I’m sorry I haven’t had a chance to blog in a while – I took a new job as the Director of the Harvard Global Health Institute and it has completely consumed my life. I’ve decided it’s time to stop whining and start writing again, and I’m leading off with a piece about adjusting for socioeconomic status. It’s pretty controversial – and a topic where I have changed my mind. I used to be against it – but having spent some more time thinking about it, it’s the right thing to do under specific circumstances. This blog is about how I came to change my mind – and the data that got me there.
Changing my mind on SES Risk Adjustment
We recently had a readmission – a straightforward case, really. Mr. Jones, a 64 year-old homeless veteran, intermittently took his diabetes medications and would often run out. He had recently been discharged from our hospital (a VA hospital) after admission for hyperglycemia. The discharging team had been meticulous in their care. At the time of discharge, they had simplified his medication regimen, called him at his shelter to check in a few days later, and set up a primary care appointment. They had done basically everything, short of finding Mr. Jones an apartment.
Ten days later, Mr. Jones was back — readmitted with a blood glucose of 600, severely dehydrated and in kidney failure. His medications had been stolen at the shelter, he reported, and he’d never made it to his primary care appointment. And then it was too late, and he was back in the hospital.
The following afternoon, I spoke with one of the best statisticians at Harvard, Alan Zaslavsky, about the case. This is why we need to adjust quality measures for socioeconomic status (SES), he said. I’m worried, I said. Hospitals shouldn’t get credit for providing bad care to poor patients. Mr. Jones had a real readmission – and the hospital should own up to it. Adjusting for SES, I worried, might create a lower standard of care for poor patients and thus, create the “soft bigotry of low expectations” that perpetuates disparities. But Alan made me wonder: would it really?
To adjust or not to adjust?
Because of Alan’s prompting, I re-examined my assumptions about adjustment for SES. As he walked me through the data, I concluded that the issue of adjustment was far more nuanced than I had appreciated.
