Evidence map›Paper›PMID 32130682›Full record

ArticleMonash bioethics review2020

A general approach to compensation for losses incurred due to public health interventions in the infectious disease context.

Søren Holm

Open access · hybridAbstract read
In one paragraph

Article in Monash bioethics review, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
3.2field-weighted citation impact, top 7% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 15 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author at 1 institution in 1 country.

Søren HolmCentre for Social Ethics and Policy, School of Law, University of Manchester, Manchester, M13 9PL, UK. Soren.holm@manchester.ac.uk.
University of Manchester · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper develops a general approach to how society should compensate for losses that individuals incur due to public health interventions aimed at controlling the spread of infectious diseases. The paper falls in three parts. The first part provides an initial introduction to the issues and briefly outlines five different kinds of public health interventions that will be used as test cases. They are all directed at individuals and aimed at controlling the spread of infectious diseases (1) isolation, (2) quarantine, (3) recommended voluntary social distancing, (4) changes in health care provision for asymptomatic carriers of multi-resistant microorganisms, and (5) vaccination. The interventions will be briefly described including the various risks, burdens and harms individuals who are subject to these interventions may incur. The second part briefly surveys current compensation mechanisms as far as any exist and argue that even where they exist they are clearly insufficient and do not provide adequate compensation. The third part will then develop a general framework for compensation for losses incurred due to public health interventions in the infectious disease context. This is the major analytical and constructive part of the paper. It first analyses pragmatic and ethical arguments supporting the existence of an obligation on the part of the state to compensate for such losses, and then considers whether this obligation can be defeated by (1) resource considerations, or (2) issues relating to personal responsibility.

Indexed as

Communicable Disease ControlCompensation and RedressHumansPublic HealthCompensationInfectious diseaseIsolationPublic health ethicsQuarantineVaccination

Identifiers

PMID32130682
PMCPMC7095444
OpenAlexW3010033530

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.