efficient measures?

When checking the infographics of the week highlighted by Nature, I came across this comparison of France and Germany for the impact of their respective vaccination mandates on health and economics. And then realised this was from a preprint from a Paris Dauphine colleague, Miquel Oliu-Barton (and co-authors). The above graphs compare the impact of governmental measures towards vaccination, short of compulsory vaccination (unfortunately).  Between Germany and France, it appears as if the measures were more effective in the latter. Which may be interpreted as either a consequence of the measures being more coercive in [unruly] France or an illustration of the higher discipline of the German society [despite the government contemplating compulsory vaccination for a while]. As an aside, I am very surprised at the higher death rate in Germany but, beside a larger percentage of people over 65 there and a lower life expectancy, the French curve is interrupted in December 2021. Looking at 2022, the peak was reached at 3.3 cases per day per million people.

Concerning the red counterfactual curves, I did not find much explanation in the preprint, apart from

“Our results are supported by the well-established econometric method of synthetic control.³⁰ We construct counterfactuals for each treated country based on a weighted average of countries that did not implement the COVID certificate and find consistent trajectories for the time period where this method is feasible, i.e., until the end of September 2021.”

and

“constructing counterfactuals ( i.e., by modelling vaccine uptake without this intervention), using innovation diffusion theory.⁶Innovation diffusion theory was introduced to model how new ideas and technologies spread”

which is not particularly helpful without further reading.

how to count excess deaths?

Another terrible graph from Nature… With vertical bars meaning nothing. Nothing more than the list of three values and both confidence intervals. But the associated article is quite interesting in investigating the difficulties in assessing the number of deaths due to COVID-19, when official death statistics are (almost) as shaky as the official COVID-19 deaths. Even in countries with sound mortality statistics and trustworthy official statistics institutes. This article opposes prediction models run by the Institute for Health Metrics and Evaluation and The Economist. The later being a machine-learning prediction procedure based on a large number of covariates. Without looking under the hood, it is unclear to me how poor entries across the array of covariates can be corrected to return a meaningful prediction. It is also striking that the model predicts much less excess deaths than those due to COVID-19 in a developed country like Japan. Survey methods are briefly mentioned at the end of the article, with interesting attempts to use satellite images of burial grounds, but no further techniques like capture-recapture or record linkage and entity resolution.

politics coming [too close to] statistics [or the reverse]

On 30 April, David Spiegelhalter wrote an opinion column in The Guardian, Coronavirus deaths: how does Britain compare with other countries?, where he pointed out the difficulty, even “for a bean-counting statistician to count deaths”, as the reported figures are undercounts, and stated that “many feel that excess deaths give a truer picture of the impact of an epidemic“. Which, on the side, I indeed believe is a more objective material, as also reported by INSEE and INED in France.

“…my cold, statistical approach is to wait until the end of the year, and the years after that, when we can count the excess deaths. Until then, this grim contest won’t produce any league tables we can rely on.” D. Spiegelhalter

My understanding of the tribune is that the quick accumulation of raw numbers, even for deaths, and their use in the comparison of procedures and countries is not helping in understanding the impacts of policies and actions-reactions from a week ago. Starting with the delays in reporting death certificates, as again illustrated by the ten day lag in the INSEE reports. And accounting for covariates such as population density, economic and health indicators. (The graph below for instance relies on deaths so far attributed to COVID-19 rather than on excess deaths, while these attributions depend on the country policy and its official statistics capacities.)

“Polite request to PM and others: please stop using my Guardian article to claim we cannot make any international comparisons yet. I refer only to detailed league tables—of course we should now use other countries to try and learn why our numbers are high.” D. Spiegelhalter

However, when on 6 May Boris Johnson used this Guardian article during prime minister’s questions in the UK Parliement, to defuse a question from the Labour leader, Keir Starmer, David Spiegelhalter reacted with the above tweet, which is indeed that even with poor and undercounted data the total number of cases is much worse than predicted by the earlier models and deadlier than in neighbouring countries. Anyway, three other fellow statisticians, Phil Brown, Jim Smith (Warwick), and Henry Wynn, also reacted to David’s tribune by complaining at the lack of statistical modelling behind it and the fatalistic message it carries, advocating for model based decision-making, which would be fine if the data was not so unreliable… or if the proposed models were equipped with uncertainty bumpers accounting for misspecification and erroneous data.

mortality rates as sounder indicators

Armistice Day [2]

Following the previous post on my grand-mother’s unknown soldier father, I came across an article in Libération that mentioned free access to archives from the French Ministry of Defence re. the death certificates of most soldiers “morts au front” during the “Great War”. The site “Mémoires des Hommes” gives “access to databases created from the digitalization and indexation of biographical records preserved in the services of the Ministry of Defence”. I thus looked for my grandfather’s father but could not find him, as he did not die in combats. I however found my great-grandmother’s brother, Florent Lefrançois, who died on September 1915 in La Marne.