OBJECTIVES We sought to determine whether aggregate national data for American Indians/Alaska Natives (AIANs) mask geographic variation and substantial subnational disparities in prenatal care utilization. METHODS We used data for US births from 1995 to 1997 and from 2000 to 2002 to examine prenatal care utilization among AIAN and non-Hispanic White mothers. The indicators we studied were late entry into prenatal care and inadequate utilization of prenatal care. We calculated rates and disparities for each indicator at the national, regional, and state levels, and we examined whether estimates for regions and states differed significantly from national estimates. We then estimated state-specific changes in prevalence rates and disparity rates over time. RESULTS Prenatal care utilization varied by region and state for AIANs and non-Hispanic Whites. In the 12 states with the largest AIAN birth populations, disparities varied dramatically. In addition, some states demonstrated substantial reductions in disparities over time, and other states showed significant increases in disparities. CONCLUSIONS Substantive conclusions about AIAN health care disparities should be geographically specific, and conclusions drawn at the national level may be unsuitable for policymaking and intervention at state and local levels. Efforts to accommodate the geographically specific data needs of AIAN health researchers and others interested in state-level comparisons are warranted.

We examined indicators of health status and healthcare utilization according to immigration status to assess the 'healthy immigrant effect' for Chinese adults. Data for Chinese in Taiwan (n = 15,549) were from the 2001 Taiwan National Health Interview Survey (NHIS). Data for U.S.-born Chinese (n = 964) and Chinese Immigrants in the U.S. (n = 253) were from the 1998-2004 U.S. NHIS. We used multivariate logistic regression to estimate the adjusted odds of perceived poor health, having ever smoked, and past year emergency room visits according to immigration status. For Chinese immigrants, more years in the U.S. were associated with lower odds of reporting poor health (OR = 0.4; 95% CI = 0.2-0.8) and past-year emergency room use (OR = 0.5; 95% CI = 0.3-0.9). Compared with recent Chinese immigrants (<5 years in U.S.), Chinese in Taiwan had higher odds of reporting poor health (OR = 6.2; 95% CI = 3.2-12.1) and having ever smoked (OR = 1.6; 95% CI = 1.1-2.5). Our results suggest that those who migrate have better health profiles than those who do not migrate. However, recent Chinese immigrants were not significantly different than U.S.-born Chinese.

We use data from the National Health Interview Survey (2000-2006) to examine the social determinants of health insurance coverage and access to care for immigrant children by 10 global regions of birth. We find dramatic differences in the social and economic characteristics of immigrant children by region of birth. Children from Mexico and Latin America fare worse than immigrant children born in the U.S. with significantly lower incomes and little or no education. These social determinants, along with U.S. public health policies regarding new immigrants, create significant barriers to access to health insurance coverage, and increase delayed or foregone care. Uninsured immigrant children had 6.5 times higher odds of delayed care compared with insured immigrant children.

Objectives. We examined rates of uninsurance among workers in the US health care workforce by health care industry subtype and workforce category. Methods. We used 2004 to 2006 National Health Interview Survey data to assess health insurance coverage rates. Multivariate logistic regression analyses were conducted to estimate the odds of uninsurance among health care workers by industry subtype. Results. Overall, 11% of the US health care workforce is uninsured. Ambulatory care workers were 3.1 times as likely as hospital workers (95% confidence interval [CI] = 2.3, 4.3) to be uninsured, and residential care workers were 4.3 times as likely to be uninsured (95% CI = 10,6.11). Health service workers had 50% greater odds of being uninsured relative to workers in health diagnosing and treating occupations (odds ratio [OR] = 1.5; 95% Cl = 1.0, 2.4). Conclusions. Because uninsurance leads to delays in seeking care, fewer prevention visits, and poorer health status, the fact that nearly 1 in 8 health care workers lacks insurance coverage is cause for concern. (Am J Public Health. 2009;99:2282-2288. doi:10.2105/AJPH.2008.152413)

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 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.

This study examines the relationship between difficulties accessing health care and voting behavior, in order to assess the possible impact that increasing constraints on access to care will have on future voting behavior. Using data from the American National Election Study we found that the proportion of people with difficulty accessing care increased significantly from 27% in 2000 to 35% in 2004. A larger proportion of those with difficulties in accessing care voted in 2004 than in 2000 and their preferences also changed to more heavily favor the Democratic candidate. If the number of those experiencing difficulties accessing care continues to grow, access to care could become a more salient campaign issue. In 2004, Democratic candidates were favored by this group, which is likely to be a constituency that both parties will try to capture in future elections.

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 ...

Medicaid and the State Children's Health Insurance Program need analytic tools to manage their programs. Drawing upon extensive discussions with experts in states, this article describes the state of the art in tool use, making several observations: (1) Several states have linked Medicaid/State Children's Health Insurance Program administrative data to other data (eg, birth and death records) to measure access to care. (2) Several states use managed care encounter data to set payment rates. (3) The analysis of pharmacy claims data appears widespread. The article also describes "lessons learned" regarding building capacity and improving data to support the implementation of management tools.

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...

The Demographic Supplement to the U.S. Current Population Survey (CPS) is used to produce state estimates of health insurance coverage and income. These estimates are used in federal allocation formulas that distribute $10-11 billion annually to states for the State Children's Health Insurance Program (SCHIP) and the Elementary and Secondary Education Act. The purpose of this article is to examine the CPS for evidence of bias in state estimates due to missing data imputation and estimate the extent of the bias for each of the fifty-one states and Washington DC. Comparing three years of CPS (1998-2000), to the Census 2000 Supplementary Survey and 1990 Decennial Census data benchmarks, we find evidence of bias in state estimates of earned income. We also extend the technique to the CPS state health insurance coverage estimates and find even more evidence of bias. In general, the "better off" states (those with higher insurance coverage rates or more income) tend to be even "better off" (have higher estimates of average income and coverage rates) after correcting for bias (and vice versa). We conclude by considering alternative strategies for the U.S. Census Bureau to alter its current imputation procedures.

A national data warehouse that links public and private data could be used to monitor trends in healthcare costs, utilization, quality of care, and adherence to quality guidelines and changes in treatment protocols. The development of the data warehouse, however, would require overcoming a number of political and technical challenges to gain access to private insurance data. This article outlines recommendations from a national conference sponsored by the Agency for Healthcare Research and Quality (AHRQ) on the private sector's role in quality monitoring and provides an operational outline for the development of a national private sector health data warehouse.