MétaCan
Menu
Back to cohort
Record W2778391255 · doi:10.25336/p60g7g

Inequality in mortality decreases with age: Evidence from developing countries using census data

2017· article· en· W2778391255 on OpenAlexvenueno aff
Rubén Castro, Eduardo Fajnzylber, Andrés Fortunato

Bibliographic record

VenueCanadian Studies in Population · 2017
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsnot available
Fundersnot available
KeywordsMicrodata (statistics)Sierra leoneSocioeconomic statusDeveloping countryCensusDemographyGeographyInequalityPopulationSocioeconomicsEconomicsEconomic growthSociologyMathematics

Abstract

fetched live from OpenAlex

With some exceptions, studies consistently find that mortality rate ratios between the highest and lowest socioeconomic status (SES) groups are substantially larger among the young-age population, rather than the old one. This pattern is relevant for policy and research, but it has seldom been explored in populations of developing countries. In this study, eight samples in the Integrated Public Use Microdata Series (IPUMS) that contain mortality data (El Salvador 1992, Rwanda 2002, Senegal 2002, Sierra Leone 2004, Uganda 2002, Malawi 2008, Brazil 2010, and Zambia 2010) and information about household assets are analyzed, and, using SES of equal relative size, results in seven out of eight cases are the same as those in developed societies: ratios are larger among the young age group and among men. Therefore, the ratio of mortality by relative-SES also decreases with age in several developing ones.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score0.302

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.006
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.709
GPT teacher head0.617
Teacher spread0.092 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Studies in PopulationSame topicGlobal Health Care IssuesFrench-language works237,207