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When and why do they die?

Video describing when and why maternal deaths occur and who has the highest risk of dying from maternal complications.
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MEGAN KILL: We’re now going to look at when and why women die. And it’s important to know when and why they die in order to understand the strategies that are used to reduce maternal mortality. So the first thing to know about maternal mortality is that the risk of maternal death is highest during delivery and the first 24 hours after childbirth. As you can see in this graph on the right, the rate of death goes down quite rapidly postnatally. And most obstetric complications require immediate attention. In particular, a woman who experiences postpartum haemorrhage can die within a matter of hours if she doesn’t receive treatments such as a blood transfusion.
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This is why it’s important to have women deliver in facilities or close by with a skilled birth attendant so they can receive emergency care in a timely manner. Now looking at causes of maternal deaths. The most important ones worldwide are haemorrhage, which is shown in the graph on the left, which accounts for about 27% of maternal deaths, and preexisting conditions which accounts for another 27%. Hypertension follows with 14%. So overall, 3/4 of maternal deaths were attributable to direct obstetric causes globally, but these causes vary greatly by low to middle and high income countries.
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So in a high income country, for example the UK, which is shown in the graph on the right, only a third died from direct obstetric causes while 2/3 died from indirect causes. And those indirect causes includes diabetes, influenza, and health diseases, suicide, cancer, et cetera. So who has the highest risk? Maternal mortality is a public health indicator with the greatest gap between rich and poor countries, with 99% of maternal deaths occurring in poor countries. For example, in Sierra Leone, the maternal mortality ratio is 1,100 per 100,000 live births while in the UK, it’s only eight.
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And within countries as well, there can be a big difference between richest and poorest women, and you can see this in the graph on the right.
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And looking at age groups, the maternal mortality ratio is highest in women over 30 years of age. Also, if you specifically look at countries with the high MMR, or low MMR, the pattern of maternal mortality over age groups is roughly the same. And this is shown by this graph by Blanc, Winfrey & Ross. Now switching from thinking about maternal mortality ratios, which again is the number of maternal deaths per 100,000 live births, this slide looks at absolute numbers of maternal deaths by age group. And by absolute numbers, I’m talking about the total number of maternal deaths. So as you can see, the largest absolute number of deaths occurs in the age groups from 20 to 34.
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And this is largely because these are the age groups that women are more likely to give birth. And this is a very important concept to understand because even though women aged over 30 are at a higher risk of dying, or in other words, there’s a higher maternal mortality ratio, more women actually die in other age groups. And this graph is an example from Bangladesh.
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When talking about preventing maternal mortality, if efforts were directed at women aged 20 to 34, it would most effectively reduce the absolute number of maternal deaths. So what role does HIV play in maternal mortality? High levels of HIV and maternal mortality go hand in hand in many regions of Sub-Saharan Africa. Therefore, understanding the interaction between pregnancy and HIV is important for the clinical management of pregnant women and the measurement of maternal mortality because the standard definition of maternal mortality excludes infectious causes of death not aggravated by pregnancy. So Zaba and colleagues looked at maternal mortality rates by pregnancy and HIV status using data from six demographic surveillance sites in Sub-Saharan Africa.
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This graph shows the maternal mortality rates per 1,000 women years for the pooled data, all sites and all time periods, by pregnancy and HIV status. We can see from this graph, the mortality rates are higher in HIV infected women compared to their uninfected counterparts. But we can also see that the mortality rates are much smaller amongst pregnant women than non-pregnant women, and among pregnant and post-partum women, the risk of death is just over eight times higher in HIV positive compared to HIV negative women, and the corresponding rate ratio is 20.5 in non-pregnant nor non-postpartum women.
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So this lower rate ratio in pregnant and post-partum women is likely to be attributable to the healthy pregnant woman effect, whereby HIV infected women who are able to become pregnant, are likely to be at an earlier stage of HIV infection.
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Similar results were found in the systematic review conducted by Calvert and Ronsmans, with a meta-analysis that analysed results from 23 studies indicating that HIV infected women had eight times the risk of pregnancy related mortality compared to uninfected women and based on two other systematic reviews, the authors concluded that most pregnancy related mortality attributable to HIV is unrelated to the pregnancy. In a review investigating the interaction between HIV status and direct obstetric complications, they showed that women who are HIV positive are 3.4 times more likely to develop sepsis, and the evidence for positive links for hypertensive diseases of pregnancy, obstructed labour, and haemorrhage were inconsistent.
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The third systematic review they conducted found no evidence pregnancy accelerates HIV disease progression with ART availability.
Knowing when and why maternal deaths occur is key to development of strategies to reduce maternal mortality.
The timing of maternal death is therefore critical, and the video considers potential complications and their timescales in more detail. At the same time, strategies must be context-specific, since the leading causes of maternal deaths vary greatly between high- and low- and middle-income countries. There are also differences within countries and by age group. Strategies that are effective in one location may be less so in another.
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