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Caries in five different socio-economic clusters in Orebro county.

机译:厄勒布鲁县五个不同的社会经济集群中的龋齿。

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This study assessed the prevalence of socio-demographic clusters in a Swedish county and the relationship of socio-demographic clusters and caries.All 2-19-year-olds (n = 58,573) who attended a routine check-up in Orebro County in 2005-2007 were involved in this study. Initially, two-stage cluster analyses were used to identify outliers. Secondly, the Ward method which is a hierarchical clustering method was used to conduct the final analysis. Bivariate logistic regression was also used to study the relationship between cluster membership and caries. The smallest study unit used in the initial analysis for geographical area is known as key code area, which is a geographical entity defined by the municipalities themselves. Decayed surface (DS/ds) has been used as a measure of dental caries.The county of Orebro clustered in five different socioeconomic clusters. Each cluster was defined by proportion of people over 75 years, native-born, single parents, and those with low incomes and low level of education. Odds ratio (OR) for having DS/ds > 0 in the last dental check-up during 2005-2007 was 1.5 (cluster 1), 1.3 (cluster 2), 1.4 (cluster 3) and 3.8 (cluster 4) compared with the most socioeconomically favoured cluster (cluster 5).Cluster analysis of socioeconomic data is a useful tool to identify neighbourhoods with different socio-economic conditions.
机译:这项研究评估了瑞典一个县的人口统计学特征和社会人口统计学特征与龋齿的关系.2005年,所有2-19岁的年轻人(n = 58,573)在厄勒布鲁县进行了例行检查-2007年参与了这项研究。最初,使用两阶段聚类分析来识别异常值。其次,使用Ward方法(一种层次聚类方法)进行最终分析。二元逻辑回归也用于研究簇成员与龋齿之间的关系。地理区域初始分析中使用的最小研究单位称为关键代码区域,它是市政当局自己定义的地理实体。腐烂的表面(DS / ds)已被用作龋齿的量度。厄勒布鲁县聚集在五个不同的社会经济集群中。每个群体的定义是:75岁以上的人口,本地出生的单亲父母以及低收入和低教育水平的人口。与2005年相比,DS / ds在上一次牙齿检查中DS / ds> 0的赔率(OR)为1.5(组1),1.3(组2),1.4(组3)和3.8(组4)。大多数社会经济偏爱的集群(集群5)。对社会经济数据的集群分析是识别具有不同社会经济条件的社区的有用工具。

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