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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.

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OBJECTIVE: To synthesize evidence on the accuracy of Medicaid reporting across state and federal surveys. DATA SOURCES: All available validation studies. STUDY DESIGN: Compare results from existing research to understand variation in reporting across surveys. DATA COLLECTION METHODS: Synthesize all available studies validating survey reports of Medicaid coverage. PRINCIPAL FINDINGS: Across all surveys, reporting some type of insurance coverage is better than reporting Medicaid specifically. Therefore, estimates of uninsurance are less biased than estimates of specific sources of coverage. The CPS stands out as being particularly inaccurate. CONCLUSIONS: Measuring health insurance coverage is prone to some level of error, yet survey overstatements of uninsurance are modest in most surveys. Accounting for all forms of bias is complex. Researchers should consider adjusting estimates of Medicaid and uninsurance in surveys prone to high levels of misreporting.

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OBJECTIVE: To assess nonresponse bias in a mixed-mode general population health survey. DATA SOURCES: Secondary analysis of linked survey sample frame and administrative data, including demographic and health-related information. STUDY DESIGN: The survey was administered by mail with telephone follow-up to nonrespondents after two mailings. To determine whether an additional mail contact or mode switch reduced nonresponse bias, we compared all respondents (N = 3,437) to respondents from each mailing and telephone respondents to the sample frame (N = 6,716). PRINCIPAL FINDINGS: Switching modes did not minimize the under-representation of younger people, nonwhites, those with congestive heart failure, high users of office-based services, and low-utilizers of the emergency room but did reduce the over-representation of older adults. CONCLUSIONS: Multiple contact and mixed-mode surveys may increase response rates, but they do not necessarily reduce nonresponse bias.

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OBJECTIVES: This report updates subnational estimates of the percentage of adults and children living in households without a landline telephone but with at least one wireless telephone (i.e., wireless-only households). State-level estimates for 2011 are presented, as well as estimates for selected U.S. counties and groups of counties, for other household telephone service use categories (e.g., those that had only landlines and those that had landlines yet received all or almost all calls on wireless telephones), and for two earlier 12-month periods (January-December 2010 and July 2010-June 2011). METHODS: Small-area statistical modeling techniques were used to estimate the prevalence of adults and children living in households with various household telephone service types for 93 disjoint geographic areas that make up the United States. This modeling was based on 2007-2011 data from the National Health Interview Survey, 2006-2010 data from the American Community Survey, and auxiliary information on the number of listed telephone lines per capita in 2007-2011. RESULTS: The prevalence of wireless-only adults and children varied substantially across states. State-level estimates for 2011 ranged from 15.3% (Rhode Island) to 44.6% (Idaho) of adults and from 15.2% (Rhode Island) to 58.6% (Mississippi) of children.

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BACKGROUND: The American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) has become an important surgical quality program in the United States, yet few studies describe their methods for handling missing data. Our study examines the impact of missing data on predictive models for short-term operative outcomes after cancer surgery in the ACS NSQIP database. STUDY DESIGN: We identified 97,230 patients who underwent oncologic resections for neoplasms in the 2005-2009 ACS NSQIP. We used multivariable logistic regression to assess the impact of pre-, intra-, and postoperative factors on short-term operative outcomes by type of procedure where missing values were included as a variable category, excluded, and imputed. RESULTS: A large proportion (72.8%) of patients had one or more missing pre-, intra-, or postoperative characteristics, particularly preoperative laboratory values. Missing data were more frequent in healthier patients and those undergoing lower-risk procedures. Although data were not missing at random, the impact of preoperative risk factors on adverse operative outcomes after cancer surgery was similar across methods for handling missing data. However, analytic approaches using only patients with complete or imputed information risk basing the analysis on a potentially nonrepresentative sample. CONCLUSIONS: Missing data present challenges to interpreting predictors of short-term operative outcomes after cancer surgery at ACS NSQIP hospitals. Similar to best practices for other data sets, this study highlights the importance of using missing values carefully when using ACS NSQIP. Given its potential to introduce bias, the approach to handling missing values should be detailed in future ACS NSQIP studies.

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OBJECTIVES: This report presents state-level estimates of the percentage of adults and children living in households that did not have a landline telephone but did have at least one wireless telephone. National estimates for the 12-month time period from July 2009 through June 2010 indicate that 23.9% of adults and 27.5% of children were living in these wireless-only households. Estimates are also presented for selected U.S. counties and groups of counties, for other household telephone service use categories (e.g., those that had only landlines and those that had landlines yet received all or almost all calls on wireless telephones), and for 12-month time periods since January-December 2007. METHODS: Small-area statistical modeling techniques were used to estimate the prevalence of adults and children living in households with various household telephone service types for 93 disjoint geographic areas that make up the entire United States. This modeling was based on January 2007-June 2010 data from the National Health Interview Survey, 2006-2009 data from the American Community Survey, and auxiliary information on the number of listed telephone lines per capita in 2007-2010. RESULTS: The prevalence of wireless-only adults and children varied substantially across states. State-level estimates for July 2009-June 2010 ranged from 12.8% (Rhode Island and New Jersey) to 35.2% (Arkansas) of adults and from 12.6% (Connecticut and New Jersey) to 46.2% (Arkansas) of children. For adults, the magnitude of the increase from 2007 to 2010 was lowest in New Jersey (7.2 percentage points) and highest in Arkansas (14.5 percentage points).

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PURPOSE: To determine the extent of authorization bias in a study linking survey and medical record data in a general population-based investigation. METHODS: Authorization status (authorized data linkage vs. not) was ascertained through a sequential mixed mode mail and telephone survey conducted in Olmsted County, MN. Respondents (regardless of authorization status) were linked to the Rochester Epidemiology Project (REP), the medical record system for health care providers in Olmsted County. The REP provided data on gender, age, race, health status (co-morbid conditions), and health care utilization (ER admission, hospital admission, clinical office visits and procedures). Authorizers (n=1357) are compared to non-authorizers (n=217) with respect to these demographic and clinical characteristics. RESULTS: 86.2% of respondents authorized data linkage. Non-authorizers were younger, healthier (lower Charlson score), and less likely to have 3 or more recent clinical office visits. In multivariate analysis, Charlson score was no longer a significant predictor of authorization while an ER visit did predict authorization. CONCLUSIONS: Younger subjects are less likely to authorize data linkages. As such, researchers should be aware of this source of potential bias when analyzing population-based linked survey and administrative data. The presence of bias with respect to health care use is more complicated. It is dependent on how the concept is operationalized with heavy clinical users more likely to authorize and those with ER visits less so.

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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.

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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.

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OBJECTIVE: To compare health insurance coverage estimates from the American Community Survey (ACS) to the Current Population Survey (CPS-ASEC). DATA SOURCES/STUDY SETTING: The 2008 ACS and CPS-ASEC, 2009. STUDY DESIGN: 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. PRINCIPAL FINDINGS: 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. CONCLUSIONS: 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.

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OBJECTIVE: To create a consistent time series to understand coverage trends by harmonizing 20 years of insurance coverage estimates from the Current Population Survey (CPS) that are an available public resource. DATA SOURCE: 1990-2009 CPS Annual Social and Economic Supplement data. STUDY DESIGN: CPS data are enhanced to account for methodological and conceptual changes in health insurance measurement and population control totals. PRINCIPAL FINDINGS: The enhancements to the CPS result in an approximately 1 percent reduction in uninsurance rates. Reductions vary over time and by age group. Changes over the last two decades differ slightly using the two data sources. For example, the enhanced data show a greater erosion of private coverage. CONCLUSION: The enhanced data provide the most consistent measure of health insurance coverage over the past two decades.

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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.

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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.

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OBJECTIVE: To resolve a conflict in the literature on whether Medicaid-Managed Care (MMC) impacts the Medicaid Undercount. DATA SOURCES/STUDY SETTING: California county-level data (1995-1997) on MMC penetration, public use data from the Current Population Survey (CPS) (1995-1997), and restricted CPS data matched to administrative records on Medicaid enrollment (2001-2002). STUDY DESIGN: We explore the robustness of previous results from the literature first using aggregate data and alternative models. We then examine CPS data linked to Medicaid enrollment data to estimate models of Medicaid reporting errors related to MMC. DATA COLLECTION/EXTRACTION METHODS: The Census Bureau linked administrative data on Medicaid enrollment to the CPS. Other data used were public use. PRINCIPAL FINDINGS: We find similar results to a previous study using aggregate data that suggest that MMC worsens reporting of Medicaid enrollment. However, using alternative methods we find those results are not statistically significant and can have opposite signs. Our linked CPS microdata analysis suggests that MMC improves reporting. The article concludes with implications of these results for policy makers. CONCLUSION: It is unlikely that increased MMC penetration explains the increased Medicaid Undercount.

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OBJECTIVE: To examine the impact of response rate variation on survey estimates and costs in three health telephone surveys. DATA SOURCE: Three telephone surveys of noninstitutionalized adults in Minnesota and Oklahoma conducted from 2003 to 2005. STUDY DESIGN: We examine differences in demographics and health measures by number of call attempts made before completion of the survey or whether the household initially refused to participate. We compare the point estimates we actually obtained with those we would have obtained with a less aggressive protocol and subsequent lower response rate. We also simulate what the effective sample sizes would have been if less aggressive protocols were followed. PRINCIPAL FINDINGS: Unweighted bivariate analyses reveal many differences between early completers and those requiring more contacts and between those who initially refused to participate and those who did not. However, after making standard poststratification adjustments, no statistically significant differences were observed in the key health variables we examined between the early responders and the estimates derived from the full reporting sample. CONCLUSIONS: Our findings demonstrate that for the surveys we examined, larger effective sample sizes (i.e., more statistical power) could have been achieved with the same amount of funding using less aggressive calling protocols. For some studies, money spent on aggressively pursuing high response rates could be better used to increase statistical power and/or to directly examine nonresponse bias.

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OBJECTIVE: To evaluate the prevalence of smoking among young adults and to describe their characteristics. METHODS: Data were examined from the Minnesota Adult Tobacco Survey, a telephone survey of 8821 residents with a sample of 1205 young adults. RESULTS: Prevalence was 39% using the adolescent definition and 32% using the adult definition. Nearly 1 in 5 young adult smokers may be considered a "previously unrecognized smoker" who would not have been identified as a cigarette user according to the standard adult definition. CONCLUSIONS: Future studies assessing prevalence should use both adolescent and adult measures.

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We discover and document errors in public-use microdata samples ("PUMS files") of the 2000 Census, the 2003-2006 American Community Survey, and the 2004-2009 Current Population Survey. For women and men age 65 and older, age- and sex-specific population estimates generated from the PUMS files differ by as much as 15 percent from counts in published data tables. Moreover, an analysis of labor-force participation and marriage rates suggests the PUMS samples are not representative of the population at individual ages for those age 65 and over. PUMS files substantially underestimate labor-force participation of those near retirement age and overestimate labor-force participation rates of those at older ages. These problems were an unintentional byproduct of the misapplication of a newer generation of disclosure-avoidance procedures carried out on the data. The resulting errors in the public-use data could significantly impact studies of people age 65 and older, particularly analyses of variables that are expected to change by age.

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Virtually all quantitative microdata used by social scientists derive from samples that incorporate clustering, stratification, and weighting adjustments (Kish 1965, 1992). Such data can yield standard error estimates that differ dramatically from those derived from a simple random sample of the same size. Researchers using historical U.S. census microdata, however, usually apply methods designed for simple random samples. The resulting p values and confidence intervals could be inaccurate and could lead to erroneous research conclusions. Because U.S. census microdata samples are among the most widely used sources for social science and policy research, the need for reliable standard error estimation is critical. We evaluate the historical microdata samples of the Integrated Public Use Microdata Series (IPUMS) project from 1850 to 1950 in order to determine (1) the impact of sample design on standard error estimates, and (2) how to apply modern standard error estimation software to historical census samples. We exploit a unique new data source from the 1880 census to validate our methods for standard error estimation, and then we apply this approach to the 1850-1870 and 1900-1950 decennial censuses. We conclude that Taylor series estimation can be used effectively with the historical decennial census microdata samples and should be applied in research analyses that have the potential for substantial clustering effects.

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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 Medicaid "undercount": Fewer people report to survey takers that they're covered by Medicaid than program administrative data show are enrolled. Our study finds that the undercount can be explained by the inability of people to recall their insurance status accurately from the previous year. We suggest that other data sources, such as Census's American Community Survey, should be studied to determine whether they would provide better estimates of the uninsured.

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