How healthy was interwar Lithuania?

My conclusion: it was about as healthy as living in Switzerland. But before 1945 Switzerland was not such a great place to live: their economic miracle came after 1945. And life expectancy was below that for the US.

Last year I wrote a post that mentioned a 499 record dataset of deaths between 1922 and 1939 for one district ("dataset A"). I have also obtained another dataset of 963 deaths for the same period for an adjacent district ("dataset B"). The deaths are almost exactly split between male and female. Deaths of infants of less than one year in dataset A were 25, and dataset B 63. I have anonymised the information due to Lithuanian privacy laws for events within 100 years.

Grouping the deaths by age in 5 year blocks and adjusting for the different numbers in each dataset ("normalising") we can see the overall pattern of age at death in the two datasets.

The chart on the left (click to expand) shows a good eyeball consistency between the two districts in the pattern of age at death. This gives us some confidence in the quality of both datasets.

Note that I have excluded deaths of infants aged one or under.

What pattern can we see?

• Up to the age of about 50 roughly similar numbers die annually and then from there the numbers rise sharply.


• There is an interesting twin peak pattern: in the range 71-75 years the number of deaths dips when we might expect a peak. What might be the reason for this? This is really very odd - we are looking at a 17 year period and there is a consistent gap in this age range. I'll look at this again later.


• The number of deaths then drops as the number of people left alive at each higher age falls. The oldest death in Dataset A was 102 and in Dataset B was 105. Now there may be some doubters out there, but I have looked at the 102 year old and traced the person back into mid 19th century revision lists and the age does seem to be correct.

How does the pattern of age at death compare to the rest of the world? I chose Switzerland as a benchmark. Switzerland is more formally known as the "Confoederatio Helvetica" or "CH" for short. I made this choice for a number of reasons. It was largely unindustrialised at the time, similar to Lithuania; it was unaffected by Great War deaths, which completely changed the demographics of France, the UK, and Germany; and reliable data is readily available from the Swiss Federal Statistical Office. I used data for 1928 and compared Dataset A with Swiss male and female deaths.

The chart on the right (click to expand) shows this comparison. (Again the total number of deaths has been normalised to facilitate this)

The eyeball consistency is remarkable: and particularly with CH female deaths.

We see the "twin peak" in the Swiss data. This means that our Litvak datasets' twin peaks were not aberrant or an artefact of my amateur analytical methods. There was a cross european phenomenon which meant that there were fewer deaths of 71-75 year olds of both sexes than one might expect.

I have no idea why this might have happened. If any historical demographer chances along here please comment. Anyone in fact: please post any ideas you might have.

Despite this gap in understanding, it is clear from the overall pattern that Litvaks and Swiss had very similar mortality patterns during the interwar years - and it's reasonable to conclude that overall health was therefore similar.

The consistency of the Litvak and Swiss data for the interwar period suggests that looking at Swiss data for earlier periods might be suggestive for the pattern of age at death for Litvaks in earlier periods, where we have much less information for Litvaks.

The chart on the left (click to expand) shows Swiss data for 1876. We see that the pattern is quite different: there are many more deaths at younger ages and many fewer very old people. The average age at death was under 50 in 1876, compared to 64 in dataset A.

This change in longevity has a genealogical impact. Litvak naming traditions were that children were named, when possible, for deceased ancestors. In the first half of the 19th century one can often see names repeated every other generation - by the time the first grandson was born the grandfather was often already dead and their name was therefore available for reuse in the family. But as time passed the grandparents were increasingly still alive - possibly for the birth of every grandchild - and so the first opportunity to reuse a name might be for a great grandchild or even a great great grandchild. The neat naming patterns break down and our task becomes that much more interesting.

To illustrate how things are today I also show 2002 deaths. People now live much longer. But note there is also a dip at 71-75. This suggests we may be seeing a culling effect with slightly more infirm people dying in their late 60s leaving a slightly healthier group with a slightly enhanced chance of survival.

These datasets have a lot more information to offer - for example, in most cases a cause of death is given and analysis of this information could be interesting. This sort of analysis, which can give us the possibility of new insights into the pattern of the lives of our ancestors is only possible with the translation of complete runs of records: this is a new sort of reason to support LitvakSIG's work.