Dataset Information

Counting uninsurance and means-tested coverage in the American community survey: a comparison to the current population survey.

ABSTRACT: To compare health insurance coverage estimates from the American Community Survey (ACS) to the Current Population Survey (CPS-ASEC).The 2008 ACS and CPS-ASEC, 2009.We compare age-specific national rates for all coverage types and state-level rates of uninsurance and means-tested coverage. We assess differences using t-tests and p-values, which are reported at <.05, <.01, and <.001. An F-test determines whether differences significantly varied by state.Despite substantial design differences, we find only modest differences in coverage estimates between the surveys. National direct purchase and state-level means-tested coverage levels for children show the largest differences.We suggest that the ACS is well poised to become a useful tool to health services researchers and policy analysts, but that further study is needed to identify sources of error and to quantify its bias.

SUBMITTER: Boudreaux M

PROVIDER: S-EPMC3034271 | biostudies-other | 2011 Feb

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Counting uninsurance and means-tested coverage in the American community survey: a comparison to the current population survey.

Boudreaux Michel M Ziegenfuss Jeanette Y JY Graven Peter P Davern Michael M Blewett Lynn A LA

Health services research 20101028 1 Pt 1

<h4>Objective</h4>To compare health insurance coverage estimates from the American Community Survey (ACS) to the Current Population Survey (CPS-ASEC).<h4>Data sources/study setting</h4>The 2008 ACS and CPS-ASEC, 2009.<h4>Study design</h4>We compare age-specific national rates for all coverage types and state-level rates of uninsurance and means-tested coverage. We assess differences using t-tests and p-values, which are reported at <.05, <.01, and <.001. An F-test determines whether differences ...[more]

PMID: 21029089

Similar Datasets

Project description:Group testing, introduced by Dorfman (1943), has been used to reduce costs when estimating the prevalence of a binary characteristic based on a screening test of [Formula: see text] groups that include [Formula: see text] independent individuals in total. If the unknown prevalence is low and the screening test suffers from misclassification, it is also possible to obtain more precise prevalence estimates than those obtained from testing all [Formula: see text] samples separately (Tu et al., 1994). In some applications, the individual binary response corresponds to whether an underlying time-to-event variable [Formula: see text] is less than an observed screening time [Formula: see text], a data structure known as current status data. Given sufficient variation in the observed [Formula: see text] values, it is possible to estimate the distribution function [Formula: see text] of [Formula: see text] nonparametrically, at least at some points in its support, using the pool-adjacent-violators algorithm (Ayer et al., 1955). Here, we consider nonparametric estimation of [Formula: see text] based on group-tested current status data for groups of size [Formula: see text] where the group tests positive if and only if any individual's unobserved [Formula: see text] is less than the corresponding observed [Formula: see text]. We investigate the performance of the group-based estimator as compared to the individual test nonparametric maximum likelihood estimator, and show that the former can be more precise in the presence of misclassification for low values of [Formula: see text]. Potential applications include testing for the presence of various diseases in pooled samples where interest focuses on the age-at-incidence distribution rather than overall prevalence. We apply this estimator to the age-at-incidence curve for hepatitis C infection in a sample of U.S. women who gave birth to a child in 2014, where group assignment is done at random and based on maternal age. We discuss connections to other work in the literature, as well as potential extensions.

Dataset Information

Counting uninsurance and means-tested coverage in the American community survey: a comparison to the current population survey.

Publications

Counting uninsurance and means-tested coverage in the American community survey: a comparison to the current population survey.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets