Exemplars in Global Health
On any average day 830 women die from pregnancy-related causes.
If all countries had the maternal mortality rate of the places with the best health, 300,000 fewer women would die each year – as I previously showed. Most of these deaths are preventable, as the World Health Organization (WHO) highlights.
A world where very few women die from pregnancy is possible. But how do we get there?
An obvious answer is to make all countries rich: all rich countries have very low maternal mortality rates. We see this in the visualization, which plots the maternal mortality rate (on the y-axis) against GDP per capita (on the x-axis).
But even if this correlation would should causation and it was the case that high incomes are the sole driver of low maternal mortality, this solution would come with two big problems: it is very difficult to make a poor country rich and economic growth is slow – it takes many decades for poor countries to become rich.
Let’s take the comparison of Rwanda and the UK as an example. The average GDP per capita in Rwanda is $1845 while it is $40,000 in the UK (see here). To increase incomes from the level of today’s Rwanda ($1845) to its current level ($40,000) took the UK more than 250 years. This works out at an average growth rate of 1.2% per year.
Even if we take Rwanda’s comparatively fast rate of economic growth – an average of 4.5% per year from 2010 to 2017 – it would still take 70 years to reach the UK’s average income today.
What else can we do than to wait for poor countries to grow rich before they can stop young mothers from dying?
While income matters, clearly there’s more to it – we see this from the very same chart: If income was the only aspect that mattered, all countries would lie on a single diagonal line in the scatterplot. Instead, there is a large spread in rates of maternal mortality at every level of income.
Look along the $10,000 income line, for example: in Egypt 33 of every 100,000 births results in the death of the mother; this is four times lower than in equally rich Indonesia and eight times lower than in Namibia, which also has a GDP per capita of around $10,000.
As my colleague Max Roser explains in his companion article, we see these differences for countries of the same level of income across many aspects of living conditions.
Which countries have low levels of maternal mortality given their income level? From the scatterplot we can identify at:
By identifying them we can try to better understand why they perform so well, and if other countries can learn from them and then adopt a similar approach. This is the concept behind the newly-launched Exemplars in Global Healthplatform. This new research publication was prepared over the course of several years and we at Our World in Data have contributed to it since the early days. Now the platform has just launched at www.exemplars.health.
If all countries had the maternal mortality rate of the places with the best health, 300,000 fewer women would die each year – as I previously showed. Most of these deaths are preventable, as the World Health Organization (WHO) highlights.
A world where very few women die from pregnancy is possible. But how do we get there?
An obvious answer is to make all countries rich: all rich countries have very low maternal mortality rates. We see this in the visualization, which plots the maternal mortality rate (on the y-axis) against GDP per capita (on the x-axis).
But even if this correlation would should causation and it was the case that high incomes are the sole driver of low maternal mortality, this solution would come with two big problems: it is very difficult to make a poor country rich and economic growth is slow – it takes many decades for poor countries to become rich.
Let’s take the comparison of Rwanda and the UK as an example. The average GDP per capita in Rwanda is $1845 while it is $40,000 in the UK (see here). To increase incomes from the level of today’s Rwanda ($1845) to its current level ($40,000) took the UK more than 250 years. This works out at an average growth rate of 1.2% per year.
Even if we take Rwanda’s comparatively fast rate of economic growth – an average of 4.5% per year from 2010 to 2017 – it would still take 70 years to reach the UK’s average income today.
What else can we do than to wait for poor countries to grow rich before they can stop young mothers from dying?
While income matters, clearly there’s more to it – we see this from the very same chart: If income was the only aspect that mattered, all countries would lie on a single diagonal line in the scatterplot. Instead, there is a large spread in rates of maternal mortality at every level of income.
Look along the $10,000 income line, for example: in Egypt 33 of every 100,000 births results in the death of the mother; this is four times lower than in equally rich Indonesia and eight times lower than in Namibia, which also has a GDP per capita of around $10,000.
As my colleague Max Roser explains in his companion article, we see these differences for countries of the same level of income across many aspects of living conditions.
Which countries have low levels of maternal mortality given their income level? From the scatterplot we can identify at:
- Low income: Nepal, Rwanda and Ethiopia.
- Despite being poorer, maternal mortality in Rwanda is 3 times lower than in Chad.
- Middle income: Egypt, Ukraine, Moldova and Tajikistan.
- Despite being slightly poorer, maternal mortality in Moldova is 35 times lower than in Nigeria.
- High income: Poland, Belarus and Greece.
- Despite having similar incomes, maternal mortality is more than 13 times lower in Poland than in Malaysia.
By identifying them we can try to better understand why they perform so well, and if other countries can learn from them and then adopt a similar approach. This is the concept behind the newly-launched Exemplars in Global Healthplatform. This new research publication was prepared over the course of several years and we at Our World in Data have contributed to it since the early days. Now the platform has just launched at www.exemplars.health.
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