# Cost Analysis of the Belgian National Antimicrobial Resistance Monitoring in Livestock: Effects on Sampling Design and Statistical Performance

## TL;DR

This paper analyzes the costs of Belgium's livestock antimicrobial resistance monitoring and how sampling size affects cost and statistical reliability.

## Contribution

It introduces the first published cost evaluation using unit cost aggregation for a national AMR monitoring program in animals.

## Key findings

- Increasing sample size leads to linearly higher costs and improved statistical power and confidence.
- Antimicrobial susceptibility testing accounts for 50.2% of total monitoring costs.
- Fattening pigs, broilers, and veal calves contribute over 18% each to total monitoring costs.

## Abstract

Background/Objectives: As part of the European Union’s harmonized monitoring framework, Belgium conducts antimicrobial resistance (AMR) monitoring in commensal bacteria from livestock. The aim of this study was to conduct a cost analysis of the national AMR monitoring in livestock, and to explore sampling size scenarios in relation to their associated costs and statistical performance (power and confidence) of monitoring. Methods: To our knowledge, this is the first published cost evaluation using unit cost aggregation of a national AMR monitoring program in animals. Results: The testing of the different sample size scenarios showed that if the sample size increases, the costs increase linearly. A sample size increase of 10 samples/isolates (e.g., from 170 to 180) can increase the yearly total costs per animal species by 5.2%. Moreover, the testing of the different scenarios showed that if the sample size increases, the power and the confidence level also increase, providing a higher level of trust in the results of the monitoring program. The highest total monitoring costs per animal category were estimated for fattening pigs, broilers and veal calves (over 18% of total costs each, using 2024 data). Among the various monitoring activities, antimicrobial susceptibility testing emerged as the costliest component, representing 50.2% of the total monitoring costs. Conclusions: The approach presented allows it to be used by other countries aiming to estimate the cost of their national AMR monitoring in animals or other similar activities. This economic and scenario testing analysis can be used to suggest informed suggestions to improve AMR monitoring in animals.