Health Policy and Technology最新文献

{"title":"Vaccination and non-pharmaceutical interventions during COVID-19: Impact on health and non-health outcomes in the US","authors":"Asmita P. Khatiwada ,&nbsp;Mesfin G. Genie ,&nbsp;Aregawi G. Gebremariam ,&nbsp;Tim C. Lai ,&nbsp;Nabin Poudel ,&nbsp;Surachat Ngorsuraches","doi":"10.1016/j.hlpt.2023.100792","DOIUrl":"10.1016/j.hlpt.2023.100792","url":null,"abstract":"<div><h3>Objective</h3><p>Little is known about the relative effectiveness of COVID-19 vaccination and its interaction with non-pharmaceutical interventions (NPIs) in reducing infections, deaths, COVID-19 reproduction rate, and job losses. This study examined the relative effectiveness of vaccination and NPIs on COVID-19 infection, deaths, reproduction rate, and unemployment rate in the US.</p></div><div><h3>Methods</h3><p>Retrospective US data at the national level were obtained from the Oxford COVID-19 Government Response Tracker (OxCGRT dataset). Unemployment rate data were obtained from the US Bureau of Labor Statistics. Time-trend analyses of the policy variables and epidemiological outcomes were performed. A regression discontinuity in time was used to investigate the effects of policy variables on health outcomes and unemployment rate.</p></div><div><h3>Results</h3><p>Based on time-trend analyses, the number of people vaccinated increased starting in March 2021, while the stringency index had steadily declined since early January 2021. A decrease in new COVID-19 cases and deaths was observed during this period. However, despite higher vaccination coverage, new COVID-19 cases and deaths peaked in late 2021 and early 2022. We found that the interaction between treatment effects (vaccinations) and stringency measures was negatively associated with total COVID-19 cases and deaths, implying that some restrictions might be required to reduce rising infections during vaccination campaigns. We also found a negative association between vaccinations and the unemployment rate.</p></div><div><h3>Conclusion</h3><p>The study findings suggested that vaccinations alone were insufficient to reduce virus spread and deaths, and that some NPIs might be required during the vaccination campaigns.</p></div>","PeriodicalId":48672,"journal":{"name":"Health Policy and Technology","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2211883723000680/pdfft?md5=692402b6a561e3282d83f2f039f7c768&pid=1-s2.0-S2211883723000680-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49564409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The interplay between global health policy and vaccination strategies in the shift towards COVID-19 endemicity","authors":"Marcello Antonini ,&nbsp;Ayman Fouda ,&nbsp;Madeleine Hinwood ,&nbsp;Adrian Melia ,&nbsp;Francesco Paolucci","doi":"10.1016/j.hlpt.2024.100854","DOIUrl":"10.1016/j.hlpt.2024.100854","url":null,"abstract":"","PeriodicalId":48672,"journal":{"name":"Health Policy and Technology","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2211883724000170/pdfft?md5=4fcbe34afc1cf0ab64a3f366cd71fc01&pid=1-s2.0-S2211883724000170-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139883744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"COVID-19 in the Netherlands: A three-phase analysis","authors":"Anouk van Amerongen ,&nbsp;Claudia Zoller ,&nbsp;Ayman Fouda","doi":"10.1016/j.hlpt.2023.100783","DOIUrl":"10.1016/j.hlpt.2023.100783","url":null,"abstract":"<div><h3>Introduction</h3><p>The COVID-19 pandemic has presented global challenges in the health, economy, society, and political sector for the past three years. For the Netherlands, the dynamic nature of the pandemic can be divided into three phases. The initial phase exclusively relied on non-pharmaceutical interventions (NPIs). The second phase was characterized by the introduction of vaccines alongside the continuation of stringent NPIs. Finally, the third phase marks the post-vaccine and booster stage, characterized by minimal or absent NPIs. This paper examines the interplay between the mitigation policies, the vaccination rollout, health outcomes, and economic indicators in the Netherlands in these three phases.</p></div><div><h3>Methods</h3><p>This analysis used national real-time data on COVID-19-related health outcomes, health service utilization, vaccination rollout, and economic indicators. Our descriptive analysis applied the “Categorising Policy &amp; Technology Interventions (CPTI)” framework.</p></div><div><h3>Results</h3><p>The number of daily deaths, hospital admission and ICU admission experienced the highest peak in the first phase, while the number of daily cases first spiked in the third phase. The containment measures reached a very significant level twice, resulting in a full lockdown twice. In the first two phases, the peak in stringency of the CPTI containment category was parallel with the peaks in health outcomes. Conversely, in the third phase, the containment measures were scaled down prior to the peak in daily cases.</p></div><div><h3>Conclusions</h3><p>Our findings suggest that the Dutch three-phased COVID-19 mitigation strategy managed to fulfil its aim and protect vulnerable individuals, prevent healthcare institutions from overload, and move from the pandemic to the endemic phase.</p></div>","PeriodicalId":48672,"journal":{"name":"Health Policy and Technology","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S221188372300059X/pdfft?md5=5bda2b559d9531a5cbdb9d7cbaab7ac1&pid=1-s2.0-S221188372300059X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47607647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Navigating economic turmoil: Chilean businesses during COVID-19 lockdowns and vaccine rollouts","authors":"Julio A. Pertuze ,&nbsp;José Pablo Montégu ,&nbsp;Cecilia González ,&nbsp;Rafael Araos ,&nbsp;Paula Daza","doi":"10.1016/j.hlpt.2023.100813","DOIUrl":"10.1016/j.hlpt.2023.100813","url":null,"abstract":"<div><h3>Objectives</h3><p>This study evaluates the effects of COVID-19 lockdowns, differentiated by their stringency, on the sales of Chilean businesses across various size categories and industries throughout 2020 and 2021. It also explores the role of the vaccination campaign and the implementation of the Mobility Pass in mitigating the negative economic effects of stringent containment measures.</p></div><div><h3>Methods</h3><p>The study uses administrative data from the Chilean Internal Revenue Service (SII), examining sales across different business sizes and industries, from March 2020 to December 2021. Through an econometric analysis, we estimate the effects of lockdowns on business sales during two distinct periods: initial reliance on dynamic non-pharmaceutical interventions (NPIs) pre-vaccine, and a subsequent stage characterized by high vaccine uptake and reduced NPI stringency.</p></div><div><h3>Results</h3><p>Lockdowns significantly reduced sales across all business sizes and most industries during the first period, with microenterprises and certain service sectors experiencing the highest decline. The national vaccination campaign and the introduction of the Mobility Pass in the second period appears to have mitigated the negative effects of lockdowns, primarily benefiting micro and small firms.</p></div><div><h3>Conclusions</h3><p>The study highlights the trade-offs between health and economic outcomes during the pandemic, stressing the importance to alleviate mobility restrictions post-vaccine rollout to ease the economic strain on businesses. The findings call for targeted support measures for MSMEs and vulnerable industries affected by NPIs.</p></div>","PeriodicalId":48672,"journal":{"name":"Health Policy and Technology","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2211883723000898/pdfft?md5=f9c5f0284ed129531c147f26758177de&pid=1-s2.0-S2211883723000898-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134995166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"THE INTERPLAY BETWEEN GLOBAL HEALTH POLICY AND VACCINATION STRATEGIES IN THE SHIFT TOWARDS COVID-19 ENDEMICITY","authors":"M. Antonini, A. Fouda, M. Hinwood, A. Melia, F. Paolucci","doi":"10.1016/j.hlpt.2024.100854","DOIUrl":"https://doi.org/10.1016/j.hlpt.2024.100854","url":null,"abstract":"","PeriodicalId":48672,"journal":{"name":"Health Policy and Technology","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139823916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Public preferences for vaccination campaigns in the COVID-19 endemic phase: Insights from the VaxPref database","authors":"M. Antonini, Dr Mesfin G. Genie, Dr Arthur Attema E, Dr Katie Attwell, Prof Zsolt J. Balogh, D. Behmane, Chiara Berardi, Dr Shuli Brammli-Greenberg, Andrew Greenland, Prof Terje P. Hagen, Dr Madeleine Hinwood, Prof Carole James, Adrian Kellner, Prof Brian Kelly, Dr Liubovė Murauskienė, Dr Neil McGregor, Prof Alessia Melegaro, Dr Naomi Moy, Dr Ana Rita Sequeira, Dr Renu Singh, Dr Aleksandra Torbica, Dr Jeremy K. Ward, Dr Dongyue Yang, Prof Francesco Paolucci","doi":"10.1016/j.hlpt.2024.100849","DOIUrl":"https://doi.org/10.1016/j.hlpt.2024.100849","url":null,"abstract":"","PeriodicalId":48672,"journal":{"name":"Health Policy and Technology","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139875924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Working With Epistemic Uncertainties: Emerging Entanglements Within Conditional Reimbursement Practices","authors":"dr. Rik Wehrens, dr. Bert de Graaff","doi":"10.1016/j.hlpt.2024.100850","DOIUrl":"https://doi.org/10.1016/j.hlpt.2024.100850","url":null,"abstract":"","PeriodicalId":48672,"journal":{"name":"Health Policy and Technology","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139874908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to ‘Towards a universal patient-centric health record sharing platform’ [Health Policy and Technology 12 (2023) 100819]","authors":"Mana Azarm ,&nbsp;Rebecca Meehan ,&nbsp;Craig Kuziemsky","doi":"10.1016/j.hlpt.2023.100821","DOIUrl":"https://doi.org/10.1016/j.hlpt.2023.100821","url":null,"abstract":"<div><p>Abstract</p></div>","PeriodicalId":48672,"journal":{"name":"Health Policy and Technology","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2023-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2211883723000977/pdfft?md5=0b347d243a5a26d272b0236c8b84f9e6&pid=1-s2.0-S2211883723000977-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138453984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to ‘Machine learning prediction of hospital patient need for post-acute care using an admission mobility measure is robust across patient diagnoses’ [Health Policy and Technology 12 (2023) 100,754]","authors":"Daniel L. Young ,&nbsp;Rebecca Engels ,&nbsp;Elizabeth Colantuoni ,&nbsp;Lisa Aronson Friedman ,&nbsp;Erik H. Hoyer","doi":"10.1016/j.hlpt.2023.100825","DOIUrl":"https://doi.org/10.1016/j.hlpt.2023.100825","url":null,"abstract":"<div><p>None</p></div>","PeriodicalId":48672,"journal":{"name":"Health Policy and Technology","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2023-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2211883723001016/pdfft?md5=127e2af5e8991d39e0edc980c50fad49&pid=1-s2.0-S2211883723001016-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138453983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence for patient scheduling in the real-world health care setting: A metanarrative review","authors":"Dacre R.T. Knight ,&nbsp;Christopher A. Aakre ,&nbsp;Christopher V. Anstine ,&nbsp;Bala Munipalli ,&nbsp;Parisa Biazar ,&nbsp;Ghada Mitri ,&nbsp;Jose Raul Valery ,&nbsp;Tara Brigham ,&nbsp;Shehzad K. Niazi ,&nbsp;Adam I. Perlman ,&nbsp;John D. Halamka ,&nbsp;Abd Moain Abu Dabrh","doi":"10.1016/j.hlpt.2023.100824","DOIUrl":"10.1016/j.hlpt.2023.100824","url":null,"abstract":"<div><h3>Objectives</h3><p>The application of artificial intelligence (AI) and machine learning (ML) to scheduling in medical practices has considerable implications for most specialties. However, the landscape of AI and ML use in scheduling optimization is unclear. We aimed to systematically summarize up-to-date evidence about application of AI and ML models for scheduling optimization in clinical settings.</p></div><div><h3>Methods</h3><p><span>We systematically searched multiple databases from inception through August 2020 to identify studies that described real-world application of AI and ML in health care scheduling and reported outcomes. Eligible studies included those conducted in any health care setting using ML or </span>predictive modeling through AI to optimize patient scheduling processes in real-time, real-world settings. Outcomes of interest included assessing impact on stakeholders (i.e., providers, patients, health systems), including impact on workload, burden, burnout, cost, utilization, patient and provider satisfaction, waste reduction, and quality. Data were extracted and reviewed in duplicates, independently and blindly by two reviewers. The results were synthesized and summarized using a metanarrative approach.</p></div><div><h3>Results</h3><p>The initial search strategy yielded 3,415 citations, of which 11 eligible studies were included. Outcome measures for studies on missed appointments covered patient double-booking volume, missed appointments, service use, and missed appointment risk. Resource allocation outcomes assessed wait time, disease-type matching performance, schedule efficiency revenue, and new patient volume wait time. Other outcomes included visit requests, examination length prediction, and surgical case time.</p></div><div><h3>Conclusions</h3><p>Available evidence shows heterogeneity in the stages of AI and ML development as they apply to patient scheduling. AI and ML applications can be used to decrease the burden on provider time, increase patient satisfaction, and ultimately provide more patient-directed health care and efficiency for medical practices. These findings help identify additional opportunities in which AI platforms can be developed to optimize patient scheduling.</p></div><div><h3>Public Interest Summary</h3><p><span>Artificial Intelligence (AI) and machine learning (ML) can help many aspects of health care. Patient scheduling has significant implications for the cost benefits of improved technology. The longstanding use of technology in medicine serves as a strong foundation for future AI applications. Here, we present an up-to-date review of the current use of AI and ML for schedule optimization in the health care clinic setting. Current evidence shows a wide variety of stages in the development, function, and application of AI and ML </span>in patient scheduling. Given the current gaps of knowledge, future studies should address feasibility, effectiveness, generalizability, and risk of A","PeriodicalId":48672,"journal":{"name":"Health Policy and Technology","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2023-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135713776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}