An overview of the statistical methods reported by studies using the Canadian community health survey

BMC Medical Research Methodology

Scott B Patten¹  Daniel J Dutton¹  Dean W Yergens¹ 

[1] Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada

关键词: Canadian community health survey;    Canada;    Population research;    Health survey;    Statistical methods;    Health statistics;   

Others  :  866475
DOI  :  10.1186/1471-2288-14-15

 received in 2013-08-07, accepted in 2014-01-23,  发布年份 2014

【 摘 要 】

Background

The Canadian Community Health Survey (CCHS) is a cross-sectional survey that has collected information on health determinants, health status and the utilization of the health system in Canada since 2001. Several hundred articles have been written utilizing the CCHS dataset. Previous analyses of statistical methods utilized in the literature have focused on a particular journal or set of journals to understand the statistical literacy required for understanding the published research. In this study, we describe the statistical methods referenced in the published literature utilizing the CCHS dataset(s).

Methods

A descriptive study was undertaken of references published in Medline, Embase, Web of Knowledge and Scopus associated with the CCHS. These references were imported into a Java application utilizing the searchable Apache Lucene text database and screened based upon pre-defined inclusion and exclusion criteria. Full-text PDF articles that met the inclusion criteria were then used for the identification of descriptive, elementary and regression statistical methods referenced in these articles. The identification of statistical methods occurred through an automated search of key words on the full-text articles utilizing the Java application.

Results

We identified 4811 references from the 4 bibliographical databases for possible inclusion. After exclusions, 663 references were used for the analysis. Descriptive statistics such as means or proportions were presented in a majority of the articles (97.7%). Elementary-level statistics such as t-tests were less frequently referenced (29.7%) than descriptive statistics. Regression methods were frequently referenced in the articles: 79.8% of articles contained reference to regression in general with logistic regression appearing most frequently in 67.1% of the articles.

Conclusions

Our study shows a diverse set of analysis methods being referenced in the CCHS literature, however, the literature heavily relies on only a subset of all possible statistical tools. This information can be used in identifying gaps in statistical methods that could be applied to future analysis of public health surveys, insight into training and educational programs, and also identifies the level of statistical literacy needed to understand the published literature.

【 授权许可】

   
2014 Yergens et al.; licensee BioMed Central Ltd.

【 参考文献 】

• [1]Statistics Canada, Statistics Division: Canadian Community Health Survey (CCHS) Cycle 3.1 (2005) - Public Use Microdata File (PUMF) User Guide. 2006.
• [2]Statistics Canada: Data Liberation Initiative (DLI). 2013. http://www.statcan.gc.ca/dli-idd/dli-idd-eng.htm webcite, Date modified: 2013-05-03
• [3]Windish DM, Huot SJ, Green ML: Medicine residents' understanding of the biostatistics and results in the medical literature. JAMA 2007, 298(9):1010-1022.
• [4]Becker PJ, Viljoen E, Wolmarans L, IJsselmuiden CB: An assessment of the statistical procedures used in original papers published in the SAMJ during 1992. South African medical journal = Suid-Afrikaanse tydskrif vir geneeskunde 1995, 85(9):881-884.
• [5]Scotch M, Duggal M, Brandt C, Lin Z, Shiffman R: Use of statistical analysis in the biomedical informatics literature. JAMIA 2010, 17(1):3-5.
• [6]Rao MH, Khan N: Comparison of statistical methods, type of articles and study design used in selected Pakistani medical journals in 1998 and 2007. JPMA 2010, 60(9):745-750.
• [7]Vahanikkila H, Nieminen P, Miettunen J, Larmas M: Use of statistical methods in dental research: comparison of four dental journals during a 10-year period. Acta Odontol Scand 2009, 67(4):206-211.
• [8]Selvin S, White MC: Description and reporting of statistical methods. Am J Infect Control 1993, 21(4):210-215.
• [9]Yergens D, Ray J, Doig CJ: KSv2: Application for Enhancing Scoping and Systematic Reviews. 2012. [American Medical Informatics Association (AMIA) 2012 Annual Symposium] November 2012. (poster)
• [10]McCandless M, Hatcher E, Gospodnetic O: Lucene in action. 2nd edition. Stanford, CT USA: Manning Publications Co; 2010.
• [11]Spat S, Cadonna B, Rakovac I, Gutl C, Leitner H, Stark G, Beck P: Enhanced information retrieval from narrative German-language clinical text documents using automated document classification. Stud Health Technol Inform 2008, 136:473-478.
• [12]Cuff AL, Sillitoe I, Lewis T, Clegg AB, Rentzsch R, Furnham N, Pellegrini-Calace M, Jones D, Thornton J, Orengo CA: Extending CATH: increasing coverage of the protein structure universe and linking structure with function. Nucleic Acids Res 2011, 39(Database issue):D420-D426.
• [13]RStudio (2012): RStudio: Integrated development environment for R (Version 0.97.336). Boston, MA: Computer software; 2012. http://www.rstudio.org webcite
• [14]R Core Team: R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2013. http://www.r-project.org/ webcite
• [15]Shields M, Gorber SC, Janssen I, Tremblay MS: Bias in self-reported estimates of obesity in Canadian health surveys: an update on correction equations for adults. Health Rep 2011, 22(3):35-45.
• [16]Feng Y, Bernier J, McIntosh C, Orpana H: Validation of disability categories derived from Health Utilities Index Mark 3 scores. Health Rep 2009, 20(2):43-50.
• [17]Kitchen P, Williams A, Pong RW, Wilson D: Socio-spatial patterns of home care use in Ontario, Canada: a case study. Health Place 2011, 17(1):195-206.
• [18]Garriguet D: Diet quality in Canada. Health Rep 2009, 20(3):41-52.
• [19]Patten SB, Gordon-Brown L, Meadows G: Simulation studies of age-specific lifetime major depression prevalence. BMC Psychiatry 2010, 10:85. BioMed Central Full Text