My mind has been blown twice this month looking at shelter data.
The push that many of us have been trying to make is to know the people touching in your system of care by name, and to cross reference those same people by shelters, outreach, and other services, as well as your By Name List or Priority List. In the case of assembling priority lists for coordinated entry, as David Tweedie on the OrgCode team has pointed out before, once you dig into the data to look at it by people that touch your system rather than people on your list, you will quickly see that there are a number of people in your shelters or served by outreach that have not been assessed and therefore are unlikely to end up on a priority list for housing. Who you are serving and who you are housing may be two different groups.
But back to having my mind blown with shelter data.
In Community A - a city of over 500,000 people - as is the case of many communities, they ran their shelter data by shelter stays in 2017. What did they find?
|Number of shelter stays||3,695|
|Average length of stay||12 days|
|Median length of stay||3 days|
|% people who leave before 14 days||79%|
|% people who stay 180+ days||0.4%|
Then they ran the SAME data but by unique individuals, and a whole different picture emerged. What did it show?
|Number of unique individuals with shelter stays||408|
|Average length of stay cumulatively||114 days|
|Median length of stay cumulatively||87.5 days|
|% people who leave before 14 days cumulatively||9%|
|% people who stay 180+ days cumulatively||21%|
Say what? Shelter stays painted a picture we are all familiar with - a large volume of short stays. Unique individuals resulted in a completely different understanding of the data. Once you started to understand cumulative engagement the world of sheltering looked completely different.
Bewildered, I had the chance to have Community B - a city of just shy of 300,000 people - run the same type of report just to make sure Community A was not an anomaly. What did they find?
|Number of shelter stays||1,888|
|Average length of stay||14 days|
|Median length of stay||4.5 days|
|% people who leave before 14 days||83%|
|% people who stay 180+ days||0.8%|
Like Community A, Community B then ran the data by unique individuals, and again a whole different picture emerged. What did it show?
|Number of unique individuals with shelter stays||211|
|Average length of stay cumulatively||106|
|Median length of stay cumulatively||91.5 days|
|% people who leave before 14 days cumulatively||8%|
|% people who stay 180+ days cumulatively||26%|
I am scratching my head. I want to see more data on unique shelter users versus shelter stayers. Is it coincidence that two communities in a row that I had contact with this month ran data that runs contrary to how we generally think shelters operate? Or were these legitimately outliers and the norm is something different? Would love to know what happens in your community when you run your data by unique individuals...let me know. We may be on to something here.