Cohort Profile: Research Advancement through Cohort Cataloguing and Harmonization (ReACH)

A Scoping Review of the Relationship Between Maternal BMI and Offspring Incidence of Respiratory Infection: Where Do We Go From Here?

Introduction

Pregnancy complications, including high maternal BMI, are associated with altered early development and child health outcomes. A growing body of work links the prenatal environment, specifically maternal BMI, with respiratory infections in offspring. In this rapid review, the authors review the literature supporting the hypothesis that high maternal BMI during pregnancy is associated with childhood respiratory infection incidence.

Methods

The authors employed systematic search criteria in known databases-EMBASE, EMCARE, MEDLINE, CINAHL, and PsychINFO-searching from inception to January 2023. Included were primary research studies that involved (1) human pregnancy, (2) pregravid or gestational overweight or obesity, and (3) childhood respiratory infection with or without hospitalization.

Results

Only 7 population-based cohort studies met the criteria, investigating maternal BMI as an exposure and childhood respiratory infection as an outcome (age 6 months to 18 years). Therefore, the authors conducted a qualitative analysis, and outcomes were reported. The authors found that >85% of the albeit few published studies support the hypothesis that maternal BMI may have independent and profound consequences on respiratory infection risk across childhood.

Discussion

This area of research needs large-scale, well-controlled studies to better understand the relationship between maternal BMI and childhood respiratory infection. Possible resources such as cohort catalogs and combined databases are discussed. These findings add to the growing evidence that early environmental factors influence lifelong respiratory health. By incorporating a life course approach to infectious disease risk, policy makers can put this research to work and target health vulnerabilities before they arise.

Evaluating the harmonization potential of oral health-related questionnaires in national longitudinal birth and child cohort surveys

BACKGROUND/OBJECTIVES

Effective use of longitudinal study data is challenging because of divergences in the construct definitions and measurement approaches over time, between studies and across disciplines. One approach to overcome these challenges is data harmonization. Data harmonization is a practice used to improve variable comparability and reduce heterogeneity across studies. This study describes the process used to evaluate the harmonization potential of oral health-related variables across each survey wave.

METHODS

National child cohort surveys with similar themes/objectives conducted in the last two decades were selected. The Maelstrom Research Guidelines were followed for harmonization potential evaluation.

RESULTS

Seven nationally representative child cohort surveys were included and questionnaires examined from 50 survey waves. Questionnaires were classified into three domains and fifteen constructs and summarized by age groups. A DataSchema (a list of core variables representing the suitable version of the oral health outcomes and risk factors) was compiled comprising 42 variables. For each study wave, the potential (or not) to generate each DataSchema variable was evaluated. Of the 2100 harmonization status assessments, 543 (26%) were complete. Approximately 50% of the DataSchema variables can be generated across at least four cohort surveys while only 10% (n = 4) variables can be generated across all surveys. For each survey, the DataSchema variables that can be generated ranged between 26% and 76%.

CONCLUSION

Data harmonization can improve the comparability of variables both within and across surveys. For future cohort surveys, the authors advocate more consistency and standardization in survey questionnaires within and between surveys.

Stress and anxiety during pregnancy and length of gestation: a federated study using data from five Canadian and European birth cohorts

While its etiology is not fully elucidated, preterm birth represents a major public health concern as it is the leading cause of child mortality and morbidity. Stress is one of the most common perinatal conditions and may increase the risk of preterm birth. In this paper we aimed to investigate the association of maternal perceived stress and anxiety with length of gestation. We used harmonized data from five birth cohorts from Canada, France, and Norway. A total of 5297 pregnancies of singletons were included in the analysis of perceived stress and gestational duration, and 55,775 pregnancies for anxiety. Federated analyses were performed through the DataSHIELD platform using Cox regression models within intervals of gestational age. The models were fit for each cohort separately, and the cohort-specific results were combined using random effects study-level meta-analysis. Moderate and high levels of perceived stress during pregnancy were associated with a shorter length of gestation in the very/moderately preterm interval [moderate: hazard ratio (HR) 1.92 (95%CI 0.83, 4.48); high: 2.04 (95%CI 0.77, 5.37)], albeit not statistically significant. No association was found for the other intervals. Anxiety was associated with gestational duration in the very/moderately preterm interval [1.66 (95%CI 1.32, 2.08)], and in the early term interval [1.15 (95%CI 1.08, 1.23)]. Our findings suggest that perceived stress and anxiety are associated with an increased risk of earlier birth, but only in the earliest gestational ages. We also found an association in the early term period for anxiety, but the result was only driven by the largest cohort, which collected information the latest in pregnancy. This raised a potential issue of reverse causality as anxiety later in pregnancy could be due to concerns about early signs of a possible preterm birth.

Evaluating the harmonisation potential of diverse cohort datasets

Data discovery, the ability to find datasets relevant to an analysis, increases scientific opportunity, improves rigour and accelerates activity. Rapid growth in the depth, breadth, quantity and availability of data provides unprecedented opportunities and challenges for data discovery. A potential tool for increasing the efficiency of data discovery, particularly across multiple datasets is data harmonisation.A set of 124 variables, identified as being of broad interest to neurodegeneration, were harmonised using the C-Surv data model. Harmonisation strategies used were simple calibration, algorithmic transformation and standardisation to the Z-distribution. Widely used data conventions, optimised for inclusiveness rather than aetiological precision, were used as harmonisation rules. The harmonisation scheme was applied to data from four diverse population cohorts.Of the 120 variables that were found in the datasets, correspondence between the harmonised data schema and cohort-specific data models was complete or close for 111 (93%). For the remainder, harmonisation was possible with a marginal a loss of granularity.Although harmonisation is not an exact science, sufficient comparability across datasets was achieved to enable data discovery with relatively little loss of informativeness. This provides a basis for further work extending harmonisation to a larger variable list, applying the harmonisation to further datasets, and incentivising the development of data discovery tools.

Life course of retrospective harmonization initiatives: key elements to consider

Optimizing research on the developmental origins of health and disease (DOHaD) involves implementing initiatives maximizing the use of the available cohort study data; achieving sufficient statistical power to support subgroup analysis; and using participant data presenting adequate follow-up and exposure heterogeneity. It also involves being able to undertake comparison, cross-validation, or replication across data sets. To answer these requirements, cohort study data need to be findable, accessible, interoperable, and reusable (FAIR), and more particularly, it often needs to be harmonized. Harmonization is required to achieve or improve comparability of the putatively equivalent measures collected by different studies on different individuals. Although the characteristics of the research initiatives generating and using harmonized data vary extensively, all are confronted by similar issues. Having to collate, understand, process, host, and co-analyze data from individual cohort studies is particularly challenging. The scientific success and timely management of projects can be facilitated by an ensemble of factors. The current document provides an overview of the 'life course' of research projects requiring harmonization of existing data and highlights key elements to be considered from the inception to the end of the project.

A harmonized analysis of five Canadian pregnancy cohort studies: exploring the characteristics and pregnancy outcomes associated with prenatal alcohol exposure

BACKGROUND

As a teratogen, alcohol exposure during pregnancy can impact fetal development and result in adverse birth outcomes. Despite the clinical and social importance of prenatal alcohol use, limited routinely collected information or epidemiological data exists in Canada. The aim of this study was to pool data from multiple Canadian cohort studies to identify sociodemographic characteristics before and during pregnancy that were associated with alcohol consumption during pregnancy and to assess the impact of different patterns of alcohol use on birth outcomes.

METHODS

We harmonized information collected (e.g., pregnant women's alcohol intake, infants' gestational age and birth weight) from five Canadian pregnancy cohort studies to consolidate a large sample (n = 11,448). Risk factors for any alcohol use during pregnancy, including any alcohol use prior to pregnancy recognition, and binge drinking, were estimated using binomial regressions including fixed effects of pregnancy cohort membership and multiple maternal risk factors. Impacts of alcohol use during pregnancy on birth outcomes (preterm birth and low birth weight for gestational) were also estimated using binomial regression models.

RESULTS

In analyses adjusting for multiple risk factors, women's alcohol use during pregnancy, both any use and any binge drinking, was associated with drinking prior to pregnancy, smoking during pregnancy, and white ethnicity. Higher income level was associated with any drinking during pregnancy. Neither drinking during pregnancy nor binge drinking during pregnancy was significantly associated with preterm delivery or low birth weight for gestational age in our sample.

CONCLUSIONS

Pooling data across pregnancy cohort studies allowed us to create a large sample of Canadian women and investigate the risk factors for alcohol consumption during pregnancy. We suggest that future pregnancy and birth cohorts should always include questions related to the frequency and amount of alcohol consumed before and during pregnancy that are prospectively harmonized to support data reusability and collaborative research.

Data harmonization and data pooling from cohort studies: a practical approach for data management

Data pooling from pre-existing datasets can be useful to increase study sample size and statistical power in order to answer a research question. However, individual datasets may contain variables that measure the same construct differently, posing challenges for data pooling. Variable harmonization, an approach that can generate comparable datasets from heterogeneous sources, can address this issue in some circumstances. As an illustrative example, this paper describes the data harmonization strategies that helped generate comparable datasets across two Canadian pregnancy cohort studies: All Our Families; and the Alberta Pregnancy Outcomes and Nutrition. Variables were harmonized considering multiple features across the datasets: the construct measured; question asked/response options; the measurement scale used; the frequency of measurement; timing of measurement, and the data structure. Completely matching, partially matching, and completely un-matching variables across the datasets were determined based on these features. Variables that were an exact match were pooled as is. Partially matching variables were harmonized or processed under a common format across the datasets considering the frequency of measurement, the timing of measurement, the measurement scale used, and response options. Variables that were completely unmatching could not be harmonized into a single variable. The variable harmonization strategies that were used to generate comparable cohort datasets for data pooling are applicable to other data sources. Future studies may employ or evaluate these strategies, which permit researchers to answer novel research questions in a statistically efficient, timely, and cost-efficient manner that could not be achieved using a single data source.

How Many Health Research Biobanks Are There?

Introduction: It is important for many research stakeholders to know how many biobanks exist. There are several potential data sources that might be expected to provide biobank numbers, such as institutions, research funders, and literature databases (e.g., PubMed), but in practice this information is rarely available and is hard to find. However, the maturation of several online health research biobank locators (also known as directories and catalogs) that relate to 12 countries and/or states has now provided some initial data to address the question of how many health research biobanks exist in relation to population size. Methods: We have analyzed four biobank locators: the Biobanking and Biomolecular Resources Research Infrastructure-European Research Infrastructure Consortium directory, the Canadian Tissue Repository Network locator, the Australian New South Wales Australia Health Pathology locator, and the UK Clinical Research Collaboration Tissue Directory. Results: We conclude that across these locators, and in those regions with potential for high research capacity as indicated by comparable gross domestic products, there are 11-30 health research biobanks/million population (2 large biobanks with >1000 samples and a further 9-28 are medium-small biobanks). Conclusion: Many locators were established primarily to increase utilization of biobanks. However, locators may be more useful in tracking the numbers of biobanks and in assisting funders and institutions to monitor research strategy and prevent unnecessary duplication of biobank resources.

Protocol for the Pregnancy During the COVID-19 Pandemic (PdP) Study: A Longitudinal Cohort Study of Mental Health Among Pregnant Canadians During the COVID-19 Pandemic and Developmental Outcomes in Their Children

BACKGROUND

The COVID-19 pandemic and countermeasures implemented by governments around the world have led to dramatically increased symptoms of depression and anxiety. Pregnant individuals may be particularly vulnerable to the negative psychological effects of COVID-19 public health measures because they represent a demographic that is most affected by disasters and because pregnancy itself entails significant life changes that require major psychosocial and emotional adjustments.

OBJECTIVE

The PdP study was designed to investigate the associations among exposure to objective hardship caused by the pandemic, perceived stress and psychological distress in pregnant individuals, and developmental outcomes in their offspring.

METHODS

The PdP study comprises a prospective longitudinal cohort of individuals who were pregnant at enrollment, with repeated follow-ups during pregnancy and the postpartum period. Participants were eligible if they were pregnant, ≥17 years old, at ≤35 weeks of gestation at study enrollment, living in Canada, and able to read and write in English or French. At enrollment, participants completed an initial survey that assessed demographic and socioeconomic characteristics, previous pregnancies and births, prepregnancy health, health conditions during pregnancy, medications, psychological distress, social support, and hardships experienced because of the COVID-19 pandemic (eg, lost employment or a loved one dying). For the first three months following the initial survey, participants received a monthly email link to complete a follow-up survey that asked about their experiences since the previous survey. After three months, follow-up surveys were sent every other month to reduce participant burden. For each of these surveys, participants were first asked if they were still pregnant and then routed either to the next prenatal survey or to the delivery survey. In the postpartum period, surveys were sent at 3, 6, and 12 months of infant age to assess maternal stress, psychological distress, and infant development.

RESULTS

Participant recruitment via social media (Facebook and Instagram) began on April 5, 2020, and is ongoing. As of April 2021, more than 11,000 individuals have started the initial survey. Follow-up data collection is ongoing.

CONCLUSIONS

This longitudinal investigation seeks to elucidate the associations among hardships, maternal psychological distress, child development during the COVID-19 pandemic, and risk and resilience factors that amplify or ameliorate these associations. The findings of this study are intended to generate knowledge about the psychological consequences of pandemics on pregnant individuals and point toward prevention and intervention targets.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/25407.