Experiences from the harmonization of Finnish national population-based health survey data.

Laura Paalanen,Hanna Tolonen

doi:10.1177/14034948211052164

Abstract

Aims: There are several advantages to pooling survey data from individual studies over time or across different countries. Our aim is to share our experiences on harmonizing data from 13 Finnish health examination surveys covering the years 1972–2017 and to describe the challenges related to harmonizing different variable types using two questionnaire variables – blood pressure measurement and total cholesterol assessment – as examples. Methods: Data from Finnish national population-based health surveys were harmonized as part of the research project ‘Projections of the Burden of Disease and Disability in Finland – Health Policy Prospects’, including variables from questionnaires, objective health measurements and results from the laboratory analysis of biological samples. The process presented in the Maelstrom Research guidelines for data harmonization was followed with minor adjustments. Results: The harmonization of data from objective measurements and biomarkers was reasonably straightforward, but questionnaire items proved more challenging. Some questions and response options had changed during the covered time period. This concerned, for example, questionnaire items on the availability and use of medication and diet. Conclusions: The long time period – 45 years – made harmonization more complicated. The survey questions or response options had changed for some topics due to changes in society. However, common core variables for topics that were especially relevant for the project, such as lifestyle factors and certain diseases or conditions, could be harmonized with sufficient comparability. For future surveys, the use of standardized survey methods and the proper documentation of data collection are recommended to facilitate harmonization.

Highlights

Pooling survey data from individual studies has several advantages, such as enhancing the statistical power of the analyses, obtaining more generalizable results, allowing cross-country comparisons and the possibility to analyze changes over time [1,2,3]
It imitated the steps of the Maelstrom Research guidelines, where the harmonization process proceeds from the first step of defining the research question, objectives and protocol to the final step of disseminating and preserving the final harmonization products [1]
The harmonization of variables, which are based on objective measurements performed by trained study nurses in the health examination part of a survey as well as biomarkers measured from blood, were fairly straightforward to harmonize

Summary

Introduction

Pooling survey data from individual studies has several advantages, such as enhancing the statistical power of the analyses, obtaining more generalizable results, allowing cross-country comparisons and the possibility to analyze changes over time [1,2,3]. The Maelstrom Research guidelines for retrospective data harmonization were published [1]. The guidelines include a step-by-step description of a harmonization process and examples of data processing models. The steps are as follows: (0) define the research question, objectives and protocol; (1) assemble pre-existing knowledge and select studies; (2) define targeted variables and evaluate the harmonization potential; (3) process the data; (4) estimate the quality of the harmonized dataset generated; and (5) disseminate and preserve the final harmonization products. The guidelines include essentials for successful harmonization. These include a collaborative framework, expert input, valid data input and output, rigorous documentation and respect for stakeholders. Potential pitfalls have been listed, such as defining a realistic but scientifically acceptable level of heterogeneity or content equivalence

Results

Discussion

Conclusion