Children's Health Insurance Patterns: A Review of the Literature. B. DYNAMICS OF THE UNINSURED


In this section, we examine available research on the dynamics of uninsured children, including the length of time they are uninsured, the events leading to loss of coverage, and the events causing them to regain insurance. When examining the dynamics of the uninsured, it is important to understand that researchers' findings may differer markedly because of methodological differences in analyzing longitudinal data. Recall from the discussion on SIPP estimates of the uninsured in Chapter 2 that estimates of the duration of spells without health insurance can vary substantially depending on whether they are based on all spells or spells in progress at a point in time. Spells in progress at a point in time contain a disproportionate number of long spells. Because of this, estimates for all spells, which are often produced using survival analysis techniques, generally give a more accurate picture of the dynamics of the uninsured.

Families USA (1997) used the 1991 SIPP panel to examine how many children were uninsured and how long they were uninsured (see Table II.3A). Families USA did not use survival analysis for these estimates, electing instead to examine all the spells in progress during the 24-month period from February 1991 to January 1993. Of the 20.5 million children uninsured at least one month during that period, 47 percent were uninsured for 12 months or more, 15 percent were uninsured throughout, and only 7 percent were uninsured for less than 3 months.

Bennefield (1996b), using a survival analysis technique on data from the 1992 SIPP panel over a 28-month period, found that the median spell of noncoverage for those under age 18 was only 4.0 months (also shown in Table II.3A).(3) This was considerably shorter than the median spell of 5.8 months or longer for all other age groups. Bennefield's report does not give any further results on length of enrollment for children.

We also looked at research on the events triggering uninsurance for both adults and children. Insurance loss for most individuals is employment-related, according to analysis of SIPP data focusing on calendar year 1994 by The Lewin Group (1997, Draft). Of the two million Americans, on average, who became uninsured each month in 1994, about 58 percent cited changes in employment as their primary reason for losing coverage. Lewin defined a change in employment as loss of employment, loss of employment for a spouse or parent, termination of an employer plan, or a shift from full-time to part-time worker status. In addition, Lewin's analysis of SIPP data from 1991 through 1993 found that only about 8 percent of all persons lost their coverage due to a change in occupation for the same employer or a shift from full-time to part-time status. They also found that those with the lowest incomes were more than twice as likely as those with higher incomes to cite a job change as the reason for losing coverage.

The events triggering uninsurance for children appear to be somewhat different from that of all persons. Using data from a special coverage supplement to the 1993 NHIS, Lewin found that individuals under age 22 who lost their health insurance coverage were less likely to cite job related reasons than all people who lost coverage (44 versus 58 percent). But, they noted that an additional 18 percent indicated that they lost private coverage because they became ineligible as dependents due to age. Therefore, 62 percent of these children became uninsured due to some break in employer coverage. The reasons that children under age 22 lost their health insurance coverage according to Lewin's analysis of the 1993 NHIS are presented in Table III.3 (reproduced from the Lewin report).

Little research has been done on the events that cause uninsured children to regain their health insurance. Blumberg et al. (1997), using data from the 1990 panel of the SIPP, examined theinsurance status of children in wave 8 of the SIPP who were uninsured in wave 1.(4) Blumberg et al. found that of those uninsured in wave 1, 52 percent were uninsured in wave 8, 29 percent were privately insured, and 19 percent were enrolled in Medicaid.

Reasons for Losing Employer Coverage Percentage
Laid off, lost job, or unemployed 11.7
Spouse or parent laid off, lost job or unemployed 28.8
Employer stopped offering coverage 1.4
Cut back to part-time status 0.6
Benefits from employer/former employer ran out 1.1
Subtotal: Job Related Reasons 43.6
Death of a spouse 0.6
Divorce or separation 1.6
Became ineligible because of age 17.7
Too expensive 22.5
Voluntary terminationb 1.5
Other 12.5
Total 100.0
a Includes all persons currently without health insurance coverage who lost their coverage within the past three years

b Voluntary termination includes: dissatisfied with previous insurance, do not believe in insurance, and free/inexpensive care available

Source: The Lewin Group (1997, Draft) tabulations of the 1993 NHIS Health Insurance Supplement.

Only somewhat dated research has been done comparing the characteristics of the long-term uninsured with the short-term uninsured. Swartz, Marcotte, and McBride (1993a, 1993b), and Swartz and McBride (1990) measured various distributions of uninsured spell lengths in the 1984 SIPP panel using survival analysis. Swartz, Marcotte, and McBride (1993a) used a hazard model of spell durations to estimate the relative effects of socio-economic and demographic characteristics on the duration of a spell without health insurance. They found that monthly family income, educational attainment, and industry of employment in the month prior to losing health insurance are the characteristics that have the greatest impact on the exit rate from being without health insurance. In particular, a low exit rate is positively correlated with low family income, low educational attainment, and employment in specific industrial sectors (agriculture/forestry/fishing and mining combined, construction, personal services and entertainment services combined, and public administration).

Monheit and Schur (1988) used the 1984 SIPP panel to examine various cohorts of the uninsured population. They did not, however, use survival analysis techniques. They found that the uninsured were heterogenous, consisting of many persons who lost coverage for relatively short periods of time, others who experienced periodic spells without coverage, and those who were persistently uninsured. The persistently uninsured, compared with all persons who lost coverage, were a much more economically disadvantaged group with far less labor market attachment and less access to employment related insurance. Monheit and Schur pointed out that longitudinal analyses of the uninsured are useful because the characteristics of the uninsured differ by spell length.