Quality, Equity and Utility of Observational Studies during 10 Years of Implementing the Structured Operational Research and Training Initiative in 72 Countries.
Ontology highlight
ABSTRACT: Introduction: Observational studies are often inadequately reported, making it difficult to assess their validity and generalizability and judge whether they can be included in systematic reviews. We assessed the publication characteristics and quality of reporting of observational studies generated by the Structured Operational Research and Training Initiative (SORT IT). Methods: A cross-sectional analysis of original publications from SORT IT courses. SORT IT is a global partnership-based initiative aimed at building sustainable capacity for conducting operational research according to country priorities and using the generated evidence for informed decision-making to improve public health. Reporting quality was independently assessed using an adapted version of 'Strengthening the Reporting of Observational Studies in Epidemiology' (STROBE) checklist. Results: In 392 publications, involving 72 countries, 50 journals, 28 publishers and 24 disease domains, low- and middle-income countries (LMICs) first authorship was seen in 370 (94%) and LMIC last authorship in 214 (55%). Publications involved LMIC-LMIC collaboration in 90% and high-income-country-LMIC collaboration in 87%. The majority (89%) of publications were in immediate open access journals. A total of 346 (88.3%) publications achieved a STROBE reporting quality score of >85% (excellent), 41 (10.4%) achieved a score of 76-85% (good) and 5 (1.3%) a score of 65-75% (fair). Conclusion: The majority of publications from SORT IT adhere to STROBE guidelines, while also ensuring LMIC equity and collaborative partnerships. SORT IT is, thus, playing an important role in ensuring high-quality reporting of evidence for informed decision-making in public health.
SUBMITTER: Zachariah R
PROVIDER: S-EPMC7709644 | biostudies-literature | 2020 Nov
REPOSITORIES: biostudies-literature
ACCESS DATA