Sheldon H. Preskorn, MD

I believe readers will find this Special Report well worth their time. Each of the authors have produced commendable articles, which are succinct but also thorough within the constraints of the space allotted and well written. I will, therefore, highlight only a couple of issues.

First is the dilemma of what adjective to use in describing these effects: should they be called side effects, adverse effects, or just other effects? 

 was originally meant to imply an effect of the drug other than a therapeutic effect. For example, sedation was considered a side effect of antipsychotic drugs like quetiapine or trazodone. However, these drugs are commonly used to produce such effects when given at generally lower doses once-daily at night. Adverse effect was meant to imply an effect that the patient found undesirable, such as weight gain from 

 and some antipsychotics, but clinicians may use such drugs for some underweight patients to promote weight gain. Whether an effect is adverse or desired can be a function of the clinical situation. For example, selective serotonin reuptake inhibitors can cause delayed ejaculation, which may be adverse for many patients, but can be beneficial for patients with premature ejaculation. For these reasons, I prefer to call them other effects of the drug.

Introduction: Finding Solutions While Managing Problems

Addressing Obesity in Patients Taking Antipsychotics

Sedation: The Ups and Downs of a Side Effect

Sex, Drugs, and Psychosis: Reviewing Psychiatric Medications’ Taboo Side Effect


Robert Drury, Brendan King, Caleb Natale, Wayne Hellstrom, MD

When using a marketed drug for an effect that is not a labeled indication, it is called an off-label indication. It is important to recall: drugs are approved and indications are labeled or not labeled. When doing so, the clinician is using an approved drug for an off-label indication. The clinician bears the responsibility for using a drug in such a manner, whether it be based on published studies of efficacy that were never submitted to the US Food and Drug Administration (FDA) for labeling purposes, or based on the known pharmacology of the drug or clinical experience. Why might a drug be used for an off-label indication? The drug developer may not have thought that indication was commercially viable to perform studies and submit the data to the FDA for approval of that indication. In some instances, the drug is either too near or past its patent life before such a potential indication was considered.

Another issue is how to know whether a member of a therapeutic class (eg, an antidepressant) is more or less likely to produce a specific other effect (eg, weight gain). In addition to the likelihood of the effect occurring, there are a number of other factors to consider, such as whether the effect is transient or enduring, the severity if it is adverse, and the nature of the data upon which these conclusions are made. From this list of issues, readers will realize that these questions are complex and go beyond the scope of this introduction to this Special Report.

I am confident that the reader will benefit from reading this series. Perhaps more importantly, this will also benefit the patients they treat.

is professor in the Department of Psychiatry and Behavioral Sciences at the Kansas University School of Medicine-Wichita. Over his 42-year academic medical career he has worked with over 140 pharmaceutical, biotechnology, device, and diagnostics companies to bring their products to the market. He has been a principal investigator at the site level on all antipsychotic and antidepressants medications approved in the US over a 25-year period of time. He has received grants/research support from or has served as a consultant, on the advisory board, or on the speaker’s bureau for Alkermes, BioXcel, Eisai, Janssen, Lyndard, Otsuka, Sunovion, and Usona Institute. All clinical trial and study contracts were with and payments made to The University of Kansas Medical Center Research Institute, a research institute affiliated with The University of Kansas School of Medicine-Wichita.

Articles

Whether an effect is adverse or desired can be a function of the clinical situation.

Finding Solutions While Managing Problems

Patients with coronavirus disease 2019 (COVID-19) who are at high risk for serious illness are also at increased risk for drug-drug interactions (DDIs) that can have a negative impact on outcomes. In elucidating this issue, this article will discuss the following main points:

1. The reasons why patients at high risk for serious illness from COVID-19 are also at high risk for DDIs.

2. The extrapolation of results from pharmacoepidemiological studies to the population at risk for serious COVID-19 illness.

3. The mechanisms underlying DDIs including their frequency and their potential complexity, and how DDIs can present clinically.

4. The methods for preventing or mitigating DDIs.

5. An introduction to University of Liverpool drug interaction checker as a tool to reduce the risk of adverse DDIs when treating patients for COVID.

Why patients at high risk for serious COVID-19 illness are also at high risk for DDIs

Patients with multiple comorbid illnesses are likely to be taking multiple medications and seeing more than 1 prescriber for their health care needs. Obviously, the more medications a patient is taking, the more likely they are to experience a DDI, and the more likely that DDI will be complex, as illustrated by the case described in 

 This situation can lead to an increased probability of unplanned and adverse DDIs, which can contribute to or directly cause a poor outcome up to and including death.

There are no published studies looking at the number of medications taken by those at high-risk of a severe form of COVID-19. However, pharmacoepidemiological studies about the frequency and complexity of multiple medication use (MMU) in the United States and other industrialized countries have been published, and these data can be generalized to patients who are at high-risk for serious COVID-19 illness.

Findings from pharmacoepidemiological studies

A pharmacoepidemiological study by Preskorn et al

 examined MMU and the complexity of drug regimes in an adult outpatient population of United States veterans treated in Region 15 of the Veterans Affairs (VA) Integrated Service Network (VISN 15). The objective was to examine the extent and nature of MMU in relation to several factors, including patient age and number of prescribers treating the patient. The outcomes were the complexity and uniqueness of the drug regimens in relation to the numbers of different medications patients were taking. Medications counted in the study were limited to those that acted systemically or gastrointestinally (SG drugs) because these agents are of particular concern given their potential to interact with each other.

The drug regimens of 5003 patients were assessed, and the researchers found that a total of 394 different SG drugs were prescribed. Only 88 (22%) of these drugs were used in at least 1% of patients (ie, 50 or more patients). The remaining 306 drugs were used in fewer than 1% of the patients. Of the 5003 patients, 80% had current prescriptions for at least 2 SG drugs and 38% were receiving 5 or more drugs. 

There were 3819 different drug regimens used in this population, 3553 of which were used in only 1 patient. Thus, 71% of the patients were receiving a unique drug regimen (ie, only 1 patient in this population of 5003 individuals was taking that same total regimen of drugs regardless of dose or schedule). If dosing and scheduling were also considered, even more patients would have been considered unique with regard to their drug treatment. These results indicate that treatment for most of these patients was quite individualized—but based on what rationale and experience?

 illustrates the rate at which the prevalence of a specified drug combination decreases when the number of drugs in the combination increases. This figure shows the most common 4 drug combinationused in the population in this VA study. Of the 5003 patients, 26.5% received aspirin and 10.6% received furosemide, but only 4.2% received both aspirin and furosemide. Fewer than 2% of the patients took a combination of aspirin, furosemide, and digoxin (a third SG drug commonly used in this population). The percentage decreased further, to fewer than 1% of patients who were receiving a combination of those 3 SG drugs plus lisinopril, which was overall the second most commonly prescribed drug to this population of outpatients. Of the 28 patients taking this 4-drug combination, only 1 patient received only these 4 drugs, 2 others received the same regimen plus a fifth drug (simvastatin, another cardiovascular-related agent), and the remaining 25 were on unique drug regimens of 5 or more drugs.

The Slone survey, published by Kaufman et al4 in 2002, was also conducted in the United States. It obtained self-reported information about prescription, over the counter, and herbal drugs from adult outpatients aged 18 years and older. This general survey of 2590 adults found that 7% had taken 5 or more prescription medications in the previous week. Women aged 65 years and older were the highest consumers of medications. In this population, 94% took at least 1 medication, 57% took 5 or more medications, and 12% took 10 or more medications. Rates of prescription drug use were similarly high in men and women in the oldest age group.

The findings from the VISN 15 study were similar to those of pharmacoepidemiological studies done in Europe. A series of studies

 looked at reimbursement for prescription medications on an average day in Fünen County, Denmark in 1994.5 Bjerrum et al5 found that 8.7% (SD 0.2%) of the patients were taking to 2 to 4 drugs and 1.2% were taking 5 or more drugs. The proportion of patients receiving 5 or more medications increased with age up to 90 years, so that two-thirds of patients aged 70 years and older were receiving 5 or more medications.

 found that 26,337 elderly patients aged 70 years or older were receiving 21,293 different drug combinations and that the 10 most prevalent combinations were used in only 2.7% of the patients. In the third study, Bjerrum et al7examined drug combinations in 5443 patients aged 16 years or older who were receiving 5 drugs. They found a total of 3890 different drug combinations, of which the 10 most prevalent combinations occurred in only 3% of patients.

A pharmacoepidemiology study by Valent et al

 assessed the prevalence of MMU in the general population of Udine, Italy, during 2017. The results of this study were comparable to those of the VISN 15 study: 63.7% of the general population were prescribed at least 1 medication during the year. Compared with the rest of the adult population, MMU was more common among the elderly, especially those aged 80 years or older, with 31.7% of those aged 65 years or older prescribed 5 or more medications at least once during the study year.

Taken together, these studies demonstrate that the frequency and complexity of MMU are similar across different countries. 

 The fact that there are numerous medications available to treat any particular condition (ie, drug class) and that there are multiple options within each of most specific pharmacological classes of drugs (eg, multiple beta blockers and serotonin reuptake inhibitors) helps explain the multitude of different medication combinations that may be taken by an elderly patient or a patient with multiple comorbid conditions. Based on these studies, many patients are receiving a unique drug regimen, where “unique” refers to the total specific drug entities a patient is receiving, regardless of dose, formulation or administration schedule. In other words, no other patient in the population sampled will be taking the same regimen. That, in turn, means that no single prescriber is likely to have extensive clinical experience with even a small fraction of the multiple total drug regimens their patients are taking.

A DDI, by definition, occurs when the presence of a co-prescribed drug (the perpetrator) alters the nature, magnitude, or duration of effect of a given dose of another drug (the victim).

To understand DDIs, it is important to know how medications act, because their action determines whether and how they will interact pharmacodynamically with other medications. It is also important to understand their pharmacokinetics (ie, the mechanisms underlying their absorption into the body, distribution throughout the body, their metabolism, and their elimination from the body), because that is the second way that drugs can interact. As previously noted, medications that act systemically or gastrointestinally are of most concern because they have the greatest potential to interact. Therefore, identifying frequently used combinations of SG medications is the first step in addressing potentially hazardous combinations.

Drugs can interact in 2 ways: pharmacodynamically and/or pharmacokinetically. The word “pharmacodynamics” refers to the action of a drug on a specific site, and the biochemical and physiological effect it produces via that site of action. It is the body’s response to the drug. The word “pharmacokinetics” refers to the movement of a drug through the body: its absorption into the body from the site of administration (usually oral) into the central compartment (ie, blood), its distribution from the central compartment to peripheral compartments (ie, various organs including the brain), its metabolism (ie, its movement through the pathways of biotransformation), and finally its excretion from the body (usually via the kidneys).

DDIs can be therapeutic or adverse; they can be planned or unplanned. A planned DDI occurs when the prescriber knows that the addition of a medication to the existing treatment regime (ie, a combination regime) will enhance the efficacy or tolerability of both. An example of a planned DDI is adding an adjunctive medication such as bupropion to an SSRI to boost the antidepressant effect experienced by a patient.In other words, these DDIs are intentionally used to benefit patients.

To understand and avoid untoward DDIs, it is important to be familiar with equation 1 and equation 2 

 The 3 variables in equation 1 determine the effect a drug will produce in a patient.First, the drug must work on a site of action that is capable of producing the effect observed. The site of action for most drugs is a receptor, transporter (uptake pump), enzyme, or ion channel. By binding to the target(s), the drug can alter the target’s functional status and thus alter human physiology. The second variable is the drug’s pharmacokinetics, which is the ability of the drug to move through the body (ie its absorption, distribution, metabolism, and eventual elimination from the body). The third variable involves the interindividual differences among patients, which can shift the dose-response curve, making patients either more or less sensitive to the effect of the drug.

 illustrates that drug concentration is a function of the dosing rate a patient is taking, rather than what has been prescribed, in relation to their ability to clear the drug.

 The time course of potential DDIs varies depending on how long the drug(s) (or their effects) persist in the body. Hence, the potential for an interaction may last for days to even months after a medication has been discontinued. For example, a medication like fluoxetine can remain in the system for many weeks. Specifically, it can last 5 weeks or more depending on the half-life of the parent drug and its essentially equally active major metabolite (norfluoxetine) in a given patient.

A drug may also have induced a change in the body that may persist for weeks after the drug has been discontinued. Examples of such drugs are those that induce (turn on the promoter genes for) cytochrome P450 drug metabolizing enzymes (eg, carbamazepine) or drugs that covalently bind to and deactivate an enzyme (eg, most monoamine oxidase inhibitors).

Hence, it is important to elicit a detailed medication history, not only of the medications the patient is currently taking but also of medications the patient has taken in the past few months.

? DDIs can present in a myriad of ways ranging from common everyday problems (which are more common but more easily missed outcomes) to catastrophic adverse effects (which are much rarer but also more easily detectable outcomes). Catastrophic adverse events due to DDIs can include sudden death, seizures, cardiac rhythm disturbances, serotonin syndrome, malignant hypertension, neuroleptic malignant syndrome, and delirium to name a few. In terms of common everyday problems, DDIs can make a patient appear to have poor tolerability for a medication(s) or to be nonresponsive (ie, lack of efficacy) to the medication(s). They may also cause symptoms that mimic the worsening of a preexisting illness or the emergence of a new illness.

The pitfall with worsening of symptoms is that it may lead the prescriber to stop a potentially efficacious treatment due to the erroneous belief that the medication that has been added is itself causing the adverse effect, rather than identifying the DDI which is the true culprit leading to the untoward outcome. If the potential for a DDI is recognized, then the prescriber can make whatever adjustments are needed (eg, lowering the dose or switching to a different agent within a class of medications, such as changing from 1 SSRI to another that does not inhibit a specific cytochrome P450 enzyme) so that the patient can benefit from the efficacy of the medication without having the adverse effect(s). Alternatively, the emergence of new symptoms as a result of a DDI may lead to a misdiagnosis, which may result in the addition of new medications to treat the apparent worsening of an existing illness or the apparent emergence of a new illness.

Example of a patient receiving multiple medications

 shows an example of a 4-medication regimen that a patient was receiving from 4 different prescribers. This patient was one of the 5003 in the Preskorn et al VISN 15 study

 previously described. This patient was taking codeine for pain, erythromycin for an infection, paroxetine for depression, and metoprolol for hypertension. Parenthetically, the pharmacoepidemiological studies reviewed in this paper documented that patients receiving such multiple medication regimes are common in clinical practice. The clinically relevant questions are: Do 2 or more of these drugs interact? If so, how does that interaction present and what might 1 or more of the 4 prescribers do in reaction to that presentation?

 illustrates the known potential interactions among the 4 medications this patient was taking. Codeine is an inactive prodrug that must be converted by CYP 2D6 to morphine to produce analgesia. Metoprolol is a beta-blocker whose clearance is principally dependent on CYP 2D6 mediated biotransformation to polar metabolites that can subsequently be eliminated via the kidneys. Paroxetine substantially to completely inhibits CYP 2D6 at the usual dose needed to produce an antidepressant response. While paroxetine is metabolized by CYP 2D6 at low concentrations, it saturates this enzyme under usual dosing conditions. At higher concentrations, paroxetine is likely dependent on CYP 3A mediated biotransformation for its elimination. CYP 3A is substantially inhibited by erythromycin under usual dosing conditions. The inhibition of CYP 3A by erythromycin will produce an increased accumulation of paroxetine, which in turn would produce more inhibition of CYP 2D6, which in turn would lead to less conversion of codeine to morphine and more accumulation of metoprolol. This is an example of a complex or multiple DDI. Such DDIs can cause many adverse effects.

 also illustrates the complex and hidden way in which such a DDI can present. Due to the inability to convert codeine to morphine, the patient will have less than optimal pain control, which may be construed as the lack of efficacy of the codeine, opiate-seeking behavior by the patient, and/or worsening depression. The increased accumulation of metoprolol can cause hypotension and the patient may subjectively complain of increased fatigue, which can again be misconstrued as a worsening of depression. The increased accumulation of paroxetine can cause more insomnia, decreased emotional reactivity, and decreased libido, which can also look like worsening of depression. If the prescriber observes this increase in depression-like symptoms and concludes that the depressive episode is worsening, they may increase the dose of paroxetine, further worsening the problem. This scenario illustrates the hidden way in which a DDI can present and how it can perhaps lead the clinician (and their patient) dangerously astray.

Preventing or mitigating DDIs in patients with COVID-19

Why it is important to consider DDIs in those diagnosed with COVID-19.

 The discussion of multiple medications and DDIs presented in the preceding section is directly applicable to patients who are at high risk for having serious COVID-19 illness, because these individuals are likely be taking multiple medications and unique combinations of those medications. On top of their regular medication regimen, these patients will likely be prescribed additional medications to treat their COVID-19 illness, which will further increase the risk of experiencing DDIs. Therefore, it is important for prescribers to be aware of and understand the implications of: (1) the extent and complexity of multiple medication use, (2) how common it is for patients to be on unique medication regimens, and (3) how to help prevent immediate and long-term adverse effects, unexpected outcomes, and even mortality.

In addition, many of the emerging treatments for COVID-19 have the potential to interact with all medications the patient is taking. If the patient is in the intensive care unit, then the treating physician may simplify the medication regimen by stopping some or even all of the previous medications. If that approach is taken, the important questions are: Which medications can be abruptly and safely stopped and why? Which medications may need to be tapered to avoid withdrawal or rebound phenomena? The answer to these questions depends on the specific agent and the indication.

When patients with COVID-19 are hospitalized but are not in the intensive care unit, their usual medications may be continued as these patients may require more supportive treatment (eg, intravenous fluids) rather than the addition of specific anti-COVID-19 medications. Determination would be made on a case-by-case basis considering both the status of the patient and their medication list.

The challenge for prescribers and health care professionals in general is the rapid expansion of our knowledge about COVID-19 and its treatment, as illustrated by the exponential growth in clinical trials and clinical experience with this illness. For example, at the end of August 2020, the US Food and Drug Administration 

the indication for remdesivir to all patients hospitalized with COVID-19.

 When considering the wider use of this drug, it is important to note that it has the potential to interact with other drugs. While the usefulness of chloroquine phosphate or hydroxychloroquine sulfate remains uncertain, for example, some providers may consider using it in combination with remdesivir. If so, the prescriber should be aware of the potential for interaction between these 2 treatments. The fact sheet concerning remdesivir issued as part of the Emergency Use Authorization from the FDA

 that “In vitro, remdesivir is a substrate for drug metabolizing enzymes CYP2C8, CYP2D6, and CYP3A4, and is a substrate for Organic Anion Transporting Polypeptides 1B1 (OATP1B1) and P-glycoprotein (P-gp) transporters. In vitro, remdesivir is an inhibitor of CYP3A4, OATP1B1, OATP1B3, BSEP, MRP4, and NTCP.” With regard to DDIs, the fact sheet states “Drug-drug interaction trials of Veklury (remdesivir) and other concomitant medications have not been conducted in humans. Due to antagonism observed in vitro, concomitant use of Veklury with chloroquine phosphate or hydroxychloroquine sulfate is not recommended.”

Caveats about specific psychiatric medications. 

Caution should be considered with fluoxetine and long-acting depot antipsychotics when treating patients who have COVID-19 and are on multiple medications , because of the long time they persist in the body even after their use is discontinued. Certain anticonvulsants such as carbamazepine also should be a red flag because of their ability to induce specific CYP enzymes such as CYP 3A, with the inducted state persisting for at least a few weeks after the inducer has been stopped. For the same reason, it is important to determine if these types of medications had recently been discontinued before patients were hospitalized, because their effects may persist for days to weeks after the drugs are discontinued.

If some of the patient’s usual medications cannot be stopped because of potential adverse health consequences or because of the potential for clinically meaningful withdrawal symptoms, then the physician or health care provider has to consider how these medications can interact with those being added to treat the COVID-19 illness. In this regard, the University of Liverpool provides access to a tool to minimize DDIs when prescribing medications for COVID-19. This is a free drug-interaction checker that prescribers can access at 

 This website lists all of the medications currently being used to treat COVID-19 and potential DDIs with other medications. It is updated regularly as new treatment regimens for COVID-19 emerge. The checker is broken down in a table format showing interactions according to drug class.

This article has highlighted numerous important clinical issues regarding DDIs. First, clinicians must recognize the frequency and complexity of MMU, particularly in older patients and those with high levels of comorbid illnesses. The article also discussed how and why drugs may interact and how such interactions can present clinically. This information is important for patients with serious forms of COVID-19 because they are a population likely to be receiving MMU before they become ill with COVID-19 and then are likely be treated with additional medicines for COVID-19 illness. Factors to consider and general actions that can be taken to prevent or mitigate untoward DDIs have been discussed. The University of Liverpool website and tool was discussed so that readers can better understand how COVID-19 treatments may interact with other medications. Comments have also been provided concerning issues related to specific psychiatric medications and treatments currently associated with COVID-19.

This article was originally published in the 

and has been adapted here with permission from Lippincott Williams & Wilkins. The original version can be found online at 

is professor in the department of Psychiatry and Behavioral Sciences. The author notes that he has received grants/research support from or has served as a consultant, on the advisory board, or on the speaker’s bureau for Alkermes, BioXcel, Eisai, Janssen, National Institute of Mental Health, Sunovion, and Usona Institute. All clinical trial and study contracts were with and payments made to The University of Kansas Medical Center Research Institute, a research institute affiliated with The University of Kansas School of Medicine-Wichita. 

is a senior and immediate past chief resident psychiatrist in the department of psychiatry at the University of Kansas School of Medicine in Wichita, KS.

1. Preskorn SH, Silkey B. Multiple medications, multiple considerations. 

2. Preskorn SH, Silkey B, Shah R, et al. Complexity of medication use in the Veterans Affairs Healthcare System: Part I: Outpatient use in relation to age and number of prescribers. 

3. Silkey B, Preskorn SH, Golbeck A, et al. Complexity of medication use in the Veterans Affairs healthcare system: Part II. Antidepressant use among younger and older outpatients. 

4. Kaufman DW, Kelly JP, Rosenberg L, et al. Recent patterns of medication use in the ambulatory adult population of the United States: The Slone Survey. 

5. Bjerrum L, Rosholm JU, Hallas J, et al. Methods for estimating the occurrence of polypharmacy by means of a prescription database. 

6. Rosholm J-U, Bjerrum L, Hallas J, et al. Polypharmacy and the risk of drug-drug interactions among Danish elderly: a prescription database study. 

7. Bjerrum L, Søgaard J, Hallas J, et al. Polypharmacy: correlations with sex, age, and drug regimen: a prescription database study. 

8. Valent F, Polypharmacy in the general population of a Northern Italian area: analysis of administrative data. Ann 1st Super Sanita. 2019;55:233-239.

9. Preskorn SH. Drug-Drug Interactions With An Emphasis On Psychiatric Medications. Professional Communications; 2018.

10. U.S. Food and Drug Administration (FDA). COVID-19 Update: FDA Broadens Emergency Use Authorization for Veklury (remdesivir) to Include All Hospitalized Patients for Treatment of COVID-19. Silver Spring, MD: FDA Press Release; August 28, 2020. Accessed October 13, 2020. 

https://www.fda.gov/news-events/press-announcements/covid-19-update-fda-broadens-emergency-use-authorization-veklury-remdesivir-include-all-hospitalized

11. Fact sheet for health care providers: emergency use authorization (EUA) of Veklury® (remdesivir). Foster City, CA: Gilead Sciences; August 2020. 

Accessed October 13, 2020. https://www.gilead.com/-/media/files/pdfs/remdesivir/eua-fact-sheet-for-hcps.pdf

12. Pinkowski J. Drug-drug interactions could imperil COVID-19 treatment. Medscape May 10, 2020. Accessed October 13, 2020. 

https://www.medscape.com/viewarticle/930265

13. University of Liverpool. Interaction Checker: COVID-19 Drug Interactions. University of Liverpool; 2020. Accessed October 13, 2020. 

Drug-drug interactions are complicating COVID-19 treatment. Read what you need to know to keep yourself and your patients safe before and during COVID infection. 

Why Are Patients With COVID-19 at Risk for Drug-Drug Interactions?

 written to help psychiatrists and other health care providers better understand the range of severity of illness that the coronavirus disease 2019 (COVID-19) virus can cause so that they can better explain the facts (as we know them today) to their patients and their families.

The mortality rate reported for COVID-19 has declined since the earliest days of the pandemic. This is partly due to better knowledge about the infection and better protocols that have been developed to treat the more serious forms of the illness. However, arguably the greatest decline in mortality rates is the result of being better able to identify who has been infected with the virus. This increase in identification of infections has increased the size of the denominator, while the numerator has remained relatively unchanged.

Equation 1. Calculation of mortality rate

“Cases” refers to individuals who have been infected with the Sudden Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2).

The calculation of the mortality rate is illustrated in 

, cases of COVID-19 initially were determined by the number of individuals who sought medical attention because of fever, cough, respiratory distress, or other flu-like symptoms and had no other potential causes for these symptoms including not having the seasonal influenza.

 Thus, it was a symptomatic diagnosis, just as it the case with most psychiatric diagnoses. Using this definition of a case, the denominator was smaller than the actual number infected, with the degree of underestimation being a function of how many individuals were infected but either were asymptomatic, did not have sufficient symptoms to seek medical attention, or for some other reason did not seek out care. The degree of underestimation would result in proportional overestimation of the mortality rate based on the 

Case example: A floating, unintentional laboratory experiment

Cruise ships can function as naturalistic research experiments with more controls than is true in clinical practice. The following summary, based on the original report published by 

, details such an example. In May 2020, a trio of Australian physicians and health researchers who traveled on an expeditionary cruise to the Antarctic Peninsula published a report about their experiences in the medical journal 

. Two of the authors, Alvin J. Ing, MBBS (Hons 1 Syd), MD FRACP, and Christine Cocks, were passengers on the cruise, and Jeffrey Green, MD, was a ship physician for this expedition.

In mid-March, the ship departed Ushuaia, Argentina, for a planned 21-day cruise of the Antarctic Peninsula to explore a route similar to the one taken by British explorer Ernest Shackleton in 1915-1917.

No one was allowed on board who had transited through then known hot spots (eg, China, Macau, Hong Kong, Taiwan, Japan, South Korea, or Iran) in the 3 weeks preceding the cruise.

 = 217) were screened for COVID-19 symptoms and body temperature as a condition of boarding.

Multiple hand hygiene stations were positioned throughout the ship, especially in the dining area.

Everyone had daily temperatures taken, which were reviewed by the ship’s 2 physicians.

The first recorded fever occurred in a passenger on day 8.

Isolation protocols were immediately initiated after this discovery. All passengers were confined to their cabins and all were issued surgical masks.

Full personal protective equipment was used for contact with any febrile patients, and N95 masks were worn for any contact between the ship staff and passengers.

By day 14, 5 additional passengers and 4 crew members developed fever, mild cough, and lethargy.

On day 17, one of these passengers, a 68-year-old man who had been a lifelong nonsmoker and had no comorbid medical conditions, deteriorated and required urgent evacuation to a hospital.

Subsequently, 7 additional passengers required medical evacuation: a 70-year-old woman with chronic obstructive pulmonary disease on day 20; a 65-year-old woman with no known comorbid conditions and 2 crew members on day 21; a 68-year-old male (after 15 days of cabin isolation) on day 24, and one of the two ship physicians on day 27.

All the evacuated patients tested positive using reverse transcription polymerase chain reaction (RT-PCR) tests to detect SARS CoV-2 RNA in respiratory specimens.

On Day 20, all 217 individuals onboard were tested with RT-PCR;

128 (59%) tested positive for SARS CoV-2. Of those that tested negative for SARS CoV-19, some were later found to have SARS CoV2 antibodies indicating prior infection.

Of the individuals who tested positive, 104 individuals (81%) were asymptomatic at the time of testing. The other individuals who tested positive (about 1 out of 5) were symptomatic at the time of testing. Of those 24 symptomatic individuals, 16 (two-thirds of the group) had only fever and mild symptoms, while 8 (one-third of the group) had to be medically evacuated. Four of those individuals required intubation and ventilation, and 1 of them died (mortality rate = 0.8% based on 1/128). He was a 50-year-old crew member with no known comorbid medical illness (personal communication, Ing 2020).

: When a population is small (as was true for this cruise ship with about 100 positive individuals), the confidence interval for a rare adverse event (ie, death, which was < 1 out of 100) is large. In such instances, a general rule for estimating the 95% confidence interval is that the upper and lower boundaries would be 3 times higher and 3 times lower, respectively, compared to what was observed. Hence, the estimate of the 95% confidence interval for the mortality rate in this case would be between 2.4% and 0.27% (ie, 0.8% multiplied by and divided by 3, respectively).

Difference in mortality rates based on symptoms versus on the total infected

In the example of the cruise ship, the mortality rate would have been calculated as 4% when the presence of symptoms was required to define a case (ie, 1 death among 24 symptomatic individuals) versus 0.8% when laboratory evidence of infection was sufficient to define a case (1 death among 128 infected cases). The result is a 5-fold difference in the calculated mortality rate depending on whether it was calculated based on symptomatic cases versus all infected cases.

The difference in practice would likely be even greater because mildly symptomatic cases may be missed in clinical practice because asymptomatic patients may not present for medical attention or because their symptoms might be attributed to a common cold or a mild flu-like syndrome. The number of infected individuals on the ship may also have been underestimated, since all 89 individuals who tested negative for actively shedding virus did not have antibody testing. It is possible that some of those who tested negative for actively shedding the virus may have been infected but the virus had already been cleared via an immune response before testing.

While the availability of reliable and valid laboratory tests is a great advance over the early symptomatic-based diagnosis (readers may want to think about the implications for symptomatic diagnosis in psychiatry), the tests can only determine if an individual is actively shedding virus. Obviously, a negative test does not tell us whether the individual will become infected in the future (even within an hour of having the test done) nor does it tell us if the individual has already been infected and has mounted a sufficient immune response to have eradicated the virus. (The first column in this series1 discussed this issue in more detail, along with the need for other reliable and valid tests to identify a history of an eradicated infection that will hopefully convey immunity against the virus—perhaps lifelong as with measles or perhaps much shorter as with seasonal influenza.)

The number of participants in this naturalistic experiment was small, which means that the 95% confidence interval for a mortality rate of 0.8% would be large as previously discussed. However, this caveat does not change the basic observation that the initial mortality rate, by virtue of being based on symptomatic cases, would be several times higher than the true mortality rate with a virus such as SARS CoV-2 because it can produce many mild and even asymptomatic cases.

The mortality rate observed on this ship is consistent with rates observed in the low-risk population discussed in the 

 This population has a low risk of developing an illness that requires hospitalization (

) and a low risk of death (below 1% but nevertheless not zero; 

) from infection with the SARS CoV-2 virus. In contrast, nearly 35% of those who die will be 85 years of age or older. The individuals younger than 85 who die will most likely have comorbid medical illnesses (

For these reasons, it is extremely important to protect high-risk populations via social distancing, the use of masks, and limited contact with anyone nonessential to the health and well-being of these individuals. The experience from this cruise ship also illustrates 2 important factors to consider when endeavoring to protect the vulnerable population. First, there is long incubation period between infection and the development of symptoms (8 days or more). Second, a high number of individuals who are infected are not symptomatic (more than 80% in the case of the experience on this ship).

The media can inform the population, but they can also hype topics to increase audience ratings to drive their revenue as has clearly been the case with COVID-19 reporting (in the author’s opinion). The most recent, egregious example is the focus on increasing numbers of cases, which is directly relevant to the discussion in this series of columns. What the media has not adequately explained (in the author’s opinion) is the nuanced understanding that a rising number of cases may not necessarily be bad but rather could be a positive development—if the cases occur in the low-risk and not the high-risk population and morbidity and mortality remain low. As explained in the second column in this series

 a higher number of low-risk cases could bring us closer to the number needed to achieve community (ie, “herd”) immunity.

Achieving such immunity is an important consideration because a reliable, safe, and effective vaccine has not been developed.While there is hope that a safe and effective vaccine may be available by late this year or early next year, the historical track record suggests it may be years before one is successfully produced for the world population. The reasons for caution relative to vaccines involve many facets, and a full discussion of this issue is beyond the scope of this column. One reason for caution is that vaccines may promote an antibody response that may alone be sufficient to prevent the illness, but it may be that a T cell (lymphocyte) response is also necessary to prevent the illness and that such a response may not occur with some vaccines. In addition, people have to be willing to receive the vaccine. Meanwhile, a recent 

found that 49% of Americans reported that they would be agreeable to vaccination against COVID-19 if such a vaccine becomes available, but 31% were not sure and 20% said they would not be vaccinated.

 Of course, these responses may change as the public becomes more familiar with the illness and more is known about the efficacy and safety of any vaccine.

With that said, there are several important concerns about a strategy of letting the virus propagate through the low-risk population while protecting the vulnerable population. First is the question of how feasible it will be to accomplish this dual goal. For example, grandparents want to see their grandchildren, but the grandchildren may be asymptomatic even though they are shedding the virus. Protecting the vulnerable populations will also be difficult if the current problem of compliance with wearing masks to decrease the spread of the virus in the United States continues.

Second, a small percentage of the low-risk population may be at risk for serious infections, and even death, for reasons that have not yet been identified, or at least not in the public domain. The mortality rate in what should be low-risk individuals is perhaps between 0.01% and 0.1% (

). However, the problem is that those numbers are so small that the 95% confidence intervals will be large, as previously discussed, so that it is difficult to calculate the rate with certainty.

While a difference between 0.01% and 0.1% may seem small, the issue is what such numbers mean in large populations. For example, a major issue is how to re-open schools. There are 72.7 million children 0 to 17 years of age in the United States (www.childstats.gov/americaschilrden/tables/tables/pop1/asp). If the mortality rate in this population is within the noted range, that would translate to between 7270 and 72,700 deaths of children 0 to 17 years of age (ie, the difference between a risk of 0.01% versus 0.1%).

Given these estimates, one must ask: What risk is acceptable (and to whom) to achieve community immunity and save more individuals in the higher risk groups? Or, should that even be a consideration? How does one balance risk when it comes to lives of young healthy individuals versus older and/or unhealthy individuals? These are the type of calculated decisions that Americans have not faced since perhaps World War II. Parenthetically, this discussion illustrates how important it is to know the mortality rate and provides a quite crude example of the type of mathematical modelling of a pandemic that is driving governmental decisions. Currently, it appears that every citizen is free to make these decisions (eg, to wear a mask or not) based on their understanding of the illness and their sense of community responsibility (because wearing a mask protects others more than the life of the person wearing the mask).

Third, little is known about whether and how long immunity lasts after a person has been infected and recovered (again highlighting the problems posed by a novel human pathogenic virus). The assumption is that immunity has occurred with recovery but even that remains to be proven. Beyond that, there is the potential problem that the virus may be able to mutate to overcome immunity. It should be kept in mind that all these caveats also apply to the development of a vaccine. Life is not always straightforward.

Finally, there is little known about whether there are after-effects of viral infection, such as shingles with herpes zoster. What has been established is that the virus can cause damage to other organs beyond the lungs in some (probably small) fraction of those infected. The virus attacks the circulatory system and, hence, can affect any organ in the body even though it probably does so relatively rarely.

The ship’s experience illustrates how difficult it is to stop the spread of the SARS CoV-2 virus unless social distancing and masks are used, especially since people can be are infectious long before they become symptomatic. In the case of the Antarctica cruise ship, SARS CoV-2 spread to the majority of the passengers and crew, even though the passengers were sheltering in their cabins and wearing masks as soon as the first individual developed symptoms.

and has been adapted here with permission from Lippincott Williams & Wilkins. The original version can be found online at www.psychiatricpractice.com.

s professor in the department of Psychiatry and Behavioral Sciences. The author notes that he has received grants/research support from or has served as a consultant, on the advisory board, or on the speaker’s bureau for Alkermes, BioXcel, Eisai, Janssen, National Institute of Mental Health, Sunovion, and Usona Institute. All clinical trial and study contracts were with and payments made to The University of Kansas Medical Center Research Institute, a research institute affiliated with The University of Kansas School of Medicine-Wichita.

1. Preskorn SH. Coronavirus disease 2019: The first wave and beyond. 

April 28, 2020. Accessed August 3, 2020. 

https://www.psychiatrictimes.com/view/coronavirus-disease-2019-first-wave-and-beyond

COVID-19: Protecting the Vulnerable and Opening the Economy

3. Ing AJ, Cocks C, Green JP. COVID-19: in the footsteps of Ernest Shackleton. Thorax. 2020;75:693-694. May 27, 2020. Accessed August 3, 2020. 

https://thorax.bmj.com/content/thoraxjnl/75/8/693.full.pdf

4. Neergaard L, Fingerhut H. AP-NORC poll: Half of Americans would get a COVID-19 vaccine. ABC News. May 27, 2020. Accessed August 3, 2020.

https://abcnews.go.com/Health/wireStory/ap-norc-poll-half-americans-covid-19-vaccine-70897024

5. Gupta A. Madhavan MV, Sehgal K, et al. Extrapulmonary manifestations of COVID-19. 

 2020;26:1017-1032. Accessed August 3, 2020. 

https://www.nature.com/articles/s41591-020-0968-3.pd

New data and an intriguing case example suggest the mortality rate may be decreasing. What does this mean for the days ahead? 

COVID-19: Has the Mortality Rate Declined?

What data are currently available about COVID-19 and how can they inform decisions? How successful are the strategies employed to date? How can we minimize damage from the pandemic? In this video, we learn about those concepts and more.

Coronavirus Disease 2019: The First Wave and Beyond

Dr Preskorn is Professor in the Department of Psychiatry and Behavioral Sciences at the University of Kansas School of Medicine-Wichita. The author notes that he has received grants/research support from or has served as a consultant on the advisory board or speaker’s bureau for Alkermes, BioXcel, Eisai, Janssen, National Institute of Mental Health, Sunovion, and Usona Institute. All clinical trial and study contracts were with and payments made to The University of Kansas Medical Center Research Institute, a research institute affiliated with The University of Kansas School of Medicine-Wichita.

Videos

This video presents Information that may help you and your patients deal with the pandemic.

Addressing Critical Questions About COVID-19

 can infect virtually everyone in the human population, based on data from China, the US, and Europe, only about 5% are susceptible to severe infection requiring admission to an intensive care unit (ICU) and/or causing a fatal outcome; this vulnerable population is identified by medical comorbidity and/or age. While 5% may initially seem like a small number, it nevertheless represents potentially 16.5 million people given a US population of 330 million. That is a tremendous number of people requiring ICU admission, potential deaths, and can easily overwhelm the US health care system, especially in hotspots. Nonetheless, it is important to consider the data for at-risk populations as part of mitigation strategies in attempts to re-open the country and the US economy.

This piece was written with the hope of helping mental health care practitioners understand the issues so that they can better explain the complexities of the current crisis to their patients. It is a personal synthesis of the implications of the extant literature in the public domain and to share thoughts on minimizing COVID-19’s damage. The piece will aim to address the following questions:

What are the 3 aspects of the race to minimize damage?

What data are currently available and how can they inform decisions?

How successful are the strategies employed to date?

Might risk stratification be a viable strategy to minimize the damage: impact of the illness and economic damage (which also can devastate and cost lives)?

 is caused by the severe acute respiratory syndrome corona virus-2 (SARS CoV-2).

 This virus, which jumped from an animal species (possibly a bat or an anteater) to humans in China in December 2019, causes a respiratory illness in humans that ranges in severity from no symptoms to cold-like symptoms to a severe, even fatal, respiratory illness.

 because of its recent emergence as a human pathogen. Due to its novel nature, humans had little to no immunity when it emerged as a human pathogen, and data were not available about the severity of the illness, the percentage of the population that could be affected, and whether risk factors could be identified for its severest forms. The concern was heightened by the fact that since 2002, 2 other novel coronaviruses (the SARS-CoV-1 and the Middle East respiratory syndrome [MERS] viruses) jumped from animals to man and caused serious, even highly fatal, illnesses. As it turned out, these earlier viruses had much higher fatality rates than COVID-19 infections: 10% for SARS-CoV-1 and 34% for MERS versus 2% for COVID-19’s SARS-CoV-2  and < 0.1% for seasonal flu.

However, it is important to keep a caveat in mind for the SARS-CoV-2 death rate. It is a general number for the whole population, but it varies widely based on subpopulations, as it is less than 1% in adults who are not of advanced age (ie, > 85 years) and those without certain specific serious comorbid medical conditions. When these two groups are excluded, most people who become infected with the SARS-CoV-2 virus have minimal to mild-moderate symptoms that do not require hospitalization. Thus, readers should keep in mind that COVID-19 fatality rate estimates are influenced by at least 2 factors: estimated rates will be higher based on the percentage of the population that has the risk factors of comorbid medical illness and/or is ≥ 85 years of age (eg, a nursing home populations versus the general population) and estimated rates may be substantially lower once sufficient results of antibody testing are available to determine the percentage of the population who have been asymptomatic or mildly symptomatic and have developed an antibody response.

Suffice it to say that COVID-19 did overwhelm the health care system in many countries and some areas of the US. Since there were no scientifically proven effective and safe treatments nor a vaccine in the earlier phases of this pandemic-and that those limitations continue as of  the writing of this article (April 20th), the response has been to try to limit the spread of the virus by good hygiene practices and social distancing. This has included orders to shelter in place, quarantining exposed individuals, and isolating infected people. This approach has resulted in shutdowns of major parts of the economy and sudden high unemployment, which has its own negative consequences.

The 3 components of the race are the pieces needed to address the previously noted unknown factors. We have needed and continue to need more knowledge about: 1- the nature of the disease and its treatment or prevention; 2 - how to best safeguard public health and avoid overwhelming the health care system; and 3- how to minimize the societal damage caused by the substantial disruption of the economy. These are 3 quite different dilemmas, and they require different expertise. To win the race, we need to balance risk in these different domains.

The good news is that we have learned a lot about the virus and the illness over the past 4 to 5 months. Within 2 weeks of the discovery of COVID-19, researchers at the 

National Institute of Allergy and Infectious Diseases

 determined how the virus enters human cells and, within 2 months, sites began Phase I trials of a potential mechanism-based 

 The problem is that treatment and vaccine trials historically take more than a year at the earliest to be successful. So, while these 2 possibilities are the most likely game changers, there will still be loss of lives and  negative economic fall-out. For this reason, there are 692 trials listed on clinicaltrials.gov examining the potential efficacy and safety of different treatments as of April 20th.

 Most-if not all-of these studies are examining the effect of already marketed drugs, because it takes many years to develop a completely new molecular entity specifically targeted for a given illness.

Much has also been learned about the natural history of the COVID-19 illness. The virus has a high enough basic reproduction number (R

 value refers to the expected number of cases directly generated by one case in a population where all individuals are susceptible to infection, as was the case for the world human population when this virus became a human pathogen. The R

 values for COVID-19 range from 1.4 to 5.7. In comparison, the COVID-19 R

 values are higher than reported for the 2 earlier coronaviruses (ie, SARS-CoV-1 and MERS) and higher than that for seasonal strains of influenza (R

 values range from 0.9 to 2.1), but are substantially lower than the R

 values for measles, one of the most infectious of all human viruses (R

 value can be used to estimate the fraction of the population that needs to be immune to the virus-whether by natural exposure or vaccination-to slow or stop the spread of the virus among nonimmune individuals. This fraction is referred to as 

 values, the threshold for effective community immunity for measles is 93% to 95% of the population being immune to the measles virus. For COVID-19, the threshold to achieve community immunity is estimated to be between 50% and 66%, according to 

, associate professor of epidemiology at Johns Hopkins University.

 Those figures are consistent with estimates that 50% to 70% of American and European populations will likely become infected with the SARS CoV-2 virus.

 Complicating this problem is that fact that somewhere between 25% and 50% of people infected with the virus are at least initially asymptomatic but nevertheless infectious to others based on data from the Centers for Disease Control and Prevention (CDC) and epidemiology studies in Iceland.

 Much has also been learned about risk factors for the seriousness of the illness as reflected in 

Clearly, age is an important risk factor but so also are comorbid medical illnesses, particularly pre-existing respiratory and cardiovascular disease, immunocompromised status, morbid obesity (ie, body mass index > 40), significant kidney or liver impairment, and/or diabetes. Age and these comorbid medical illnesses are correlated-that is, the older a person is, the more likely they have one or more of these comorbid illnesses. However, what is not known is whether age is simply a surrogate for having a comorbid illness or an independent risk factor. When younger people (< 55 years) have serious and even fatal outcomes, they most often have one or more of the medical illnesses listed, including severe asthma. In fact, almost 90% of all patients hospitalized for COVID-19 independent of age had one or more of the listed comorbid conditions.

 That means that 90% of the individuals who were hospitalized, admitted to the ICU, or died (

) had comorbid medical illness. When these individuals are removed, the likelihood of those 3 adverse outcomes drops dramatically, in some cases approaching zero. For these reasons, it is probably best to consider both age and medical status when stratifying risk; this was not done in 

. Age is likely mainly a surrogate for comorbid medical illness, but by age 85 and above, the vital capacity of many organs, such as forced vital capacity, a measure of lung function, has often declined to a significant degree. On the basis of this information, 5 potential severity levels can be identified (

There are some caveats concerning the data in 

, which represents cases from February 12 to March 16, 2020. First, early recognition and medical care have since improved substantially, which could produce a drop in these numbers even in vulnerable populations. Second and even more important, the data are based on approximately 2500 cases; more than 775,000 cases have now been reported, a number that almost undoubtedly includes more individuals at severity levels 0 or 1 because cases in the early days were first diagnosed on the basis of symptoms that would have been missed in these people. Because the number of cases is now so much higher, the CDC will be able to determine whether the larger numbers replicate the earlier findings shown in 

, which will give greater confidence in the results. Such an analysis has not yet been posted in the public domain, but this will likely happen and/or the results will be shared with policy makers.

Guidance for mental health care practitioners to better explain the complexities of the current crisis to their patients.