Data pooling from multiple studies into a single dataset provides opportunities to increase the statistical power of a study and to answer novel research questions that could not be addressed using data from a single study [1, 2]. Data pooling from existing data sources allows investigators to conduct research more rapidly and at a lower cost than primary data collection would allow, providing opportunities for timely translation of knowledge into practice.

Individual datasets from different studies or data sources often measure the same construct differently, which poses challenges for data pooling. These challenges are addressed by data harmonization. Data harmonization refers to efforts that provide comparability of datasets from heterogeneous sources and allows for combining, pooling, or integrating them in a coherent way [3].

Data harmonization can take a prospective or retrospective approach. Prospective data harmonization occurs at the initial stage of study design, or at least before data collection. For this, investigators agree on a common core set of variables or measures, compatible data collection tools, and standard operating procedures, often leading to a high degree of homogeneity [3, 4]. Retrospective harmonization is a flexible approach, which targets the synthesis of already-collected information. For this, researchers define a core set of variables, and then assess the compatibility of information collected and the potential for creating single harmonized variables. If harmonization is possible, strategies for data processing are developed [3–6].

Data harmonization is particularly valuable when the outcome and/or risk factor is rare, since examining interactions among risk factors and investigating population subgroups requires a large sample size to ensure adequate study power. It is not always feasible to accomplish this with primary data collection from a single study given the resources required. Additionally, measurement of the same construct using multiple measurement scales is generally unreasonable or unfeasible for a single study unless the primary aim of the study is to compare the results from the multiple scales.

The use of multiple existing datasets from the studies that were conducted in similar target populations using comparable methodologies but different measurement scales can address these issues. However, data harmonization (in this case, retrospective) involves extensive data processing or data cleaning and management and variable transformation processes. While these processes are critical [6], the literature or guidelines on how to do this remain limited [6].

Our research project aimed to improve the understanding of risk factors for preterm birth using data from two pregnancy cohort studies conducted in Alberta, Canada–All Our Families (AOF: n = 3,351) and Alberta Pregnancy Outcomes and Nutrition (APrON: n = 2,187) [7–10]. Specifically, our research intended to develop and validate a prediction model for preterm birth, to evaluate the suitability of and comparability of multiple anxiety scales to measure anxiety during pregnancy, and to examine if neighborhood socioeconomic status modified the association between anxiety and/or depression status during pregnancy and preterm birth [11–13]. Achieving these goals required data harmonization.

This paper describes the data harmonization strategies that helped generate comparable datasets across these two studies to address our research objectives. It presents examples of data harmonization strategies that were used to generate comparable datasets. These strategies may be employed or evaluated in subsequent studies, and may serve as useful starting points for other projects.

A total of 20 variables were considered for harmonization, and of those, 18 variables (90.0%) were successfully harmonized. Of 20 variables, three variables (15.0%) were completely matching and 14 (70.0%) were partially matching. These variables were successfully harmonized across the datasets and pooled/combined (i.e., appended into a single variable). One variable (5.0%) was completely unmatching across the datasets due to the different measurement scales used to measure the same construct, this variable was harmonized across the datasets for the purpose of data merging (i.e., pooling data as two different variables). Two variables (10.0%) were only available in one dataset; thus, variable harmonization was not applicable (Supplement Table 2). Characteristics (or distribution) of participants across the studies were similar in harmonized data, except drug abuse and smoking status (Supplementary Table 3). There were discrepancies in the proportion of missing data for some variables, particularly body mass index and gestational age at delivery. These discrepancies also existed in the original datasets; thus, they were not related to the data harmonization process.

Several partially matching variables such as marital status, ethnicity, income, parity, depression, and smoking were successfully harmonized (Supplement Table 2). For example, one variable, current marital status, was partially identical across the datasets as the construct measured (or question asked) was completely identical across both datasets but the variable response categories and the value level coding were different across the datasets. As the variable response categories were collapsible to identical and meaningful categories across the datasets, the variable response was re-organized into three identical categories in both datasets. Another variable, depression symptoms during pregnancy - which was measured in both datasets using the same scale, the EPDS - was not compatible in terms of frequency of measurement and gestational age at each measurement. Accordingly, the depression variables were harmonized by creating three unique variables in each dataset that indicated the depression score in each trimester of pregnancy.

Similarly, the EPDS-3A-based anxiety variables, which were made by extracting data on three items of the EPDS, were harmonized by creating three unique variables in each dataset that indicated the anxiety score in each trimester of pregnancy. The anxiety variables measured by two different anxiety scales (i.e., STAI-20 and SCL-90) were harmonized by creating three unique variables in each dataset that indicated the anxiety score (measured by different scales across the datasets) in each trimester of pregnancy (Table 1).

The harmonized combined cohort dataset (n = 5,538) contained several important variables, including maternal age, gestational age at delivery, marital status, ethnicity, duration of stay in Canada, body mass index, parity, smoking, and anxiety (measured by EPDS-3A), and depression during pregnancy for each trimester. Additionally, variables that were important for our research but were only available in one of the datasets (previous preterm birth and prenatal care visits) or measured by different anxiety measurement scales (anxiety during pregnancy) were included in the combined dataset.

The anxiety data measured by two different scales across the datasets were pooled as two different variables, with missing values recorded for measures on the scale not included in the original study. Anxiety data or values were available for both anxiety-related variables for participants who participated in both cohort studies (overlapping study cohort, n = 231) (Table 1). Similarly, the combined dataset contained missing values for the cohort with no measurement of previous preterm birth and prenatal care visits variables.

This study describes data harmonization strategies, which helped create comparable datasets across two cohort studies and enabled the datasets pooling. The combined dataset created unique research opportunities to answering our clinically relevant research questions, by providing a large sample size (thus increased study power and efficiency), additional variables, and data measured by multiple different scales [11–13]. The use of the harmonized, combined dataset facilitated statistical analysis to answer our research questions and added comprehensiveness to our research, which would have been less feasible using either of the datasets alone.

For example, the large sample size provided an opportunity to analyze the risk of preterm birth (relatively a rare outcome) across the several strata of risk factors, such as anxiety alone, depression alone, and both anxiety and depression and their stratification across socioeconomic variables [13]. Similarly, we evaluated the performance of multiple anxiety scales in measuring anxiety during pregnancy: the suitability of STAI- 20 and SCL-90 anxiety screening scales in the individual study cohort and the comparability of these scales (correlation between the anxiety scores measured by two scales) restricting our analysis in the overlapping study cohort [12]. We performed analyses including those variables that were available in both datasets [11–13]. We also performed sensitivity analyses using the additional variables available in one dataset [11–13].

The harmonized data are stored in a secure data repository (SAGE - Secondary Analysis to Generate Evidence) which also houses the cohort-specific datasets. The dataset may be available upon request from the AOF and APrON data custodians. The harmonization strategies described are applicable to generate comparable data across administrative databases, survey cycles, jurisdictions (provincial, national or international), and measures repeated over time. However, the strategies may not be necessarily directly applicable to different contexts, such as harmonizing data from a larger number of studies or data sources. Heterogeneity across studies or datasets becomes more persistent and data harmonization process becomes complex as the number of datasets or data sources increases. Nevertheless, it may be worthwhile to evaluate the utility and applicability of these strategies in subsequent studies.

The success or the scientific impact of any data harmonization and integration research project depends on the quality of the data harmonization process, the quality of the information collected by the primary studies, and the ability to access the data collected [1–6, 17]. Hence, a series of procedures should be considered as a part of the data harmonization and synthesis initiatives to ensure the quality and validity of the harmonized databases created. To illustrate, the potential to harmonize and integrate information depends on homogeneity across a range of study-specific factors. These include the study design, target population, time period, and duration of follow-up; the type of information and samples collected; the specific tools and standard operating procedures used to collect or generate data; and the data coding and data management systems employed. The incompatibility of these study-specific factors can affect whether variables recorded in different data sources are actually measuring the same construct. Access to documentation from the primary studies, dialogue with their research teams, and preliminary exploration of the dataset before the actual harmonization allow researchers to understand the level of substantive heterogeneity across studies [5, 6]. These strategies ultimately facilitate the selection of variables to be harmonized or combined and helps decide harmonization strategies to be employed.

Additionally, agreement on data access and intellectual property from each study and ethical approval must be obtained before data harmonization. Finally, it is important that the person(s) involved in data harmonization always create new files for the harmonization and document the harmonization process [17]. This facilitates the evaluation of the data harmonization process and reproducibility.

While the need for additional statistical power has often led investigators to employ data harmonization and data pooling, there are several other benefits as well [2–4]. These include increased use of existing data, strengthening the scientific impact of individual studies, and optimal return on investments. To illustrate, compared to building new studies involving thousands of participants, employing data harmonization on existing data can permit the generation of research projects relatively rapidly and at a lower cost, with timely knowledge translation opportunities. This also allows researchers to properly explore similarities and differences across time and place. Ultimately, data harmonization initiatives leverage national and international collaborations, facilitates the emergence of leading-edge collaborative and cross-disciplinary research initiatives and innovations, and thereby minimizes the duplication of research efforts [21].

While data harmonization is an important component in research, its application (harmonization process and harmonized data) has some challenges and limitations. Recent publications provide high-level guideline on harmonization [5, 6], but literature on how to perform data processing and evaluate harmonization quality (practical approaches) is limited [5, 6]. The data harmonization process is resource intensive. It involves a repetitive/iterative and time-consuming process, requires thorough preparatory work, and has many elements that must be worked through carefully and systematically with rigorous documentation.

To illustrate, data processing and integration in a systematic manner requires a comprehensive understanding of previous studies (study-specific designs, standard operating procedures, data collection devices, data format and data content, and quality of study-specific data) and requires research content knowledge and analytical skills [5, 6]. Even if harmonization procedures (variable selection and pairing rules definition and data processing) are done under the consensus and advice from experts, there is inevitably an element of subjectivity in harmonization procedures. Evaluation of the quality of the harmonized data is required to understand its scientific performance [6]. At least two independent individuals are needed to evaluate inter-coder agreement with regard to their data harmonization procedures or processes (such as Cohen’s k statistic) [5]. Furthermore, data harmonization may lead to limited use of information (in terms of the number of variables, variable categories) collected by specific primary studies.

For example, in our research context, maternal ethnicity and household income variables were categorized differently across datasets, broad categories vs. specific categories. Using harmonized data, we had to analyze the data by broad categories. We also had to analyze the anxiety data on the subsample. In contrast, the use of a single study or dataset is less resource-intensive, with more flexibility on using the information collected by primary studies, but has other limitations as described. Additionally, the harmonization strategies used in one context may not necessarily be directly applicable to different contexts due to the variation in heterogeneities, such as large number of studies or data sources and heterogeneous target population and data collections and management systems across studies.

Data harmonization is an important aspect of conducting research using multiple datasets. It generates comparable data across different data sources and facilitates pooling of relevant data across data sources, leading to unique opportunities for research. Data harmonization and pooling augment the utility and scientific impact of existing data or individual studies, creates a collaborative research environment, minimize the duplication of research, and increase research feasibility. Hence, data harmonization is a very promising avenue to support advancement in population health research that can result in improvements to the health and well-being of populations.

Ethics approval for this study was obtained from the Conjoint Health Research Ethics Board at the University of Calgary (REB16-2548). This study used secondary data and all the data were anonymized; therefore, did not require informed consent.