Evidence map›Paper›PMID 31427320›Full record

ArticleBMJ open2019

Variability in the burden of disease estimates with or without age weighting and discounting: a methodological study.

Oluwaseun Egunsola, Jacques Raubenheimer, Nicholas Buckley

Abstract read
In one paragraph

Article in BMJ open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 4 pooled it
–field-weighted citation impact
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

12 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Observational
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  9. Reflections on key methodological decisions in national burden of disease assessments.Archives of public health = Archives belges de sante publique · 2020
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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

3 authors.

Oluwaseun EgunsolaDiscipline of Pharmacology, University of Sydney, Sydney, New South Wales, Australia seunegunsola@yahoo.com.ORCID 0000-0002-0500-9501
Jacques RaubenheimerDiscipline of Pharmacology, University of Sydney, Sydney, New South Wales, Australia.
Nicholas BuckleyDiscipline of Pharmacology, University of Sydney, Sydney, New South Wales, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThis study examines the impact of the type of method used on the estimation of the burden of diseases.

designComparison of methods of estimating disease burden.

settingFour metrics of burden of disease estimation, namely, years of potential life lost (YPLL), non-age weighted years of life lost (YLL) without discounting and YLL with uniform or non-uniform age weighting and discounting were used to calculate the burden of selected diseases in three countries: Australia, USA and South Africa.

participantsMortality data for all individuals from birth were obtained from the WHO database. OUTCOMES: The burden of 10 common diseases with four metrices, and the relative contribution of each disease to the overall national burden when each metric is used.

resultsThere were variations in the burden of disease estimates with the four methods. The standardised YPLL estimates were higher than other methods of calculation for diseases common among young adults and lower for diseases common among the elderly. In the three countries, discounting decreased the contributions of diseases common among younger adults to the total burden of disease, while the contributions of diseases of the elderly increased. After discounting with age weighting, there were no distinct patterns for diseases of the elderly and young adults in the three countries.

conclusionsGiven the variability in the estimates of the burden of disease with different approaches, there should be transparency regarding the type of metric used and a generally acceptable method that incorporates all the relevant social values should be developed.

Indexed as

Cost of IllnessAdolescentAdultAgedAged, 80 and overAge DistributionAustraliaChildChild, PreschoolDatabases, FactualFemaleHumansInfantInfant, NewbornMaleMiddle Agedepidemiologyhealth economicspublic health

Identifiers

PMID31427320
PMCPMC6701586

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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.