Burden of disease and productivity impact of Streptococcus suis infection in Thailand

Abstract

Background: Streptoccocus suis (S.suis) infection is a neglected zoonosis disease in humans mainly affects men of working age. We estimated the health and economic burden of S.suis infection in Thailand in terms of years of life lost, quality-adjusted life years (QALYs) lost, and productivity-adjusted life years (PALYs) lost which is a novel measure that adjusts years of life lived for productivity loss attributable to disease.

Methods: A decision-analytic Markov model was developed to simulate the impact of S. suis infection and its major complications: death, meningitis and infective endocarditis among Thai people in 2019 with starting age of 51 years. Transition probabilities, and inputs pertaining to costs, utilities and productivity impairment associated with long-term complications were derived from published sources. A lifetime time horizon with follow-up until death or age 100 years was adopted. The simulation was repeated assuming that the cohort had not been infected with S.suis. The differences between the two set of model outputs in years of life, QALYs, and PALYs lived reflected the impact of S.suis infection. An annual discount rate of 3% was applied to both costs and outcomes. One-way sensitivity analyses and Monte Carlo simulation modeling technique using 10,000 iterations were performed to assess the impact of uncertainty in the model.

Key results: This cohort incurred 769 (95% uncertainty interval [UI]: 695 to 841) years of life lost (14% of predicted years of life lived if infection had not occurred), 826 (95% UI: 588 to 1,098) QALYs lost (21%) and 793 (95%UI: 717 to 867) PALYs (15%) lost. These equated to an average of 2.46 years of life, 2.64 QALYs and 2.54 PALYs lost per person. The loss in PALYs was associated with a loss of 346 (95% UI: 240 to 461) million Thai baht (US$11.3 million) in GDP, which equated to 1.1 million Thai baht (US$ 36,033) lost per person.

Conclusions: S.suis infection imposes a significant economic burden both in terms of health and productivity. Further research to investigate the effectiveness of public health awareness programs and disease control interventions should be mandated to provide a clearer picture for decision making in public health strategies and resource allocations.

Fig 1. A decision tree and Markov models.

The infection involves acute phase and post-infection phase in which there are different health states from complete recovery, partial recovery (hearing loss, hearing loss w VD, not recovered from IE) and death.

Fig 2. One-way sensitivity analysis–QALYs.

Change in estimated QALYs lost to S.suis infection compared with base case (%).

Fig 3. One-way sensitivity analysis–PALYs.

Change in estimated PALYs lost to S.suis infection compared with base case (%).