In response to the need for health economics modelers to apply more appropriate complex systems models to address complex challenges in public health, an international team of more than 40 experts in the field of complex systems models and economic evaluation has developed and recently published a guideline on the application of complex systems models to the economic evaluation of public health interventions. This paper introduces the development process and main content of the guidelines, which can provide references to facilitate the application of the guidelines by domestic researchers, aiming to ultimately improve the overall quality of public health research and services and improve the health of the population in China.
ObjectiveTo understand the current status of research methods in disease burden systematic reviews, identify limitations and shortcomings of existing research methods, and provide suggestions to address relevant issues. MethodsA computer search of the PubMed database was conducted to collect systematic reviews on disease burden, with search limits set from database inception to December 21, 2023. Two independent researchers utilized Endnote 20 for literature screening and Excel 2019 for data extraction and descriptive analysis. ResultsA total of 216 articles were included in the review, revealing a year-on-year increase in the number of systematic reviews on disease burden since 2004. The journal PharmacoEconomics published the most articles (n=22), while research on certain infectious diseases and parasitic infections was the most prevalent (n=51). Only 31 articles provided a complete account of the entire systematic review process. The reporting rates for inclusion/exclusion criteria, information retrieval, literature screening, and statistical analysis steps were all 100%. However, the rate of protocol registration was relatively low at 19%. Eighty-eight percent of the articles utilized software such as Excel and Epidata for data extraction, yet only 32% adhered to the reproducibility principles outlined in AMSTAR-2. In terms of quality assessment, 105 articles underwent evaluation, with the Joanna Briggs Institute checklist and Newcastle-Ottawa scale being the most commonly used quality assessment tools for epidemiological studies, while economic studies preferred the Drummond checklist (n=9). Regarding the details of inclusion/exclusion criteria, only 53% of studies reported their study design in detail, and less than one-sixth provided a comprehensive description of the interventions and control measures. Statistical analyses predominantly employed qualitative methods (80%), with quantitative analyses comprising a minority (20%), all of which were conducted using meta-analysis techniques, primarily utilizing R software (n=15). ConclusionThe number of systematic reviews on disease burden has shown a yearly increasing trend; however, most studies have failed to comprehensively adhere to the fundamental processes of systematic reviews, significantly limiting their quality. Currently, the primary issues include a lack of protocol registration, incomplete supplementary searches, mismatched quality assessment tools, and insufficiently comprehensive outcome measures. To address these challenges, it is essential to develop a methodological guideline for systematic reviews on disease burden that incorporates these concerns. Such a guideline would standardize researchers' practices and ensure strict adherence to systematic review methodologies, thereby enhancing the scientific rigor of the research and its support for clinical decision-making.
Clinical prediction models typically utilize a combination of multiple variables to predict individual health outcomes. However, multiple prediction models for the same outcome often exist, making it challenging to determine the suitable model for guiding clinical practice. In recent years, an increasing number of studies have evaluated and summarized prediction models using the systematic review/meta-analysis method. However, they often report poorly on critical information. To enhance the reporting quality of systematic reviews/meta-analyses of prediction models, foreign scholars published the TRIPOD-SRMA reporting guideline in BMJ in March 2023. As the number of such systematic reviews/meta-analyses is increasing rapidly domestically, this paper interprets the reporting guideline with a published example. This study aims to assist domestic scholars in better understanding and applying this reporting guideline, ultimately improving the overall quality of relevant research.