In the United States, 50.7 million people, or 16.7% of the U.S. population, had no health insurance for at least some of 2009. This is the highest rate of uninsurance over the past decade. In fact, during the past decade, the aggregate uninsurance rate was never less than 13.4%. Uninsured individuals are, on average, more likely to forgo needed health care than insured individuals, including services for preventable or chronic conditions. In addition, the uninsured are less likely to have a "usual source of care," or a provider to oversee individual health care. For these and other reasons, the uninsured often have worse health outcomes than the insured. Among the most likely to be uninsured in 2009 were adults between 19 and 25, Hispanics, those living in families with incomes below 200% of the federal poverty guidelines, and those in families with relatively weak attachments to the labor force. This report characterizes the health insurance status of individuals in 2009. Following a brief discussion of the data, the report examines the relationship between types of health insurance (including employment-sponsored, other private, and Medicaid) and individual characteristics (including age, other demographic characteristics, and ties to the labor market). Next, the report demonstrates the different conclusions that might be drawn from different analyses of the uninsurance data. The report then compares the likelihood that various individuals with different demographic characteristics were uninsured. The report concludes with a discussion of trends in uninsurance since 1999. This report uses data from the 2010 Current Population Survey (CPS) conducted by the Census Bureau of the U.S. Department of Commerce. The CPS is a monthly survey of non-institutionalized civilian households primarily used to collect employment data. The Annual Social and Economic Supplement (ASEC) to the CPS collects information on individual health insurance status, income, and poverty. The ASEC is also known as the March Supplement, because most of the surveys are completed in March, with many questions covering the prior year. About 100,000 addresses comprise the sample households to be interviewed. Statistical techniques adjust the data to represent all households in the nation. The key variable in this report is whether each person was uninsured in 2009. More specifically, the uninsurance variable measures whether an individual lacked health insurance on any one representative day in 2009. This report, therefore, uses the term "uninsured" to mean at a point in 2009, and not necessarily over the entire year. The March Supplement to the CPS is one of several popular sources used to estimate the levels of uninsurance in the United States. Additional prominent data sources include the American Community Survey (ACS), the Medical Expenditure Panel Survey (MEPS), and the Survey of Income and Program Participation (SIPP). Each dataset gives broadly similar results, and CRS analyzes the data from the two largest data sets, the ACS and the CPS. This section covers the relationships between health insurance and an individual's demographic and employment characteristics. An individual's age and many other demographic characteristics cannot be changed by policy. Public policies, however, can affect income and labor force attachments. Table 1 provides a breakdown of health insurance coverage by type of insurance and age in 2009, in order of increasing age. Compared with other age groups, those under age five had the highest rates of coverage (40.0%) in Medicaid, CHIP, or some other public program for low-income individuals. On the other hand, young adults aged 19 to 25 were the most likely to have gone without coverage. While half in the young-adult age group (50.1%) were covered under an employment-based plan, almost one-third (32.7%) had no health insurance. Among people aged 65 and older, 93.5% were covered by Medicare, and less than 2% were uninsured. The remainder of this section focuses on the nonelderly population because of the high insurance rates among the elderly. In addition, Medicare, private-nongroup, and military/veterans insurance are combined into an "other category" because these insurance types enroll a relatively small number of individuals in the 64-and-under age cohort. Table 2 shows the rate of health insurance coverage by type of health insurance and selected demographic characteristics for people under age 65. In 2009, whites were least likely to be uninsured (14.0%), while Hispanics were most likely to be uninsured (33.9%). The rate of employment-based health coverage was highest among whites and Asians (69.1% and 63.1%, respectively), and the rate of public coverage was highest among blacks (30.3%). Although these differences are quantitative estimates of the racial differentials in insurance coverage, they in and of themselves do not provide reasons for the differences. In fact, this dichotomy is true throughout this report; while it is straightforward to estimate differences in uninsurance patterns across individual characteristics (or over time), the reasons for the differences usually remain speculative. Individuals livings in different family types have different insurance patterns. As indicated in Table 2 , people residing in two-parent families were most likely to have employment-based health insurance (68.7%) and least likely to be uninsured (13.1%). People in a family headed by a single mother were most likely to have public coverage (43.9%), compared with other family types, and those in a family headed by a single father, or a single man living alone, were most likely to be uninsured. Turning to regional differences, those under 65 years old were less likely to be uninsured if they lived in the Northeast or Midwest (14.2% and 15.1%, respectively) than if they lived in the South or West (22.3% and 20.3%, respectively). Employment-based health insurance covered about 65% in the Northeast and Midwest, compared with about 57% in the South and West. Income, as measured by poverty status, is a strong predictor of health insurance status. Among individuals with family incomes at least two times the poverty threshold, 12.1% went without health insurance, compared with 34.0% of the poor (i.e., those with family incomes below the poverty threshold). Only 15.6% of the poor received health coverage through employment, while 49.5% had public coverage. Of people with family incomes at least two times the poverty threshold, 77.1% were covered through an employer and 7.6% had public coverage. Finally, noncitizens had higher uninsured rates (48.0%) and lower employment-based coverage rates (34.5%) than native-born U.S. citizens (16.1% and 62.6%, respectively). Table 3 shows the rate of health insurance coverage for people under age 65 in 2009 by the employment characteristics of the primary worker in the family. Families where no one worked are omitted from the table. Family members were increasingly less likely to be uninsured as the primary worker's firm size increased. For example, members of a family where the primary worker was employed by a firm with less than 10 employees were more than three times as likely to be uninsured as family members where the primary worker was employed by a firm with over 1,000 employees. Some of this difference may occur because large firms frequently face lower costs for health insurance benefits than smaller firms. This cost differential occurs because larger firms are better able to spread the risks associated with high-insurance-cost individuals across many individuals. In addition, administration and marketing costs are lower in larger firms than in smaller firms. Members of a family where the primary worker had a strong attachment to the labor force were less likely to be uninsured than others. Members of a family where the primary worker was employed full-time for a full year were about half as likely to be uninsured as those in families where the primary worker was employed full-time for only part of the year (13.7% versus 26.0%, respectively). Those who worked part-time, either for all of 2009 or for a part of it, were even more likely to be uninsured. This section demonstrates that, in evaluating groups of uninsured individuals, it is important to decide on an appropriate comparison group. Although citizenship status is used as an example, the issues covered in this section are applicable to other traits as well. The conclusion is that different representations of the same data can lead to different conclusions if care is not taken when evaluating the data. Figure 1 looks at the total number of uninsured individuals and displays the percentage of uninsured individuals by citizenship status. From this picture alone, it appears that the greatest number of individuals in need of policy support are those who are native-born citizens. This is because almost 75% of the total pool of the uninsured are native-born citizens, while about 20% of the total pool of the uninsured are not citizens, with the remaining 5% being naturalized citizens. In this example, it is important to remember that all comparisons are relative to the total number of uninsured. The remaining figures, however, demonstrate that different conclusions might be drawn if the analysis compares the percentage of uninsured individuals within each group's citizenship status. These comparisons are illustrated in Figure 2 , Figure 3 , and Figure 4 for native-born citizens, noncitizens, and naturalized citizens. Figure 2 demonstrates that far more native-born citizens are insured (about 84%) than are uninsured (about 16%). Even though Figure 1 demonstrates that native-born citizens are the largest group of uninsured citizens, Figure 2 demonstrate that, of all native born citizens, 16% are uninsured. The comparison across the two measurements of noncitizens is also instructive. Figure 1 demonstrates that noncitizens comprise the smallest citizenship group of the uninsured. On the other hand, Figure 3 demonstrates that almost one half of noncitizens are uninsured. Similar disparities can be found when comparing the relative importance of uninsured naturalized citizens in Figure 1 and Figure 4 .