Before we get to that, let’s take a step back for a moment and ask: why bother investigating health demographics in the first place? Well, to be fair, it is very atypical in a workplace context. But that doesn’t mean it shouldn’t be done.
Supporting employees to be healthy and thriving at work necessitates better understanding of your population. It’s through this kind of work that you can ensure benefits help tackle some of your business’ most pressing issues. Take employee absence as an example.
The latest annual Labour Force Survey data for the UK shows that working days lost per employee due to self-reported illness or injury was broadly flat over recent years, but increased markedly in 2019/20 to reach 38.8 million; an average of 17.6 days lost per employee. This can only be explained in part by COVID-19 as some of the data was collected before the pandemic hit.
Health demographics on a country level
The collection of health demographic data is happening all the time across populations both local and global. This is done to ensure that healthcare systems such as the NHS are meeting needs; that they’re equipped to support population health. It’s done by assessing influences on population structure and health, such as size and age, the proportions of different ethnic groups, affluence, social status etc.
Healthcare ecosystems simply wouldn’t stand a chance of providing the support people need without such insights. So, when you think about it, it seems a bit crazy that health benefits are traditionally put in place within companies in the absence of such insights.
These are expensive benefits. So, wouldn’t it be prudent to take the time to find out how – or if – they’re meeting your multigenerational, multicultural workforce needs?
Health demographics on a company level
Here’s what we found when we carried out such segmentation for one client. As referenced earlier, we found that the segments of the workforce most in need of health benefits – those with fair to poor, or very poor, health ratings – were the least likely to select them. This included people across various age groups, with and without children at home, and across various levels of financial security.
In the context of a Flex programme, the appetite for preventative benefits, such as health screening, was particularly low. Employees were much more likely to take a cash savings scheme or even the cash itself. This was the case across all segments, with the appetite for health screening only slightly higher for those under age 40, who are least likely to have family commitments.
From data interrogation to actions
From this, we could perhaps ask the question, is it time to rethink the way healthcare programmes are structured? Instead of employers viewing PMI as the thing people are likely to strive for – or the perk for senior execs only – why not divert investment from core or ‘exclusive’ benefits and instead focus on a more ‘inclusive’ prevention rather than cure strategy, such as health screening.
Affordability may be an issue. And employees might not want to pay for such a service themselves. But delivered by their employer it says a lot about how seriously the company takes employee wellbeing. Not only that, but it helps shoulder the public healthcare burden when it comes to prevention of lifestyle diseases.
Communication and participation
Communication is going to be an essential part of any approach. And with the segmented information to understand where your workforce is on the journey to a healthier lifestyle, you can fine tune your communication activities to maximise employee adoption.
Understanding the health of a workforce and their propensity to consider health benefits as a priority was achieved through our Amplify Insight tool. Using geodemographic segmentation, combining discrete workforce data with population data sources – such as ONS census – we can gain a much deeper understanding of who their employees are, what their interests are and even what their health rating is.
Of course, this is just one take-away from this exercise. The client concerned gained a lot of very rich data; data which could be used to design and communicate a fit-for-purpose benefits programme; fit for people, for business and for the future.
This article was written for, and features in REBA, February 2021.