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?

A Cluster Randomised Trial to Determine the Efficacy of the "Feeding Buddies" Programme in Improving Exclusive Breastfeeding Rates Among HIV-Infected Women in Rural KwaZulu-Natal, South Africa.

This cluster randomised trial in KwaZulu-Natal South Africa, evaluated the implementation of a Feeding Buddies (FB) programme to improve exclusive breastfeeding (EBF) amongst human immunodeficiency virus infected mothers. Eight clinics were randomly allocated to intervention and control arms respectively. Pregnant women attending the prevention of mother-to-child transmission program and intending to EBF were enrolled: control (n = 326), intervention (n = 299). Intervention mothers selected FBs to support them and they were trained together (four sessions). Interviews of mothers occurred prenatally and at post-natal visits (day 3, weeks 6, 14 and 22). Breastfeeding results were analysed (Stata) as interval-censored time-to-event data, with up to four time intervals per mother. EBF rates at the final interview were similar for control and intervention groups: 44.68% (105/235) and 42.75% (109/255) respectively (p = 0.67). In Cox regression analysis better EBF rates were observed in mothers who received the appropriate training (p = 0.036), had a community care giver visit (p = 0.044), while controlling for other factors. Implementation realities reduced the potential effectiveness of the FBs.

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.

Environmental predictors of the seasonality of malaria transmission in Africa: the challenge.

A description of malaria seasonality is important for planning and optimizing malaria control in both time and space, but adequate malariologic data are not available for many disease-endemic areas. We analyzed the relationship between seasonality in the entomologic inoculation rate (EIR) and environmental factors in sites across sub-Saharan Africa with the objective of predicting seasonality from environmental data. The degree of EIR seasonality in each site was quantified using an index previously used for rainfall. The results showed that seasonality of rainfall, minimum temperature, and irrigation are important determinants of seasonality in EIR. Model fit was poor in areas characterized by two rainfall peaks and by irrigation activities. Two rainfall peaks probably dampen seasonality and irrigation creates perennial breeding habitats for vectors independent of rainfall. This complex interplay between the seasonal dynamics of environmental determinants and malaria pose a great challenge and highlights the need for improved models of malaria seasonality.