Minimizing Population Health Loss in Times of Scarce Surgical Capacity During the Coronavirus Disease 2019 Crisis and Beyond: A Modeling Study

Abstract

Objectives: Coronavirus disease 2019 has put unprecedented pressure on healthcare systems worldwide, leading to a reduction of the available healthcare capacity. Our objective was to develop a decision model to estimate the impact of postponing semielective surgical procedures on health, to support prioritization of care from a utilitarian perspective.

Methods: A cohort state-transition model was developed and applied to 43 semielective nonpediatric surgical procedures commonly performed in academic hospitals. Scenarios of delaying surgery from 2 weeks were compared with delaying up to 1 year and no surgery at all. Model parameters were based on registries, scientific literature, and the World Health Organization Global Burden of Disease study. For each surgical procedure, the model estimated the average expected disability-adjusted life-years (DALYs) per month of delay.

Results: Given the best available evidence, the 2 surgical procedures associated with most DALYs owing to delay were bypass surgery for Fontaine III/IV peripheral arterial disease (0.23 DALY/month, 95% confidence interval [CI]: 0.13-0.36) and transaortic valve implantation (0.15 DALY/month, 95% CI: 0.09-0.24). The 2 surgical procedures with the least DALYs were placing a shunt for dialysis (0.01, 95% CI: 0.005-0.01) and thyroid carcinoma resection (0.01, 95% CI: 0.01-0.02).

Conclusion: Expected health loss owing to surgical delay can be objectively calculated with our decision model based on best available evidence, which can guide prioritization of surgical procedures to minimize population health loss in times of scarcity. The model results should be placed in the context of different ethical perspectives and combined with capacity management tools to facilitate large-scale implementation.

Keywords: COVID-19; healthcare planning; population health; prioritization; simulation model; surgery delay.

Figure 1

State-transition diagram of the cohort model. The model is a state-transition cohort model with 3 health states, a preoperative health states (preop), a postoperative state (postop), and dead. All patients start in the preop health states. This is the health state where patient eligible for surgery start in our simulation. We follow these patients over time using fixed time intervals of 1 week; these fixed time intervals are called cycles. Every cycle, patients can transition to one of the other health states or they can remain in the health states they currently are. From the preop health state they either die (transition to dead health state) or continue to wait for their surgical procedure (stay in the preop health state, the arrow points back into the health state). At the time of surgical procedure, which is determined by the selected model scenario of surgical delay, all individuals still alive in the preop health state transition to the postop health state. The cohort is followed their remaining lifetime, defined as up to 100 years of age. While they are followed, they can die (transition from the postop state to dead state) or stay alive in the postop health state (transition back to the postop state). Finally, patients in the dead state remain dead, so every cycle they stay in the dead state.

Figure 2

This figure shows the distribution of the parameter values as used during the probabilistic sensitivity analysis (PSA). For each PSA iteration (100 iterations in total), a value for each parameter was sampled from the original source input as described in Appendix A (in Supplemental Materials found at https://doi.org/10.1016/j.jval.2020.12.010). The distribution of the final values used in the model is shown here. The y-axis shows the names of the surgical procedures. In the column called survival the x-axis represents the weekly probability of surviving. In the column Time until no Survival effect the x-as represents the days until treatment is not effective. (For a full list of input parameters per disease and source, see Appendix A in Supplemental Materials found at https://doi.org/10.1016/j.jval.2020.12.010.)

Figure 3

The average DALYs and YLLs per month of delay for the investigated surgical procedures based on the simulation of surgery delay of 52 weeks. The estimates (gray bars) and 95% confidence intervals (black lines) are shown. The actual data are presented in Appendix B in Supplemental Materials found at https://doi.org/10.1016/j.jval.2020.12.010.