Rivers and flooded areas identified by medium-resolution remote sensing improve risk prediction of the highly pathogenic avian influenza H5N1 in Thailand

Thailand experienced several epidemic waves of the highly pathogenic avian influenza (HPAI) H5N1 between 2004 and 2005. This study investigated the role of water in the landscape, which has not been previously assessed because of a lack of high-resolution information on the distribution of flooded land at the time of the epidemic. Nine Landsat 7 - Enhanced Thematic Mapper Plus scenes covering 174,610 km2 were processed using k-means unsupervised classification to map the distribution of flooded areas as well as permanent lakes and reservoirs at the time of the main epidemic HPAI H5N1 wave of October 2004. These variables, together with other factors previously identified as significantly associated with risk, were entered into an autologistic regression model in order to quantify the gain in risk explanation over previously published models. We found that, in addition to other factors previously identified as associated with risk, the proportion of land covered by flooding along with expansion of rivers and streams, derived from an existing, sub-district level (administrative level no. 3) geographical information system database, was a highly significant risk factor in this 2004 HPAI epidemic. These results suggest that water-borne transmission could have partly contributed to the spread of HPAI H5N1 during the epidemic. Future work stemming from these results should involve studies where the actual distribution of small canals, rivers, ponds, rice paddy fields and farms are mapped and tested against farm-level data with respect to HPAI H5N1

Point pattern simulation modelling of extensive and intensive chicken farming in Thailand : accounting for clustering and landscape characteristics

In recent decades, intensification of animal production has been occurring rapidly in transition economies to meet the growing demands of increasingly urban populations. This comes with significant environmental, health and social impacts. To assess these impacts, detailed maps of livestock distributions have been developed by downscaling census data at the pixel level (10 km or 1 km), providing estimates of the density of animals in each pixel. However, these data remain at fairly coarse scale and many epidemiological or environmental science applications would make better use of data where the distribution and size of farms are predicted rather than the number of animals per pixel. Based on detailed 2010 census data, we investigated the spatial point pattern distribution of extensive and intensive chicken farms in Thailand. We parameterized point pattern simulation models for extensive and intensive chicken farms and evaluated these models in different parts of Thailand for their capacity to reproduce the correct level of spatial clustering and the most likely locations of the farm clusters. We found that both the level of clustering and location of clusters could be simulated with reasonable accuracy by our farm distribution models. Furthermore, intensive chicken farms tended to be much more clustered than extensive farms, and their locations less easily predicted using simple spatial factors such as human populations. These point-pattern simulation models could be used to downscale coarse administrative level livestock census data into farm locations. This methodology could be of particular value in countries where farm location data are unavailable

Australian chiropractic sports medicine: half way there or living on a prayer?

Sports chiropractic within Australia has a chequered historical background of unorthodox individualistic displays of egocentric treatment approaches that emphasise specific technique preference and individual prowess rather than standardised evidence based management. This situation has changed in recent years with the acceptance of many within sports chiropractic to operate under an evidence informed banner and to embrace a research culture. Despite recent developments within the sports chiropractic movement, the profession is still plagued by a minority of practitioners continuing to espouse certain marginal and outlandish technique systems that beleaguer the mainstream core of sports chiropractic as a cohesive and homogeneous group. Modern chiropractic management is frequently multimodal in nature and incorporates components of passive and active care. Such management typically incorporates spinal and peripheral manipulation, mobilisation, soft tissue techniques, rehabilitation and therapeutic exercises. Externally, sports chiropractic has faced hurdles too, with a lack of recognition and acceptance by organized and orthodox sports medical groups. Whilst some arguments against the inclusion of chiropractic may be legitimate due to its historical baggage, much of the argument appears to be anti-competitive, insecure and driven by a closed-shop mentality.sequently, chiropractic as a profession still remains a pariah to the organised sports medicine world. Add to this an uncertain continuing education system, a lack of protection for the title 'sports chiropractor', a lack of a recognized specialist status and a lack of support from traditional chiropractic, the challenges for the growth and acceptance of the sports chiropractor are considerable. This article outlines the historical and current challenges, both internal and external, faced by sports chiropractic within Australia and proposes positive changes that will assist in recognition and inclusion of sports chiropractic in both chiropractic and multi-disciplinary sports medicine alike

Downscaling livestock census data using multivariate predictive models: Sensitivity to modifiable areal unit problem

The analysis of census data aggregated by administrative units introduces a statistical bias known as the modifiable areal unit problem (MAUP). Previous researches have mostly assessed the effect of MAUP on upscaling models. The present study contributes to clarify the effects of MAUP on the downscaling methodologies, highlighting how a priori choices of scales and shapes could influence the results. We aggregated chicken and duck fine-resolution census in Thailand, using three administrative census levels in regular and irregular shapes. We then disaggregated the data within the Gridded Livestock of the World analytical framework, sampling predictors in two different ways. A sensitivity analysis on Pearson’s r correlation statistics and RMSE was carried out to understand how size and shapes of the response variables affect the goodness-of-fit and downscaling performances. We showed that scale, rather than shapes and sampling methods, affected downscaling precision, suggesting that training the model using the finest administrative level available is preferable. Moreover, datasets showing non-homogeneous distribution but instead spatial clustering seemed less affected by MAUP, yielding higher Pearson’s r values and lower RMSE compared to a more spatially homogenous dataset. Implementing aggregation sensitivity analysis in spatial studies could help to interpret complex results and disseminate robust products