scholarly journals The use of Qualitative Comparative Analysis (QCA) to address causality in complex systems: a systematic review of research on public health interventions

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Benjamin Hanckel ◽  
Mark Petticrew ◽  
James Thomas ◽  
Judith Green
Keyword(s):  
Public Health ◽  
Comparative Analysis ◽  
Complex Systems ◽  
Qualitative Comparative Analysis ◽  
Complex Interventions ◽  
Health Interventions ◽  
Data Availability ◽  
Future Research ◽  
Public Health Interventions ◽  
Minimum Criteria

Abstract Background Qualitative Comparative Analysis (QCA) is a method for identifying the configurations of conditions that lead to specific outcomes. Given its potential for providing evidence of causality in complex systems, QCA is increasingly used in evaluative research to examine the uptake or impacts of public health interventions. We map this emerging field, assessing the strengths and weaknesses of QCA approaches identified in published studies, and identify implications for future research and reporting. Methods PubMed, Scopus and Web of Science were systematically searched for peer-reviewed studies published in English up to December 2019 that had used QCA methods to identify the conditions associated with the uptake and/or effectiveness of interventions for public health. Data relating to the interventions studied (settings/level of intervention/populations), methods (type of QCA, case level, source of data, other methods used) and reported strengths and weaknesses of QCA were extracted and synthesised narratively. Results The search identified 1384 papers, of which 27 (describing 26 studies) met the inclusion criteria. Interventions evaluated ranged across: nutrition/obesity (n = 8); physical activity (n = 4); health inequalities (n = 3); mental health (n = 2); community engagement (n = 3); chronic condition management (n = 3); vaccine adoption or implementation (n = 2); programme implementation (n = 3); breastfeeding (n = 2), and general population health (n = 1). The majority of studies (n = 24) were of interventions solely or predominantly in high income countries. Key strengths reported were that QCA provides a method for addressing causal complexity; and that it provides a systematic approach for understanding the mechanisms at work in implementation across contexts. Weaknesses reported related to data availability limitations, especially on ineffective interventions. The majority of papers demonstrated good knowledge of cases, and justification of case selection, but other criteria of methodological quality were less comprehensively met. Conclusion QCA is a promising approach for addressing the role of context in complex interventions, and for identifying causal configurations of conditions that predict implementation and/or outcomes when there is sufficiently detailed understanding of a series of comparable cases. As the use of QCA in evaluative health research increases, there may be a need to develop advice for public health researchers and journals on minimum criteria for quality and reporting.

scholarly journals What methods and challenges for taking account of context when transferring complex interventions?

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
S Villadsen ◽  
S Dias
Keyword(s):  
Public Health ◽  
Health Care ◽  
Reproductive Health ◽  
Health Care Providers ◽  
Complex Interventions ◽  
Health Interventions ◽  
Care Providers ◽  
Public Health Interventions ◽  
Reproductive Health Care

Abstract For complex public health interventions to be effective their implementation needs to adapt to the situation of those implementing and those receiving the intervention. While context matter for intervention implementation and effect, we still insist on learning from cross-country comparison of implementation. Next methodological challenges include how to increase learning from implementation of complex public health interventions from various context. The interventions presented in this workshop all aims to improve quality of reproductive health care for immigrants, however with different focus: contraceptive care in Sweden, group based antenatal care in France, and management of pregnancy complications in Denmark. What does these interventions have in common and are there cross cutting themes that help us to identify the larger challenges of reproductive health care for immigrant women in Europe? Issues shared across the interventions relate to improved interactional dynamics between women and the health care system, and theory around a woman-centered approach and cultural competence of health care providers and systems might enlighten shared learnings across the different interventions and context. Could the mechanisms of change be understood using theoretical underpinnings that allow us to better generalize the finding across context? What adaption would for example be needed, if the Swedish contraceptive intervention should work in a different European setting? Should we distinguish between adaption of function and form, where the latter might be less important for intervention fidelity? These issues will shortly be introduced during this presentation using insights from the three intervention presentations and thereafter we will open up for discussion with the audience.

scholarly journals Engaging Ethical Issues Associated with Research and Public Health Interventions During Humanitarian Crises: Review of a Dialogic Workshop

2018 ◽  
Vol 5 ◽  
Author(s):  
Anushree Dave ◽  
Julie Cumin ◽  
Ryoa Chung ◽  
Matthew Hunt
Keyword(s):  
Public Health ◽  
Ethical Issues ◽  
Healthcare Delivery ◽  
Health Interventions ◽  
Future Research ◽  
Public Health Interventions ◽  
Extensive Discussion ◽  
Health Ethics ◽  
Humanitarian Crises ◽  
Health Equity Research

On November 7th, 2014 the Humanitarian Health Ethics Workshop was held at McGill University, in Montreal. Co-hosted by the Montreal Health Equity Research Consortium and the Humanitarian Health Ethics Network, the event included six presentations and extensive discussion amongst participants, including researchers from Canada, Haiti, India, Switzerland and the US. Participants had training in disciplines including anthropology, bioethics, medicine, occupational therapy, philosophy, physical therapy, political science, public administration and public health. The objective of the workshop was to create a forum for discussion amongst scholars and practitioners interested in the ethics of healthcare delivery, research and public health interventions during humanitarian crises. This review is a summary of the presentations given, key themes that emerged during the day’s discussions, and avenues for future research that were identified.

Development and evaluation of complex multicomponent interventions in public health

2021 ◽  
pp. 351-364
Author(s):  
Rona Campbell ◽  
Chris Bonell
Keyword(s):  
Public Health ◽  
Qualitative Research ◽  
Systems Theory ◽  
Health Inequalities ◽  
Complex Interventions ◽  
Health Interventions ◽  
Intervention Design ◽  
Public Health Interventions ◽  
Quantitative And Qualitative Research ◽  
Multicomponent Interventions

This chapter examines the issues to consider when developing and evaluating complex public health interventions and signposts where more detailed guidance can be found. It starts by considering what complexity means in this context, including the contribution that systems theory has made. When developing complex interventions we suggest: (i) reading quantitative and qualitative research on similar interventions, preferably within systematic reviews; (ii) consulting stakeholders, including those that the intervention is intended to benefit, to help ensure its relevance, acceptability and ownership; (iii) considering using theory to inform the intervention design and hypotheses to assess in evaluations; (iv) assessing whether the intervention could operate at more than one level (from individual through to policy) to increase its chances of success; and (v) reflecting on issues of equity and how the intervention could reduce health inequalities.

Applying methods of economic evaluation to public health: Contemporary solutions to traditional challenges

2019 ◽  
pp. 53-86
Author(s):  
Rhiannon T. Edwards ◽  
Emma McIntosh
Keyword(s):  
Public Health ◽  
Economic Evaluation ◽  
Study Design ◽  
Controlled Trial ◽  
Good Practice ◽  
Health Sector ◽  
Complex Interventions ◽  
Health Interventions ◽  
Natural Experiments ◽  
Public Health Interventions

Chapter 3 opens with a discussion of the role of study design, the gold standard traditionally being a randomized controlled trial, and widens this to consider other types of study design such as cohort studies and natural experiments. Readers are introduced to the idea that many public health interventions are ‘complex interventions’ and there is a need for a ‘systems-based approach’ to understanding their potential effectiveness and cost-effectiveness. The chapter highlights the relevance of behavioural economics to the evaluation of public health interventions. This chapter goes on to summarize a range of challenges faced by economists, used to evaluate healthcare technologies in a healthcare setting, when they start evaluating public health interventions, which are often delivered outside the health sector in, for example, schools and workplaces. UK guidance from NICE is presented on good practice in economic evaluation of public health interventions along with ideas about how such evaluations are best reported in the literature.

scholarly journals Inferring global-scale temporal latent topics from news reports to predict public health interventions for COVID-19

2021 ◽  
Author(s):  
Zhi Wen ◽  
Guido Powell ◽  
Imane Chafi ◽  
David Buckeridge ◽  
Yue Li
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Global Scale ◽  
Health Interventions ◽  
Future Research ◽  
Public Health Interventions ◽  
News Reports ◽  
Country Level ◽  
Global Pandemic ◽  
Latent Topics

The COVID-19 global pandemic has highlighted the importance of non-pharmacological interventions (NPI) for controlling epidemics of emerging infectious diseases. Despite the importance of NPI, their implementation has been monitored in an ad hoc and uncoordinated manner, mainly through the manual efforts of volunteers. Given the absence of systematic NPI tracking, authorities and researchers are limited in their ability to quantify the effectiveness of NPI and guide decisions regarding their use during the progression of a global pandemic. To address this issue, we propose 3-stage machine learning framework called EpiTopics to facilitate the surveillance of NPI by mining the vast amount of unlabelled news reports about these interventions. Building on topic modeling, our method characterizes online government reports and media articles related to COVID-19 as a mixture of latent topics. Our key contribution is the use of transfer-learning to address the limited number of NPI-labelled documents and topic modelling to support interpretation of the results. At stage 1, we trained a modified version of the unsupervised dynamic embedded topic model (DETM) on 1.2 million international news reports related to COVID-19. At stage 2, we used the trained DETM to infer topic mixture from a small set of 2000 NPI-labelled WHO documents as the input features for predicting NPI labels on each document. At stage 3, we supply the inferred country-level temporal topics from the DETM to the pretrained document-level NPI classifier to predict country-level NPIs. We identified 25 interpretable topics, over 4 distinct and coherent COVID-related themes. These topics contributed to significant improvements in predicting the NPIs labelled in the WHO documents and in predicting country-level NPIs. Together, our work lay the machine learning methodological foundation for future research in global-scale surveillance of public health interventions. The EpiTopics code is available at GitHub: https://github.com/li-lab-mcgill/covid-npi.

scholarly journals Inferring global-scale temporal latent topics from news reports to predict public health interventions for COVID-19

2021 ◽  
Author(s):  
Zhi Wen ◽  
Guido Powell ◽  
Imane Chafi ◽  
David L Buckeridge ◽  
Yue Li
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Global Scale ◽  
Health Interventions ◽  
Future Research ◽  
Public Health Interventions ◽  
News Reports ◽  
Country Level ◽  
Global Pandemic ◽  
Latent Topics

Abstract The COVID-19 global pandemic has highlighted the importance of non-pharmacological interventions (NPI) for controlling epidemics of emerging infectious diseases. Despite the importance of NPI, their implementation has been monitored in an ad hoc and uncoordinated manner, mainly through the manual efforts of volunteers. Given the absence of systematic NPI tracking, authorities and researchers are limited in their ability to quantify the effectiveness of NPI and guide decisions regarding their use during the progression of a global pandemic. To address this issue, we propose 3-stage machine learning framework called EpiTopics to facilitate the surveillance of NPI by mining the vast amount of unlabelled news reports about these interventions. Building on topic modeling, our method characterizes online government reports and media articles related to COVID-19 as a mixture of latent topics. Our key contribution is the use of transfer-learning to address the limited number of NPI-labelled documents and topic modelling to support interpretation of the results. At stage 1, we trained a modified version of the unsupervised dynamic embedded topic model (DETM) on 1.2 million international news reports related to COVID-19. At stage 2, we used the trained DETM to infer topic mixture from a small set of 2000 NPI-labelled WHO documents as the input features for predicting NPI labels on each document. At stage 3, we supply the inferred country-level temporal topics from the DETM to the pretrained document-level NPI classifier to predict country-level NPIs. We identified 25 interpretable topics, over 4 distinct and coherent COVID-related themes. These topics contributed to significant improvements in predicting the NPIs labelled in the WHO documents and in predicting country-level NPIs. Together, our work lay the machine learning methodological foundation for future research in global-scale surveillance of public health interventions. The EpiTopics code is available at GitHub: https://github.com/li-lab-mcgill/covid-npi.

scholarly journals Evaluation of public health interventions from a complex systems perspective: A research methods review

2021 ◽  
Vol 272 ◽  
pp. 113697
Author(s):  
Elizabeth McGill ◽  
Vanessa Er ◽  
Tarra Penney ◽  
Matt Egan ◽  
Martin White ◽  
...  
Keyword(s):  
Public Health ◽  
Complex Systems ◽  
Research Methods ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Systems Perspective

scholarly journals Addressing complexity in population health intervention research: the context/intervention interface

2018 ◽  
Vol 72 (4) ◽  
pp. 319-323 ◽  
Author(s):  
Laetitia Minary ◽  
François Alla ◽  
Linda Cambon ◽  
Joelle Kivits ◽  
Louise Potvin
Keyword(s):  
Public Health ◽  
Complex Systems ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Specific Context ◽  
Potential Effectiveness ◽  
Population Health Intervention Research ◽  
Population Health Intervention ◽  
Contextual Elements ◽  
Intervention Context

BackgroundPublic health interventions are increasingly being recognised as complex and context dependent. Related to this is the need for a systemic and dynamic conception of interventions that raises the question of delineating the scope and contours of interventions in complex systems. This means identifying which elements belong to the intervention (and therefore participate in its effects and can be transferred), which ones belong to the context and interact with the former to influence results (and therefore must be taken into account when transferring the intervention) and which contextual elements are irrelevant to the intervention.DiscussionThis paper, from which derives criteria based on a network framework, operationalises how the context and intervention systems interact and identify what needs to be replicated as interventions are implemented in different contexts. Representing interventions as networks (composed of human and non-human entities), we introduce the idea that the density of interconnections among the various entities provides a criterion for distinguishing core intervention from intervention context without disconnecting the two systems. This differentiates endogenous and exogenous intervention contexts and the mediators that connect them, which form the fuzzy and constantly changing intervention/context interface.ConclusionWe propose that a network framework representing intervention/context systems constitutes a promising approach for deriving empirical criteria to delineate the scope and contour of what is replicable in an intervention. This approach should allow better identification and description of the entities that have to be transferred to ensure the potential effectiveness of an intervention in a specific context.

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