scholarly journals Prescribing patterns and compliance with World Health Organization recommendations for the management of severe malaria: a modified cohort event monitoring study in public health facilities in Ghana and Uganda

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
H. Hilda Ampadu ◽  
Kwaku Poku Asante ◽  
Samuel Bosomprah ◽  
Samantha Akakpo ◽  
Pierre Hugo ◽  
...  
2016 ◽  
Vol 36 (5) ◽  
pp. 401-411 ◽  
Author(s):  
Alambo K. Mssusa ◽  
Adam M. Fimbo ◽  
Alex F. Nkayamba ◽  
Henry F. Irunde ◽  
Hiiti B. Sillo ◽  
...  

Author(s):  
Bereket Bahiru Tefera ◽  
Melese Getachew ◽  
Bekalu Kebede

Abstract Background Drug use evaluation is a structured, methodological, and criteria-based drug assessment system that helps to evaluate the actual trend of drug use in a particular setting. If drug prescription practices are inappropriate, need to examine the patterns of drug use is necessary to change prescribing patterns accordingly. Therefore, this review aimed to determine the drug prescription pattern in public health facilities found in Ethiopia using prescribing indicators developed by the World Health Organization. Methods This review was conducted as per the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guideline. Extensive searching to identify articles was conducted in PubMed, Medline, Web of Science, Research Gate, Africa Journal of Online, and Google scholar. Finally, 10 eligible articles were selected for analysis based on inclusion and exclusion criteria. The median value, as well as the 25th and 75th percentiles for each WHO prescribing indicator, were computed. Result The pooled median value of WHO prescribing indicators was reported as follows: the average number of drugs prescribed per encounter = 2.14 (IQR 1.79–2.52), the percentage of encounters with antibiotics prescribed = 43.46% (IQR 30.01–58.67), the percentage of encounters with an injection prescribed = 13.20% (6.47–40.7), percentage of drugs prescribed by generic name = 93.49% (89.13–97.96), and the percentage of medicines prescribed from essential medicines list = 92.54% (85.10–97.7). The forest plots determined for each prescribing indicator indicated that there is a high degree of heterogeneity across articles. Conclusion All of the prescribing indicators were not consistent with the standard values recommended by the World Health Organization. Therefore, public health facilities should take appropriate measures for improving the prescription patterns as per the recommendation set by the World Health Organization.


2021 ◽  
Author(s):  
Sarah Kreps

BACKGROUND Misinformation about COVID-19 has presented challenges to public health authorities during pandemics. Understanding the prevalence and type of misinformation across contexts offers a way to understand the discourse around COVID-19 while informing potential countermeasures. OBJECTIVE The aim of the study was to study COVID-19 content on two prominent microblogging platform, Twitter, based in the United States, and Sina Weibo, based in China, and compare the content and relative prevalence of misinformation to better understand public discourse of public health issues across social media and cultural contexts. METHODS A total of 3,579,575 posts were scraped from both Weibo and Twitter, focusing on content from January 30th, 2020, when the World Health Organization (WHO) declared COVID-19 a “Public Health Emergency of International Concern” and February 6th, 2020. A 1% random sample of tweets that contained both the English keywords “coronavirus” and “covid-19” and the equivalent Chinese characters was extracted and analyzed based on changes in the frequencies of keywords and hashtags. Misinformation on each platform was compared by manually coding and comparing posts using the World Health Organization fact-check page to adjudicate accuracy of content. RESULTS Both platforms posted about the outbreak and transmission but posts on Sina Weibo were less likely to reference controversial topics such as the World Health Organization and death and more likely to cite themes of resisting, fighting, and cheering against the coronavirus. Misinformation constituted 1.1% of Twitter content and 0.3% of Weibo content. CONCLUSIONS Quantitative and qualitative analysis of content on both platforms points to cross-platform differences in public discourse surrounding the pandemic and informs potential countermeasures for online misinformation.


2012 ◽  
Vol 7 (2) ◽  
pp. 51
Author(s):  
Oedojo Soedirham

Kota Sehat merupakan proyek World Health Organization (WHO) yang diluncurkan pada pertengahan tahun 1980-an dengan mengambil tempat untuk yang pertama kali adalah kota-kota di Eropa. Konsep Kota Sehat adalah konsep lama sekaligus baru. “Lama” berarti telah lama manusia berusaha untuk membuat kota lebih sehat sejak awal peradaban perkotaan (urban civilization). “Baru” dalam manifestasinya sebagai satu sarana utama promosi kesehatan – kesehatan masyarakat baru (new public health) – dalam pencarian Sehat untuk Semua (Health for All). Hal tersebut dipandang sebagai “a means of legitimizing, nurturing, and supporting the process of community empowerment”. Artikel ini mengulas Kota Sehat dalam konteks sustainable communities.Kata kunci: Kota sehat, kesehatan masyarakat baru, pemberdayaan, sustainable communitiesAbstractHealthy City is a World Health Organization (WHO) project that launched in mid 1980s with cities at Europe as first attempts. The Healthy City concept is old and new. “Old” means that since the early urban civilization, humanbeing striving for better and healthier places to live. “New” means that it’s one primary manifestation for health promotion – new public health – in seeking “Health for All”. This is seen as “a means of legitimizing, nurturing, and supporting the process of community empowerment”. The paper reviewed Healthy City in sustainable communities context.Key words: Healthy city, new public health, empowerment, sustainable communities


2017 ◽  
Vol 22 (1) ◽  
pp. 46
Author(s):  
SaurabhRamBihariLal Shrivastava ◽  
PrateekSaurabh Shrivastava ◽  
Jegadeesh Ramasamy

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