Measuring Returns to Healthcare (2010)

Healthcare spending in the United States comprises 16 percent of GDP--nearly 80 percent more than in the median OECD country and 45 per-cent above that of the second-highest spending nation, France. Across countries, and across markets within the United States, the vast disparities in spending are not associated with better measures of health-outcome. (1) However, evidence from time series and panel data suggest that higher healthcare spending has generated benefits that, when converted to dollar magnitudes in various ways, appear to exceed their costs) Of course, the type of variation in treatment intensity differs across these two types of comparisons, but the question remains: are the returns to healthcare large or small? Estimating such returns can be confounded because medical providers attempt to provide each patient with a particular level of care. With heterogeneous returns, greater care is likely provided to those with the highest returns. This would tend to bias results toward finding beneficial effects of treatment. At the same time, patients with the highest returns may be those in relatively poor health. Indeed, hospitalized patients who receive more care are much more likely to die in the hospital, even after controlling for a host of observable characteristics: more care is provided to patients in worse health. With the raw correlation between treatment and health seemingly negative, estimating returns is an uphill battle. In a series of research studies, my coauthors and I have explored natural experiments that can shed some light on the returns to healthcare. Most of these papers consider conditions where selection bias associated with admission into the hospital is less of an issue: childbirth--the most common reason for hospitalization in the United States--and emergency admissions. This research summary briefly describes this work and points to future work in the area. Evidence from At-Risk Newborns One project, joint with Douglas Almond, Amanda Kowalski, and Heidi Williams, uses the idea that diagnostic thresholds can offer the potential to estimate returns to healthcare. (3) If physicians provide greater levels of care to patients falling just above a diagnostic criterion, then researchers can compare treatment and health outcomes for patients just above and below the threshold. The nature of the variation allows us to measure marginal returns, which are crucial for the interpretation of whether additional care saves lives. Our work focuses on at-risk newborns on either side of the low birthweight threshold of 1500 grams (3lbs. 5oz.) The underlying health of newborns who weigh 1499 grams is similar to those weighing 1500 grams, yet the rules of thumb used by physicians and hospital protocols call for additional attention for newborns below the threshold. By comparing newborns on either side of the threshold, we are able to avoid some of the confounding factors that usually affect measurements of the returns to health care. We show that newborns with birth-weights just below 1500 grams have discontinuously higher hospital costs than slightly heavier newborns, on the order of $10,000 each. When we study data from the census of U.S. births over twenty years, we find that newborns with birth weights just below 1500 grams have a single percentage-point lower infant mortality rate than newborns with birth weights just above this cutoff, even though mortality risk tends to decrease with birth-weight. This constitutes a relatively large reduction when compared to a mortality rate of 5.5 percent just above 1500 grams. We conclude that the additional medical attention afforded to very low birth-weight newborns is highly cost effective at saving lives. The same project shows that hospitals with the most state-of-the-art neonatal intensive care units (NICUs) are less likely to use the threshold, whereas changes in treatment and mortality are found at those hospitals with lower-level or no NICUs. …