Having access to this data means I can ask why so much co-proxamol (a medication that has no evidence of effectiveness, but which is also very dangerous) is being prescribed in Gloucestershire.
I was taking a look around some of the data for antidepressants and was wondering why there are sometimes such drastic decreases in spending. Spending for Escitalopram dropped over 80% in a single month[1]. There are a few others that had sharp drops in spending. Just curious, do you know why?
In any case, it would be really interesting to see how legislation, lobbying, and current events (relative) may have affected this data.
(First choice is a good quality talking therapy, but meds may be useful)
> The full guideline on depression concluded that antidepressants have largely equal efficacy and that choice should mainly depend on side‑effect profile, people's preference and previous experience of treatments, propensity to cause discontinuation symptoms, safety in overdose, interactions and cost. However, a generic SSRI is recommended as first‑choice because of its favourable risk–benefit ratio. Neither escitalopram nor any of the available 'dual action' antidepressants, such as venlafaxine and duloxetine, were judged to have any clinically important advantages over other antidepressants. Results from meta‑analyses (Comparative benefits and harms of second-generation antidepressants for treating major depressive disorder, Gartlehner et al. 2011 and 2 Cochrane reviews: Cipriani et al. 2012, CD006534 and Cipriani et al. 2012, CD006533) have provided no evidence to depart from NICE guidance when selecting antidepressants for people with depression.
> Citalopram and escitalopram have been found to prolong the QT interval and are now contraindicated in people who are already taking medication known to prolong the QT interval as there may be an additive effect [Lundbeck Ltd, 2011a; Lundbeck Ltd, 2011b; MHRA, 2011]. Caution is advised in people with congenital long QT interval or who have pre-existing QT interval prolongation as they have a higher risk than average of developing a ventricular arrhythmia including Torsade de Pointes.
Completely agree, I was blown away at the level of transparency this brings to the world of pharma prescriptions. Inefficient care, overprescribing, otherwise suspect prescriptions, all of this laid out very clearly.
I searched for the medicines I get from my GP and it turns out I'm the only one getting them. You can see the blips in the graph from zero when I got the prescriptions!
Isn't this a privacy issue? There are some very small GP offices. From the HSCIC FAQ:
I am prescribed a drug for a rare condition; can I be identified in this dataset?
All practice level information down to presentation level is being released, but no
information about patients is contained in the data. It is not possible to identify
individual patients in the data.
In line with the recent High Court ruling on the release of abortion statistics, data can
be released unless an individual can be identified from the data or from other data
that is already in the public domain. The release of practice level prescribing data
does not enable the identification of individual patients.
If you are the only patient receiving a certain drug in your practice then the number
of items prescribed and their cost for that medicine will be in this dataset but it will
not show which patient received it. Note that information about the price of drugs is
already available in the public domain.
That last paragraph doesn't make sense. I can easily imagine someone who knows a friend/relative has a rare condition using a data source like this to see how often it's being prescribed, and (perhaps) whether they're taking the medicine as often as they're supposed to.
That was my first thought. I think it's brilliant that people with background knowledge can assess unusual prescription patterns at GP practices, but this is bound to release data that can give you a pretty good idea of [some of] what some individuals with a particular rare condition are being prescribed.
This is an amazing dataset. I did some work with it in 2012 at a hackathon, in particular linking it to wiki entities. I used the redirects as drug synonyms and automatically extracted and linked drug names to their Wikipedia pages, which then offered more structured info on the drugs (eg side effects etc). From that, I then did some basic NLP to identify diseases that mentioned those drugs, thereby linking the diseases each drugs can be used to treat for further analysis. The next step we didn't do much with was linking it to the British national formulary - the uk clinical guidelines on drug use (a digital version wasn't readily available then). You can see some of those wiki datasets on my website (http://www.stewh.com - I'm on a train so can't get the exact url, but you should find it in the menu). I had more datasets and the MapReduce code for working with the dataset and wiki dumps if anyone could use it.
I do some work around suicide prevention.
Having access to this data means I can ask why so much co-proxamol (a medication that has no evidence of effectiveness, but which is also very dangerous) is being prescribed in Gloucestershire.