Spatial scale effects in environmental risk-factor modelling for diseases

Abstract

Abstract. Studies attempting to identify environmental risk factors for diseases can be seen to extract candidate variables from remotely sensed datasets, using a single buffer-zone surrounding locations from where disease status are recorded. A retrospective case-control study using canine leptospirosis data was conducted to verify the effects of changing buffer-zones (spatial extents) on the risk factors derived. The case-control study included 94 case dogs predominantly selected based on positive polymerase chain reaction (PCR) test for leptospires in urine, and 185 control dogs based on negative PCR. Land cover features from National Land Cover Dataset (NLCD) and Kansas Gap Analysis Program (KS GAP) around geocoded addresses of cases/controls were extracted using multiple buffers at every 500 m up to 5,000 m, and multivariable logistic models were used to estimate the risk of different land cover variables to dogs. The types and statistical significance of risk factors identified changed with an increase in spatial extent in both datasets. Leptospirosis status in dogs was significantly associated with developed high-intensity areas in models that used variables extracted from spatial extents of 500-2000 m, developed medium-intensity areas beyond 2,000 m and up to 3,000 m, and evergreen forests beyond 3,500 m and up to 5,000 m in individual models in the NLCD. Significant associations were seen in urban areas in models that used variables extracted from spatial extents of 500-2,500 m and forest/woodland areas beyond 2,500 m and up to 5,000 m in individual models in Kansas gap analysis programme datasets. The use of ad hoc spatial extents can be misleading or wrong, and the determination of an appropriate spatial extent is critical when extracting environmental variables for studies. Potential work-arounds for this problem are discussed.

Spatial epidemiological approaches to inform leptospirosis surveillance and control: A systematic review and critical appraisal of methods

“…Only 40 studies (34.78%, n = 40/115) used infection data generated by surveys. Most studies were cross‐sectional (86.95%, n = 100/115), few were case–control studies (6.08%, n = 7/115) (Ghneim et al, ; Hennebelle, Sykes, Carpenter, & Foley, ; Raghavan, Brenner, Higgins, Van der Merwe, & Harkin, ; Raghavan, Brenner, Harrington, Higgins, & Harkin, ; Suryani, Pramoedyo, Sudarto, & Andarini, ; Ward, ; Ward, Guptill, & Wu, ), and only six studies (5.21%) employed a prospective cohort design (Deshmukh et al, ; Hagan et al, ; Ko, Galvão Reis, Ribeiro Dourado, Johnson, & Riley, ; Ledien et al, ; Mišić‐Majerus, ; Reis et al, ).…”

“…Seventeen studies (43.36%, n = 17/39) conducted in six countries assessed the association between incidence ( n = 7; Ghneim et al, ; Major, Schweighauser, & Francey, ; Raghavan et al, ; Raghavan et al, ; Raghavan et al, ; Ward et al, ; White et al, ) or prevalence ( n = 10; Alton et al, ; Bier et al, ; Bier et al, ; Biscornet et al, ; Elder et al, ; Elder & Ward, ; Himsworth et al, ; Ivanova et al, ) with various predictors at national ( n = 6) and subnational ( n = 9) levels. As with human studies, the effect of physical environmental (64.70%, n = 11/17; Alton et al, ; Biscornet et al, ; Elder et al, ; Ghneim et al, ; Ivanova et al, ; Raghavan et al, ; Raghavan et al, ; Silva et al, ; Raghavan et al, ; Ward et al, ; White et al, ) and climatic factors (52.94%, n = 9/17; Elder et al, ; Elder & Ward, ; Ghneim et al, ; Himsworth et al, ; Ivanova et al, ; Silva et al, ; Major et al, ; Ward et al, ; White et al, ) on animal infections were the most commonly studied. Nine studies used RS‐based environmental data (Dobigny et al, ; Ghneim et al, ; Ivanova et al, ; Raghavan et al, ; Raghavan et al, ; Silva et al, ; Ward et al, ; White et al, ) including land cover/land use, elevation or slope (Supporting Information Table ).…”

Mapping Of Leptospirosis Environmental Risk Factors and Determining the Level of Leptospirosis Vulnerable Zone In Demak District Using Remote Sensing Image

“…Both technologies are very useful in the management of spatial information of earth surface condition [26], [27]. Recently, the use of GIS and spatial representation of various health issues make professionals arrive at conclusions in a faster and better way in the field of both public health and decisionmaking.…”

Bayesian Spatio-Temporal Analysis and Geospatial Risk Factors of Human Monocytic Ehrlichiosis

“…Influential factors affecting the prevalence and distribution of diseases could differ from one region to another due to natural changes in the geography, and they are quite scale-dependent [6], [7]. In addition, the relevance of certain spatial determinants often becomes evident only in the presence of others.…”