Davern Michael, Rodin Holly, Blewett Lynn A, Call Kathleen Thiede
Division of Health Policy and Management, University of Minnesota, School of Public Health, 2221 University Avenue, S.E., Minneapolis, MN, USA.
Health Serv Res. 2007 Oct;42(5):2038-55. doi: 10.1111/j.1475-6773.2007.00703.x.
To determine whether the imputation procedure used to replace missing data by the U.S. Census Bureau produces bias in the estimates of health insurance coverage in the Current Population Survey's (CPS) Annual Social and Economic Supplement (ASEC).
2004 CPS-ASEC.
Eleven percent of the respondents to the monthly CPS do not take the ASEC supplement and the entire supplement for these respondents is imputed by the Census Bureau. We compare the health insurance coverage of these "full-supplement imputations" with those respondents answering the ASEC supplement. We then compare demographic characteristics of the two groups and model the likelihood of having insurance coverage given the data are imputed controlling for demographic characteristics. Finally, in order to gauge the impact of imputation on the uninsurance rate we remove the full-supplement imputations and reweight the data, and we also use the multivariate regression model to simulate what the uninsurance rate would be under the counter-factual simulation that no cases had the full-supplement imputation.
The noninstitutionalized U.S. population under 65 years of age in 2004.
The CPS-ASEC survey was extracted from the U.S. Census Bureau's FTP web page in September of 2004 (http://www.bls.census.gov/ferretftp.htm).
In the 2004 CPS-ASEC, 59.3 percent of the full-supplement imputations under age 65 years had private health insurance coverage as compared with 69.1 percent of the nonfull-supplement imputations. Furthermore, full-supplement imputations have a 26.4 percent uninsurance rate while all others have an uninsurance rate of 16.6 percent. Having imputed data remains a significant predictor of health insurance coverage in multivariate models with demographic controls. Both our reweighting strategy and our counterfactual modeling show that the uninsured rate is approximately one percentage point higher than it should be for people under 65 (i.e., approximately 2.5 million more people are counted as uninsured due to this imputation bias).
The imputed ASEC data are coding too many people to be uninsured. The situation is complicated by the current survey items in the ASEC instrument allowing all members of a household to be assigned coverage with the single press of a button. The Census Bureau should consider altering its imputation specifications and, more importantly, altering how it collects survey data from those who respond to the supplement. IMPLICATIONS FOR POLICY DELIVERY OR PRACTICE: The bias affects many different policy simulations, policy evaluations and federal funding allocations that rely on the CPS-ASEC data.
The Robert Wood Johnson Foundation.
确定美国人口普查局用于替换缺失数据的插补程序是否会在当前人口调查（CPS）的年度社会经济补充调查（ASEC）中对医疗保险覆盖范围的估计产生偏差。
2004年CPS - ASEC。
每月CPS的受访者中有11%不参加ASEC补充调查，这些受访者的整个补充调查数据由人口普查局进行插补。我们将这些“完全补充插补”受访者的医疗保险覆盖情况与回答了ASEC补充调查的受访者进行比较。然后我们比较两组的人口特征，并在控制人口特征的情况下对给定插补数据时拥有保险覆盖的可能性进行建模。最后，为了评估插补对未参保率的影响，我们去除完全补充插补数据并对数据重新加权，并且我们还使用多元回归模型来模拟在没有任何案例进行完全补充插补的反事实模拟下未参保率会是多少。
2004年65岁以下的非机构化美国人口。
CPS - ASEC调查于2004年9月从美国人口普查局的FTP网页（http://www.bls.census.gov/ferretftp.htm）提取。
在2004年CPS - ASEC中，65岁以下完全补充插补受访者中有59.3%拥有私人医疗保险覆盖，而非完全补充插补受访者这一比例为69.1%。此外，完全补充插补受访者的未参保率为26.4%，而其他所有人的未参保率为16.6%。在有人口控制的多元模型中，拥有插补数据仍然是医疗保险覆盖的一个重要预测因素。我们的重新加权策略和反事实建模均表明，65岁以下人群的未参保率比应有的水平高出约一个百分点（即由于这种插补偏差，大约多算了250万人未参保）。
插补后的ASEC数据将过多的人编码为未参保。ASEC工具当前的调查项目允许通过单次按键为一个家庭的所有成员分配保险覆盖，这使情况变得复杂。人口普查局应考虑改变其插补规范，更重要的是，改变其从回答补充调查的人那里收集调查数据的方式。对政策实施或实践的影响：这种偏差影响了许多依赖CPS - ASEC数据的不同政策模拟、政策评估和联邦资金分配。
罗伯特·伍德·约翰逊基金会。
