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.

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.

Control of Mosquito-Borne Illnesses: A Challenge to Public Health Ethics

2019 ◽  
pp. 458-471
Author(s):  
Laura Greisman ◽  
Barbara Koenig ◽  
Michele Barry
Keyword(s):  
Public Health ◽  
Ethical Issues ◽  
Mosquito Control ◽  
Virus Disease ◽  
Health Interventions ◽  
Ethical Challenges ◽  
Insecticide Treated Nets ◽  
Public Health Interventions ◽  
Middle Income ◽  
Public Health Agencies

This chapter delves into the ethical issues surrounding the implementation of public health interventions for control of mosquito-borne illnesses. Emerging and reemerging mosquito-borne infections remain a public health threat worldwide, prompting public health agencies to strengthen individual and population-wide measures for mosquito control. Ethical issues surrounding surveillance activities and key public health interventions for mosquito control are discussed, including provision of insecticide-treated nets (ITNs), the spraying of aerial pesticides, and the introduction of genetically modified mosquitoes. A case study of Zika virus disease highlights specific ethical challenges surrounding the safety of insect repellent use in pregnancy and the complex issue of women’s reproductive rights arising in a fast-moving epidemic. The chapter emphasizes the need for community engagement at all levels of mosquito control interventions, and it highlights the disproportionate impact of mosquito-borne disease on the poor, calling to action the need to strengthen health systems in low- and middle-income countries.

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.

Telehealth: technological, legal, and ethical issues

2021 ◽  
pp. 169-178
Author(s):  
Angelina Ivanova ◽  
Keyword(s):  
Public Health ◽  
Healthcare Professionals ◽  
Ethical Issues ◽  
Clinical Care ◽  
Legal Issues ◽  
Health Interventions ◽  
Patient Relationship ◽  
Public Health Interventions ◽  
Ethical And Legal Issues ◽  
Legal And Ethical Issues

The use of telehealth is an increasingly common avenue for providing clinical care, performing research and conducting public health interventions. However, with the growth of telecommunication technologies, healthcare professionals have encountered an emerging new set of ethical and legal issues relating to the doctor-patient relationship, standarts, privacy, cost and liability. This article explores the main benefits and challenges that come with growth of telehealth.

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 Evidence on public health interventions in humanitarian crises

The Lancet ◽  
2017 ◽  
Vol 390 (10109) ◽  
pp. 2287-2296 ◽  
Author(s):  
Karl Blanchet ◽  
Anita Ramesh ◽  
Severine Frison ◽  
Emily Warren ◽  
Mazeda Hossain ◽  
...  
Keyword(s):  
Public Health ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Humanitarian Crises
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