Health expenditure growth: Looking beyond the average through decomposition of the full distribution

Meijer, Claudine de; O’Donnell, Owen; Koopmanschap, Marc; Doorslaer, Eddy van

doi:10.1016/j.jhealeco.2012.10.009

Cited by 39 publications

(32 citation statements)

References 42 publications

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“…As a result, the Netherlands is now the country with the highest level of spending on health care in the world after the United States (OECD, 2015). Much of the increase in spending since 2001 was the result of a greater propensity to use hospital care, as well as the introduction of new technologies (De Meijer et al 2013). …”

Section: Trends In Aggregate Health Expenditurementioning

confidence: 99%

Spending on Health Care in the Netherlands: Not Going So Dutch

2017

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractThe Netherlands is among the top spenders on health in the OECD. We document the lifecycle profile, concentration and persistence of this expenditure using claims data covering both curative and long-term care expenses for the full Dutch population. Spending on health care is strongly concentrated: the one per cent of individuals with the highest levels of expenditure account for one quarter of the aggregate in any one year. Averaged over three years, the top one per cent still accounts for more than a fifth of the total, indicating a very high degree of persistence in the largest expenses. Spending on long-term care, which amounts to one third of all expenditure on health care, is even more concentrated: the top one per cent accounts for more than half of total spending on this type of care. Average expenditure rises steeply with age and even more so with proximity to death. Spending on individuals in their last year of life absorbs one tenth of aggregate health care expenditure. In a given year, spending on health care is highly skewed toward individuals with lower incomes. Average expenditure on the poorest fifth is more than three times that on the richest fifth. Spending on long-term care is five times more concentrated on the poor. Policy Points1. Spending on health care is highly concentrated on a small share of the population and is strongly persistent over time. 2. Public spending on long-term care is high and is even more concentrated and persistent. 3. In any given year, health spending is highly skewed towards poorer individuals. 4. The high concentration and persistence imply substantial interpersonal redistribution. 5. Steeply rising health expenditures with age and proximity to death imply substantial intrapersonal redistribution across the life cycle. JEL Codes: D12, I13, I14

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Section: Trends In Aggregate Health Expenditurementioning

confidence: 99%

Spending on Health Care in the Netherlands: Not Going So Dutch

2017

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“…The difference is explained by the sum of a composition effect produced by the differences in the characteristics of the two populations and a structure effect generated by the differences in the population-specific distributions. The latter is also called a conditional distribution effect by De Meijer et al (2013).…”

Section: Simulating a Counterfactual Distributionmentioning

confidence: 99%

The Effect of Introducing a Minimum Price on the Distribution of Alcohol Purchase: A Counterfactual Analysis

2016

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We use counterfactual analysis techniques to evaluate the impact of a $2 minimum unit price (MUP) on the distribution of Australian (Victorian) household off-trade alcohol purchases. Our estimates suggest that a $2 MUP significantly reduces the purchases of at-risk households by up to -0.92 [90% CI: -1.55, -0.28] standard drinks at the highest quantiles and has substantially less effect on households purchasing at light and moderate levels. A $2 MUP may reduce the proportions of male and female shoppers purchasing at the public health threshold of more than two standard drinks per household member per day by -3.03 [90% CI: -4.83, -1.22] percentage points (relative variation: -17%); and -1.85 [90% CI: -2.60, -1.10] percentage points (relative variation:-22%), respectively. Implementing an MUP on alcohol thus promises significant positive impacts on public health. Copyright © 2016 John Wiley & Sons, Ltd.

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“…A continuum of indicator variables needs to be generated and then regression models are used to construct the conditional distribution functions for each value. Given the computational demand of this approach and lack of variation in the indicator variables at low and high costs, de Meijer et al () use linear probability models in place of logit regressions. We also adopt this approach in our comparison, because preliminary work showed that, where it was possible to estimate both logit and linear probability models, there was little difference between the methods.…”

Section: Empirical Modelsmentioning

confidence: 99%

“…More recently, Chernozhukov et al () propose that a continuum of logits should be estimated (one for each unique value of the outcome variable) to allow for an even greater range of estimates for the effect of a covariate. In an application to Dutch health expenditures, de Meijer et al () use the Chernozhukov et al () method to decompose changes in the distribution of health expenditures between two periods. The authors find that the effect of covariates varies across the distribution of health expenditures, which would have been missed if analysis had focused solely on the mean.…”

Section: Introductionmentioning

confidence: 99%

Healthcare Cost Regressions: Going Beyond the Mean to Estimate the Full Distribution

2015

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Understanding the data generating process behind healthcare costs remains a key empirical issue. Although much research to date has focused on the prediction of the conditional mean cost, this can potentially miss important features of the full distribution such as tail probabilities. We conduct a quasi-Monte Carlo experiment using the English National Health Service inpatient data to compare 14 approaches in modelling the distribution of healthcare costs: nine of which are parametric and have commonly been used to fit healthcare costs, and five others are designed specifically to construct a counterfactual distribution. Our results indicate that no one method is clearly dominant and that there is a trade-off between bias and precision of tail probability forecasts. We find that distributional methods demonstrate significant potential, particularly with larger sample sizes where the variability of predictions is reduced. Parametric distributions such as log-normal, generalised gamma and generalised beta of the second kind are found to estimate tail probabilities with high precision but with varying bias depending upon the cost threshold being considered.

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Health expenditure growth: Looking beyond the average through decomposition of the full distribution

Cited by 39 publications

References 42 publications

Spending on Health Care in the Netherlands: Not Going So Dutch

Spending on Health Care in the Netherlands: Not Going So Dutch

The Effect of Introducing a Minimum Price on the Distribution of Alcohol Purchase: A Counterfactual Analysis

Healthcare Cost Regressions: Going Beyond the Mean to Estimate the Full Distribution

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