初级保健心血管预防临床实验室的地理统计学分析

Salomón Martín Pérez , Teresa Arrobas Velilla , Juan Fabiani de la Iglesia , Ignacio Vázquez Rico , Gema Varo Sánchez , Antonio León-Justel
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引用次数: 0

摘要

引言和目的心血管疾病继续在西班牙的死亡率排名中名列前茅。地质统计学分析技术在临床实验室的应用是创新的工具,可以设计心血管疾病初级预防的新策略。本研究的目的是研究所研究健康地区严重血脂异常的患病率和地理位置,以便在初级保健中实施预防策略。对2019年和2020年低密度蛋白结合胆固醇、甘油三酯和脂蛋白(A)水平进行了回顾性队列研究。此外,还使用每个分析的人口统计数据中包含的邮政编码格式的地理信息进行了地质统计分析,包括在choropleth地图中的表示和聚类聚类检测。结果纳入研究的分析数据为甘油三酯(n=365384)、低密度蛋白结合胆固醇(n=289594)和脂蛋白-脂蛋白(a)(n=502)。具有最高和最低病例百分比的区域被确定为LDL-C>;190mg/dl和TG>;150mg/dl。对于cLDL>;190mg/dl,并且对于TG值>;150 mg/dl.结论聚类的检测以及choropleth图的表示,可以对检测需要更多关注干预和改善心血管风险的地理区域有很大帮助。
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Geostatistical analysis from the clinical laboratory in cardiovascular prevention for primary care

Introduction and objectives

Cardiovascular diseases continue to lead the ranking of mortality in Spain. The implementation of geostatistical analysis techniques in the clinical laboratory are innovative tools that allow the design of new strategies in primary prevention of cardiovascular disease. The aim of this study was to study the prevalence and geolocation of severe dyslipidemia in the health areas under study in order to implement prevention strategies in primary care. A retrospective cohort study of low-density protein-bound cholesterol, triglyceride and lipoprotein (a) levels in the years 2019 and 2020 were carried out. In addition, a geostatistical analysis was performed including representation in choropleth maps and the detection of clustering clusters, using geographic information in zip code format included in the demographic data of each analytic.

Results

The analytical data included in the study were triglycerides (n = 365,384), low density protein-bound cholesterol (n = 289,594) and lipoprotein to lipoprotein (a) (n = 502). Areas with the highest and lowest percentage of cases were identified for the established cut-off points of LDL-C > 190 mg/dl and TG > 150 mg/dl. Two clustering clusters with statistical significance were detected for cLDL > 190 mg/dl and a total of 6 clusters for TG values > 150 mg/dl.

Conclusions

The detection of clusters, as well as the representation of choropleth maps, can be of great help in detecting geographic areas that require greater attention to intervene and improve cardiovascular risk.

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