The Doctor Might See You Now: The Supply Side Effects of Public Health Insurance Expansions

In the United States, public health insurance programs cover over 90 million individuals. Changes in the scope of these programs, such as the Medicaid expansions under the recently passed Patient Protection and Affordable Care Act, may have large effects on physician behavior. This study finds that following the implementation of the State Children’s Health Insurance Program, physicians decreased the number of hours spent with patients, but increased their participation in the expanded program. Suggestive evidence is found that this decrease in hours was a result of shorter office visits. These findings are consistent with the predictions from a mixed-economy model of physician behavior with public and private payers and also provide evidence of crowd out resulting from the creation of SCHIP.

The Returns to Medical Inventions

Medical innovation is perhaps the most important driver of health care spending and quality. Economists have studied pharmaceutical innovation for decades, and their findings have contributed to the debate about optimal Food and Drug Administration policy. Despite their importance to health care spending and value, there is no similar literature to inform an optimal regulation system for novel and valuable medical procedures. In this paper, we begin to fill this gap by documenting the incentives for developing medical procedures and the process through which they are approved for use. Drawing on the work of Sam Peltzman and George Stigler, we argue that the largely ad hoc system of rewards and review for medical procedures may explain the slow pace of innovation, particularly when compared with drug innovation

Giving Mom a Break: The Impact of Higher EITC Payments on Maternal Health

The 1993 expansions of the Earned Income Tax Credit created the first meaningful separation in the benefit level for families based on the number of children, with families containing two or more children now receiving substantially more in benefits. If income is protective of health, we should see improvements over time in the health for mothers eligible for the EITC with two or more children compared to those with only one child. Using data from the Behavioral Risk Factors Surveillance Survey, we find in difference-in-difference models that for low-educated mothers of two or more children, the number of days with poor mental health and the fraction reporting excellent or very good health improved relative to the mothers with only one child. Using data from the National Health Examination and Nutrition Survey, we find evidence that the probability of having risky levels of biomarkers fell for these same low-educated women impacted more by the 1993 expansions, especially biomarkers that indicate inflammation.

Estimating Heterogeneity in the Benefits of Medical Treatment Intensity

Federal and state laws passed in the late 1990 increased considerably postpartum stays for newborns. Using all births in California over the 1995-2001 period, 2SLS estimates suggest that for the average newborn impacted by the law, increased treatment intensity had modest and statistically insignificant (p-value>0.05) impacts on readmission probabilities. Allowing the treatment effect to vary by pre-existing conditions or the pre-law propensity score of being discharged early, two objective measures of medical need, demonstrates that the law had large and statistically significant impacts for those with the greatest likelihood of a readmission. These results demonstrate heterogeneity in the returns to greater treatment intensity, and the returns to the average and marginal patient vary considerably.

The Orphan Drug Act at 35: Observations and an Outlook for the Twenty-First Century

On the thirty-fifth anniversary of the adoption of the Orphan Drug Act (ODA), we describe the enormous changes in the markets for therapies for rare diseases that have emerged over recent decades. The most prominent example is the fact that the profit-maximizing price of new orphan drugs appears to be greater today than it was in 1983. All else equal, this should reduce the threshold for research and development (R&D) investment in an economically viable product. Further, the small size of patient populations for orphan drugs, together with the increasing prevalence of biologics among orphan drugs, have created a set of natural monopoly-like markets in which firms face little competition, even after the end of formal periods of patent protection and market exclusivity. Additionally, the evolving technologies of drug development—in particular, the increasingly common use of auxiliary endpoints in clinical trials and the use of biomarkers for patient selection for treatment—now allow manufacturers to target smaller populations. Taken together, these changes raise doubts about whether the ODA encourages the development of products that otherwise would not have been brought to market—or whether, instead, it simply rewards the producers of inframarginal products. After presenting empirical support for our claims of an evolving marketplace, we discuss the tradeoffs associated with reshaping the ODA for the twenty-first century

The White/Black Educational Gap, Stalled Progress, and the Long Term Consequences of the Crack Epidemic

We propose the rise of crack cocaine as an explanation for the end to the convergence in black-white educational outcomes beginning in the mid-1980s. After constructing a measure of the arrival of crack arrival in cities and states, we first show there are large increases in incarceration and murder rates after the arrival of the drug. We show that the emergence of crack accounts for between 39 and 71 percent of the fall in black male high school graduation rates. The results suggest that, in line with human capital theory, educational investments declined in response to decreased returns to schooling

Assessment of the learning curve in health technologies: a systematic review

Objective: We reviewed and appraised the methods by which the issue of the learning curve has been addressed during health technology assessment in the past. Method: We performed a systematic review of papers in clinical databases (BIOSIS, CINAHL, Cochrane Library, EMBASE, HealthSTAR, MEDLINE, Science Citation Index, and Social Science Citation Index) using the search term "learning curve:" Results: The clinical search retrieved 4,571 abstracts for assessment, of which 559 (12%) published articles were eligible for review. Of these, 272 were judged to have formally assessed a learning curve. The procedures assessed were minimal access (51%), other surgical (41%), and diagnostic (8%). The majority of the studies were case series (95%). Some 47% of studies addressed only individual operator performance and 52% addressed institutional performance. The data were collected prospectively in 40%, retrospectively in 26%, and the method was unclear for 31%. The statistical methods used were simple graphs (44%), splitting the data chronologically and performing a t test or chi-squared test (60%), curve fitting (12%), and other model fitting (5%). Conclusions: Learning curves are rarely considered formally in health technology assessment. Where they are, the reporting of the studies and the statistical methods used are weak. As a minimum, reporting of learning should include the number and experience of the operators and a detailed description of data collection. Improved statistical methods would enhance the assessment of health technologies that require learning

Assessing the learning curve effect in health technologies: Lessons from the non-clinical literature

Introduction: Many health technologies exhibit some form of learning effect, and this represents a barrier to rigorous assessment. It has been shown that the statistical methods used are relatively crude. Methods to describe learning curves in fields outside medicine, for example, psychology and engineering, may be better. Methods: To systematically search non–health technology assessment literature (for example, PsycLit and Econlit databases) to identify novel statistical techniques applied to learning curves. Results: The search retrieved 9,431 abstracts for assessment, of which 18 used a statistical technique for analyzing learning effects that had not previously been identified in the clinical literature. The newly identified methods were combined with those previously used in health technology assessment, and categorized into four groups of increasing complexity: a) exploratory data analysis; b) simple data analysis; c) complex data analysis; and d) generic methods. All the complex structured data techniques for analyzing learning effects were identified in the nonclinical literature, and these emphasized the importance of estimating intra- and interindividual learning effects. Conclusion: A good dividend of more sophisticated methods was obtained by searching in nonclinical fields. These methods now require formal testing on health technology data sets

Evaluation of elicitation methods to quantify Bayes linear models

The Bayes linear methodology allows decision makers to express their subjective beliefs and adjust these beliefs as observations are made. It is similar in spirit to probabilistic Bayesian approaches, but differs as it uses expectation as its primitive. While substantial work has been carried out in Bayes linear analysis, both in terms of theory development and application, there is little published material on the elicitation of structured expert judgement to quantify models. This paper investigates different methods that could be used by analysts when creating an elicitation process. The theoretical underpinnings of the elicitation methods developed are explored and an evaluation of their use is presented. This work was motivated by, and is a precursor to, an industrial application of Bayes linear modelling of the reliability of defence systems. An illustrative example demonstrates how the methods can be used in practice