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Biro S, Scott K, Nagy E, Slipp N, Beck K, Catley C, Hart E. Tracking emergency response actions during COVID-19 leads to development of an innovative public health evaluation tool. CANADIAN JOURNAL OF PUBLIC HEALTH = REVUE CANADIENNE DE SANTE PUBLIQUE 2023; 114:737-744. [PMID: 37548890 PMCID: PMC10484822 DOI: 10.17269/s41997-023-00811-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 06/29/2023] [Indexed: 08/08/2023]
Abstract
SETTING Early in the pandemic, KFL&A Public Health needed a way to capture, organize, and display COVID-19-related events to be accountable for and evaluate our actions. INTERVENTION We used accessible software (Microsoft Office 365 suite, Microsoft PowerBI) to develop a data collection and visualization system. The Canadian Institute for Health Information (CIHI) developed a timeline and categorization approach for provincial and national COVID-related interventions, which was used to develop a regional version for local events using similar categories. We collected and displayed qualitative data alongside epidemiological data that allowed users to display different timelines of actions and outcomes and evaluate our response. OUTCOMES In developing the timeline, we took stock of the information and data we wanted to collect, sort, and display locally. Next, we collected information on response actions, case and contact tracing, and staffing changes in a database that we displayed on a timeline. We included CIHI's data set to provide insight into pandemic response across all jurisdictions. IMPLICATIONS Our timeline tool has many advantages for public health authorities beyond responding to a rapidly evolving emergency. By collecting information on events as they occur, decisions and actions are documented that may otherwise be overlooked. This enables decision-makers to visualize the impact of public health actions on health outcomes over time. The tool is completely customizable and scalable depending on the project scope and we plan to apply this method to other public health programming. Finally, we include lessons learned from quickly developing these tools in a real-time pandemic setting, both locally at KFL&A Public Health and nationally at CIHI.
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Affiliation(s)
| | | | - Emma Nagy
- KFL&A Public Health, Kingston, ON, Canada
| | | | - Kinsey Beck
- Canadian Institute for Health Information, Ottawa, ON, Canada
| | | | - Ezra Hart
- Canadian Institute for Health Information, Ottawa, ON, Canada
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Khazaee-Pool M, Pashaei T, Ponnet K. Social innovation in health and community-driven engagement as a key strategy for addressing COVID-19 crisis challenges: insights and reflections from the multicultural society of Iran. Front Public Health 2023; 11:1174385. [PMID: 37346112 PMCID: PMC10279867 DOI: 10.3389/fpubh.2023.1174385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 05/17/2023] [Indexed: 06/23/2023] Open
Abstract
Background Social innovation is one of the strategies for appealing to people and encouraging social cooperation and engagement in interventions during crisis periods. In this regard, community engagement is an operative and innovative community health approach for achieving successful health outcomes. There is limited information about the role and operational impact of social innovation on community engagement during the challenges posed by the COVID-19 crisis. In this study, we aim to contribute to the understanding of innovative social strategies to attract social participation in crises such as the COVID-19 pandemic by highlighting the experience of social innovative strategies based on community-driven engagement in Iran. Methods This qualitative study was conducted in seven provinces of Iran-Mazandaran, Zanjan, Golestan, Lorestan, Tehran, Kurdistan, and Khuzestan-from 4 September 2021 to 1 March 2022. A sample of Iranians (15-71 years) was selected by purposeful and snowball sampling methods to participate in the study, and 187 semi-structured telephone interviews were conducted. Participants were recruited from three levels of the community: community leaders, healthcare providers, and laypeople. The data collection tool was an interview guide, which was designed based on a review of the literature. The data were analyzed using conventional content analysis. Exploratory analyses were performed to identify social innovative strategies based on community engagement used during the COVID-19 crisis in Iran. The interviews continued until data saturation was reached. Results Based on our findings, we distilled innovative strategies into 6 main themes and 37 categories: (1) information giving/sharing, (2) consultation, (3) involvement/collaboration, (4) health education and prevention, (5) empowering, and (6) advocacy. The results revealed that the participants were very driven to engage in the management and control of the COVID-19 crisis, even though they faced significant challenges. Conclusion The spread of the COVID-19 pandemic required social- and community-based responses. These reactions increased the possibility of fair access to health services, especially for vulnerable groups and minorities. As with other epidemics, applying the experience of the comprehensive participation of communities played an important and active role in the prevention and control of COVID-19. In this regard, giving and sharing information, consultation, involvement/collaboration, health education/prevention, empowerment, and advocacy are the most important innovative strategies that might encourage the community to perform COVID-19 crisis management and control.
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Affiliation(s)
- Maryam Khazaee-Pool
- Department of Health Education and Promotion, School of Health, Health Sciences Research Center, Mazandaran University of Medical Sciences, Sari, Iran
| | - Tahereh Pashaei
- Social Determinants of Health Research Center, Research Institute for Health Development, Kurdistan University of Medical Sciences, Sanandaj, Iran
| | - Koen Ponnet
- Department of Communication Sciences, imec-mict-Ghent University, Ghent, Belgium
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Uddin S, Imam T, Khushi M, Khan A, Ali M. How did socio-demographic status and personal attributes influence compliance to COVID-19 preventive behaviours during the early outbreak in Japan? Lessons for pandemic management. PERSONALITY AND INDIVIDUAL DIFFERENCES 2021; 175:110692. [PMID: 33526954 PMCID: PMC7839830 DOI: 10.1016/j.paid.2021.110692] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 12/23/2020] [Accepted: 01/22/2021] [Indexed: 12/27/2022]
Abstract
This study focuses on how socio-demographic status and personal attributes influence self-protective behaviours during a pandemic, with protection behaviours being assessed through three perspectives – social distancing, personal protection behaviour and social responsibility awareness. The research considers a publicly available and recently collected dataset on Japanese citizens during the COVID-19 early outbreak and utilises a data analysis framework combining Classification and Regression Tree (CART), a data mining approach, and regression analysis to gain deep insights. The analysis reveals Socio-demographic attributes – sex, marital family status and having children – as having played an influential role in Japanese citizens' abiding by the COVID-19 protection behaviours. Especially women with children are noted as more conscious than their male counterparts. Work status also appears to have some impact concerning social distancing. Trust in government also appears as a significant factor. The analysis further identifies smoking behaviour as a factor characterising subjective prevention actions with non-smokers or less-frequent smokers being more compliant to the protection behaviours. Overall, the findings imply the need of public policy campaigning to account for variations in protection behaviour due to socio-demographic and personal attributes during pandemics and national emergencies.
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Affiliation(s)
- Shahadat Uddin
- School of Project Management, Faculty of Engineering, The University of Sydney, Room 5050, ABS Building, Darlington, NSW 2008, Australia
| | - Tasadduq Imam
- School of Business and Law, CQUniversity (Melbourne), VIC 3000, L4, 120 Spencer Street, Melbourne, VIC 3000, Australia
| | - Matloob Khushi
- School of Computer Science, J12, Computer Science Building, The University of Sydney, Australia
| | - Arif Khan
- Faculty of Engineering, J12, Computer Science Building, The University of Sydney, Australia
| | - Mohammad Ali
- WHO Collaborating Centre on eHealth, School of Public Health and Community Medicine, Faculty of Medicine, The University of New South wales, Sydney, NSW 2052, Australia.,WHO Collaborating Centre on eHealth, UNSW Digital Health, School of Public Health and Community Medicine, Faculty of Medicine, UNSW, Sydney, Australia
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Abbas K, Nawaz SMA, Amin N, Soomro FM, Abid K, Ahmed M, Sayeed KA, Ghazanfar S, Qureshi N. A web-based health education module and its impact on the preventive practices of health-care workers during the COVID-19 pandemic. HEALTH EDUCATION RESEARCH 2020; 35:353-361. [PMID: 32951026 PMCID: PMC7543561 DOI: 10.1093/her/cyaa034] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Accepted: 08/28/2020] [Indexed: 05/09/2023]
Abstract
Proper training on the preventive measures against COVID-19 among health-care workers is crucial for mitigating the spread of viral infection. The present study evaluated the efficacy of a brief web-based module on the practice of hand hygiene and respiratory etiquette among respective health-care workers. A comparative study was conducted with a total of 500 participants. A self-reported questionnaire was used for both pre- and post-intervention evaluation. The post-intervention assessment was conducted 1-2 weeks following the intervention. The difference in the practice of hand hygiene and respiratory etiquettes during work hours was recorded. We found that the intervention resulted in an evident difference in the use of alcohol-based hand sanitizer by the participating doctors before examining the patient. Interns showed a much higher propensity to wash their hands for at least 20 s, relative to other health-care workers. The difference between pre- and post-intervention handwashing for >5 times/day was 6.5% in females and 4.5% in males. In short, the study was able to demonstrate that a web-based health education module is an effective tool for the education and promotion of preventative measures in hospital setups, which may ultimately aid in halting the spread of COVID-19 among health-care workers.
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Affiliation(s)
- Kiran Abbas
- Department of Medicine, Jinnah Postgraduate Medical Center, Karachi 75230, Pakistan
| | - S Muhammad A Nawaz
- Department of Urology, Glan Clwyd Hospital—Betsi Cadwaladr University Health Board, Denbighshire LL16, Wales, UK
| | - Nazish Amin
- Department of Medicine, Jinnah Sindh Medical University, Karachi 75950, Pakistan
| | - Fareena M Soomro
- Department of Psychiatry, Liaquat University of Medical and Health Sciences, Jamshoro, Sindh 75950, Pakistan
| | - Kanza Abid
- Department of Medicine, Jinnah Sindh Medical University, Karachi 75950, Pakistan
| | - Moiz Ahmed
- Department of Medicine, Jinnah Postgraduate Medical Centre, Karachi 75640, Pakistan
| | - Khalid A Sayeed
- Department of Medicine, Liaquat College of Medicine and Dentistry, Karachi 75220, Pakistan
| | - Shamas Ghazanfar
- Department of Medicine, Dow University of Health Sciences, Karachi 75500, Pakistan
| | - Noorulain Qureshi
- Department of Medicine, Jinnah Sindh Medical University, Karachi 75950, Pakistan
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McCoy LG, Smith J, Anchuri K, Berry I, Pineda J, Harish V, Lam AT, Yi SE, Hu S, Rosella L, Fine B. Characterizing early Canadian federal, provincial, territorial and municipal nonpharmaceutical interventions in response to COVID-19: a descriptive analysis. CMAJ Open 2020; 8:E545-E553. [PMID: 32873583 PMCID: PMC7641155 DOI: 10.9778/cmajo.20200100] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Nonpharmaceutical interventions (NPIs) are the primary tools to mitigate early spread of the coronavirus disease 2019 (COVID-19) pandemic; however, such policies are implemented variably at the federal, provincial or territorial, and municipal levels without centralized documentation. We describe the development of the comprehensive open Canadian Non-Pharmaceutical Intervention (CAN-NPI) data set, which identifies and classifies all NPIs implemented in regions across Canada in response to COVID-19, and provides an accompanying description of geographic and temporal heterogeneity. METHODS We performed an environmental scan of government websites, news media and verified government social media accounts to identify NPIs implemented in Canada between Jan. 1 and Apr. 19, 2020. The CAN-NPI data set contains information about each intervention's timing, location, type, target population and alignment with a response stringency measure. We conducted descriptive analyses to characterize the temporal and geographic variation in early NPI implementation. RESULTS We recorded 2517 NPIs grouped in 63 distinct categories during this period. The median date of NPI implementation in Canada was Mar. 24, 2020. Most jurisdictions heightened the stringency of their response following the World Health Organization's global pandemic declaration on Mar. 11, 2020. However, there was variation among provinces or territories in the timing and stringency of NPI implementation, with 8 out of 13 provinces or territories declaring a state of emergency by Mar. 18, and all by Mar. 22, 2020. INTERPRETATION There was substantial geographic and temporal heterogeneity in NPI implementation across Canada, highlighting the importance of a subnational lens in evaluating the COVID-19 pandemic response. Our comprehensive open-access data set will enable researchers to conduct robust interjurisdictional analyses of NPI impact in curtailing COVID-19 transmission.
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Affiliation(s)
- Liam G McCoy
- Faculty of Medicine (McCoy, Harish, Lam) and Institute of Health Policy, Management and Evaluation (McCoy, Harish), University of Toronto; Layer 6 AI (Smith, Yi), Toronto, Ont.; Cumming School of Medicine (Anchuri, Hu), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Berry, Harish, Rosella) and the Department of Computer Science (Pineda), University of Toronto; Ontario Institute for Cancer Research (Pineda); Operational Analytics Lab, Institute for Better Health (Fine), Trillium Health Partners; Department of Medical Imaging (Fine), University of Toronto, Toronto, Ont.
| | - Jonathan Smith
- Faculty of Medicine (McCoy, Harish, Lam) and Institute of Health Policy, Management and Evaluation (McCoy, Harish), University of Toronto; Layer 6 AI (Smith, Yi), Toronto, Ont.; Cumming School of Medicine (Anchuri, Hu), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Berry, Harish, Rosella) and the Department of Computer Science (Pineda), University of Toronto; Ontario Institute for Cancer Research (Pineda); Operational Analytics Lab, Institute for Better Health (Fine), Trillium Health Partners; Department of Medical Imaging (Fine), University of Toronto, Toronto, Ont
| | - Kavya Anchuri
- Faculty of Medicine (McCoy, Harish, Lam) and Institute of Health Policy, Management and Evaluation (McCoy, Harish), University of Toronto; Layer 6 AI (Smith, Yi), Toronto, Ont.; Cumming School of Medicine (Anchuri, Hu), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Berry, Harish, Rosella) and the Department of Computer Science (Pineda), University of Toronto; Ontario Institute for Cancer Research (Pineda); Operational Analytics Lab, Institute for Better Health (Fine), Trillium Health Partners; Department of Medical Imaging (Fine), University of Toronto, Toronto, Ont
| | - Isha Berry
- Faculty of Medicine (McCoy, Harish, Lam) and Institute of Health Policy, Management and Evaluation (McCoy, Harish), University of Toronto; Layer 6 AI (Smith, Yi), Toronto, Ont.; Cumming School of Medicine (Anchuri, Hu), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Berry, Harish, Rosella) and the Department of Computer Science (Pineda), University of Toronto; Ontario Institute for Cancer Research (Pineda); Operational Analytics Lab, Institute for Better Health (Fine), Trillium Health Partners; Department of Medical Imaging (Fine), University of Toronto, Toronto, Ont
| | - Joanna Pineda
- Faculty of Medicine (McCoy, Harish, Lam) and Institute of Health Policy, Management and Evaluation (McCoy, Harish), University of Toronto; Layer 6 AI (Smith, Yi), Toronto, Ont.; Cumming School of Medicine (Anchuri, Hu), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Berry, Harish, Rosella) and the Department of Computer Science (Pineda), University of Toronto; Ontario Institute for Cancer Research (Pineda); Operational Analytics Lab, Institute for Better Health (Fine), Trillium Health Partners; Department of Medical Imaging (Fine), University of Toronto, Toronto, Ont
| | - Vinyas Harish
- Faculty of Medicine (McCoy, Harish, Lam) and Institute of Health Policy, Management and Evaluation (McCoy, Harish), University of Toronto; Layer 6 AI (Smith, Yi), Toronto, Ont.; Cumming School of Medicine (Anchuri, Hu), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Berry, Harish, Rosella) and the Department of Computer Science (Pineda), University of Toronto; Ontario Institute for Cancer Research (Pineda); Operational Analytics Lab, Institute for Better Health (Fine), Trillium Health Partners; Department of Medical Imaging (Fine), University of Toronto, Toronto, Ont
| | - Andrew T Lam
- Faculty of Medicine (McCoy, Harish, Lam) and Institute of Health Policy, Management and Evaluation (McCoy, Harish), University of Toronto; Layer 6 AI (Smith, Yi), Toronto, Ont.; Cumming School of Medicine (Anchuri, Hu), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Berry, Harish, Rosella) and the Department of Computer Science (Pineda), University of Toronto; Ontario Institute for Cancer Research (Pineda); Operational Analytics Lab, Institute for Better Health (Fine), Trillium Health Partners; Department of Medical Imaging (Fine), University of Toronto, Toronto, Ont
| | - Seung Eun Yi
- Faculty of Medicine (McCoy, Harish, Lam) and Institute of Health Policy, Management and Evaluation (McCoy, Harish), University of Toronto; Layer 6 AI (Smith, Yi), Toronto, Ont.; Cumming School of Medicine (Anchuri, Hu), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Berry, Harish, Rosella) and the Department of Computer Science (Pineda), University of Toronto; Ontario Institute for Cancer Research (Pineda); Operational Analytics Lab, Institute for Better Health (Fine), Trillium Health Partners; Department of Medical Imaging (Fine), University of Toronto, Toronto, Ont
| | - Sophie Hu
- Faculty of Medicine (McCoy, Harish, Lam) and Institute of Health Policy, Management and Evaluation (McCoy, Harish), University of Toronto; Layer 6 AI (Smith, Yi), Toronto, Ont.; Cumming School of Medicine (Anchuri, Hu), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Berry, Harish, Rosella) and the Department of Computer Science (Pineda), University of Toronto; Ontario Institute for Cancer Research (Pineda); Operational Analytics Lab, Institute for Better Health (Fine), Trillium Health Partners; Department of Medical Imaging (Fine), University of Toronto, Toronto, Ont
| | - Laura Rosella
- Faculty of Medicine (McCoy, Harish, Lam) and Institute of Health Policy, Management and Evaluation (McCoy, Harish), University of Toronto; Layer 6 AI (Smith, Yi), Toronto, Ont.; Cumming School of Medicine (Anchuri, Hu), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Berry, Harish, Rosella) and the Department of Computer Science (Pineda), University of Toronto; Ontario Institute for Cancer Research (Pineda); Operational Analytics Lab, Institute for Better Health (Fine), Trillium Health Partners; Department of Medical Imaging (Fine), University of Toronto, Toronto, Ont
| | - Benjamin Fine
- Faculty of Medicine (McCoy, Harish, Lam) and Institute of Health Policy, Management and Evaluation (McCoy, Harish), University of Toronto; Layer 6 AI (Smith, Yi), Toronto, Ont.; Cumming School of Medicine (Anchuri, Hu), University of Calgary, Calgary, Alta.; Dalla Lana School of Public Health (Berry, Harish, Rosella) and the Department of Computer Science (Pineda), University of Toronto; Ontario Institute for Cancer Research (Pineda); Operational Analytics Lab, Institute for Better Health (Fine), Trillium Health Partners; Department of Medical Imaging (Fine), University of Toronto, Toronto, Ont
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