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Why are people dying following surgery in Africa?

Bruce Biccard's picture

Surgery is a core component of health. It is a cost-effective intervention1 which is important for global health.2 However, to fully realize the health benefits of surgery, it needs to be safe. In the African continent—with a population of 1.2 billion people—it is estimated that approximately 95% do not have access to safe and affordable surgery. The Lancet Commission on Global Surgery has established six indicators to indicate the success of providing access to safe and affordable surgery.3 Four of them are included in the World Bank’s World Development Indicators (WDI) database. The perioperative mortality rate (POMR)—the number of in-hospital deaths from any cause in patients who have undergone a procedure done in an operating theatre, divided by the total number of procedures—is one of the indicators the success in achieving safe surgery, yet it is not included in the WDI as the data is sparse, including the one from Africa. The recent publication of the African Surgical Outcomes Study (ASOS) has cast an important light on the POMR in Africa.4

ASOS has shown that for patients in Africa fortunate enough to access surgical care, the patient outcomes following surgery are relatively poor. ASOS demonstrated that African surgical patients were twice as likely to die following surgery when compared to the global average, despite a similar complication rate to the global average (Table 1). This is despite the fact that surgical patients in Africa are relatively healthy when compared with similar international surgical patient cohorts,5 and one would thus expect them to do well postoperatively. Therefore, if the data from ASOS had been risk-adjusted for patient comorbidities, it is likely that the mortality following surgery in Africa is more than twice the global average. The results from ASOS are compelling as they provide comprehensive data on surgical outcomes in Africa, from 25 countries, 247 hospitals, and over 11,000 patients.

Table 1. Mortality, complications and ‘failure to rescue’ following surgery

Source: ISOS International Surgical Outcomes Study ASOS African Surgical Outcomes Study4
  ISOS
(elective surgery)
ASOS
(elective surgery)
ASOS
(elective and emergency surgery)
Mortality 207/44 814 (0.5%) 48/4792 (1.0%) 239/11193 (2.1%)
Complications 7508/44814 (16.8%) 624/4658 (13.4%) 1977/10885 (18.2%)
Death following complication
(failure to rescue)
207/7508 (2.8%) 30/620 (4.8%) 188/1970 (9.5%)

Introducing two new dashboards in the Health, Nutrition and Population data portal

Haruna Kashiwase's picture

We’re pleased to launch new dashboards in the Health, Nutrition and Population Portal, following the portal’s revamp last year. The renewed HNP portal has two main dashboards covering Population and Health. Both dashboards are designed to be interactive data visualization tools where users can see various population and health indicators. Users can access various charts and maps by selecting specific time, country or region and indicators. We have added new indicators, charts and new health topics such as Universal Health Coverage and Surgery and Anesthesia. Below are some examples of stories gleaned from our dashboards.

India’s population is projected to surpass that of China around 2022

China, with 1.4 billion people, is the most populous country in the world in 2017. However, India, the second most populous country with 1.3 billion people, is projected to surpass China’s population by 2022. China’s total fertility rate (the number of children per woman) has also declined sharply since the 1970s.

Data quality in research: what if we’re watering the garden while the house is on fire?

Michael M. Lokshin's picture

A colleague stopped me by the elevators while I was leaving the office.

“Do you know of any paper on (some complicated adjustment) of standard errors?”

I tried to remember, but nothing came to mind – “No, why do you need it?”

“A reviewer is asking for a correction.”

I mechanically took off my glasses and started to rub my eyes – “But it will make no difference. And even if it does, wouldn’t it be trivial compared to the other errors in your data?”

“Yes, I know. But I can’t control those other errors, so I’m doing my best I can, where I can.”

This happens again and again — how many times have I been in his shoes? In my previous life as an applied micro-economist, I was happily delegating control of data quality to “survey professionals” — national statistical offices or international organizations involved in data collection, without much interest in looking at the nitty-gritty details of how those data were collected. It was only after I got directly involved in survey work that I realized the extent to which data quality is affected by myriad extrinsic factors, from the technical (survey standards, protocols, methodology) to the practical (a surprise rainstorm, buggy software, broken equipment) to the contextual (the credentials and incentives of the interviewers, proper training and piloting), and a universe of other factors which are obvious to data producers but usually obscure and typically hidden from data users.

New country classifications by income level: 2018-2019

World Bank Data Team's picture
Also available in: 中文 | Français | العربية | Español

Updated country income classifications for the World Bank’s 2019 fiscal year are available here.

The World Bank assigns the world's economies into four income groups — high, upper-middle, lower-middle, and low. We base this assignment on GNI per capita calculated using the Atlas method. The units for this measure and for the thresholds is current US Dollars.

At the Bank, these classifications are used to aggregate data for groups of similar countries. The income-category of a country is not one of the factors used that influence lending decisions.

Each year on July 1st, we update the classifications. They change for two reasons:

1. In each country, factors such as income growth, inflation, exchange rates, and population change, influence GNI per capita.

2. To keep the dollar thresholds which separate the classifications fixed in real terms, we adjust them for inflation.

The data for the first adjustment come from estimates of 2017 GNI per capita which are now available. This year, the thresholds have moved down slightly because of low price inflation and the strengthening of the US dollar. Click here for information about how the World Bank classifies countries.

Updated Thresholds

New thresholds are determined at the start of the Bank’s fiscal year in July and remain fixed for 12 months regardless of subsequent revisions to estimates. As of July 1 2018, the new thresholds for classification by income are:

Threshold GNI/Capita (current US$)
Low-income < 995
Lower-middle income 996 - 3,895
Upper-middle income 3,896 - 12,055
High-income > 12,055

Changes in Classification

The following countries have new income groups:

Country Old group New group
Argentina Upper-middle High-income
Armenia Lower-middle Upper-middle
Croatia Upper-middle High-income
Guatemala Lower-middle Upper-middle
Jordan Lower-middle Upper-middle
Panama Upper-middle High-income
Syrian Arab Rep. Lower-middle Low-income
Tajikistan Lower-middle Low-income
Yemen Rep. Lower-middle Low-income

The country and lending groups page provides a complete list of economies classified by income, region, and lending status and links to previous years’ classifications. The classification tables include all World Bank members, plus all other economies with populations of more than 30,000. The term country, used interchangeably with economy, does not imply political independence but refers to any territory for which authorities report separate social or economic statistics.

Tables showing 2017 GNI, GNI per capita, GDP, GDP PPP, and Population data are also available as part of the World Bank's Open Data Catalog. Note that these are preliminary estimates and may be revised. For more information, please contact us at [email protected]

Q2 2018 update of World Development Indicators available

World Bank Data Team's picture

The World Development Indicators database has been updated. This is a regular quarterly update to 1,600 indicators and includes both new indicators and updates to existing indicators. 

Data for population and national accounts, including GDP and GNI-related indicators, have been released for countries and aggregates.

The methodology for presenting value added for the services sector has been revised, and financial intermediary services indirectly measured (FISIM) are presented separately. Historically, FISIM was used in the calculation of the “Services, etc” indicator. Starting with July 2018 update of the WDI, FISIM is presented as a separate series, where available. In addition, the “Final consumption expenditure, etc” and “Household consumption expenditure, etc” data included any existing statistical discrepancy between GDP according to production methodology and GDP according to expenditure methodology. Starting with this update, these two series will no longer be published. Instead, indicators for final consumption expenditure and household consumption expenditure are now available. Users can find the statistical discrepancy listed as a separate indicator. You can access the latest list of indicator additions, deletions, descriptions and code changes here. The methodology for calculating value added shares has also been updated.  
 
Other data that have been updated include FDI, tariffs, monetary and prices indicators, balance of payments, trade, health, military expenditure, air traffic, CPIA ratings, and fisheries. Purchasing Power Parities (PPP) have been updated for OECD and Eurostat countries to show the latest release. The country classification hierarchies and group aggregate data reflect the new fiscal year 2019 income classifications. Historical data have been revised as necessary.

Data can be accessed via various means including:

- The World Bank’s main multi-lingual and mobile-friendly data website, http://data.worldbank.org 
- The DataBank query tool: http://databank.worldbank.org which includes archived versions of WDI
Bulk download in XLS and CSV formats and directly from the API
 

Applications open for third round of funding for collaborative data innovation projects

World Bank Data Team's picture
Photo Credit: The Crowd and The Cloud


The Global Partnership for Sustainable Development Data and the World Bank Development Data Group are pleased to announce that applications are now open for a third round of support for innovative collaborations for data production, dissemination, and use. This follows two previous rounds of funding awarded in 2017 and earlier in 2018.

This initiative is supported by the World Bank’s Trust Fund for Statistical Capacity Building (TFSCB) with financing from the United Kingdom’s Department for International Development (DFID), the Government of Korea and the Department of Foreign Affairs and Trade of Ireland.

Scaling local data and synergies with official statistics

The themes for this year’s call for proposals are scaling local data for impact, which aims to target innovations that have an established proof of concept which benefits local decision-making, and fostering synergies between the communities of non-official data and official statistics, which looks for collaborations that take advantage of the relative strengths and responsibilities of official (i.e. governmental) and non-official (e.g.,private sector, civil society, social enterprises and academia) actors in the data ecosystem.

Official Statistics in a Post-Truth World

Haishan Fu's picture
Photo Credit:  2018 Edelman Trust Barometer Report

I've been thinking about the role of data and digital technology in today's information landscape. New platforms and technologies have democratized access to much of the world’s knowledge, but they’ve also amplified disinformation that affects public discourse. In this context, the official statistics community plays a critical role in bringing credible, evidence-based information to the public.
 
A “post-truth” society is not an inevitable state of affairs that we must accept; it's an unacceptable state of affairs that we must address. To do so, we need reliable data that are trusted by the public. Institutions like national statistical offices must go beyond their traditional data production remit to become a trusted, visible force for reason in people’s lives by building trust, embracing relevance, and communicating better.

If development data is so important, why is it chronically underfinanced?

Michael M. Lokshin's picture

Few will argue against the idea that data is essential for the design of effective policies. Every international development organization emphasizes the importance of data for development. Nevertheless, raising funds for data-related activities remains a major challenge for development practitioners, particularly for research on techniques for data collection and the development of methodologies to produce quality data.

If we focus on the many challenges of raising funds for microdata collected through surveys, three reasons stand out in particular: the spectrum of difficulties associated with data quality; the problem of quantifying the value of data; and the (un-fun) reality that data is an intermediate input.

Data quality

First things first – survey data quality is hard to define and even harder to measure. Every survey collects new information; it’s often prohibitively expensive to validate this information and so it’s rarely done. The quality of survey data is most often evaluated based on how closely the survey protocol was followed.

The concept of Total Survey Error sets out a universe of factors which condition the likelihood of survey errors (Weisbeg 2005). These conditioning factors include, among many other things: how well the interviewers are trained; whether the questionnaire was tested and piloted and to what degree; whether the interviewers’ individual profiles could affect the respondent answers, etc. Measuring some of these indicators precisely is effectively impossible—most of the indicators are subjective by nature. It may be even harder to separate the individual effects of these components in the total survey error.

Imagine you are approached with a proposal to conduct a cognitive analysis of your questionnaire. - How often were you bothered by the pain in the stomach over the last year? A cognitive psychologist will tell you that this is a badly formulated question: the definition of stomach varies drastically among the respondents; last year could be interpreted as last calendar year, 12 months back from now, or from January 1st until now; one respondent said: it hurt like hell, but it did not bother me, I am a Marine... (from a seminar by Gordon Willis)

Beyond Proof of Concept: do we have the right structure to take disruptive technologies to production?

Michael M. Lokshin's picture
Figure 1: Azure Cognitive Services Algorithm compliments authors’
youthful appearances

“Every company is a technology company”. This idea, popularized by Gartner, can be seen unfolding in every sector of the economy as firms and governments adopt increasingly sophisticated technologies to achieve their goals. The development sector is no exception, and like others, we’re learning a lot about what it takes to apply new technologies to our work at scale.

Last week we published a blog about our experience in using Machine Learning (ML) to reduce the cost of survey data collection. This exercise highlighted some challenges that teams working on innovative projects might face in bringing their innovative ideas to useful implementations. In this post, we argue that:

  1. Disruptive technologies can make things look easy. The cost of experimentation, especially in the software domain, is often low. But quickly developed prototypes belie the complexity of creating robust systems that work at scale. There’s a lot more investment needed to get a prototype into production that you’d think.

  2. Organizations should monitor and invest in many proofs of concept because they can relatively inexpensively learn about their potential, quickly kill the ones that aren’t going anywhere, and identify the narrower pool of promising approaches to continue monitoring and investing resources in.

  3. But organizations should also recognize that the skills needed to make a proof of concept are very different to the skills needed to scale an idea to production. Without a structure or environment to support promising initiatives, even the best projects will die. And without an appetite for long-term investment, applications of disruptive technologies in international development will not reach any meaningful level of scale or usefulness.

The 2018 Atlas of Sustainable Development Goals: an all-new visual guide to data and development

World Bank Data Team's picture
Also available in: Español | العربية | Français
Download PDF (30Mb) / View Online

“The World Bank is one of the world’s largest producers of development data and research. But our responsibility does not stop with making these global public goods available; we need to make them understandable to a general audience.

When both the public and policy makers share an evidence-based view of the world, real advances in social and economic development, such as achieving the Sustainable Development Goals (SDGs), become possible.” - Shanta Devarajan

We’re pleased to release the 2018 Atlas of Sustainable Development Goals. With over 180 maps and charts, the new publication shows the progress societies are making towards the 17 SDGs.

It’s filled with annotated data visualizations, which can be reproducibly built from source code and data. You can view the SDG Atlas online, download the PDF publication (30Mb), and access the data and source code behind the figures.

This Atlas would not be possible without the efforts of statisticians and data scientists working in national and international agencies around the world. It is produced in collaboration with the professionals across the World Bank’s data and research groups, and our sectoral global practices.
 

Trends and analysis for the 17 SDGs

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