Covid and the Value of Statistical Life

 COVID-19 and Uncertainties in the Value Per Statistical Life

Do the Benefits of COVID‐19 Policies Exceed the Costs? Exploring Uncertainties in the Age–VSL Relationship

For an individual, VSL can be derived by dividing WTP by the risk reduction. A population-average VSL of $10 million indicates that the typical individual is willing to pay $1,000 to decrease the chance of dying in a given year by 1 in 10,000. Individual WTP also can be summed across individuals expected to accrue the risk reduction. If 10,000 people will experience a 1 in 10,000 risk reduction and are each willing to pay $1,000 for the risk change, the total value is $10 million (10,000 times $1,000), and one less person would be expected to die that year as calculated by 10,000 times 1/10,000.

But

Individual WTP, then, is the fundamental measure—the $1,000 in this case. The conversion to a $10 million VSL is simply for convenience. 

 In a recent study with Ryan Sullivan and Jason F. Shogren, I compare the effects of three approaches often used to adjust for age: an invariant population-average VSL; a constant value per statistical life-year (VSLY); and a VSL that follows an inverse-U pattern, peaking in middle age. We find that when applied to the U.S. age distribution of COVID-19 deaths, these approaches result in average VSL estimates of $10.6 million, $4.5 million, and $8.5 million. The differences in these values is substantial enough to alter the conclusions of frequently cited analyses of social distancing.

 Table II. VSL by Age Group (in 2019 millions of dollars)

Age Group	Invariant VSL	Constant VSLY	Inverse‐U Relationship
Under 1 year	$10.63	$13.88	$5.38
1–4 years	$10.63	$13.74	$5.38
5–14 years	$10.63	$13.37	$5.38
15–24 years	$10.63	$12.64	$5.38
25–34 years	$10.63	$11.76	$8.50
35–44 years	$10.63	$10.63	$10.63
45–54 years	$10.63	$9.19	$10.72
55–64 years	$10.63	$7.54	$8.15
65–74 years	$10.63	$5.68	$8.15
75–84 years	$10.63	$3.72	$8.15
85 years and over	$10.63	$2.03	$8.15

 Table III. COVID‐19 Age‐Weighted Value (in 2019 millions of dollars)

	Invariant VSL	Constant VSLY	Inverse‐U Relationship
Total value, all COVID‐19 deaths	$937.6 billion	$394.8 billion	$773.4 billion
Average VSL, weighted by COVID‐19 deaths by age	$10.63 million	$4.47 million	$8.31 million

Table IV. Effect of Alternative Approaches on Analytic Results

		————————————————‐Benefits————————————‐————
		Lives	Original	Invariant	Constant	Inverse‐U
	Costs	Saved	Approach	VSL	VSLY	Relationship
Thunström et al. (2020)	$7.2 trillion	1.24 million	$12.4 trillion	$13.16 trillion	$5.54 trillion	$10.30 trillion
Greenstone and Nigam (2020)	N/A	1.76 million	$7.94 trillion	$18.72 trillion	$7.88 trillion	$14.64 trillion
Acemoglu et al. (2020)	$2.15 trillion	8.7 million	N/A	$92.44 trillion	$38.93 trillion	$72.31 trillion

Does this make sense? It seems quite high. If we value identified life more than a statistical life, can you imagine the final figure?

Whether the social distancing policy considered by Thunström et al. (2020) yields net benefits varies depending on the valuation approach. The authors use an invariant VSL but apply a somewhat lower value than we use in our analysis ($10 million rather than $10.63 million). However, both our invariant VSL and inverse‐U approaches lead to positive net benefits. Under our invariant approach, the benefits increase by almost $800 billion due to differences between the VSL estimates. Benefits decrease when using the inverse‐U approach, but not by a large enough amount to drop below estimated costs. Under the constant VSLY approach, benefits decrease by a substantial amount and the policy no longer appears cost‐beneficial.

While Greenstone and Nigam (2020) do not include a cost estimate in their calculations, the effects of our three approaches on their featured benefit estimates are significant. The benefit estimates more than double when applying the invariant VSL approach rather than their age‐adjusted approach. Interestingly, their estimates are very similar ($7.94 vs. $7.88 trillion) to the results using our constant VSLY method, while applying our inverse‐U estimates almost doubles the value in comparison to their inverse‐U approach. This result reflects the relative steepness of their curve at older ages as well as our assumption that values level off at older ages under the inverse‐U approach. As noted earlier, the additional sensitivity analyses reported by the authors also show siginificant variation in the results.

Acemoglu et al. (2020) have by far the largest estimates of lives saved across the three social distancing studies, which naturally increases the benefit values. Under all three approaches, we find that benefits exceed costs by an order of magnitude. However, Acemoglu et al. (2020) find that approaches other than the scenario reflected in Table IV are more cost‐effective, particularly if they target higher risk, older age groups.

Anyway, let's search a little bit more on that. Let's take the press. I don't think that this helps to take policy decisions in the current pandemic. It's just recreational research.

PS. The price of freedom is 103€ per day in my country (cost of non being free by mistake). Explained here.

Hockney

Covid Costs, who knows?

 The COVID-19 Pandemic and the $16 Trillion Virus

Summary of the estimates by Cutler and Summers:

And a video:

Covid vaccine landscape

 Evolution of the COVID-19 vaccine development landscape

Currently there are 321 vaccine candidates for COVID, however only 33 have entered clinical trials.

Although the leading COVID-19 vaccine candidates have progressed to advanced stages of clinical development at exceptional speed, many uncertainties remain given the lack of robust clinical data so far. Moreover, given the highly unusual circumstances associated with developing a vaccine during the evolution of a novel global pandemic, probability of success benchmarks for traditional vaccine development are likely to underrepresent the risks associated with delivering a licensed vaccine for COVID-19. The most advanced candidates are expected to begin reporting data from pivotal studies over the coming months, which if positive will be used to support accelerated licensure of the first COVID-19 vaccines. 

 

Population Health amidst pandemic

 Fitting Community-Centered Population Health (CCPH) Into the Existing Health Care Delivery  Patchwork. The Politics of CCPH

COVID-19 Crisis Creates Opportunities for Community-Centered Population Health Community Health Workers at the Center

Norbert Goldfield et al provides a useful reflection on the current moment in US. And says:

The virus has exposed glaring weaknesses that cannot be unseen. The US health care system’s embrace of patient-centered care is being tested.

We need a new paradigm, one that moves from a patient-centered care system to a community-centered health and social care ecosystem. Unlike much of the current US medical care system, the community sector has lacked funding and development. Strong and consistent sources of support are needed to make this sector viable and keep it flourishing.

Politics will ultimately determine how our current patient-centered acute care–focused health care system will change. The wealthy undoubtedly will continue to have an outsize influence on any legislation promoting CCPH. Despite this fact, we will at least think about becoming better in improving our response before the next pandemic hits.

Both articles deserve being read. 

Hockney

 

Primary care: measuring performance

 Taking Stock of the Global Primary Health Care Measurement Landscape

Better measurement for performance improvement in Low- and Middle-Income Countries: The Primary Health Care Performance Initiative (PHCPI) Experience of Conceptual Framework Development and Indicator Selection

During the pandemic everybody agree about the need for a strong primary care. What does this mean?

Check this framework:

And its Core indicators. Unfortunately they have forgotten the Central de Resultats indicators and data.

That's it.

Hockney

Primary care is the house in the center of the painting.

Health thinking

 El paciente del futuro

Dual practice regulation

Dual practice regulatory mechanisms in the health sector A systematic review of approaches and implementation 

Dual practice refers to physicians concurrent activity, public and private. The conflicts of incentives arise and some regulatory mechanisms are needed.

The regulatory mechanisms that have been employed across countries can be divided into three categories: those that advocate for total banning of DP, those that allow it with restrictions and those that allow it without restriction. Countries that attempted total banning of dual practice, as in Portugal and Greece, could not easily stamp it out. DP continued to exist on a wide scale in Portugal until the ban was lifted in 1993 (Oliveira and Pinto, 2005). Similarly, the ban in Greece from 1983 to 2002 did not prevent public doctors from practising privately (Mossialos et al., 2005). Efforts to ban dual practice failed because of lack of capacity to enforce it. The resources needed to enforce it may not be commensurate with the benefits a country gets from banning it. Moreover, banning dual practice has in some countries been associated with the migration of health workers, especially specialists, from the public to the private sector as well as an international brain drain (Buchan and Sochalski, 2004; Mossialos et al., 2005). In LMIC settings where health workers are underpaid and members of the general population are willing to pay for more convenient and possibly better services, this option might not be viewed as legitimate or even feasible.

 The second category is allowing dual practice with restrictions. This was the most frequent approach used by countries. Financial and licensure restrictions as well as promotional incentives were employed. Financial restrictions included limiting private sector earnings, providing incentives to limit private sector activities, salary increases for public sector workers and performance-based payments. All financial restrictions intrinsically require well-established and adequate health financing systems to fund and monitor public and private sector activity. A combination of tax-based public financing, mandatory health insurance and private insurance might be necessary to counter the financial resource demands of this approach, while supervision, monitoring systems and transparent bureaucracies would be necessary to ensure that private sector activities and earnings are indeed limited and payments are matched by performance.

 Allowing DP without restrictions was noted in countries like Indonesia and Egypt, where DP is routine and accepted. An interesting point to note is that in both countries, the productivity of physicians far exceeded the capacity of the public sector to employ them. Because of the low salaries offered in the public sector, physicians are allowed to supplement their incomes with private sector earnings. This approach is unlikely to be feasible in countries with health worker shortages. Considering the three options of total ban, allowing dual practice with restrictions and allowing it without restrictions, the most feasible for the LMICs is allowing it with restrictions. With health workers who are underpaid, in short supply and working in areas with a high burden of disease, they will scarcely be able to satisfy the demands of the public or the private sector alone.

Hockney

 

DRGs 101

 DIAGNOSIS-RELATED GROUPS: a question and answer guide on case-based classification and payment systems

WHO has released a report on DRGs that is useful as introduction to the concept and the design of payment systems.

The document consists of four parts:

Part 1 outlines definitions, terminology and the main conceptual aspects related to CBG and DRG.

Part 2 covers the assessment phase and highlights questions and issues that policy-makers should consider before taking the decision to introduce a CBG system.

Part 3 delves into the preparation phase by exploring policy and design aspects once a country has decided to introduce a CBG system.

Part 4 is concerned with the implementation phase and discusses implementation questions, requirements for system adjustments and the need for monitoring and revision in order to identify and address unintended impacts of a CBG system.

 

 

Improving CRISPR, a crowd of proteins

Improving CRISPR from Mammoth Biosciences; 

 Genome editing is the process researchers use to make targeted changes to an organism’s DNA (its genome). Scientists have used a variety of technologies for genome editing (see the history of genome editing here). However, since ~2012, CRISPR has made the genome editing processes much easier. CRISPR associated or “Cas” proteins drive this process. They are relatively easy to target to specific DNA sequences. They also work in many organisms.

Yet, the main Cas protein currently used for CRISPR genome editing, SpCas9, has limitations. In this post, we cover SpCas9’s limitations and how newly discovered Cas protein families, Cas14 and CasΦ, potentially overcome these limitations. We hope Cas14 and CasΦ will enable more efficient genome editing in diverse organisms and tissues.

 

Spillover effects of payment systems

 Randomized trial shows healthcare payment reform has equal-sized spillover effects on patients not targeted by reform

From PNAS: 

Changes in the way health insurers pay healthcare providers may not only directly affect the insurer’s patients but may also affect patients covered by other insurers.

This is the research question. And this is the result:

We use a payment reform in TM, which was randomly applied to some markets but not others, to study spillovers of healthcare payment reform. We find spillovers of the same sign and similar magnitude on privately insured MA patients. Naturally, our findings are specific to our setting; the existence, sign and magnitude of any spillovers may well vary across contexts.

Sounds good. However, there is a previous research question, which is the insurer's market share that allows to have the option to change the payment system. This former question is as relevant as the later one.

 

Hockney

Health impact of social isolation and loneliness

Social Isolation And Health

The lonely century

Social isolation and loneliness are both terms that denote a degree of social disconnection. Social isolation is an objective state marked by few or infrequent social contacts. Loneliness is the subjective and distressing feeling of social isolation, often defined as the discrepancy between actual and desired level of social connection.

Social connection and connectedness encompass a variety of terms used in the scientific literature (for example, social support, social integration, social cohesion) that document the ways that being physically or emotionally connected to others can influence health and well-being. 

In this HA Brief, the author explains that loneliness impact on health may be greater than we think. Rather than being alarming, she shows a description of the situation, the evidence and a proposal for policy agenda.

And if you are not convinced, than I would suggest this book:

An unprecedented discovery

Yesterday was World Crispr Day

 

Mapping genetic regulation

The GTEx Consortium atlas of genetic regulatory effects across human tissues

A great step in research:

The GTEx v8 data release represents a deep survey of both intra- and interindividual transcriptome variation across a large number of tissues. With 838 donors and 15,201 samples—approximately twice the size of the v6 release used in the previous set of GTEx Consortium papers—we have created a comprehensive resource of genetic variants that influence gene expression and splicing in cis. This substantially expands and updates the GTEx catalog of sQTLs, doubles the number of eGenes per tissue, and saturates the discovery of eQTLs with greater than twofold effect sizes in ~40 tissues. The fine-mapping data of GTEx cis-eQTLs provide a set of thousands of likely causal functional variants. While trans-QTL discovery and the characterization of sex- and population-specific genetic effects are still limited by sample size, analyses of the v8 data provide important insights into each.

On being honest in politics

 Eliciting preferences for truth-telling in a survey of politicians

From PNAS: 

Voters who would like to accurately evaluate the performance of politicians in office often rely on incomplete information and are uncertain whether politicians’ words can be trusted. Honesty is highly valued in politics because politicians who are averse to lying should in principle provide more trustworthy information. Despite the importance of honesty in politics, there is no scientific evidence on politicians’ lying aversion. We measured preferences for truth-telling in a sample of 816 elected politicians and study observable characteristics associated with honesty. We find that in our sample, politicians who are averse to lying have lower reelection rates, suggesting that honesty may not pay off in politics.

Therefore, in this case, truth-telling is a dominant non-rational strategy in politics. We still do need more evidence. Maybe the next presidential election will provide us a confirmation of this article.

Fact-Checker from WP

Bill Brandt at KBR-Mapfre Barcelona