Application of landscape epidemiology to assess potential public health risk due to poor sanitation.

Clear identification of areas vulnerable to waterborne diseases is essential for protecting community health. This is particularly important in developing countries where unsafe disposal of domestic wastewater and limited potable water supply pose potential public health risks. However, data paucity can be a compounding issue. Under these circumstances, landscape epidemiology can be applied as a resource efficient approach for mapping potential disease risk areas associated with poor sanitation. However, in order to realise the full potential offered by this approach, an in-depth understanding of the impact of different classes of an explanatory variable on a target disease and the validity of hotspot analysis using limited datasets is needed. Accordingly, this research study focused on typhoid and diarrhoea incidence with respect to different classes of elevation, flood inundation, land use, soil permeability, population density and rainfall as explanatory variables. An integrated methodology consisting of hot spot analysis and Poisson regression was employed to map potential disease risk areas. The study findings confirmed the significant differences in the influence exerted by the various classes of an explanatory variable in relation to a target disease. The results also confirmed the feasibility of the hotspot analysis for identifying areas vulnerable to the target diseases using a limited dataset. The study outcomes are expected to contribute to creating an in-depth understanding of the relationship between disease prevalence and associated landscape factors for the delineation of disease risk zones in the context of data paucity.

Single nucleotide polymorphisms in the open reading frame of the stearoyl-CoA desaturase gene and resulting genetic variants in Canadian Holstein and Jersey cows.

Stearoyl-CoA desaturase (SCD) catalyzes the synthesis of conjugated linoleic acid (CLA) and mono-unsaturated fatty acids (MUFA) from their saturated counterparts in the mammary gland and adipose tissue of ruminant animals. We hypothesize that single nucleotide polymorphisms (SNPs) in the SCD gene account for some of the differences in SCD activity, and consequently for some of the variations in CLA and MUFA content of milk fat between Holsteins and Jersey cows and within these two breeds. We analyzed the open reading frame of the SCD gene of 44 Holsteins and 48 Jerseys for SNPs by sequencing. Three SNPs: 702A --> G, 762T --> C and 878C --> T were identified in both breeds and a further SNP, 435G --> A, was unique to Holsteins. The SNPs characterized four different genetic variants in Holsteins: A (G(435)A(702)T(762)C(878)), A1 (A(435)A(702)T(762)C(878)), B (G(435)G(702)C(762)T(878)) and B1 (A(435)G(702)C(762)T(878)), with only variants A and B in Jerseys. SNP 878C --> T resulted in a non-synonymous codon change while the rest resulted in synonymous codon changes giving rise to two protein variants, A having alanine and B having valine. Allele A was the most prevalent in the two breeds. These differences may, therefore, contribute to existing variations in CLA and fat content between and within Canadian Holstein and Jersey cows.

Spatial response surface modelling in the presence of data paucity for the evaluation of potential human health risk due to the contamination of potable water resources.

Potential human health risk from waterborne diseases arising from unsatisfactory performance of on-site wastewater treatment systems is driven by landscape factors such as topography, soil characteristics, depth to water table, drainage characteristics and the presence of surface water bodies. These factors are present as random variables which are spatially distributed across a region. A methodological framework is presented that can be applied to model and evaluate the influence of various factors on waterborne disease potential. This framework is informed by spatial data and expert knowledge. For prediction at unsampled sites, interpolation methods were used to derive a spatially smoothed surface of disease potential which takes into account the uncertainty due to spatial variation at any pre-determined level of significance. This surface was constructed by accounting for the influence of multiple variables which appear to contribute to disease potential. The framework developed in this work strengthens the understanding of the characteristics of disease potential and provides predictions of this potential across a region. The study outcomes presented constitutes an innovative approach to environmental monitoring and management in the face of data paucity.

Spatiotemporal variation of notified Barmah Forest virus infections in Queensland, Australia, 1993-2001.

The spatiotemporal variations of Barmah Forest virus (BFv) infections were assessed in Queensland, Australia, between 1993 and 2001 utilizing a Geographic Information System. The notified BFv cases came from 441 localities between 1993 and 1995, 512 between 1996 and 1998, and 546 between 1999 and 2001. A statistically significant increase was observed in the number of localities from which the cases were notified by 16.1% for the period of 1996-1998 and 23.8% for 1999-2001, compared with that for 1993-1995 (chi2 (df=2) = 11.5; p < 0.01). These results indicate that there has been an expanded geographic distribution of the notified BFv infections in Queensland over the last decade. Socioecological changes may be causal factors in this expansion.