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David E. Nelson, MD, MPH, Julie Bolen, PhD, Henry E. Wells, MS, Suzanne M. Smith, MD, MPH, MPA and Shayne Bland, MS

David E. Nelson is with the Office on Smoking and Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Ga. Julie Bolen and Suzanne M. Smith are with the Division of Adult and Community Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention. Henry E. Wells is with the Research Triangle Institute, Atlanta, Ga. At the time of the study, Shayne Bland was with Children’s Hospital, Denver, Colo.

Correspondence: Correspondence should be sent to David E. Nelson, MD, MPH, Centers for Disease Control and Prevention, 4770 Buford Hwy NE, Mail Stop K-50, Atlanta, GA 30341 (e-mail: den2{at}cdc.gov).

	ABSTRACT

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION CONCLUSIONS References

 

Objectives. We analyzed state-specific uninsurance trends among US adults aged 18 to 64 years.

Methods. We used logistic regression models to examine Behavioral Risk Factor Surveillance System data for uninsurance from 1992 to 2001 in 47 states.

Results. Overall, uninsurance rates increased in 35 states and remained unchanged in 12 states. Increases were observed among people aged 30 to 49 years (in 34 states) and 50 to 64 years (in 24 states), and increases were also observed among individuals at middle and low income levels (in 39 states and 19 states, respectively), individuals employed for wages (in 33 states), and the self-employed (in 18 states).

Conclusions. Among adults aged 18–64, rates of uninsurance increased in most states from 1992 through 2001. Decreased availability of employer-sponsored health insurance, rising health care costs, and state fiscal crises are likely to worsen the growing uninsurance problem.

	INTRODUCTION

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Lack of health insurance has been a longstanding concern in the United States,¹ especially because uninsurance has well-documented negative consequences for the public’s health.^2–4 Furthermore, uncompensated care provided to uninsured individuals strains a health care system that is struggling to meet the escalating costs of new technologies and treatments as well as the health care needs of an aging population.⁵

Historically, employment and health insurance status in the United States have been closely linked.⁶ Although percentages have generally declined over time,⁶ nearly 63% of people with insurance are covered through employment-based policies.^7–10 The United States experienced its longest peacetime economic expansion during the 1990s, with the national unemployment rate declining substantially over this period. Despite this economic expansion, there was little change in national uninsurance estimates; uninsurance increased from 15.0% in 1992 to 16.3% in 1998 and then decreased to 14.6% in 2001.^7,11,12 However, recent reports suggest that uninsurance is increasing among individuals at middle and high income levels and among those aged 50 to 64 years.^7,13–15

But national data can mask important variations in uninsurance across states. According to 3-year state averages calculated for the period 1999 through 2001, uninsurance rates ranged from 7.2% in Rhode Island to 23.2% in New Mexico.⁷ States have different demographics, economies, and policies for public funding of health insurance, all of which contribute to levels of uninsurance.⁶ For example, some states enacted measures to broaden health care access during the 1980s and 1990s.^16,17

To date, only a few state-level analyses of uninsurance trends during the 1990s have been conducted, primarily based on Current Population Survey (CPS) data.^7,9,12,18,19 Unfortunately, CPS data must be pooled across years, because annual sample sizes are small for many states⁸ and extensive changes in survey wording made in 1995 have raised concerns about the accuracy of trend analyses.^8,20 Other ongoing federal and privately funded research data collection efforts have not been designed to track state-level trends in uninsurance,⁸ have included data from only a limited number of states,⁸ or have examined changes in health insurance during this time period on populations in only a few states or metropolitan areas.⁸

Little is known about broader, population-wide trends in uninsurance among adults at the state level, especially in the case of certain demographic subgroups (e.g., groups categorized according to age or employment status) among which uninsurance estimates are known to vary widely.^21,22 With most states currently facing fiscal difficulties, rising unemployment, and increasing health care costs, and with the burden of paying for uninsured health care falling disproportionately on state and local governments, detailed state trend data are needed to describe the extent of the uninsurance problem among adults and help guide policymakers’ efforts.

State-specific adult data on uninsurance are available from the Behavioral Risk Factor Surveillance System (BRFSS), which has used the same question over time to gather information on insurance status. We analyzed BRFSS data from 1992 through 2001 to assess state uninsurance trends among adults aged 18 to 64 years. We limited our analyses to these ages because most adults aged 65 years or older are covered by Medicare.^7,12 In addition to overall state uninsurance trends, we also examined state-specific trends according to age group, household income, and employment classification.

	METHODS

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Using the BRFSS, we obtained data from the period 1992 to 2001 for adults aged 18 to 64 years who resided in 47 states. The BRFSS is coordinated by the Centers for Disease Control and Prevention and all state health departments in the United States. Each month, state health departments conduct telephone surveys of randomly selected, noninstitutionalized adults 18 years and older to obtain information on personal behaviors associated with increased risks for chronic diseases and with other health-related factors. More details regarding the BRFSS, including its design, sampling frame, and analytical methods, are available elsewhere.^23,24

Arkansas, Wyoming, and the District of Columbia were excluded because of missing data on health insurance status during 1 or more years. Alaska was excluded because of the substantial misclassification of respondents in this state who were covered by Alaska Native health and medical services. Median state samples ranged from 1494 in 1992 to 3218 in 2001, and state samples across the study period ranged from 948 to 7019. Median state response rates, based on percentages of eligible individuals reached by telephone, ranged from 70.6% in 1992 to 51.1% in 2001.

During all years covered in the study period, respondents’ health insurance status was defined according to their responses to the question "Do you have any kind of health care coverage, including health insurance, pre-paid plans such as HMOs, or government plans such as Medicare?" Those who responded "no" were classified as uninsured. Respondents were excluded if they had missing or unknown responses or refused to answer this question. In the case of all of the study years, each state was missing less than 1% of relevant insurance status data.

Age, employment, and household (not individual) income levels were included in the analyses because of their known associations with health insurance status.^{1,15,21,22,25} Individuals were categorized into 3 age groups—18 through 29, 30 through 49, and 50 through 64 years—because, at the state level, numbers were insufficient to analyze data for smaller age groups. Employment status was categorized as follows: employed for wages, self-employed, unemployed, or other employment. Income level was categorized as less than $25 000, $25 000 through $49999, and $50 000 or more per year. We also included gender and race/ethnicity in the models. Race/ethnicity was classified as White non-Hispanic, Black non-Hispanic, Hispanic, or other.

Because of the complex survey design, we used SUDAAN²⁶ to analyze these data. Data were weighted for each year through the use of intercensal estimates based on the age, gender, and race distribution of each state’s population. Initially, we calculated descriptive statistics to characterize the samples of uninsured individuals during each year.

We tested for nonlinear time trends by fitting state-specific logit models that included uninsurance (outcome variable) and linear and quadratic terms for year (independent variables). If the quadratic term was not significant (P > .01), we assumed that a linear model was appropriate for trend analyses. The logit models for 7 states (Arizona, California, Georgia, Mississippi, Nebraska, New York, Oregon) exhibited significant nonlinear trends, while the models for the other 40 states met the linearity assumption.

We then created state-specific logistic regression models to examine uninsurance trends. These models included uninsurance as the dependent variable and gender, race/ethnicity, survey year, age group, employment category, and income level as independent variables. We also created state-specific models stratified by each age, employment, and income level. In these stratified models, we controlled for other demographic variables. For example, each age-stratified model controlled for gender, race/ethnicity, employment category, and income level. We restricted our models to subgroups with at least 50 state respondents in each year. Data from several states included fewer than 50 respondents in certain race/ethnicity subgroups (e.g., Black non-Hispanic and Hispanic); for these states, we collapsed race categories as necessary.

Although each model contained the same dependent and independent variables, we used one of a pair of modeling strategies based on the results of the linear time trend analyses. For the 40 states with linear trends, the overall and stratified state-specific models contained a linear term for year, and we used odds ratios to estimate average annual changes in the odds of being uninsured during the study period. We used an alternate approach to analyze data for the 7 states with nonlinear time trends. Models for these states treated year as a 10-level categorical variable, and we compared the odds of being uninsured in 2001 with the odds of being uninsured in 1992 (the referent).

We used our models to determine the predicted overall prevalence rates of uninsurance in 1992 and 2001 in each state. Estimates were indirectly standardized to the age, gender, race, and educational level of an "average" person.^27,28 Although model-based values are not actual prevalence estimates, they allow for appropriate comparisons across states over time and are similar to published state estimates.²⁴ Because of the number of analyses, and to minimize the role of statistical chance, we considered odds ratios statistically significant only when P values were less than .01.

	RESULTS

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Table 1 presents median state uninsurance estimates and ranges for 1992, 1996, and 2001 according to demographic group. Uninsurance was more frequent among people aged 18 to 29 years, Black non-Hispanics, Hispanics, people with low incomes, and people who were self-employed or unemployed. Estimates of uninsurance by demographic subgroup varied widely within the states. The most striking increases in uninsurance occurred among the low- and middle-income groups (less than $25 000 and $25 000–$49999).

Uninsurance increased in 33 states among people employed for wages, in 18 states among self-employed people, and in none of the states among the unemployed; no declines by employment category occurred in any state. Again, there was no particular geographic clustering of states exhibiting increases in uninsurance.

There were marked differences in demographic-specific trends in uninsurance among states. Eleven states exhibited increases for 6 or more of the 9 subgroups analyzed here: Colorado, Idaho, Indiana, Kentucky, Maine, Montana, North Carolina, Pennsylvania, South Carolina, South Dakota, and West Virginia. In contrast, no increases in uninsurance estimates were found for any of the demographic subgroups in California, Missouri, Oregon, and Tennessee.

	DISCUSSION

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Findings from this study of state uninsurance trends among adults younger than 65 years are troubling. Despite the economic expansion occurring during much of the study period, uninsurance increased in the vast majority of states. Particularly striking were the large number of states experiencing increases in uninsurance among persons aged 30 to 64 years, those with middle-level incomes, and those employed for wages. The best that can be said is that, in some states and for some demographic groups, the uninsurance situation among adults aged younger than 65 years did not worsen over this period. Only 4 states (California, Missouri, Oregon, and Tennessee) experienced no increase in uninsurance estimates overall for any of the demographic subgroups analyzed, while 11 states experienced increases for 6 or more of 9 demographic subpopulations.

State trends in uninsurance observed among people aged 50 to 64 years confirm previous research indicating that uninsurance is a growing problem for this age group.^14,15,25,29 Individuals in this group are typically dependent on private health care coverage, and they may lose insurance coverage because of early retirement, job transitions, higher cost sharing (e.g., increased deductibles), changes in marital status, or declining health.^10,14,15,25 Even more evident from our study is the growing problem of uninsurance among individuals aged 30 to 49 years. Some of the same issues facing the 50- to 64-year age group may affect individuals aged 30 to 49 years; however, further research is warranted on reasons for the increases in uninsurance among the latter group. With the exception of one state (North Carolina), uninsurance among people aged 18 to 29 years did not change over the study period. Reasons for trends in this group are not clear, but it should be remembered that uninsurance estimates among young adults are higher than those of any other age group.^30,31

Income- and employment-specific state trends indicate that uninsurance is no longer a concern only in the case of economically disadvantaged groups.^6,32 Although the largest increases were most evident for adults at middle and low income levels, some states also exhibited increases among those at the highest income levels. The trend of increasing uninsurance among those employed for wages provides more evidence that employment-based health care coverage is becoming less available to adults in many states.^10,33,34 The increase in uninsurance among the self-employed may reflect the increased cost of health insurance for those who need to obtain coverage directly from private insurers.³⁵ An increasing proportion of adults are either not being offered insurance through their employers or cannot afford to pay for private insurance policies.^1,34,36

Although there were broad overall state uninsurance trends, there was also much state-to-state variability. Some states exhibited widespread increases in uninsurance across many subpopulations, while others showed minimal or no changes. Of note was that 3 of the 4 states with no increases for any population group (California, Oregon, and Tennessee) instituted fairly comprehensive efforts to expand health care coverage during the 1990s, which may have contributed to the trends observed in these states.^37,38

However, reasons for state variations in uninsurance trends cannot be directly ascertained from the present data. Findings from surveillance systems such as the BRFSS are best viewed as hypothesis generating in nature, as opposed to explanatory. Our results should not be viewed as sufficient evidence for evaluating the effects of specific state health insurance programs or policies. Further research is needed to better understand the effects of policy or program changes within states.

Uninsurance is a multifaceted, dynamic problem influenced by many factors, none of which occur in isolation. States differ in terms of economic level of activity and employment base (and, by extension, the degree of employer-sponsored coverage), health care inflation, and health care access policies or programs.^39,40 For example, the shift over time in employment from manufacturing to service industries may play a role, as service industry employers are less likely to offer health benefits.^18,33,41

A comparison of our findings with those of other surveys is not possible for a number of reasons. First, several surveys (e.g., the Medical Expenditure Panel Survey, the Survey of Income Program and Participation, and the Community Tracking Survey) are not designed to provide state estimates of insurance coverage.⁸ The National Survey of America’s Families provides state-specific estimates, but only for 13 states, and it does not include all the years covered in this study.^8,42 Second, as mentioned previously, state-level estimates from the CPS are based on small numbers of respondents in many cases, published state data are not limited to adults aged 18 to 64 years, and the CPS involves a different definition of uninsurance, one that changed in 1995. Third, in our trend analyses, we used regression modeling to control for changes over time in state demographics.

Our study involved several limitations. Our findings are applicable only to adults aged 18 to 64 years in the general populations of the states examined; previous research has shown that some state and federal health reform efforts, such as the State Children’s Health Insurance Program, can increase health insurance coverage rates among children and the indigent.^5,16 We relied on self-reports to ascertain uninsurance status; however, previous studies suggest that self-reported information on health insurance status is highly valid.^43,44 In addition, our results were based on self-reported insurance status at the time of the interview, whereas other surveys have used different criteria, such as presence or absence of insurance coverage during the previous 12 months, which potentially can result in different estimates.⁸ However, a major strength of the BRFSS data is that the same question was used to assess insurance status across all 10 years of the study period.

Although these limitations are unlikely to have a substantial impact on the trends observed, our data probably underestimate the prevalence of uninsurance, given that people without telephones are more likely to have low incomes,⁴⁵ and low income levels are associated with uninsurance.¹² Typical of other telephone surveys conducted during the 1990s,⁴⁶ BRFSS response rates declined over the study period in nearly all of the states examined. BRFSS weighting procedures partially adjust for nonresponse, but the impact of declining response rates on trends is uncertain.

	CONCLUSIONS

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION CONCLUSIONS References

 
Uninsurance remained a persistent problem from 1992 through 2001 and actually worsened in most states among adults aged 18 to 64 years, despite the booming economy present during most of this period. In particular, state uninsurance trends worsened among population groups not traditionally considered to be at risk for uninsurance. Even in states such as Oregon and Tennessee that greatly expanded health care coverage during this period, there was little population-wide improvement in uninsurance rates, and the efforts in these 2 states have been sharply curtailed recently because of substantial funding reductions.^37,38

This study strongly suggests that state-level health reform efforts have not been successful in producing population-wide reductions in the prevalence of uninsurance among persons aged 18 to 64 years. Given the fiscal challenges experienced by states over the past few years, the financial difficulties facing many employers, and rapidly increasing health care costs,⁴⁷ the problem of uninsurance among adults aged younger than 65 years is likely to increase in the absence of broader efforts to address this issue.

	Acknowledgments

 
Shayne Bland passed away on March 20, 2002.

We thank the state BRFSS coordinators for their assistance with data collection and Paul Mowery, MS, and Glenn Laird, PhD, for their input on statistical analyses. In addition, we thank Karen Davis, PhD, Katherine Swartz, PhD, and Betsy Thompson, MD, MSPH, for their constructive reviews of an earlier version of this article.

Human Participant Protection
No protocol approval was needed for this study.

	Footnotes

 
Peer Reviewed

Contributors
D.E. Nelson, J. Bolen, and S.M. Smith participated in the conception and design of the study, interpretation of the data, and the writing of the article. H.E. Wells conducted the data analyses. S. Bland participated in the initial planning of the study and preliminary data analyses.

Accepted for publication September 12, 2003.

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