Objectives. To estimate the prevalence and experience of dental caries among 15-year-old adolescents in north-west Russia between 2007 and 2008. Study design. A cross-sectional study. Methods. In total, 352 adolescents at the age of 15 were selected at random from 3 urban and 4 rural areas in the Arkhangelsk region. Girls comprised 53.4% of the sample. Caries experience was assessed at D3 level by a single calibrated examiner and was estimated as a sum of decayed, missing and filled teeth (DMFT). Results. The prevalence of caries was 91.8% with a mean DMFT of 4.92. On average, there were 2.61 decayed, 0.13 missing and 2.18 filled teeth per participant. No gender differences in the prevalence of caries in any of the settings or in the full sample were observed. In urban areas, the average number of decayed teeth was lower (2.15 vs. 2.95, p=0.006), while the number of filled teeth was greater (2.71 vs. 1.79, p<0.001) than in rural areas. Conclusions. Under assumption of the representativeness of the sample, no improvements in the overall caries prevalence among 15-year-old children in the Arkhangelsk region occurred since 1997-1998. Urban-rural variations, but not gender variations, in caries experience were observed. The levels are considerably higher than those in neighbouring Nordic countries and the Russian average. Urgent public health measures on both population and individual levels are needed to improve the situation. (Int J Circumpolar Health 2011; 70(3):232-235)
In this paper, we have described the main principles of cross-sectional studies planning and data analysis. A theoretical base for cross-sectional studies' design has been presented as well as advantages and disadvantages of this type of studies. We present the methods for sample size calculation and data analysis using statistical software. Calculation of confidence intervals using free software "Epi Info" and online calculators has also been presented. The main effect measures used in cross-sectional studies have been described. Examples of cross-sectional studies in the fields of clinical medicine, dentistry and public health performed in the Arkhangelsk region have been given. The primary audience for this article consists of master and doctoral students whose research is still in the planning phase. This paper supplements, but does not substitute the literature in the field of clinical epidemiology.
Sample size calculation prior to data collection is still relatively rare in Russian research practice. This situation threatens validity of the conclusion of many projects due to insufficient statistical power to estimate the parameters of interest with desired precision or to detect the differences of interest. Moreover, in a substantial proportion of cases where sample size calculations are performed simplified formulas with assumption of a normal distribution of the studied variables are used in spite of the fact that this assumption does not hold for many research questions in biomedical research. Correlation analysis is still one of the most commonly used methods of statistical analysis used in Russia. Pearson’s correlation coefficient despite its well-known limitations appears in a greater proportion of publications that non-parametric coefficients. We calculated minimal sample sizes for the parametric Pearson’s coefficient as well its non-parametric alternatives — Spearman’s rho and Kendall’s tau-b correlation coefficients to assist junior researchers with the tool to be able to plan data collection and analysis for several types of data, various expected strengths of associations and research questions. The results are presented in ready-for-use tables with required sample size for the three abovementioned coefficients within the range from 0,10 through 0,90 by 0,05 for statistical power 0,8 and 0,9 and alpha-error or 5% as well as for estimation of the same correlation coefficients with the 95% confidence intervals width equal to 0,1 and 0,2.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.