Several recent studies have examined the consequences of uninsurance in a near-elderly population using data from the longitudinal Health and Retirement Survey (Heeringa and Conner 1995). Baker et al. (2001, 2002) found that those who were continuously or intermittently uninsured, or lost their insurance coverage over a 2-4 year period, experienced greater health declines than those who were continuously insured. McWilliams et al. (2004, 2003) found that lack of insurance was associated with significantly increased mortality, and that previously uninsured near-elderly adults who survived to age 65 increased their use of basic clinical services after they obtained Medicare coverage more than those who had been fully insured.

These research findings raise two important questions. Does lack of insurance prior to age 65 result in people qualifying for Medicare in worse health than if they had been insured? If so, is public insurance spending through Medicare and Medicaid on newly enrolled beneficiaries greater than it would be if people had continuous insurance coverage prior to age 65?

Our analysis extends these previous studies in several ways. As the prior studies were not specifically interested in the question of health status at entry to Medicare, they included changes in health for people as young as 57, as well as people who were older than 65 and had already aged into Medicare coverage. If attaining Medicare coverage improves health (Lichtenberg 2002), then the previous results may understate the impact of lack of insurance on health status at age 65. We also analyze data from the Health and Retirement Survey (HRS), but define our endpoint as health status at the last survey before turning 65.
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Second, Baker et al. (2001, 2002) did not adjust for possible bias in the estimation of the health insurance effect because of the selection of people into insurance states based on their unobserved health. This bias could occur through a combination of mechanisms. People who are uninsured at this age and in good health may forego insurance coverage, especially nongroup coverage, because of its very high cost for older people. At the other extreme, people in poor health who are unable to work may qualify for Medicaid and/ or Medicare coverage because of a work-limiting disability. Similarly, people in less than perfect health with employer-sponsored insurance may be more likely to continue working to keep their insurance coverage, as opposed to taking early retirement without coverage. These behaviors raise the possibility that unobserved health, which affects future health, may be better among the uninsured and worse among the insured than if people were randomly assigned to alternative insurance states in an experiment.

McWilliams et al. (2004) used a propensity score method based on health insurance status in 1992 to adjust for the effects of observable differences associated with insurance coverage. However, this adjustment may not fully account for the effects of unobserved factors. We use instrumental variable (IV) analysis (McClellan and Newhouse 2000) to adjust for possible biases because of unobserved factors, focusing on the percentage of time a person was insured over the entire observation period prior to turning 65. (McWilliams et al. [2004] adjust only for insurance status at baseline, 1992.)

Third, Baker et al. (2001, 2002) measured the change in health by two categorical variables: a “major decline in health,” defined as a change in self-reported health status between baseline and endpoint either (1) from excellent, very good, or good health to fair or poor, or (2) from fair to poor, and a “new difficulty with mobility,” defined from specific questions asking whether the person had “no difficulty” with an activity at baseline, but was unable to perform the activity at the endpoint. Consequently, people already in poor health or unable to perform the mobility activities at baseline, as well as people who died, were excluded from the analysis. McWilliams et al. (2004) analyzed only mortality, ignoring changes in health status among survivors. We analyze a broader and more detailed measure of health prior to age 65, taking into account mortality, self-reported health status, and the presence of instrumental activities of daily living (IADL) or activities of daily living (ADL) limitations.

Finally, we use the results from our analysis of the relationship between insurance coverage and health prior to age 65 to simulate whether medical spending by newly enrolled, aged Medicare beneficiaries might be affected by extending continuous insurance coverage to all people between the ages of 55 and 64. We use data on health and medical care spending from the Medicare Current Beneficiary Survey (MCBS) to simulate the effects of a change in the distribution of initial health states on both total and public (Medicare plus Medicaid) medical care spending by 66-70-year olds. (1)

1. FEDERALLY QUALIFIED HEALTH CENTERS

These not-for-profit clinics get money from the government to treat members of the community who have no health insurance and therefore use Medicaid. “These clinics often fill up, but they are obligated to treat patients,” says Rhonda Hagler, a New Jersey physician who runs a private practice. Locate a center near you by visiting cms.hhs.gov/center/fqhc.asp.

2. PARISH NURSING CENTERS

Many churches and synagogues have community outreach health centers at which nurses give free health-care services. Keep in mind that these programs are provided by the church and may come with a little proselytizing as well. Find the best option available to you by searching the Internet using the key words parish nursing or by calling your local churches and temples.

3. CHARITY-CARE AND REDUCED-PAYMENT PLANS
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“Some doctors have charity-care programs based on your income,” says Hagler. “Call the doctors you’re interested in seeing and ask if they offer it. If they don’t, ask them if they know anyone in the area who does.” Take note: High-earning specialists are less likely to offer free care than internists.

And even the M.D.’s who don’t offer charity care may be willing to help in other ways. “Doctors will reduce the cost and let you spread out your payments over a period of time,” Hagler says. “As long as you’re willing to pay something, they’ll often work with you.”

4. MINICLINICS

Housed within large chain stores like Wal-Mart, these small health centers treat patients with common ailments (strep throat, sinus infection, bronchitis and so forth) at an affordable cost. The rates for treatments at CVS Minute-Clinics, for example, range between $28 and $110. While some doctors argue against the quickly spreading phenomenon because these clinics are mostly run by nurse practitioners instead of doctors, other health experts like Patricia Carroll, R.N., author of What Nurses Know and Doctors Don’t Have Time to Tell You (Perigee), say that miniclinics are as efficient as any other center providing health-care services. “Nurse practitioners are completely qualified to give care in a setting like that,” she explains. “R.N.’s have seen as many patients as a doctor and do a great deal of clinical work.”–K.H.

This study investigated the effect of patient insurance status upon physicians’ decisions to write do-not-resuscitate orders (DNRs). Ninety-four physicians completed a questionnaire consisting of demographic data and a case vignette. In addition to the main research question, the study explored the effect of religious affiliation on writing DNRs and performing “slow codes.” Results indicate that insurance status has a significant effect upon the likelihood of writing a DNR, with physicians more likely to write DNRs for patients covered by public (i.e., government-funded, as compared to private) insurance. Religious affiliation was also significant, with greater church attendance associated with a lesser likelihood of writing a DNR. Results should be interpreted with caution; however, findings from this study support related research, and warrant further exploration.

Keywords: health care, do-not-resuscitate, uninsured

The availability and affordability of health insurance is a major policy concern in the U.S. About one-fifth of the U.S. population between the ages of 18 and 64 (36.3 million) lacks health insurance and the size of this population is expected to rise as health insurance becomes more costly relative to income for many people (DeNavas-Walt, Proctor, and Mills 2004; Rowland 2004). For most of the last decade, yearly job-based health insurance premium increases were higher than increases in both earnings and inflation (Gabel et al. 2001). If the premiums for health insurance continue to increase faster than personal income, recent estimates suggest that the percentage of uninsured workers could increase by more than 50 percent by 2009 (Gilmer and Kronick 2001).

The U.S. uninsured population is not homogenously distributed across Census regions, states, and communities. For example, 2001 uninsurance rates for the adult population under the age of 65 range from 10.4 percent in the New England region to 24.3 percent in the West South Central region, and from 8.7 percent in Iowa to 26 percent in Texas (Fronstin 2002). Cunningham and Ginsburg (2001) show that variation in uninsurance rates across 60 selected U.S. communities is high, ranging from a low of 4.7 percent in Rochester, NY to a high of 28.9 percent in Miami, FL.

When Leandra Ollie saw her sister rack up a $33,000 bill following a two-week hospital stay, she realized how the rising cost of healthcare could wreak havoc on household finances. “My sister was in her early 20s, still in grad school, and couldn’t afford to pay the bill,” she says. “As a result, I definitely know that if something happens to you suddenly, it can ruin any financial planning if you aren’t covered.”

A Harvard University study found that nearly half of all people who filed for bankruptcy in 2004 did so because of medical bills they couldn’t pay. While health insurance is vital, it doesn’t guarantee immunity from a medical-related financial crisis.

The challenge of paying medical debt is further complicated by the lag in wage increases as healthcare costs continue to rise, says Elise Gould, a health economist at the Economic Policy Institute. “For families barely making ends meet, unexpected healthcare costs of a few thousand dollars could put them over the edge,” she says. Insurance premiums for employer-sponsored health plans increased 9.2% last year, while worker earnings went up only 2.8%.

To help cover unexpected medical costs, Ollie, 33, purchased long-term care insurance. For $88 a month, she’s covered for extended hospital stays, nursing homes, assisted living, hospice, and home care visits.

In an ABC News/Washington Post poll last fall, 62 percent of the respondents favored a universal, government-run medical insurance program. Such surveys reflect a widespread frustration with a health care system that is too expensive, too uncertain, and too complicated.

The answer proposed by John Kerry and John Edwards is to continue the creeping socialization of medicine that Americans have been experiencing since the 1960s. That course would mean the end of private health care in the U.S., and with it the unparalleled medical progress that has benefited patients in this country and throughout the world. It would have a disastrous impact on medical innovation and the quality of care.

The Bush administration, for its part, has failed to offer a coherent alternative to piecemeal nationalization of health care. But the increasingly successful campaigns to privatize Social Security and expand school vouchers suggest a way out: mandatory private health insurance. Under this system, in effect, purchasing health insurance would be not much different from buying car or homeowner’s insurance today. As a result, we could preserve and extend the advantages of a free market with a minimal amount of coercion.

What has led to the current malpractice crisis? There are 2 main theories.

Physicians, insurers, and hospitals generally blame lawyers and the litigation system for increasing the number of claims filed (claim frequency) and the average payout on claims (claims severity).

Attorneys and consumer groups argue that malpractice insurance goes through natural cycles in costs and charges. For the rise in premiums in the current crisis, they particularly blame decreased investment returns and poor pricing decisions by insurers.

* Who’s right?

Research suggests that neither argument alone is persuasive. For instance, a study of the National Practitioner Data Bank, which collects results of all malpractice claims payments, found that claims severity did increase since 1991, but not during the current malpractice crisis period when adjusted for inflation: 52% from 1991 to 2003 but only 6% from 2000 to 2003. (1) The highest growth rate has been in medium-sized awards, not the large ones you often read about. And, as always, claims severity growth varies among states (FIGURE 1). (1)

The 1990s were a decade of relative prosperity, yet the percentage of Americans without health insurance coverage rose over 17 percent between 1990 and 1998. This decline generally reflects a drop in the rates of employer-sponsored coverage, a trend that began in the late 1970s (Farber and Levy 2000). The drop in coverage has raised concern among policy makers in light of a variety of studies that highlight the difficulty that the uninsured have in accessing care, and their resulting poorer outcomes (Institute of Medicine 2002; Serafini and Stone 2002). Designing policies that will effectively address this problem requires understanding why coverage rates have fallen and anticipating how coverage will change in the future. Despite a relatively large literature investigating the determinants of insurance coverage, relatively few studies use multivariate techniques to examine factors contributing to the decline in coverage over time. These studies show that increased reliance on part-time workers (Fronstin and Snider 1996), industry shifts (Long and Rogers 1995), a combination of labor market factors (Kronick and Gilmer 1999; Glied and Stabile 2000; Glied and Jack 2003), or crowdout (Curler and Gruber 1996a, b; Currie and Yelowitz 1999; Blumberg, Dubay, and Norton 2000) only partially explain the decline in employer-provided insurance.

An alternative explanation is that coverage has dropped because the cost of insurance has risen. In contrast to substantial media coverage linking rising premiums to declining coverage rates, empirical evidence quantifying the relationship between premiums and coverage is limited. The studies that use multivariate techniques to examine the relationship between health care costs and coverage rates find support for the view that increasing costs decrease coverage (Fronstin and Snider 1996; Kronick and Gilmer 1999; Curler 2002; Glied and Jack 2003). Kronick and Gilmer (1999) rely on national measures of health care costs, relative to income, and generate most of the variance in the cost to income ratio from variation in income, not health care costs. Fronstin and Snider (1996) analyze state-level data from 1988 to 1992 and include only one cost proxy, the price of a hospital day. Cutler (2002) uses national-level data on employee contributions. Glied and Jack (2003) use state-level Medicare per capita spending excluding home health, adjusted by the ratio of private spending per enrollee to Medicare spending per enrollee. Thus these studies do not directly measure the effects of rising premiums on coverage, nor do they attempt to adjust for potential reverse causality that arises because declining coverage may lead to higher premiums. Further, existing studies typically focus on employer-sponsored coverage, which, although important, does not give a full picture of the effects of rising premiums on coverage because some individuals may substitute public for private coverage. Finally, these studies typically do not devote substantial attention to controlling for potential confounding explanations for the decline in coverage such as the expansion in Medicaid or changing tax policy.

One of the greatest challenges a statistical agency faces is keeping up to date with developments in the economy and with the evolving information needs of the agency’s customers. In addition to resuming a regular program of reports on the incidence and characteristics of employee benefits plans, the 2003 National Compensation Survey (NCS) employee benefits publications introduced a variety of new data tabulations. These new data items range from information on the percentage of establishments offering major types of benefits to their employees and the percentage of total medical premiums paid by employers and employees, to tabulations that link benefit plan coverage to workers’ wages, to new details on such topics as the types of bonuses offered employees, employer contributions to cash balance pension plans, and orthodontic coverage for dependents of employees.

The new tabulations stem from several sources. First, employee compensation programs have long been a dynamic part of our economy. Wages and salaries, on the one hand, and employee benefit packages, on the other, evolve in response to a variety of pressures and needs. Employers seek competitive advantage in recruiting and retaining employees, while at the same time trying to control labor costs. Some compensation programs follow trends in collective bargaining; others reflect prevailing practices in an industry or among associated employers. Employee benefit plans are rewritten to meet legal or regulatory mandates. Second, customer requests have impelled the Bureau of Labor Statistics to introduce many new data tabulations. In some cases, these data focus on new elements of the compensation package; in other cases, the tabulations highlight fresh perspectives on employee compensation. Third, some of the new items result from a central goal of the NCS: to combine in a single place all of the data that were formerly collected and stored in several separate survey programs. (1) This integration of separate programs into one makes possible, for the first time, comparisons that look across the various forms of employee compensation data.