Background In 2010, overweight and obesity were estimated to cause 3.4 million deaths, 3.9% of years of life lost, and 3.8% of DALYs globally. The rise in obesity has led to widespread calls for regular monitoring of changes in overweight and obesity prevalence in all populations. Comparative, up-to-date information on levels and trends is essential both to quantify population health effects and to prompt decision-makers to prioritize action. Methods We systematically identified surveys, reports, and published studies (n = 1,769) that included information on height and weight, both through physical measurements and self-reports. Mixed effects linear regression was used to correct for the bias in self-reports. Age-sex-country-year observations (n = 19,244) on prevalence of obesity and overweight were synthesized using a spatio-temporal Gaussian Process Regression model to estimate prevalence with 95% uncertainty intervals. Findings Globally, the proportion of adults with a body mass index (BMI) of 25 or greater increased from 28.8% (95% UI: 28.4-29.3) in 1980 to 36.9% (36.3-37.4) in 2013 for men and from 29.8% (29.3-30.2) to 38.0% (37.5-38.5) for women. Increases were observed in both developed and developing countries. There have been substantial increases in prevalence among children and adolescents in developed countries, with 23.8% (22.9-24.7) of boys and 22.6% (21.7-23.6) of girls being either overweight or obese in 2013. The prevalence of overweight and obesity is also rising among children and adolescents in developing countries as well, rising from 8.1% (7.7-8.6) to 12.9% (12.3-13.5) in 2013 for boys and from 8.4% (8.1-8.8) to 13.4% (13.0-13.9) in girls. Among adults, estimated prevalence of obesity exceeds 50% among men in Tonga and women in Kuwait, Kiribati, Federated States of Micronesia, Libya, Qatar, Tonga, and Samoa. Since 2006, the increase in adult obesity in developed countries has stabilized. Interpretation Because of the established health risks and substantial increases in prevalence, obesity has become a major global health challenge. Contrary to other major global risks, there is little evidence of successful population-level intervention strategies to reduce exposure. Not only is obesity increasing, but there are no national success stories over the past 33 years. Urgent global action and leadership is required to assist countries to more effectively intervene.

Unmanned aerial vehicles (UAVs) or drones have been used by disaster relief organizations in the United States since 2005. However, their place in the disaster response ecosystemthe standardization, utility, ethical, and legal challenges of drone use-remains largely unstudied. This case series describes how UAVs were used by two teams of responders for damage assessment purposes during the 2017 southeastern US Hurricanes Harvey and Irma. Data streams ranged from social media, direct observation, participant-observation and semi-directed interviews. Qualitative analysis was performed for thematic content derived from field observation and from post-hoc interviews. Outcomes of the qualitative analysis emphasize the barriers to deploying drones in the disaster context, their tactical implementation, programmatic integration, and ethical and legal challenges. These observations lay the groundwork for both future research on the utilization of drones and the prudent and ethical implementation of programs that employ drones in post-disaster settings.

The humanitarian sector is increasingly adopting geospatial data to support operations. However, the utilization of these data in the humanitarian health arena is predominantly in thematic map format, thereby limiting the full insight and utility of geospatial information.Geospatial analytics, in contrast, including pattern analysis, interpolation, and predictive modeling, have tremendous potential within the field of humanitarian health. This paper explores a variety of historical and contemporary geospatial applications in the public health and humanitarian fields and argues for greater integration of geospatial analysis into humanitarian health research and programming. From remote sensing to create sampling frames, to spatial interpolation for environmental exposure analysis, and multi-objective optimization algorithms for humanitarian logistics, spatial analysis has transformed epistemological paradigms, research methods and programming landscapes across diverse disciplines.The field of humanitarian health, which is inextricably bounded by geography and resource limitations, should leverage the unique capacities of spatial methods and strategically integrate geospatial analytics into research and programming not only to fortify the academic legitimacy and professionalization of the field but also to improve operational efficiency and mitigation strategies.