Economic impact of generic antiretrovirals in France for HIV patients' care: a simulation between 2019 and 2023.

BACKGROUND: In a context where the economic burden of HIV is increasing as HIV patients now have a close to normal lifespan, the availability of generic antiretrovirals commonly prescribed in 2017 and the imminence of patent expiration are expected to provide substantial savings in the coming years. This article aims to assess the economic impact of these generic antiretrovirals in France and specifically over a five-year period. METHODS: An agent-based model was developed to simulate patient trajectories and treatment use over a five-year period. By comparing the results of costs for trajectories simulated under different predefined scenarios, a budget impact model can be created and sensitivity analyses performed on several parameters of importance. RESULTS: The potential economic savings from 2019 to 2023 generated by generic antiretrovirals range from €309 million when the penetration rate of generics is set at 10% to €1.5 billion at 70%. These savings range from €984 million to €993 million as the delay between patent and generic marketing authorisation varies from 10 to 15 years, and from €965 million to €993 million as the Negotiated Price per Unit (NPU) of generics at market-entry varies from 40 to 50% of the NPU for patents. DISCUSSION: This economic savings simulation could help decision makers to anticipate resource allocations for further innovation in antiretrovirals therapies as well as prevention, especially by funding the Pre-Exposure Prophylaxis (PrEP) or HIV screening.

Identification and Economic Evaluation of Differentiated Thyroid Cancer Care Consumption Patterns Using Sequence Analysis.

Objectives: This study aims to assess the impact of care consumption patterns and individual characteristics on the cost of treating differentiated thyroid carcinoma (DTC), in France, with a specific emphasis on socioeconomic position. Methods: The methodology involved a net cost approach utilising cases from the EVATHYR cohort and controls from the French National Health Insurance database. Care consumption patterns were created using Optimal Matching and clustering techniques. The individual characteristics influence on patterns was assessed using multinomial logistic regression. The individual characteristics and patterns influence on care costs was assessed using generalised estimating equations. Results: The findings revealed an average cost of €13,753 per patient during the initial 3 years. Regression models suggested the main predictors of high DTC specific care consumption tended to include having a high risk of cancer recurrence (OR = 4.97), being a woman (OR = 2.00), and experiencing socio-economic deprivation (OR = 1.26), though not reaching statistical significance. Finally, high DTC-specific care consumers also incurred higher general care costs (RR = 1.35). Conclusion: The study underscores the increased costs of managing DTC, shaped by consumption habits and socioeconomic position, emphasising the need for more nuanced DTC management strategies.

Agent based modeling in health care economics: examples in the field of thyroid cancer.

Although they remain little used in the field of Health Care Economics, Agent Based Models (ABM) are potentially powerful decision-making tools that open up great prospects. The reasons for this lack of popularity are essentially to be found in a methodology that should be further clarified. This article hence aims to illustrate the methodology by means of two applications to medical examples. The first example of ABM illustrates the construction of a Baseline Data Cohort by means of a Virtual Baseline Generator. The aim is to describe the prevalence of thyroid cancer in the French population over the long term according to different scenarios of evolution of this population. The second study considers a setting where the Baseline Data Cohort is an established cohort of (real) patients: the EVATHYR cohort. The aim of the ABM is to describe the long-term costs associated with different scenarios of thyroid cancer management. The results are evaluated using several simulation runs in order to observe the variability of simulations and to derive prediction intervals. The ABM approach is very flexible since several sources of data can be involved and a large variety of simulation models can be calibrated to generate observations according to different evolution scenarios.

Influence of deprivation on hospital care pathways and costs for people with ischaemic stroke: A trajectory modelling study.

BACKGROUND: Socioeconomic characteristics have a strong impact on the incidence of stroke. The highest levels of morbidity and mortality are associated with the lowest socioeconomic positions. Moreover, the health insurance budget for stroke is significant. OBJECTIVES: To describe typical Hospital Care Pathways (HCPs) and their costs 1 year after acute stroke and to assess the impact of individuals' socioeconomic characteristics on HCPs and costs. METHODS: This retrospective observational study used hospital data on people with primary ischaemic stroke in a region of France (Haute-Garonne). HCPs were identified by sequential analysis. Multinomial logistic regression was used to analyse the impact of socioeconomic characteristics on HCPs as measured using an ecological index of deprivation, and a gamma regression model was used to analyse costs. RESULTS: The analysis identified 4 typical HCPs. The first HCP consisted exclusively of consultations, the second was composed of consultations and hospitalisations, the third included hospitalisations during the first 2 months after stroke followed by consultations, and the fourth included long-term hospitalisations. After adjustment, deprivation was associated with HCPs and determined its costs: a high level of social disadvantage determined the type of pathway and increased the costs. CONCLUSIONS: This study is one of the first to assess the impact of social inequalities in health on post-stroke HCPs in France and their corresponding costs. Our results confirm the hypothesis that healthcare costs differ according to the level of deprivation.