Defining the research agenda to measure and reduce tuberculosis stigmas

This is an Open Access article, © 2017 International Union Against Tuberculosis and Lung Disease. Content in the UH Research Archive is made available for personal research, educational, and non-commercial purposes only. Unless otherwise stated, all content is protected by copyright, and in the absence of an open license, permissions for further re-use should be sought from the publisher, the author, or other copyright holder.Crucial to finding and treating the 4 million tuberculosis (TB) patients currently missed by National TB Programs, TB stigma is receiving well-deserved and long-delayed attention at the global level. However, the ability to measure and evaluate the success of TB stigma reduction efforts is limited by the need for additional tools. At a 2016 TB stigma measurement meeting held in The Hague, stigma experts discussed and proposed a research agenda around four themes: (1) Drivers: What are the main drivers and domains of TB stigma(s)?; (2) Consequences: How consequential are TB stigmas? How are negative impacts most felt?; (3) Burden: What is the global prevalence and distribution of TB stigma(s)? What explains any variation? (4): Intervention: What can be done to reduce the extent and impact of TB stigma(s)? Each theme was further subdivided into research topics to be addressed to move the agenda forward. These include more clarity on what causes TB stigmas to emerge and thrive, the difficulty of measuring the complexity of stigma, and the improbability of a universal stigma ‘cure’. Notwithstanding, these challenges should not hinder investments in TB stigma measurement and reduction. We believe it is time to focus on how and not whether the global community should measure and reduce TB stigma.Peer reviewedFinal Published versio

Geographical and temporal distribution of the residual clusters of human leptospirosis in China, 2005–2016

Human leptospirosis outbreaks still persistently occur in part of China, indicating that leptospirosis remains an important zoonotic disease in the country. Spatiotemporal pattern of the high-risk leptospirosis cluster and the key characteristics of high-risk areas for leptospirosis across the country are still poorly understood. Using spatial analytical approaches, we analyzed 8,158 human leptospirosis cases notified during 2005-2016 across China to explore the geographical distribution of leptospirosis hotspots and to characterize demographical, ecological and socioeconomic conditions of high-risk counties for leptospirosis in China. During the period studied, leptospirosis incidence was geographically clustered with the highest rate observed in the south of the Province of Yunnan. The degree of spatial clustering decreased over time suggesting changes in local risk factors. However, we detected residual high-risk counties for leptospirosis including counties in the southwest, central, and southeast China. High-risk counties differed from low-risk counties in terms of its demographical, ecological and socioeconomic characteristics. In high-risk clusters, leptospirosis was predominantly observed on younger population, more males and farmers. Additionally, high-risk counties are characterized by larger rural and less developed areas, had less livestock density and crops production, and located at higher elevation with higher level of precipitation compare to low-risk counties. In conclusion, leptospirosis distribution in China appears to be highly clustered to a discrete number of counties highlighting opportunities for elimination; hence, public health interventions should be effectively targeted to high-risk counties identified in this study

Conservation status of Asian elephants: the influence of habitat and governance

Understanding the drivers of Asian elephant (Elephas maximus) abundance and distribution is critical for effective elephant conservation, yet no such analysis exists despite decades of assessments and planning. We explored the influence of habitat- and governance-related drivers on elephant abundance across the 13 Asian elephant range countries. We tested competing statistical models by integrating a binary index of elephant abundance (IEA) derived from expert knowledge with different predictor variables including habitat, human population, socioeconomics, and governance data. We employed logistic regression and model-averaging techniques based on Akaike’s Information Criterion to identify the best-performing subset among our 12 candidate models and used the model-averaged results to predict IEA in other areas in Asia where elephant population status is currently unknown. Forest area was our strongest single predictor variable. The best performing model, however, featured a combination of habitat and governance variables including forest area, level of corruption, proportional mix of forest and agriculture, and total agricultural area. Our predictive model identified five areas with medium–high to high probability to have populations with >150 elephants, which we believe should be surveyed to assess their status. Asian elephants persist in areas that are dominated by forest but also seem to benefit from a mix of agricultural activities. A relatively low level of corruption is also important and we conclude that effective governance is essential for maintaining Asian elephant populations. Asian elephant populations cannot be maintained solely in protected areas but need well-managed, mixed-use landscapes where people and elephants coexist