END OF 2018 OPEN ENROLLMENT PERIOD (41 states)

Time: D H M S

Arizona: Case Study in what a pain in the ass trying to track the data can be

Hat Tip To: 
Maurice H.

When I first read this article submitted by contributor Maurice H., I was pretty concerned, as it made it sound like Arizona's exchange QHP total was only around perhaps 60,100 as of March 18 (the artilce was posted on the 19th):

Final numbers for Arizona enrollment will not be available for a few weeks, but more than 80,000 Arizonans have enrolled in Medicaid and more than 60,000 have enrolled in private health care plans through the site, said Herb K. Schultz, regional director of the Health and Human Services Department.

This concerned me because Arizona already had 57,611 QHPs as of March 1st...and had 43,495 on February 1st. That means that AZ's February average was 504/day.

If the 3/18 number was only around 60,100, that would mean they were only at 2,489 for March, or only 146/day…a 71% plummet from February.

HOWEVER, when you watch the video at the link, he says, at the very beginning:

"as of the end of February, there were approximately 60,000 people enrolled in the marketplace… (and then a minute later) …there have been over 80,000 who've enrolled in Medicaid, 60,000 in that marketplace…"

This is irritating for two reasons. First, he's rounding the 57,611 in the last report up to 60,000 (in fact it was closer to 57K even at "the end of February" since that 57,611 figure included the first day of March).

Second, the article makes it sound like the 60K figure is within the past few days, when it's actually over 3 weeks out of date.

Yes, I round up or down as well, but generally only to the nearest thousand, and usually only in the headlines; I provide the exact numbers in the entry itself if it's provided. In the projection formula I really need as close to the exact numbers and dates (or as close to exact as possible) as possible.

So, while I can't use this data in the spreadsheet or on the projection chart, it does make for a useful example in data accuracy and sourcing (in this case, the direct source was the regional director of the HHS Dept. Make of that what you will...)