Evidence map›Paper›PMID 24349130›Full record

ArticlePloS one2013

Year of birth effects in the historical decline of tuberculosis mortality: a reconsideration.

Romola J Davenport

Abstract read
In one paragraph

Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Causes of child mortality (1 to 4 years of age) from 1983 to 2012 in the Republic of Korea: national vital data.Journal of preventive medicine and public health = Yebang Uihakhoe chi · 2014
    Article
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

1 author.

Romola J DavenportDepartment of Geography, University of Cambridge, Cambridge, United Kingdom.

Funding

Wellcome Trust
6 · The paper itself

Abstract

Birth cohort patterns in mortality are often used to infer long-lasting impacts of early life conditions. One of the most widely accepted examples of a birth cohort effect is that of tuberculosis mortality before the late 1940s. However the evidential basis for claims of cohort-specific declines in tuberculosis mortality is very slight. Reanalysis of original or enhanced versions of datasets used previously to support claims of cohort effects in tuberculosis mortality indicated that: 1. where the initial decline in tuberculosis mortality occurred within the period of observation, onset of decline occurred simultaneously in many age groups, in a pattern indicative of 'period' not cohort-dependent effects. 2. there was little evidence of 'proportional hazard'-type cohort patterns in tuberculosis mortality for any female population studied. Therefore any mechanisms proposed to underlie this type of cohort pattern in male mortality must be sex-specific. 3. sex ratios of tuberculosis mortality at older ages peaked in cohorts born around 1900, and resembled cohort sex ratios of lung cancer mortality. This analysis indicates that age-specific patterns in the decline in tuberculosis mortality before 1950 are unlikely to reflect improvements in early life conditions. The patterns observed are generally more consistent with the influence of factors that reduced mortality simultaneously in most age groups. Additional influences, possibly smoking habits, impeded the decline of tuberculosis in older adult males, and produced the sex-specific shifts in age distributions of mortality that were previously interpreted as evidence of cohort-dependent mortality decline.

Indexed as

AdolescentAdultAgedAged, 80 and overAge DistributionAge FactorsChildChild, PreschoolCohort EffectCohort StudiesEuropeFemaleHumansInfantInfant, NewbornJapan

Identifiers

PMID24349130
PMCPMC3859563

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.