While talking to some healthcare providers in my local, and a few other regions, I've heard statements about the apparent impacts of poverty on COVID-19 risk, mostly based on anecdotal evidence. I honestly don't doubt it exists, and although I don't have the data available to prove it ... others do.
That led to a creation of a measure request based on social determinants of health in The SANER Project.
Not much after we added measure requests for staffing and supplies, CDC added two new COVID-19 modules into their reporting for similar items. We had already agreed we were not going to spend much time deciding on "experimental measures" for our Connectathon release of the guide. But I did reference the recently released CDC guidelines because it has the categories they think are important, and frankly, I don't need to second guess them.
Having thus concluded that for the purposes of COVID measurement, we'd try to use the CDC as an authority where possible, it occurred to me to look into how CDC was evaluating social determinants of health. The National Center for Health Statistics publishes an annual report, titled Health, United States, and in it, you will find rather detailed descriptions of how they classify certain categories that impact Social Determinants of Health.
Age, Gender, Race and Ethnicity would likely be covered in an existing measure request, and the singular for "Gender" is likely to get into a discussion around gender, sexual preference, and sex at birth. Given these are deemed a given in EHR systems certified by ONC, I think we can take it for granted that the data should be available, though perhaps not always readily accessible. Age gets interesting because current reporting (available to the public) is in 20 year chunks, though I think I've seen some data in 10-year chunks and one which pulled out 0-2 for special attention, but other reporting looks at 0-18, 19-44, 45 - 64, and 65+. We're rapidly reaching a point where 65 is no longer the age break for social security or medicare benefits, and the justifications for 18 because it is the age of adulthood is perhaps questionable. I'd stick with what people are using for COVID reporting right now though (e.g., 10 or 20 year brackets up to 80) because it's relatively simple.
The regional classification associated with the patient (urban, rural, et cetera) is likely a readily available datum for stratification if you can get to the demographics for the patients counted by the measure. But, as you can see if you clicked the link, there are at least 3 different classification systems that might be used. Geodata can get to census tract from an address, hospital counties are readily accessible, and for -ish sorts of things, that might be good enough (though some note that the Grand Canyon is classified as metopolitan, which, if you've ever been, surely isn't).
That leaves a few other factors to address:
That led to a creation of a measure request based on social determinants of health in The SANER Project.
Not much after we added measure requests for staffing and supplies, CDC added two new COVID-19 modules into their reporting for similar items. We had already agreed we were not going to spend much time deciding on "experimental measures" for our Connectathon release of the guide. But I did reference the recently released CDC guidelines because it has the categories they think are important, and frankly, I don't need to second guess them.
Having thus concluded that for the purposes of COVID measurement, we'd try to use the CDC as an authority where possible, it occurred to me to look into how CDC was evaluating social determinants of health. The National Center for Health Statistics publishes an annual report, titled Health, United States, and in it, you will find rather detailed descriptions of how they classify certain categories that impact Social Determinants of Health.
Age, Gender, Race and Ethnicity would likely be covered in an existing measure request, and the singular for "Gender" is likely to get into a discussion around gender, sexual preference, and sex at birth. Given these are deemed a given in EHR systems certified by ONC, I think we can take it for granted that the data should be available, though perhaps not always readily accessible. Age gets interesting because current reporting (available to the public) is in 20 year chunks, though I think I've seen some data in 10-year chunks and one which pulled out 0-2 for special attention, but other reporting looks at 0-18, 19-44, 45 - 64, and 65+. We're rapidly reaching a point where 65 is no longer the age break for social security or medicare benefits, and the justifications for 18 because it is the age of adulthood is perhaps questionable. I'd stick with what people are using for COVID reporting right now though (e.g., 10 or 20 year brackets up to 80) because it's relatively simple.
The regional classification associated with the patient (urban, rural, et cetera) is likely a readily available datum for stratification if you can get to the demographics for the patients counted by the measure. But, as you can see if you clicked the link, there are at least 3 different classification systems that might be used. Geodata can get to census tract from an address, hospital counties are readily accessible, and for -ish sorts of things, that might be good enough (though some note that the Grand Canyon is classified as metopolitan, which, if you've ever been, surely isn't).
That leaves a few other factors to address:
Disability
This one doesn't seem that hard. In the Health, United States report, it's a simple three tiered classification no difficulty, some, a lot or cannot do (where the last two are clustered into one bucket). The determination is based on the report of one or more categories related to ability to function (see the report for details on how they classify). If we want to make SDOH data useful, it should be aligned with where existing research has already gone. I'd stick with the 3-tiered classification.
Education Level
It gets as granular as years of schooling, but the key categories are no high school diploma, diploma or GED, some college, bachelors or higher. Some include AA degree as a stratum, but it's not much different from "some college" according to this chart.
Insurance
Do you have it? Yes or No. If Yes, is it private, or is it Medicaid? These are the important strata used in the report.
Food Security
I'm not really clear here on where to go. There's not really anything I spotted in the Health US report, and well, it's late as I write this. Honestly, I think income and housing are probably as indicative of food security. But, I also learn, there's a Z-code for food insecurity in ICD-10.
Income Level
This one gets tricky, because it's different based on the size of the family unit, and it changes annually. The Health, US report covers it as number of 100%-ile units above/below the HHS poverty guidelines generally, rather than as the census covers it with Poverty Thresholds. If you want to understand the difference, Google it. What's interesting here is that other research on poverty and health in the CDC uses two breakpoints: 130%, and 350%. Part of the reason for that is that many Federal guidelines use 130% as a qualification point for certain types of federal assistance, and 350% splits the remaining population into generally equal sized chunks. I'd go with the latter, because fewer is less work, and the 130% mark would seem to address some confounding challenges around food security. But then there's a Z-code, and it breaks at 100% and 200%, and that aligns with Gravity work in HL7, and the PRAPARE tool in use by Kaiser Permanente and others. I think we go with that because it's already accessible to some.
Housing Security
Again, not much to go on, but I'd guess there might be three big strata: Homeless, rent, or own. For homelessness, there's a Z-code in ICD-10 (see previous link under Food Security).Employment Status
I'm not sure how good an indicator this is given the current rapid rise in unemployment. I'm sure it's a factor though. There's a Z-code for this in ICD-10.
My Proposed SDOH Strata for COVID
A lot of the data above is not readily obtainable without additional efforts on the hospital side, which is likely something to avoid. What's likely already known: Insurance, Patient Address (and a proxy for homeless vs. Rent/Own), and functional/disability status (though not a completely), and possibly employment status. The ICD-10 Z-codes are also somewhat in alignment with the PRAPARE tool that others have examined, including the Gravity project in HL7. Z-Codes have the benefit of having been around long enough to already be in the EHR system.
So, what I'd go with is the minimum set of Insurance class (none, Private, Medicaid), the six Z codes covering employment, education, homelessness, food insecurity, and income level, as individual strata for COVID+/All patients.
This isn't a perfect stratification, and I'm sure we could debate the merits of other formulations. It's going to be marked experimental (like anything other than the CDC/NHSN or FEMA measures for connectathon, and I think it's good enough to see what others can do with it.
Keith
P.S. It's amazing how analysis paralysis disappears when you need it yesterday, and you have to work with what's available now, not next month, or with a little more work and research, and that's a key attribute in your decision making criteria.
0 comments:
Post a Comment