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February 2018

Surgical care – an overlooked entity in health systems

Emi Suzuki's picture
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Five billion peopletwo thirds of world populationlack access to safe and affordable surgical, anesthesia and obstetric (SAO) care while a third of the global burden of disease requires surgical and/or anesthesia decision-making or treatment. Treating the sick very often requires surgery and anesthesia. Despite such huge burden of disease, safe and affordable SAO care is often overlooked.

Why? It may be because surgery and anesthesia are not disease entities. They are treatment modalities that address the breadth of human disease — infections, non-communicable, maternal, child, geriatric and trauma-related disease and injuries, and international development agencies have been focusing on vertical disease-based programs.

Prior to 2015, global data on surgery, anesthesia and obstetric care was virtually nonexistent. With the idea that “We can’t manage what we don’t measure”, the Lancet Commission on Global Surgery developed six Surgical, Obstetric and Anesthesia (SAO) indicators (discussed here) and collected data for them. The analysis of these data show large gaps in SAO care across countries by income groups.

There are 70-times as many surgical workers per 100,000 people in high-income countries compared with low-income countries

The SAO or “surgical” workforce is extremely small in low-income countries (1 SAOs per 100,000 population) and lower middle-income countries (10 SAOs per 100,000 population) whereas there are 69 SAOs per 100,000 population in high-income countries. The discrepancy between high-income countries and low- and middle-income countries is even greater for surgical workforce density than that of physician density.

Measuring surgical systems worldwide: an update

Parisa Kamali's picture
Photo: Chhor Sokunthea / World Bank

Five billion people—two thirds of world population—lack access to safe and affordable surgical, obstetric and anesthesia care with low and middle income countries (LMICs) taking a lead.1-3 Surgical care is a crucial component of building strong health systems and one that is often overlooked (Dr. Jim Kim UHC 2017 video). All people are entitled to quality essential health services, no matter who they are, where they live, or how much money they have. This simple but powerful belief underpins the growing movement towards universal health coverage (UHC), a global commitment under the Sustainable Development Goals (SDGs). Inherent in the framework of UHC is access to safe surgical, obstetric and anesthesia (SOA) care.

An estimated 33 million undergo financial hardship every year from the direct costs of surgical care. And those are the individuals fortunate enough to have access to care.4 Moreover, about 11% of the world’s disability-adjusted life years are attributable to diseases that are often treated with surgery such as heart and cerebrovascular diseases, cancer, and injuries from road traffic accidents.2,5 Other surgically treatable disorders such as obstructed labour, obstetric fistulas, and congenital birth defects are major causes of morbidity and mortality in the developing world.5,7 The delivery of safe and quality SOA care is critical for the realization of many of the Sustainable Development Goals, including: Good health and well-being (Goal 3); No poverty (Goal 1); Gender equality (Goal 5), and Reducing inequalities (Goal 10).

Conversations with Chatbots: Exploring AI’s Potential for Development

Haishan Fu's picture

Development work is getting more technologically sophisticated by the day. The World Bank’s Information and Technology Solutions (ITS) department recently started an Artificial Intelligence (AI) Initiative. At the launch event, we explored the role of AI in development and what it might mean for the work that we do here at the Bank. In short: AI is already here, international organizations have an important role to play, and we need to invest in our skills and expertise.

AI is already being incorporated into development projects

A growing family of Artificial Intelligence techniques are being employed in development. Using machine learning for classification and prediction tasks is becoming as routine as running regressions. Our team recently launched a data science competition on poverty prediction and has been evaluating the performance of different machine learning algorithms. This includes the use of automated machine learning where the machine itself helps to select and tune models in a way a data scientist ordinarily would.