Evidence map›Paper›PMID 26400682›Full record

ArticleBMC public health2015

Beyond crystal balls: crosscutting solutions in global health to prepare for an unpredictable future.

Wladimir Jimenez Alonso, Benjamin Joseph James McCormick, Mark A Miller, Cynthia Schuck-Paim, Ghassem R Asrar

Abstract read
In one paragraph

Article in BMC public health, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Wladimir Jimenez AlonsoFogarty International Center, National Institutes of Health, Bethesda, Maryland, 20892, USA. alonsow@mail.nih.gov.
Benjamin Joseph James McCormickFogarty International Center, National Institutes of Health, Bethesda, Maryland, 20892, USA. ben.mccormick@nih.gov.
Mark A MillerFogarty International Center, National Institutes of Health, Bethesda, Maryland, 20892, USA. millemar@mail.nih.gov.
Cynthia Schuck-PaimOrigem Scientifica, São Paulo, São Paulo, Brazil. cynthia.paim@wolfson.oxon.org.
Ghassem R AsrarJoint Global Change Research Institute, University of Maryland, College Park, MD, 20740, USA. ghassem.asrar@pnnl.gov.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEfforts in global heath need to deal not only with current challenges, but also to anticipate new scenarios, which sometimes unfold at lightning speed. Predictive modeling is frequently used to assist planning, but outcomes depend heavily on a subset of critical assumptions, which are mostly hampered by our limited knowledge about the many factors, mechanisms and relationships that determine the dynamics of disease systems, by a lack of data to parameterize and validate models, and by uncertainties about future scenarios. DISCUSSION: We propose a shift from a focus on the prediction of individual disease patterns to the identification and mitigation of broader fragilities in public health systems. Modeling capabilities should be used to perform "stress tests" on how interrelated fragilities respond when faced with a range of possible or plausible threats of different nature and intensity. This system should be able to reveal crosscutting solutions with the potential to address not only one threat, but multiple areas of vulnerability to future health risks. Actionable knowledge not based on a narrow subset of threats and conditions can better guide policy, build societal resilience and ensure effective prevention in an uncertain world.

Indexed as

Models, TheoreticalForecastingGlobal HealthHumansPublic Health

Identifiers

PMID26400682
PMCPMC4581487

What OpenQuestion holds

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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.