Quantifying risk of injury from usual alcohol consumption: An instrumental variable analysis.

BACKGROUND: Numerous studies of roadside accidents among emergency room patients show elevated risk of injury from acute alcohol consumption, i.e., recent drinking precedes the injury event. The observed effects are large and show a dose-response relationship. In contrast, studies quantifying the association between injury risk and chronic consumption, such as past-year average volume, show lower relative risk estimates than those from acute consumption. METHODS: Combining data from 4 waves of US National Alcohol Surveys (NAS) for years 2000-2015 (N = 29,571, 53% overall cooperation rate), we estimated the risk of any past-year injury from past-year volume using logistic regression. This was contrasted with an instrumental variable (IV) analysis utilizing a 2-stage residual inclusion (2SRI) approach to estimate injury risk from volume, which adjusted for unobserved confounders using state beer and spirits tax rates, zip code-level outlet and bar density, and control state status as instruments. RESULTS: Based on the combined US population surveys and controlling for sociodemographics, using conventional logistic regression, the odds ratios of injury from an average volume of 1, 2, and 5 drinks per day were 1.12 [95% confidence interval: 1.02, 1.24], 1.10 [1.00, 1.22], and 1.04 [0.88, 1.22], respectively. These compared with 1.67 [1.00, 2.78], 2.38 [0.87, 6.54], and 6.98 [0.57, 85.89] using the IV method. The proportion of injury attributed to alcohol also increased in magnitude, from 6.2% [0.3%, 11.9%] using the conventional approach to 17.9% [8.2%, 27.7%] using the IV method. CONCLUSIONS: The association between injury and chronic alcohol consumption may be confounded by unobserved factors, resulting in a possible downward bias of the risk estimate.

Regression-Based Causal Analysis from the Potential Outcomes Perspective.

Most empirical economic research is conducted with the goal of providing scientific evidence that will be informative in assessing causal relationships of interest based on relevant counterfactuals. The implementation of regression methods in this context is ubiquitous. With this as motivation, we detail a comprehensive regression-based potential outcomes framework for causal modeling, estimation and inference. This framework facilitates rigorous specification of the effect parameter of interest and makes clear the sense in which it is causally interpretable, when appropriately defined in a potential outcomes setting. It also serves to crystallize the conditions under which the effect parameter and the underlying regression parameters are identified. The consistent sample analog estimator of the effect parameter is discussed. Juxtaposing this framework with a stylized version of a commonly implemented and routinely applied modeling and estimation protocol reveals how the latter is deficient in recognizing, and fully accounting for, conditions required for identification of the relevant effect parameter and the causal interpretability of estimation results. In the context of an example, we demonstrate the conceptual advantages of this general potential outcomes framework for regression modeling by showing how it resolves fundamental shortcomings in the conventional approach to characterizing and remedying omitted variable bias.

Two-Stage Residual Inclusion Estimation in Health Services Research and Health Economics.

OBJECTIVES: Empirical analyses in health services research and health economics often require implementation of nonlinear models whose regressors include one or more endogenous variables-regressors that are correlated with the unobserved random component of the model. In such cases, implementation of conventional regression methods that ignore endogeneity will likely produce results that are biased and not causally interpretable. Terza et al. (2008) discuss a relatively simple estimation method that avoids endogeneity bias and is applicable in a wide variety of nonlinear regression contexts. They call this method two-stage residual inclusion (2SRI). In the present paper, I offer a 2SRI how-to guide for practitioners and a step-by-step protocol that can be implemented with any of the popular statistical or econometric software packages. STUDY DESIGN: We introduce the protocol and its Stata implementation in the context of a real data example. Implementation of 2SRI for a very broad class of nonlinear models is then discussed. Additional examples are given. EMPIRICAL APPLICATION: We analyze cigarette smoking as a determinant of infant birthweight using data from Mullahy (1997). CONCLUSION: It is hoped that the discussion will serve as a practical guide to implementation of the 2SRI protocol for applied researchers.

Emergency room visits and hospitalizations among participants with spinal cord injury.

BACKGROUND: Literature examining emergency room visits (ERV) and emergency room related hospitalizations (ERH) after spinal cord injury (SCI) is limited. OBJECTIVE: Identify (1) the annual frequency of ERV and ERH and (2) their likelihood as a function of demographic, injury, and socioeconomic characteristics. METHODS: Participants (n = 1,579) with SCI completed mailed self-report questionnaires. RESULTS: 37% reported at least one ERV, with an average of 85 ERV per 100 participants. 19% reported at least one ERH and an average of 33 ERH annually per 100 participants. A greater likelihood of ERV was observed among non-whites, those with more severe SCI, less education, and lower income. Among those with at least one ERV, greater risk of ERH was observed among non-Hispanic whites, those with more severe SCI, lower education, and higher age. CONCLUSIONS: ERV are common after SCI and should be accounted for when predicting SCI related expenses. Those with the most severe SCI and those in the oldest age group were most likely to be hospitalized after an ERV.

U.S. alcohol affordability and real tax rates, 1950-2011.

BACKGROUND: The affordability of alcoholic beverages, determined by the relationship of prices to incomes, may be an important factor in relation to heavy drinking, but little is known about how affordability has changed over time. PURPOSE: To calculate real prices and affordability measures for alcoholic beverages in the U.S. over the period from 1950 to 2011. METHODS: Affordability is calculated as the percentage of mean disposable income required to purchase 1 drink per day of the cheapest spirits, as well as popular brands of spirits, beer, and wine. Alternative income and price measures also are considered. Analyses were conducted in 2012. RESULTS: One drink per day of the cheapest brand of spirits required 0.29% of U.S. mean per capita disposable income in 2011 as compared to 1.02% in 1980, 2.24% in 1970, 3.61% in 1960, and 4.46% in 1950. One drink per day of a popular beer required 0.96% of income in 2010 compared to 4.87% in 1950, whereas a low-priced wine in 2011 required 0.36% of income compared to 1.05% in 1978. Reduced real federal and state tax rates were an important source of the declines in real prices. CONCLUSIONS: Alcoholic beverages sold for off-premises consumption are more affordable today than at any time in the past 60 years; dramatic increases in affordability occurred particularly in the 1960s and 1970s. Declines in real prices are a major component of this change. Increases in alcoholic beverage tax rates and/or implementing minimum prices, together with indexing these to inflation could be used to mitigate further declines in real prices.

What U.S. data should be used to measure the price elasticity of demand for alcohol?

This paper examines how estimates of the price elasticity of demand for beer vary with the choice of alcohol price series examined. Our most important finding is that the commonly used ACCRA price data are unlikely to reliably indicate alcohol demand elasticities-estimates obtained from this source vary drastically and unpredictably. As an alternative, researchers often use beer taxes to proxy for alcohol prices. While the estimated beer taxes elasticities are more stable, there are several problems with using taxes, including difficulties in accounting for cross-price effects. We believe that the most useful estimates reported in this paper are obtained using annual Uniform Product Code (UPC) "barcode" scanner data on grocery store alcohol prices. These estimates suggest relatively low demand elasticity, probably around -0.3, with evidence that the elasticities are considerably overstated in models that control for beer but not wine or spirits prices.

Prediction of postinjury employment and percentage of time worked after spinal cord injury.

OBJECTIVE: To use a 2-part model to identify biographic, injury, educational, and vocational predictors of postinjury employment and the percentage of time employed after spinal cord injury (SCI) onset. DESIGN: Survey. SETTING: Data were collected at 3 hospitals in the Southeastern and Midwestern United States. PARTICIPANTS: Participants were adults with traumatic SCI of at least 1 year duration, all under 65 years at the time of SCI onset. A total of 1329 observations were used in the analysis. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Postinjury employment, defined by whether the individual had ever been employed after SCI and percentage of time employed after SCI onset. RESULTS: Almost 52% of participants worked at some point in time postinjury. Among those who had worked postinjury, the mean portion of time spent working was 0.56. Several factors were significantly related to postinjury employment and portion of time worked postinjury. The probability of postinjury employment increased with successively less severe injury. However, only ambulatory participants were found to have a significantly greater portion of time postinjury among those who became employed. Having obtained either a 4-year or graduate degree after injury was associated with a greater likelihood of postinjury employment. Conversely, among those who worked postinjury, having obtained those degrees prior to injury was associated with a greater portion of time employed. Being white, a man, having completed a 4-year degree or a graduate degree, and having worked in the service industry prior to SCI onset were all associated with a greater portion of time working among those who had worked. CONCLUSIONS: The factors precipitating PE are not identical to those associated with a greater portion of time employed after SCI onset.

Pediatric palliative care: describing hospice users and identifying factors that affect hospice expenditures.

OBJECTIVE: Each year approximately 50,000 children die. These children could benefit from pediatric palliative care, and hospice is one important provider of palliative care. However, little information exists to understand pediatric hospice care. This study seeks to describe Medicaid pediatric hospice and nonhospice users and to identify factors that affect hospice expenditures. DESIGN: Analyses of Medicaid administrative data and death certificate data. PARTICIPANTS: A total of 1527 children in Florida Medicaid program. RESULTS: Few children in the sample used hospice services (11%) and the dominant location of death was home for hospice users (55%). Descriptive analyses show that pediatric hospice users had higher inpatient, outpatient, emergency department, and pharmacy expenditures than nonhospice users. Regression results suggest that black non-Hispanic, Hispanic, and children of other races had $730 to $880 fewer hospice expenditures than Whites. Higher hospice expenditures ($970) were associated with longer enrollment spans. CONCLUSIONS: Descriptive analyses suggest that there are differences between pediatric hospice and nonhospice users. Minority race/ethnicities, as well as shortened Medicaid enrollment spans, are both associated with decreased hospice expenditures. Information from this study can be used to develop interventions aimed at increasing the prevalence of and reducing inequalities in hospice care.

Assessing the impact of drug use on hospital costs.

OBJECTIVE: To assess whether outpatient prescription drug utilization produces offsets in the cost of hospitalization for Medicare beneficiaries. DATA SOURCES/STUDY SETTING: The study analyzed a sample (N=3,101) of community-dwelling fee-for-service U.S. Medicare beneficiaries drawn from the 1999 and 2000 Medicare Current Beneficiary Surveys. STUDY DESIGN: Using a two-part model specification, we regressed any hospital admission (part 1: probit) and hospital spending by those with one or more admissions (part 2: nonlinear least squares regression) on drug use in a standard model with strong covariate controls and a residual inclusion instrumental variable (IV) model using an exogenous measure of drug coverage as the instrument. PRINCIPAL FINDINGS: The covariate control model predicted that each additional prescription drug used (mean=30) raised hospital spending by $16 (p<.001). The residual inclusion IV model prediction was that each additional prescription fill reduced hospital spending by $104 (p<.001). CONCLUSIONS: The findings indicate that drug use is associated with cost offsets in hospitalization among Medicare beneficiaries, once omitted variable bias is corrected using an IV technique appropriate for nonlinear applications.

Earnings among people with spinal cord injury.

OBJECTIVE: To identify differences in conditional and unconditional earnings among participants with spinal cord injury (SCI) attributable to biographic, injury, educational, and employment factors by using a 2-part model (employment, earnings). DESIGN: A secondary analysis of cross-sectional survey data. SETTING: A Midwestern university hospital and a private hospital in the Southeastern United States. PARTICIPANTS: All participants (N=1296) were adults between the ages of 18 and 64 who had a traumatic SCI at least 1 year before study initiation. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Earnings were defined by earnings within the previous 12 months and were measured by a single categoric item. Conditional earnings reflect the earnings of employed participants, whereas unconditional earnings reflect all participants with $0 in earnings recorded for those unemployed. RESULTS: Sex and race were significantly related to conditional earnings, even after controlling for educational and vocational variables. Additionally, conditional earnings (employed participants only) were related to 16 or more years of education, number of years employed, the percentage of time after SCI spent employed, and working in either government or private industry (not self-employed or family business). There was a greater number of significant variables for unconditional earnings, largely reflective of the influence of the portion employed (those not working having $0 in earnings). CONCLUSIONS: Efforts to improve employment outcomes should focus on facilitating return to work immediately after injury, returning to preinjury job, maintaining regular employment, and working for placement in government or private industry. Special efforts may be needed to promote vocational outcomes among women and nonwhites.

The use of linear instrumental variables methods in health services research and health economics: a cautionary note.

OBJECTIVE: To investigate potential bias in the use of the conventional linear instrumental variables (IV) method for the estimation of causal effects in inherently nonlinear regression settings. DATA SOURCES: Smoking Supplement to the 1979 National Health Interview Survey, National Longitudinal Alcohol Epidemiologic Survey, and simulated data. STUDY DESIGN: Potential bias from the use of the linear IV method in nonlinear models is assessed via simulation studies and real world data analyses in two commonly encountered regression setting: (1) models with a nonnegative outcome (e.g., a count) and a continuous endogenous regressor; and (2) models with a binary outcome and a binary endogenous regressor. PRINCIPAL FINDINGS: The simulation analyses show that substantial bias in the estimation of causal effects can result from applying the conventional IV method in inherently nonlinear regression settings. Moreover, the bias is not attenuated as the sample size increases. This point is further illustrated in the survey data analyses in which IV-based estimates of the relevant causal effects diverge substantially from those obtained with appropriate nonlinear estimation methods. CONCLUSIONS: We offer this research as a cautionary note to those who would opt for the use of linear specifications in inherently nonlinear settings involving endogeneity.

Two-stage residual inclusion estimation: addressing endogeneity in health econometric modeling.

The paper focuses on two estimation methods that have been widely used to address endogeneity in empirical research in health economics and health services research-two-stage predictor substitution (2SPS) and two-stage residual inclusion (2SRI). 2SPS is the rote extension (to nonlinear models) of the popular linear two-stage least squares estimator. The 2SRI estimator is similar except that in the second-stage regression, the endogenous variables are not replaced by first-stage predictors. Instead, first-stage residuals are included as additional regressors. In a generic parametric framework, we show that 2SRI is consistent and 2SPS is not. Results from a simulation study and an illustrative example also recommend against 2SPS and favor 2SRI. Our findings are important given that there are many prominent examples of the application of inconsistent 2SPS in the recent literature. This study can be used as a guide by future researchers in health economics who are confronted with endogeneity in their empirical work.

Care-giver advice as a preventive measure for drinking during pregnancy: zeros, categorical outcome responses, and endogeneity.

We conduct an empirical investigation of the impact of prenatal care-giver advice on alcohol consumption by pregnant women. In the design of the model and estimator, we pay particular attention to three aspects of the data. First, a large proportion of pregnant women do not drink at all. To accommodate this aspect of the sample we base the essential formulation of the model on the modified version of the two-part approach of Duan et al. (Journal of Business and Economic Statistics 1983; 1: 115-126.) suggested by Mullahy (Journal of Health Economics 1998; 17: 247-281.). Second, in the survey that we analyze (the 1988 National Maternal and Infant Health Survey - NMIHS), respondents were only required to report their consumption up to a specified range of values (e.g. 1-2 drinks per week, 2-5 drinks per week, and so on). For this reason, the model is cast in the grouped regression framework of Stewart (Review of Economic Studies 1983; 50: 141-149.). Third, the binary physician advice variable is likely to be endogenous and the econometric specification explicitly accounts for this possibility. To summarize the results, we find that failing to account for endogeneity leads to the counterintuitive conclusion that advice has a positive and statistically significant influence on drinking during pregnancy. When the model is extended to allow for potential endogeneity, we find that advice has a negative and statistically significant impact.

Estimating the effects of prescription drug coverage for Medicare beneficiaries.

OBJECTIVE: To identify the effect of insurance coverage on prescription utilization by Medicare beneficiaries. DATA SOURCES/STUDY SETTING: Secondary data from the 1999 Medicare Current Beneficiary Survey (MCBS) Cost and Use files, a nationally representative survey of Medicare enrollees. STUDY DESIGN: The paper uses a cross-sectional design with (1) a standard regression framework to estimate the impact of prescription coverage on utilization controlling for potential selection bias with covariate control based on the Diagnostic Cost Group/Hierarchical Condition Category (DCG/HCC) risk adjuster, and (2) a multistage residual inclusion method using instrumental variables to control for selection bias and identify the insurance coverage effect. DATA COLLECTION/EXTRACTION METHODS: Data were extracted from the 1999 MCBS. Study inclusion criteria are community-dwelling MCBS respondents with full-year Medicare enrollment and supplemental medical insurance with or without full-year drug benefits. The final sample totaled 5,270 Medicare beneficiaries. PRINCIPAL FINDINGS: Both the model using the DCG/HCC risk adjuster and the model using the residual inclusion method produced similar results. The estimated price elasticity of demand for prescription drugs for the Medicare beneficiaries in our sample was -0.54. CONCLUSIONS: Our results confirm that selection into prescription coverage is predictable based on observable health. Our results further confirm prior estimates of price sensitivity of prescription drug demand for Medicare beneficiaries, though our estimate is slightly above prior results.

Injury and demographic factors predictive of disparities in earnings after spinal cord injury.

OBJECTIVE: To identify differences in earnings after spinal cord injury (SCI) attributable to demographic factors, injury severity, and education using a regression model that accounts for employment status, conditional earnings (earnings of those employed only), and unconditional earnings (earnings from employment for all participants with $0 recorded for those unemployed). DESIGN: Secondary analysis of cross-sectional survey data. SETTING: A midwestern university hospital and a private hospital in the southeastern United States. PARTICIPANTS: Adults with traumatic SCI of at least 2 years duration and under the traditional retirement age of 65 completed mailed surveys (n=615). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Conditional and unconditional earnings. Earnings were measured by a single item that grouped earnings into the following 8 categories: (1) less than $10,000, (2) $10,000 to $14,999, (3) $15,000 to 19,999, (4) $20,000 to 24,999, (5) $25,000 to 34,999, (6) $35,000 to 49,999, (7) $50,000 to 74,999, and (8) $75,000 or more. RESULTS: Several factors investigated were significantly associated with employment status (sex, race, age, neurologic level of injury, ambulatory status, years since injury, educational level), but conditional earnings were significantly related to only 3 factors. Higher conditional earnings were obtained by men, non-African Americans, and those with a college degree. Unconditional earnings were significantly higher among those with the following characteristics: male, non-African Americans, age 34 and less, ambulatory, and those who completed some education beyond high school. CONCLUSIONS: There are substantial differences in the likelihood of postinjury employment as a function of participant characteristics. These disparities are compounded for women, African Americans, and those with less than a college degree by differences in conditional earnings among those employed.