How long does it take for new prescription drugs to become mainstream?

You probably don’t want to hear your doctor proclaiming “I’m so indie, I was prescribing that *way* before it was cool.” Or maybe you do?

If you’re a hipster and you really need to know when a particular band stops being underground and teeters on the edge of being mainstream so you can only like it in the cool ironic way, then you would want to know how quickly it passes from the early adopters into the hands of the mainstream.

It’s the same for prescribed drugs in primary care – we want to know how long it takes for new prescription drugs to become part of mainstream practice.

tl;dr – we had a go at working out how long it takes for prescription drugs to be fully adopted in Australia, and published it here.

We already know quite a lot about how an individual prescriber makes a decision to change his or her behaviour for a new drug on the Pharmaceutical Benefits Scheme (PBS). Sometimes it has something to do with the evidence being produced in clinical trials and aggregated in systematic reviews. But often it is all about what the prescriber’s colleagues are saying and the opinions of influential people and companies.

It’s evidence of social contagion. And it’s been shown to be important for innovations in healthcare.

What we haven’t seen are good models for describing (or even better, predicting) the rate of adoption within a large population the size of a country. So in a new paper in BMC Health Services Research I wrote about a well-studied model and its application to prescription volumes in Australian general practice. Together with some of my more senior colleagues, I applied a simple model to over a hundred drugs that were introduced to Australia since ‘96.

It turns out that, in Australia, your average sort of drug takes over 8 years to reach a steady level of prescriptions.

The model is arguably too simple. It assumes an initial external ‘push’, which falls away as social contagion grows. The problem is that these external pushes don’t all happen at once when a new drug is released onto the PBS but more likely exist as a series of perturbations which correspond to new evidence, new marketing efforts, other drugs, and changes and restrictions. So while the model produces some very accurate curves that correspond to the adoption we have seen historically, it wouldn’t be particularly good at predicting adoption based on some early information.

For that, I think we need to create a strong link between the decision-making of individuals and the structure of the network through which diffusion of information and social contagion flows. I’ve started something like this already. I think we still have quite a way to go before we can work out why some drugs take over a decade and some are adopted within a couple of years.

Spatial ecological networks – where physics, ecology, geography and computational science meet

It’s part physics, part ecology, and part geography – and that’s probably why it is so much fun. Whenever I fly from city to city my favourite part of the trip is looking out of the window to see the patterns made in the landscapes. Most of the time, the patterns are carved out by humans using the land for agriculture, forestry, mining or just as places to live. Other times the landscape pattern is a consequence of natural stuff like weather and bushfires. It’s even easier to see these patterns with Google Maps – you can just zoom in to the south-west corner of Australia and see a patchwork of farms, towns, roads and less disturbed habitats where the more old-school ecosystems are.

For people working in restoration ecology, the whole idea is to work out the best and most efficient way to improve (or at least maintain) the quality of an ecosystem by helping the right kinds of animals move around and getting the right kinds of plants to disperse seeds around the place. Of course it would be nearly impossible to simply reclaim the vast majority of the land and hand it back over to nature because people still need to eat and also extract stuff out of the ground to make more stuff to put in their homes or store in their garages.

But what restoration can do is to look for the best ways to improve connectivity between the areas of land that are safe from most human disturbance – and that is where the modelling of connectivity and corridors has its place. In this type of work, we look for the locations that are most important to the connectivity and improve or maintain them, having a sort of multiplicative positive effect on the surrounding areas. I’ve worked in this area quite extensively in the past and the science still has quite a way to go. Sadly, I’ve also moved on but it remains a passion of mine to “be more efficient with the resources at your disposal.”

The science itself essentially comes down to finding efficient ways to model, simulate or otherwise estimate the movement of organisms through a landscape. In my summer break, I re-implemented four methods (one based on circuit theory, one firmly established in social network analysis, one based directly on 3rd year shortest path algorithms, and a simulation method I developed based on multi-level cellular automata applications) and wrote it up succinctly for a book chapter, for which the book may yet be a long way from completion.

Admittedly, it’s been quite a while since I have been monitoring recent literature updates in spatial modelling within landscape ecology although I have noticed that one piece of software for doing analysis of corridors has become available and I didn’t notice if they had fixed the issues I wrote about in Ecography, which would mean that people using the application may not be getting the best results.

It also doesn’t help that other research in the area (not the particular methods I discuss above) is mired by unusual discrepancies in the methods – in one case, I found two papers published with the exact same network, yet claiming completely different methods for construction. Let’s hope a new brand of responsible and rigorous researchers can come and revolutionise the field.