MEDICINE AND HEALTH CARE

THE HISTORICAL ROLE OF MEDICINE AND HEALTH CARE

Many medical historians agree that practitioner-provided medical interventions played only a small, perhaps negligible, role in the historical decline in population mortality rates. Effective medicine is a fairly recent phenomenon, and the delivery of effective medical interventions on a scale sufficient to affect population health indicators most likely appeared only well into the twentieth century. Though the magnitudes of other causes of mortality declines are still disputed, it is clear that a larger role, one of the most significant ones, might be attributed to public health measures and the spread of knowledge of the sources of disease. However, a number of scholars in this field attribute the largest share of the credit to improvements in environment, particularly to the greatly increased supply of foodstuffs that became available due to the agricultural and industrial revolutions.

The Rising Population and the Role of Medicine
The notion that medicine played a relatively minor historical role is certainly not new, and it has been asserted by researchers of various ideologies. This point of view is associated with the work of Thomas McKeown (1976), who focused on the dramatic rise in population in England and Wales from 1750 to the modern day. The pattern of world population growth, including population growth in England and Wales, has interested many scholars, including McKeown. World population is hard to estimate for the distant past, but research by the United Nations (1996) and others show that something extraordinary happened during the last 300 years. In the first century the population was roughly 300 million. For a thousand years thereafter, until the era of Viking ships, little or no change occurred. By the Age of Enlightenment, starting just before 1700, the population may have risen to 600 million. Then things began to change rapidly. Within a single century, the world population passed 1 billion people. The next 5 billion arrived within a mere 200 years. What had happened? Figure 5-2, based on United Nations data, reveals this startling pattern.

Returning to the history of England and Wales, the large rise in their populations in the period following 1750 is to a large degree a story of the population’s health. Population increase comes from increased birth rates, reduced mortality, or increased net in-migration. Migration was not an important source of population increase in England and Wales; when accurate birth rate and death rate data became available from 1841, these data alone proved able to account for the population change. Likewise, fertility probably did not account for the change because recorded birth rates have declined during the period since data have become available. Declines in birth rates are a common finding in countries undergoing industrialization and modernization. In contrast, recorded mortality rates did decline substantially. McKeown began by investigating which diseases contributed to the decline in death rates. Mortality data are very limited prior to the mid-1800s, but the records revealed an emerging picture. Table 5-1 shows death rates by disease category for three time periods. The table shows that airborne infectious diseases account for the largest single portion of mortality reduction, and waterborne infectious diseases also make up a substantial portion of known causes. Regarding the airborne diseases, other data suggest that the main airborne diseases showing a decline in mortality include tuberculosis, bronchitis, pneumonia, and influenza.

MEASURES OF HEALTH?

HOW DOES HEALTH CARE AFFECT OTHER MEASURES OF HEALTH?

Mortality rates are part of the story, but only a part, and for many, not even the most important part. Other measures include morbidity data, disability days, work loss days, and other indicators. Research available also measures the quality of life. The RAND Health Insurance Experiment (RHIE) is one of the largest randomly controlled economic experiments ever conducted. It was designed to test the effect of alternative health insurance policies on the demand for health care and on the health status of a large and closely observed group of people from all walks of life. RAND researchers discovered that the greater the portion of the health care bill that individuals are required to pay, the less health care they choose to purchase. While this should not have been surprising, what did surprise most health economists was how great the difference was; the fully insured purchased roughly 40 percent more health care than those who had to pay their own bills. This provides an opportunity to ask whether those with 40 percent more health care were also 40 percent more healthy. This was serendipitous from a research standpoint, but a perfectly valid way to test the real contribution of health care to people’s health within the context of a scientifically controlled experiment. Fortunately, RHIE analysts kept detailed records on each person, including a dozen or more measurements under each category of physical health, mental health, social health, and general health index. They also examined their subjects’ dental health, persistence of symptoms, health habits, and disability days. The results are easy to summarize. For dozens of items, virtually no differences were found between the groups studied; health care and health insurance did not seem to matter. A simple example from the RHIE illustrates the point. Table 5-4 provides detail on workloss days per employed person per year—a measure of health status and morbidity that some economic researchers like to use because it ties directly to both health and productivity. This table separates the RAND subjects into four groups, which differ by type of health insurance policy. Some subjects pay nothing out of pocket for their health care/health insurance package; some pay 25 percent to 50 percent of their bill themselves; others pay all of their health care bills up to a certain amount, called a deductible. The subject’s out-of-pocket cost ranges from zero (free) to about 95 percent of the bill. Newhouse et al. (1993) summarize: “Our results show that the 40 percent increase in services on the free-care plan had little or no effect on health status for the average adult.” The effects on children showed a somewhat similar pattern. Valdez et al. (1985) examined data for 1,844 children in the RAND study—children who differed primarily by the type of insurance plan their families obtained. Children under the cost-sharing plans consumed up to one-third less care. However, the reduction in care was not significantly related to health status measures. It may seem from the RAND results that public provision of health insurance to both adults and children might not be justifiable on the basis of benefits to health. However, as Jonathan Gruber (2008) points out, this conclusion does not follow. No one in the RAND Experiment was “uninsured,” completely without insurance, as are close to 50 million Americans as of this writing (the Patient Protection and Affordable Care Act begins to address them in 2014). The least insured individuals studied by RAND had full coverage for health expenditures above a deductible, which was $1,000. Studies of the truly uninsured began to appear showing significant health gains from the provision of public insurance (Currie and Gruber, 1996; Doyle, 2005; Hanratty, 1996). These studies report reductions in infant and neonate deaths of around 5 to 10 percent. The Institute of Medicine estimates suggest that even larger gains are possible; they claim that the uninsured face a 25 percent greater mortality risks. Gruber further explains why these studies do not conflict with RAND. He proposes that the marginal effectiveness of medical expenditures is quite high for the first expenditures but then drops off precipitously, a plausible pattern given diminishing marginal returns. Eventually, additional spending does no good and the effectiveness curve flattens out . . . This appears to be the case as we move from less to more generous coverage, as in the RAND Health Insurances Experiment (Gruber, 2008: 584).