Left-sided skin cancer: Importance of age, gender, body site, and tumor subtype in studying skin cancer laterality and implications for future research and public health interventions

2011 ◽  
Vol 65 (1) ◽  
pp. 208-210 ◽  
Author(s):  
Scott W. Fosko ◽  
Susan T. Butler ◽  
Eric S. Armbrecht
Keyword(s):  
Public Health ◽  
Skin Cancer ◽  
Health Interventions ◽  
Future Research ◽  
Body Site ◽  
Tumor Subtype ◽  
Public Health Interventions

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 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 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 An analysis of COVID-19 clusters in India

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Pooja Sengupta ◽  
Bhaswati Ganguli ◽  
Sugata SenRoy ◽  
Aditya Chatterjee
Keyword(s):  
Public Health ◽  
Cluster Analysis ◽  
Population Density ◽  
Case Studies ◽  
Compartment Model ◽  
Health Interventions ◽  
Disease Spread ◽  
Second Phase ◽  
Public Health Interventions ◽  
Homogeneous Groups

Abstract Background In this study we cluster the districts of India in terms of the spread of COVID-19 and related variables such as population density and the number of specialty hospitals. Simulation using a compartment model is used to provide insight into differences in response to public health interventions. Two case studies of interest from Nizamuddin and Dharavi provide contrasting pictures of the success in curbing spread. Methods A cluster analysis of the worst affected districts in India provides insight about the similarities between them. The effects of public health interventions in flattening the curve in their respective states is studied using the individual contact SEIQHRF model, a stochastic individual compartment model which simulates disease prevalence in the susceptible, infected, recovered and fatal compartments. Results The clustering of hotspot districts provide homogeneous groups that can be discriminated in terms of number of cases and related covariates. The cluster analysis reveal that the distribution of number of COVID-19 hospitals in the districts does not correlate with the distribution of confirmed COVID-19 cases. From the SEIQHRF model for Nizamuddin we observe in the second phase the number of infected individuals had seen a multitudinous increase in the states where Nizamuddin attendees returned, increasing the risk of the disease spread. However, the simulations reveal that implementing administrative interventions, flatten the curve. In Dharavi, through tracing, tracking, testing and treating, massive breakout of COVID-19 was brought under control. Conclusions The cluster analysis performed on the districts reveal homogeneous groups of districts that can be ranked based on the burden placed on the healthcare system in terms of number of confirmed cases, population density and number of hospitals dedicated to COVID-19 treatment. The study rounds up with two important case studies on Nizamuddin basti and Dharavi to illustrate the growth curve of COVID-19 in two very densely populated regions in India. In the case of Nizamuddin, the study showed that there was a manifold increase in the risk of infection. In contrast it is seen that there was a rapid decline in the number of cases in Dharavi within a span of about one month.

Sign in / Sign up

Export Citation Format

Share Document