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A multidisciplinary journal that focuses on the intersection of public health and technology, public health informatics, mass media campaigns, surveillance, participatory epidemiology, and innovation in public health practice and research.
JMIR Public Health & Surveillance (JPHS, Editor-in-chief: Travis Sanchez, Emory University/Rollins School of Public Health) is a PubMed-indexed, peer-reviewed sister journal of the Journal of Medical Internet Research (JMIR), the top cited journal in health informatics (Impact Factor 2016: 5.175). JPH is a multidisciplinary journal with a unique focus on the intersection of innovation and technology in public health, and includes topics like health communication, public health informatics, surveillance, participatory epidemiology, infodemiology and infoveillance, digital disease detection, digital public health interventions, mass media/social media campaigns, and emerging population health analysis systems and tools.
We publish regular articles, reviews, protocols/system descriptions and viewpoint papers on all aspects of public health, with a focus on innovation and technology in public health.
Apart from publishing traditional public health research and viewpoint papers as well as reports from traditional surveillance systems, JPH was one of the first (if not the only) peer-reviewed journal which publishes papers with surveillance or pharmacovigilance data from non-traditional, unstructured big data and text sources such as social media and the Internet (infoveillance, digital disease detection), or reports on novel participatory epidemiology projects, where observations are solicited from the public.
Among other innovations, JPH is also dedicated to support rapid open data sharing and rapid open access to surveillance and outbreak data. As one of the novel features we plan to publish rapid or even real-time surveillance reports and open data. The methods and description of the surveillance system may be peer-reviewed and published only once in detail, in a "baseline report" (in a JMIR Res Protoc or a JMIR Public Health & Surveill paper), and authors then have the possibility to publish data and reports in frequent intervals rapidly and with only minimal additional peer-review (we call this article type "Rapid Surveillance Reports"). JMIR Publications may even work with authors/researchers and developers of selected surveillance systems on APIs for semi-automated reports (e.g. weekly reports to be automatically published in JPHS and indexed in PubMed, based on data-feeds from surveillance systems and minmal narratives and abstracts).
Furthermore, duing epidemics and public health emergencies, submissions with critical data will be processed with expedited peer-review to enable publication within days or even in real-time.
We also publish descriptions of open data resources and open source software. Where possible, we can and want to publish or even host the actual software or dataset on the journal website.
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Low- and middle-income countries (LIMCs) are undergoing an ‘epidemiological transition’, in which the burden of non-communicable diseases (NCDs) is rising and mortality will shift from infectious...
Low- and middle-income countries (LIMCs) are undergoing an ‘epidemiological transition’, in which the burden of non-communicable diseases (NCDs) is rising and mortality will shift from infectious diseases to NCDs. Specifically, cardiovascular disease, diabetes, renal diseases, chronic respiratory diseases, and cancer are becoming more prevalent. In some regions, particularly sub-Saharan Africa, the dual HIV and NCD epidemics will pose challenges as joint burden will have adverse effects on quality of life and will likely increase global inequities. Given the austere clinical infrastructure in many LMICs, innovative models of care delivery are needed to provide comprehensive care in resource-limited settings. Improved data collection and surveillance of NCDs among HIV-infected persons in LMICs are necessary to inform integrated NCD-HIV prevention, care, and treatment models that are effective across a range of geographic settings. These efforts will preserve the considerable investments that have been made to prevent lives lost to HIV, promote healthy aging of PLHIV, and contribute to meeting United Nations Sustainable Development Goals.
Background: Rapid reporting of human infections with novel influenza A viruses accelerates detection of viruses with pandemic potential and implementation of effective public health responses. After d...
Background: Rapid reporting of human infections with novel influenza A viruses accelerates detection of viruses with pandemic potential and implementation of effective public health responses. After detection of human infections with influenza A(H3N2) variant viruses (“H3N2v”) associated with agricultural fairs during August of 2016, the Michigan Department of Health and Human Services worked with Centers for Disease Control and Prevention (CDC) to identify infections with variant influenza viruses using a text-based illness monitoring system. Objective: To enhance detections of influenza infections using text-based monitoring and evaluate the feasibility and acceptability of the system for use in future outbreaks of novel influenza viruses. Methods: During an outbreak of H3N2v virus infections among agricultural fair attendees, we deployed text-illness monitoring (TIM) to conduct active illness surveillance among households of youth who exhibited swine at fairs. We selected fairs with suspected H3N2v virus infections and fairs without suspect infections that met predefined criteria. Eligible respondents were identified and recruited through email outreach and/or on-site meetings at fairs. During and for 10 days after selected fairs, enrolled households received daily, automated text-messages inquiring about illness; reports of illness were investigated by local health departments. To understand the feasibility and acceptability of the system, we monitored enrollment and trends in participation and distributed a web-based survey to households of exhibitors from 5 fairs. Results: Among an estimated 500 households with a member who exhibited swine at one of 9 selected fairs, representatives of 87 (17%) households were enrolled, representing 392 household members. For fairs that were ongoing when TIM was deployed, the number of respondents peaked at 56 on the third day of the fair and then steadily declined throughout the rest of the monitoring period; 26 (30%) of 87 household representatives responded through the end of the 10-day monitoring period. We detected 2 H3N2v virus infections using TIM, which represents 17% (2/12) of all H3N2v virus infections detected during this outbreak in Michigan. Of the 70 survey respondents, 16 (23%) had participated in TIM. Of those, 73% (11/15) participated because it was recommended by fair coordinators and 80% (11/15) said they would participate again. Conclusions: Using a text-message system, we were able to monitor a large number of individuals and households for illness and detected H3N2v virus infections through active surveillance. Text-based illness monitoring systems are useful to detect novel influenza virus infections when active monitoring is deemed necessary. Participant retention and testing of persons reporting illness are critical elements for system improvement.
Background: Social media have been increasingly adopted by health agencies and professionals to disseminate information, interact with public, and understand public opinion. Among them, the Centers fo...
Background: Social media have been increasingly adopted by health agencies and professionals to disseminate information, interact with public, and understand public opinion. Among them, the Centers for Disease Control and Prevention (CDC) is arguably one of the first government health agencies to adopt social media during health emergencies and crisis. It had been active on Twitter during the 2016 Zika epidemic that caused 5,168 domestic cases in the United States. Objective: This study aims to quantify the temporal variabilities in CDC’s tweeting activities over the course of Zika epidemic, public engagement in these CDC-initiated tweets (i.e., retweets and replies), and Zika case counts. It then compares the patterns of these three sets of data to identify the discrepancy and consistency among actual domestic Zika case counts, CDC response on Twitter, and public engagement in this topic. Methods: All of the CDC-initiated tweets published in 2016 with corresponding retweets and replies were collected from 67 CDC-associated Twitter accounts. Both univariate (ARIMA model) and multivariate time series analyses (CCF, mutual Shannon information entropy, ARIMAX model, Granger test) were performed in each quarter of 2016 for domestic Zika case counts, CDC tweeting activities, and public engagement in the CDC-initiated tweets. Results: CDC sent out more than 84% of its Zika tweets in the first quarter of 2016 when Zika case counts were low in the 50 U.S. states and territories. While Zika case counts increased dramatically in the second and the third quarters, CDC efforts on Twitter plunged. Time series of public engagement in the CDC-initiated tweets generally differed among quarters and from that of original CDC tweets based on ARIMA model parameters. Original CDC tweets and public engagement both had highest mutual information with Zika case counts in the 2nd quarter. Public engagement in the original CDC tweets was also substantially influenced by and preceded actual Zika epidemic. Conclusions: There were substantial discrepancies among CDC’s original tweets regarding Zika, public engagement in these tweets, and actual Zika epidemic. The patterns of these discrepancies also varied between different quarters in 2016. We discovered that although CDC was very effective in early warning of Zika in the first quarter of 2016, it failed to respond in a timely manner on Twitter during the rest of the Zika epidemic. Public engagement in CDC’s original tweets served as a more prominent predictor of actual Zika epidemic than number of CDC’s original tweets later in the year.
Background: In order to understand public sentiment regarding the Zika virus, social media can be leveraged to understand how positive, negative, and neutral sentiments are expressed in society. Speci...
Background: In order to understand public sentiment regarding the Zika virus, social media can be leveraged to understand how positive, negative, and neutral sentiments are expressed in society. Specifically, understanding the characteristics of negative sentiment could help inform federal disease control agencies’ efforts to disseminate relevant information to the public about Zika related issues. Objective: The purpose of this study was to analyze public sentiment concerning Zika using posts on Twitter and determine the qualitative characteristics of positive, negative and neutral sentiments expressed. Methods: Machine learning techniques and algorithms were used to analyze the sentiment of tweets concerning Zika. A supervised machine learning classifier was built to classify tweets into three sentiment categories: positive, neutral, and negative. Tweets in each category were then examined using a topic modeling approach in order to determine the main topics for each category, with focus on the negative category. Results: A total of 5,303 tweets were manually annotated and used to train multiple classifiers. These performed moderately well (F1 score = 0.69, 0.68) with text-based feature extraction. All 48,734 tweets were then categorized into the sentiment categories. Ten topics for each sentiment category were identified using topic modeling with a focus on the negative sentiment category. Conclusions: Our study demonstrates how sentiment expressed within discussions of epidemics on Twitter can be discovered. This allows public health officials to understand public sentiment regarding an epidemic and enables them to address specific elements of negative sentiment in real-time. Our negative sentiment classifier was able to identify tweets concerning Zika with three broad themes: neural defects, Zika abnormalities, and reports and findings. These broad themes were based on domain expertise and from topics discussed in journals such as MMWR and Vaccine. Since the majority of topics in the negative sentiment category concerned symptoms, officials should focus on spreading information about prevention and treatment research.
Background: Individuals with diabetes are using social media as a method to share and gather information about their health via the diabetes online community. Infoveillance is one methodological appro...
Background: Individuals with diabetes are using social media as a method to share and gather information about their health via the diabetes online community. Infoveillance is one methodological approach to examine healthcare trends. Infoveillance, however, while very effective in identifying many real-world health trends, may miss opportunities which use photographs as primary sources for data. We propose a new methodology, photosurveillance, in which photographs are analyzed to examine real-world trends. Objective: The purpose of this research is to 1) assess the use of photosurveillance as a research method to examine real-world trends in diabetes, and 2) report on real-world use of continuous glucose monitoring on Instagram. Methods: This exploratory mixed method study examined all photographs posted on Instagram identified with the hashtag, #dexcom, over a 2-month period. Photographs were coded by CGM location on the body. Original posts and corresponding comments were textually coded for length of CGM wear and CGM failure and analyzed for emerging themes. Results: 2923 photographs were manually screened, 12.1% (N=353) depicted a photograph with a CGM site location. The majority (64%, n=225) of the photographs showed a CGM site in an off-label location, while 26.2% where in an FDA approved location (abdomen), and 10.2% (n=36) were in unidentifiable locations There were no significant differences in the number of likes or comments based on FDA approval. Four themes emerged from the analysis of original posts (N-353) and corresponding comments (N=2364): 1) endorsement of CGM as providing a sense of wellbeing, 2) reciprocating encouragement and support, and 3) life hacks to optimize CGM use, and 4) sharing and learning about off-label CGM activity. Conclusions: Our results indicate that individuals successfully used CGM in off-label locations with greater frequency than the abdomen, with no indication of sensor failure, although these photographs only capture a snapshot in time. There were instances in which sensors were worn beyond the FDA-approved 7-days, however, they represented the minority in this study.
This paper discusses the acceptance of and resistance to new medical technologies in healthcare settings such as hospitals, doctors’ offices, and eHealth Centers. As users’ acceptance of new techn...
This paper discusses the acceptance of and resistance to new medical technologies in healthcare settings such as hospitals, doctors’ offices, and eHealth Centers. As users’ acceptance of new technologies is critical for successful implementation, it is important to understand what factors influence acceptance and resistance. Therefore, a method is proposed for identifying the factors that influence the acceptance of and resistance to new technologies by both medical staff and patients. The method draws on concepts from the Technology Acceptance Model and Unified Technology Acceptance and Use of Technology model. The target groups are patients in Brandenburg and key players in the local healthcare structure, such as medical institutions and professionals. In addition, practical suggestions are given for facilitating users’ acceptance of digital solutions and innovative medical technology.