Using seasonal-trend decomposition based on loess (STL) to explore temporal patterns of pneumonic lesions in finishing pigs slaughtered in England, 2005-2011

MJ Sanchez-Vazquez, M Nielen, GJ Gunn, FI Lewis

Research output: Contribution to journalArticle

Abstract

Enzootic pneumonia (EP) is responsible for considerable economic losses in pig production. This study analyses temporal variations of pneumonic lesions present in slaughtered finishing pigs utilising a novel analytical tool – STL decomposition. Using data collected over a 6-year period starting in July 2005, time-series analyses were conducted to identify trend and the presence of seasonal variations to support industry led measures to monitor and control this important respiratory disease. In England, the BPEX Pig Health Scheme monitors the occurrence of EP in slaughtered finished pigs by identifying its gross pathology, enzootic pneumonia-like (EP-like) lesions. For visual analytics, the monthly prevalence for EP-like lesions was modelled using STL, a seasonal-trend decomposition method based on locally-weighted regression. A binomial generalised linear mixed-effects model (GLMM), accounting for clustering at batch level, was used to test the significance of the trend and seasonality. A mean of 12,370 pigs was assessed per month across 12 pig abattoirs over the study period. A trend toward reduction in prevalence of EP-like lesions during the first 3 years of BPHS, followed by an increasing trend, was identified with STL. This feature was consistent with the presence of a statistically significant positive quadratic term (“U” shape) as identified using the GLMM inference model. November and December appeared in the STL explorations as higher seasonal peaks of the occurrence of EP-like lesions. These 2 months had a significantly higher risk of this disease (OR = 1.38, 95% CI: 1.24–1.54 and OR = 1.4, 95% CI: 1.25–1.58, respectively, with July taken as baseline). The results were reported back to the pig industry as part of the national monitoring investigations.
Original languageEnglish
Pages (from-to)65 - 73
Number of pages9
JournalPreventive Veterinary Medicine
Volume104
Publication statusFirst published - 2011

Bibliographical note

2032599

Keywords

  • Pig-information systems
  • Pneumonia
  • Seasonal dynamics
  • Time-series analysis

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