Infant Mortality

On the advent of my first son, Aashay, the discussions around the dinner table with the grandparents center on the massive difference in the way they were brought up. Activities taken for granted in our generation living in the US – sterilization of bottles & feeding equipment, usage of hypoallergenic washing machine soap, the constant bombardment of hand sanitizer – are a stark contrast to the grandparent’s generation. Furthermore, they said all the knowledge about baby-care, the dos and don’ts were solely passed down by the previous generation. There was no Google.

And yet, they have survived. “Do you really need all this? We made it alright; our parents didn’t have santizers… we didn’t even use hand soap most of the times. We’re still alive and well!”, said my son’s grandparents to me.

My counter argument was to look at average infant mortality: it did not matter that only they survived (well, I’m extremely pleased they survived, obviously), but the effect of improved infant care must be evaluated by looking at mortality rates averaged across populations.

How did better knowledge, healthcare, immunizations, access to clean water etc impact infant mortality?

What a great opportunity to do some data mining, and attempt my first Rmarkdown code.

The entire code is published on rpubs.

Where can we find this data?

Infant mortality rates can be easily found, among other things, at the World Bank. Raw data in CSV format can be downloaded here: Link.

Infant mortality is defined as the number of deaths per 1000 births, within 1 year of birth. The data available is for the years 1960 to 2015.


The first graph is most striking! Each line represents a country. A few observations:

  • Even in 1960, the mean and spread of infant mortality of low/low-mid/upper-mid income group countries is significantly higher than the high income OECD and some of the nonOECD countries
  • The high income countries have made significant improvements within a 20 year timeframe: 1960-1980. Notice the steeper slopes in this period. This is followed by a leveling off, almost asymptotic in nature.
  • Lower income countries have huge spreads in mortality rates, even today.
  • For some countries, mortality rates drop and then peak again – Reasons could vary from epidemics to droughts to wars or regime changes
  • There is a direct correlation between mortality rates and the income group. The discrepancy in average infant mortality rates is alarming.
##            IncomeGroup     AvgMR
##                 (fctr)     (dbl)
## 1    High income: OECD  3.334375
## 2 High income: nonOECD  9.718519
## 3  Upper middle income 17.376923
## 4              Unknown 25.614644
## 5  Lower middle income 33.028000
## 6           Low income 54.125806

The bar chart shows mortality rates for the year 2015. Another great way to look at this data is using the rworldmap package.


How does my home country fare?

From the bar graph, I can see India is the 45th worse country in the world. How has my country done in the past 50 years?


We have a reduction in mortality rate by ~23 deaths every 10 years. While this is encouraging, it seems like awfully slow progress to me. We are still at 38 deaths every 1000 births in my home country.

How does India compare against it’s geographical neighbours? Hopefully we are doing better. Let’s find out.


This plot shows India’s performance against our seven neighbours. We are clearly not the best performing nation, not even average. As of 2015, India is only second to Pakistan among our neighbours. Surprisingly, Maldives has done wonderfully since 1960; so has Sri Lanka. Unsurprisingly, China has a low mortality rate, given their 1-child policy – one takes care of their only offspring. As a corollary to that, does that mean India & Pakistan don’t value their offspring, since we produce so many?

What income classes are my neighbouring countries?

Although Sri Lanka is also a part of the Lower middle income, and Nepal is in the Low income category, both countries are doing significantly better than India.

##   Country.Name Country.Code   Year MortalityRate              Region
##         (fctr)       (fctr) (fctr)         (dbl)              (fctr)
## 1     Maldives          MDV   2015           7.4          South Asia
## 2    Sri Lanka          LKA   2015           8.4          South Asia
## 3        China          CHN   2015           9.2 East Asia & Pacific
## 4       Bhutan          BTN   2015          27.2          South Asia
## 5        Nepal          NPL   2015          29.4          South Asia
## 6   Bangladesh          BGD   2015          30.7          South Asia
## 7        India          IND   2015          37.9          South Asia
## 8     Pakistan          PAK   2015          65.8          South Asia

How do the top 10 countries perform? It’s a massive difference. Top performing countries in this indicator are at mortality rates of 1.5 to 2.4.

##    Country.Name Country.Code   Year MortalityRate                Region
##          (fctr)       (fctr) (fctr)         (dbl)                (fctr)
## 1    Luxembourg          LUX   2015           1.5 Europe & Central Asia
## 2       Iceland          ISL   2015           1.6 Europe & Central Asia
## 3       Finland          FIN   2015           1.9 Europe & Central Asia
## 4         Japan          JPN   2015           2.0   East Asia & Pacific
## 5        Norway          NOR   2015           2.0 Europe & Central Asia
## 6       Andorra          AND   2015           2.1 Europe & Central Asia
## 7     Singapore          SGP   2015           2.1   East Asia & Pacific
## 8      Slovenia          SVN   2015           2.1 Europe & Central Asia
## 9       Estonia          EST   2015           2.3 Europe & Central Asia
## 10       Sweden          SWE   2015           2.4 Europe & Central Asia


Certainly India has a ways to go before we catch up to some of our neighbours, let alone developed high-income countries.


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This entry was posted on February 16, 2016 by in Analytics, DataIsBeautiful and tagged , .


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