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在死亡率社会阶层差异研究中,与排除非经济活动人口相关的偏差。

Bias related to the exclusion of the economically inactive in studies on social class differences in mortality.

作者信息

Martikainen P, Valkonen T

机构信息

Department of Sociology, University of Helsinki, Finland.

出版信息

Int J Epidemiol. 1999 Oct;28(5):899-904. doi: 10.1093/ije/28.5.899.

DOI:10.1093/ije/28.5.899
PMID:10597989
Abstract

BACKGROUND

To assess how the exclusion of the economically inactive affects levels and trends in social class differences in mortality among men and women at different durations of follow-up.

METHODS

Records of the 1970, 1975, 1980 and 1985 censuses on Finnish men and women aged 35-64 linked with records of all deaths during 1971-1990.

RESULTS

Exclusion of the economically inactive population underestimates the class differences in the total population by about 25% among men and 60% among women. The bias does not disappear if the first 5 years of follow-up are excluded and the bias can lead to erroneous conclusions about the trends in social class differences in mortality.

CONCLUSIONS

Analyses based on the economically active population may lead to significant underestimation of social class differences in mortality, introduce biases in international comparison and may only partially capture the causal mechanisms underlying these mortality differences. Our results further show that although the bias diminishes during the follow-up, it is by no means eliminated after the first 5 years. The underestimation of social class differences in mortality created by the exclusion of the inactive should be more widely recognized and more accurate data on previous occupations should be collected.

摘要

背景

评估排除无经济活动人口对不同随访时长的男性和女性死亡率社会阶层差异水平及趋势的影响。

方法

将1970年、1975年、1980年和1985年芬兰35 - 64岁男性和女性的人口普查记录与1971 - 1990年期间所有死亡记录相链接。

结果

排除无经济活动人口会使男性总人口中的阶层差异低估约25%,女性低估约60%。如果排除随访的前5年,这种偏差不会消失,且该偏差可能导致关于死亡率社会阶层差异趋势的错误结论。

结论

基于有经济活动人口的分析可能会显著低估死亡率的社会阶层差异,在国际比较中引入偏差,并且可能只能部分捕捉这些死亡率差异背后的因果机制。我们的结果进一步表明,尽管这种偏差在随访期间会减小,但在前5年后绝没有消除。排除无经济活动人口造成的死亡率社会阶层差异低估应得到更广泛的认识,并且应收集关于以往职业的更准确数据。

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