Risk adjustment and observation time: comparison between cross-sectional and 2-year panel data from the Medical Expenditure Panel Survey (MEPS)

et al. 2018

Front. Public Health

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IntroductionThe stages of biological development are not clearly defined despite the fact that they have been used to refer to concepts such as adolescence and aging. This study aimed to (1) propose and test a framework to search for stages of representative components and determine stages of stability and transition, (2) identify stages of biological development based on health questionnaire and biomarker data, and (3) interpret the major trajectories in a health and biomarker database.MethodsThis study analyzed the data on the Canadian Health Measures Survey (CHMS) interviewees from cycle 1 to 3 (2007–2013) in Canada. We selected 282 variables containing information from questionnaire and on biomarkers after removing redundant variables based on high correlation. Fifty-nine nominal variables were replaced by 122 binominal variables, leaving 345 variables for analysis. Principal component (PC) analysis was conducted to summarize the data and the loadings were used to interpret the PCs. A stable stage was assumed to be the age groups without significantly different values of PCs.ResultsThe CHMS interviewed 16,340 Canadians. Of all, 51.25% were female. The age ranged from 6 to 79 years (mean = 34.41 years, 95% CI = 34.74–34.08). The proportions of total variance explained by the first three PCs were 12.14, 4.03, and 3.19%, respectively. The differences of the first PC were not significant, especially between age 22 and 33, 34 and 40, 41 and 45, 46 and 71, and 72 and 79 years (adjusted p > 0.05 for all). The leading variable, in terms of the variance contributed to PC1, was time spent in physical activities, followed by variables related to alcohol consumption, and smoking. The 13 leading contributors to PC2 variances were all lung function measures.Discussion and conclusionThere are stages of stability and transition across all age groups based on the first PCs. The first and second PCs are related to physical development and lung function. The identification of stable stages is the first step to understanding how human biology develops in a population perspective and will be important for research that relies on a research population with similar characteristics to draw samples for observation or intervention.

Section: Discussionmentioning

confidence: 64%

Stages of Biological Development across Age: An Analysis of Canadian Health Measure Survey 2007–2011

et al. 2018

Front. Public Health

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“…This research framework can be extended to other major surveys with similar data structure, variable naming systems, missing value identification strategies and sampling frames, especially the Canadian Community Health Survey[ 48 , 56 ]. For other major surveys that provide cleaned data[ 61 ] or do not use bootstrap weights[ 35 ], it requires minimal revision to replicate this research framework to conduct trend analysis for all variables. The automated process for visualization of trend analysis is suggested for researchers to look for neglected trends and for survey administrators to search and correct data errors that can be demonstrated with trends of extreme rates of change across cycles or time points.…”

Section: Discussionmentioning

confidence: 99%

“…The values ending in 4, 5, 6, 7, 8, and 9 might represent “values higher than limits of detection”, “values less than limits of detection”, “not applicable”, “don’t know”, “refusal” and “not stated”[ 30 , 31 , 33 ]. For other surveys, missing values might be represented with certain values[ 34 ] or be coded with reserve values, such as -1 to -3[ 35 ]. To prevent computer memory from being exhausted, the data sets were always removed from the memory if unused.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2018

PLoS ONE

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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.

“…[16] The degrees of freedom were specified as required. [16, 18, 20, 24–28] Data processing and statistical analysis were conducted with R (v3.20)[29] and RStudio (v0.98.113). [30]…”

Section: Methodsmentioning

confidence: 99%

Drug trends among non-institutionalized Canadians and the impact of data collection changes in the Canadian Health Measures Survey 2007 to 2015

et al. 2019

PLoS ONE

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Background There is a global trend of increasing use in prescription and over-the-counter (OTC) drugs. This hasn’t been verified in Canada. In addition, there are changes made to the collection method of medication information after the Canadian Health Measures Survey (CHMS) cycle 2. This study aims to review the potential impact of the changes in medication data collection and the trends in medication use if data quality remains similar throughout the CHMS cycles 1 to 4. This is fundamental for the analysis of this biomonitoring database. Methods The CHMS cycle 1 to 4 medication and household data were used to study the trends of medication use between 2007 and 2015. The use of prescription or OTC drugs was grouped based on the first levels of the Anatomical Therapeutic Chemical (ATC) Classification system. The total numbers of medications were asked in all cycles. However, only a maximum of 15 and 5 drugs could be respectively reported for existing and new prescription or OTC drugs in cycles 1 and 2. There were no restrictions on drug reporting after cycle 2. The trends of medication use were described as ratios, compared to cycle 1. Results The total numbers of the types of medication ever identified decreased from 739 to 603 between cycles 1 and 4. The proportions of using any drugs were from 0.90 to 0.88 between cycles 1 and 4 (ratio = 1.08 in cycle 4, 95% CI = 0.89 to 1.26). The numbers of drugs in use were from 3.9 to 3.8 (ratio = 1.05 in cycle 4, 95% CI = 0.86 to 1.24). The proportions of prescription drug use were from 0.53 to 0.55 (ratio = 1.13 in cycle 4, 95% CI = 0.89 to 1.37), while the numbers of prescription were from 1.51 to 1.68 (ratio = 1.20 in cycle 4, 95% CI = 0.92 to 1.48). The use of diabetes and thyroid medication had trends similar to the respective disease prevalence. The use and the numbers of drugs for blood and blood forming organs significantly increased between cycles 1 and 4 (ratio = 1.56 in cycle 4, 95% CI = 1.03 to 2.10). Conclusions There is an increasing trend in the use of blood and blood forming agents through cycles 2 to 4 and cardiovascular drugs in cycle 3. For diabetes and thyroid medication, the proportions of medication use increase proportionally with disease prevalence. The changes in the medication information collection method may not have important impact on the reporting of the use of prescription or OTC drugs.