A global map of travel time to cities to assess inequalities in accessibility in 2015.

The economic and man-made resources that sustain human wellbeing are not distributed evenly across the world, but are instead heavily concentrated in cities. Poor access to opportunities and services offered by urban centres (a function of distance, transport infrastructure, and the spatial distribution of cities) is a major barrier to improved livelihoods and overall development. Advancing accessibility worldwide underpins the equity agenda of 'leaving no one behind' established by the Sustainable Development Goals of the United Nations. This has renewed international efforts to accurately measure accessibility and generate a metric that can inform the design and implementation of development policies. The only previous attempt to reliably map accessibility worldwide, which was published nearly a decade ago, predated the baseline for the Sustainable Development Goals and excluded the recent expansion in infrastructure networks, particularly in lower-resource settings. In parallel, new data sources provided by Open Street Map and Google now capture transportation networks with unprecedented detail and precision. Here we develop and validate a map that quantifies travel time to cities for 2015 at a spatial resolution of approximately one by one kilometre by integrating ten global-scale surfaces that characterize factors affecting human movement rates and 13,840 high-density urban centres within an established geospatial-modelling framework. Our results highlight disparities in accessibility relative to wealth as 50.9% of individuals living in low-income settings (concentrated in sub-Saharan Africa) reside within an hour of a city compared to 90.7% of individuals in high-income settings. By further triangulating this map against socioeconomic datasets, we demonstrate how access to urban centres stratifies the economic, educational, and health status of humanity.

The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015.

Since the year 2000, a concerted campaign against malaria has led to unprecedented levels of intervention coverage across sub-Saharan Africa. Understanding the effect of this control effort is vital to inform future control planning. However, the effect of malaria interventions across the varied epidemiological settings of Africa remains poorly understood owing to the absence of reliable surveillance data and the simplistic approaches underlying current disease estimates. Here we link a large database of malaria field surveys with detailed reconstructions of changing intervention coverage to directly evaluate trends from 2000 to 2015, and quantify the attributable effect of malaria disease control efforts. We found that Plasmodium falciparum infection prevalence in endemic Africa halved and the incidence of clinical disease fell by 40% between 2000 and 2015. We estimate that interventions have averted 663 (542-753 credible interval) million clinical cases since 2000. Insecticide-treated nets, the most widespread intervention, were by far the largest contributor (68% of cases averted). Although still below target levels, current malaria interventions have substantially reduced malaria disease incidence across the continent. Increasing access to these interventions, and maintaining their effectiveness in the face of insecticide and drug resistance, should form a cornerstone of post-2015 control strategies.

Global maps of travel time to healthcare facilities.

Access to healthcare is a requirement for human well-being that is constrained, in part, by the allocation of healthcare resources relative to the geographically dispersed human population1-3. Quantifying access to care globally is challenging due to the absence of a comprehensive database of healthcare facilities. We harness major data collection efforts underway by OpenStreetMap, Google Maps and academic researchers to compile the most complete collection of facility locations to date. Leveraging the geographically variable strengths of our facility datasets, we use an established methodology4 to characterize travel time to healthcare facilities in unprecedented detail. We produce maps of travel time with and without access to motorized transport, thus characterizing travel time to healthcare for populations distributed across the wealth spectrum. We find that just 8.9% of the global population (646 million people) cannot reach healthcare within one hour if they have access to motorized transport, and that 43.3% (3.16 billion people) cannot reach a healthcare facility by foot within one hour. Our maps highlight an additional vulnerability faced by poorer individuals in remote areas and can help to estimate whether individuals will seek healthcare when it is needed, as well as providing an evidence base for efficiently distributing limited healthcare and transportation resources to underserved populations both now and in the future.

Effect of several interventions on the frequency of handwashing among elementary public school children.

BACKGROUND: The purpose of this educational project was to assess the effect of several interventions on the frequency of handwashing among elementary public school children. METHODS: Participants in this project were first-graders and fourth-graders from jurisdictions within a mid-Atlantic metropolitan area. Phase I included a baseline assessment of bathroom cleanliness as well as adequacy of supplies for handwashing in each school. During phase 2, the frequency of handwashing before lunch or after bathroom use was monitored and recorded during a 2-month period. The schools were separated into four groups: a peer education group, a hand wipes and instructional poster group, a combination of the education and hand wipes/poster groups, and a (control) comparison school. RESULTS: Overall, a significant increase occurred in the proportion of handwashing frequency from preintervention to postintervention for each intervention group (wipes: 0.50 vs 0.66, p = 0.03; education only: 0.64 vs 0.72, p = 0.02; and education and wipes: 0.45 vs 0.67, p = 0.03) but not in the control group (0.42 vs 0.46, p = 0.26). When the first 3 weeks and the last 3 weeks after intervention were compared, handwashing frequency remained unchanged in the wipes only group (0.66 vs 0.66, p = 0.96), decreased in the education group (0.77 vs 0.65, p = 0.006), and increased in the education and wipes group (0.58 vs 0.75, p = 0.003), as well as in the control group (0.37 vs 0.52, p = 0.01). CONCLUSION: Education combined with accessible convenient hand hygiene may result in a sustainable increase in the frequency of handwashing among elementary school children.