ArticleThe Pan African medical journal2020
Algorithm for asthma diagnosis and management at Chitungwiza Central Hospital, Zimbabwe.
Article in The Pan African medical journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed, 3 citations in OpenAlex.
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 at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionasthma is a chronic inflammatory and a heterogeneous condition of respiratory system whose pathogenesis is linked with variable structural changes. The clinical manifestation of asthma includes attacks of breathlessness, cough, chest tightness and wheezing. Provision of basic equipment and test for asthma diagnosis and access to essential medicines by asthmatic patients reduces morbidity and mortality rates. Significant progress has been made in the diagnosis and management of asthma in other countries but not in the health care delivery system in Zimbabwe. Therefore, the aim of this study was to develop algorithm for asthma diagnosis and management for Zimbabwe.
methodsa two stage Delphi model was used to collect data in order to develop an algorithm of asthma diagnosis and management. A baseline interview with 44 doctors was done to understand their experiences and knowledge regarding asthma diagnosis and management. We collected data using the KoBo Collect Toolbox installed on Android mobile phone and transferred the data to an Excel 2016 spreadsheet for cleaning. The data was qualitatively analysed and themes were constructed. These themes were further explored in stage two at an algorithm development workshop which was led by 4 medical expert panellists in order to develop consensus on the information to be included in the algorithms for asthma diagnosis and management. A total of 15 doctors and 30 nurses participated at the workshop.
resultsdoctors who attended the workshop described the challenges in asthma diagnosis and management that they experienced. These challenges were attributed to lack of basic equipment such as spirometers and Peak Expiratory Flow Meters and tests which included IgE tests, Skin Allergen Tests and RAST. Asthma diagnosis clinical history and management was based on the doctors' knowledge. The doctors indicated the need for refresher courses to update their knowledge on asthma diagnosis and enhance their diagnostic skills. A draft algorithm framework for asthma diagnosis was developed at the workshop and later refined by the core-research team. The final algorithm described in this paper was circulated for further contributions and endorsement by the asthma experts.
conclusionwe established the need for doctors to receive constant refresher courses on asthma diagnosis for upskilling. We recommend adoption by the Zimbabwe's Ministry of Health and Child Care of the asthma diagnostic algorithm we developed.
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.