Suchergebnisse

IntroductionPooling data from cohort studies can be used to increase sample size. However, individual datasets may contain variables that measure the same construct differently, posing challenges in the usefulness of combined datasets. Variable harmonization (an effort that provides comparable view of data from different studies) may address this issue.
Objectives and ApproachThis study harmonized existing datasets from two prospective pregnancy cohort studies in Alberta Canada (All Our Families (n=3,351) and Alberta Pregnancy Outcome and Nutrition (n=2,187)). Given the comparability of the characteristics of the two cohorts and similarities of the core data elements of interest, data harmonization was justifiable. Data harmonization was performed considering multiple factors, such as complete or partial variable matching regarding question asked/responded, the response coded (value level, value definition, data type), the frequency of measurement, the pregnancy time-period of measurement, and missing values. Multiple imputation was used to address missing data resulting from the data harmonization process.
ResultsSeveral variables such as ethnicity, income, parity, gestational age, anxiety, and depression were harmonized using different procedures. If the question asked/answered and the response recorded was the same in both datasets, no variable manipulation was done. If the response recorded was different, the response was re-categorized/re-organized to optimize comparability of data from both datasets. Missing values were created for each resulting unmatched variables and were replaced using multiple imputation if the same construct was measured in both datasets but using different ways/scales. A scale that was used in both datasets was identified as a reference standard. If the variables were measured in multiple times and/or different time-periods, variables were synchronized using pregnancy trimesters data. Finally, harmonized datasets were then combined/pooled into a single dataset (n=5,588).
Conclusion/ImplicationsVariable harmonization is an important aspect of conducting research using multiple datasets. It provides an opportunity to increase study power through maximizing sample size, permitting more sophisticated statistical analyses, and to answer novel research questions that could not be addressed using a single study.

Data pooling from pre-existing multiple datasets can be useful to increase study sample size and statistical power 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– the 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 synchronized across the datasets considering the frequency of measurement, the timing of measurement, 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. Variable harmonization and pooling provide an opportunity to increase study power and the utility of existing data, permitting 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.

Objectives: Our aim is to understand the effect of the COVID-19 pandemic on families who have been followed longitudinally in two cohorts studied in Alberta, Canada. We will examine household infections during the COVID-19 pandemic, financial impact, domestic violence, substance use, child school and daily life and relationships in the home. We will identify risk and protective factors for maternal mental health outcomes using longitudinal data that can inform policy and government resource allocation in future disasters. Methods: Mothers who are currently participating in two longitudinal studies, Alberta Pregnancy Outcomes and Nutrition (APrON; N = 1,800) and All Our Families (AOF: N = 2,534) were eligible to participate. Mothers were invited to complete the baseline COVID-19 Impact Survey (20–30 min) within 4 months of March 15, 2020, which was when the province of Alberta, Canada, implemented school closures and physical-distancing measures to prevent the spread of COVID-19. Mothers were asked to report on their own, their child's and their family's functioning. Mothers were re-surveyed at 6 months after completion of the initial COVID-19 Impact Survey, and will be re-surveyed again at 12 months. Results: Responses from participants in both cohorts will be examined in harmonized analyses as well as separately. Descriptive, multivariable analysis will be undertaken to examine risk and resiliency over time and factors that predict mental health and well-being. Conclusions: This study will provide timely information on the impact of COVID-19 for Albertan families. It will identify risk and protective factors for mental health and well-being among contemporary urban families supported by a publicly funded health care system to inform allocation of resources to support those most vulnerable during a global pandemic.