Achieving universal health care coverage—a key target of the United Nations Sustainable Development Goal number 3—requires accessibility to health care services for all. Currently, in sub-Saharan Africa, at least one-sixth of the population lives more than 2 h away from a public hospital, and one in eight people is no less than 1 h away from the nearest health center. We combine high-resolution data on the location of different typologies of public health care facilities [J. Maina et al., Sci. Data 6, 134 (2019)] with population distribution maps and terrain-specific accessibility algorithms to develop a multiobjective geographic information system framework for assessing the optimal allocation of new health care facilities and assessing hospitals expansion requirements. The proposed methodology ensures universal accessibility to public health care services within prespecified travel times while guaranteeing sufficient available hospital beds. Our analysis suggests that to meet commonly accepted universal health care accessibility targets, sub-Saharan African countries will need to build ∼6,200 new facilities by 2030. We also estimate that about 2.5 million new hospital beds need to be allocated between new facilities and ∼1,100 existing structures that require expansion or densification. Optimized location, type, and capacity of each facility can be explored in an interactive dashboard. Our methodology and the results of our analysis can inform local policy makers in their assessment and prioritization of health care infrastructure. This is particularly relevant to tackle health care accessibility inequality, which is not only prominent within and between countries of sub-Saharan Africa but also, relative to the level of service provided by health care facilities.
In response to the 2020 COVID-19 pandemic, policymakers worldwide adopted unprecedented measures to limit disease spread, with major repercussions on economic activities and the environment. Here we provide empirical evidence of the impact of a lockdown policy on satellite-measured agricultural land greenness in Badung, a highly populated regency of Bali, Indonesia. Using machine learning and satellite data, we estimate what the Enhanced Vegetation Index (EVI) of cropland would have been without a lockdown. Based on on this counterfactual, we estimate a significant increase in the EVI over agricultural land after the beginning of the lockdown period. The finding is robust to a placebo test. Based on evidence from official reports and international press outlets, we suggest that the observed increase in EVI might be caused by labour reallocation to agriculture from the tourism sector, hardly hit by the lockdown measures. Our results show that machine learning and satellite data can be effectively combined to estimate the effects of exogenous events on land productivity.
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