27 July 2022 Blog Post: Population Prevalence and the 'Luck' of COVID Acquisition
For those interested in a contrarian COVID-19 approach, I recommend following Dr. Vinay Prasad of UCSF on Twitter (@VPrasadMDMPH). While his snap opinions seem to be written, at times, with the sole intention of being inflammatory, he does offer a competing perspective as a researcher in an academic setting.
Readers will be interested to know that despite his opinion that SARS-CoV-2 infection is ‘inevitable’ and unavoidable, Dr. Prasad himself has never had COVID-19. Something he attributes to ‘luck.’ In fact, the biggest predictor of acquiring SARS-CoV-2 its frequency in the population and Dr. Prasad has the good fortune of working in a city that has aggressively limited the spread of the virus, perhaps more.so than any other US metropolitan area.
Now forgive a slight aside, we will return to the dynamics of SARS-CoV-2 transmission. Luck is a very unsatisfying (and unscientific) explanation for any phenomenon. Imagine if Darwin upon observing the finches in the Galapagos simply decided that their unique evolutionary patterns was just ‘luck’. Or, if physicians rather than discussing preventive measures simply chalked heart disease risk up to luck.
In reality, the acquisition of any illness is multifactorial in nature. Take, for instance, breast cancer – the risk factors of which have been elucidated over decades of research. Approximately half of breast cancers can be explained by known risk factors, like reproductive factors. An additional 10 percent are associated with family history and genetics.
Delving deeper into these risk factors shows that breast cancer likelihood is influenced by age (older age groups are at higher risk), gender (women are at higher risk although there is male breast cancer), tall stature (increased height associated with increased risk), and estrogen levels (higher levels, higher risk). Now are these absolutes? Of course not. But they do all come together to inform risk.
A similar effect can be seen when it comes to the risk of acquiring a SARS-CoV-2 infection with the biggest predictor of risk being population prevalence. Prevalence is defined as the proportion of a population who have a specific characteristic (in this case SARS-CoV-2 infection) in a given time period. While it can be expressed as rate, in the graphs below we express prevalence as a proportion of 100 individuals (technically this is a ‘point prevalence’ for those keeping track at home).
The frequency of COVID-19 in LA County has varied tremendously throughout the pandemic (Figure 1 below), with two very obvious spikes in the Winters of 2020/2021 and 2021/2022 (Omicron). The small blip seen in August of 2021 is the Delta wave, tiny by comparison. But look at the right side of the graph, you see a rising prevalence beginning in May of this year.
If we focus in on the right side of the graph (Figure 2 below), we see that Omicron faded out by the end of February and the prevalence was very low until mid May. Rates have risen slowly but steadily and now we stand at about an 8% population prevalence – or, in other words, 8 of every 100 people have an active SARS-CoV-2 infection in the County.
In the same way that multiple factors can modify one’s likelihood of developing cancer, so is it true for SARS-CoV-2 infection. Occupation (healthcare workers, essential workers early on), mask usage, time and proximity to a known case all modify risk. Individuals with wider social circles (people who are more popular or have more friends) are also at higher risk. It makes sense in this context that Tom Hanks and two US Presidents have had the infection as stated simply, they interact with more people. Population prevalence itself may be the strongest predictor – and therein lies the ‘luck’ of Dr. Prasad. He works in San Francisco which has had the lowest COVID-19 case rates in the country. To date, 167,931 cases have been reported in San Francisco (population: 883,305 – a rate of 19011 cases per 100,000) as compared to 3,153,690 in Los Angeles (population: 10,004,000 – a rate of 31524 cases per 100,000). So simply working in a city which has aggressively sought to contain the spread of the virus might be sufficient to explain his ‘luck.’
𝗦𝗶𝗴𝗻 𝗨𝗽 𝗳𝗼𝗿 𝗢𝘂𝗿 𝗡𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿
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