The propensity score--the probability of exposure to a specific treatment conditional on observed variables--is increasingly being used in observational studies. Creating strata in which subjects are matched on the propensity score allows one to balance measured variables between treated and untreated subjects. There is an ongoing controversy in the literature as to which variables to include in the propensity score model. Some advocate including those variables that predict treatment assignment, while others suggest including all variables potentially related to the outcome, and still others advocate including only variables that are associated with both treatment and outcome. We provide a case study of the association between drug exposure and mortality to show that including a variable that is related to treatment, but not outcome, does not improve balance and reduces the number of matched pairs available for analysis. In order to investigate this issue more comprehensively, we conducted a series of Monte Carlo simulations of the performance of propensity score models that contained variables related to treatment allocation, or variables that were confounders for the treatment-outcome pair, or variables related to outcome or all variables related to either outcome or treatment or neither. We compared the use of these different propensity scores models in matching and stratification in terms of the extent to which they balanced variables. We demonstrated that all propensity scores models balanced measured confounders between treated and untreated subjects in a propensity-score matched sample. However, including only the true confounders or the variables predictive of the outcome in the propensity score model resulted in a substantially larger number of matched pairs than did using the treatment-allocation model. Stratifying on the quintiles of any propensity score model resulted in residual imbalance between treated and untreated subjects in the upper and lower quintiles. Greater balance between treated and untreated subjects was obtained after matching on the propensity score than after stratifying on the quintiles of the propensity score. When a confounding variable was omitted from any of the propensity score models, then matching or stratifying on the propensity score resulted in residual imbalance in prognostically important variables between treated and untreated subjects. We considered four propensity score models for estimating treatment effects: the model that included only true confounders; the model that included all variables associated with the outcome; the model that included all measured variables; and the model that included all variables associated with treatment selection. Reduction in bias when estimating a null treatment effect was equivalent for all four propensity score models when propensity score matching was used. Reduction in bias was marginally greater for the first two propensity score models than for the last two propensity score models when stratification on the quintiles of the pr...

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Background-Administrative health care databases are increasingly used for health services and comparative effectiveness research. When comparing outcomes between different treatments, interventions or exposures, the ability to adjust for differences in the risk of the outcome occurring between treatment groups is important. Similarly, when conducting health care provider profiling, adequate risk-adjustment is necessary for conclusions about provider performance to be valid. There are limited validated methods for risk-adjustment in ambulatory populations using administrative health care databases.Objectives-To examine the ability of the Johns Hopkins' Aggregated Diagnosis Groups (ADGs) to predict mortality in a general ambulatory population cohort.Research Design-Retrospective cohort constructed using population-based administrative data. Conclusions-Logistic regression models using age, sex, and the John Hopkins ADGs were able to accurately predict one-year mortality in a general ambulatory population of subjects. Subjects-All

Propensity score methods are increasingly being used to estimate causal treatment effects in the medical literature. Conditioning on the propensity score results in unbiased estimation of the expected difference in observed responses to two treatments. The degree to which conditioning on the propensity score introduces bias into the estimation of the conditional odds ratio or conditional hazard ratio, which are frequently used as measures of treatment effect in observational studies, has not been extensively studied. We conducted Monte Carlo simulations to determine the degree to which propensity score matching, stratification on the quintiles of the propensity score, and covariate adjustment using the propensity score result in biased estimation of conditional odds ratios, hazard ratios, and rate ratios. We found that conditioning on the propensity score resulted in biased estimation of the true conditional odds ratio and the true conditional hazard ratio. In all scenarios examined, treatment effects were biased towards the null treatment effect. However, conditioning on the propensity score did not result in biased estimation of the true conditional rate ratio. In contrast, conventional regression methods allowed unbiased estimation of the true conditional treatment effect when all variables associated with the outcome were included in the regression model. The observed bias in propensity score methods is due to the fact that regression models allow one to estimate conditional treatment effects, whereas propensity score methods allow one to estimate marginal treatment effects. In several settings with non-linear treatment effects, marginal and conditional treatment effects do not coincide.

Insurance coverage is important, but it does not always assure access to health care. A comparison of Canadian and U.S. cities shows that persons in low-income areas are more likely to put off getting care until it is too late to avoid hospitalization.by John Billings, Geoffrey M. Anderson, and Laurie S. Newman ABSTRACT: Disparities in health outcomes for low-income populations as documented by rates of preventable hospital admissions remain large in the United States, even with the moderate expansion of Medicaid and efforts at the state and local levels to improve primary care services that began in the mid-1980s. These differences in outcome for rich and poor are not an isolated phenomenon of a few old and decaying Northeast urban centers but are documented in a broad range of urban areas. Much smaller differences are found in urban areas in Ontario, where universal coverage may help to reduce barriers to care. R ATES OF PREVENTABLE hospitalization often are used to document potential barriers to ambulatory care, to assess the performance of the primary care delivery system, and to identify possible deficiencies in the quality of outpatient care.1 Delay in receiving or failure to obtain timely, effective ambulatory care can result in avoidable hospital admissions for many common conditions such as asthma, diabetes, congestive heart failure, and cellulitis. Higher rates of admission for these conditions in an area or among a population subgroup can be an indication of serious access or performance problems.With the demise of national health care reform in the United States, impending Medicare and Medicaid cutbacks, and the traumatic alteration of the health system ecology resulting from the growth of managed care

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The existence and importance of excitement in gambling, the effects of runs of wins and losses on gambling behaviour and the relationships of both with sensation-seeking were investigated using samples of students and experienced gamblers in real and artificial gambling situations. Heart-rate increases, gambling behaviour and events such as 'stake decision time' were recorded as subjects played blackjack. Significant differences between real and artificial casinos were found for mean heart-rate increases over base-lines, for gambling behaviour and in the relationships between sensation-seeking, arousal and gambling in the two conditions. Doubt is cast on laboratory gambling as a valid analogue of the real gambling situation. Sensation-seeking and arousal are discussed briefly in relation to explanations of gambling.