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Google Trends Assessment of Keywords Related to Smoking and Smoking Cessation During the COVID-19 Pandemic in 4 European Countries: Retrospective Analysis

Google Trends Assessment of Keywords Related to Smoking and Smoking Cessation During the COVID-19 Pandemic in 4 European Countries: Retrospective Analysis

SARS-Co V-2, the virus responsible for COVID-19 infection, initially surfaced in Wuhan, China, in December 2019 and quickly spread worldwide [1]. The first cases in Europe were reported in January 2020, shortly after it was declared a global pandemic by the World Health Organization (WHO) [2].

Tobias Jagomast, Jule Finck, Imke Tangemann-Münstedt, Katharina Auth, Daniel Drömann, Klaas F Franzen

Online J Public Health Inform 2024;16:e57718

A Patient-Driven Mobile Health Innovation in Cystic Fibrosis Care: Comparative Cross-Case Study

A Patient-Driven Mobile Health Innovation in Cystic Fibrosis Care: Comparative Cross-Case Study

However, the current literature does not examine the factors that influence the adoption, spread, and scale-up of patient-driven innovations in health care organizations [3]. The paucity of research studies evaluating and reporting the outcomes of patient-driven innovations has been suggested as a potential obstacle to their adoption in health care [4]. Cystic fibrosis (CF) is a complex chronic and genetic condition that affects respiratory and other organ systems [5].

Pamela Mazzocato, Jamie Linnea Luckhaus, Moa Malmqvist Castillo, Johan Burnett, Andreas Hager, Gabriela Oates, Carolina Wannheden, Carl Savage

J Med Internet Res 2024;26:e50527

Spontaneous Scaling of a Primary Care Innovation in Real-Life Conditions: Protocol for a Case Study

Spontaneous Scaling of a Primary Care Innovation in Real-Life Conditions: Protocol for a Case Study

Many of these propose modifiers, such as scaling up or out and vertical or horizontal scaling, are used in an attempt to describe the different ways in which innovations are scaled or spread. Expand Net, a World Health Organization–affiliated group, has produced several widely used practical guides on the topic and defines scaling up as an action that aims to increase the impacts of successfully tested innovations to benefit more people [6].

France Légaré, Diogo G V Mochcovitch, Roberta de Carvalho Corôa, Amédé Gogovor, Ali Ben Charif, Cynthia Cameron, Annie Plamondon, Marie Cimon, Sabrina Guay-Bélanger, Geneviève Roch, Maxine Dumas Pilon, Jean-Sébastien Paquette, Robert K D McLean, Andrew Milat

JMIR Res Protoc 2023;12:e54855

Toward Inclusive Approaches in the Design, Development, and Implementation of eHealth in the Intellectual Disability Sector: Scoping Review

Toward Inclusive Approaches in the Design, Development, and Implementation of eHealth in the Intellectual Disability Sector: Scoping Review

Another example is the Nonadoption, Abandonment, and challenges to the Scale-up, Spread, and Sustainability (NASSS) framework, which reviews the implementation of health care technology in multiple domains (eg, technology, value proposition, adopters, and organization) [30]. According to the NASSS framework, the development of technology is a never-ending process in which the technology can be adjusted to fit each specific setting and context [31].

Julia F E van Calis, Kirsten E Bevelander, Anneke W C van der Cruijsen, Geraline L Leusink, Jenneken Naaldenberg

J Med Internet Res 2023;25:e45819

Syndromic Surveillance Using Structured Telehealth Data: Case Study of the First Wave of COVID-19 in Brazil

Syndromic Surveillance Using Structured Telehealth Data: Case Study of the First Wave of COVID-19 in Brazil

Such data can be used as a proxy for the spread of infectious diseases for health surveillance [6-11]. The mathematical modeling of epidemics is a well-established field that has been successfully used for tracking the spread of SARS-Co V-2 infection, supporting the decision-making process on diverse aspects of public health response to the COVID-19 pandemic. Such models are usually defined as compartmental models.

Viviane S Boaventura, Malú Grave, Thiago Cerqueira-Silva, Roberto Carreiro, Adélia Pinheiro, Alvaro Coutinho, Manoel Barral Netto

JMIR Public Health Surveill 2023;9:e40036

Spread of COVID-19 Vaccine Misinformation in the Ninth Inning: Retrospective Observational Infodemic Study

Spread of COVID-19 Vaccine Misinformation in the Ninth Inning: Retrospective Observational Infodemic Study

Data provided by VAERS has been increasingly used by antivaccine advocates to spread misinformation [2]. This database has also seen increased reports since COVID-19 vaccines received emergency use authorization from the Food and Drug Administration [2].

Alec J Calac, Michael R Haupt, Zhuoran Li, Tim Mackey

JMIR Infodemiology 2022;2(1):e33587

Assessing COVID-19 Health Information on Google Using the Quality Evaluation Scoring Tool (QUEST): Cross-sectional and Readability Analysis

Assessing COVID-19 Health Information on Google Using the Quality Evaluation Scoring Tool (QUEST): Cross-sectional and Readability Analysis

Since the onset of the COVID-19 pandemic, new information is released daily, if not hourly, regarding disease spread, symptomatology, and health and economic consequences. In some cases, news has been rapidly spread only to be contradicted days later. For example, at the beginning of the pandemic, hydroxychloroquine was regularly discussed in lay news and scientific journals alike.

Vismaya S Bachu, Heba Mahjoub, Albert E Holler, Tudor Crihalmeanu, Dheevena M Bachu, Varun Ayyaswami, Pearman D Parker, Arpan V Prabhu

JMIR Form Res 2022;6(2):e32443

Utility of Facebook’s Social Connectedness Index in Modeling COVID-19 Spread: Exponential Random Graph Modeling Study

Utility of Facebook’s Social Connectedness Index in Modeling COVID-19 Spread: Exponential Random Graph Modeling Study

In this cross-sectional study, we analyzed publicly available data to test the association between the SCI and COVID-19 spread in Missouri using exponential random graph models (ERGMs). This study was reviewed by the institutional review board and deemed nonhuman participant research. The SCI was obtained through the Facebook Data for Good program.

Beth Prusaczyk, Kathryn Pietka, Joshua M Landman, Douglas A Luke

JMIR Public Health Surveill 2021;7(12):e33617

Characterization of Unlinked Cases of COVID-19 and Implications for Contact Tracing Measures: Retrospective Analysis of Surveillance Data

Characterization of Unlinked Cases of COVID-19 and Implications for Contact Tracing Measures: Retrospective Analysis of Surveillance Data

As COVID-19 cases are still rising around the world and new variants are emerging, nonpharmaceutical interventions (NPIs) are essential for controlling the spread of COVID-19 [1,2] in many countries, especially when vaccination programs are impeded by factors such as vaccine hesitancy, reduced efficacy against some new variants, and vaccine shortage.

Ka Chun Chong, Katherine Jia, Shui Shan Lee, Chi Tim Hung, Ngai Sze Wong, Francisco Tsz Tsun Lai, Nancy Chau, Carrie Ho Kwan Yam, Tsz Yu Chow, Yuchen Wei, Zihao Guo, Eng Kiong Yeoh

JMIR Public Health Surveill 2021;7(11):e30968

Effectiveness of Contact Tracing for Viral Disease Mitigation and Suppression: Evidence-Based Review

Effectiveness of Contact Tracing for Viral Disease Mitigation and Suppression: Evidence-Based Review

To mitigate the spread of disease, contact tracing involves interviewing people who are infected to identify which other individuals they might have exposed to the virus, finding those exposed contacts, isolating contacts who are infected, and placing exposed contacts in quarantine until they are not deemed infectious [4]. Public health agencies use contact tracing as one strategy among many to break the chain of viral transmission.

Kelly Jean Thomas Craig, Rubina Rizvi, Van C Willis, William J Kassler, Gretchen Purcell Jackson

JMIR Public Health Surveill 2021;7(10):e32468