RESUMO
Diabetes is one of the most widespread global epidemics and has become the main component of the global disease burden. Based on data regarding the prevalence of diabetes in 203 countries and territories from 2013 to 2017, we employed the Bayesian space-time model to investigate the spatiotemporal trends in the global diabetes prevalence. The factors influencing the diabetes prevalence were assessed by the Bayesian LASSO regression model. We identified 77 (37.9%) hotspots with a higher diabetes prevalence than the global average, 10 (0.4%) warm spots with global average level and 116 (57.1%) cold spots with lower level than global average. Of the 203 countries and territories, 68 (33.5%), including 31 hotspots, 5 warm spots and 32 cold spots, exhibited an increasing trend. Of these, 60 experienced an annual increase of more than 0.25%, and 8 showed an increasing trend. Three populous countries, namely China, the USA and Mexico, exhibited a high prevalence and an increasing trend simultaneously. Three socioeconomic factors, body mass index (BMI), urbanization rate (UR) and gross domestic product per capita (GDP-PC), and PM2.5 pollution were found to significantly influence the prevalence of diabetes. BMI was the strongest factor; for every 1% increase in BMI, the prevalence of diabetes increased by 2.371% (95% confidence interval (95% CI): 0.957%, 3.890%) in 2013 and by 3.045% (95% CI: 1.803%, 4.397%) in 2015 and 2017. PM2.5 pollution could be a risk factor, and its influencing magnitude gradually increased as well. With an annual PM2.5 concentrations increase of 1.0% in a country, the prevalence of diabetes increased by 0.196% (95% CI: 0.020%, 0.356%). The UR, on the other hand, was found to be inversely associated with the prevalence of diabetes; with each UR increase of 1%, the prevalence of diabetes decreased by 0.006% (95% CI: 0.001%, 0.011%).
Assuntos
Diabetes Mellitus , Saúde Global , Teorema de Bayes , China/epidemiologia , Diabetes Mellitus/epidemiologia , Humanos , México/epidemiologia , Prevalência , Análise Espacial , Fatores de TempoRESUMO
Unsustainable hunting outside protected areas is threatening tropical biodiversity worldwide and requires conservationists to engage increasingly in antipoaching activities. Following the example of ecocertified logging companies, we argue that other extractive industries managing large concessions should engage in antipoaching activities as part of their environmental management plans. Onshore hydrocarbon concessions should also adopt antipoaching protocols as a standard because they represent a biodiversity threat comparable to logging. We examined the spatiotemporal patterns of small- and large-mammal poaching in an onshore oil concession in Gabon, Central Africa, with a Bayesian occupancy model based on signs of poaching collected from 2010 to 2015 on antipoaching patrols. Patrol locations were initially determined based on local intelligence and past patrol successes (adaptive management) and subsequently with a systematic sampling of the concession. We generated maps of poaching probability in the concession and determined the temporal trends of this threat over 5 years. The spatiotemporal patterns of large- and small-mammal poaching differed throughout the concession, and likely these groups will need different management strategies. By elucidating the relationship between site-specific sampling effort and detection probability, the Bayesian method allowed us to set goals for future antipoaching patrols. Our results indicate that a combination of systematic sampling and adaptive management data is necessary to infer spatiotemporal patterns with the statistical method we used. On the basis of our case study, we recommend hydrocarbon companies interested in implementing efficient antipoaching activities in their onshore concessions to lay the foundation of long-needed industry standards by: adequately measuring antipoaching effort; mixing adaptive management and balanced sampling; setting goals for antipoaching effort; pairing patrols with large-mammal monitoring; supporting antipoaching patrols across the landscape; restricting access to their concessions; performing random searches for bushmeat and mammal products at points of entry; controlling urban and agricultural expansion; supporting bushmeat alternatives; and supporting land-use planning.