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

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.

Exploring Spatial and Temporal Distribution of Cutaneous Leishmaniasis in the Americas, 2001–2011

“…These exploratory methods and their graphic representations are an essential part for the understanding of the complexity of the data correlated over time [40]. This is particularly true in this study where our “sample” is the whole population under surveillance, and for more practical purposes it could be assumed that cases represented approximately the occurrence of CL in the population; in which statistical inference is not as useful as the descriptive method [41]. …”

Temporal patterns of human and canine Giardia infection in the United States: 2003–2009

“…These methods are commonly used in fields such as econometrics, but their application to veterinary medical data has been limited (Benschop et al, 2008;Sanchez-Vazquez et al, 2012). A recent paper (Christiansen et al, 2012) that reviewed methods used to assess seasonality in epidemiological studies of human infectious disease did not include STL and ARIMA/SARIMA.…”

Using Bayesian networks to explore the role of weather as a potential determinant of disease in pigs

“…Several reports indicate the presence of seasonal changes in disease occurrence in farmed pigs, suggesting that climatic factors play a substantial role driving pathogen (Sanchez-Vazquez et al, 2012b; Wagner and Polley, 1999) or disease patterns (Sanchez-Vazquez et al, 2012c; Stärk, 2000). …”

“…In analyses whose main focus is identifying environmental risk factors or disease determinants, choices are often made about summarizing a few key variables, for example, through stepwise selection (Acevedo et al, 2010; McCluskey et al, 2003), data reduction with principle component analysis (Duchateau et al, 1997; Estrada-peña and Venzal, 2007) and partial Fourier series (Purse et al, 2007). These variables are then incorporated into modelling frameworks that might include regressions of bio-geographic variables on the presence of disease (Gilbert et al, 2005), pattern matching disease observations with climatic descriptors (Olwoch et al, 2003; Purse et al, 2007, 2004), utilizing expert opinion of major risk factors (Sumption et al, 2008) or comparison of time series (Sanchez-Vazquez et al, 2012c). …”

“…In this case study we use information on pig herd health, obtained from abattoir surveillance data, to contrast a number of pathologies that respond differently to climatic factors (Sanchez-Vazquez et al, 2012b,c, 2010). The collection of ten pathologies that are of importance to the economic productivity of farms describe a number of different aetiological routes; for example, papular dermatitis is related to sarcoptic mange (Cargill et al, 1997), enzootic-pneumonia is most closely associated with Mycoplasma hyopneumoniae (Maes et al, 1996) and milk spots are the result of Ascaris suum infection (Bindseil, 1973).…”