Accuracy of Cardiovascular Risk Prediction Varies by Neighborhood Socioeconomic Position: A Retrospective Cohort Study.
Accuracy of Cardiovascular Risk Prediction Varies by Neighborhood Socioeconomic Position: A Retrospective Cohort Study.
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DOI：
10.7326/m16-2543
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发表时间：
2017-10-03
影响因子：
39.2
通讯作者：
Dawson NV
中科院分区：
文献类型：
--
作者：
Dalton JE;Perzynski AT;Zidar DA;Rothberg MB;Coulton CJ;Milinovich AT;Einstadter D;Karichu JK;Dawson NV
与美国人的社会经济地位相关的健康结果的不平等正在加剧。第一，评估邻里劣势与主要动脉粥样硬化性心血管疾病(ASCVD)相关事件之间的空间关系；第二，评估邻里劣势和生理风险对ASCVD事件发生率的邻里差异的相对程度。地理编码纵向电子健康记录的观察性队列分析。俄亥俄州东北部的一个单一的学术健康中心和周围社区。109,793名克利夫兰临床健康系统(CCHS)患者，他们在2007至2010年间进行了门诊血脂检查。第一个合格的脂类小组的日期作为研究基线。从基线到5年内首次发生重大ASCVD事件(心肌梗死、中风或心血管死亡)的时间，建模为1)局部派生的邻里不利指数(NDI)和2)美国心脏病学院/美国心脏协会联合队列方程风险模型(PCERM)预测的5年ASCVD事件发生率的函数。如果连续两年没有遇到CCHS，或者当不再有州死亡数据时(即从2015年起)，结果数据被审查。PCERM系统地低估了来自弱势社区患者的ASCVD事件风险。这些患者的模型辨别力(一致性指数[95%可信区间]：0.70[0.67-0.74])比来自最富裕社区的患者(0.80[0.78-0.81])差。仅NDI一项就解释了ASCVD事件发生率在人口普查区域水平变化的32.0%，而PCERM解释的这一比例为10.0%。来自富裕社区的患者比例过高。在克利夫兰诊所接受心血管疾病诊断治疗的患者的结果被认为与患者来自贫困社区还是富裕社区无关。邻里劣势可能是ASCVD事件风险的有力调节因素。除了补充风险模型和临床筛查标准外，还需要以人群为基础的解决方案，以改善邻里不利对健康结果的有害影响。
Inequality in health outcomes in relation to Americans’ socioeconomic position is rising. First, to evaluate the spatial relationship between neighborhood disadvantage and major atherosclerotic cardiovascular disease (ASCVD)-related events; and second, to evaluate the relative extent to which neighborhood disadvantage and physiological risk account for neighborhood-level variation in ASCVD event rates. Observational cohort analysis of geocoded longitudinal electronic health records. A single academic health center and surrounding neighborhoods in Northeast Ohio. 109,793 Cleveland Clinic Health System (CCHS) patients who had had an outpatient lipid panel drawn between 2007 and 2010. The date of the first qualifying lipid panel served as study baseline. Time from baseline to the first occurrence of a major ASCVD event (myocardial infarction, stroke, or cardiovascular death) within 5 years, modeled as a function of 1) a locally-derived neighborhood disadvantage index (NDI) and 2) the predicted 5-year ASCVD event rate from the American College of Cardiology/American Heart Association Pooled Cohort Equations Risk Model (PCERM). Outcome data were censored if there were no CCHS encounters for two consecutive years or when state death data were no longer available (i.e., 2015 onward). The PCERM systematically under-predicted ASCVD event risk among patients from disadvantaged communities. Model discrimination was poorer among these patients (concordance index [95% confidence interval]: 0.70 [0.67 – 0.74]) than among patients from the most affluent communities (0.80 [0.78 – 0.81]). The NDI alone accounted for 32.0% of census-tract-level variation in ASCVD event rates, compared to 10.0% accounted for by the PCERM. Patients from affluent communities were over-represented. Outcomes of patients treated for cardiovascular disease diagnoses at Cleveland Clinic were assumed to be independent of whether patients came from a disadvantaged or affluent neighborhood. Neighborhood disadvantage may be a powerful regulator of ASCVD event risk. In addition to supplemental risk models and clinical screening criteria, population-based solutions to ameliorating the deleterious effects of neighborhood disadvantage on health outcomes are needed.