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With the uncertainty brought about by the Brexit vote, Pharma and Biotech companies need to consider a number of unique opportunities to chart future growth in the UK and EU

On 23 June 2016 the UK government held a referendum on either exiting or remaining a part of the EU and 52% of voters opted to leave the EU. Political upheaval and speculation about what this actually means for the UK and the current EU is ongoing, and while we wait for the dust to settle the one thing that is clear is that until the UK government invokes article 50, NOTHING HAS HAPPENED YET.

What we do know is this:

  • The UK government must officially invoke Article 50 of the Lisbon Treaty to start the process for negotiating its exit.
  • The referendum is technically “advisory” and isn’t legally binding for the UK government to act.
  • To date there has been no indication if/when the UK government will invoke Article 50, so until then, nothing changes.

This was certainly a divisive vote for the country and the resulting uncertainty has heightened emotions and speculation from both camps.  Now, if you view this vote at the highest level, it was a binary vote about whether the people of the UK wanted change (Brexit) or status quo (Bremain). So part of the reason this has been so emotional for many is that change is uncomfortable.

And we are now starting to the see the short-term effects of Brexit described by Mark Carney, Governor of the Bank of England, as the UK having “entered a period of uncertainty and significant economic adjustment”. Lack of clarity is leading to a lot of debate combined with fear, uncertainty and doubt.

W2O Group is known for its fluidity (H2O, W2O, get it?!).  We spend a lot of time sitting in discomfort as we challenge ourselves in devising new approaches, alternative thinking and challenging traditional approaches in order to help our clients achieve their objectives. If we look at the Digital revolution, while it created far reaching change to both our clients’ and our own business it created, for many, opportunities. Our aim is to apply this fluid approach and work with clients and the industry to identify the opportunities and minimize the risks as the implications of Brexit become apparent.

So what should our healthcare clients be considering as the situation plays out:

What could Brexit mean for Pharma?

Pharma and biotech companies currently employ more than 222,000 people in the UK and spend some £4 billion each year on research and development.  Prior to the referendum, big UK based drug companies had said that they wanted the country to remain in the EU. Initial uncertainty in the market on the news of Brexit had an impact on pharmaceutical stocks due to major exposure to the European market.  However, the life sciences business sector has shown recovery, but uncertainty remains.  In terms of the geo-political situation three key considerations for the industry include:

  • Potential instability of the UK as an economy (long-term) and the degree to which the UK will continue to be a priority market or part of the EU Big 5, will have far reaching impact from commercial decisions, to reimbursement negotiations, to clinical trial planning.
  • Lack of clarity about the UK’s future relationship with Europe and how this will affect medicines regulations, licensing, R&D funding, and costs for import/export of medicines.
  • Political uncertainty in the UK (short-term) and if/when Article 50 may be invoked and how the sector’s needs will be championed. Key negotiation points that will most impact our clients will be in relation to the “Four Freedoms” which include free movement of goods, services, people and capital across borders.  This is the foundation of the European Union and once the UK is no longer a part of it, how these points will be either included or excluded from a new arrangement will have the greatest effect on our how our clients can do business.

Considerations about the future relationship between the UK and the EU is warranted as it relates to participation in the centralised EMA (European Medicines Agency) regulatory system:

  • Being outside the centralised system could increase the workload for pharma company regulatory departments. As well as necessitating the shoring up of the UK national regulatory body, there will be uncertainty over how or even if the scope of responsibilities will change, both of which could lead to disruption in providing new medicines to patients across the UK and Europe. Our clients should evaluate their current regulatory department SOPs to determine how increased flexibility can be built into their operations.
  • There is speculation that there would be uncertainty within the EMA about the granting of new drug licences or the renewal of existing ones as the default period for initial licensing is five years, followed by an open-ended renewal. The EMA would therefore face a dilemma on whether to approve a drug from a UK company that would not be part of the EU for the lifetime of the licence. Licences may need to be transferred to businesses inside remaining member states and new medicines approved by the EU would not be automatically placed on the British market, but may need to undergo a protracted approval process. Our clients need to be both expediting submission of marketing authorisations and also scenario planning for those that are not yet ready for submission.
  • It could be necessary to relocate the EMA out of London. This means clients should start looking at proximity of regulatory departments to international transport.

Probably the most time-critical consideration is around the new EU Clinical Trials Directive, which was agreed in 2014, introducing a raft of changes that were expected to be implemented by the end of 2017 at the earliest and by October 2018 at the latest, when the new EU CT portal and database are fully functional. The new directive is aimed at the introduction of a simplified submission process that would ease the regulatory burden on trial sponsors by effectively using a single application to carry out multi-site trials across the EU.

With Brexit in the air it now remains to be determined what the impact on CT in the UK will be and our clients need to assess what this could mean for them. Currently, when it comes to non-EU countries operating within EMA rulings there is a precedent with EEA countries which also abide by the EMA’s regulations, so in the best of cases nothing would change.

What could Brexit mean for science, research and funding?

Research funding is one of the few areas where the UK gains more money than it spends.  Of the country’s gross contribution to the EU, £5.4bn (€6.84bn; $7.77bn) can be attributed to the community’s research, development, and innovation activities. But the UK gets back £8.8bn in research grants, so exiting the EU would in theory leave a gap of £3.4bn to be filled. Through programmes such as Horizon 2020 (H2020) and the Innovative Medicines Initiatives (IMI), the EU provides funding and coordinates research collaborations. UK-based companies without research facilities in other EU countries are likely to lose access to these programmes.

Clients should be looking to emphasise their robust research programs to attract talent and also looking to more closely align with leading UK universities and institutions to establish/maintain a sustainable pipeline of talent, funding and engagement with the scientific community.

What could the impact be on the NHS?

11% of UK doctors and nurses (according to the General Medical Council) hold qualifications from another EU country.  This could mean a loss of non-UK healthcare workers as well as the significant problem of the loss of capacity (a loss of EU healthcare services abroad).

Clients should be looking at devising value-add programmes which support both efficiency and quality of care.  These programmes will be important whether or not the worst fears of the NHS are realised, but it is timely to look at current support programmes and determine if they are truly making a difference, how they can be optimised, and where investment should focus next.

What could Brexit mean for UK public health?

The European Centre for Disease Control and Prevention (ECDC) is at the centre of a network of communication between EU and EEA member states to monitor, communicate and assist in response to a threat of communicable disease, forming an early warning and response system for the prevention and control of communicable diseases.  The UK will be on the outside of this network which could impact, for example, procurement of pandemic vaccines, where the EU’s greater purchasing power might push the UK down the queue.

Clients who do have vaccine programmes, should look at how these are administered, how they can support the government in shoring up critical medicines, and discuss how to information share in a potentially dis-jointed system.

Everything entirely depends on the direction that the UK government wishes to take when negotiating its exit under Article 50…IF it negotiates its exit under Article 50.  Depending on how negotiations proceed, it may even be possible to keep the UK within the European system for drug approval, and allow UK scientists and companies to continue participating in the EU’s research programmes.

So as we look at our clients’ programming needs for 2017, we are thinking in more dimensions…how we interpret global/EU challenges, how we can help clients confidently move forward with key decisions and programmes/campaigns, how we can support infrastructure changes within organisations, and how we can help UK-based clients do more with less in a challenging environment.  Staying fluid will help define new approaches for our clients’ businesses, helping them to find value in uncertainty.


 This article was written by W2O Group London-based leaders: Annalise Coady President of tWist Marketing; Danielle Whitney, Healthcare Lead EMEA: Effie Baoutis, Medical Communications Global Lead

We started our Twitterendum series asking what would happen if only Twitter users voted in the referendum. We concluded that post by saying:

“Of course, tweets are not votes. Twitter users do not reflect the UK population as a whole. Twitter users account for roughly a quarter of the population (23%) and tend to skew young and urban.”

While we were well aware of the limitations in the Twitter dataset, we were equally curious to see what it could tell us about voting intentions. So, now the UK has voted and the results are in, how did the Twitter model fare?

WHAT WORKED
All in all our Twitter analysis accurately forecast the direction of the vote (whether the location skewed ‘stay’ or ‘leave’) in 248 out of 381 Local Authority Districts (LADs). It inaccurately forecast the direction in 91 LADs, and a further 39 LADs didn’t have enough data so were unable to be placed in either camp.

W2O Group, Brexit Vote, analytics

The predictions for the frontrunners in the ‘remain’ camp were very accurately predicted. Cambridge, Oxford, Exeter, Cardiff, Brighton and Hove, Glasgow, Edinburgh and parts of London, all led the referendum’s ‘remain’ category with a margin of 20% or more. Ceredigion was the only exception, which was forecast as the ‘remain’ frontrunner by the model, but only did so by a margin of 10%. The model accurately predicted the top Bremain locations in England, and only fared slightly worse in Scotland (it didn’t predict as much intensity for remain in places like East Dunbartonshire), and obviously didn’t include places like Gibraltar (where an impressive 96% voted ‘remain’).

Brexit Vote W2O Group analytics

The ‘leave’ camp were marginally less well represented. All leading members of ‘leave’ in the Twitter model were accounted for in the final vote tally, but not with the same level of intensity. Burnley, Hartlepool, Kingston upon Hull and Wakefield scored margins of above 35% in favour of ‘leave’, and experienced similar levels in the model. Predictions for Eastbourne and Oldham were also broadly in line with voting outcomes, albeit less so. However, Twitter frontrunners for ‘leave’ didn’t line up with actual outcomes. Boston, South Holland, Castle Point, Thurrock, Great Yarmouth and Fenland experienced margins of 40% and above for Brexit in voting and had much lower ratios in the twitter model. A large part of this was due to the fact that our model de-emphasised areas with a low Twitter handle representation, a factor in those six locations. Havering was easily the worst call made by the model, which was seen to be slightly in the ‘stay’ camp; final referendum result placed Havering as radically in favour of Brexit, with 70% of the population there in favour of ‘leave’.

WHAT DIDN’T
It wasn’t so much the direction of the vote in LADs that was erroneous, but the extent of the vote. The referendum is a total voting tally, and is called when either ‘Leave’ or ‘Remain’ passes the winning post by achieving 50% of ballots cast, plus at least one vote, so the actual margin in each area is extremely important. That is to say, rather than determining victory on a per LAD basis, the overall number of votes were the most important. Our model was constructed primarily using unique accounts backing either camp from the LAD, and the percentage of the population they represented. In almost all LADs, Twitter results overestimated the margin in favour of the remain camp, overemphasising victory margins and downplaying the losses, pointing to a firm ‘remain’ victory. London was especially problematic, which we estimated as a single entity rather than breaking it into multiple zones. This effect was greatest in outer London areas, proving completely inaccurate in forecasting the result in the aforementioned Havering.

W2O Group analytics and Brexit vote

YOUNG VERSUS OLD
No matter what the size of the sample, uncontrolled bias skewed the results. Age was a major determining factor for the model’s shortcomings. Simply put, people aged 45 and above were scarcely represented.

Twitter skews young, urban and only accounted for approximately 23% of the population (30% of internet users in UK). Our model forecast a decisive victory, and under the above conditions it was fairly predictive. However, not only did voters aged 18-34 account for only a fraction of the population, they appeared to have voted a lot less (only 36% of 18-24s and 58% of 25-34s voted, according to Sky Data.

As a result, the model’s forecasting generally biased towards a group that leaned towards ‘remain’ (75% of 18-24s voted to ‘remain’ according to YouGov). Whilst this explains the underlying lean toward Bremain, the areas with a disproportionate amount of older voters were inherently less accurate. We can see that for the majority of areas with a disproportionate amount of people above 45, the model predicted completely inaccurate results.

URBAN BIAS
The same bias was was even more present in urban centres, where data was much more concentrated.

When choosing the source of a tweet, we assigned based on self-reported locations in each twitter handle bio.  Overall, twitter users reporting their location were far more likely to identify a major city than a rural place, even if only peripherally attached to it. The result was that LADs containing a major city had a disproportionate amount of content, higher percentage of representation and thus higher scores in our model.

Ultimately, cities likely had even more of a ‘stay’ lean due to high proportion of younger people combined with higher scores due to a larger amount of unique accounts identified.

OTHER MISCELLANEA
We calibrated the model to only include people living UK through analysing self-reported city of origin and the usage of the English language. While this is not necessarily a bad way of representing people who live in the UK, there is no doubt a high number of non-voting migrants in our model. According to a House of Commons Briefing Paper on migration, 5.3 million migrants lived in the UK, 2.9m of which came from the 27 EU member countries. It’s not unreasonable to imagine that this population would have been very active in the run up to the vote. This lends further bias to the ‘remain’ camp, as reflected in Figure 3 above.

IN CONCLUSION
So, Twitter is useful at understanding a very specific audience in a very specific context, but we must be wary of stretching it further. A referendum is a truly seismic event, with all walks of life represented in the electorate. While public and accessible to most, Twitter is far from representative of the UK population as a whole. The value of its data lies in illuminating a particular part of society and in the ease of access and quantity of its data. Size of sample is certainly important (the number of unique accounts observed is much higher than that accessible through traditional polling), but with such heavy bias it ultimately overpowers the ability for the model to make predictions about events encompassing the totality of society. The Brexit result was surprising to many, and shows how easily we’re caught in our own echo-chamber, surrounded by like-minded people and unable to fathom the full spectrum of opinion. Looking at Brexit through Twitter underscores this phenomena and the importance of a balanced dataset if one is to make observations of any kind.

Predictions using data fare well when the underlying elements follow specific rules. Politics can be hard to predict because those rules tend to be opaque, or only sporadically followed. Recent failures in data-driven models of the political landscape (The Brexit result or Trump’s nomination, for example) could be down to the fact that the electorate is ultimately changing the underlying rules on what they will vote for. Making sense of the world using data is an important advantage and a cornerstone to better making better predictions. Nonetheless, when making predictions with data (irrespective of robustness of model and its accompanying data), it is good to remember that the world remains an uncertain place, and to approach predicting it with a healthy dose of humility and scepticism.

On the eve of tomorrow’s EU referendum vote, Britain braces itself for a momentous decision in deciding whether the country should “remain a member of the European Union” or “leave the European Union.”  Week after week we have been tracking the data and while polls continue to show a tight vote we’re ready to make a call on the Twitterendum.

Brexit_GIF3

Come tomorrow, we believe that UK Twitter would crown the #StrongerIn campaign victorious.

BrexitWk26

At least on Twitter, the results are quite conclusive. The Brexit camp enjoyed a lead during a large part of the campaign, with far more outspoken supporters. Every ‘leaver’ contributed an average of 9.63 tweets to the cause, twice as many as Bremainers. However, in the end, raw account numbers proved far more important.

Brexit Analysis

Since the official campaign launch, the #StrongerIn camp has carved a growing lead in the total number of supporters, consolidating its lead in urban centers. This trend accelerated dramatically in the last three weeks, which experienced a sharp increase of unique accounts pledging their allegiance to staying in the EU. The effect of this swing is impressive, with the ‘stay’ camp ending with a 17% lead in unique accounts over ‘leave’ by week 26, having almost doubled its lead in the final three weeks.

Brexit4

So there you have it. A look at extremely an extremely complex political vote through the somewhat reductive lens of Twitter conversation. While the correlation between the Twitterendum results and the actual referendum outcome of the remains to be seen, I’m not suggesting that Twitter is representative of the totality of the British voting public. But it might serve as a good indicator, an alternative tool to augment traditional polling.

We plan to compare the actual results of the referendum to our model and investigate potential connections in the weeks following the final vote. In the meantime, happy voting!

Earlier this month I began to explore the correlation between Twitter analytics and Britain’s possible departure from the EU. Things are heating up in the Twitterendum, but not necessarily getting any clearer. The volume of tweets in the five weeks following our last post was effectively the same as the previous twenty combined. This represents an huge increase in the raw number of tweets, but also in unique contributors, meaning a lot more people are being drawn into the debate.

Brexit_GIF

In spite of the increase in activity, the Twitterendum results remained remarkably static. The number of ‘stay’ unique accounts remained around 10% higher than their opposition whilst Brexiters continued to generate more per capita tweets than Bremainers, around 20% more content.

Week24_Brexit2

In the ‘leave’ camp, Burnley, Eastbourne and South Northamptonshire all doubled down on their positions. The number of ‘stay’ supporters increased dramatically in Woking and Manchester, however, though they are now more contested, both still generated a far greater number of ‘leave’ tweets.

Only one new joiner stood out – Oldham. Instead of making gains in key decisive swings of opinion, the Brexit camp found success in edging several smaller Local Authorities. Doncaster, Sunderland and Kingston upon Hull all moved from ‘undecided’ to ‘leave’.

Similarly, the ‘stay’ camp continued to reinforce its position in university towns and Labour strongholds. All ‘stay’ frontrunners from our last update increased their #StrongerIn scores.

London continued to be the centre of extremely heated debate with the total number of tweets only just favoring the stay camp. However, in terms of unique users it enjoyed one of the highest ratios of Bremainers: Brexiters in the entire country…perhaps unsurprisingly. The animation we’ve included shows London’s gradual movement from undecided firmly into the ‘stay’ camp, mobilizing more and more ‘stay’ supporters with each passing week.

The remaining most populous centers experienced very similar phenomena. Sheffield, Birmingham and Leeds all recorded surges in the number of unique ‘stay’ supporters, while simultaneously recording disproportionate levels of ‘leave’ tweets.

Analysis1v2_Brexit2

From this, it seems like the race is rather contested… though the higher proportion of unique accounts means that the ‘remain’ camp is pulling slightly ahead. What will the results show on the eve of the vote? Will there be any correlation between Twitter trends and the final referendum results? Join us next week!

Lucas-Galan-headshotLucas Galan currently serves as the Head of Analytics Productization at W2O Group’s London office. Connect with him on LinkedIn!