A new study by Mujaheed Shaikh and co-authors in PNAS shows the impact of “non-pharmaceutical” measures in the US.
To curb the spread of COVID-19, governments around the world implemented so-called non-pharmaceutical interventions (NPIs) – measures like social distancing. But how well have they stemmed the spread of the disease, and what happened when the rules were eased – who did they benefit, and who was most vulnerable?
In a study published online by the Proceedings of the National Academy of Science of the US (PNAS) on 3 March 2021, researchers at the Hertie School, Oxford University and Imperial College London examine the effects of NPIs through county-level data in the United States between 1 January and 3 June 2020. The researchers look at measures such as shelter-in-place, the closure of dine-in restaurants and non-essential businesses, limiting social gatherings, closing daycare, schools and universities, and banning people from visiting nursing homes, to name a few.
Surya Singh of Oxford University, Mujaheed Shaikh of the Hertie School and Katharina Hauck and Marisa Miraldo, both of Imperial College London, discovered that such NPIs reduced the COVID-19 spread in US counties on average, but their impact diminished after they were lifted. NPIs had the greatest impact in high-income counties and in those with large non-White populations. On the other hand, NPIs targeting specific vulnerable populations, like school closures, did not have a significant impact.
The measures may vary in their effectiveness, as not everyone has the privilege of social distancing, the authors say. The privilege of social distancing is thus “highly intersected with ethnicity and socioeconomic status,” they note.
“Notably, we find heterogenous impacts due to population characteristics, implying that measures can mitigate the disproportionate burden of COVID-19 on marginalised populations,” say the authors. In addition, they found that “selectively targeting populations may not be effective.”
The researchers used several methods such as difference-in-differences, propensity score matching, and event study designs that all pointed to the same findings.
Read about the study in the PNAS article here.
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