
                • 50. Tomfohr-Madsen LM, Racine N, Giesbrecht GF, Lebel C, Madigan S. Depression and anxiety in pregnancy during COVID-19: A rapid review and meta-analysis. Psychiatry Research. 2021:113912. pmid:33836471
                • 51. Lebel C, MacKinnon , Anna , Bagshawe , Mercedes , Tomfohr-Madsen , Lianne , Giesbrecht , Gerald . Elevated depression and anxiety symptoms among pregnant individuals during the COVID-19 pandemic. Journal of Affective Disorders. 2020;277:5–13. pmid:32777604
                • 52. Khoury JE, Atkinson L., Bennett T., Jack S. M., & Gonzalez A. COVID-19 and mental health during pregnancy: The importance of cognitive appraisal and social support. Journal of Affective Disorders. 2021a;282:1161–9. pmid:33601691
                • 53. Almeida M, Shrestha AD, Stojanac D, Miller LJ. The impact of the COVID-19 pandemic on women’s mental health. Archives of women’s mental health. 2020:1–8. pmid:33263142
                • 54. Fallon V, Davies SM, Silverio SA, Jackson L, De Pascalis L, Harrold JA. Psychosocial experiences of postnatal women during the COVID-19 pandemic. A UK-wide study of prevalence rates and risk factors for clinically relevant depression and anxiety. Journal of Psychiatric Research. 2021;136:157–66. pmid:33596462
                • 55. Frey BN, Vigod S, de Azevedo Cardoso T, Librenza-Garcia D, Favotto L, Perez R, et al. The Early Burden of Disability in Individuals With Mood and Other Common Mental Disorders in Ontario, Canada. JAMA Network Open. 2020;3(10):e2020213–e. pmid:33104205
                • 56. Healy P, Devane D. Methodological considerations in cohort study designs. Nurse researcher. 2011;18(3):32–6. pmid:21560924
                • 57. Watson P, McDonald B. Seasonal variation of nutrient intake in pregnancy: effects on infant measures and possible influence on diseases related to season of birth. European journal of clinical nutrition. 2007;61(11):1271–80. pmid:17299488
                • 58. McGrath JJ, Barnett AG, Eyles DW. The association between birth weight, season of birth and latitude. Annals of Human Biology. 2005;32(5):547–59. pmid:16316912
                • 59. Statistics Canada. Table 13-10-0414-01 Live births, by place of residence of mother. Ottawa: Government of Canada, 2021 Contract No.: https://doi.org/10.25318/1310041401-eng.
                  • 60. Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol. 1996;49(12):1373–9. Epub 1996/12/01. pmid:8970487.
                  • 61. Peduzzi P, Concato J, Feinstein AR, Holford TR. Importance of events per independent variable in proportional hazards regression analysis. II. Accuracy and precision of regression estimates. J Clin Epidemiol. 1995;48(12):1503–10. Epub 1995/12/01. pmid:8543964.
                  • 62. Neergaard MA, Olesen F, Andersen RS, Sondergaard J. Qualitative description–the poor cousin of health research? BMC medical research methodology. 2009;9(1):1–5. pmid:19607668
                  • 63. Sandelowski M. Whatever happened to qualitative description? Research in nursing & health. 2000;23(4):334–40. pmid:10940958
                  • 64. Sandelowski M. What’s in a name? Qualitative description revisited. Research in nursing & health. 2010;33(1):77–84.
                  • 65. Vanstone M, Taneja S, Popoola A, Panday J, Greyson D, McDonald SD. Why do people use cannabis during pregnancy and lactation? A qualitative study. Canadian Medical Association Journal. 2021;193(50):E1906–14.
                  • 66. Malterud K, Siersma VD, Guassora AD. Sample size in qualitative interview studies: guided by information power. Qualitative health research. 2016;26(13):1753–60. pmid:26613970
                  • 67. Hsieh H-F, Shannon SE. Three approaches to qualitative content analysis. Qualitative health research. 2005;15(9):1277–88. pmid:16204405
                  • 68. Elo S, Kyngäs H. The qualitative content analysis process. Journal of advanced nursing. 2008;62(1):107–15. pmid:18352969
                  • 69. Charmaz K. Constructing grounded theory: sage; 2014.
                  • 70. Jenkins EK, Slemon A, Haines-Saah RJ, Oliffe J. A guide to multisite qualitative analysis. Qualitative health research. 2018;28(12):1969–77. pmid:30024317
                  • 71. Creswell JW, Clark VLP. Designing and conducting mixed methods research: Sage publications; Thousand Oaks, CA. 2017.
                    • 72. Johnson RE, Grove AL, Clarke A. Pillar integration process: A joint display technique to integrate data in mixed methods research. Journal of Mixed Methods Research. 2019;13(3):301–20.