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Trend analysis for national surveys: Application to all variables from the Canadian Health Measures Survey cycle 1 to 4

TitleTrend analysis for national surveys: Application to all variables from the Canadian Health Measures Survey cycle 1 to 4
Year of Publication2018
AuthorsChao, Y-S., Wu C-J., Wu H-C., and Chen W-C.
JournalPLoS ONE
Volume13
Pages1 - 15
Keywordsadult, age factors, aged, blood pressure, body mass index, canada, data interpretation, environmental biomarkers, female, health surveys/*trends, humans, male, middle aged, sex factors, socioeconomic factors, statistical, time factors
Abstract

Background Trend analysis summarizes patterns over time in the data to show the direction of change and can be used to investigate uncertainties in different time points and associations with other factors. However, this approach is not widely applied to national surveys and only selected outcomes are investigated. This study demonstrates a research framework to conduct trend analysis for all variables in a national survey, the Canadian Health Measures Survey (CHMS). Data and methods The CHMS cycle 1 to 4 was implemented between 2007 and 2015. The characteristics of all variables were screened and associated to the weight variables. Missing values were identified and cleaned according to the User Guide. The characteristics of all variables were extracted and used to guide data cleaning. Trend analysis examined the statistical significance of candidate predictors: the cycles, age, sex, education, household income and body mass index (BMI). R (v3.2) and RStudio (v0.98.113) were used to develop the framework. Results There were 26557 variables in 79 data files from four cycles. There were 1055 variables significantly associated with the CHMS cycles and 2154 associated with the BMI after controlling for other predictors. The trend of blood pressure was similar to those published. Conclusion Trend analysis for all variables in the CHMS is feasible and is a systematic approach to understand the data. Because of trend analysis, we have detected data errors and identified several environmental biomarkers with extreme rates of change across cycles. The impact of these biomarkers has not been well studied by Statistics Canada or others. This framework can be extended to other surveys, especially the Canadian Community Health Survey.

URLhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6084849/
Document URLhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6084849/pdf/pone.0200127.pdfhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6084849/epub/