Yadav Vikender Singh, Monga Nitika, Jose Nisha K, Priya Harsh
Division of Periodontics, Centre for Dental Education and Research, All India Institute of Medical Sciences, New Delhi, India.
Division of Non-Communicable Diseases, Indian Council of Medical Research Headquarters, New Delhi, India.
J Indian Soc Periodontol. 2023 Sep-Oct;27(5):461-464. doi: 10.4103/jisp.jisp_400_22. Epub 2023 Sep 1.
Gingival recession (GR) is considered a public health problem which is highly prevalent across different populations. Accuracy of psychometric properties of prevalence estimates of GR reported in epidemiological studies is important to facilitate setting public health goals, planning of public health programs, implementation of best practices and thereby developing public health policy. However, the reported prevalence estimates are influenced by the methodological variations among different studies, as observed in our recently published systematic review and meta-analysis on the global prevalence of GR. It substantially limits the comparability between studies and inferences about the true global variation in the prevalence of GR are difficult to establish. To address these issues, this commentary suggests to follow the standardized principles related to study design, clinical examination protocol, and characteristics of study subjects in future epidemiological studies on prevalence estimates of GR. Furthermore, the inclusion of additional domains in the reporting data is suggested for a deeper insight into the patterns of GR in different populations. Our suggestions are derived from a pragmatic approach and their consistent implementation would improve the reporting quality and achieve uniformity in future studies, thus benefitting the research in this area.
牙龈退缩(GR)被视为一个公共卫生问题,在不同人群中普遍存在。流行病学研究报告的GR患病率估计值的心理测量特性的准确性对于促进设定公共卫生目标、规划公共卫生项目、实施最佳实践以及制定公共卫生政策至关重要。然而,正如我们最近发表的关于GR全球患病率的系统评价和荟萃分析中所观察到的,报告的患病率估计值受到不同研究方法差异的影响。这极大地限制了研究之间的可比性,并且难以确定GR患病率的真实全球差异。为了解决这些问题,本评论建议在未来关于GR患病率估计的流行病学研究中遵循与研究设计、临床检查方案和研究对象特征相关的标准化原则。此外,建议在报告数据中纳入更多领域,以便更深入地了解不同人群中GR的模式。我们的建议源自一种务实的方法,其一致实施将提高报告质量并在未来研究中实现一致性,从而使该领域的研究受益。