Dataset Information

Systematic Review and Weight of Evidence Are Integral to Ecological and Human Health Assessments: They Need an Integrated Framework.

ABSTRACT: Scientific assessments synthesize the various results of scientific research for policy and decision making. Synthesizing evidence in environmental assessments can involve either or both of 2 systems: systematic review (SR) and weight of evidence (WoE). Systematic review was developed to systematically assemble results of clinical trials to be combined by meta-analysis. Weight-of-evidence approaches have evolved from jurisprudence to make inferences from diverse bodies of evidence in various fields. Our objectives are to describe the similarities and differences between SR and WoE and to suggest how their best practices can be combined into a general framework that is applicable to human health and ecological assessments. Integrating SR and WoE is based on the recognition that 2 processes are required, assembling evidence and making an inference. Systematic review is characterized by methodical literature searching, screening, and data extraction, originally for meta-analysis but now for various inferential methods. Weight of evidence is characterized by systematically relating heterogeneous evidence to considerations appropriate to the inference and making the inference by weighing the evidence. Systematic review enables the unbiased assembly of evidence from literature, but methods for assembling other information must be considered as well. If only 1 type of quantitative study estimates the assessment endpoint, meta-analysis is appropriate for inference. Otherwise, the heterogeneous evidence must be weighed. A framework is presented that integrates best practices into a methodical assembly and weighing of evidence. A glossary of terms for the combined practice and a history of the origins of SR and WoE are provided in Supplemental Data. Integr Environ Assess Manag 2020;16:718-728. Published 2020. This article is a US Government work and is in the public domain in the USA.

SUBMITTER: Suter G

PROVIDER: S-EPMC7551547 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:A growing challenge for risk, vulnerability, and resilience assessment is the ability to understand, characterize, and model the complexities of our joint socioecological systems, often delineated with differing natural (e.g., watershed) and imposed (e.g., political) boundaries at the landscape scale. To effectively manage such systems in the increasingly dynamic, adaptive context of environmental change, we need to understand not just food web interactions of contaminants or the flooding impacts of sea level rise and storm surges, but rather the interplay between social and ecological components within the inherent and induced feedforward and feedback system mechanisms. Risk assessment, in its traditional implementation, is a simplification of a complex problem to understand the basic cause-and-effect relationships within a system. This approach allows risk assessors to distill a complex issue into a manageable model that quantifies, or semiquantifies, the effects of an adverse stressor. Alternatively, an integrated risk and resilience assessment moves toward a solution-based assessment with the incorporation of adaptive management practices as 1 of 4 parts of system resilience (i.e., prepare, absorb, recover, and adapt), and directly considers the complexities of the systems being modeled. We present the Multilevel Risk and Resilience Assessment Parameterization framework for the systematic parameterization and deconstruction of management objectives and goals into assessment metrics and quantifiable risk measurement metrics and complementary resilience measurement metrics. As a proof-of-concept, the presented framework is paired with the Bayesian Network-Relative Risk Model for a human-focused subset of a larger risk and resilience assessment of climate change impacts within the Charleston Harbor Watershed of South Carolina. This new parameterization framework goes beyond traditional simplification and embraces the complexity of the system as a whole, which is necessary for a more representative analysis of an open, dynamic complex system. Integr Environ Assess Manag 2021;17:131-146. © 2020 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Project description:ObjectiveValidity refers to the extent to which evidence and theory support the adequacy and appropriateness of inferences based on score interpretations. The health sector is lacking a theoretically-driven framework for the development, testing and use of health assessments. This study used the Standards for Educational and Psychological Testing framework of five sources of validity evidence to assess the types of evidence reported for health literacy assessments, and to identify studies that referred to a theoretical validity testing framework.MethodsA systematic descriptive literature review investigated methods and results in health literacy assessment development, application and validity testing studies. Electronic searches were conducted in EBSCOhost, Embase, Open Access Theses and Dissertations and ProQuest Dissertations. Data were coded to the Standards' five sources of validity evidence, and for reference to a validity testing framework.ResultsCoding on 46 studies resulted in 195 instances of validity evidence across the five sources. Only nine studies directly or indirectly referenced a validity testing framework. Evidence based on relations to other variables is most frequently reported.ConclusionsThe health and health equity of individuals and populations are increasingly dependent on decisions based on data collected through health assessments. An evidence-based theoretical framework provides structure and coherence to existing evidence and stipulates where further evidence is required to evaluate the extent to which data are valid for an intended purpose. This review demonstrates the use of the Standards' theoretical validity testing framework to evaluate sources of evidence reported for health literacy assessments. Findings indicate that theoretical validity testing frameworks are rarely used to collate and evaluate evidence in validation practice for health literacy assessments. Use of the Standards' theoretical validity testing framework would improve evaluation of the evidence for inferences derived from health assessment data on which public health and health equity decisions are based.

Project description:BackgroundHealthy behaviors are crucial for maintaining a person's health and well-being. The effects of health behavior interventions are mediated by individual and contextual factors that vary over time. Recently emerging smartphone-based ecological momentary interventions (EMIs) can use real-time user reports (ecological momentary assessments [EMAs]) to trigger appropriate support when needed in daily life.ObjectiveThis systematic review aims to assess the characteristics of smartphone-delivered EMIs using self-reported EMAs in relation to their effects on health behaviors, user engagement, and user perspectives.MethodsWe searched MEDLINE, Embase, PsycINFO, and CINAHL in June 2019 and updated the search in March 2020. We included experimental studies that incorporated EMIs based on EMAs delivered through smartphone apps to promote health behaviors in any health domain. Studies were independently screened. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed. We performed a narrative synthesis of intervention effects, user perspectives and engagement, and intervention design and characteristics. Quality appraisal was conducted for all included studies.ResultsWe included 19 papers describing 17 unique studies and comprising 652 participants. Most studies were quasi-experimental (13/17, 76%), had small sample sizes, and great heterogeneity in intervention designs and measurements. EMIs were most popular in the mental health domain (8/17, 47%), followed by substance abuse (3/17, 18%), diet, weight loss, physical activity (4/17, 24%), and smoking (2/17, 12%). Of the 17 studies, the 4 (24%) included randomized controlled trials reported nonstatistically significant effects on health behaviors, and 4 (24%) quasi-experimental studies reported statistically significant pre-post improvements in self-reported primary outcomes, namely depressive (P<.001) and psychotic symptoms (P=.03), drinking frequency (P<.001), and eating patterns (P=.01). EMA was commonly used to capture subjective experiences as well as behaviors, whereas sensors were rarely used. Generally, users perceived EMIs to be helpful. Common suggestions for improvement included enhancing personalization, multimedia and interactive capabilities (eg, voice recording), and lowering the EMA reporting burden. EMI and EMA components were rarely reported and were not described in a standardized manner across studies, hampering progress in this field. A reporting checklist was developed to facilitate the interpretation and comparison of findings and enhance the transparency and replicability of future studies using EMAs and EMIs.ConclusionsThe use of smartphone-delivered EMIs using self-reported EMAs to promote behavior change is an emerging area of research, with few studies evaluating efficacy. Such interventions could present an opportunity to enhance health but need further assessment in larger participant cohorts and well-designed evaluations following reporting checklists. Future research should explore combining self-reported EMAs of subjective experiences with objective data passively collected via sensors to promote personalization while minimizing user burden, as well as explore different EMA data collection methods (eg, chatbots).Trial registrationPROSPERO CRD42019138739; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=138739.

Dataset Information

Systematic Review and Weight of Evidence Are Integral to Ecological and Human Health Assessments: They Need an Integrated Framework.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets