Modular literature review: a novel systematic search and review method to support priority setting in health policy and practice

Background There is an unmet need for review methods to support priority-setting, policy-making and strategic planning when a wide variety of interventions from differing disciplines may have the potential to impact a health outcome of interest. This article describes a Modular Literature Review, a novel systematic search and review method that employs systematic search strategies together with a hierarchy-based appraisal and synthesis of the resulting evidence. Methods We designed the Modular Review to examine the effects of 43 interventions on a health problem of global significance. Using the PICOS (Population, Intervention, Comparison, Outcome, Study design) framework, we developed a single four-module search template in which population, comparison and outcome modules were the same for each search and the intervention module was different for each of the 43 interventions. A series of literature searches were performed in five databases, followed by screening, extraction and analysis of data. “ES documents”, source documents for effect size (ES) estimates, were systematically identified based on a hierarchy of evidence. The evidence was categorised according to the likely effect on the outcome and presented in a standardised format with quantitative effect estimates, meta-analyses and narrative reporting. We compared the Modular Review to other review methods in health research for its strengths and limitations. Results The Modular Review method was used to review the impact of 46 antenatal interventions on four specified birth outcomes within 12 months. A total of 61,279 records were found; 35,244 were screened by title-abstract. Six thousand two hundred seventy-two full articles were reviewed against the inclusion criteria resulting in 365 eligible articles. Conclusions The Modular Review preserves principles that have traditionally been important to systematic reviews but can address multiple research questions simultaneously. The result is an accessible, reliable answer to the question of “what works?”. Thus, it is a well-suited literature review method to support prioritisation, decisions and planning to implement an agenda for health improvement.

The making and unmaking of Hashimoto’s thyroiditis: On the mismatch between illness and disease. Results from an Italian study

The diagnostic process in contemporary medical practice is increasingly technical, specialised and relying on population-based ranges of biological normalcy. Disease is defined according to a hierarchy of evidence that privileges specialist knowledge and marginalises subjective experiences of illness. Medical and individual definitions of the situation can conflict in two ways: (i) a diagnosis is made in the absence of symptoms, (ii) individual suffering does not constitute ‘real’ disease if it is not validated by scientific evidence. This article investigates how the discrepancy between specialist and embodied knowledge is experienced and tentatively solved by patients’ self-narratives. Starting from the analysis of 22 in-depth interviews with people affected by autoimmune diseases, we focus on the subgroup affected by Hashimoto’s thyroiditis. Applying the most-different-systems design, we confront two flesh-and-blood ideal-types of illness narratives characterised by a mismatch between illness and disease. Their diagnostic trajectories are outlined and discussed as poles of a continuum of experiences resulting from different configurations of medical evidence of disease and subjective evidence of illness.

Miasmas, mental models and preventive public health: some philosophical reflections on science in the COVID-19 pandemic

When the history of the COVID-19 pandemic is written, it is likely to show that the mental models held by scientists sometimes facilitated their thinking, thereby leading to lives saved, and at other times constrained their thinking, thereby leading to lives lost. This paper explores some competing mental models of how infectious diseases spread and shows how these models influenced the scientific process and the kinds of facts that were generated, legitimized and used to support policy. A central theme in the paper is the relative weight given by dominant scientific voices to probabilistic arguments based on experimental measurements versus mechanistic arguments based on theory. Two examples are explored: the cholera epidemic in nineteenth century London—in which the story of John Snow and the Broad Street pump is retold—and the unfolding of the COVID-19 pandemic in 2020 and early 2021—in which the evidence-based medicine movement and its hierarchy of evidence features prominently. In each case, it is shown that prevailing mental models—which were assumed by some to transcend theory but were actually heavily theory-laden—powerfully shaped both science and policy, with fatal consequences for some.

Pharmacogenetic testing for adverse drug reaction prevention: systematic review of economic evaluations and the appraisal of quality matters for clinical practice and implementation

Background Genetic testing has potential roles in identifying whether an individual would have risk of adverse drug reactions (ADRs) from a particular medicine. Robust cost-effectiveness results on genetic testing would be useful for clinical practice and policy decision-making on allocating resources effectively. This study aimed to update a systematic review on economic evaluations of pharmacogenetic testing to prevent ADRs and critically appraise the quality of reporting and sources of evidence for model input parameters. Methods We searched studies through Medline via PubMed, Scopus and CRD’s NHS Economic Evaluation up to October 2019. Studies investigating polymorphism-based pharmacogenetic testing, which guided drug therapies to prevent ADRs, using economic evaluation methods were included. Two reviewers independently performed data extraction and assessed the quality of reporting using the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) guidelines and the quality of data sources using the hierarchy of evidence developed by Cooper et al. Results Fifty-nine economic evaluations of pharmacogenetic testing to avoid drug-induced ADRs were found between 2002 and 2018. Cost-utility and cost-effectiveness analyses were the most common methods of economic evaluation of pharmacogenetic testing. Most studies complied with the CHEERS checklist, except for single study-based economic evaluations which did not report uncertainty analysis (78%). There was a lack of high-quality evidence not only for estimating the clinical effectiveness of pharmacogenetic testing, but also baseline clinical data. About 14% of the studies obtained clinical effectiveness data of testing from a meta-analysis of case-control studies with direct comparison, which was not listed in the hierarchy of evidence used. Conclusions Our review suggested that future single study-based economic evaluations of pharmacogenetic testing should report uncertainty analysis, as this could significantly affect the robustness of economic evaluation results. A specific ranking system for the quality of evidence is needed for the economic evaluation of pharmacogenetic testing of ADRs. Differences in parameters, methods and outcomes across studies, as well as population-level and system-level differences, may lead to the difficulty of comparing cost-effectiveness results across countries.

Approaches to Assessing the Quality of Observational Studies of Clinical Practice Based on the Big Data Analysis

The article is devoted to the discussion of the problems of assessing the quality of observational studies in real clinical practice and determining their place in the hierarchy of evidence-based information. The concept of “big data” and the acceptability of using such a term to refer to large observational studies is being discussed. Data on the limitations of administrative and claims databases when performing observational studies to assess the effects of interventions are presented. The concept of confounding factors influencing the results of observational studies is discussed. Modern approaches to reducing the severity of bias in real-life clinical practice studies are presented. The criteria for assessing the quality of observational pharmacoepidemiological studies and the fundamental differences between such studies and randomized clinical trials are presented. The results of systematic reviews of real-life clinical trials to assess the effects of direct oral anticoagulants are discussed.

A Hierarchical Framework for Assessing Transmission Causality of Respiratory Viruses

We propose a hierarchical framework based on our experience of systematically reviewing and synthesizing 378 primary studies for an evidence-based update of the modes of transmission for SARS-CoV-2. These studies revealed significant methodological shortcomings with a lack of standardization in the design, conduct, testing and reporting of SARS-CoV-2 transmission. While this situation is in part excusable at the outset of a pandemic, evidence rules of proof for assessing the transmission of this virus are needed for this and future pandemics of viral respiratory pathogens. We review the history of causality assessment related to microbial etiologies with a focus on respiratory viruses and suggest a hierarchy of evidence to integrate clinical, epidemiologic, molecular and laboratory perspectives on transmission. The hierarchy, if applied to future studies, should narrow the uncertainty over the twin concepts of causality and transmission of human respiratory viruses. We attempt to address the translational gap between the current research evidence and the assessment of causality in the transmission of respiratory viruses with a focus on SARS-CoV-2. Experimentation, consistency and independent replication of research alongside our proposed framework provide a chain of evidence that can reduce the uncertainty over the transmission of respiratory viruses and increase the level of confidence in specific modes of transmission and the measures that should be undertaken to prevent transmission

Challenges to Evidence Synthesis and Identification of Data Gaps in Human Biomonitoring

The increasing number of human biomonitoring (HBM) studies undertaken in recent decades has brought to light the need to harmonise procedures along all phases of the study, including sampling, data collection and analytical methods to allow data comparability. The first steps towards harmonisation are the identification and collation of HBM methodological information of existing studies and data gaps. Systematic literature reviews and meta-analyses have been traditionally put at the top of the hierarchy of evidence, being increasingly applied to map available evidence on health risks linked to exposure to chemicals. However, these methods mainly capture peer-reviewed articles, failing to comprehensively identify other important, unpublished sources of information that are pivotal to gather a complete map of the produced evidence in the area of HBM. Within the framework of the European Human Biomonitoring Initiative (HBM4EU) initiative—a project that joins 30 countries, 29 from Europe plus Israel, the European Environment Agency and the European Commission—a comprehensive work of data triangulation has been made to identify existing HBM studies and data gaps across countries within the consortium. The use of documentary analysis together with an up-to-date platform to fulfil this need and its implications for research and practice are discussed.

Analysis of the Type and Study Design of Publications in the Journal of Indian Orthodontic Society and the Changes in Trends Over 2 Decades

Background: The aim of the study was to analyze the type and study design of publications in the Journal of Indian Orthodontic Society (JIOS) and the changes in their trends from 2001 to 2020. Materials and Methods: All the online publications in JIOS were classified as background resource, original articles, and case reports. The original articles were further divided into 7 types based on study design. The annual and overall data were compiled. To evaluate the change in trends, the study period was divided into 2 block years: 2001 to 2010 and 2011 to 2020. The differences in the total number of articles and distribution of articles by type and study design were analyzed using Mann–Whitney and chi-square/Fisher’s exact tests, respectively. Results: Background resource, original articles, and case reports comprised 38.7%, 43.9%, and 17.4% of the total articles (n = 845), respectively. Non-clinical (34.5%) and cross-sectional (32.35%) studies formed the bulk of original articles. There were 17 randomized controlled trials (RCTs) (4.58%) and 5 systematic reviews (1.35%) among the original articles. The total number of publications were higher for years in the second block compared to those in the first (Mdn of 63.5 vs 14.5, P < .001). The differences in the distribution according to type were not statistically significant [ X2(2) = 2.052, P = .35]. A reduction in the share of cohort studies and increase in RCTs were found in the second block (F = 19.174, P = .002). Conclusions: The higher proportion of background resource publications and those with study designs lower in the hierarchy of evidence is a matter of concern. Though slow paced, the increase in the number of RCTs and systematic reviews over the past few years is encouraging.