Total Numbers of Clients Versus Caseload: why the latter is a better measure
I – and now others* – have cast doubt on the number of children and young people that the charity Kids Company claim to support. I have two reasons for doubt. The first is my personal experience of low attendance when I worked at the Urban Academy some years ago, the second concerns the caseload figures suggested by Kids Company’s numbers.
Let’s set aside my experience. After all, as I’ve said before, it was inevitably partial, and it isn’t all that recent. Let’s look instead at caseload. Caseload is, potentially, a simple and useful way of comparing social programmes since it gives a sense of both staff working conditions and the time and resource that can be dedicated to the people a programme aims to help.
Before getting stuck into the detail of calculating caseload, there’s an important questions to answer: why do accurate numbers matter?
Beyond general concerns about probity on the part of charities and their leaders, accurate numbers matter because they give us the right information with which to determine the allocation of resources.
Put simply: if organisations delivering social programmes inflate their figures to gain funding, they mandate funders and commissioners to underestimate the true cost of frontline work. This in turn leaves social programmes underfunded, and vulnerable people at risk.
This is bad for everyone.
Caseload could help to cut through this muddle. It gives us a real sense of how programmes are working and how much time they can give to supporting their clients. A focus on caseload could help move us away from big – inaccurate – aggregate numbers towards a more realistic picture of frontline delivery.
There is already some excellent work on which to build. One programme, the Family Nurse Partnership, which has an exemplary record of data collection and evaluation, also provides relatively detailed information on caseload. FNP nurses manage an average caseload of 25 cases. The level of detail provided by this programme makes it very clear that this is a demanding level of support. That finding is echoed by the 2012 Community Care survey of social work which found an average caseload of 25 amongst adult services social workers and 17 in children’s services, based on 925 social workers surveyed. It also found high sickness rates and evidence of staff resigning due to the pressures of an increasing, and increasingly complex, caseload. A more recent Unison survey from April 2014 found that on any one day social workers were, on average, responsible for 22 cases. All research suggests these are high, and demanding, caseload levels.
We can conclude that managing upward of 20 cases is a tough ask, for any 1:1 support worker and needs appropriate funding and effective support.
Based on a calculation using published staff and client numbers (assuming 400 full time key workers and 18,000 children and young people receiving “intensive support”) Kids Company has an average caseload of 45. That is almost three times the national average in children’s services, which the charity’s leader has repeatedly described as overstretched and unable to cope. Even if every single member of staff – 650 in latest claims to the media – was a full time key worker, the caseload for Kids Company staff would be 27, still well above the average in children’s services, and higher even than the average in adult services. It should also be noted that these estimates use the lower of two figures usually quoted by the charity. In many other reports the number of beneficiaries reached is twice as high, doubling caseload estimates to an astronomical 90.
If these numbers are accurate, Kids Company’s intensive support with a caseload average of 45 equates to a maximum of 3.5 days a year for each one of its clients. And of course for each young person who receives more than the equal distribution of hours, another must receive fewer.
It just doesn’t seem possible that these figures and Camila Batmanghelidjh’s claim to be “obsessed with protecting” her workers from stress and to “always have a key worker present to support that young person” can both be correct. Claims about the Kids Company model and claims about its reach appear to be straightforwardly incompatible. That in turn begs questions about the model itself.
But let’s leave Kids Company and take a look at another example altogether. Over the last few months I have been researching caseload across the national Troubled Families (TF) programme. The TF programme publishes regular tables charting progress assessed by the number of families ‘turned around’. It operates across the whole of England and Wales and therefore offers a relatively rich comparative field. The TF programme is based on family intervention led by a “single dedicated worker” to quote the DCLG guidance. It is a programme, like the Family Nurse Partnership, like Kids Company, that relies on strong relationships between workers and clients. It is, therefore, a programme in which caseload is likely to play an important role.
As with many social programmes in the non-profit sector, however, when you take a closer look at the tables, things begin to look muddy.
I collected TF data through FoI requests as a means of testing the published progress tables. All but one of the requests were made online using the Freedom of Information portal What Do They Know and can be viewed on the public account here.
The requests were made in two series. The first request was sent out to 111 Local Authorities. The second was sent to a further 37 Local Authorities who were not contacted in the first round. These final 37 requests tested some of the findings that arose from the build of the initial dataset. In total the requests produced 76 usable sets of data for 2013/14, and 75 for 2014/15. Data is still coming in for the second round, but 10 usable sets of data are included here for reference.
So what does the data tell us?
It certainly bears out Community Care’s survey findings on sickness absence. The data shows an average number of 6 days – 2 more than the ONS average of 4 –off sick amongst TF staff. That average masks wide fluctuations, with the highest reported figure an astonishing 24 days, and the lowest 0. Twenty TF teams had sickness absence above the average; nine of these had average sickness of 10 days or more.
These numbers might well indicate an overstretched workforce with impossible caseloads.
The data also reveals that Camila Batmanghelidjh has competition for the record UK caseload calculated according to staff and client numbers. The highest, at 107, from Southwark, can probably be put down to the inevitable inaccuracies that come from FoI requests and their variable interpretation, but Westminster and Haringay’s 51 and 60 respectively might more nearly reflect reality. That said Kids Company’s caseload is still right up at the top of this dataset. Only 4 of the 75 LAs in the 2014/15 table had average caseload figures above 40 (down from 20 LAs in 13/14).
To be clear, the client/staff calculations show that the vast majority of Local Authorities in the TF programme have more capacity for supporting families than Kids Company (according to the charity’s own figures).
As well as calculating by client and staff numbers, the dataset includes the caseload average given by each council in response to a direct question in the FoI requests.
This is where things get interesting.
There is a consistent discrepancy between the caseload calculated using client and staff numbers and the averages given directly in the FoI responses. To take the example of Southwark, whose client/staff figures suggest a whopping average caseload of 107, caseload given via FoI is a very modest 10.
The average gap between calculated caseload and FoI caseload is 22, rather lower than Southwark’s 97, but that’s still a big gap that needs explaining: 22 families for every member of staff, unaccounted for in the FoI figures.
One way of explaining the discrepancy between client/staff caseload and FoI caseload might be turnover, that is the rate at which supported families enter and exit the various TF support programmes. This could mean that a key worker does annually support 32 families (the dataset average), but with a rolling caseload of 10 or so families at a time. This would mean that most TF workers are managing three entirely separate caseloads of 10 families in any one year, turning each cohort around every four months. That is a fast pace of change and provides us with a much more precise picture of what might lie behind the progress charts published by the government, and the sickness rates in the FoI responses.
It takes a good bit of effort to get here though, the tables themselves don’t tell this story.
But, again as with Kids Company, it is equally possible that the numbers are wrong, or inaccurate, or misleading, or just not quite what they seem.
To investigate the discrepancies, the second round of FoI requests that I submitted asked LAs for client numbers as well as average caseload. In every single one of the answers to the FoI request the figure for the number of clients was different to the number in the government data release. In all cases but one, the data release figure was higher than the FoI figure, with an average discrepancy of 82 families.
Bluntly, you might reasonably conclude that the figures in the government data releases are inflated, just as Kids Company’s aggregate figures are, in all likelihood, inflated.
Inflated figures are a huge hurdle for all social programmes. They create an impossible baseline of care by mandating commissioners and funders to underestimate the time and resource that effective support really requires. They create a painful vicious circle: social programmes inflate their figures to attract funding, but remain chronically underfunded as a direct result of their own inflated figures, continually chasing higher numbers with limited resources. This might explain some of Kids Company’s own funding problems.
This is where caseload analysis – and other similar forms of analysis – are needed to recalibrate the system away from unrealistic aggregate numbers towards models that do justice to the hard work of frontline workers and the continual challenge of delivering support to those who need it most. A realistic assessment of the support that can effectively be provided by any given programme is essential; we should actively demand that all funders and commissioners make use of these kinds of models in funding and evaluating programmes.
In short we don’t just need transparency and accountability through the publication of numbers, we need simple, useful and comparable data that helps us to understand social programmes better, and we need it to be accurate. Exaggerated figures don’t help anyone, least of all the vulnerable. Caseload might be a good place to start creating better forms of measurement.
[To read the full analysis of the TF dataset, visit our downloads page here.]
*Access to content requires subscription
Image credit: redjar