Research Paper: _*Health care visits during the COVID-19 pandemic: A spatial and temporal analysis of mobile device data*_

Be sure to check out this publication by @Jueyu_Wang_UNC_Chapel_Hill, Noreen McDonald, Lindsey Oluyede, Mary Wolfe, and Lauren Prunkl titled Health care visits during the COVID-19 pandemic: A spatial and temporal analysis of mobile device data. You can read the full paper here! :point_left:

They used SafeGraph data to explore temporal patterns of visits to health care POI’s and how these patterns are associated with demographic and spatial characteristics at a census block group level in North Carolina. Two key findings were:

(1) CBGs with higher percentages of elderly persons, minorities, low-income individuals, and people without vehicle access had lower use of health care before the pandemic and experienced a slower recovery in medical visits after the lockdown

(2) CBGs in the central areas of large metropolitans or with higher population density tend to have a slow recovery of health care visits.

The COVID-19 pandemic has been found to exacerbate already-existing disparities in a number of fields, and this work suggests health care access is no different. Among other suggestions, the paper offers motivation for further adoption of tele-medicine solutions, even in non-pandemic times.

This topic was automatically generated from Slack. You can find the original thread here.

Hey @Jojo_Zhou ! Flagging this study for you since I know you’re interested in looking at changes in foot traffic during COVID-19. Are you focusing on any specific types of POIs in your research?

Hi @Xueming_Chen_Virginia_Commonwealth_University ! Have you this seen this publication by @Jueyu_Wang_UNC_Chapel_Hill and Abigail Cochran ? I recall from our onboarding call that you wanted to look at foot traffic to hospitals. Are you still pursuing this line of research? This publication might have a lot of overlap with your work - be sure to check it out!

Hey @Gordon_Ngai! Hope all is well since our last conversation. Just wanted to flag this study since it reminded me of your “hot-spotting” publication. Are you still working on a follow up to the study?

@Tanmoy_Bhowmik_University_of_Central_Florida Thought this publication by Jueyu and Abigail fell in line with your publication on hospitalization and ICU capacity. Have you already checked it out?

This is very interesting! As someone who is working on a paper that relies on data from medical records in 2020, I will forward on to my coauthors to make sure we are aware of the biases we may have.

  1. I’m somewhat concerned about the CBGs with no sampled devices; given the data you have on who is undersampled, it seems like you would be most likely to miss CBGs containing many Hispanic residents or with a low household income. How many of the cut CBGs are for no data vs. limited data? How do the excluded CBGs differ from included CBGs?
  2. Since you aggregate to home CBGs, am I correct thinking you do not determine where the POIs of each cluster are, or what types of medical facilities are contained in each? I found it difficult to interpret the results, or determine what the policy implications would be, when I did not know anything about the medical care being sought among each cluster. One could imagine that a once-yearly primary care appointment is very important, but, say, a delay of three months to see your dermatologist is less likely to have long term health implications. (I mean, dermatology is important, but it is, comparatively, less likely to be life-or-death.)
  3. At the end of the paper, you make a recommendation that bikeshare services might help people access healthcare. This, too, is somewhere I could find knowing where POIs were wrt. the CBGs was useful – for instance, a 1 mile average travel time is much more substitutable with a bike than a 10 mile one.
    This concern is somewhat addressed by noting these are high population density areas, so presumably the distances traveled are shorter than those at the fringes of metro areas, but this didn’t fully address my concerns. The most underserved people might likely to live in neighborhoods where there are few medical facilities, and thus, still have non-trivial transit distances to medical facilities.