Evidence map›Paper›PMID 24853081›Full record

ArticlePloS one2014

Modeling age-specific mortality for countries with generalized HIV epidemics.

David J Sharrow, Samuel J Clark, Adrian E Raftery

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In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
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  5. Observational
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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.

David J SharrowDepartment of Sociology, University of Washington, Seattle, Washington, United States of America.
Samuel J ClarkDepartment of Sociology, University of Washington, Seattle, Washington, United States of America; Institute of Behavioral Science (IBS), University of Colorado at Boulder, Boulder, Colorado, United States of America; MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; ALPHA Network, London School of Hygiene and Tropical Medicine, London, United Kingdom; INDEPTH Network, Accra, Ghana.
Adrian E RafteryDepartment of Statistics, University of Washington, Seattle, Washington, United States of America; Department of Sociology, University of Washington, Seattle, Washington, United States of America.

Funding

Methods and Data CoreR24HD042828 · NICHD · UNIVERSITY OF WASHINGTON · PI MAR, CORINNE M · 2002 to 2016
$9.3M
Statistical Methods for Population ProjectionsR01HD070936 · NICHD · UNIVERSITY OF WASHINGTON · PI RAFTERY, ADRIAN E · 2012 to 2021
$3.3M
Sexually Transmitted Infection Epidemics: Bayesian Prevalence & Size EstimateR01HD054511 · NICHD · UNIVERSITY OF WASHINGTON · PI RAFTERY, ADRIAN E · 2007 to 2016
$3.2M
Predicting Impacts of Infectious Disease on Structure and Dynamics of PopulationsK01HD057246 · NICHD · UNIVERSITY OF WASHINGTON · PI CLARK, SAMUEL J · 2008 to 2012
$620k
NICHD NIH HHS K01 HD057246NICHD NIH HHS R01 HD054511NICHD NIH HHS R01 HD070936NICHD NIH HHS R24 HD042828
6 · The paper itself

Abstract

backgroundIn a given population the age pattern of mortality is an important determinant of total number of deaths, age structure, and through effects on age structure, the number of births and thereby growth. Good mortality models exist for most populations except those experiencing generalized HIV epidemics and some developing country populations. The large number of deaths concentrated at very young and adult ages in HIV-affected populations produce a unique 'humped' age pattern of mortality that is not reproduced by any existing mortality models. Both burden of disease reporting and population projection methods require age-specific mortality rates to estimate numbers of deaths and produce plausible age structures. For countries with generalized HIV epidemics these estimates should take into account the future trajectory of HIV prevalence and its effects on age-specific mortality. In this paper we present a parsimonious model of age-specific mortality for countries with generalized HIV/AIDS epidemics. METHODS AND

findingsThe model represents a vector of age-specific mortality rates as the weighted sum of three independent age-varying components. We derive the age-varying components from a Singular Value Decomposition of the matrix of age-specific mortality rate schedules. The weights are modeled as a function of HIV prevalence and one of three possible sets of inputs: life expectancy at birth, a measure of child mortality, or child mortality with a measure of adult mortality. We calibrate the model with 320 five-year life tables for each sex from the World Population Prospects 2010 revision that come from the 40 countries of the world that have and are experiencing a generalized HIV epidemic. Cross validation shows that the model is able to outperform several existing model life table systems.

conclusionsWe present a flexible, parsimonious model of age-specific mortality for countries with generalized HIV epidemics. Combined with the outputs of existing epidemiological and demographic models, this model makes it possible to project future age-specific mortality profiles and number of deaths for countries with generalized HIV epidemics.

Indexed as

Life TablesModels, TheoreticalAge FactorsEpidemicsHIV InfectionsHumansPrevalence

Identifiers

PMID24853081
PMCPMC4031074

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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.