What Impacts Maternal Mortality?

It’s been a while since my last post—so sorry about that! I have been working on this post for a few days, and it was prompted by a segment I saw on GMA recently about Christy Turlington-Burns’ efforts to bring awareness to maternal mortality throughout the world through her organization, Every Mother Counts. It really hit home with me—if it weren’t for great medical care, my son and I most likely would not be alive right now. (We’re so grateful!) Her work really resonated with me, and so I wanted to share it with you.

So, here’s my agenda for this post, and a few more to follow it: I want to show how easy it is to use data visualization to tell a compelling story about how cultural circumstances can have major impacts on the lives of women and children around the world. I was teaching a class last week, and when I showed a draft of this, people frowned. It’s sad stuff! No doubt about it. But it’s very real.

The first person I talked to about this was my mom. (Hi, Mom!) She’s a nurse practitioner, and she has worked in some of the very remote areas in the map below that have abysmal rates of preventable maternal deaths. I asked her—what do you think the causes are? Without hesitation, she said, “Teenagers giving birth, and the lack of skilled help during births.” That makes a lot of sense to me.

So, I got a hold of the most recent World Bank Indicators, a version of which ships with Tableau, and then spent an eon transforming it in SQL Server so that I could load only the most recent numbers for each country for the metrics in question. (More on that tomorrow!) It’s a very rich data source, and it includes economic measures that, along with literacy and health data, describes some of the living conditions in a country fairly well.

My first question is which areas of the world have higher instances of maternal death. I started with a familiar map—it’s a great way of showing disparities across the world. The countries are ranked in descending order by the likelihood that a woman will die in or after childbirth—countries with high ranks (like #1, South Sudan) are really bad places to be pregnant. (The US is in the middle…below several former Eastern Bloc countries, which is a surprise.) My friend Nelson Davis @nelsondavis recent blogged about the relationship between life expectancy and war—there have been several notable genocides and civil wars in Sub-Saharan Africa, and consequently, they are not places one should expect to live very long or in good health.

The countries are colored by percentiles (great new table calc in Tableau 8.2, along with rank) of maternal mortality rates. When you click on a country, the scatter plots below, which show correlations between the percentage of maternal deaths that are preventable and other public health measures, will highlight. The area map of our aid to those countries also filters.

The scatter plots are significant, and they prove numerically what my mother told me about the correlations between teenaged pregnancies, unattended births, and maternal mortality. I added in literacy rate—notice that it’s trend line is nearly identical to that of unattended births, though the median is a little bit lower. The relationship between percent of GDP spend on health is less significant, though the clustering is obvious—there are some outliers that I would question, like Liberia and Sierra Leone in the upper right—especially what we know about the quick spread of Ebola there recently.

Talk to me about your thoughts on this and what you think I should add in the future.

Age is a number, but what does it mean?

The genesis for this blog post actually is my father. He had a birthday recently, which coincided with the announcement that he is engaged (congratulations, Dad!) to a lady who is from the country where he resides. So I was curious—how would his age compare in the country where they live? And what would his age in America be if he had been born elsewhere? And now that I’m entering middle-age, what would that look like in, for instance, Africa?

I used this World Bank data, which actually ships with Tableau, to create a multiplication factor for each country that relates its life expectancy, both for men and for women, to that of the US, and then I used a couple of parameters to allow you to select the country where you’re from—or one that holds your interest—and then input your age and gender. The labels over the countries tell you what your age would be if you lived there. If the local age is less than your current age, then their life expectancy is less than ours.

The countries are colored by the percentage of life that would be complete if you lived in a specific country. The news for our friends in Africa isn’t so good—their life expectancies are significantly shorter than ours in The Americas and in Europe. There’s a significant relationship between birth rate and life expectancy in each country. I added trend lines to the scatter plot below the map, and the relationships are logarithmic.

The birth rates translate into children per woman: for some perspective, 49 births per 1,000 in Niger translates into 7.6 births per woman; in the Netherlands, 9-ish births per 1,000 translates into 1.9 births per woman. (I found the births per woman data and will add it later this week.)

Click on a country or region to filter the scatter plot and the histogram. (I tested our friendly p-values and R2 values to confirm that this is the best model. If you love stats and Tableau, send me a message!)

Feel free to download the workbook and check out what I did with the parameters. This data is from 2010—I have found an updated data set, but it needs some major transformations; the metrics here haven’t changed significantly since published, but I will be updating it later in the week, and I plan to add more analyses of the infrastructure/educational/public health factors that contribute so such wide variations in birth rates and life expectancies over the next few weeks.