Welcome to the February 2024 Newsletter for

Santa Monica Primary Care

This month’s newsletter arrives just under the end of the month wire, but we will use that to our advantage as we dive into the latest COVID prevalence estimates from Los Angeles County’s data as well as those from our own practice. The seasonal patterns to COVID now seem to be better defined based on information collated from 2022 and 2023 and can inform our activities moving forward. But before COVID, we will cover Lp(a), the ‘horrible cholesterol’ along with new clinical trial opportunities in its treatment. We conclude with the slow demise of the CDC’s 5 day COVID isolation guidelines.

Lp(a): The “Horrible” Cholesterol

Some of you, particularly our Medicare patients, may have noticed over the past year or so that Lp(a) has been added to your annual cholesterol panel (Note: I have not generally included this measure for commercial PPO patients as insurance coverage of the test has been spotty but it makes it no less important). Lp(a) is a subtype of LDL cholesterol (‘bad’ cholesterol) and higher concentrations of Lp(a) have a strong association with the future development of coronary artery disease. Interestingly, most racial/ethnic groups tend to have low Lp(a) levels with the notable exception of African Americans, Africans and individuals from India amongst whom Lp(a) levels
trend higher

Statistical footnote here: for most ethnic groups, Lp(a) levels show a “right skewed” distribution towards lower values as represented in the first Figure. Among African Americans, Africans and individuals from India, Lp(a) values show a more “normal” or Gaussian distribution but centered at a higher mean value.

Both the European Society of Cardiology and the American Heart Association recommend that Lp(a) be measured once in the lifetime, in particular to identify those with very high levels (>430 nmol/L with an upper limit of population normal being 75 nmol/L using LabCorp’s reference values). The American Heart Association notes that Lp(a) levels over 125 nmol/L function as a ‘risk enhancing’ factor for cardiovascular disease. The molar concentration provided by LabCorp (amongst others but not all labs) is a better predictor of cardiovascular disease events than are mass units
(mg/dL).

The once a lifetime measurement may be appropriate as Lp(a) levels stay fairly steady although there are age-related increases related to sex steroid deficiency, inflammation and decline in renal function. And, at least currently, there is no intervention that can specifically target Lp(a) levels. Instead we emphasize more traditional cardiovascular risk reduction techniques including diet/exercise, statins, Zetia (Ezetimibe) and the more recently approved injectable PCSK9 inhibitors. Statins raise Lp(a), Zetia has no effect and PCSK9 inhibitors reduce Lp(a) and likely
represent the current best treatment for high risk patients with elevated Lp(a)

Through our Cardiology colleagues, we do now have the ability to refer qualified patients to one of three clinical trials opening this Spring testing Lepodisiran, which is a once yearly injectable agent that lowers Lp(a) levels by 90%. It is not anticipated that this agent will reach the market for another 3 years, and these sorts of outcome trials are critical in understanding the relationship between Lp(a), familial hypercholesterolemia and risk of cardiovascular and cerebrovascular disease.

COVID-19: 2022 and 2023 Data from Santa Monica Primary Care

In the last two newsletters we have discussed the bimodal distribution of COVID cases that corresponds to two distinct seasonal peaks: one in summer and the second in November and December (Figure below). Inversely, we have relative lulls in the Spring and Fall; the latter interesting because the return to school has always been implicated as a primary driver of respiratory viruses. This does not seem to be the case in our practice (which admittedly has very few school aged patients)

Total Cases of COVID-19 by Month: Santa Monica Primary Care

So how do we estimate the impact of COVID infection now that we have a couple of years of data? From the graph above it is clear that 2023 was a less impactful year than 2022 in terms of overall caseload with 116 total infections (19% of the practice) reported in 2022 as compared to 239 (40% of the practice) in 2022. Converting this into more conventional epidemiologic units, our 2023 rate corresponds to 39833 cases per 100,000 population. For comparison, infection rates for other communicable diseases are listed below:

COVID-19: A Seasonal Illness with Two Peaks - 2022 and 2023 Data from Los Angeles County

Turning now to incidence data provided by the Los Angeles County Health Department, we see a similar pattern with a May-August peak followed by a second November/December peak. Note in the Figures below that I removed the first weeks of January as there was a massive peak in January 2022 that renders the rest of the graph unreadable. Similar to what was seen in our practice, infection rates for the County were higher in 2022 than in 2023, although this may correspond to underreporting and an increase in home testing.

Smoothed Daily Incidence Case Rate (per 100,000 population) of SARS-CoV2: Los Angeles County, California for 2022 (Blue) and 2023 (Red)

Given that Los Angeles County data relies on passive surveillance from laboratory testing and that they no longer accept home testing as a valid reporting tool, an estimate of prevalence rates are a better measure by which to estimate the county-wide impact of COVID-19 infections. The two seasonal peaks of COVID are even more clear when looking at prevalence data (again with 2022 having significantly higher rates than 2023).

Smoothed Daily Prevalnce Rate (Active Cases per 100 population) of SARS-CoV2: Los Angeles County, California for 2020 (Blue), 2021(Red), 2022(Yellow),and 2023(Green)

The cumulative prevalence of COVID-19 in 2023 for the County is actually higher than that of our practice in Santa Monica – an estimated 24% of County residents had a COVID infection in 2023 based on these data (19% for Santa Monica Primary Care).

COVID-19 Mortality Data: Los Angeles County

One of the unqualified wins regarding COVID infection is the significant improvement in mortality rates that we have seen throughout the pandemic. This is, no doubt, a conjoint effect of vaccination, early outpatient treatment with antivirals and improvement in protocols and supportive care for those hospitalized. The mortality rate from COVID peaked in the first half of January 2021 in Los Angeles County at 2.80 daily deaths per 100,00 population. The most recent rate is 0.007 daily deaths per 100,000 population (Figure below). That’s a 4000 fold improvement.

Log Transformed and Smothed Daily Mortality Rate (Per 100,000 population) of SARS-CoV2: Los Angeles County,

Unsurprisingly, mortality rates correlate strongly with case rates, reinforcing the adage “more cases, more deaths.” However, the trend lines are markedly different with greater separation between the two becoming apparent in mid-2022. While a number of factors have contributed to this observed improvement, it is important to note that Paxlovid came to market in December of 2021. Its more widespread use, particularly amongst those at highest risk, may be part of what is driving this effect (Figure below with cases in red and mortality in blue).

Smoothed Daily Mortality (Blue) and Incidence (Red) Rates (per 100,000 population) of SARS-CoV2: Los Angeles County,

No More 5 Day Isolation: New California and Upcoming CDC Guidelines

To very little fanfare, the CDC began discussions to loosen its COVID isolation
recommendations such that those who test positive for coronavirus no longer need to routinely stay home from work and school. This, ostensibly, is to align it with guidance for RSV, Influenza and other respiratory pathogens to reinforce our “showing up sick to work” approach. Interestingly, a recent study showed that 89% of Americans still show up to work when they feel unwell, with many (45%) doing so because they don’t want to use up sick days. Compounding the issue, two-thirds express “stress, guilt, fear or anxiety” when calling in sick and 80% of managers admit to being skeptical of such requests (link:
(link: https://www.bamboohr.com/resources/guides/sick-guilt-2023).

To summarize the absolute bind the CDC has gotten itself into, Dr. Osterholm, an Infectious Disease expert from the University of Minnesota, offers the following
spell-binding conclusion:

“Public health has to be realistic. In making recommendations to the public today,
we have to try to get the most out of what people are willing to do. […] You can be
absolutely right in the science and yet accomplish nothing because no one will
listen to you.”

[“Right in the science” is probably the most perplexing phrase in this very curious statement. But that can be a topic for another newsletter.]

Under the CDC’s new approach, people who test positive for COVID should now use clinical symptoms to determine when to end isolation – namely being fever free for 24 hours off of medication (fever is no longer a commonly reported manifestation of acute COVID infection but is one that can be quantified so often kept in guidelines) and having mild and improving symptoms. Fever, loss of taste/smell and difficulty breathing were more hallmarks of SARS-CoV-2 infection in an immunologically naive population. Now with widespread blended immunity (from community acquired infection and vaccination) we see mostly runny nose, cough and sore throat. For those living in California, any federal recommendations have little impact because on January 30th, the state already loosened isolation recommendations
(link: https://covid19.ca.gov/isolation/#guidelines)

Rather than being disingenuous about COVID isolation guidelines, the CDC and the California DHS would be more consistent by abandoning testing entirely. If a positive test won’t change your actions, then why test in the first place? Of course here I am being contrarian to illustrate a point, because there is inherent value in knowing what is making you ill and applying an appropriate treatment, particularly at high risk of severe disease. But, beyond such, the antigen test provides no formal public health benefit under current California guidelines.

However, the home rapid antigen test has a dual value insofar as it identifies infection as well as infectiousness. As long as a positive line is visible, one is still capable of spreading the infection and it takes little mental effort to ask the question – “if a coworker has a positive test and comes to a large meeting in a poorly ventilated conference room, do I really want to be in that meeting?”

Isolation, defined as the public health practice used to protect the public by preventing exposure to those that have a contagious disease, separates those who are sick and contagious from those who are not. It is distinct from quarantine which restricts the movement of those who have been exposed to the disease but may not be contagious. With the widespread availability of rapid antigen tests, there is little need for quarantine as we can identify who is infectious, but isolation still has value when we consider COVID infection which occurred amongst 24% of County residents in 2023.

My general advice follows a University of Chicago study looking at healthcare workers returning to work in early 2022 after acute COVID infection. In that study 58% of healthcare workers returning to work on Day #5 were still positive, but only 26% were positive on Days #8 and #9. This fits with out general experience here where I call Day #5 a ‘coin flip’ and recommend it as the first day of retesting. Most antigen testing converts to negative between Days #7 and #10 with 90-95% of patients testing negative by Day #10. Some small proportion do, however, continue to test positive and the longest sequence of positive testing I have seen was 16 days! As long as there is a positive rapid antigen test, one still runs the risk of spreading the illness. The faintness of the line is an additional barometer of relative infectiousness, so the darker the

line the more likely one is to spread the infection. Spread is influenced by viral load, proximity and duration of exposure so all of those can be taken into account as well in determining the relative risk to others of leaving isolation.

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