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Categorized | Hartford

News Analysis: COVID Data Presents Blind-Spots

By Thomas Nocera, Staff Writer

HARTFORD — Across the country, combating the spread of coronavirus has increasingly relied on data zeroing on each zip code in urban, rural and suburban towns. With such tight focus, local and state governments have been more effective at tracking trends, pivoting to new needs, and allocating resources where most needed.

Here in Connecticut, however, information that detailed is simply not available. Currently coronavirus numbers that make their way to local authorities from the state are not refined any further than the county and town level.

This lack may leave the most vulnerable and hard hit communities without the proper resources and, according to James Trostle, Professor of Anthropology at Trinity College and author of ‘Epidemiology and Culture,’ can hinder overall response efforts.

“This kind of information is called geo-located data,” Trostle says. “It provides an important way to look at the spatial distribution of both risks and infections. In fact, ‘place’ is one of the three core organizing principles in epidemiology, along with person and time.”

Thomas Nocera

Trostle, whose expertise is in medical anthropology and public health, believes that sharing of highly specified data can be one of the keys to successfully combating pandemics. Currently, the Connecticut Department of Public Health acts as a central corral point for COVID-19 data it receives from hospitals and other health facilitates. From there, numbers are input into a frequently updated database for relevant authorities on the ground to use as they develop strategies. But the lack of more detail may create a blind-spot of sorts for local health officials.

In Hartford, for example, Vasishth Srivastava, Mayor Bronin’s chief-of-staff, confirmed that the database does not allow them to see detailed demographic or geographic information, though they’ve been attempting to work out a plan to receive such data. In cities and states outside of Connecticut, however, access to that information has allowed officials to fine-tune responses.

In New York, like in Connecticut, analysis of geo-located data revealed that poor and minority neighborhoods had far higher rates of infection. That initiated a quick and forceful response which included the opening of new clinics and testing facilities in these areas. The worrying revelation that vulnerable communities were bearing the brunt of the virus’s effects is by no means isolated.

Illinois and North Carolina have also dug deeper into their data and identified higher rates of infection linked to ethnicity and income in particular areas or neighborhoods. By scaling down the data, authorities in these states hope to have a better chance at staying a step ahead of the ever-changing patterns of infection. Not doing so though can seriously hinder responses.

“Ignoring, or failing to collect, place-based data like zip code or census tract or latitude/longitude,” Trostle claims, “makes it difficult – if not impossible – to see how important location might be in the growth or distribution of any particular epidemic infection.”

Strategies that meticulously map out infections are nothing new when it comes to combating diseases. In one well known effort, the father of modern epidemiology – John Snow – painstakingly traced the source of a cholera outbreak in London in 1854. According to Trostle, Snow did this by walking “door-to-door and counting cholera cases house-by-house.”

Snow’s efforts were groundbreaking at the time. Today though, the precedent for tracing cases as locally as possible is a well-proven method in the fight against pandemics.

By responding to small scale hotspots, authorities better their chances of avoiding larger pools of infection in the future. As of now, cities across Connecticut will have to wait for that level of information as no plans currently exist to refine data and provide deeper location information, according to a spokesperson in an email from the state’s Department of Public Health. 

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