Concentration indices are frequently used to measure inequality in one variable over the distribution of another. Most commonly, they are applied to the measurement of socioeconomic-related inequality in health. We introduce the user-written command conindex, which provides point estimates and standard errors of a range of concentration indices. The command also graphs concentration curves (and Lorenz curves) and performs statistical inference for the comparison of inequality between groups. We offer an accessible introduction to the various concentration indices that have been proposed to suit different measurement scales and ethical responses to inequality. We also demonstrate the command's capabilities and syntax by analyzing wealth-related inequality in health and health care in Cambodia.

Difference-in-differences (DiD) estimators provide unbiased treatment effect estimates when, in the absence of treatment, the average outcomes for the treated and control groups would have followed parallel trends over time. This assumption is implausible in many settings. An alternative assumption is that the potential outcomes are independent of treatment status, conditional on past outcomes. This paper considers three methods that share this assumption: the synthetic control method, a lagged dependent variable (LDV) regression approach, and matching on past outcomes. Our motivating empirical study is an evaluation of a hospital pay-for-performance scheme in England, the best practice tariffs programme. The conclusions of the original DiD analysis are sensitive to the choice of approach. We conduct a Monte Carlo simulation study that investigates these methods’ performance. While DiD produces unbiased estimates when the parallel trends assumption holds, the alternative approaches provide less biased estimates of treatment effects when it is violated. In these cases, the LDV approach produces the most efficient and least biased estimates.

Severe refractory asthma poses a substantial burden in terms of healthcare costs but relatively little is known about the factors which drive these costs. This study uses data from the British Thoracic Society Difficult Asthma Registry (n=596) to estimate direct healthcare treatment costs from an National Health Service perspective and examines factors that explain variations in costs. Annual mean treatment costs among severe refractory asthma patients were £2912 (SD £2212) to £4217 (SD £2449). Significant predictors of costs were FEV₁% predicted, location of care, maintenance oral corticosteroid treatment and body mass index. Treating individuals with severe refractory asthma presents a substantial cost to the health service.

Objective: To compare interactive fixed effects (IFE) and generalized synthetic control (GSC) methods to methods prevalent in health policy evaluation and re‐evaluate the impact of the hip fracture best practice tariffs introduced for hospitals in England in 2010.Data Sources: Simulations and Hospital Episode Statistics.Study Design: Best practice tariffs aimed to incentivize providers to deliver care in line with guidelines. Under the scheme, 62 providers received an additional payment for each hip fracture admission, while 49 providers did not. We estimate the impact using difference‐in‐differences (DiD), synthetic control (SC), IFE, and GSC methods. We contrast the estimation methods' performance in a Monte Carlo simulation study.Principal Findings: Unlike DiD, SC, and IFE methods, the GSC method provided reliable estimates across a range of simulation scenarios and was preferred for this case study. The introduction of best practice tariffs led to a 5.9 (confidence interval: 2.0 to 9.9) percentage point increase in the proportion of patients having surgery within 48 hours and a statistically insignificant 0.6 (confidence interval: −1.4 to 0.4) percentage point reduction in 30‐day mortality.Conclusions: The GSC approach is an attractive method for health policy evaluation. We cannot be confident that best practice tariffs were effective.