IHME failed to update their model for behavioral changes"...Unfortunately, the IHME modelers’ findings contained an error that even minimal scrutiny should have caught. The projected number of lives saved, and the implied case for a mask mandate, are based on a faulty statistic. Using a months-old survey, IHME modelers assumed erroneously that the U.S. mask-adoption rate stood at only 49% as of late September, and therefore had plenty of room to increase to “universal adoption,” defined as 95%, or to a more plausible 85%. According to more recent survey findings, however, America’s mask-adoption rate has hovered around 80% since the summer...."

Phillip Magness, "Case for Mask Mandate Rests on Bad Data,"Wall Street Journal, November 11, 2020, https://www.wsj.com/articles/case-for-mask-mandate-rests-on-bad-data-11605113310

James Capretta, "CBO presents options for covering the uninsured," American Enterprise Institute, October 5, 2020, https://www.aei.org/articles/cbo-presents-options-for-covering-the-uninsured/

Allison Percy and Karen Stockley, “Who Went Without Health Insurance In 2019, and Why?” Congressional Budget Office, September 2020, https://www.cbo.gov/system/files/2020-09/56504-Health-Insurance.pdf

Jared Maeda and Karen Stockley, “Policies to Achieve Near-Universal Health Insurance Coverage,” Congressional Budget Office, October 2020, https://www.cbo.gov/system/files/2020-10/56620-near-universal-coverage.pdf

Capretta here makes no sense according to CBO“…0 million people are uninsured because they are residing in the U.S. without proper legal status; as such, they are ineligible for subsidized coverage.6 million people are ineligible for employer coverage but have incomes too high to qualify for subsidized premiums through the ACA exchanges; they could enroll in plans offered in the nongroup market but would be required to pay the full premiums themselves. Finally, there are 3.2 million people who have incomes below the federal poverty line and live in states that did not expand Medicaid as allowed by the ACA.This group falls into a “coverage gap” in current law; their incomes are too low to qualify for subsidized premiums in the ACA exchanges and yet too high to qualify for Medicaid. The first priority of any future effort to cover the uninsured should be to ensure this population can enroll in either Medicaid or another alternative that requires no premium payment….”

And only 1/3 of persons not insured not eligible for subsidized coverage“…In 2019, an estimated 30 million people under the age of 65, or 12 percent of that population, were uninsured. One reason for the lack of insurance coverage was a lack of subsidized options. About one-third of uninsured people under the age of 65 did not have access to coverage that was subsidized by the government or an employer: 13 percent were noncitizens who were not lawfully present in this country; 11 percent had income that was less than 100 percent of the FPL and lived in a state that did not expand Medicaid; and 9 percent had income that was too high to qualify for marketplace subsidies(see Figure 1)…”

“…About two-thirds of the 30 million uninsured people under the age of 65 had access to some form of subsidized coverage but were not enrolled, although those options were subsidized to different degrees: 17 percent were eligible for Medicaid or CHIP; 19 percent were eligible for subsidized coverage through the marketplaces established under the ACA; and 31 percent had access to coverage through an employer. Most of those people could have purchased health insurance that cost less than 10 percent of their income, but fewer people had an option that cost less than 5 percent of their income. Those people lacked health insurance coverage because they did not consider it to be worth the cost or because of the complexities of the enrollment process, among other reasons….”

But bottom line according toCBO only 9.8mm people who are currently uninsured are not eligible for subsidized coverage under the current systems, and 4mm are not lawfully present in the country, so only5.8mm peoplearen’t being captured by the current system. The other 20mm people don’t have health insurance because to quote CBO “…Those people lacked health insurance coverage because they did not consider it to be worth the cost or because of the complexities of the enrollment process, among other reasons…”

This is Heritage’s health care proposal you asked about. It’s not a comprehensive plan but offers a series of potential, in many cases current innovations based on the current system (some of these, like telemedicine, likely have been accelerated by the pandemic)

Steve

1. Provide opportunity for individuals to delink health care from employment. “…In an ideal world, most people would own their own health insurance and take it with them as they traveled from job to job and in and out of the labor market. Some employers may have better insurance than is available on the open market. But others might prefer to make a cash contribution to help employees pay their own premiums rather than provide insurance directly. Some employers were actually doing that before Obamacare. They used an account called aHealth Reimbursement Arrangement (HRA), providing tax-free funds employees could use to buy their own health insurance. But with Obamacare came regulations and threats of steep employer fines that effectively deep-sixed this option. Thankfully, the Trump administration is reversing course. Beginning next January, employers will be able to use HRAs to help employees obtain their own coverage with the administration’s blessing…”
2. Make Medicare more open to “direct primary care.”“….Under the arrangement, Medicare would pay a fixed monthly fee to a physician or physician group instead of the traditional fee-for-service payments. In return, the physicians would provide virtually all primary care. The fees would range from $90 to $120 a month, depending on the patient’s age and medical complexity….”
3. Expanded use of telemedicine“…Medicare doesn’t pay for any of this. And since private insurers and employers tend to pay the way Medicare pays, the entire country is missing out on incredible advances in telemedical technology….Federal law (the Social Security Act) allows Medicare to pay for telemedicine only under strictly limited circumstances… The CMS is acting aggressively to change this. As of January 1 of this year, doctors in Medicare Advantage plans and accountable-care organizations can now bill Medicare if they use phone, email, Skype, and other technologies to consult with patients remotely to determine if they need an in-office visit. Patients can be anywhere, including their own homes. Doctors can also bill Medicare to review and analyze medical images patients send them. And they can bill for telemedical consultations with other doctors. But these are still baby steps. Congress needs to liberate telemedicine once and for all….”
4. Allow specialized healthcare plans“….The most successful Obamacare insurers are Medicaid contractors. The plans that have survived in the exchanges look like Medicaid managed care with a high deductible. The networks include only those doctors who will accept Medicaid fees coupled with all the hassle of managed-care bureaucracy….Before Obamacare, most states had risk pools for the small number of people who entered the individual market with an expensive preexisting condition and were denied access to ordinary insurance coverage. The risk-pool plans looked like garden-variety Blue Cross plans, with access to almost all doctors and hospitals. While some risk pools had problems — such as being over-subscribed and unable to take new customers — those problems were discrete and addressable…..Health Care Choices Proposal, developed by the Health Policy Consensus Group and supported by more than 100 conservative leaders across the country. This proposal would block-grant Obamacare funds to the states and give them wide discretion to reform their individual health-insurance markets. The Center for Health and the Economy estimates that this proposal would lower health-care premiums by as much as a third, would insure about the same number of people as Obamacare, and would better protect people with preexisting conditions and high health costs….”
5. Expand use of Health Savings Accounts, “…To take advantage of the full potential of HSAs, we need three policy changes: (1) People should be able to use completely flexible HSAs, wrapping them around any health-insurance plan and using them to pay for any medical costs the plan does cover; (2) they should be able to use their HSAs to pay premiums as well as out-of-pocket expenses; and (3) health plans should be allowed to have “shared savings programs,” where enrollees who choose better and cheaper care get to keep their share of their savings in their HSA…”
   Marie Fishpaw and John Goodman, "A Health Plan For President Trump," Heritage Foundation, August 2, 2019, https://www.heritage.org/health-care-reform/commentary/health-plan-president-trump

Editorial Board, "Pre-Existing Condition Fiction,"Wall Street Journal, September 30, 2020, https://www.wsj.com/articles/pre-existing-condition-fiction-11601507970

Gary Claxton, Cynthia Cox, Anthony Damico, Larry Levitt and Karen Pollitz, "Pre-Existing Condition Prevalence for Individuals and Families," Kaiser Family Foundation, October 4, 2019, https://www.kff.org/health-reform/issue-brief/pre-existing-condition-prevalence-for-individuals-and-families/

DHHS, “Health Insurance Coverage for Americans with Pre-Existing Conditions: The Impact of the Affordable Care Act,“ Department of Health And Human Services, January 5, 2017, https://aspe.hhs.gov/system/files/pdf/255396/Pre-ExistingConditions.pdf

Rachel Fehr, Cynthia Cox and Larry Levitt, “Data Note: Changes In Enrollment In The Individual Health Insurance Market Through Early 2019,” Kaiser Family Foundation, August 21, 2019, https://www.kff.org/private-insurance/issue-brief/data-note-changes-in-enrollment-in-the-individual-health-insurance-market-through-early-2019/

I haven’t found an estimate of what % of the 13.8mm persons have a pre-existing condition.

So that’s plans accessed though an exchange. Anotherstudyby the Kaiser Family Foundation found that in 2018 13.8mm individual were insured through plans purchased on the exchange (however that’s not everyone buying individuals plans, I have always purchased directly from Blue Cross/Blue Shield without going through an exchange)“…Total individual market enrollment, measured on an average monthly basis, increased from 10.6 million in 2013 to a peak of 17.4 million in 2015, before declining to13.8 million in 2018….”If you use Kiser’s 27% of non-elderly adults who had pre-existing conditions against that total enrolment 13.8mm *.27 that would imply3.726mm individuals with such a condition were accesses health care on the exchanges cheaper than they otherwise would be. Now that’s prob not a fair assumption as the exchanges tend to be younger people who don’t get health care though their employer.

"... The question is not how many Americans have a health condition,but how many Americans buying insurance in the individual insurance market have a condition that makes them difficult to insure at prices they can pay. Keep in mind that the Affordable Care Act set up a subsidized transitional plan for anyone with pre-existing conditions denied insurance in the individual market. Peak enrollment: about 115,000 in 2013.The White House in an executive order last week noted that, by the Obama Administration’s own report, a mere 2.7% of an estimated roughly 130 million people with pre-existing conditions gained access to health insurance through the Affordable Care Act.... "

Okay, so basically the WSJ editorial board argued that the number that really matters is the number of American’s who are not covered by Medicare/Medicaid nor employer provided health insurance who get covered though plans purchased on exchange.

Table from HHS:

Kaiser goes on to note, and I think this is a good answer to your questions, 53.8mm (non-elderly) have a condition that would have left them uninsurable in the individuals markets, however most of these people are getting insurance though their employer so they can avoid individual plans“…A larger share of non-elderly adult women (30%) than men (24%) have declinable pre-existing conditions in 2018, unchanged from 2015. We estimate that23.7 million men have a pre-existing condition that would have left them uninsurable in the individual market pre-ACA, compared to 30.1 million women. Pregnancy explains part (about 2 million women) but not all of this difference….”

Using HHS’s narrow definition that number goes to61mm non-elderly Americans.That HHS estimate was mirrored by the Kaiser Family Foundation, "... estimate that 27% of nonelderly adults have a declinable health condition, which is about53.8 million people in 2018…. The prevalence of declinable conditions also increases with age among non-elderly adults: ranging from 18% of those in the 18-34 age group to 44% for those in the 55-64 age group….”

You are correct Biden was using a pretty broad definition of “Pre-existing condition” including high blood pressure and behavioral issues, using HHS’s broad definition you can say 133mm. HHS“…As shown in Table 1, we find that theACA is protecting between 23 and 51 percent of nonelderly Americans--61 to 133 million people--with some type of pre-existing health condition from being denied coverage, charged significantly higher premiums, subjected to an extended waiting period, or having their health insurance benefits curtailed should they need individual market health insurance coverage….”

I think a good answer to your question, how many people (non-elderly) have a condition significant enough to have an impact on the price of insurance is somewhere btw 53.8mm-61mm.

“…Remember George Box’swise wordsabout models: “All models are wrong but some models are useful”. Or as he put it elsewhere, less dramatically but morespecifically:“Models, of course, are never true, but fortunately it is only necessary that they be useful. For this it is usually needful only that they not be grossly wrong”….”

Jeffrey K Aronson, Jon Brassey, Kamal R Mahtani, "“When will it be over?”: An introduction to viral reproduction numbers, R0 and Re," Centre for Evidence-Based Medicine, Nuffield Department of Primary Care Health Sciences, University of Oxford, April 14, 2020, https://www.cebm.net/covid-19/when-will-it-be-over-an-introduction-to-viral-reproduction-numbers-r0-and-re/

When will it be over?”: An introduction to viral reproduction numbers, R0 and R

The UK Government is regularly reviewing the current lockdown measures, as it is required to do every 3 weeks, by law. With the peak of the pandemic yet to arrive in the UK, it seems certain that the current measures will continue. So when will the UK lockdown end?

On 30 March 2020, Sir Patrick Vallance, the Government’s Chief Scientific Officer, gave an indication of how this decision may be guided, saying that Britain’s lockdown was having a “very big effect” on the R0, bringing it down “below one”.

The figure to which he was referring is the basic reproduction number (or reproductive ratio) of a virus, called R0 (R nought or R zero). And it is important to know how to interpret it.

We include some definitions in the box at the end.

The basic reproduction number

The basic reproduction number is defined as the number of cases that are expected to occur on average in a homogeneous population as a result of infection by a single individual, when the population is susceptible at the start of an epidemic, before widespread immunity starts to develop and before any attempt has been made at immunization. So if one person develops the infection and passes it on to two others, the R0 is 2.

If the average R0 in the population is greater than 1, the infection will spread exponentially. If R0 is less than 1, the infection will spread only slowly, and it will eventually die out. The higher the value of R0, the faster an epidemic will progress.

R0 is estimated from data collected in the field and entered into mathematical models. The estimated value depends on the model used and the data that inform it.

R0 is affected by:

the proportion of susceptible people at the start and the density of the population;
the infectiousness of the organism;
the rate of disappearance of cases by recovery or death, the first of which depends on the time for which an individual is infective;

The denser the population, the more people are susceptible, and the more infective the virus, the larger R0 will be for a given virus; the faster the rate of removal of infected individuals, by recovery or death, the smaller R0 will be.

The zero in “R zero” means that it is estimated when there is zero immunity in the population, even though not everyone will necessarily be susceptible to infection, although that is the usual assumption. In an epidemic with a completely new virus, the earlier the measurements are made the nearer the calculated value is likely to be to the true value of R0, assuming high-quality data. For this reason, it is better to talk about the transmissibility of the virus at the time that it is measured, using a different symbol, Re, the effective reproduction number.

The effective reproduction number, Re

The effective reproduction number, Re, sometimes also called Rt, is the number of people in a population who can be infected by an individual at any specific time. It changes as the population becomes increasingly immunized, either by individual immunity following infection or by vaccination, and also as people die.

Re is affected by the number of people with the infection and the number of susceptibles with whom infected people are in contact. People’s behaviour (e.g. social distancing) can also affect Re.

The number of susceptibles falls as people die or become immunized by exposure. The sooner people recover or die, the smaller the value of Re will be at any given time.

Unfortunately, the symbol R0 is often used in publications when Re is meant. This can be confusing.

Herd immunity

Initial reports suggested that one of the UK Government’s strategies in tackling the pandemic was to allow the virus to spread within the community, in a controlled way, so that immunity, so-called herd immunity, could develop across the population. However, the health secretary, Matt Hancock, later said that this was not part of the UK response to the virus. In contrast, other countries, such as Sweden, have responded to the pandemic in ways that avoid full lockdown. The problem with leaving people to catch the infection spontaneously, leading to herd immunity, is that the death rate would increase as a result. For example, on 10 April, the number of confirmed cases in Sweden was 9685 with 870 deaths (9.0%), compared with Norway with 6219 confirmed cases and 108 deaths (1.7%) and Denmark with 5830 confirmed cases and 237 deaths (4.4%).

R0 predicts the extent of immunization that a population requires if herd immunity is to be achieved, the spread of the infection limited, and the population protected against future infection. To prevent sustained spread of the infection the proportion of the population that has to be immunized (Pi) has to be greater than 1 − 1/R0. The relation between Pi and 1 - 1/R0 is shown in Figure 1.

For example, if R0 = 2, immunization needs to be achieved in 50% of the population. However, if R0 = 5 the proportion rises steeply, to 80%. Beyond that the rise is less steep; an increase in R0 to 10 increases the need for immunization to 90%. Measles has an R0 greater than 10, which is why immunization of a large proportion of the population is so important in preventing the disease.

Thus, if R0 is 10, a child with measles will infect 10 others if they are susceptible. When other children become immune the infected child who encounters 10 children will not be able to infect them all; the number infected will depend on Re. When immunity is 90% or more the chances that the child will meet enough unimmunized children to pass on the disease falls to near zero, and the population is protected.

At any time, Re = R0 × (1 - Pi), where Pi is the proportion of the population who are immune at that time. This is illustrated in Figure 2.

For example, immunization of 50% of the population (blue line) will bring Re down below one only if R0 was no greater than 2; in contrast immunization of 90% of the population (brown line) will bring Re down to 1 or less for all values of R0 up to 10.

How R0 is estimated

A typical epidemiological model by which R0 is estimated is based on three factors: individual Susceptibility to the infection, the rate at which infections actually occur (Infectivity), and the rate of Removal of infection from the population, by either recovery or death. This is the so-called S-I-R model.The model involves the solution of simultaneous linear differential equations and was described as long ago as 1927 by W. O. Kermack and A. G. McKendrick. A version of their model is shown in Figure 3.

However, many other methods have been used, including both statistical models and simple calculations. The former range from methods such as the Reed-Frost stochastic model, developed by Lowell J. Reed and Wade Hampton Frost in 1928, similar to a method that was already being developed by H E Soper and which he described in 1929, to the IDEA method, first described by Fisman and colleagues in 2013. Of the methods involving simple mathematical calculation of R0, one that is commonly used involves the following equation: R0 = βS/γ, where β is the transmission rate, S is the number of susceptibles at the start of the epidemic, and γ is the removal rate. There are many other variants of a similar nature.

Figure 4. Estimated values of R0 in different viral infections, culled from a variety of published sources

What is the R0 of SARS-CoV-2?

Estimates of the R0 of SARS-CoV-2 vary widely. Figure 5 shows nine estimates, all from studies in China or South Korea. The mean estimate of R0 is 2.63 (95% CI = 1.85,3.41). From a wider survey of 16 published estimates, the mean estimate is 2.65 (1.97, 3.09). In both cases, the range is 0.4-4.6.

This means that in order to protect us against future epidemics, herd immunity of around 62% will be needed, taking the mean estimate across all these studies. Taking the highest value (4.6), 78% immunization will be needed, and it would be wise to aim for at least that. When a test for neutralizing antibodies to SARS-CoV-2 and a vaccine are available, we shall be able to tell to what extent the population has been immunized by exposure and vaccination.

In the UK, one of the key findings that influenced the Government’s decision to start the lockdown was the model presented by the Imperial College COVID-19 response team. The model explores the effect of two strategies (a) suppression, by which interventions are instituted to bring Re to below 1, and (b) mitigation, by which strategies are instituted to reduce the impact of the epidemic, but not interrupt viral transmission completely, thus reducing Re, but not necessarily below 1. The model assumed an R0 of about 2.4 and predicted that, in the absence of any control measures, 81% of the UK population would become infected, with over half a million deaths. However, this number could be reduced to under 50,000 (and possibly under 6000 deaths) with a suite of suppression strategies in place. Little wonder, given these figures, that the UK government swiftly began the lockdown.

It is important to note that the value of R0 varies considerably in the models used in the COVID-19 pandemic. One systematic review reported that the mean of 29 reported values of R0 from 21 studies was estimated at 3.32 (2.81-3.82), with a range of 1.9 to 6.49; all the included studies were from China. In a statement on 23 January, 2020 about the outbreak of COVID-19 the World Health Organization (WHO) gave a preliminary R0 estimate of 1.4-2.5.

Interpreting the news

The Chinese data suggest little change in the reproduction numbers measured at various times over the last 3 months. So when you read a report that the reproduction number (Re) has fallen below 1, which should be cause for rejoicing, be cautious. The calculated value depends on the quality of the data, which may be poor, and the epidemiological model used, which may distort the outcome. Remember George Box’s wise words about models:

“All models are wrong but some models are useful”.

Or as he put it elsewhere, less dramatically but more specifically:

“Models, of course, are never true, but fortunately it is only necessary that they be useful. For this it is usually needful only that they not be grossly wrong”.

Definitions
Some definitions of terms used in this article are given below.
Immunity to a viral infection is the state of being non-susceptible to it, by virtue of being able to produce neutralizing antibodies when challenged by it; such antibodies may be produced by prior exposure or by immunization following vaccination.
Herd immunity is a form of protection from an infectious disease that results when a large enough proportion of the population has become immune, conferring indirect protection from infection on individuals who are not themselves immune.
Vaccination against a viral infection is the administration of a vaccine to an individual with the intention of immunizing that individual or boosting pre-existing immunity.
Immunization against a viral infection is the production of immunity to the infection by virtue of successful vaccination or exposure to the virus.

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Disclaimer: the article has not been peer-reviewed; it should not replace individual clinical judgement and the sources cited should be checked. The views expressed in this commentary represent the views of the authors and not necessarily those of the host institution, the NHS, the NIHR, or the Department of Health and Social Care. The views are not a substitute for professional medical advice.