External liquid calibration method for iodide chemical ionization mass spectrometry enables quantification of gas-phase per- and polyfluoroalkyl substances (PFAS) dynamics in indoor air.

Per- and polyfluoroalkyl substances (PFAS) are manufactured chemicals that have been detected across the globe. Fluorotelomer alcohols (FTOHs) are one PFAS class commonly found in indoor air due to emissions from consumer products (e.g., textiles and food packaging) and are human metabolic, atmospheric oxidative, and industrial precursors of perfluoroalkyl carboxylic acids (PFCAs). We developed a quantitative method for real-time analysis of gas-phase FTOHs, perfluoroalkyl acids (PFCAs and GenX), one perfluorooctane sulfonamide (EtFOSA), one fluorotelomer diol (FTdiOH), and one fluorinated ether (E2) using high-resolution time-of-flight chemical ionization mass spectrometry equipped with iodide reagent ion chemistry (I-HR-ToF-CIMS). Herein, we present a direct liquid injection method for external calibration, providing detection limits of 0.19-3.1 pptv for 3 s averaging and 0.02-0.44 pptv for 120 s averaging, with the exception of E2, which had detection limits of 1700 and 220 pptv for 3- and 120 s averaging, respectively. These calibrations enabled real-time gas-phase quantification of 6 : 2 FTOH in room air while microwaving popcorn, with an average peak air concentration of 31.6 ± 4.5 pptv measured 2 meters from a closed microwave. Additionally, 8 : 2 and 10 : 2 FTOH concentrations in indoor air were measured in the presence and absence of a rain jacket, with observed peak concentrations of 110 and 25 pptv, respectively. Our work demonstrates the ability of I-HR-ToF-CIMS to provide real-time air measurements of PFAS relevant to indoor human exposure settings and allow for PFAS source identification. We expect that real-time quantification of other gas-phase PFAS classes is possible, enabling advances in understanding PFAS sources, chemistry, and partitioning.

Bias in telephone surveys that do not sample cell phones: uses and limits of poststratification adjustments.

OBJECTIVE: To examine how biased health surveys are when they omit cell phone-only households (CPOH) and to explore whether poststratification can reduce this bias. METHODS: We used data from the 2008 National Health Interview Survey (NHIS), which uses area probability sampling and in-person interviews; as a result people of all phone statuses are included. First, we examined whether people living in CPOH are different from those not living in CPOH with respect to several important health surveillance domains. We compared standard NHIS estimates to a set of "reweighted" estimates that exclude people living in CPHO. The reweighted NHIS cases were fitted through a series of poststratification adjustments to NHIS control totals. In addition to poststratification adjustments for region, race or ethnicity, and age, we examined adjustments for home ownership, age by education, and household structure. RESULTS: Poststratification reduces bias in all health-related estimates for the nonelderly population. However, these adjustments work less well for Hispanics and blacks and even worse for young adults (18 to 30 y). Reduction in bias is greatest for estimates of uninsurance and having no usual source of care, and worse for estimates of drinking, smoking, and forgone or delayed care because of costs. CONCLUSIONS: Applying poststratification adjustments to data that exclude CPOH works well at the total population level for estimates such as health insurance, and less well for access and health behaviors. However, poststratification adjustments do not do enough to reduce bias in health-related estimates at the subpopulation level, particularly for those interested in measuring and monitoring racial, ethnic, and age disparities.

Health Insurance Portability and Accountability Act (HIPAA) authorization and survey nonresponse bias.

OBJECTIVES: To extend earlier work (Beebe et al, Med Care. 2007;45:959-965) that demonstrated Health Insurance Portability and Accountability Act authorization form (HAF) introduced potential nonresponse bias (toward healthier respondents). RESEARCH DESIGN: The sample frame from the earlier experiment was linked to administrative medical record data, enabling the comparison of background and clinical characteristics of each set of respondents (HAF and No HAF) to the sample frame. SUBJECTS: A total of 6939 individuals residing in Olmsted County, Minnesota who were mailed a survey in September 2005 assessing recent gastrointestinal symptoms with an embedded HAF experiment comprised the study population. MEASURES: The outcomes of interest were response status (survey returned vs. not) by HAF condition (randomized to receive HAF or not). Sociodemographic indicators included gender, age, and race. Health status was measured using the severity-weighted Charlson Score and utilization was measured using emergency room visits, hospital admissions, clinic office visits, and procedures. RESULTS: Younger and nonwhite residents were under-represented and those with more clinical office visits were over-represented in both conditions. Those responding to the survey in the HAF condition were significantly more likely to be in poor health compared with the population (27.3% with 2+ comorbidities vs. 24.6%, P=0.02). CONCLUSIONS: The HAF did not influence the demographic composition of the respondents. However, in contrast to earlier findings based on self-reported health status (Beebe et al, Med Care. 2007;45:959-965), responders in the HAF condition were slightly sicker than in the non-HAF condition. The HAF may introduce a small amount of measurement error by suppressing reports of poor health. Furthermore, researchers should consider the effect of the HAF on resultant precision, respondent burden, and available financial resources.

Disparities in public use data availability for race, ethnic, and immigrant groups: national surveys for healthcare disparities research.

BACKGROUND: Disparities in healthcare coverage and access have a prominent place on the national health policy agenda. It is, therefore, essential to understand strengths and limitations of national surveys that provide annual or periodic data for population-based healthcare disparities research and monitoring. Importantly, publicly available data on healthcare coverage and access are needed for disparities populations defined by race, ethnicity, or immigrant group (REI). OBJECTIVE: To document public use data availability for REI groups, insurance coverage, and access to care measures in selected national surveys used for healthcare disparities research. DESIGN: We examined public use data for general population surveys that collect information on healthcare coverage and access on an annual or periodic basis for the nation. Data sources examined include the following: Current Population Survey, Survey of Income and Program Participation, National Health Interview Survey (NHIS), National Health and Nutrition Examining Survey, National Survey of Children's Health, Behavioral Risk Factor Surveillance System, and Medical Expenditure Panel Survey-Household Component. RESULTS: Although each survey has strengths for healthcare disparities research, there is no single survey that has detailed REI group identifiers, comprehensive measures of coverage and access, and geographic identifiers. CONCLUSIONS: Current Population Survey and NHIS have detailed REI identifiers. NHIS and Medical Expenditure Panel Survey-Household Component have comprehensive measures of coverage and access but are limited by smaller samples and no geography. Findings summarized in this article will assist with identifying existing data to examine healthcare coverage and access disparities and highlight areas for improvement in public use data availability.

American Indian/Alaska Native uninsurance disparities: a comparison of 3 surveys.

OBJECTIVES: We examined whether 3 nationally representative data sources produce consistent estimates of disparities and rates of uninsurance among the American Indian/Alaska Native (AIAN) population and to demonstrate how choice of data source impacts study conclusions. METHODS: We estimated all-year and point-in-time uninsurance rates for AIANs and non-Hispanic Whites younger than 65 years using 3 surveys: Current Population Survey (CPS), National Health Interview Survey (NHIS), and Medical Expenditure Panel Survey (MEPS). RESULTS: Sociodemographic differences across surveys suggest that national samples produce differing estimates of the AIAN population. AIAN all-year uninsurance rates varied across surveys (3%-23% for children and 18%-35% for adults). Measures of disparity also differed by survey. For all-year uninsurance, the unadjusted rate for AIAN children was 2.9 times higher than the rate for White children with the CPS, but there were no significant disparities with the NHIS or MEPS. Compared with White adults, AIAN adults had unadjusted rate ratios of 2.5 with the CPS and 2.2 with the NHIS or MEPS. CONCLUSIONS: Different data sources produce substantially different estimates for the same population. Consequently, conclusions about health care disparities may be influenced by the data source used.

Measuring adequacy of coverage for the privately insured: new state estimates to monitor trends in health insurance coverage.

The privately insured are assuming a greater share of the costs of their health care, yet little is known about changes in out-of-pocket spending at the state level. The central problem is that national surveys with the relevant data are not designed to generate state-level estimates. The study addresses this shortcoming by using a two-sample modeling approach to estimate state-level measures of out-of-pocket spending relative to income for privately insured adults and children. National data from the Medical Expenditure Panel Survey-Household Component and state representative data from the Current Population Survey are used. Variation in out-of-pocket spending over time and across states is shown, highlighting concern about the adequacy of coverage for 2.9% of privately insured children and 7.8% of privately insured adults. Out-of-pocket spending relative to income is an important indicator of access to care and should be monitored at the state level.

The effects of survey mode and asking about future intentions on self-reports of colorectal cancer screening.

Colorectal cancer (CRC) screening rates are often ascertained via self-reports but can be subject to overreporting bias. Asking about intention to get screened before asking about past screening may minimize overreporting of cancer screening. In a statewide survey conducted from July through October of 2005, we embedded an experiment that tested the effect of question ordering (asking about future intention to get screened before or after asking about past screening; "future first" and "future second," respectively), crossed with survey mode (mail versus telephone), on CRC screening rates. Weighted analysis focused on 752 respondents who were ages 50 years or older. We found (a) that asking about future intentions to get screened before asking about past screening (future first) statistically significantly lowers reports of past CRC screening [70.9% future second versus 58.0% future first; odds ratio (OR), 1.83; 95% confidence interval (95% CI), 1.08-3.13]; (b) that there was no main effect of survey mode; and (c) that the effect of the ordering of the future intentions item varies by survey mode. In the mailed survey, the odds of reporting past CRC screening were almost thrice greater in the future second condition compared with the future first condition (72.4% versus 49.0%, respectively; OR, 2.74; 95% CI, 1.22-6.17). In the telephone condition, the odds of reporting were only 28% higher in the future second (69.5%) condition than in the future first condition (63.9%; OR, 1.28; 95% CI, 0.64-2.57). The results suggest that asking about future intentions to get screened before the actual behavior elicits lower, and arguably more truthful reports of CRC screening but mainly in mailed surveys.

Transition to adulthood: delays and unmet needs among adolescents and young adults with asthma.

OBJECTIVE: To examine the effect of the transition to adulthood on financial and non-financial barriers to care in youth with asthma. STUDY DESIGN: With National Health Interview Survey data from 2000 to 2005, we examined delays and unmet needs because of financial and non-financial barriers, evaluating the effect of adolescent (age, 12-17 years; n = 1539) versus young adult age (age, 18-24 years; N = 833), controlling for insurance, usual source of care, and sociodemographic characteristics. We also simulated the effects of providing public insurance to uninsured patients and a usual source of care to patients without one. RESULTS: More young adults than adolescents encountered financial barriers resulting in delays (18.6% versus 8%, P < .05) and unmet needs (26.6% versus 11.4%, P < .05), although delays caused by non-financial barriers were similar (17.3% versus 14.9%, P = not significant). In logistic models young adults were more likely than adolescents to report delays (odds ratio [OR], 1.45; 95% CI, 1.02-2.08) and unmet needs (OR, 1.8; 95% CI, 1.29-2.52) caused by financial barriers. CONCLUSIONS: Delays and unmet needs for care caused by financial reasons are significantly higher for young adults than they are for adolescents with asthma.

Four decades of population health data: the integrated health interview series as an epidemiologic resource.

The National Health Interview Survey (NHIS) is a primary source of information on the changing health of the US population over the past 4 decades. The full potential of NHIS data for analyzing long-term change, however, has rarely been exploited. Time series analysis is complicated by several factors: large numbers of data files and voluminous documentation; complexity of file structures; and changing sample designs, questionnaires, and variable-coding schemes. We describe a major data integration project that will simplify cross-temporal analysis of population health data available in the NHIS. The Integrated Health Interview Series (IHIS) is a Web-based system that provides an integrated set of data and documentation based on the NHIS public use files from 1969 to the present. The Integrated Health Interview Series enhances the value of NHIS data for researchers by allowing them to make consistent comparisons across 4 decades of dramatic changes in health status, health behavior, and healthcare.

Local access to care programs (LACPs): new developments in the access to care for the uninsured.

CONTEXT: New, locally based health care access programs are emerging in response to the growing number of uninsured, providing an alternative to health insurance and traditional safety net providers. Although these programs have been largely overlooked in health services research and health policy, they are becoming an important local supplement to the historically overburdened safety net. METHODS: This article is based on a literature review, Internet search, and key actor interviews to document programs in the United States, using a typology to classify the programs and document key characteristics. FINDINGS: Local access to care programs (LACPs) fall outside traditional private and publicly subsidized insurance programs. They have a formal enrollment process, eligibility determination, and enrollment fees that give enrollees access to a network of providers that have agreed to offer free or reduced-price health care services. The forty-seven LACPs documented in this article were categorized into four general models: three-share programs, national-provider networks, county-based indigent care, and local provider-based programs. CONCLUSIONS: New, locally based health access programs are being developed to meet the health care needs of the growing number of uninsured adults. These programs offer an alternative to traditional health insurance and build on the tradition of county-based care for the indigent. It is important that these locally based, alternative paths to health care services be documented and monitored, as the number of uninsured adults is continuing to grow and these programs are becoming a larger component of the U.S. health care safety net.

The impact of smoking-cessation intervention by multiple health professionals.

BACKGROUND: Smokers have contact with many different types of health professionals. The impact of tobacco intervention by multiple types of heath professionals is not known. METHODS AND MATERIALS: As part of the 2003 Minnesota Adult Tobacco Survey, smokers (n=1723) reported on tobacco treatment by medical doctors, nurses, dentists, pharmacists, or other health professionals. This analysis examined: (1) smokers' report of tobacco intervention by different types of healthcare providers, (2) the proportion of smokers who report intervention by multiple provider types, and (3) the relationship between smokers' report of intervention by multiple provider types and readiness to quit, quit attempts, and recent quitting. RESULTS: Among past-year smokers, 65% had visits with two or more types of health professionals. Among smokers who visited health professionals (n=1523), only 34% reported being asked about smoking by two or more types of professionals. Among current smokers (n=1324), advice or assistance from more than one type of professional was uncommon (26% and 7%, respectively). Being asked about smoking by two or more types of professionals substantially increased the odds of recent quitting (OR=2.37; 95% CI=1.15-4.88). Among current smokers, being advised to quit by two or more types of professionals increased the odds of having made a quit attempt in the past year (OR=2.92; 95% CI=1.56-5.45) or intending to quit in the next 6 months (OR=2.17; 95% CI=1.10-4.29). CONCLUSIONS: Smoking-cessation interventions by more than one type of health professional have the potential to substantially increase quitting and readiness to quit in the population.

Medicaid undercount and bias to estimates of uninsurance: new estimates and existing evidence.

OBJECTIVE: To examine whether known Medicaid enrollees misreport their health insurance coverage in surveys and the extent to which misreports of lack of coverage bias estimates of uninsurance. DATA SOURCE: Primary survey data from the Medicaid Undercount Experiment. STUDY DESIGN: Analyze new data from surveys of Medicaid enrollees in California, Florida, and Pennsylvania and summarize existing research examining bias in coverage estimates due to misreports among Medicaid enrollees. DATA COLLECTION METHOD: Subjects were randomly drawn from Medicaid administrative records and were surveyed by telephone. PRINCIPAL FINDINGS AND CONCLUSIONS: Cumulative evidence shows that a small percentage of Medicaid enrollees mistakenly report being uninsured, resulting in modest upward bias in estimates of uninsurance. A somewhat larger percentage of enrollees report having some other type of coverage than no coverage, biasing Medicaid enrollment estimates downward but not biasing estimates of uninsurance significantly upward. Implications for policy makers' confidence in survey estimates of coverage are discussed.

Accuracy in self-reported health insurance coverage among Medicaid enrollees.

The largest portion of the Medicaid undercount is caused by survey reporting error--that is, Medicaid recipients misreport their enrollment in health insurance coverage surveys. In this study, we sampled known Medicaid enrollees to learn how they respond to health insurance questions and to document correlates of accurate and inaccurate reports. We found that Medicaid enrollees are fairly accurate reporters of insurance status and type of coverage, but some do report being uninsured. Multivariate analyses point to the prominent role of program-related factors in the accuracy of reports. Our findings suggest that the Medicaid undercount should not undermine confidence in survey-based estimates of uninsurance.

Persistent disparities in health insurance coverage: Hispanic children, 1996 to 2005.

OBJECTIVE: To identify how health insurance coverage trends changed for Hispanic children between 1996 and 2005. METHODS: Data from the Current Population Survey Annual Social and Economic Supplement were analyzed to determine health insurance coverage rates for Hispanic children and logistic regression was used to determine the role of race/ethnicity on health insurance status, adjusting for citizenship status, child characteristics, migration status, and geography. RESULTS: The proportion of uninsured Hispanic children decreased significantly. However, the increased likelihood of a Hispanic child being uninsured relative to non-Hispanic White children did not change during this period. CONCLUSIONS: Expansions in public health insurance programs between 1996 and 2005 increased health insurance coverage for Hispanic children but disparities between Hispanic and non-Hispanic White children persist.

Comparison of two within-household selection methods in a telephone survey of substance abuse and dependence.

PURPOSE: Random-digit dial telephone surveys often rely on the random selection of one respondent within the household. We compared a new method of within-household selection to a standard "next birthday" approach on selected survey process measures, respondent characteristics, and substantive results. METHODS: From October 2004 through June 2005, we conducted a survey of adults in Minnesota to obtain information about substance use and need for treatment. Control group respondents (n = 1944) were selected using the "next birthday" method, and experimental group respondents (n = 1086) were selected using a new method developed by Rizzo, Brick, and Park. We assessed group differences for survey process measures, such as the number of attempts to interview and the refusal, response, and cooperation rates. We also examined whether the groups differed in demographic factors, substance use, and mental health. RESULTS: The experimental group had higher rates of refusal and lower response and cooperation rates. Demographic factors and most measures of substance use and mental health did not differ significantly between groups. CONCLUSIONS: The experimental method of within-household selection developed by Rizzo and colleagues does not offer advantages over the classic "next birthday" method. Study limitations are discussed and opportunities for future research are identified.

Mixed-mode administration reduced bias and enhanced poststratification adjustments in a health behavior survey.

OBJECTIVE: To assess whether a mixed-mode survey design reduced bias and enhanced methods commonly used to correct for bias (poststratification weighting). STUDY DESIGN AND SETTING: The data for this paper are from a study of 1,900 adult patients enrolled in a randomized controlled trial to promote repeat treatment for relapsed smokers at five Veteran's Affairs Medical Centers. A sequential mixed-mode design was used for data collection whereby the initial attempt was conducted using phone administration, with mail follow-up for nonresponders. Analyses examined demographic, health, and smoking cessation treatment seeking differences between telephone responders, mail responders, and nonresponders and compared the relative effectiveness of global vs. targeted poststratification weighting adjustments for correcting for response bias. RESULTS: The findings suggest (1) that responders to the additional survey mode (mail) did not significantly differ from responders to the first mode (phone) or nonresponders and (2) that poststratification weighting adjustments that take this additional information into account perform better than the standard global adjustments. CONCLUSIONS: A mixed-mode design can improve survey representativeness and enhance the performance of poststratification weighting adjustments.

Are the Current Population Survey uninsurance estimates too high? An examination of the imputation process.

RESEARCH OBJECTIVE: 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). DATA SOURCE: 2004 CPS-ASEC. STUDY DESIGN: 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. POPULATION STUDIED: The noninstitutionalized U.S. population under 65 years of age in 2004. DATA EXTRACTION METHODS: 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). PRINCIPAL FINDINGS: 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). CONCLUSIONS: 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. PRIMARY FUNDING SOURCE: The Robert Wood Johnson Foundation.

Agreement between self-reported and administrative race and ethnicity data among Medicaid enrollees in Minnesota.

OBJECTIVE: This paper measures agreement between survey and administrative measures of race/ethnicity for Medicaid enrollees. Level of agreement and the demographic and health-related characteristics associated with misclassification on the administrative measure are examined. DATA SOURCES: Minnesota Medicaid enrollee files matched to self-report information from a telephone/mail survey of 4,902 enrollees conducted in 2003. STUDY DESIGN: Measures of agreement between the two measures of race/ethnicity are computed. Using logistic regression, we also assess whether misclassification of race/ethnicity on administrative files is associated with demographic factors, health status, health care utilization, or ratings of quality of health care. DATA EXTRACTION: Race/ethnicity fields from administrative Medicaid files were extracted and merged with self-report data. PRINCIPAL FINDINGS: The administrative data correctly classified 94 percent of cases on race/ethnicity. Persons who self-identified as Hispanic and those whose home language was English had the greater odds (compared with persons who self-identified as white and those whose home language was not English) of being misclassified in administrative data. Persons classified as unknown/other on administrative data were more likely to self-identify as white. CONCLUSIONS: In this case study in Minnesota, researchers can be reasonably confident that the racial designations on Medicaid administrative data comport with how enrollees self-identify. Moreover, misclassification is not associated with common measures of health status, utilization, and ratings of quality of care. Further replication is recommended given variation in how race information is collected and coded by Medicaid agencies in different states.

Mixing web and mail methods in a survey of physicians.

OBJECTIVE: To assess the effects of two different mixed-mode (mail and web survey) combinations on response rates, response times, and nonresponse bias in a sample of primary care and specialty internal medicine physicians. DATA SOURCES/STUDY SETTING: Primary data were collected from 500 physicians with an appointment in the Mayo Clinic Department of Medicine (DOM) between February and March 2005. STUDY DESIGN: Physicians were randomly assigned to receive either an initial mailed survey evaluating the Electronic Medical Record (EMR) with a web survey follow-up to nonrespondents or its converse-an initial web survey followed by a mailed survey to nonrespondents. Response rates for each condition were calculated using standard formula. Response times were determined as well. Nonresponse bias was measured by comparing selected characteristics of survey respondents to similar characteristics in the full sample frame. In addition, the distributions of results on key outcome variables were compared overall and by data collection condition and phase. PRINCIPAL FINDINGS: Overall response rates were somewhat higher in the mail/web condition (70.5 percent) than in the web/mail condition (62.9 percent); differences were more pronounced before the mode switch prior to the mailing to nonrespondents. Median response time was 2 days faster in the web/mail condition than in the mail/web (median=5 and 7 days, respectively) but there was evidence of under-representation of specialist physicians and those who used the EMR a half a day or less each day in the web/mail condition before introduction of the mailed component. This did not translate into significant inconsistencies or differences in the distributions of key outcome variables, however. CONCLUSIONS: A methodology that uses an initial mailing of a self-administered form followed by a web survey to nonrespondents provides slightly higher response rates and a more representative sample than one that starts with web and ends with a mailed survey. However, if the length of the data collection period is limited and rapid response is important, perhaps the web survey followed by a mailed questionnaire is to be preferred. Key outcome variables appear to be unaffected by the data collection method.

Estimating regression standard errors with data from the Current Population Survey's public use file.

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.