Accounting for autocorrelation in multi-drug resistant tuberculosis predictors using a set of parsimonious orthogonal eigenvectors aggregated in geographic space

Abstract

Abstract. Spatial autocorrelation is problematic for classical hierarchical cluster detection tests commonly used in multidrug resistant tuberculosis (MDR-TB) analyses as considerable random error can occur. Therefore, when MDR-TB clusters are spatially autocorrelated the assumption that the clusters are independently random is invalid. In this research, a product moment correlation coefficient (i.e. the Moran's coefficient) was used to quantify local spatial variation in multiple clinical and environmental predictor variables sampled in San Juan de Lurigancho, Lima, Peru. Initially, QuickBird (spatial resolution = 0.61 m) data, encompassing visible bands and the near infra-red bands, were selected to synthesize images of land cover attributes of the study site. Data of residential addresses of individual patients with smear-positive MDR-TB were geocoded, prevalence rates calculated and then digitally overlaid onto the satellite data within a 2 km buffer of 31 georeferenced health centres, using a 10 m 2 grid-based algorithm. Geographical information system (GIS)-gridded measurements of each health centre were generated based on preliminary base maps of the georeferenced data aggregated to block groups and census tracts within each buffered area. A three-dimensional model of the study site was constructed based on a digital elevation model (DEM) to determine terrain covariates associated with the sampled MDR-TB covariates. Pearson's correlation was used to evaluate the linear relationship between the DEM and the sampled MDR-TB data. A SAS/GIS ® module was then used to calculate univariate statistics and to perform linear and non-linear regression analyses using the sampled predictor variables. The estimates generated from a global autocorrelation analyses were then spatially decomposed into empirical orthogonal bases, using a negative binomial regression with a non-homogeneous mean. Results of the DEM analyses indicated a statistically non-significant, linear relationship between georeferenced health centres and the sampled covariate elevation. The data exhibited positive spatial autocorrelation and the decomposition of Moran's coefficient into uncorrelated, orthogonal map pattern components which revealed global spatial heterogeneities necessary to capture latent autocorrelation in the MDR-TB model. It was thus shown that Poisson regression analyses and spatial eigenvector mapping can elucidate the mechanics of MDR-TB transmission by prioritizing clinical and environmental-sampled predictor variables for identifying high risk populations.

Information Technologies in Biomedicine

“…Such studies outcomes could, however, be expected due to the need for a different perspective on issues in the field of biomedicine and health care by relevant experts in the domain. [7], [9], [13], [23], [24], [29], [30], [33], [35], [37], [39], [41], [43], [44], [51], [57], [63], [64], [65], [71], [73] 21 HP [4], [6], [12], [16], [21], [22], [25], [36], [48], [49], [53], [77] 12 JECH [8], [10], [17], [31], [42], [47], [50], [52], [54], [55], [59], [60], [61], [62], [75] 15 Total 48 Source: Own elaboration [5], [14], [15] 3 GH …”

“…Using the adjusted and simplified version of software SIMCEP, prepared by Lehmussola and coworkers, described in [17] and available in internet [33] we propose to create a collection of artificial images. This software originally has been created for simulating fluorescent microscope full color images of living cells.…”

“…An individual should confront his expectations with what is physically possible in the surrounding geographical, social and cultural environment and find ways to fulfil them [31], [33]. Self-fulfilment at work, i.e.…”

Trends in obesity at the national and local level among South Korean adolescents

“…In addition, interpreting and understanding health data usually requires the consideration of the appropriate spatial context because many health datasets come with geographic information. Some health studies in the past incorporated the spatial context in the data analysis (Edsall, 2003;Ezzati et al, 2006;Jacob et al, 2010). Most of the past studies, however, used only simple images and maps, such as aerial images, choropleth or isopleth maps for the visualisation of health data.…”

Geospatial Health: the first five years

“…In the "big three" group, malaria was the predominant disease (11.2%) (Aron, 2006;Connor et al, 2006;Gosoniu et al, 2006;Kiang et al, 2006;Kazembe et al, 2007;Sogoba et al, 2007;Briët et al, 2008;Capinha et al, 2009;Dongus et al, 2009;Mboera et al, 2010;Wayant et al, 2010;Yang et al, 2010;Delgado-Petrocelli et al, 2011;Machault et al, 2011). On the other hand, HIV/AIDS and tuberculosis were discussed in only one and two studies, respectively (Zendejas-Martínez et al, 2008;Jacob et al, 2010;Peng et al, 2011).…”