1/n There are a few things we couldn't get into in our story this morning on the higher death rates in for-profit LTC homes, including the extent to which Wave 2 was relatively much *worse* in for-profits.
2/n Here are three factors that stood out the most in our analysis:
1. Ownership type 2. A home's age (based on the age of design standards) 3. The local infection rate.
Orange is for-profit. Blue the rest.
Higher = more deaths per 100 beds.
3/n You can see home age and infection rate are clearly correlated with mortality — but of course ownership type matters, too.
You can see that when you compare across the 3 factors.
This was the key graphic in our story.
(It's what the industry said we didn't do last May.)
4/n We looked at other factors too. Those — home size, chain ownership and the presence of an outside management agreement — don't seem to have such a large correlation.
But, the for-profits were still *clearly* worst:
5/n One interesting thing is we found the difference between for-profits and the rest was narrower in Wave 1 than Wave 2.
Left is Wave 1. Right is Wave 2.
About 50% higher mortality than non-profits in Wave 1, more than 2x in Wave 2.
6/n I found this interesting (and validating) because although academic work done on Wave 1 agreed with the finding of higher-mortality, it found that design age and local context were a strong explanation for that difference.
7/n If we found that Wave 2 was not clearly different than Wave 1 in our analysis, we would expect that academic method to find the same thing all over again.
Instead: We found a much larger for-profit difference in Wave 2.
2-3x higher, in every combination of age + context.
8/n And indeed, as we were getting ready to publish, the Ministry of Health repeated @NathanStall's research, this time including Wave 2.
Like us: Their research found a significant ownership effect on mortality in Wave 2.
10/n That research is at the link here. Have a look:
The ministry's study did not find a significant ownership effect looking at the full pandemic.
(This means the confidence range of the for-profit effect — second from top — included the null result)
2/n For-profits have, in fact, reported higher death rates even accounting for factors the industry says explain the difference: Their homes’ older design & community infection rates.
Older or newer, hard-hit cities or no, for-profits saw more deaths per capita across the board.
3/n To be clear, older homes and those in areas with higher infection rates have *clearly* seen elevated death tolls.
We don't dispute that.
But: For-profits had worse avgs. than non-profits & municipal homes across virtually every combination of factors we looked at.
First, it was an emergency. They shut schools and the border was closed. But not at Pearson (and we went on March break.) They closed parks for the cherry blossoms. We lined up outside grocery stores. Then it was Stage 1, 2 and 3. Then it was “modified stage 2”.
We were told not to dine indoors — but you still could. They said "everyone can get tested," then it was only a few of us. Then appointments only. We all had our bubbles. Then there was colour coding. Then they changed the colours. And grey was worse than red.
Then there was “grey +” which was also a “lockdown” (but for most of us only after Boxing Day). The border was still closed, but you could fly to Cancun. Still can. Our kids went to school during the lockdown, then they didn’t. They will again soon. Probably.
2/n The problem is that all the ups and downs in these lines strongly suggest *narrative* — but for the most part, the data doesn't have anywhere near that fidelity.
Big turnarounds happen in pandemics, yes, but you won't know for sure you've had one until weeks later.
3/n Think of all the times we've heard about flattening, or a plateau, or a spike after some holiday.
You can see those moments clearly in the ups and downs of the fall wave.
At the same time: You can also draw a remarkably straight line through the same curve.
1/n Not long after I did the attached chart of cases by age, I realized I could use the Ontario database to predict how deadly each day's set of new cases might be, based on the age breakdown and their average death rates.