Evidence map›Paper›PMID 34327002›Full record

ArticleJournal of global health2021

Using evidence, expert opinion and epidemiological model to understand pathways to survival and mortality: The Pathways to Survival (PATHS) Tool.

Igor Rudan, Evropi Theodoratou, Kit Yee Chan, Davies Adeloye, Ozren Polašek, Harry Campbell, Mickey Chopra

Open access · goldAbstract read
In one paragraph

Article in Journal of global health, 2021. 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
1.0field-weighted citation impact, top 22% 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, 4 citations in OpenAlex.

  1. Article
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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

7 authors at 3 institutions in 3 countries.

Igor RudanCentre for Global Health, The Usher Institute, University of Edinburgh, Edinburgh, UK.
Evropi TheodoratouCentre for Global Health, The Usher Institute, University of Edinburgh, Edinburgh, UK.
Kit Yee ChanCentre for Global Health, The Usher Institute, University of Edinburgh, Edinburgh, UK.
Davies AdeloyeCentre for Global Health, The Usher Institute, University of Edinburgh, Edinburgh, UK.
Ozren PolašekCroatian Centre for Global Health, University of Split School of Medicine, Split, Croatia.
Harry CampbellCentre for Global Health, The Usher Institute, University of Edinburgh, Edinburgh, UK.
Mickey ChopraHealth Nutrition and Population, The World Bank, Washington, DC, USA.
University of Edinburgh · GBUniversity of Split · HRWorld Bank · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe reasons why episodes of illness can lead to fatal outcomes in affected persons in low resource settings are numerous and complex. A tool that allows policy makers to better understand those complexities could be useful to improve success of programmes that are implemented globally to reduce mortality.

methodsWe developed a "Pathways to Survival" (PATHS) tool: an epidemiological model using decision trees, available evidence and expert opinion. PATHS visualises the "architecture" of mortality in the population by following the entire population cohort over a certain period of time. It explains how initially healthy persons progress through health systems to lethal outcomes at the end of the specified time period. We developed an illustrative example based on the 136 million newborns and an estimated 907 000 deaths from newborn sepsis in the year 2008. This allowed us to develop an epidemiological model that described pathways to deaths from neonatal sepsis globally in 2010.

resultsThe model described the "status quo' situation in 2010 with 907 000 deaths to allow an assessment of the potential impact and feasibility of different interventions and programmes at various level of health systems in reducing this cause of mortality. A useful model should incorporate both a 'horizontal' and a 'vertical' component. The 'horizontal' would track the progress of all neonates globally through time, ie, their first 28 days of life, and separate them into different 'pathways' every time a change in their risk of dying from neonatal infection occurs because of their specific contextual circumstances. The 'vertical' would track their position within the health systems of their countries and separate them into different categories based on the ability of health system to intervene and reduce their risk of dying. Based on those requirements, PATHS tool was developed which is based on decision trees where different "branches" of the trees are associated with varying case-fatality rates.

conclusionsThe application of the PATHS tool on the example of newborn sepsis revealed that novel diagnostic tests could save many lives, so we should continue to invest in them to improve their validity, deliverability and affordability. However, PATHS showed that investments in better diagnostics have limited impact unless they are coupled with improvements of the context. Programs for parental education improve compliance and care seeking. Promoting legislation change to empower community health workers (CHWs) to actively engage in prevention, diagnosis and care also makes a difference, as well as programs for training CHWs to use diagnostic tests and administer treatments correctly. Care-seeking behaviour can also be improved through programs of conditional cash transfers. Finally, PATHS demonstrated that improving access to primary and secondary health care for everyone is the most powerful contextual change.

Indexed as

Infant MortalityModels, BiologicalSurvival AnalysisEvidence-Based PracticeHumansInfantInfant, Newborn

Identifiers

PMID34327002
PMCPMC8310575
OpenAlexW3166385040

What OpenQuestion holds

Textmetadata
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.