We have been working as part of a multi-institution team to make county level estimates for the U.S. of the time until health systems are overwhelmed with patients. The analyses use a 28 day look forward window from 3/24/2020 and identify numerous counties where the health care system is expected to be overwhelmed; 28-day look forward analyses will be re-done weekly. Projections of time to health care systems being overwhelmed have been made for various levels of social distancing and various levels of intensity of hospital response to patient surges. A paper detailing all of the estimates will be uploaded to pre-press sites soon.
Our online mapping tool is currently displaying maps based on these analyses that show which counties are projected to experience patient volumes that exceed their hospital capacity over the next 28 days, under three scenarios: 1) no-social distancing and low intensity hospital response to patient surge; 2) no-social distancing and medium intensity hospital response to patient surge; and 3) no-social distancing and high intensity hospital response to patient surge. The estimates combine Jeffrey Shaman and colleague’s models of disease spread and the estimates posted previously of how many critical care hospital beds can be made available under various assumptions of hospital responses to patient surges.
As in prior posts, the mapping site is a work in progress and will be updated frequently.
Time to patient demand exceeding hospital capacity: 28 day look forward from 3/24/20, no social distancing and a medium surge response
Our geographer extraordinaire, James Quinn, built a new version of our interactive mapping tool for severe COVID-19. The map depicts populations at high risk of severe COVID-19 due to older age or underlying health conditions, the availability of ICU beds and the ratios of high risk populations to ICU beds. The interactive mapping tool is here. This is an ongoing project and we will keep updating the maps with new data and features as the pandemic continues.
We have continued our partnership with PolicyMap.Com and they created us an interactive mapping widget that shows the data on populations at risk of severe COVID-19. The at risk populations we mapped are: the number of people 65 years and older; number of people 75 years and older; the numbers of people with underlying chronic conditions linked to severe COVID-19 disease; and the number of hospital beds (note that at any given time in the U.S. about 66% of beds are occupied by patients).
We have continued to work with Policymap.com‘s excellent data portal tool to map populations at risk of severe COVID-19. Our prior post is here. The map below shows Counties in purple with high numbers of adults 65 years or older and low availability of hospital beds. High numbers of adults 65 years or older was defined as more the U.S. median across counties (greater than 4,698 people) and low availability was defined as less than the median number of hospital beds (<49 beds). Population counts are from the Census Bureau for 2014-2018 and counts of hospital beds are from the Health Resources and Services Administration (HRSA) for 2016.
Counties in purple have large populations of older adults and low numbers of hospital beds.
Like the vast majority of the world, we have been obsessing over the COVID-19 pandemic. Given that certain populations are at risk for severe COVID-19 disease we have been wondering where the at risk populations are in the U.S. To generate some quick maps we used Policymap.com‘s excellent data portal and mapping tool. By county, we mapped the number of people 65 years and older, 75 years and older and the numbers of people with underlying chronic conditions linked to severe COVID-19 disease. Counts of people rather than percentages of the population were mapped because it is the number of people, not the percent, that can strain the health care system. We also mapped the number of hospital beds – note that at any given time in the U.S. about 66% of beds are occupied by patients.
The evidence on links between neighborhood walkability and physical activity and body mass index remains limited because there have been few longitudinal studies with repeated measures of neighborhood walkability and health behavior and outcomes. While large cohort studies with long-term follow-up, residential address history, and health outcomes are available, the lack of neighborhood walkability measures with the same temporal and geographic coverage limits the use of these cohorts to study how urban form shapes health. We recently published a paper in the Journal of Urban Health describing a new measure of neighborhood walkability, the Built Environment and Health-Neighborhood Walkability Index (BEH-NWI), that can be calculated across communities in the U.S. and historically over the past three decades.
We retrospectively measured neighborhood walkability for 2010 for 1 km circles centered on each Census block in NYC (N=38,526) using the BEH-NWI and using our prior NWI. The correlation between walkability scores calculated from our BEH-NWI and our prior NWI across NYC is 86%, and BEH-NWI scores across NYC are also highly correlated with circa 2010 WalkScore data.
We used the BEH-NWI with two studies that previously collected physical activity, health and residential address data, the NYU Women’s Health Study and the NYC Department of Health and Mental Hygiene’s 2011 Physical Activity and Transit (PAT) Survey. We calculated BEH-NWI scores for the residential neighborhoods of participants in the NYU Women’s Health Study when they first enrolled into the study circa 1990. Higher BEH-NWI scores were significantly associated with greater self-reported walking per week and lower body mass index among study participants. PAT Survey participant’s wore accelerometers for a week to objectively measure their physical activity and we found that higher BEH-NWI scores were significantly associated higher levels of physical activity. BEH-NWI scores and WalkScore data were equivalently predictive of total physical activity among PAT Survey participants, but the BEH-NWI has the advantage that it can be retrospectively calculated across the U.S. back to 1990.
Differences in BMI by Quartile of Residential Neighborhood Walkability among NYU Women’s Health Study participants circa 1990.
The BEH-NWI can be a valuable new resource for research on how urban form and built environments affect physical activity, obesity, and health. The BEH-NWI is grounded conceptually in urban planning/design theory and uses data that are available nationwide and historically as far back as 1990. This measure will allow researchers to leverage existing longitudinal human health datasets for new insights into the role of neighborhood features in shaping health.
Just a quick note: The April 2019 issue of National Geographic focuses on Cities and how to redesign them to support health, sustainability and community. The issue covers transit oriented design, China’s new urban design regulations, walking through Tokyo, the evolution of a refugee settlement in Uganda into an urban hub and rats in NYC, all with Nat Geo’s excellent maps and info-graphics. Check it out.
Nat Geo Cities Issue
Posted in Active Transport, Economic Development, Injury, Parks, Pedestrian Injury, Physical Activity, Safety, Transportation, Urban Design, Urban Forestry, Walkability
We recently published a paper in the Journal of Urban Health, led by BEH alum Tanya Kaufman and frequent BEH collaborator Jana A. Hirsch, which found that individuals living near more commercial physical activity facilities (e.g. health club, tennis club, martial arts school, dance studio) were more likely to report having a membership at a gym or recreational facility. Additionally, while amount of facilities within a neighborhood was associated with more measured physical activity, this association was stronger for individuals who reported having a gym membership.
This study used the New York City Department of Health and Mental Hygiene’s “New York City Physical Activity and Transit (PAT)” survey data. We evaluated associations between counts of commercial physical activity facilities (from the National Establishment Time Series database) within 1 km of participants’ home addresses with both facility membership and accelerometry-measured physical activity.
Often efforts to increase physical activity have focused on either individuals (e.g., educational campaigns) or neighborhoods (e.g., access to additional recreational facilities). Little work looks at the interaction between spatial proximity (having a facility nearby) and individual characteristics that could be related to facility use. Our study findings suggest that interventions aiming to increase physical activity should consider both neighborhood amenities and potential barriers, including the financial and social barriers of membership to the neighborhood amenities. Similarly, evaluation of neighborhood opportunities should expand beyond physical presence to consider other factors that make an amenity accessible to different populations.