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We study the effect of Medicare on financial strain, measured by annual changes in medical debt in collections, using credit bureau data. We exploit the program’s eligibility age at 65 and ...

SUMMARY A host of recent research has used reweighting methods to analyze the extent to which observable characteristics predict between-group differences in the distribution of an outcome. Less attention has been paid to using reweighting methods to isolate the roles of individual covariates. We analyze two approaches that have been used in previous studies, and we propose a new approach that examines the role of one covariate while holding the marginal distribution of the other covariates constant. We illustrate the differences between the methods with a numerical example and an empirical analysis of black–white wage differentials among males. Copyright © 2015 John Wiley & Sons, Ltd.

Objectives. We examined how changes in demographic, geographic, and childbearing risk factors were related to changes in the Black–White infant mortality rate (IMR) gap over 2 decades. Methods. Using 1983–2004 Vital Statistics, we applied inverse probability weighting methods to examine the relationship between risk factors and 3 outcomes: the overall IMR gap, its birth weight component, and its conditional (on birth weight) IMR component. Results. The unexplained IMR gap (the part not related to observed risk factors) was stable, changing from 5.0 to 5.3 deaths per 1000 live births. By contrast, the explained gap declined from 4.6 to 1.9. The decline in the explained gap was driven by the changing relationship between risk factors and IMR. Further analysis revealed that most of the unexplained gap occurred among infants weighing less than 1000 grams at birth, whereas most of the explained gap occurred among infants weighing more than 1000 grams. Conclusions. The unexplained gap was stable over the last 2 decades, but the explained gap declined markedly. If the stability of the unexplained gap continues, even complete convergence of risk factors would reduce the Black–White IMR gap by only one quarter.

We assess the extent to which differences in socio-economic status are associated with racial and ethnic gaps in a fundamental measure of population health: the rate at which infants die. Using micro-level Vital Statistics data from 2000 to 2004, we examine mortality gaps of infants born to white, black, Mexican, Puerto Rican, Asian, and Native American mothers. We find that between-group mortality gaps are strongly and consistently (except for Mexican infants) associated with maternal marital status, education, and age, and that these same characteristics are powerful predictors of income and poverty for new mothers in U.S. Census data. Despite these similarities, we document a fundamental difference in the mortality gap for the three high mortality groups: whereas the black-white and Puerto Rican-white mortality gaps mainly occur at low birth weights, the Native American-white gap occurs almost exclusively at higher birth weights. We further examine the one group whose IMR is anomalous compared to the other groups: infants of Mexican mothers die at relatively low rates given their socio-economic disadvantage. We find that this anomaly is driven by lower infant mortality among foreign-born mothers, a pattern found within many racial/ethnic groups. Overall, we conclude that the infant mortality gaps for our six racial/ethnic groups exhibit many commonalities, and these commonalities suggest a prominent role for socio-economic differences.

We analyze four methods to measure unexplained gaps in mean outcomes: three decompositions based on the seminal work of Oaxaca (1973) and Blinder (1973) and an approach involving a seemingly naive regression that includes a group indicator variable. Our analysis yields two principal findings. We show that the coefficient on a group indicator variable from an OLS regression is an attractive approach for obtaining a single measure of the unexplained gap. We also show that a commonly-used pooling decomposition systematically overstates the contribution of observable characteristics to mean outcome differences when compared to OLS regression, therefore understating unexplained differences. We then provide three empirical examples that explore the practical importance of our analytic results.

OBJECTIVES Investigators and clinicians almost always rely on Diagnostic and Statistical Manual of Mental Disorder, 4th edition's (DSM-IV) somatoform disorders (and its derivative diagnoses) to characterize and identify patients with medically unexplained symptoms (MUS). Our objective was to evaluate this use by determining the prevalence of DSM-IV somatoform and nonsomatoform disorders in patients with MUS proven by a gold standard chart review. METHODS In a community-based staff model HMO, we identified subjects for a clinical trial using a systematic and reliable chart rating procedure among high-utilizing MUS patients. Only baseline data are reported here. The World Health Organization Composite International Diagnostic Interview provided full and abridged DSM-IV diagnoses. Patients with full or abridged DSM-IV somatoform diagnoses were labeled "DSM somatoform-positive," whereas those without them were labeled "DSM somatoform-negative." RESULTS Two hundred six MUS patients averaged 13.6 visits in the year preceding study, 79.1% were females, and the average age was 47.7 years. We found that 124 patients (60.2%) had a nonsomatoform ("psychiatric") DSM-IV diagnosis of any type; 36 (17.5%) had 2 full nonsomatoform diagnoses, and 41 (19.9%) had >2; 92 (44.7%) had some full anxiety diagnosis and 94 (45.6%) had either full depression or minor depression diagnoses. However, only 9 of 206 (4.4%) had any full DSM-IV somatoform diagnosis, and only 39 (18.9%) had abridged somatization disorder. Thus, 48 (23.3%) were "DSM somatoform-positive" and 158 (76.7%) were "DSM somatoform-negative." The latter exhibited less anxiety, depression, mental dysfunction, and psychosomatic symptoms (all p

We study the efficiency and distributional effects of financing universal health-insurance coverage, using a computational general equilibrium model of the United States for 1991, with considerable disaggregation among families. Aggregate efficiency losses (primarily from labor supply distortions) range from 0.2 percent to nearly 1 percent of net output. Losses are considerably smaller for a "mandate-with-tax-credit" plan than for full tax finance. All plans redistribute in favor of the poor. The mandate with credit is much better for the highest income groups, but worse for the lower-middle class. The elderly lose in all plans we consider.

The conversion of a nonprofit firm to the for-profit form is commercialism carried to an extreme. Conversion is increasingly common, most notably in health care. We seek to advance understanding of why nonprofit conversions occur and what public policy should be toward them. A transfer of control—the essence of a conversion—over nonprofit assets can be accomplished in various ways. Thus, it would be a public policy mistake to focus narrowly on formal legal conversions or outright sales. We explore the possible motives for conversion, and speculate about the reasons for the flurry of conversion activity in hospitals and health maintenance organizations (HMOs). We conclude that there are three central public policy questions raised by conversions: (1) under what circumstances is conversion appropriate—when does it represent an efficient reallocation of resources and when only a redistribution of wealth?; (2) how should the nonprofit's assets be valued?; and (3) what should happen to the financial assets that remain after a conversion?

We analyze four methods to measure unexplained gaps in mean outcomes: three decompositions based on the seminal work of Oaxaca (1973) and Blinder (1973) and an approach involving a seemingly naive regression that includes a group indicator variable. Our analysis yields two principal findings. We first show that a commonly-used pooling decomposition systematically overstates the contribution of observable characteristics to mean outcome differences, therefore understating unexplained differences. We also show that the coefficient on a group indicator variable from an OLS regression is an attractive approach for obtaining a single measure of the unexplained gap. We then provide three empirical examples that explore the practical importance of our analytic results.

There is no proven primary care treatment for patients with medically unexplained symptoms (MUS). We hypothesized that a long-term, multidimensional intervention by primary care providers would improve MUS patients' mental health.Clinical trial.HMO in Lansing, MI.Patients from 18 to 65 years old with 2 consecutive years of high utilization were identified as having MUS by a reliable chart rating procedure; 206 subjects were randomized and 200 completed the study.From May 2000 to January 2003, 4 primary care clinicians deployed a 12-month intervention consisting of cognitive-behavioral, pharmacological, and other treatment modalities. A behaviorally defined patient-centered method was used by clinicians to facilitate this treatment and the provider-patient relationship.The primary endpoint was an improvement from baseline to 12 months of 4 or more points on the Mental Component Summary of the SF-36.Two hundred patients averaged 13.6 visits for the year preceding study. The average age was 47.7 years and 79.1% were females. Using intent to treat, 48 treatment and 34 control patients improved (odds ratio [OR]=1.92, 95% confidence interval [CI]: 1.08 to 3.40; P=.02). The relative benefit (relative "risk" for improving) was 1.47 (CI: 1.05 to 2.07), and the number needed to treat was 6.4 (95% CI: 0.89 to 11.89). The following baseline measures predicted improvement: severe mental dysfunction (P.001), severe body pain (P=.039), nonsevere physical dysfunction (P=.003), and at least 16 years of education (P=.022); c-statistic=0.75.The first multidimensional intervention by primary care clinicians led to clinically significant improvement in MUS patients.

BACKGROUND: There are no proven, comprehensive treatments in primary care for patients with medically unexplained symptoms (MUS) even though these patients have high levels of psychosocial distress, medical disability, costs, and utilization. Despite extensive care, these common patients often become worse. OBJECTIVE: We sought to identify an effective, research-based treatment that can be conducted by primary care personnel. DESIGN: We used our own experiences and files, consulted with experts, and conducted an extensive review of the literature to identify two things: 1) effective treatments from randomized controlled trials for MUS patients in primary care and in specialty settings; and 2) any type of treatment study in a related area that might inform primary care treatment, for example, depression, provider-patient relationship. MAIN RESULTS: We developed a multidimensional treatment plan by integrating several areas of the literature: collaborative/stepped care, cognitive-behavioral treatment, and the provider-patient relationship. The treatment is designed for primary care personnel (physicians, physician assistants, nurse practitioners) and deployed intensively at the outset; visit intervals are progressively increased as stability and improvement occur. CONCLUSION: Providing a comprehensive treatment plan for chronic, high-utilizing MUS patients removes one barrier to treating this common problem effectively in primary care by primary care personnel.

Somatization is a common, costly problem with great morbidity, but there has been no effective screening method to identify these patients and target them for treatment.We tested a hypothesis that we could identify high utilizing somatizing patients from a management information system (MIS) by total number of visits and what we termed "somatization potential," the percentage of visits for which ICD-9 primary diagnosis codes represented disorders in the musculoskeletal, nervous, or gastrointestinal systems or ill-defined complaints.We identified 883 high users from the MIS of a large staff model HMO as those having six or more visits during the year studied (65th percentile). A physician rater, without knowledge of hypotheses and predictors, then reviewed the medical records of these patients and identified somatizing patients (n = 122) and nonsomatizing patients (n = 761). In two-thirds of the population (the derivation set), we used logistic regression to refine our hypothesis and identify predictors of somatization available from the MIS: demographic data, all medical encounters, and primary diagnoses made by usual care physicians (ICD-9 codes). We then tested our prediction model in the remaining one-third of the population (the validation set) to validate its usefulness.The derivation set contained the following significant correlates of somatization: gender, total number of visits, and percent of visits with somatization potential. The c-statistic, equivalent to the area under the ROC curve, was 0.90. In the validation set, the explanatory power was less with a still impressive c-statistic of 0.78. A predicted probability of 0.04 identified almost all somatizers, whereas a predicted probability of 0.40 identified about half of all somatizers but produced few false positives.We have developed and validated a prediction model from the MIS that helps to distinguish chronic somatizing patients from other high utilizing patients. Our method requires corroboration but carries the promise of providing clinicians and health plan directors with an inexpensive, simple approach for identifying the common somatizing patient and, in turn, targeting them for treatment. The screener does not require clinicians' time.

Survival of extremely low-birth-weight (ELBW) infants weighing less than 1000 g has improved impressively, but as children and adolescents, they continue to have problems in cognition, achievement at school, behavior, and social adaptation. Only recently have survivors from the early era of postneonatal intensive care reached their young adult years. The investigators report a prospective population-based study carried out in Ontario, Canada, that enrolled 166 ELBW subjects weighing 501 to 1000 g at birth who presently were 22 to 25 years of age. The comparison group consisted of 145 normal-birth-weight (NBW) young adults. Face-to-face interviews were conducted without knowledge of the interviewee's status. Complete data were available for 149 of the ELBW participants and 133 of the NBW group at mean ages of 23.3 and 23.6 years, respectively. Neurosensory impairment of various types had been documented in 27% of the ELBW group and 2% of the NBW group. Average total years of education were significantly fewer in the ELBW group (13.9 vs 14.5 years). The proportion of each group who graduated high school was comparable to that for the overall Canadian population, but ELBW subjects were less likely to have completed the advanced course needed for entering the university (36% vs 57%). Half of the 22 ELBW participants with less than a high school education had neurosensory impairments. Approximately 5% could not be enrolled in special resource classes. Males were overrepresented among ELBW participants with the least education, and they were less likely than females to be enrolled in-or graduated from-a college or university. Rates of permanent employment were 48% for ELBW participants and 57% for the NBW group, both figures being comparable to Canadian norms. A higher proportion of the ELBW group was not in school or employed. More of the ELBW group was unemployed because of either chronic illness or permanent disability (46% vs 15%). Overall living arrangements did not differ, and there were no significant group differences, overall or by gender, in marital status. Contrary to what many have expected, a significant majority of young adults in this study who had been ELBW newborn infants have made a generally successful transition from adolescence to young adulthood, even when those with neurosensory impairment are taken into account. Many of them have overcome earlier problems to become functioning members of society. Parents of ELBW children are entitled to hope that, in the longer term, their children will do as well as NBW children.

This article develops a framework for benefit-cost analysis of screening tests and applies it to compare tests for gonorrhea in asymptomatic women. Some analytic results on the dependence of net benefits on the values of various parameters are derived. A benefit-cost analysis is then performed to compare the modified Thayer-Martin culture technique and the Gonozyme immunoassay with each other and with a no screening alternative. Analysis of the sensitivity of results to changes in assumed parameter values is also carried out. The findings suggest that the Thayer-Martin technique has positive net benefits and is preferred to Gonozyme under a broad range of plausible conditions.

This paper models individual choice between two types of jobs as dependent on the difference i n potential earnings and on preferences for nonpecuniary compensation. The model leads to simultaneous estimation of earnings and job choice functions in a manner that takes account of self-selection of individuals into the sector of highest utility. An application to lawyers choosing between private and "public-interest" law casts doubt on the notion that public-interest lawyers are accepting substantially lower earnings by virtue of their choice-an impression derived from es timation of earnings functions without accounting for self-selection. The estimation technique also takes proper account of the "'choice-based" nature of the sample. Copyright 1988 by University of Chicago Press.

Concerns have been expressed about the welfare effects of expensive innovations in medical care financed largely through medical insurance. This paper develops a model which considers such issues. It is shown that even if insurance is purchased optimally (subject to a plausible constraint on the form of the contract), innovations may be adopted that have the effect of reducing expected welfare. The question of how the benefits of medical innovations ought to be measured in the presence of insurance is also explored.

able to gather on amnesties in other states. Do tax amnesties generate revenue that In our view, information about the types would not otherwise be collected? Will tax of taxpayers (or nontaxpayers) who paramnesties provide continuing future reve- ticipate in amnesties and the circumnue gains? And is an amnesty appropriate stances that lead them to participate can in the transition to a new tax enforcement be of considerable use in evaluating the regime? Based on economic analysis of the effects of amnesties and for predicting the choice to participate in an amnesty and e-x- prospects for a federal tax amnesty from amination of the characteristics of partic the state results. ipants in Michigan and other states, it ap- Under a tax amnesty, taxpayers are ofpears that amnesties are not particularly fered an opportunity to come forward during a specified period of time and pay preeffective in identifying tax evaders, so that I

Economists have frequently pointed to the distorting effects of health insurance as a major contributor to rapidly increasing health costs. Relatively little is known, however, about the influence of changing medical technology on expenditures. If increases in expenditures are in part the result of development of costly but worthwhile new technologies (a common view among noneconomists), failure to take account of this may lead to over-statement of the distortive effects of insurance. On the other hand some observers have questioned whether the benefits derived from expensive medical innovations always exceed the costs [18]. Others have suggested that resources devoted to the development of very expensive technologies might be better used for other kinds of medical research [16; 3]. In light of these arguments, it appears that the relationships between medical insurance and technological change may be quite important. If the extent of insurance coverage influences the rate and direction of technological change in medicine, then the long run welfare effects of insurance are more complex than current models [5; 6] allow. It is sometimes argued informally that such an influence existsthat the presence of insurance biases the system toward innovations that expand capabilities while increasing expenditures, and away from those that reduce costs [15; 20]. Such a hypothesis has, however, never been developed as an implication of a formal economic model.' Even the term bias in the argument is unclear; it appears to be used in a normative sense, but it has not been explained clearly why such a bias, if it exists, leads to welfare losses. Even if it is true, for example, that the presence of insurance leads to the adoption of a more costly mix of medical innovations than would otherwise occur, this is not obviously undesirable. To facilitate the purchase of costly but beneficial medical care is the purpose of insurance. This paper defines bias in technological change in a normative sense. It employs a simple model of insurance and profit maximizing innovators, and develops sufficient