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Paul Fronstin - One of the best experts on this subject based on the ideXlab platform.
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sources of health insurance and characteristics of the uninsured analysis of the march 2012 Current Population Survey
Social Science Research Network, 2012Co-Authors: Paul FronstinAbstract:This paper provides historical data through 2011 on the number and percentage of nonelderly individuals with and without health insurance. Based on EBRI estimates from the U.S. Census Bureau’s March 2012 Current Population Survey (CPS), it reflects 2011 data. It also discusses trends in coverage for the 1994-2011 period, as well as characteristics that typically indicate whether an individual is insured. The percentage of the nonelderly Population (under age 65) with health insurance coverage increased to 82 percent in 2011, notable since increases in health insurance coverage have been recorded in only five years since 1994. Employment-based health benefits remain the most common form of health coverage in the United States, though it represents a declining share. In 2011, 58.4 percent of the nonelderly Population had employment-based health benefits, down from the peak of 69.3 percent in 2000, during the 1994-2011 period. Public program health coverage expanded as a percentage of the Population in 2011, accounting for 22.5 percent of the nonelderly Population. Enrollment in Medicaid and the State Children’s Health Insurance Program (S-CHIP) also increased to a combined 46.9 million in 2011, covering 17.6 percent of the nonelderly Population, significantly above the 10.2 percent level of 1999. The percentage represented by individually purchased health coverage was unchanged in 2011 and has basically hovered in the 6-7 percent range since 1994. The unemployment rate in 2012 has been about 8 percent since the beginning of the year, and remains high amidst a still-sluggish economy. As a result, the nation is likely to see a corresponding erosion of employment-based health benefits when the data for 2012 are released next year. Until the economy gains enough strength to have a substantial impact on the labor market, a rebound in employment-based coverage is unlikely.
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sources of health insurance and characteristics of the uninsured analysis of the march 2011 Current Population Survey
EBRI issue brief Employee Benefit Research Institute, 2011Co-Authors: Paul FronstinAbstract:LATEST CENSUS DATA: This Issue Brief provides historical data through 2010 on the number and percentage of nonelderly individuals with and without health insurance. Based on EBRI estimates from the U.S. Census Bureau's March 2011 Current Population Survey (CPS), it reflects 2010 data. It also discusses trends in coverage for the 1994-2010 period and highlights characteristics that typically indicate whether an individual is insured. HEALTH COVERAGE RATE CONTINUES TO DECREASE, UNINSURED INCREASE: The percentage of the nonelderly Population (under age 65) with health insurance coverage decreased to 81.5 percent in 2010. Increases in health insurance coverage have been recorded in only three years since 1994, when 36.5 million nonelderly individuals were uninsured. The percentage of nonelderly individuals without health insurance coverage was 18.5 percent in 2010, up from 18.3 percent in 2009, and its highest level during the 1994-2010 period. EMPLOYMENT-BASED COVERAGE REMAINS DOMINANT SOURCE OF HEALTH COVERAGE, BUT CONTINUES TO ERODE: Employment-based health benefits remain the most common form of health coverage in the United States. In 2010, 58.7 percent of the nonelderly Population had employment-based health benefits, down from 69.3 percent in 2000. SHIFTING COMPOSITION OF EMPLOYMENT-BASED COVERAGE: Between 2007 and 2010, the percentage of individuals under age 65 with employment-based coverage in their own name has dropped. In 2007, 54.2 percent had coverage in their own name. By 2010, it was down to 51.5 percent. Dependent coverage during this time period fell slightly from 17.5 percent to 17.1 percent, and increased slightly from 16.8 percent to 17.1 percent between 2009 and 2010. PUBLIC PROGRAM COVERAGE IS GROWING: Public program health coverage expanded as a percentage of the Population in 2010, accounting for 21.6 percent of the nonelderly Population. Enrollment in Medicaid and the State Children's Health Insurance Program increased, reaching a combined 45 million in 2010, and covering 16.9 percent of the nonelderly Population, significantly above the 10.2 percent level of 1999. INDIVIDUAL COVERAGE STABLE: Individually purchased health coverage was unchanged in 2010 and has basically hovered in the 6-7 percent range since 1994. WHAT TO EXPECT IN 2011: 2010 is the most recent year for data on sources of health coverage. Unemployment in 2011 has been about 9 percent since the beginning of the year. While down from the 2010 average of 9.6 percent, it remains high and there is a continued threat of a double-dip recession increasing it even further. As a result, the nation is likely to see continued erosion of employment-based health benefits when the data for 2011 are released in 2012. Fewer working individuals translates into fewer individuals with access to health benefits in the work place, especially after COBRA subsidies have been exhausted.
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sources of health insurance and characteristics of the uninsured analysis of the march 2011 Current Population Survey
Social Science Research Network, 2011Co-Authors: Paul FronstinAbstract:This paper provides historical data through 2010 on the number and percentage of nonelderly individuals with and without health insurance. Based on EBRI estimates from the U.S. Census Bureau’s March 2011 Current Population Survey (CPS), it reflects 2010 data. It also discusses trends in coverage for the 1994-2010 period and highlights characteristics that typically indicate whether an individual is insured. The percentage of the nonelderly Population (under age 65) with health insurance coverage decreased to 81.5 percent in 2010. Increases in health insurance coverage have been recorded in only three years since 1994, when 36.5 million nonelderly individuals were uninsured. The percentage of nonelderly individuals without health insurance coverage was 18.5 percent in 2010, up from 18.3 percent in 2009, and its highest level during the 1994-2010 period. Employment-based health benefits remain the most common form of health coverage in the United States. In 2010, 58.7 percent of the nonelderly Population had employment-based health benefits, down from 69.3 percent in 2000. Between 2007 and 2010, the percentage of individuals under age 65 with employment-based coverage in their own name has dropped. In 2007, 54.2 percent had coverage in their own name. By 2010, it was down to 51.5 percent. Dependent coverage during this time period fell slightly from 17.5 percent to 17.1 percent, and increased slightly from 16.8 percent to 17.1 percent between 2009 and 2010. Public program health coverage expanded as a percentage of the Population in 2010, accounting for 21.6 percent of the nonelderly Population. Enrollment in Medicaid and the State Children’s Health Insurance Program increased, reaching a combined 45 million in 2010, and covering 16.9 percent of the nonelderly Population, significantly above the 10.2 percent level of 1999. Individually purchased health coverage was unchanged in 2010 and has basically hovered in the 6-7 percent range since 1994. 2010 is the most recent year for data on sources of health coverage. Unemployment in 2011 has been about 9 percent since the beginning of the year. While down from the 2010 average of 9.6 percent, it remains high and there is a continued threat of a double-dip recession increasing it even further. As a result, the nation is likely to see continued erosion of employment-based health benefits when the data for 2011 are released in 2012. Fewer working individuals translates into fewer individuals with access to health benefits in the work place, especially after COBRA subsidies have been exhausted.
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sources of health insurance and characteristics of the uninsured analysis of the march 2010 Current Population Survey
Social Science Research Network, 2010Co-Authors: Paul FronstinAbstract:This paper provides historic data through 2009 on the number and percentage of nonelderly individuals with and without health insurance. Based on EBRI® estimates from the U.S. Census Bureau’s March 2010 Current Population Survey (CPS), it reflects 2009 data. It also discusses trends in coverage for the 1994-2009 period and highlights characteristics that typically indicate whether an individual is insured. The percentage of the nonelderly Population (under age 65) with health insurance coverage decreased to 81.1 percent in 2009. Increases in health insurance coverage have been recorded in only four years since 1994, when 36.5 million nonelderly individuals were uninsured. The percentage of nonelderly individuals without health insurance coverage was 18.9 percent in 2009, up from 17.4 percent in 2008, and its highest level during the 1994-2009 period. These trends are due to job losses resulting from the recent recession and slow economic recovery, fewer workers being eligible for coverage, and more workers with coverage dropping it. Employment-based health benefits remain the most common form of health coverage in the United States. In 2009, 59 percent of the nonelderly Population had employment-based health benefits, down from 68.4 percent in 2000. Public program health coverage expanded as a percentage of the Population in 2009, accounting for 21.1 percent of the nonelderly. Enrollment in Medicaid and the State Children’s Health Insurance Program increased, reaching a combined 44.1 million in 2009, and covering 16.7 percent of the nonelderly Population, significantly above the 10.5 percent level of 1999. Individually purchased health coverage was unchanged in 2009 and has basically hovered in the 6-7 percent range since 1994. 2009 is the most recent year for data on sources of health coverage. Unemployment in 2010 averaged 9.7 percent between January and August and reached a high of 9.9 percent in April. As a result, the nation is likely to see continued erosion of employment-based health benefits when the data for 2010 are released in 2011. Fewer individuals will be working, which means fewer individuals with access to health benefits in the work place, and coupled with uncertainty about the economy, the future of job security, and prospects for health reform, an increasing number of workers are likely to forego coverage when it is available. In addition, COBRA subsidies that were meant to stem the erosion in employment-based coverage expired during the summer of 2010.
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sources of health insurance and characteristics of the uninsured analysis of the march 2010 Current Population Survey
EBRI issue brief Employee Benefit Research Institute, 2010Co-Authors: Paul FronstinAbstract:LATEST CENSUS DATA: This Issue Brief provides historic data through 2009 on the number and percentage of nonelderly individuals with and without health insurance. Based on EBRI estimates from the U.S. Census Bureau’s March 2010 Current Population Survey (CPS), it reflects 2009 data. It also discusses trends in coverage for the 1994–2009 period and highlights characteristics that typically indicate whether an individual is insured.
David A Macpherson - One of the best experts on this subject based on the ideXlab platform.
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the affordable care act and the growth of involuntary part time employment
Industrial and Labor Relations Review, 2019Co-Authors: William E Even, David A MacphersonAbstract:This study tests whether the employer mandate under the Affordable Care Act (ACA) increased involuntary part-time (IPT) employment. Using data from the Current Population Survey between 1994 and 20...
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union membership and coverage database from the Current Population Survey note
Industrial and Labor Relations Review, 2003Co-Authors: Barry T Hirsch, David A MacphersonAbstract:This note describes the construction and provision of an Internet database providing private and public sector union membership, coverage, and density estimates compiled from the Current Population Survey (CPS). Economy-wide estimates are provided beginning in 1973, estimates by state, detailed industry, and detailed occupation begin in 1983, and estimates by metropolitan area begin in 1986. The database will be updated annually and can be accessed at http://www.unionstats.com/.
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union membership and coverage database from the Current Population Survey note
Industrial and Labor Relations Review, 2003Co-Authors: Barry T Hirsch, David A MacphersonAbstract:This note describes the construction and provision of an Internet database providing private and public sector union membership, coverage, and density estimates compiled from the Current Population Survey (CPS). Economy-wide estimates are provided beginning in 1973, estimates by state, detailed industry, and detailed occupation begin in 1983, and estimates by metropolitan area begin in 1986. The database is updated annually and can be accessed at http://www.unionstats.com/.
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union membership and coverage database from the Current Population Survey note
Social Science Research Network, 2002Co-Authors: Barry T Hirsch, David A MacphersonAbstract:This note describes the construction and provision of an Internet database providing private and public sector union membership, coverage, and density estimates compiled from the Current Population Survey (CPS). Economy-wide estimates are provided beginning in 1973, estimates by state, detailed industry, and detailed occupation begin in 1983, and estimates by metropolitan area begin in 1986. The database is updated annually and can be accessed on the web.
Michael Davern - One of the best experts on this subject based on the ideXlab platform.
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twenty years of coverage an enhanced Current Population Survey 1989 2008
Health Services Research, 2011Co-Authors: Jeanette Y Ziegenfuss, Michael DavernAbstract:In order to appropriately evaluate state and national health reform efforts, researchers need to have a firm understanding of the historical developments concerning the rates of uninsurance and the rates of public and private coverage. This paper reports on an enhanced time series that corrects the Annual Social and Economic Supplement to the Current Population Survey (CPS) microdata for many of the changes that have occurred over the past 20 years. The CPS is the only data source that provides historical annual national and state-level estimates of the number of people of all ages in the United States without health insurance. Further, it provides details on the type of coverage that people with insurance have. The CPS will likely provide similar data into the foreseeable future. As a result of the strengths of the CPS, it is the most frequently used source of insurance estimates by national and state-level health policy analysts (Blewett et al. 2004). There are many other valuable sources for health insurance estimation, including the Medical Expenditure Panel Survey (MEPS), the National Health Interview Survey (NHIS), the Survey of Income and Program Participation (SIPP), and beginning in Fall 2009, the American Community Survey (ACS). A rich literature exists on the differences between these Surveys and the pros and cons of each (Swartz 1986; Lewis, Ellwood, and Czajka 1998; Congressional Budget Office 2003; Davern et al. 2009b; State Health Access Data Assistance Center [SHADAC] 2009;). The analysis presented here is agnostic to which Survey provides the most accurate estimate of coverage at any given point in time. Here, we use the CPS because it offers a relatively consistent measure over two decades on which we can improve. In this analysis, we are also agnostic about whether the CPS is capturing the true rate of uninsurance. There is substantial evidence that the CPS undercounts Medicaid coverage (Klerman et al. 2009; Davern et al. 2009a;); however, this issue is not unique to the CPS (Call et al. 2008). There is also substantial debate regarding whether the CPS captures an all-year measure of uninsurance or is closer to a point-in-time estimate (Swartz 1986; Lewis, Ellwood, and Czajka 1998; Congressional Budget Office 2003; Klerman et al. 2009;). This paper does not tackle these issues; rather, it highlights the unique ability of the CPS to provide a consistent trend controlling for known measurement, processing, and adjustment errors. Our enhanced data series makes changes to the individual records of the CPS to adjust for known consistency problems, discussed in detail below. By making changes to the individual-level microdata, it allows for the greatest utility to health policy analysts because they will be able to consistently run their specific analysis using our enhanced microdata series. Our enhanced data make changes both to the individual-level variables measuring health insurance coverage as well as to the person-level weights. Tabulated estimates using these data (i.e., predefined tables) are available from the SHADAC Data Center and the individual-level microdata are available through Integrated Public Use Microdata Series (IPUMS) CPS through the University of Minnesota's Population Center. Here, we present a summary of the results using these enhanced data examining rates of coverage over the past two decades and compare them with those available from the nonenhanced CPS.
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counting uninsurance and means tested coverage in the american community Survey a comparison to the Current Population Survey
Health Services Research, 2011Co-Authors: Michel Boudreaux, Michael Davern, Jeanette Y Ziegenfuss, Peter Graven, Lynn A BlewettAbstract:In 2008, the U.S. Census Bureau began fielding a health insurance coverage question in the American Community Survey (ACS), establishing a valuable new resource for health services researchers and policy makers. Survey estimates of health insurance coverage are important to state and national policy makers who develop programs for their communities, to analysts who estimate the fiscal impact of new programs, and to researchers who work to identify the correlates and consequences of coverage status. Because the Current Population Survey (CPS-ASEC) produces annual state-level coverage estimates for all age groups, it has historically been the primary data source for such activities (Blewett and Davern 2006). Now, data users have an alternative in the ACS. Not surprisingly, the two Surveys have different goals and methods (Davern et al. 2009). As such, they have different strengths and weaknesses for the purposes of health policy analysis. This paper focuses on the ACS, describing its relative advantages and disadvantages for estimating health insurance coverage levels compared with the CPS-ASEC. We provide side-by-side estimates from both Surveys, offer conjectures for why the Surveys compare the way they do, and give ideas for further investigation. The CPS-ASEC was chosen as a point of comparison for two reasons. First, it is the only federal Survey, other than the ACS, to produce annual state-level estimates of health insurance coverage for all age groups and there is no gold standard for uninsurance. Second, the CPS-ASEC has historically served as the Survey of record for the distribution of public program funds (Blewett and Davern 2007). Data users and stake-holders have an obvious interest in how the ACS compares to the CPS.
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Understanding the Current Population Survey's insurance estimates and the Medicaid 'undercount'.
Health Affairs, 2009Co-Authors: Jacob Alex Klerman, Michael Davern, Victoria Lynch, Kathleen Thiede Call, Jeanne S. RingelAbstract:ABSTRACT: The widely cited Census Bureau estimates of the number of uninsured people, based on the Current Population Survey, probably overstate the number of uninsured people. This is because of a...
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an examination of the medicaid undercount in the Current Population Survey preliminary results from record linking
Health Services Research, 2009Co-Authors: Michael Davern, Kathleen Thiede Call, Jacob Alex Klerman, David K Baugh, George D GreenbergAbstract:Survey estimates of public program enrollment are substantially lower than estimates of program enrollment compiled from administrative data for Medicaid, Temporary Assistance for Needy Families, and Food Stamps (C. Taeuber, D. Resnick, S. Love, J. Staveley, P. Wilde, and R. Larson, unpublished data; Lynch et al. 2007; Call et al. 2008). This discordance is particularly apparent for Medicaid and has become known as the “Medicaid undercount” (Lewis, Elwood, and Czajka 1998; Klerman, Ringel, and Roth 2005; Call et al. 2008; Davern et al. 2008). The crude Medicaid undercount in the 2001 Current Population Survey (CPS)'s Annual Social and Economic Supplement (ASEC) was 42 percent, corresponding to calendar year 2000; in the 2002 CPS it was 43 percent, corresponding to calendar year 2001.1 This large Medicaid undercount in the CPS is particularly problematic because the CPS is widely used for official and unofficial health policy research purposes at the national and state level (Blewett et al. 2004). At the national level, CPS estimates are used by statute in the allocation of State Children's Health Insurance Program (SCHIP) funds to states (Davern et al. 2003). In addition, the Congressional Budget Office uses CPS-based estimates to “score” (i.e., estimate the cost of) legislation (Glied, Remler, and Zivin 2002). The CPS data are also used by state health policy analysts to examine the potential cost and impact of state-level health reform legislation and to report to the federal government on their progress toward insuring low-income uninsured children through SCHIP and other efforts (Blewett and Davern 2006). Unofficially the CPS is widely used by the academic and policy research community to evaluate health policy reforms and to estimate policy-relevant Populations within each state, such as the number of people who are eligible for but not enrolled in public health insurance coverage (Blewett et al. 2004). These uses of the CPS data emphasize the importance of an improved understanding of the Medicaid undercount in the CPS. Toward this end, this paper reports preliminary results from a project that linked MSIS Medicaid enrollment data to CPS Survey data. The U.S. Census Bureau constructed files and performed tabulations that allow us to break the undercount into two components: (1) MSIS counts of people outside the CPS sampling frame and (2) Survey response errors among those in the CPS sample. The resulting analysis presented in this paper provides insight into the contributions of these two components of the undercount, although an exact accounting is still not possible.
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are the Current Population Survey uninsurance estimates too high an examination of the imputation process
Health Services Research, 2007Co-Authors: Michael Davern, Lynn A Blewett, Holly Rodin, Kathleen Thiede CallAbstract:The U.S. Census Bureau's Annual Social and Economic Supplement (ASEC) to the Current Population Survey (CPS) provides the most visible estimate of the number of uninsured people in the United States. The ASEC has become the Survey of record for estimates of health insurance coverage because it produces both national and state estimates of health insurance coverage, makes its micro data available to analysts within 6 months after the data are collected, contains a wealth of demographic information (including family structure and income), and releases its detailed report on an annual basis (Blewett et al. 2004). The ASEC estimates of health insurance coverage are widely used in academic research literature and media outlets, and it is the Survey to which all other Surveys are compared for coverage measurement. The ASEC estimates of health insurance coverage are used for a variety of purposes. The Congressional Budget Office makes use of the ASEC to help score legislation (Glied, Remler, and Zivin 2002), and states use the ASEC to monitor progress in determining the success of the State Children's Health Insurance Program (SCHIP) in reducing the number of low income uninsured children in each state (Davern, Blewett et al. 2003). The ASEC is also used to allocate three to four billion dollars per year to states to fund SCHIP based, in part, on the number of low income uninsured children in each state and the number of low-income children in each state (Davern, Blewett et al. 2003). Because of their many uses, the ASEC uninsurance estimates have been scrutinized over the years, especially as they tend to be higher than most Surveys that ask about health insurance coverage at a single point in time (Lewis, Ellwood, and Czajka 1998; Fronstin 2000; Short 2001; Congressional Budget Office 2003). Some of the national Surveys that measure health insurance coverage include the National Health Interview Survey (NHIS), the Household Component of the Medical Expenditure Panel Survey (MEPS-HC), and the Survey of Income and Program Participation (SIPP) (Blewett et al. 2004). The ASEC estimate of the number of people with no health insurance for the entire previous calendar year, is typically higher than the full-year uninsurance estimates produced using data from these other Surveys. In addition, the ASEC full-year uninsurance estimates are even higher than many “point-in-time” uninsurance estimates from other Surveys (Congressional Budget Office 2003; Czajka 2005; Peterson 2005). Several authors have offered potential reasons why the health insurance estimates differ across the various federal Surveys, including: differences in sample frame; sample selection and Population coverage; mode of Survey administration; Survey operationalization of the concept of uninsurance; misreporting by respondents in the Survey; and data processing (Lewis, Ellwood, and Czajka 1998; Fronstin 2000; Short 2001; Congressional Budget Office 2003). One of the least explored reasons why the ASEC differs from other Surveys is the impact of missing data imputation on health insurance coverage estimates from the ASEC. Davern et al. (2004) explicitly examined this issue with respect to state Survey estimates of health insurance coverage, finding that the process used by the Census Bureau to impute missing insurance data in the ASEC biases the state estimates of uninsurance. Some states had higher and some states had lower rates of coverage due to bias, but the national rate was unbiased (Davern et al. 2004). In this paper we focus explicitly on the national estimate of uninsurance to explore whether the imputation process used by the Census Bureau explains the higher uninsurance estimates found in the ASEC relative to other national Surveys. Specifically, we examine whether there is a significant difference in estimates of uninsurance for those cases that have health insurance data imputed (11 percent of the ASEC sample) and those that do not. We begin by describing the imputation methodology the Census Bureau uses for the ASEC health insurance items and why we think the Current methodology may impact the overall rates of coverage to produce an upward bias in the national estimates of uninsurance.
Anne M Hartman - One of the best experts on this subject based on the ideXlab platform.
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reliability of adult self reported smoking history data from the tobacco use supplement to the Current Population Survey 2002 2003 cohort
Nicotine & Tobacco Research, 2012Co-Authors: Julia N Soulakova, Anne M Hartman, Benmei Liu, Gordon Willis, Steve AugustineAbstract:INTRODUCTION This study examined the reliability of self-reported smoking history measures. The key measures of interest were time since completely quitting smoking among former smokers; age at which fairly regular smoking was initiated among former and Current smokers; the number of cigarettes smoked per day and the number of years of daily smoking among former smokers; and never smoking. Another goal was to examine sociodemographic factors and interview method as potential predictors of the odds of strict agreement in responses. METHODS Data from the 2002-2003 Tobacco Use Supplement to the Current Population Survey were examined. Descriptive analysis was performed to detect discrepant data patterns, and intraclass and Pearson correlations and kappa coefficients were used to assess reporting consistency over the 12-month interval. Multiple logistic regression models with replicate weights were built and fitted to identify factors influencing the logit of agreement for each measure of interest. RESULTS All measures revealed at least moderate levels of overall agreement. However, upon closer examination, a few measures also showed some considerable differences in absolute value. The highest percentage of these differences was observed for former smokers' reports of the number of years smoking every day. CONCLUSIONS Overall, the data suggest that self-reported smoking history characteristics are reliable. The logit of agreement over a 12-month period is shown to depend on a few sociodemographic characteristics as well as their interactions with each other and with interview method.
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changes in smoking prevalence among u s adults by state and region estimates from the tobacco use supplement to the Current Population Survey 1992 2007
BMC Public Health, 2011Co-Authors: Ahmedin Jemal, Anne M Hartman, Michael J Thun, Vilma Cokkinides, Hana Ross, Elizabeth WardAbstract:Background: Tobacco control policies at the state level have been a critical impetus for reduction in smoking prevalence. We examine the association between recent changes in smoking prevalence and state-specific tobacco control policies and activities in the entire U.S. Methods: We analyzed the 1992-93, 1998-99, and 2006-07 Tobacco Use Supplement to the Current Population Survey (TUS-CPS) by state and two indices of state tobacco control policies or activities [initial outcome index (IOI) and the strength of tobacco control (SOTC) index] measured in 1998-1999. The IOI reflects cigarette excise taxes and indoor air legislation, whereas the SOTC reflects tobacco control program resources and capacity. Pearson Correlation coefficient between the proportionate change in smoking prevalence from 1992-93 to 2006-07 and indices of tobacco control activities or programs was the main outcome measure. Results: Smoking prevalence decreased from 1992-93 to 2006-07 in both men and women in all states except Wyoming, where no reduction was observed among men, and only a 6.9% relative reduction among women. The percentage reductions in smoking in men and women respectively were the largest in the West (average decrease of 28.5% and 33.3%) and the smallest in the Midwest (18.6% and 20.3%), although there were notable exceptions to this pattern. The decline in smoking prevalence by state was correlated with the state’s IOI in both women and men (r = -0.49, p < 0.001; r = -0.31, p = 0.03; respectively) and with state’s SOTC index in women(r = -0.30, p = 0.03 0), but not men (r = -0.21, p = 0.14). Conclusion: State level policies on cigarette excise taxes and indoor air legislation correlate strongly with reductions in smoking prevalence since 1992. Strengthening and systematically implementing these policies could greatly accelerate further reductions in smoking.
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translation of a tobacco Survey into spanish and asian languages the tobacco use supplement to the Current Population Survey
Nicotine & Tobacco Research, 2008Co-Authors: Gordon Willis, Anne M Hartman, Deirdre Lawrence, Martha Stapleton Kudela, Kerry Y Levin, Barbara ForsythAbstract:Because of the vital need to attain cross-cultural comparability of estimates of tobacco use across subgroups of the U.S. Population that differ in primary language use, the National Cancer Institute (NCI) Tobacco Use Special Cessation Supplement to the Current Population Survey (TUSCS-CPS) was translated into Spanish, Chinese (Mandarin and Cantonese), Korean, Vietnamese, and Khmer (Cambodian). The questionnaire translations were extensively tested using an eight-step process that focused on both translation procedures and empirical pretesting. The resulting translations are available on the Internet at http://riskfactor.cancer.gov/studies/tus-cps/translation/questionnaires.html for tobacco researchers to use in their own Surveys, either in full, or as material to be selected as appropriate. This manuscript provides information to guide researchers in accessing and using the translations, and describes the empirical procedures used to develop and pretest them (cognitive interviewing and behavior coding). We also provide recommendations concerning the further development of questionnaire translations.
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distribution of daily smokers by stage of change Current Population Survey results
Preventive Medicine, 2003Co-Authors: Mary Ellen Wewers, Anne M Hartman, Frances A Stillman, Donald R. ShoplandAbstract:Abstract Background Population-based national estimates of stage of change among daily smokers are unknown. This study described the proportion of U.S. daily smokers, 18 and older, by stage of change. Selected sociodemographic characteristics were delineated. Methods Cross-sectional data were collected via telephone or face-to-face interview in daily smokers who responded to the Current Population Survey in 1992–1993 ( n = 39,706), 1995–1996 ( n = 34,865), or 1998–1999 ( n = 30,153). Main outcomes included stage of change: (1) Precontemplation—not interested in quitting smoking in next 6 months; (2) Contemplation—interested in quitting smoking in next 6 months but not next 30 days; (3) Preparation—interested in quitting smoking in next 30 days and stopped at least 1 day during past year. Results During 1992–1993, 59.1% of respondents were precontemplators, 33.2% contemplators, and 7.7% in preparation stage. This distribution was similar in subsequent Surveys (1995–1996; 1998–1999). Gender differences were not apparent. Whites were more likely to be precontemplators. As education and income increased, the percentage in precontemplation decreased. Rural residents were more likely in precontemplation and less frequently in preparation. Conclusions Among daily smokers, little movement in stage of change was apparent in the United States during the 1990s. Tobacco control efforts must receive high priority to address these static patterns.
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state specific trends in smoke free workplace policy coverage the Current Population Survey tobacco use supplement 1993 to 1999
Journal of Occupational and Environmental Medicine, 2001Co-Authors: Donald R. Shopland, Anne M Hartman, Karen K Gerlach, David M Burns, James T. GibsonAbstract:We examined trends in smoke-free workplace policies among all indoor workers in the United States using the National Cancer Institute's Tobacco Use Supplement to the Census Bureau's Current Population Survey (total n = 270,063). Smoke-free was defined as smoking not permitted in public or common areas or in work areas of a worksite. Nationally, we found that nearly 70% of the US workforce worked under a smoke-free policy in 1999. At the state level, a greater than 30-percentage-point differential existed in the proportion of workers with such policies. Although significant progress has been made to reduce worker exposure to environmental tobacco smoke on the job, we predict further progress may be difficult unless comprehensive regulations to protect all workers are implemented at the national, state, or local level.
Lynn A Blewett - One of the best experts on this subject based on the ideXlab platform.
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counting uninsurance and means tested coverage in the american community Survey a comparison to the Current Population Survey
Health Services Research, 2011Co-Authors: Michel Boudreaux, Michael Davern, Jeanette Y Ziegenfuss, Peter Graven, Lynn A BlewettAbstract:In 2008, the U.S. Census Bureau began fielding a health insurance coverage question in the American Community Survey (ACS), establishing a valuable new resource for health services researchers and policy makers. Survey estimates of health insurance coverage are important to state and national policy makers who develop programs for their communities, to analysts who estimate the fiscal impact of new programs, and to researchers who work to identify the correlates and consequences of coverage status. Because the Current Population Survey (CPS-ASEC) produces annual state-level coverage estimates for all age groups, it has historically been the primary data source for such activities (Blewett and Davern 2006). Now, data users have an alternative in the ACS. Not surprisingly, the two Surveys have different goals and methods (Davern et al. 2009). As such, they have different strengths and weaknesses for the purposes of health policy analysis. This paper focuses on the ACS, describing its relative advantages and disadvantages for estimating health insurance coverage levels compared with the CPS-ASEC. We provide side-by-side estimates from both Surveys, offer conjectures for why the Surveys compare the way they do, and give ideas for further investigation. The CPS-ASEC was chosen as a point of comparison for two reasons. First, it is the only federal Survey, other than the ACS, to produce annual state-level estimates of health insurance coverage for all age groups and there is no gold standard for uninsurance. Second, the CPS-ASEC has historically served as the Survey of record for the distribution of public program funds (Blewett and Davern 2007). Data users and stake-holders have an obvious interest in how the ACS compares to the CPS.
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are the Current Population Survey uninsurance estimates too high an examination of the imputation process
Health Services Research, 2007Co-Authors: Michael Davern, Lynn A Blewett, Holly Rodin, Kathleen Thiede CallAbstract:The U.S. Census Bureau's Annual Social and Economic Supplement (ASEC) to the Current Population Survey (CPS) provides the most visible estimate of the number of uninsured people in the United States. The ASEC has become the Survey of record for estimates of health insurance coverage because it produces both national and state estimates of health insurance coverage, makes its micro data available to analysts within 6 months after the data are collected, contains a wealth of demographic information (including family structure and income), and releases its detailed report on an annual basis (Blewett et al. 2004). The ASEC estimates of health insurance coverage are widely used in academic research literature and media outlets, and it is the Survey to which all other Surveys are compared for coverage measurement. The ASEC estimates of health insurance coverage are used for a variety of purposes. The Congressional Budget Office makes use of the ASEC to help score legislation (Glied, Remler, and Zivin 2002), and states use the ASEC to monitor progress in determining the success of the State Children's Health Insurance Program (SCHIP) in reducing the number of low income uninsured children in each state (Davern, Blewett et al. 2003). The ASEC is also used to allocate three to four billion dollars per year to states to fund SCHIP based, in part, on the number of low income uninsured children in each state and the number of low-income children in each state (Davern, Blewett et al. 2003). Because of their many uses, the ASEC uninsurance estimates have been scrutinized over the years, especially as they tend to be higher than most Surveys that ask about health insurance coverage at a single point in time (Lewis, Ellwood, and Czajka 1998; Fronstin 2000; Short 2001; Congressional Budget Office 2003). Some of the national Surveys that measure health insurance coverage include the National Health Interview Survey (NHIS), the Household Component of the Medical Expenditure Panel Survey (MEPS-HC), and the Survey of Income and Program Participation (SIPP) (Blewett et al. 2004). The ASEC estimate of the number of people with no health insurance for the entire previous calendar year, is typically higher than the full-year uninsurance estimates produced using data from these other Surveys. In addition, the ASEC full-year uninsurance estimates are even higher than many “point-in-time” uninsurance estimates from other Surveys (Congressional Budget Office 2003; Czajka 2005; Peterson 2005). Several authors have offered potential reasons why the health insurance estimates differ across the various federal Surveys, including: differences in sample frame; sample selection and Population coverage; mode of Survey administration; Survey operationalization of the concept of uninsurance; misreporting by respondents in the Survey; and data processing (Lewis, Ellwood, and Czajka 1998; Fronstin 2000; Short 2001; Congressional Budget Office 2003). One of the least explored reasons why the ASEC differs from other Surveys is the impact of missing data imputation on health insurance coverage estimates from the ASEC. Davern et al. (2004) explicitly examined this issue with respect to state Survey estimates of health insurance coverage, finding that the process used by the Census Bureau to impute missing insurance data in the ASEC biases the state estimates of uninsurance. Some states had higher and some states had lower rates of coverage due to bias, but the national rate was unbiased (Davern et al. 2004). In this paper we focus explicitly on the national estimate of uninsurance to explore whether the imputation process used by the Census Bureau explains the higher uninsurance estimates found in the ASEC relative to other national Surveys. Specifically, we examine whether there is a significant difference in estimates of uninsurance for those cases that have health insurance data imputed (11 percent of the ASEC sample) and those that do not. We begin by describing the imputation methodology the Census Bureau uses for the ASEC health insurance items and why we think the Current methodology may impact the overall rates of coverage to produce an upward bias in the national estimates of uninsurance.
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estimates of health insurance coverage comparing state Surveys with the Current Population Survey
Health Affairs, 2007Co-Authors: Kathleen Thiede Call, Michael Davern, Lynn A BlewettAbstract:The Census Bureau produces annual state-level estimates of health insurance coverage using the Current Population Survey (CPS) Annual Social and Economic Supplement. Many states also conduct their own Population Surveys of health insurance status; in most cases, the state Survey estimates of uninsurance are lower than the estimates produced by the CPS. This discrepancy fuels debate about the true count of uninsured Americans and changes in that number over time. This paper compares state Survey and CPS estimates of uninsurance, highlights key reasons for these differences, and discusses the policy implications of this persistent discrepancy.
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estimating regression standard errors with data from the Current Population Survey s public use file
Inquiry: Critical Thinking Across the Disciplines, 2007Co-Authors: Michael Davern, Arthur Jones, James M Lepkowski, Gestur Davidson, Lynn A BlewettAbstract:This study examines whether reasonable standard errors for multivariate models can be calculated using the public use file of the Current Population Survey’s Annual Social and Economic Supplement (CPS ASEC). We restrict our analysis to the 2003 CPS ASEC and model three dependent variables at the individual level: income, poverty, and health insurance coverage. We compare standard error estimates performed on the CPS ASEC public use file with those obtained from the Census Bureau’s restricted internal data that include all the relevant sampling information needed to compute standard errors adjusted for the complex Survey sample design. Our analysis shows that the multivariate standard error estimates derived from the public use CPS ASEC following our specification perform relatively well compared to the estimates derived from the internal Census Bureau file. However, it is essential that users of CPS ASEC data do not simply choose any available method since three of the methods commonly used for adjusting for the complex sample design produce substantially different estimates.
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unstable inferences an examination of complex Survey sample design adjustments using the Current Population Survey for health services research
Inquiry, 2006Co-Authors: Michael Davern, Arthur Jones, James M Lepkowski, Gestur Davidson, Lynn A BlewettAbstract:Statistical analysis of the Current Population Survey's Annual Social and Economic Supplement is used widely in health services research. However, the statistical evidence cited from the Current Population Survey (CPS) is not always consistent because researchers use a variety of methods to produce standard errors that are fundamental to significance tests. This analysis examines the 2002 Annual Social and Economic Supplement's (ASEC) estimates of national and state average income, national and state poverty rates, and national and state health insurance coverage rates. Findings show that the standard error estimates derived from the public use CPS data perform poorly compared with the Survey design-based estimates derived from restricted internal data, and that the generalized variance parameters Currently used by the U.S. Census Bureau in its ASEC reports and funding formula inputs perform erratically. Because the majority of published research (both by academics and Census Bureau analysts) does not mak...
