ReviewInfluenza and other respiratory viruses2013
Clinical care for severe influenza and other severe illness in resource-limited settings: the need for evidence and guidelines.
Review in Influenza and other respiratory viruses, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
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.
Who cites it
6 citing papers in PubMed.
- Resources and Geographic Access to Care for Severe Pediatric Pneumonia in Four Resource-limited Settings.American journal of respiratory and critical care medicine · 2022Article
- Management of cancer patients during COVID-19 pandemic at developing countries.World journal of clinical cases · 2020Review
- Whole Genome Sequencing of Influenza A and B Viruses With the MinION Sequencer in the Clinical Setting: A Pilot Study.Frontiers in microbiology · 2018Article
- Article
- Global burden of influenza as a cause of cardiopulmonary morbidity and mortality.Global heart · 2014Review
- Clinical care for severe influenza and other severe illness in resource-limited settings: the need for evidence and guidelines.Influenza and other respiratory viruses · 2013Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The 2009 influenza A (H1N1) pandemic highlighted the importance of quality hospital care of the severely ill, yet there is evidence that the impact of the 2009 pandemic was highest in low- and middle-income countries with fewer resources. Recent data indicate that death and suffering from seasonal influenza and severe illness in general are increased in resource-limited settings. However, there are limited clinical data and guidelines for the management of influenza and other severe illness in these settings. Life-saving supportive care through syndromic case management is used successfully in high-resource intensive care units and in global programs such as the Integrated Management of Childhood Illness (IMCI). While there are a variety of challenges to the management of the severely ill in resource-limited settings, several new international initiatives have begun to develop syndromic management strategies for these environments, including the World Health Organization's Integrated Management of Adult and Adolescent Illness Program. These standardized clinical guidelines emphasize syndromic case management and do not require high-resource intensive care units. These efforts must be enhanced by quality clinical research to provide missing evidence and to refine recommendations, which must be carefully integrated into existing healthcare systems. Realizing a sustainable, global impact on death and suffering due to severe influenza and other severe illness necessitates an ongoing and concerted international effort to iteratively generate, implement, and evaluate best-practice management guidelines for use in resource-limited settings.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.