Interpersonal comparison of welfare in Harsanyi’s veil-of-ignorance model

Selon le voile d’ignorance proposé par John Harsanyi (1953, 1955), l’observateur rationnel derrière le voile d’ignorance cherche à maximiser la somme des utilités individuelles. Cependant, le modèle d’Harsanyi est fondé sur une hypothèse erronée que la fonction d’utilité à la von Neumann-Morgenstern de l’observateur permet la comparaison interpersonnelle de bien-être. Ce papier suggère une modification du modèle d’Harsanyi qui permet la comparaison interpersonnelle de bien-être, en utilisant les années de vie en parfaite utilité ou les années de vie heureuse comme mesure du bien-être

Pharmaceutical expenditure and gross domestic product: Evidence of simultaneous effects using a two-step instrumental variables strategy

This paper estimates the income elasticity of government pharmaceutical spending and assesses the simultaneous effect of such spending on gross domestic product (GDP). Using a panel dataset for 136 countries from 1995 to 2006, we employ a two-step instrumental variable procedure where we first estimate the effect of GDP on public pharmaceutical expenditure using tourist receipts as an Instrument for GDP. In the secondstep,we construct an adjusted pharmaceutical expenditure series where the response of public pharmaceutical expenditure to GDP is partialled out and use this endogeneity adjusted series as an instrument for pharmaceutical expenditure. Our estimations show that GDP has a strong positive impact on pharmaceutical spending with elasticity in excess of unity in countries with low spending on pharmaceuticals and countries with large economic freedom. In the second step, we find that when the quantitatively large reverse effect of GDP is accounted for, public pharmaceutical spending has a negative effect on GDP per capita particularly in countries with limited economic freedom

Effects of increased drug copayments on the demand for drugs and physician visits in Germany

'Background: On July 1, 1997, the German government raised drug copayment by EUR2.60 to EUR 4.60, EUR 5.60 or EUR 6.60 per prescription, depending on package size. The aim of this study was to examine the effects of increased copayments on the demand for drugs and physician visits one year later. Lower income groups and the chronically ill were analysed separately. Methods: A survey of 695 non-exempted adult pharmacy customers who suffered from acute or chronic health conditions in the previous 12 months was conducted. Logistic regression was performed to analyse the effect of different income levels on the demand for drugs and physician visits. Results: Of the respondents, 19.9% reported reduced physician visits, 22.6% reported reduced prescription drug purchases, 44.9% reported increased use of over-the-counter products and 46.3% reported increased use of non-drugs such as household remedies. A total of 11.2% waived more than one visit to the physician. Almost all of those respondents who reduced their purchases of prescription packages waived 1 to 5 packages (82.5%). There was distinctly less change in purchasing patterns among persons with chronic diseases. Logistic regression confirmed that lower income households were more likely to change demand patterns than households in other income brackets. Conclusion: Increased copayments had little effect on drugs and physician visits of adult pharmacy customers, especially among those with chronic conditions. Negative effects on low income households were observed.' (excerpt)

Validation of Models That Estimate the Cost-Effectiveness of Improving Patient Adherence

this note suggests a test for internal validation of models that estimate the costs and effects of improving patient adherence. We apply the validation test to two published cost-effectiveness models on adherence improvement

Aging diseases - do they prevent preventive health care from saving costs?

The potential of preventive health-care services to save costs is intensely debated. On the one hand, a longer life span increases the probability that new and costly diseases occur. On the other hand, a higher life expectancy postpones the expensive last year of life (LYOL), which becomes cheaper with age. Using US expenditure data on survivors and decedents the paper shows that prevention in the general population causes expenditures for additional diseases that are larger than the savings from postponing the LYOL. This result may also hold for prevention in diseased individuals. Copyright © 2008 John Wiley & Sons, Ltd.

COVID-19 and the forgone health benefits of elective operations

Abstract Background and aim The first SARS-CoV-2 pandemic wave in Germany involved a tradeoff between saving the lives of COVID-19 patients by providing sufficient intensive care unit (ICU) capacity and foregoing the health benefits of elective procedures. This study aims to quantify this tradeoff. Methods The analysis is conducted at both the individual and population levels. The analysis calculates quality-adjusted life years (QALYs) to facilitate a comparison between the health gains from saving the lives of COVID-19 patients in the ICU and the health losses associated with postponing operative procedures. The QALYs gained from saving the lives of COVID-19 patients are calculated based on both the real-world ICU admissions and deaths averted from flattening the first wave. Scenario analysis was used to account for variation in input factors. Results At the individual level, the resource-adjusted QALY gain of saving one COVID-19 life is predicted to be 3 to 15 times larger than the QALY loss of deferring one operation (the average multiplier is 9). The real-world QALY gain at the population level is estimated to fall within the range of the QALY loss due to delayed procedures. The modeled QALY gain by flattening the first wave is 3 to 31 times larger than the QALY loss due to delayed procedures (the average multiplier is 17). Conclusion During the first wave of the pandemic, the resource-adjusted health gain from treating one COVID-19 patient in the ICU was found to be much larger than the health loss from deferring one operation. At the population level, flattening the first wave led to a much larger health gain than the health loss from delaying operative procedures

The clinical and economic value of a successful shutdown during the SARS-CoV-2 pandemic in Germany

Background and aim A shutdown of businesses enacted during the SARS-CoV-2 pandemic can serve different goals, e.g., preventing the intensive care unit (ICU) capacity from being overwhelmed (‘flattening the curve’) or keeping the reproduction number substantially below one (‘squashing the curve’). The aim of this study was to determine the clinical and economic value of a shutdown that is successful in ‘flattening’ or ‘squashing the curve’ in Germany. Methods In the base case, the study compared a successful shutdown to a worst-case scenario with no ICU capacity left to treat COVID-19 patients. To this end, a decision model was developed using, e.g., information on age-specific fatality rates, ICU outcomes, and the herd protection threshold. The value of an additional life year was borrowed from new, innovative oncological drugs, as cancer reflects a condition with a similar morbidity and mortality burden in the general population in the short term as COVID-19. Results A shutdown that is successful in ‘flattening the curve’ is projected to yield an average health gain between 0.02 and 0.08 life years (0.2 to 0.9 months) per capita in the German population. The corresponding economic value ranges between €1543 and €8027 per capita or, extrapolated to the total population, 4% to 19% of the gross domestic product (GDP) in 2019. A shutdown that is successful in ‘squashing the curve’ is expected to yield a minimum health gain of 0.10 life years (1.2 months) per capita, corresponding to 24% of the GDP in 2019. Results are particularly sensitive to mortality data and the prevalence of undetected cases. Conclusion A successful shutdown is forecasted to yield a considerable gain in life years in the German population. Nevertheless, questions around the affordability and underfunding of other parts of the healthcare system emerge

A parsimonious model to validate cost-effectiveness analyses on preventive health care

Background!#!The effect of preventive health care on health expenditures is ambiguous. On the one hand, prevention reduces the costs of future morbidity. On the other hand, prevention leads to costs of life extension. The purpose of this paper is to develop a parsimonious model that determines for a preventive measure of interest whether savings from preventing morbidity are more than offset by the costs of living longer, resulting in a net expenditure increase.!##!Methods!#!A theoretical model was built based on a Weibull survival function. It includes savings and life extension costs over the remaining lifetime. The model was applied to the example of obesity prevention.!##!Results!#!The model shows that the cost consequences of prevention are essentially driven by two factors: i) the relative reduction of morbidity-related costs, which determines the amount of savings from avoiding morbidity; and ii) the hazard ratio of death, which determines the amount of life extension costs. In the application example, the model is able to validate the results of a more complex cost-effectiveness model on obesity prevention.!##!Conclusions!#!This work provides new insight into the lifetime cost consequences of prevention. The model can be used both to check plausibility of the results of other models and to conduct an independent analysis

Optimal allocation of scarce PCR tests during the COVID-19 pandemic.

Background/aimDuring the coronavirus disease (COVID-19) pandemic, Germany and various other countries experienced a shortage of polymerase chain reaction (PCR) laboratory tests due to the highly transmissible SARS-CoV-2 Omicron variant that drove an unprecedented surge of infections. This study developed a mathematical model that optimizes diagnostic capacity with lab-based PCR testing.MethodsA mathematical model was constructed to determine the value of PCR testing in relation to the pre-test probability of COVID-19. Furthermore, the model derives the lower and upper bounds for the threshold pre-test probability of the designated priority group. The model was applied in a German setting using the PCR test-positivity rate at the beginning of February 2022.ResultsThe value function of PCR testing is bell-shaped with respect to the pre-test probability, reaching a maximum at a pre-test probability of 0.5. Based on a PCR test-positivity rate of 0.3 and assuming that at least two thirds of the tested population have a pre-test probability below, lower and higher pre-test probability thresholds are ≥ 0.1 and 0.7, respectively. Therefore, individuals who have a 25% likelihood of testing positive because they exhibit symptoms should be a higher priority for PCR testing. Furthermore, a positive rapid antigen test in asymptomatic individuals with no known exposure to COVID-19 should be confirmed using PCR. Yet, symptomatic individuals with a positive RAT should be excluded from PCR testing.ConclusionA mathematical model that allows for the optimal allocation of scarce PCR tests during the COVID-19 pandemic was developed