Evidence map›Paper›PMID 29428888›Full record

ArticleSocial science & medicine (1982)2018

Medical provision and urban-rural differences in maternal mortality in late nineteenth century Scotland.

Alice Reid, Eilidh Garrett

Open access · hybridAbstract readHistorical Article
In one paragraph

Article in Social science & medicine (1982), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
6.2field-weighted citation impact, top 4% of its field
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

2 citing papers in PubMed, 13 citations in OpenAlex.

  1. Article
  2. 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

2 authors at 2 institutions in 2 countries.

Alice ReidDepartment of Geography, University of Cambridge, UK. Electronic address: amr1001@cam.ac.uk.
Eilidh GarrettDepartment of History, University of Essex, UK.
University of Cambridge · GBUniversity of Essex · GB

Funding

Wellcome Trust
6 · The paper itself

Abstract

This paper examines the effect of variable reporting and coding practices on the measurement of maternal mortality in urban and rural Scotland, 1861-1901, using recorded causes of death and women who died within six weeks of childbirth. This setting provides data (n = 604 maternal deaths) to compare maternal mortality identified by cause of death with maternal mortality identified by record linkage and to contrast urban and rural settings with different certification practices. We find that underreporting was most significant for indirect causes, and that indirect causes accounted for a high proportion of maternal mortality where the infectious disease load was high. However, distinguishing between indirect and direct maternal mortality can be problematic even where cause of death reporting appears accurate. Paradoxically, underreporting of maternal deaths was higher in urban areas where deaths were routinely certified by doctors, and we argue that where there are significant differences in medical provision and reported deaths, differences in maternal mortality may reflect certification practices as much as true differences. Better health services might therefore give the impression that maternal mortality was lower than it actually was. We end with reflections on the interpretation of maternal mortality statistics and implications for the concept of the obstetric transition.

Indexed as

Health Status DisparitiesDeath CertificatesFemaleHistory, 19th CenturyHumansMaternal Health ServicesMaternal MortalityPregnancyRural PopulationScotlandUrban Population19th centuryCertification of deathDoctorsMaternal mortalityObstetric transitionScotlandUnder-recording

Identifiers

PMID29428888
PMCPMC6565842
OpenAlexW2792242457

What OpenQuestion holds

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LicenceCC BY
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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.