Evidence map›Paper›PMID 34251434›Full record

ArticleDemography2021

Estimating Adult Death Rates From Sibling Histories: A Network Approach.

Dennis M Feehan, Gabriel M Borges

Abstract read
In one paragraph

Article in Demography, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–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

6 citing papers in PubMed.

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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

2 authors.

Dennis M FeehanDepartment of Demography, University of California, Berkeley, CA, USA.ORCID 0000-0001-7008-4375
Gabriel M BorgesBrazilian Institute of Geography and Statistics (IBGE), Rio de Janeiro, Brazil.ORCID 0000-0002-8840-328X

Funding

Program Development (Pilot)P30AG012839 · NIA · UNIVERSITY OF CALIFORNIA BERKELEY · PI William H Dow · 1999 to 2026
$18.6M
Dev-Core-001P2CHD073964 · NICHD · UNIVERSITY OF CALIFORNIA BERKELEY · PI EDWARD ANDREW MIGUEL · 2019 to 2026
$2.9M
Berkeley Population CenterR24HD073964 · NICHD · UNIVERSITY OF CALIFORNIA BERKELEY · PI GOLDSTEIN, JOSHUA R. · 2014 to 2017
$466k
NIA NIH HHS P30 AG012839NICHD NIH HHS P2C HD073964NICHD NIH HHS R24 HD073964
6 · The paper itself

Abstract

Hundreds of millions of people live in countries that do not have complete death registration systems, meaning that most deaths are not recorded and that critical quantities, such as life expectancy, cannot be directly measured. The sibling survival method is a leading approach to estimating adult mortality in the absence of death registration. The idea is to ask survey respondents to enumerate their siblings and to report about their survival status. In many countries and periods, sibling survival data are the only nationally representative source of information about adult mortality. Although a vast amount of sibling survival data has been collected, important methodological questions about the method remain unresolved. To help make progress on this issue, we propose reframing the sibling survival method as a network sampling problem. This approach enables a formal derivation of statistical estimators for sibling survival data. Our derivation clarifies the precise conditions that sibling history estimates rely on, leads to internal consistency checks that can help assess data and reporting quality, and reveals important quantities that could potentially be measured to relax assumptions in the future. We introduce the R package siblingsurvival, which implements the methods we describe.

Indexed as

Life ExpectancySiblingsAdultHumansResearch DesignSurveys and QuestionnairesDemographic and health surveysMortalityNetworksSampling

Identifiers

PMID34251434
PMCPMC10923937

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.