Category: Data

Euthanasia in the Netherlands and Belgium, 1990-2015

While parsing the general literature, I found this paper from van der Heide et al. (2017) giving some numbers about end-of-life decisions in the Netherlands these past 25 years. I was wondering if one could see similar evolution in Belgium. And I didn’t have to look very far: van der Heide cited another NEJM paper with Belgian numbers (Chambaere et al., 2015 ; an attentive reader will notice “Belgian” data is “only” about Flanders, not the whole Belgium).

If you put together the data about euthanasia itself (not counting other type of end-of-life assistance), you obtain approximately the same proportion and evolution:

euthanasia_NL_BE

I’m not aware of more recent Belgian data using the same methodology (i.e. physician interviews). The Belgian Commission fédérale de Contrôle et d’Évaluation de l’Euthanasie (CFCEE) presented its last report in October 2016. This report contained numbers for years 2014 and 2015. But these numbers were related to euthanasia that were officially requested (and granted) by the Commission. For instance, the Commission granted 1 928 euthanasia for a total of 104 723 deaths in Belgium in 2014 (i.e. 1.84% ; deaths in Belgium in the Open Data repository). If we focus only on requests written in Flemish, we find 2.59% of euthanasia in 2014 (1 523 euthanasia for a total of 58 858 deaths) (note: Flemish is the language spoken in Flanders – the region targeted by interviews in the Chambaere et al. paper – but requests in Flemish might have originated from other regions). One might have found different numbers if one would have used interviews like van der Heide or Chambaere.

Dataset (note there is more data in a Wikipedia article)

Activity tracker: waist vs. wrist

A few weekends ago, I was challenged by a friend to do more steps than him. Of course, I won 😉 But I noticed he was wearing his activity tracker on his wrist while I was wearing mine on my waist. As I noticed several times before, when I had an activity tracker on my wrist, these devices tend to capture some movements even if you don’t actually walk (while typing energetically on the computer or while driving for instance).

So I took the opportunity of a small trip to wear 2 activity trackers, one Fitbit One on my waist and one Fitbit Charge HR on my wrist. Continue reading “Activity tracker: waist vs. wrist”

Digitize you charts with Engauge Digitizer

A few words of appreciation for an open source software that can help you a lot in your work, Engauge Digitizer (ED) from Mark Mitchell. ED is a simple, straightforward curve digitizer: it takes images with graphs like the one below and transform them (with a little help) in data you can use later on.

170804-Engauge-survival0

Continue reading “Digitize you charts with Engauge Digitizer”

Counting steps is the easiest way to reduce cardiovascular risk

After abandoning my Fitbit device in January because using it didn’t see improvement in my weight (see previous post), I was wondering if I could still measure my risk to develop cardiovascular diseases and other preventable chronic diseases (diabetes e.g.). So, still sitting at my desk (something I do for more than 8 hours a day in theory – probably more in practice), I looked into the ways to monitor my risk for these diseases …

Continue reading “Counting steps is the easiest way to reduce cardiovascular risk”

Evolution of the number and causes of death in Belgium (2010-2014)

Statbel, the Belgian governmental organisation for data and statistics, just released mortality data for 2014 (press release in French, dataset). The headline of their press release was that, for the first time, tumors were the first cause of death for Belgian men. Diseases of the circulatory system remains the main cause of death in Belgium, for women and for both sex together.

While the death of someone is a bad news in itself, I’m more interested here in the evolution of death causes. I’m interested in the evolution of causes of death because it might be a consequence of the evolution of the Belgian society and, as a proxy, of any (most) developed, occidental countries.

If you look at the data, the number of Belgians dying is stable and natural death is still the main cause (and also stable, around 93%). Note that if we look at data before 2010, it seems that mortality is slightly increasing since around 2005.

Evolution of the number of deaths in Belgium, all causes, 2010-2014

If the total number of deaths seems stable, the press release seemed to indicate that tumors (cancers) are on the rise, especially in men. The breakdown in categories is made following the international classification ICD-10 and, because the names of the different chapters are quite long for graphs, I will use the corresponding chapter numbers instead. Here is the key:

Chapter Header
I Certain infectious and parasitic diseases (A00-B99)
II Neoplasms (C00-D48)
III Diseases of the blood and blood-forming organs and certain disorders involving the immune mechanism (D50-D89)
IV Endocrine, nutritional and metabolic diseases (E00-E90)
V Mental and behavioural disorders (F00-F99)
VI Diseases of the nervous system (G00-G99)
VII Diseases of the eye and adnexa (H00-H59)
VIII Diseases of the ear and mastoid process (H60-H95)
IX Diseases of the circulatory system (I00-I99)
X Diseases of the respiratory system (J00-J99)
XI Diseases of the digestive system (K00-K93)
XII Diseases of the skin and subcutaneous tissue (L00-L99)
XIII Diseases of the musculoskeletal system and connective tissue (M00-M99)
XIV Diseases of the genitourinary system (N00-N99)
XV Pregnancy, childbirth and the puerperium (O00-O99)
XVI Certain conditions originating in the perinatal period (P00-P96)
XVII Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)
XVIII Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified (R00-R99)
XX External causes of morbidity and mortality (V01-Y98)

One thing to notice is that, for chapter IV, Statbel only counts categories E00 to E88 while the WHO includes 2 more, from category E00 to E90 ; I would assume here that it has no important impact. Also note that, below, R ordered the chapters in a strange way – I’ll see how to fix that.

Excluding natural causes, we see that indeed, diseases of the circulatory system (chapter IX) are still the first cause of death, followed by neoplasms (chapter II) and diseases of the respiratory system (chapter X). If we compare the relative ratio of all these causes (second graph below), we also find the same conclusion – but the relative decline in deaths due to diseases of the circulatory system is better shown. And we can see that neoplasms take back approximately the same relative percentage of death, in 2014 (although they returned to the absolute number of deaths of 2012, approximately).

Causes of death in Belgium, 2010-2014

Causes of death in Belgium, 2010-2014, relative numbers

The available data set doesn’t go into more details than numbers by ICD-10 chapters. Therefore we cannot tell from that what kind of neoplasm is the most prevalent or what kind of infectious disease is the most present in Belgium, for instance. The press release however mentions that respiratory, colorectal and breast cancers are the top three killers and that flu was not very present in 2014.

As the cancer occurrence is increasing with age, and as the Belgian population is aging, one of the explanation for a high number of deaths due to neoplasms can be age ; however we don’t see a dramatic increase of neoplasms (fortunately!). Another potential factor is the impact of screening for cancers. Due to a very intelligent political split (sarcasm!), prevention (and therefore screening) is not a federal duty. Therefore regions started different screening programs, at different times, with different results. Screening data and their results are therefore difficult to obtain. The Belgian Cancer Registry doesn’t publish data on screening in oncology – although its latest report (revised version of April 2016) very often mentions screening as a main factor for change in the number of cases diagnosed. In its 2016 report (PDF), the Flemish Center for the Detection of Cancer (Centrum voor kankeropsporing) indicates that they increased the number of women screened for breast cancer by more than 8% between 2011 and 2015 (especially in 2015), with a quality of test between 90% and 95%. They also showed an increase in cancer diagnostics (without linking it directly to the increase in screening).

screening-flanders

This is by no means an exhaustive review of the data. There are other potentially interesting things to look at: the geographical disparities between the three regions, the gender ratio evolution (as some of these diseases are known or by definition affecting more one sex than the other), etc.

It would also be interesting to follow these trends as some changes occurred recently in the Belgian curative landscape. New drugs in cancer immunotherapy were recently authorised and reimbursed, for melanoma, lung – and other indications will follow. These costs have a price (less than what is in the press, however, I may come back on this in a future post) but they delay death (unfortunately they don’t avoid it). However, for some of them, in some indications, their administration and reimbursement is sometimes also linked with screening, testing and prior treatment failure ; that might decrease their impact on overall mortality. New drugs for Hepatitis C also arrived in 2015 and 2016 and the Belgian health minister decided to reimburse these drugs for patients in their early stage 2 of the disease. Studies showed that treating at this stage may prevent hepatitis C from progressing to later stages and, in some cases, studies showed patients cured from the disease. This is an opportunity to see a decline in mortality due to this infectious disease (although it is already quite low – compared to other diseases).

2013 in review: how to use your users’ collected data

With a few days of interval I received two very different ways of reviewing data collected by users of “activity trackers”.

Jawbone_20140117-075010b The first one came from Jawbone (although I don’t own the UP, I might have subscribed to one of their mailing-lists earlier) and is also publicly available here. Named “2013, the big sleep” it a kind of infographics of how public (and mostly American) events influenced sleep of the “UP Community”. Here data about all (or at least a lot of) UP users were aggregated and shown. This is Big Data! This is a wonderful and quantitative insight on the impact of public event on sleep! But this is also a public display of (aggregated) individual data (something that UP users most probably agreed by default when accepting the policy, sometimes when they first used their device).

The second way came from Fitbit, also via e-mail. There was written how many steps I took in total as well as my most and least active periods / days of 2013. At the bottom there was a link to a public page comparing distances traveled in general with what it could mean in the animal kingdom (see below or here). This is not Big Data (although I am sure Fitbit have access to all these data). But at the same time (aggregated) individual data are not shared with the general public (although here again I am sure a similar policy apply to Fitbit users).

Different companies, different ways to handle the data … I hope people will realise the implication of sharing their data in an automated ways in such centralized services.

Fitbit2_20140117-075745

More sleep with Fitbits

After a bit less than 2 hours, jepsfitbitapp retrieved my sleep data from Fitbit for the whole 2013 (read previous post for the why (*)). Since this dataset covers the period I didn’t have a tracking device and, more broadly, I always slept at least a little bit at night, I removed all data point where it indicates I didn’t sleep.

hours alseep with FitbitSo I slept 5 hours and 37 minutes on average in 2013 with one very short night of 92 minutes and one very nice night of 12 hours and 44 minutes. Fitbits devices do not detect when you go to sleep and when you wake up: you have to tell tem (for instance by tapping 5 times on the Flex) that you go to sleep or you wake up (by the way this is a very clever way to use the Flex that has no button). Once told you are in bed the Flex manages to determine the number of minutes to fall asleep, after wakeup, asleep, awake, … The duration mentioned here is the real duration the Fitbit device considers I sleep (variable minutesAsleep).

Visually it looks like there is a tendency to sleep more as 2013 passes. But, although the best linear fit shows an angle, the difference between sleep in March and sleep in December is not significant.

R allows to study the data in many different ways (of course!). When plotting the distribution of durations asleep it seems this may be distributed like a normal (Gaussian) distribution (see the graph below). But the Shapiro-wilk normality test shows that the data doesn’t belong to a normal distribution.

Histogram of hours asleep in 2013Hours asleep in 2013 - Normal?As mentioned above, Fitbit devices are tracking other sleep parameters. Among them there is the number of awakenings and the sleep efficiency.

Awakenings in 2013

The simple plot of the number of awakenings over time shows the same non-significant trend as the sleep duration (above). The histogram of these awakenings shows a more skewed distribution to the left (to a low number of awakenings) (than the sleep duration). This however shows there is a relation between the two variables: the more I sleep the more the Flex detects awakenings (see second graph below).

Number of awakenings in 2013 (histogram)Relation between sleep duration and awakenings with Fitbit FlexSleep efficiency is the ratio between the total time asleep by the total time in bed from the moment I fell asleep. This is therefore not something related to the different sleep stages. However it may indicate an issue worth investigating with a real doctor. In my case, although I woke up 9 time per night on average in 2013, my sleep efficiency is very high (93.7% on average) …

Sleep efficiency in 2013… or very low. There are indeed some nights where my sleep efficiency is below 10% (see the 4 points at the bottom of the chart). These correspond with nights when I didn’t sleep a lot and also with very little awakenings (since these are related).

There is no mood tracking with Fitbit (except one additional tracker that you can define by yourself and must enter a value manually): everything tracked has to be a numerical value either automatically tracked or manually entered. It would be interesting to couple these tracked variables with the level of fatigue at wake-up time or the mood you feel during the subsequent day. I guess there are apps for that too …

The code is updated on Github (this post is in the sleep.R file).

(*) Note: I just discovered that there is in fact a specific call in the API for time series … This is for a next post!