Allocating treatment resources for hepatitis C in the UK: a constrained optimization modelling approach

Area of Science:

• Health Economics
• Public Health Policy
• Hepatology

Background:

• Direct-acting antivirals have advanced chronic hepatitis C (CHC) treatment, but uptake remains low among marginalized UK populations like people who inject drugs (PWID) and men who have sex with men (MSM).
• Addressing health inequalities is a key objective for healthcare agencies.
• Current CHC treatment budget allocation in the UK may not be optimal for minimizing liver-related mortality.

Purpose of the Study:

• To identify the optimal allocation of the CHC treatment budget across different populations and treatments in the UK.
• To minimize liver-related mortality in CHC patients within budget and equity constraints.
• To inform healthcare policy for more effective resource distribution.

Main Methods:

• A constrained optimization model was developed using Excel for resource allocation in CHC treatment.
• The model aimed to minimize liver-related deaths by adjusting treatment numbers for elbasvir-grazoprevir, ombitasvir-paritaprevir-ritonavir-dasabuvir, sofosbuvir-ledipasvir, and pegylated interferon-ribavirin.
• Linear programming was applied to UK data, incorporating budget and equity constraints, with scenario analyses for robustness.

Main Results:

• An optimal allocation strategy was identified, increasing patient treatment by 20.7% (2,430 more patients) and averting 0.3% (78) of liver-related deaths.
• Treatment numbers significantly increased for PWID (60.1% rise) and MSM (35.8% rise) within existing budget and equity considerations.
• The optimized allocation favored ombitasvir-paritaprevir-ritonavir-dasabuvir for specific patient groups.

Conclusions:

• The current UK allocation of the CHC treatment budget is suboptimal.
• A constrained optimization model demonstrates that reallocating funds can increase patient treatment and reduce liver-related deaths without additional expenditure.
• Prioritizing specific treatments and populations through optimized resource allocation is crucial for improving health equity and outcomes in CHC management.