Evidence map›Paper›PMID 29258452›Full record

ArticleBMC pregnancy and childbirth2017

How can we make international comparisons of infant mortality in high income countries based on aggregate data more relevant to policy?

Ania Zylbersztejn, Ruth Gilbert, Anders Hjern, Pia Hardelid

Open access · goldAbstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC pregnancy and childbirth, 2017. 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
2.5field-weighted citation impact, top 9% 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

3 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
  3. 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

4 authors at 3 institutions in 3 countries.

Ania ZylbersztejnThe Farr Institute of Health Informatics Research, 222 Euston Road, London, NW1 2DA, UK. ania.zylbersztejn.14@ucl.ac.uk.ORCID http://orcid.org/0000-0003-1035-1448
Ruth GilbertUCL Great Ormond Street Institute of Child Health, 30 Guilford Street, London, WC1N 1EH, UK.
Anders HjernCentre for Health Equity Studies (CHESS), Stockholm University, SE-10691, Stockholm, Sweden.
Pia HardelidUCL Great Ormond Street Institute of Child Health, 30 Guilford Street, London, WC1N 1EH, UK.
Great Ormond Street Hospital · GBInstitute of Child Health · GRStockholm University · SE

Funding

Awards to establish the Farr Institute of Health Informatics Research, London, from the Medical Research Council, Arthritis Research UK, British Heart Foundation, Cancer Research UK, Chief Scientist Office, Economic and Social Research Council, Engineering MR/K006584/1Department of Health PDF-2013-06-004Medical Research Council MR/K006584/1National Institute for Health Research PDF-2013-06-004Wellcome Trust
6 · The paper itself

Abstract

backgroundInfant mortality rates are commonly used to compare the health of populations. Observed differences are often attributed to variation in child health care quality. However, any differences are at least partly explained by variation in the prevalence of risk factors at birth, such as low birth weight. This distinction is important for designing interventions to reduce infant mortality. We suggest a simple method for decomposing inter-country differences in crude infant mortality rates into two metrics representing risk factors operating before and after birth.

methodsWe used data from 7 European countries participating in the EURO-PERISTAT project in 2010. We calculated crude and birth weight-standardised stillbirth and infant mortality rates using Norway as the standard population. We decomposed between-country differences in crude stillbirth and infant mortality rates into the within-country difference in crude and birth weight-standardised stillbirth and infant mortality rates (metric 1), reflecting prenatal risk factors, and the between-country difference in birth weight-standardised stillbirth and infant mortality rates (metric 2), reflecting risk factors operating after birth. We also calculated birth weight-specific mortality.

resultsUsing our metrics, we showed that for England, Wales and Scotland risk factors before and after birth contributed equally to the differences in crude stillbirth and infant mortality rates relative to Norway. In Austria, Czech Republic and Switzerland the differences were driven primarily by metric 1, reflecting high rate of low birth weight. The highest values of metric 2 observed in Poland partially reflected high rates of congenital anomalies.

conclusionsOur suggested metrics can be used to guide policy decisions on preventing infant deaths through reducing risk factors at birth or improving the care of babies after birth. Aggregate data tabulated by birth weight/gestational age should be routinely collected and published in high-income countries where birth weight is reported on birth certificates.

Indexed as

Birth WeightInfant MortalityDeveloped CountriesEuropeGestational AgeHealth PolicyHumansInfantPostnatal CarePostpartum PeriodRisk FactorsStillbirthBirth weightInfant mortalityInternational comparisonNeonatal mortalityPost-neonatal mortalityStillbirth

Identifiers

PMID29258452
PMCPMC5738161
OpenAlexW2779279595

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.