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Coronavirus: Why New York and San Francisco Mortality Are So Different

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Both San Francisco and New York City are first COVID-19 (New Coronavirus Infection) (# If there is no character limit, add parentheses when first appearing Incident during the first week of March. March 16 San Francisco announcement He instructed the residents to stay home to avoid the spread of the coronavirus. New York did the same In less than a week. But by the end of May, San Francisco 43 death COVID-19 shows the death toll in New York City Over 20,000..

What explains the clear difference in COVID-19-related mortality between these two cities? Are you responsible for delays in home orders? What about the city-specific measures taken to mitigate COVID-19 prior to ordering? is there anything else?

The forked trails of San Francisco and New York City are particularly striking, but not unique. World wide, COVID-19 has a great variety of effects. In the U.S.Infections, hospitalizations, and deaths have skyrocketed in nearly every major city in the northeast, but remain fairly low in other large cities such as Houston, Phoenix, and San Diego.

How cities and states Implemented Public health interventions such as school closures and telecommuting vary. Regardless of how they worked for whom, comparing these interventions can provide insight into disease and improve future policy decisions. But making an accurate comparison is not easy.

Scope of COVID-19 interventions implemented in the United States and around the world Was not random, Make it difficult to compare them. Population density, household size, public transport availability, and hospital capacity, among others, may contribute to the difference in deaths from COVID-19 in San Francisco and New York City. These types of differences complicate the analysis of response efficacy for the COVID-19 pandemic.

So Biostatistician And Epidemiologist, We use statistical methods to categorize causes and effects by controlling differences between communities. COVID-19 often sees comparisons that do not adjust for these differences. The following experiment shows why it matters.

Urban simulation reveals the paradox

To explain the danger of comparisons where differences can’t be adjusted, a simple Computer simulation We use only three virtual variables: the size of five up to 15 cities, the timing of home orders, and the cumulative death of COVID-19.

COVID-19 mortality was plotted for 300 simulated cities, with a delay defined as the number of days between March 1 and the order placed. In cities of comparable size, delays in implementing home-based orders have been associated with increased COVID-19 mortality. Specifically, 40-63 deaths are expected for every 10-day delay. The fictitious policy recommendations from this analysis are for immediate implementation of home orders.

Next, consider a plot of the same 300 simulated cities without considering the size of the cities. The relationship between delay and death is reversed. Early implementations in this simulation are strongly associated with more deaths and later implementations are associated with fewer deaths. This obvious Paradox Caused by the causal relationship between city size, delays and COVID-19 mortality. A strong connection or association between two variables does not guarantee that one variable will cause another. Correlation does not mean causality..

Failure to properly address these relationships can be misleading and dramatically impact policymakers. In these simulations, an analysis that does not consider the size of the city leads to a false policy recommendation that the home order be delayed or never executed.

Gets more complicated

Of course, inferring causal relationships in real life is more complicated than computer simulations of only three variables.

In addition to confounding factors such as community size, substantial evidence suggests that public health interventions do not protect all people equally.

In San Francisco, there is a severe gap. For example, a comprehensive test in the Mission District shows that 95% of those who test positive Hispanic.. Socioeconomic status, race, ethnicity and many other factors Varies by community COVID-19 infection and mortality can be affected. Differences in locals make it more difficult to properly interpret comparisons such as San Francisco and New York.

So how do you effectively learn in your current environment?

This is a particularly pressing issue, but the analytical challenges of COVID-19 are not new. Public health professionals have long used data from nonrandomized studies, even in the midst of the epidemic. When the cholera occurred in London in 1849, John SnowWell-known in the epidemiological community, we used available data, simple tools, and careful consideration to identify water pumps as the cause of the epidemic of the disease. Evidence-based decisions require both data and appropriate methods to analyze the data.

Cities and communities around the world differ in important ways that can complicate public health research. Strict application of Causal reasoning method Taking into account population differences is necessary to guide policy and avoid misleading conclusions.

Laura B. Balzer is an associate professor of biostatistics and a director of the U Mass Causality Lab at the University of Massachusetts Amherst. Brian W. Whitcom is an Associate Professor of Epidemiology at the University of Massachusetts Amherst University.

This article has been reissued from conversation Under Creative Commons license. Read Original work..

Image: Reuters

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