Activity Level as a Mortality Predictor in a Population Sample after Typical Underwriting Exclusions and Laboratory Scoring.

Steven J Rigatti, Robert Stout
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Abstract

Objectives.- To quantify the effect of physical activity on the mortality rates of healthy individuals in a population sample, after controlling for other sources of mortality risk. Background.- The widespread availability of activity monitors has spurred life insurance companies to consider incorporating such data into their underwriting practices. Studies have shown that sedentary lifestyles are associated with poor health outcomes and higher risks of death. The aim of this paper is to investigate how well certain measures of activity predict mortality when controlled for other known predictors of mortality including a multivariate laboratory based risk score. Methods.- Data were obtained from the National Health and Nutrition Examination Survey (NHANES) for the years 1999 through 2014. Laboratory and biometric data were scored for mortality risk using a previously developed proprietary algorithm (CRL SmartScore). Data on activity were obtained from the NHANES questionnaires pertaining to activity. In a second analysis, data were obtained from pedometers worn for 1 week by NHANES participants (years 2003-2004, and 2005-2006 only). Before analysis, cases were selected based on commonly used life insurance underwriting criteria to remove from consideration those who have major health issues, which would ordinarily preclude an offer of life insurance. Results.-In fully-adjusted Cox model which included survey-based MET*hours per day as a 3-level categorical variable, the moderate and minimal levels of activity were associated with hazard ratios of 1.15 (95% CI: 1.04-1.28) and 1.38 (95% CI: 1.23-1.56), respectively, when compared to the highest level of activity. When treated as a continuous variable, the fully adjusted model the HR for MET*hours per day was 0.91 (95% CI: 0.87-0.95). In fully adjusted models using pedometer data, the percentage of wear time spent sedentary was associated with mortality (HR: 1.19, 95% CI: 1.09-1.31), while average counts per minute were negatively associated with mortality (HR: 0.82, CI: 0.75-0.90). Conclusions.-It is clear from these results that high proportions of sedentary time are associated with increased mortality, whether the sedentary time is quantified via questionnaire or pedometer. Because both laboratory scores and activity levels remain significant in Cox models where both are included, these factors are largely independent, indicating that they are measuring distinct influences on the risk of mortality.

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活动水平作为典型核保排除和实验室评分后人口样本的死亡率预测指标。
目标--在控制了其他死亡风险来源之后,量化体育锻炼对人口样本中健康人死亡率的影响。背景--活动监测器的普及促使人寿保险公司考虑将此类数据纳入其承保实践。研究表明,久坐不动的生活方式与不良的健康状况和较高的死亡风险有关。本文旨在研究在控制其他已知死亡率预测因素(包括基于实验室的多变量风险评分)的情况下,某些活动量能在多大程度上预测死亡率。方法:数据来自 1999 年至 2014 年的美国国家健康与营养调查(NHANES)。使用之前开发的专有算法(CRL SmartScore)对实验室和生物测量数据进行死亡风险评分。活动数据来自 NHANES 有关活动的调查问卷。在第二项分析中,数据来自 NHANES 参与者佩戴一周的计步器(仅 2003-2004 年和 2005-2006 年)。在分析之前,根据常用的人寿保险承保标准对案例进行了筛选,以剔除那些有重大健康问题的人,因为这些问题通常会排除人寿保险的提供。结果......在完全调整的 Cox 模型中,将基于调查的每天 MET* 小时数作为 3 级分类变量,与最高活动水平相比,中等和最低活动水平的危险比分别为 1.15(95% CI:1.04-1.28)和 1.38(95% CI:1.23-1.56)。当作为连续变量处理时,MET*每天小时数的完全调整模型HR为0.91(95% CI:0.87-0.95)。在使用计步器数据的完全调整模型中,久坐不动的佩戴时间百分比与死亡率相关(HR:1.19,95% CI:1.09-1.31),而每分钟平均计数与死亡率呈负相关(HR:0.82,CI:0.75-0.90)。结论:这些结果清楚地表明,无论久坐时间是通过问卷还是计步器量化,久坐时间比例高都与死亡率增加有关。由于实验室评分和活动水平在同时纳入这两个因素的 Cox 模型中仍具有显著性,因此这两个因素在很大程度上是独立的,这表明它们测量的是对死亡风险的不同影响因素。
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来源期刊
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0.50
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6
期刊介绍: The Journal of Insurance Medicine is a peer reviewed scientific journal sponsored by the American Academy of Insurance Medicine, and is published quarterly. Subscriptions to the Journal of Insurance Medicine are included in your AAIM membership.
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