Idiopathic focal eosinophilic enteritis (IFEE), an emerging cause of abdominal pain in horses: the effect of age, time and geographical location on risk.

BACKGROUND: Idiopathic focal eosinophilic enteritis (IFEE) is an emerging cause of abdominal pain (colic) in horses that frequently requires surgical intervention to prevent death. The epidemiology of IFEE is poorly understood and it is difficult to diagnose pre-operatively. The aetiology of this condition and methods of possible prevention are currently unknown. The aims of this study were to investigate temporal and spatial heterogeneity in IFEE risk and to ascertain the effect of horse age on risk. METHODOLOGY/PRINCIPAL FINDINGS: A retrospective, nested case-control study was undertaken using data from 85 IFEE cases and 848 randomly selected controls admitted to a UK equine hospital for exploratory laparotomy to investigate the cause of colic over a 10-year period. Generalised additive models (GAMs) were used to quantify temporal and age effects on the odds of IFEE and to provide mapped estimates of 'residual' risk over the study region. The relative risk of IFEE increased over the study period (p = 0.001) and a seasonal pattern was evident (p<0.01) with greatest risk of IFEE being identified between the months of July and November. IFEE risk decreased with increasing age (p<0.001) with younger (0-5 years old) horses being at greatest risk. The mapped surface estimate exhibited significantly atypical sub-regions (p<0.001) with increased IFEE risk in horses residing in the North-West of the study region. CONCLUSIONS/SIGNIFICANCE: IFEE was found to exhibit both spatial and temporal variation in risk and is more likely to occur in younger horses. This information may help to identify horses at increased risk of IFEE, provide clues about the aetiology of this condition and to identify areas that require further research.

Bayesian partitioning for mapping disease risk using a matched case-control approach to confounding.

Disease maps are useful for exploring geographical heterogeneity in health outcomes. Typically interest lies in unearthing atypical regions after adjusting for known confounders. This paper presents a Bayesian partitioning approach for analyses when individual-level matching has been used to control confounding. The model makes few assumptions about the surface form and, in particular, permits discontinuity. The specification is inherently parsimonious and posterior sampling permits direct assessment of surface uncertainty; additional unmatched covariates can also be incorporated. The method is used to investigate spatial variation in perinatal mortality in the North-West Thames region, England.

Bayesian partitioning for modeling and mapping spatial case-control data.

Methods for modeling and mapping spatial variation in disease risk continue to motivate much research. In particular, spatial analyses provide a useful tool for exploring geographical heterogeneity in health outcomes, and consequently can yield clues as to disease etiology, direct public health management, and generate research hypotheses. This article presents a Bayesian partitioning approach for the analysis of individual level geo-referenced health data. The model makes few assumptions about the underlying form of the risk surface, is data adaptive, and allows for the inclusion of known determinants of disease. The methodology is used to model spatial variation in neonatal mortality in Porto Alegre, Brazil.