New York [US], March 21 (ANI): A study led by a team of researchers at the New York University presented a computational approach to measure exposure density at a high spatial and temporal resolution to understand neighbourhood disparities in transmission risk of COVID-19.
The findings were published in the journal PNAS.
Researchers report that neighbourhood changes in exposure density--a measure of activity associated with increased risk of exposure to COVID-19-infected individuals--in New York City in response to a statewide stay-at-home order were associated with COVID-19 case, fatality, and test positivity rates; low-income and predominantly minority communities had the smallest relative decreases in exposure density and disproportionately poor health outcomes, according to the authors.
By integrating geolocation data and granular land-use information, the researchers are able to establish both the extent of activity in a particular neighbourhood and the nature of that activity across residential, non-residential, and outdoor activities.
The researchers then analyse the differential behavioural response to social-distancing policies based on local risk factors, built-environment characteristics, and socioeconomic inequality.
Our results highlight the significant disparities in health outcomes for racial and ethnic minorities and lower-income households. Exposure density provides an additional metric to further explain and understand the disparate impact of COVID-19 on vulnerable communities.
Although there is increasing awareness of disparities in COVID-19 infection risk among vulnerable communities, the effect of behavioural interventions at the scale of individual neighbourhoods has not been fully studied.
The team of researchers develop a method to quantify neighbourhood activity behaviours at high spatial and temporal resolutions and test whether, and to what extent, behavioural responses to social-distancing policies vary with socioeconomic and demographic characteristics.
Researchers define exposure density (ExrExr) as a measure of both the localised volume of activity in a defined area and the proportion of activity occurring in distinct land-use types.
Using detailed neighbourhood data for New York City, they quantify neighbourhood exposure density using anonymized smartphone geolocation data over a 3-mo period covering more than 12 million unique devices and rasterize granular land-use information to contextualize observed activity.
Next, researchers analyse disparities in community social distancing by estimating variations in neighbourhood activity by land-use type before and after a mandated stay-at-home order.
Finally, they evaluate the effects of localized demographic, socioeconomic, and built-environment density characteristics on infection rates and deaths in order to identify disparities in health outcomes related to exposure risk.
The findings demonstrate distinct behavioural patterns across neighbourhoods after the stay-at-home order and that these variations in exposure density had a direct and measurable impact on the risk of infection.
Notably, they find that an additional 10 per cent reduction in exposure density city-wide could have saved between 1,849 and 4,068 lives during the study period, predominantly in lower-income and minority communities. (ANI)