ArticlePloS one2018
Trend analysis for national surveys: Application to all variables from the Canadian Health Measures Survey cycle 1 to 4.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Extended Joinpoint Regression Methodology for Complex Survey Data.Statistics in medicine · 2026Article
- Prenatal alcohol use effects on birth, development, and educational outcomes in the Aboriginal and non-Aboriginal populations in the Northern Territory of Australia: a study protocol.BMC pregnancy and childbirth · 2025Article
- Impacts of Homecare Investments in Alberta: Ecological and Economic Trend Analysis.Healthcare policy = Politiques de sante · 2025Article
- Availability and readiness of the health facilities to provide HIV counseling and testing and prevention of mother-to-child transmission services in Burkina Faso: a trend analysis from 2012 to 2018.BMC health services research · 2023Article
- Trend of metabolic risk factors among the population aged 25-64 years for non-communicable diseases over time in Vietnam: A time series analysis using national STEPs survey data.Frontiers in public health · 2022Article
- Article
- Opportunities and Challenges From Leading Trends in a Biomonitoring Project: Canadian Health Measures Survey 2007-2017.Frontiers in public health · 2020Article
- Article
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4 authors.
Funding
Abstract
backgroundTrend 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
methodsThe 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.
resultsThere 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.
conclusionTrend 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.
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