Evidence map›Paper›PMID 34728480›Full record

ReviewBMJ global health2021

Mortality estimates for WHO SEAR countries: problems and prospects.

Chalapati Rao, Kanitta Bundhamcharoen, Matthew Kelly, Viroj Tangcharoensathien

Abstract readReview
In one paragraph

Review in BMJ global health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Review
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.

Chalapati RaoResearch School of Population Health, Australian National University, Canberra, Australian Capital Territory, Australia chalapati.rao@anu.edu.au.ORCID 0000-0002-9554-0581
Kanitta BundhamcharoenInternational Health Policy Program, Ministry of Public Health, Nonthaburi, Thailand.
Matthew KellyResearch School of Population Health, Australian National University, Canberra, Australian Capital Territory, Australia.ORCID 0000-0001-7963-2139
Viroj TangcharoensathienInternational Health Policy Program, Ministry of Public Health, Nonthaburi, Thailand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cause-specific mortality estimates for 11 countries located in the WHO's South East Asia Region (WHO SEAR) are generated periodically by the Global Burden of Disease (GBD) and the WHO Global Health Estimates (GHE) analyses. A comparison of GBD and GHE estimates for 2019 for 11 specific causes of epidemiological importance to South East Asia was undertaken. An index of relative difference (RD) between the estimated numbers of deaths by sex for each cause from the two sources for each country was calculated, and categorised as marginal (RD=±0%-9%), moderate (RD=±10%-19%), high (RD=±20%-39%) and extreme (RD>±40%). The comparison identified that the RD was >10% in two-thirds of all instances. The RD was 'high' or 'extreme' for deaths from tuberculosis, diarrhoea, road injuries and suicide for most SEAR countries, and for deaths from most of the 11 causes in Bangladesh, DPR Korea, Myanmar, Nepal and Sri Lanka. For all WHO SEAR countries, mortality estimates from both sources are based on statistical models developed from an international historical cause-specific mortality data series that included very limited empirical data from the region. Also, there is no scientific rationale available to justify the reliability of one set of estimates over the other. The characteristics of national mortality statistics systems for each WHO SEAR country were analysed, to understand the reasons for weaknesses in empirical data. The systems analysis identified specific limitations in structure, organisation and implementation that affect data completeness, validity of causes of death and vital statistics production, which vary across countries. Therefore, customised national strategies are required to strengthen mortality statistics systems to meet immediate and long-term data needs for health policy and research, and reduce dependence on current unreliable modelled estimates.

Indexed as

Global Burden of DiseaseGlobal HealthCause of DeathHumansReproducibility of ResultsWorld Health Organizationepidemiologyhealth policypublic health

Identifiers

PMID34728480
PMCPMC8568533

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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.