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We discuss 'cost-value analysis', a method for health care resource allocation suggested by Nord et al. (Nord E et al. Incorporating societal concerns for fairness in numerical valuations of health programmes. Health Econ 1999; 8: 25-39). Some difficulties and issues for future research are pointed out.
This paper examines whether the responsiveness of health care utilization to cost-sharing varies by health status and the implications of such heterogeneity. First, we show theoretically that if health care utilization of those in poor health is less responsive to cost sharing, this, combined with the skewness of health expenditures in health status, leads to overestimates of the effect of cost sharing. This bias is exacerbated when elasticities are generalized to populations with greater expenditure skewness. Second, we show empirically that cost-sharing responsiveness does differ by health status using data from the Medicare Current Beneficiary Survey. Medicare beneficiaries are stratified into health status groups based on activity of daily living (ADL) impairments and self-reported health status. Separately, for each of the health status groups, we estimate the effect of Medigap insurance on Part B utilization using a two-part expenditure model. We find that the change in expenditures associated with Medigap is smaller for those in poorer health. For example, when stratified using ADLs, Medigap insurance increases expenditures for 'healthy' groups by 36.4%, while the increase for the 'sick' group is 12.7%. Results are qualitatively the same for different forms of supplemental insurance and different methods of health status stratification. We develop a test to demonstrate that adjusting our results for selection bias would result in estimates of greater heterogeneity. Our results imply that a lowerbound estimate of the bias from neglecting heterogeneity is about 2-7%.
This paper discusses different design techniques for stated preference surveys in health economic applications. In particular, we focus on different design techniques, i.e. how to combine the attribute levels into alternatives and choice sets, for choice experiments. Design is a vital issue in choice experiments since the combination of alternatives in the choice sets will determine the degree of precision obtainable from the estimates and welfare measures. In this paper we compare orthogonal, cyclical and D-optimal designs, where the latter allows expectations about the true parameters to be included when creating the design. Moreover, we discuss how to obtain prior information on the parameters and how to conduct a sequential design procedure during the actual experiment in order to improve the precision in the estimates. The designs are evaluated according to their ability to predict the true marginal willingness to pay under different specifications of the utility function in Monte Carlo simulations. Our results suggest that the designs produce unbiased estimations, but orthogonal designs result in larger mean square error in comparison to D-optimal designs. This result is expected when using correct priors on the parameters in D-optimal designs. However, the simulations show that welfare measures are not very sensitive if the choice sets are generated from a D-optimal design with biased priors.
This paper examines selection and matching incentives of performance-based contracting (PBC) in a model of patient heterogeneity, provider horizontal differentiation and asymmetric information. Treatment effectiveness is affected by the match between a patient's illness severity and a provider's treatment intensity. Before PBC, a provider's revenue is unrelated to treatment effectiveness; therefore, providers supply treatments even if their treatment intensities do not match with the patients' severities. Under PBC, budget allocation is positively related to treatment performance; patient-provider mismatch is reduced because patients are referred more often. Using data from the state of Maine, we show that PBC leads to more referrals and better match between illness severity and treatment intensity. Moreover, we find that PBC has a positive but insignificant effect on dumping.
There is evidence in the economics literature that restrictions on Medicaid funding for abortion reduces the demand for abortion. The unresolved question is whether such restrictions also increase safe sex (that is, pregnancy avoidance) behavior among women. This study explores that issue using state-level gonorrhea rates among women for 1975-1995. The rationale is that sexual behavior that leads to greater risk of accidental pregnancies is likely to be highly correlated with sexual behavior leading to greater risk of STD infection. Since gonorrhea has an incubation period of about a week, and is transmitted almost exclusively through sexual intercourse, a change in sexual behavior should soon be followed by a change in gonorrhea rates. The study used a partial adjustment model with lagged-dependent variables estimated using Arellano-Bond's GMM method. Results fail to find any statistically significant evidence that Medicaid funding restrictions are effective in reducing gonorrhea rates. This finding is robust to a variety of alternate specifications and tests. This suggests that restrictions on Medicaid funding for abortion fail to promote safe sex behavior among women.
This paper seeks to enlighten the readers on the potential complexities involved in including cost variables in conjoint analysis, with the aim of emphasising that interpretation of implicit WTP values should be tackled with caution. To illustrate the potential pitfalls, a large data set from a recent Danish study is applied. The data consists of 1991 interviews in which participants are required to perform three discrete choice tasks regarding choice of hospitals, and three choice tasks involving health-care systems in general. Model comparisons are performed which test the effect of (1) the cost range applied and (2) the effect of including a dummy variable to represent the utility associated with payment per se. A wider cost range including higher payments is associated with lower parameter weights associated with the payment variable, and thus increased WTP values. Including a dummy variable to explain utility associated with payment per se has significant effects on the model incurring some of the other variables to become insignificant, and others to change sign. Results suggest that inclusion of a two-dimensional structure to explain the relationship between cost and utility may avoid erroneous conclusions and give rise to significant changes in implicit WTP estimates.
We evaluated three questions that commonly arise when unit costing exercises for multinational trials are conducted: (1). In countries where investigators plan to collect hospital unit cost estimates for a selected set of diagnoses, how should one estimate unit costs for the remaining diagnoses observed in the trial for which cost data were not collected? (2). For how many hospital diagnoses should estimates be obtained? (3). For how many countries should they be obtained? We addressed these questions using unit cost data collected in four western European countries and three relative value measures from the US Medicare diagnosis-related group (DRG) payment system. We found that the arithmetic mean length of stay from the US DRG payment system was a good predictor of unit costs in four countries in Europe. We also found that the imputation error decreased as the number of hospital diagnoses and countries sampled increased, but that the rate of reduction in error shrank. Finally, we found that - given the existence of a reliable method for cost imputation - from a pure information standpoint, it is better to obtain estimates for fewer hospital diagnoses from more countries than the reverse.
The World Health Report 2000 focuses on the performance of health-care systems around the globe. The report uses efficiency measurement techniques to create a league table of health-care systems, highlighting good and bad performers. Efficiency is measured using panel data methods. This paper suggests that the WHO's estimation procedure is too narrow and that contextual information is hidden by the use of one method. This paper uses and validates a range of parametric and non-parametric empirical methods to measure efficiency using the WHO data. The rankings obtained are compared to the WHO league table and we demonstrate that there are trends and movements of interest within the league tables. We recommend that the WHO broaden its range of techniques in order to reveal this hidden information.
Health status varies across socio-economic groups and health status is generally assumed to predict health care needs. Therefore the need for health care varies across socio-economic groups, and studies of equity in the distribution of health care between socio-economic groups must compare levels of utilisation with levels of need. Economic studies of equity in health care generally assume that health care needs can be derived from the current health-health care relationship. They therefore do not consider whether the current health-health care relationship is (vertically) equitable and the focus is restricted to horizontal inequity. This paper proposes a framework for incorporating the implications of vertical inequity for the socio-economic distribution of health care. An alternative to the current health-health care relationship is proposed using a restriction on the health-elasticity of health care. The health-elasticity of general practitioner contacts in Scotland is found to be generally negative, but positive at low levels of health status. Pro-rich estimates of horizontal inequity and vertical inequity are obtained but neither is statistically significant. Further analysis demonstrates that the magnitude of vertical inequity in health care may be larger than horizontal inequity.
Using a unique longitudinal dataset tracking the experiences of patients diagnosed with HIV+ disease, this paper develops and estimates a model capable of recovering the effect of revisions in life expectancy on labor market outcomes. The data allow us to estimate the effect of changes in health status (as objectively measured by CD4 counts) and the impact of learning that one is HIV+, which we interpret as a negative shock to life expectancy. Both parametric and distribution-free models robustly indicate that decreases in health have little effect on labor demand but decrease probability of employment. We conclude that, in this sample, negative association between income and health is attributable mostly to the effect of altered incentives induced by changes in life expectancy.
Cost-efficiency targets, used to encourage downward pressure on hospital unit costs, have been employed within the UK NHS for many years. There has been considerable speculation that these targets create incentives to reduce beds and increase occupancy rates at the expense of holding spare capacity to accommodate fluctuations in emergency admissions. This research used panel data for the period 1994/1995-1999/2000, supplemented by a series of semi-structured interviews, to explore the strategies Trusts employ to reduce unit costs. No relationship could be found between published targets and changes in unit costs, nor that targets were successful in reducing the dispersion of unit costs over time. Interviews revealed that efficiency gains required of Trusts, usually dictated by the local health economy, often bore no correspondence to the national or regional published targets. Results further indicated that contrary to prior speculation, Trusts divide into two distinct groups, those with high occupancy rates and those with a high proportion of free beds to accommodate emergencies, with Trust characteristics displaying stability over time. A pressing need for future work is the development of measures to encourage efficiency that take account of quality improvement.
This paper uses new cross-country data to assess the relationship between public spending on health care and the health status of the poor. Data are drawn from two sources: (i) existing data on health status by income quintile tabulated from demographic health surveys in 44 countries; and (ii) our estimates of the health status of the poor in over 70 countries drawn from a new technique in decomposing social indicators. Our estimates confirm that the poor have significantly worse health status than the nonpoor and the regression results provide new evidence that public spending on health care matters more to them. However, the results suggest that increased public spending alone will not be sufficient to significantly improve health status.
In the recent past, considerable effort in health economics has been made on applying stated preference methods such as contingent valuation and choice experiments. Despite this increased use, there is still considerable scepticism concerning the value of these approaches. The application of contingent valuation in environmental economics has a long history and has been widely accepted. Whilst choice experiments were introduced to the environmental and health economics literature at a similar time, the wider acceptance of monetary measures of benefit in environmental economics has meant that they have also been more widely applied. The purpose of this paper is to identify some of the key issues and debates that have taken place in the environmental economics literature, summarise the state of the art with respect to these issues, and consider how health economists have addressed these issues. Important areas for future research in health economics are identified.
Estimating elasticities of cigarette demand has become commonplace amongst economists and policymakers. Synthesizing the various elasticities into a coherent message is quite challenging, however, as the point estimates are obtained using quite disparate modeling techniques and data. In this study, we perform a meta-analysis to explore factors that influence variations within and across studies. Empirical results suggest that demand specification, data issues, and estimation methodology have varying degrees of influence on reported estimates of price, income, and advertising elasticities.
Ordinary least squares (OLS) and stochastic frontier (SF) analyses are commonly used to estimate industry-level and firm-specific efficiency. Using cross-sectional data for English public hospitals, a total cost function based on a specification developed by the English Department of Health is estimated. Confidence intervals are calculated around the OLS residuals and around the inefficiency component of the SF residuals. Sensitivity analysis is conducted to assess whether conclusions about relative performance are robust to choices of error distribution, functional form and model specification. It is concluded that estimates of relative hospital efficiency are sensitive to estimation decisions and that little confidence can be placed in the point estimates for individual hospitals. The use of these techniques to set annual performance targets should be avoided.
Statistical models have been used to assess the influence of clinical and demographic factors on hospital charge and length of stay (LOS). Hospital costs constitute a significant proportion of overall expenditure in health care. With escalating costs, knowing the correlates of LOS and in-hospital cost is important for decisions on allocating resources. However, hospital charge and LOS are correlated. We describe two regression models that permit estimation of mean charges as a function of patient hospital stay and adjust for the influence of patient characteristics and treatment procedures on LOS and charge. In the first model, the mean charge over a specified duration is a weighted average of the expected cumulative charge, with weighting determined by the distribution of LOS. The second model for LOS and charge explicitly accounts for their correlation and yields estimates of the average charge per average LOS. The methods are applied to assess mean charges and mean charge per day by cardiac procedure in a cohort of patients hospitalized for acute myocardial infarction, while adjusting for the impact of patient demographic and clinical factors on LOS and charge. For relatively short hospital stays, and when only total hospital charges are available, these models provide a flexible approach to estimating summary measures on resource use while controlling for the effects of covariates on LOS and charge.
This paper presents and compares two threshold approaches to measuring the fairness of health care payments, one requiring that payments do not exceed a pre-specified proportion of pre-payment income, the other that they do not drive households into poverty. We develop indices for 'catastrophe' that capture the intensity of catastrophe as well as its incidence and also allow the analyst to capture the degree to which catastrophic payments occur disproportionately among poor households. Measures of poverty impact capturing both intensity and incidence are also developed. The arguments and methods are empirically illustrated with data on out-of-pocket payments from Vietnam in 1993 and 1998. This is not an uninteresting application given that 80% of health spending in that country was paid out-of-pocket in 1998. We find that the incidence and intensity of 'catastrophic' payments - both in terms of pre-payment income as well as ability to pay - were reduced between 1993 and 1998, and that both incidence and intensity of 'catastrophe' became less concentrated among the poor. We also find that the incidence and intensity of the poverty impact of out-of-pocket payments diminished over the period in question. Finally, we find that the poverty impact of out-of-pocket payments is primarily due to poor people becoming even poorer rather than the non-poor being made poor, and that it was not expenses associated with inpatient care that increased poverty but rather non-hospital expenditures.
A key theoretical prediction concerning willingness to pay is that it is positively correlated with benefit size and is assessed by testing the 'sensitivity to scale (scope)'. 'External' (between-sample) sensitivity tests are usually regarded as less powerful than 'internal' (within-subject) tests. However, the latter may suffer from 'anchoring' effects. This paper studies the statistical power of these tests by questioning the distributional assumption of empirical data. We present an integrated model to capture both internal and external variations, while controlling for sample heterogeneity, applied to data from a survey estimating the value of reducing symptom-days. Results indicate that once data is properly transformed, WTP becomes 'scale sensitive' and consistent with diminishing marginal utility theory.