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Texas A&M researchers help highlight COVID-19 risk factors
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Texas A&M researchers help highlight COVID-19 risk factors

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There are associations between COVID-19 positivity rates and several socioeconomic factors, according to a recent study by Texas A&M University researchers. 

Specifically, the study says that there is an association between detected COVID-19 cases and dependent children under 18 years old, population density, median household income and race. More precisely, it explains that neighborhoods with a large dependent youth population, that are densely populated, low-income or have predominantly Black populations have higher positivity rates. 

Data for the ecological study, which was published in BMC Medicine, was collected in New York City between March 1 and April 5. Research was completed by Texas A&M aerospace engineering doctoral student Richard Whittle and aerospace engineering assistant professor Ana Diaz-Artiles.

“This is an unprecedented situation in the U.S.,” Whittle told Texas A&M Today, “so it’s important to get as much understanding as quickly as we can.” 

The study states that its aim was to find potential neighborhood-level socioeconomic determinants of the COVID-19 test positivity rate and explain variations between neighborhoods throughout the start of the pandemic in New York City, which saw its first detected case on March 1. 

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Diaz-Artiles and Whittle say in their report that the study brings attention to “the importance of public health management during and after the current COVID-19 pandemic.” They said additional research is needed to understand “the mechanisms by which these factors may have affected the positivity rate, either in terms of the true number of cases or access to testing.”

During the time period that data was collected, people with mild symptoms were asked not to get tested; the researchers say in their study that because of this, the data probably only reflects information about people with severe symptoms. 

Several potential predictors looked at in the study were found to be statistically insignificant, including the 65-and-older population. Diaz-Artiles and Whittle hypothesized that this could be because publicity about older groups dying from the virus could have caused those populations to abide by recommended safety precautions. Alternatively, the researchers think that it also could be because information about older groups being impacted could have made younger people think they were not at risk, causing them to be the ones engaging in “riskier behaviors.” 

The percentage of uninsured residents was also a potential predictor that ended up being statistically insignificant. While the researchers point out that insurance has in the past been a barrier for people trying to receive treatment for other medical needs, they said it was “unsurprising” that this was not reflected here, since there has been “unprecedented” access to free testing as state funds went into responding to the pandemic. 

Studies looking into previous pandemics, including H1N1 pandemics in 1918 and 2009, say that socioeconomic factors on a national level can affect detection rates and medical outcomes, the researchers say in their report.

Whittle told Texas A&M Today that understanding the landscape of a situation is critical so that resources can be better allocated. 

“If you go back and look at the Spanish flu, there were three waves that hit the U.S., of which the second wave was the worst,” Whittle said. “So having an earlier understanding of the socioeconomic risk factors allows policy makers to better target where they want to focus their efforts.”

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