vop_poc_nz: A Python Framework for Distributional Cost-Effectiveness and Value of Perspective Analysis

Abstract

Health economic evaluations are sensitive to the choice of analytical perspective (e.g., health system vs. societal). We present vop_poc_nz, a Python package for Distributional Cost-Effectiveness Analysis (DCEA), Markov cohort modeling, probabilistic sensitivity analysis, value of information, and explicit comparison of analytical perspectives. We define Value of Perspective (VoP) as a directional expected opportunity loss: the welfare loss, measured under a declared reference perspective, when decisions are selected under another perspective. Five synthetic Aotearoa New Zealand demonstration models produced deterministic decision discordance in two cases at a NZ$20,000/QALY threshold. Seeded probabilistic analysis estimated mean per-person directional VoP of NZ$128,706 [$0, $279,088] for smoking cessation and NZ$30,853 [$0, $89,122] for housing insulation (95% simulation intervals in brackets); the other three cases had zero loss under the simulated strategy choices. These values test the software workflow and are not estimates for policy adoption. Release 0.2.3 supports Python 3.12-3.14 and records typed inputs, random seeds, software versions, and Arrow-schema identities for reproducible analysis.