Evidence map›Paper›PMID 29019084›Full record

ArticleDemography2017

A Flexible Bayesian Model for Estimating Subnational Mortality.

Monica Alexander, Emilio Zagheni, Magali Barbieri

Abstract read
In one paragraph

Article in Demography, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing 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

31 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

3 authors.

Monica AlexanderDepartment of Demography, University of California, Berkeley, 2232 Piedmont Avenue, Berkeley, CA, 94720-2120, USA. monicaalexander@berkeley.edu.
Emilio ZagheniDepartment of Sociology, University of Washington, Seattle, 211 Savery Hall, Box 353340, Seattle, WA, 98195-3340, USA.
Magali BarbieriDepartment of Demography, University of California, Berkeley, 2232 Piedmont Avenue, Berkeley, CA, 94720-2120, USA.

Funding

Program Development (Pilot)P30AG012839 · NIA · UNIVERSITY OF CALIFORNIA BERKELEY · PI William H Dow · 1999 to 2026
$18.6M
Workshops in Formal DemographyR25HD083136 · NICHD · UNIVERSITY OF CALIFORNIA BERKELEY · PI JOSHUA R. GOLDSTEIN · 2015 to 2026
$1.7M
NIA NIH HHS P30 AG012839NICHD NIH HHS R25 HD083136
6 · The paper itself

Abstract

Reliable subnational mortality estimates are essential in the study of health inequalities within a country. One of the difficulties in producing such estimates is the presence of small populations among which the stochastic variation in death counts is relatively high, and thus the underlying mortality levels are unclear. We present a Bayesian hierarchical model to estimate mortality at the subnational level. The model builds on characteristic age patterns in mortality curves, which are constructed using principal components from a set of reference mortality curves. Information on mortality rates are pooled across geographic space and are smoothed over time. Testing of the model shows reasonable estimates and uncertainty levels when it is applied both to simulated data that mimic U.S. counties and to real data for French départements. The model estimates have direct applications to the study of subregional health patterns and disparities.

Indexed as

Bayes TheoremModels, StatisticalMortalityAdolescentAdultAgedAged, 80 and overAge DistributionChildChild, PreschoolComputer SimulationFemaleFranceGeographyHumansInfantBayesian hierarchical modelFranceMortalityPrincipal componentsSubnational estimation

Identifiers

PMID29019084
PMCPMC5948000

What OpenQuestion holds

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Registered trials

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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.