TLDR

Background

In this report we take cursory look at slot-level COVID appointment availability data as gathered by USDR’s UNIVAF project. The main question we’re looking at is whether this kind of data can be used to identify issues in vaccine access by different demographics. Another question is whether this can help us understand the role of barriers to access, as opposed to more ideological barriers, can help us understand trailing vaccination rates.

Stats:

Provider breakdown:

## 
##    centura_driveup_event        kroger_citymarket             kroger_covid 
##                     2705                    37170                       25 
##       kroger_kingsoopers kroger_the_little_clinic                walgreens 
##                   126875                    18623                   155090 
##                  walmart 
##                    40665

Coverage (county)

Roughly, what share of vaccinations can be traced to “bookings” in our appointment availability data? This is a very coarse estimate that smooths over a lot of uncertainties like: when do shots get counted? what about doses? can appointments get overbooked? do they get canceled? Keeping these uncertainties in mind, here is the distribution of the coverage rate by county, as compared to the national distribution:

Here are the number of slots per day (weekend days highlighted), whether they were “booked”, and how long they were available for:

Appointment availability over time (state)

Number of slots

Total number of slots that were ever available, unwrapped by dates. Mondays are highlighted:

  • Less availability outside of the work day and outside working hours in the weekend.

Aggregate stats per provider:

Provider Number of Slots Avg. hours available Relative weekend slots
centura_driveup_event 2705 247.8 1.24
kroger_citymarket 37170 60.5 0.88
kroger_covid 25 21.8 0.00
kroger_kingsoopers 126875 53.9 0.75
kroger_the_little_clinic 18623 71.0 0.95
walgreens 155090 102.5 0.70
walmart 40665 2.1 0.60
  • Statistics will be mostly driven by Walgreens and to a lesser extend Krogers.

Average hours slots are available for

Average number of hours that slots are available for. More is better as it gives the person more time to book an appointment.

  • The last week sees a big increase in time available for, as the release schedule switched from weekly on Friday to daily two weeks in advance.

Averaged by hour of day and day of week:

  • Slots outside working hours are available for a shorter amount of time.

Availability (county)

Availability by vulnerability (zip)

We can compare appointment availability by the demographics of the area that the distribution location is in. The most granular level is the zip-code. Of course, people can travel to locations outside of their zip-code, and so a better analysis would, for every zip code, take an average of the nearby locations weighted by their distance.

We do univariate correlations between vulnerability indices like SVI and availability statistics. In this plot, the points represent individual zip-codes in CO. The red line is a linear fit of the relationship between the vulnerability statistic and the availbility statistic. The blue line represents that same relationship for all zipcodes in the US.

For most indices, we see a negative correlation between vulnerability and availability (household SVI excluded), and the trend for CO mostly tracks the national average.

Note: because we don’t have vaccination data on the zip-code level, we can’t (unlike below for counties) adjust slots/person by coverage, nor can we plot the SVI statistics against vaccination rates on a zip-code level.

Does availability predict vaccinations? (county)

How does appointment availability relate to vaccinations? Unfortunately we don’t have vaccination data on the zip-code level, so we’ll do this analysis on a county level. Vaccination counts by county by day are pulled from CovicActNow.org. The outcome we look at is the difference in vaccination rate over the six weeks that we’re looking at. We run a simple linear regression with counties as units. First just the availability statistics, and then taking into account other drivers like hesitancy (taken from this CDC survey and prior vaccination rate).

## 
## =====================================================
##                              Dependent variable:     
##                         -----------------------------
##                               n_vax/population       
##                            (1)       (2)       (3)   
## -----------------------------------------------------
## Slots/Person (week)       0.106              -0.067  
##                          (0.086)             (0.094) 
## Slots/Person (weekend)    0.173              0.371** 
##                          (0.170)             (0.165) 
## Average range (hrs)      0.0002              0.0002  
##                         (0.0001)            (0.0001) 
## Share Black                         0.086     0.165  
##                                    (0.154)   (0.135) 
## Trump vote share                  -0.066**   -0.029  
##                                    (0.032)   (0.029) 
## % hesitant (CDC survey)           -0.638**  -0.513** 
##                                    (0.258)   (0.243) 
## Prior vax rate                     -0.0001   0.0001  
##                                   (0.0004)  (0.0003) 
## Intercept               0.031***  0.199***  0.139*** 
##                          (0.009)   (0.047)   (0.048) 
## -----------------------------------------------------
## Observations               35        35        35    
## Adjusted R2               0.485     0.431     0.605  
## =====================================================
## Note:                     *p<0.1; **p<0.05; ***p<0.01