A WHO-led global strategy to control greenhouse gas emissions: a call for action.

BACKGROUND: Climate change, driven by anthropogenic greenhouse gas emissions, is among the greatest threats to human health. The World Health Organisation (WHO), has led global efforts to respond to emerging public health threats including the control of hazardous substances such as tobacco, alcohol, lead and asbestos, with remarkable health gains. BODY: Despite WHO's clear messaging on the enormous and growing health risks of climate change, greenhouse gases are not yet classified as hazardous substances, requiring control through a global strategy or framework. Additionally, WHO has not classified disease attributable to climate change as a result of the promulgation of these hazards as a Public Health Emergency of International Concern (PHEIC), despite the serious and preventable health risks it poses globally. Several historical precedents set the stage for WHO to declare excess greenhouse gases as health hazards, including the control of ozone-depleting substances and breast-milk substitutes where the public benefit of control exceeded the potential benefit of their promulgation. In addition, WHO's undertaking within the International Health Regulations to protect global health, providing imperative to declare climate change a PHEIC, with Tedros Adhanom Ghebreyesus, director-general of WHO, declaring: "The climate crisis is a health crisis, fuelling outbreaks, contributing to higher rates of noncommunicable diseases, and threatening to overwhelm our health workforce and health infrastructure". Importantly, the health sector, perhaps more than other sectors, has successfully overcome formidable, vested interests in combatting these threats to health. CONCLUSION: It is thus imperative that WHO make full use of their credibility and influence to establish a global framework for the control of greenhouse gases through the declaration of excess greenhouse gas emissions as a hazardous substance, and declaring climate change a PHEIC. Who else is better placed to drive the considerable societal transformation needed to secure a liveable future?

Developing a spatial-statistical model and map of historical malaria prevalence in Botswana using a staged variable selection procedure.

BACKGROUND: Several malaria risk maps have been developed in recent years, many from the prevalence of infection data collated by the MARA (Mapping Malaria Risk in Africa) project, and using various environmental data sets as predictors. Variable selection is a major obstacle due to analytical problems caused by over-fitting, confounding and non-independence in the data. Testing and comparing every combination of explanatory variables in a Bayesian spatial framework remains unfeasible for most researchers. The aim of this study was to develop a malaria risk map using a systematic and practicable variable selection process for spatial analysis and mapping of historical malaria risk in Botswana. RESULTS: Of 50 potential explanatory variables from eight environmental data themes, 42 were significantly associated with malaria prevalence in univariate logistic regression and were ranked by the Akaike Information Criterion. Those correlated with higher-ranking relatives of the same environmental theme, were temporarily excluded. The remaining 14 candidates were ranked by selection frequency after running automated step-wise selection procedures on 1000 bootstrap samples drawn from the data. A non-spatial multiple-variable model was developed through step-wise inclusion in order of selection frequency. Previously excluded variables were then re-evaluated for inclusion, using further step-wise bootstrap procedures, resulting in the exclusion of another variable. Finally a Bayesian geo-statistical model using Markov Chain Monte Carlo simulation was fitted to the data, resulting in a final model of three predictor variables, namely summer rainfall, mean annual temperature and altitude. Each was independently and significantly associated with malaria prevalence after allowing for spatial correlation. This model was used to predict malaria prevalence at unobserved locations, producing a smooth risk map for the whole country. CONCLUSION: We have produced a highly plausible and parsimonious model of historical malaria risk for Botswana from point-referenced data from a 1961/2 prevalence survey of malaria infection in 1-14 year old children. After starting with a list of 50 potential variables we ended with three highly plausible predictors, by applying a systematic and repeatable staged variable selection procedure that included a spatial analysis, which has application for other environmentally determined infectious diseases. All this was accomplished using general-purpose statistical software.