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Journal Description

JMIR Public Health & Surveillance (JPHS, Editor-in-chief: Travis Sanchez, Emory University/Rollins School of Public Health) is an ESCI, PMC/PubMed- and MEDLINE-indexed, peer-reviewed international multidisciplinary journal with a unique focus on the intersection of innovation and technology in public health, and includes topics like public health informatics, surveillance (surveillance systems and rapid reports), participatory epidemiology, infodemiology and infoveillance, digital disease detection, digital epidemiology, electronic public health interventions, mass media/social media campaigns, health communication, and emerging population health analysis systems and tools. 

JPHS has an international author- and readership and welcomes submissions from around the world.

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. The main themes/topics covered by this journal can be found here.

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 journals to publish 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, JPHS 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 minimal narratives and abstracts).

Furthermore, during 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.

 

Recent Articles:

  • Source: freepik; Copyright: tirachardz; URL: https://www.freepik.com/free-photo/asian-freelance-business-woman-wearing-protective-mask-using-tablet-working-living-room-from-home-when-social-distancing-stay-home-self-quarantine-time-coronavirus-concept_7685895.htm#page=1&query=asian+person+mask&posit; License: Licensed by JMIR.

    General Model for COVID-19 Spreading With Consideration of Intercity Migration, Insufficient Testing, and Active Intervention: Modeling Study of Pandemic...

    Abstract:

    Background: The coronavirus disease (COVID-19) began to spread in mid-December 2019 from Wuhan, China, to most provinces in China and over 200 other countries through an active travel network. Limited by the ability of the country or city to perform tests, the officially reported number of confirmed cases is expected to be much smaller than the true number of infected cases. Objective: This study aims to develop a new susceptible-exposed-infected-confirmed-removed (SEICR) model for predicting the spreading progression of COVID-19 with consideration of intercity travel and the difference between the number of confirmed cases and actual infected cases, and to apply the model to provide a realistic prediction for the United States and Japan under different scenarios of active intervention. Methods: The model introduces a new state variable corresponding to the actual number of infected cases, integrates intercity travel data to track the movement of exposed and infected individuals among cities, and allows different levels of active intervention to be considered so that a realistic prediction of the number of infected individuals can be performed. Moreover, the model generates future progression profiles for different levels of intervention by setting the parameters relative to the values found from the data fitting. Results: By fitting the model with the data of the COVID-19 infection cases and the intercity travel data for Japan (January 15 to March 20, 2020) and the United States (February 20 to March 20, 2020), model parameters were found and then used to predict the pandemic progression in 47 regions of Japan and 50 states (plus a federal district) in the United States. The model revealed that, as of March 19, 2020, the number of infected individuals in Japan and the United States could be 20-fold and 5-fold as many as the number of confirmed cases, respectively. The results showed that, without tightening the implementation of active intervention, Japan and the United States will see about 6.55% and 18.2% of the population eventually infected, respectively, and with a drastic 10-fold elevated active intervention, the number of people eventually infected can be reduced by up to 95% in Japan and 70% in the United States. Conclusions: The new SEICR model has revealed the effectiveness of active intervention for controlling the spread of COVID-19. Stepping up active intervention would be more effective for Japan, and raising the level of public vigilance in maintaining personal hygiene and social distancing is comparatively more important for the United States.

  • Source: freepik; Copyright: pressfoto; URL: https://www.freepik.com/free-photo/discussing-chest-x-ray_5534956.htm#page=1&query=doctor%20with%20computer&position=39; License: Licensed by JMIR.

    The Oxford Royal College of General Practitioners Clinical Informatics Digital Hub: Protocol to Develop Extended COVID-19 Surveillance and Trial Platforms

    Abstract:

    Background: Routinely recorded primary care data have been used for many years by sentinel networks for surveillance. More recently, real world data have been used for a wider range of research projects to support rapid, inexpensive clinical trials. Because the partial national lockdown in the United Kingdom due to the coronavirus disease (COVID-19) pandemic has resulted in decreasing community disease incidence, much larger numbers of general practices are needed to deliver effective COVID-19 surveillance and contribute to in-pandemic clinical trials. Objective: The aim of this protocol is to describe the rapid design and development of the Oxford Royal College of General Practitioners Clinical Informatics Digital Hub (ORCHID) and its first two platforms. The Surveillance Platform will provide extended primary care surveillance, while the Trials Platform is a streamlined clinical trials platform that will be integrated into routine primary care practice. Methods: We will apply the FAIR (Findable, Accessible, Interoperable, and Reusable) metadata principles to a new, integrated digital health hub that will extract routinely collected general practice electronic health data for use in clinical trials and provide enhanced communicable disease surveillance. The hub will be findable through membership in Health Data Research UK and European metadata repositories. Accessibility through an online application system will provide access to study-ready data sets or developed custom data sets. Interoperability will be facilitated by fixed linkage to other key sources such as Hospital Episodes Statistics and the Office of National Statistics using pseudonymized data. All semantic descriptors (ie, ontologies) and code used for analysis will be made available to accelerate analyses. We will also make data available using common data models, starting with the US Food and Drug Administration Sentinel and Observational Medical Outcomes Partnership approaches, to facilitate international studies. The Surveillance Platform will provide access to data for health protection and promotion work as authorized through agreements between Oxford, the Royal College of General Practitioners, and Public Health England. All studies using the Trials Platform will go through appropriate ethical and other regulatory approval processes. Results: The hub will be a bottom-up, professionally led network that will provide benefits for member practices, our health service, and the population served. Data will only be used for SQUIRE (surveillance, quality improvement, research, and education) purposes. We have already received positive responses from practices, and the number of practices in the network has doubled to over 1150 since February 2020. COVID-19 surveillance has resulted in tripling of the number of virology sites to 293 (target 300), which has aided the collection of the largest ever weekly total of surveillance swabs in the United Kingdom as well as over 3000 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) serology samples. Practices are recruiting to the PRINCIPLE (Platform Randomised trial of INterventions against COVID-19 In older PeopLE) trial, and these participants will be followed up through ORCHID. These initial outputs demonstrate the feasibility of ORCHID to provide an extended national digital health hub. Conclusions: ORCHID will provide equitable and innovative use of big data through a professionally led national primary care network and the application of FAIR principles. The secure data hub will host routinely collected general practice data linked to other key health care repositories for clinical trials and support enhanced in situ surveillance without always requiring large volume data extracts. ORCHID will support rapid data extraction, analysis, and dissemination with the aim of improving future research and development in general practice to positively impact patient care.

  • Untitled. Source: Freepik; Copyright: freepik; URL: https://www.freepik.com/free-photo/white-pills-falling-out-bottle_3376635.htm; License: Licensed by JMIR.

    Online Conversation Monitoring to Understand the Opioid Epidemic: Epidemiological Surveillance Study

    Abstract:

    Background: Between 2016 and 2017, the national mortality rate involving opioids continued its escalation; opioid deaths rose from 42,249 to 47,600, bringing the public health crisis to a new height. Considering that 69% of adults in the United States use online social media sites, a resource that builds a more complete understanding of prescription drug misuse and abuse could supplement traditional surveillance instruments. The Food and Drug Administration has identified 5 key risks and consequences of opioid drugs—misuse, abuse, addiction, overdose, and death. Identifying posts that discuss these key risks could lead to novel information that is not typically captured by traditional surveillance systems. Objective: The goal of this study was to describe the trends of online posts (frequency over time) involving abuse, misuse, addiction, overdose, and death in the United States and to describe the types of websites that host these discussions. Internet posts that mentioned fentanyl, hydrocodone, oxycodone, or oxymorphone were examined. Methods: Posts that did not refer to personal experiences were removed, after which 3.1 million posts remained. A stratified sample of 61,000 was selected. Unstructured data were classified into 5 key risks by manually coding for key outcomes of misuse, abuse, addiction, overdose, and death. Sampling probabilities of the coded posts were used to estimate the total post volume for each key risk. Results: Addiction and misuse were the two most commonly discussed key risks for hydrocodone, oxycodone, and oxymorphone. For fentanyl, overdose and death were the most discussed key risks. Fentanyl had the highest estimated number of misuse-, overdose-, and death-related mentions (41,808, 42,659, and 94,169, respectively). Oxycodone had the highest estimated number of abuse- and addiction-related mentions (3548 and 12,679, respectively). The estimated volume of online posts for fentanyl increased by more than 10-fold in late 2017 and 2018. The odds of discussing fentanyl overdose (odds ratios [OR] 4.32, 95% CI 2.43-7.66) and death (OR 5.05, 95% CI 3.10-8.21) were higher for social media, while the odds of discussing fentanyl abuse (OR 0.10, 95% CI 0.04-0.22) and addiction (OR 0.24, 95% CI 0.15-0.38) were higher for blogs and forums. Conclusions: Of the 5 FDA-defined key risks, fentanyl overdose and death has dominated discussion in recent years, while discussion of oxycodone, hydrocodone, and oxymorphone has decreased. As drug-related deaths continue to increase, an understanding of the motivations, circumstances, and consequences of drug abuse would assist in developing policy responses. Furthermore, content was notably different based on media origin, and studies that exclusively use either social media sites (such as Twitter) or blogs and forums could miss important content. This study sets out sustainable, ongoing methodology for surveilling internet postings regarding these drugs.

  • Source: freepik; Copyright: freepik; URL: https://www.freepik.com/free-photo/woman-wearing-surgical-face-mask-working-from-home_7329290.htm; License: Licensed by JMIR.

    Suitability and Sufficiency of Telehealth Clinician-Observed, Participant-Collected Samples for SARS-CoV-2 Testing: The iCollect Cohort Pilot Study

    Abstract:

    Background: The severe acute respiratory coronavirus 2 (SARS-CoV-2) pandemic calls for expanded opportunities for testing, including novel testing strategies such as home-collected specimens. Objective: We aimed to understand whether oropharyngeal swab (OPS), saliva, and dried blood spot (DBS) specimens collected by participants at home and mailed to a laboratory were sufficient for use in diagnostic and serology tests of SARS-CoV-2. Methods: Eligible participants consented online and were mailed a participant-collection kit to support collection of three specimens for SARS-CoV-2 testing: saliva, OPS, and DBS. Participants performed the specimen collection procedures during a telehealth video appointment while clinical observers watched and documented the suitability of the collection. The biological sufficiency of the specimens for detection of SARS-CoV-2 by reverse transcriptase–polymerase chain reaction and serology testing was assessed by laboratorians using visual inspection and quantification of the nucleic acid contents of the samples by ribonuclease P (RNase P) measurements. Results: Of the enrolled participants,153/159 (96.2%) returned their kits, which were included in this analysis. All these participants attended their video appointments. Clinical observers assessed that of the samples collected, 147/153 (96.1%) of the saliva samples, 146/151 (96.7%) of the oropharyngeal samples, and 135/145 (93.1%) of the DBS samples were of sufficient quality for submission for laboratory testing; 100% of the OPS samples and 98% of the saliva samples had cycle threshold values for RNase P <30, indicating that the samples contained sufficient nucleic acid for RNA-PCR testing for SARS-CoV-2. Conclusions: These pilot data indicate that most participant-collected OPS, saliva, and DBS specimens are suitable and sufficient for testing for SARS-CoV-2 RNA and serology. Clinical observers rated the collection of specimens as suitable for testing, and visual and quantitative laboratory assessment indicated that the specimens were biologically sufficient. These data support the utility of participant-collected and mailed-in specimens for SARS-CoV-2 testing.

  • Untitled. Source: iStock by Getty Images; Copyright: filadendron; URL: https://www.istockphoto.com/ca/photo/video-call-with-doctor-gm1216946068-355032303; License: Licensed by the authors.

    Telehealth as a Bright Spot of the COVID-19 Pandemic: Recommendations From the Virtual Frontlines ("Frontweb")

    Abstract:

    The coronavirus disease (COVID-19) pandemic has accelerated the telehealth tipping point in the practice of family medicine and primary care in the United States, making telehealth not just a novel approach to care but also a necessary one for public health safety. Social distancing requirements and stay-at-home orders have shifted patient care from face-to-face consultations in primary care offices to virtual care from clinicians’ homes or offices, moving to a new frontline, which we call the “frontweb.” Our telehealth workgroup employed the Clinical Transformation in Technology implementation framework to accelerate telehealth expansion and to develop a consensus document for clinician recommendations in providing remote virtual care during the pandemic. In a few weeks, telehealth went from under 5% of patient visits to almost 93%, while maintaining high levels of patient satisfaction. In this paper, we share clinician recommendations and guidance gleaned from this transition to the frontweb and offer a systematic approach for ensuring “webside” success.

  • Untitled. Source: Freepik; Copyright: freepik; URL: https://www.freepik.com/free-photo/circular-white-pills-spilling-from-glass-bottle-yellow-background_2690208.htm; License: Licensed by JMIR.

    Using Twitter to Surveil the Opioid Epidemic in North Carolina: An Exploratory Study

    Abstract:

    Background: Over the last two decades, deaths associated with opioids have escalated in number and geographic spread, impacting more and more individuals, families, and communities. Reflecting on the shifting nature of the opioid overdose crisis, Dasgupta, Beletsky, and Ciccarone offer a triphasic framework to explain that opioid overdose deaths (OODs) shifted from prescription opioids for pain (beginning in 2000), to heroin (2010 to 2015), and then to synthetic opioids (beginning in 2013). Given the rapidly shifting nature of OODs, timelier surveillance data are critical to inform strategies that combat the opioid crisis. Using easily accessible and near real-time social media data to improve public health surveillance efforts related to the opioid crisis is a promising area of research. Objective: This study explored the potential of using Twitter data to monitor the opioid epidemic. Specifically, this study investigated the extent to which the content of opioid-related tweets corresponds with the triphasic nature of the opioid crisis and correlates with OODs in North Carolina between 2009 and 2017. Methods: Opioid-related Twitter posts were obtained using Crimson Hexagon, and were classified as relating to prescription opioids, heroin, and synthetic opioids using natural language processing. This process resulted in a corpus of 100,777 posts consisting of tweets, retweets, mentions, and replies. Using a random sample of 10,000 posts from the corpus, we identified opioid-related terms by analyzing word frequency for each year. OODs were obtained from the Multiple Cause of Death database from the Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research (CDC WONDER). Least squares regression and Granger tests compared patterns of opioid-related posts with OODs. Results: The pattern of tweets related to prescription opioids, heroin, and synthetic opioids resembled the triphasic nature of OODs. For prescription opioids, tweet counts and OODs were statistically unrelated. Tweets mentioning heroin and synthetic opioids were significantly associated with heroin OODs and synthetic OODs in the same year (P=.01 and P<.001, respectively), as well as in the following year (P=.03 and P=.01, respectively). Moreover, heroin tweets in a given year predicted heroin deaths better than lagged heroin OODs alone (P=.03). Conclusions: Findings support using Twitter data as a timely indicator of opioid overdose mortality, especially for heroin.

  • Field Officers of the Total Control of the Epidemic community-based HIV testing program, Zambezi region, Namibia. Source: Image created by the Authors; Copyright: The Authors; URL: http://publichealth.jmir.org/2020/2/e17107/; License: Licensed by the authors.

    Rates and Correlates of HIV Incidence in Namibia’s Zambezi Region From 2014 to 2016: Sentinel, Community-Based Cohort Study

    Abstract:

    Background: Direct measures of HIV incidence are needed to assess the population-level impact of prevention programs but are scarcely available in the subnational epidemic hotspots of sub-Saharan Africa. We created a sentinel HIV incidence cohort within a community-based program that provided home-based HIV testing to all residents of Namibia’s Zambezi region, where approximately 24% of the adult population was estimated to be living with HIV. Objective: The aim of this study was to estimate HIV incidence, detect correlates of HIV acquisition, and assess the feasibility of the sentinel, community-based approach to HIV incidence surveillance in a subnational epidemic hotspot. Methods: Following the program’s initial home-based testing (December 2014-July 2015), we purposefully selected 10 clusters of 60 to 70 households each and invited residents who were HIV negative and aged ≥15 years to participate in the cohort. Consenting participants completed behavioral interviews and a second HIV test approximately 1 year later (March-September 2016). We used Poisson models to calculate HIV incidence rates between baseline and follow-up and multivariable Cox proportional hazard models to assess the correlates of seroconversion. Results: Among 1742 HIV-negative participants, 1624 (93.23%) completed follow-up. We observed 26 seroconversions in 1954 person-years (PY) of follow-up, equating to an overall incidence rate of 1.33 per 100 PY (95% CI 0.91-1.95). Among women, the incidence was 1.55 per 100 PY (95% CI 1.12-2.17) and significantly higher among those aged 15 to 24 years and residing in rural areas (adjusted hazard ratio [aHR] 4.26, 95% CI 1.39-13.13; P=.01), residing in the Ngweze suburb of Katima Mulilo city (aHR 2.34, 95% CI 1.25-4.40; P=.01), who had no prior HIV testing in the year before cohort enrollment (aHR 3.38, 95% CI 1.04-10.95; P=.05), and who had engaged in transactional sex (aHR 17.64, 95% CI 2.88-108.14; P=.02). Among men, HIV incidence was 1.05 per 100 PY (95% CI 0.54-2.31) and significantly higher among those aged 40 to 44 years (aHR 13.04, 95% CI 5.98-28.41; P<.001) and had sought HIV testing outside the study between baseline and follow-up (aHR 8.28, 95% CI 1.39-49.38; P=.02). No seroconversions occurred among persons with HIV-positive partners on antiretroviral treatment. Conclusions: Nearly three decades into Namibia’s generalized HIV epidemic, these are the first estimates of HIV incidence for its highest prevalence region. By creating a sentinel incidence cohort from the infrastructure of an existing community-based testing program, we were able to characterize current transmission patterns, corroborate known risk factors for HIV acquisition, and provide insight into the efficacy of prevention interventions in a subnational epidemic hotspot. This study demonstrates an efficient and scalable framework for longitudinal HIV incidence surveillance that can be implemented in diverse sentinel sites and populations.

  • Source: Freepik; Copyright: peoplecreations; URL: https://www.freepik.com/free-photo/surgeons-performing-operation-operation-room_1008446.htm; License: Licensed by JMIR.

    COVID-19 and Laparoscopic Surgery: Scoping Review of Current Literature and Local Expertise

    Abstract:

    Background: The current coronavirus disease (COVID-19) pandemic is holding the world in its grip. Epidemiologists have shown that the mortality risks are higher when the health care system is subjected to pressure from COVID-19. It is therefore of great importance to maintain the health of health care providers and prevent contamination. An important group who will be required to treat patients with COVID-19 are health care providers during semiacute surgery. There are concerns that laparoscopic surgery increases the risk of contamination more than open surgery; therefore, balancing the safety of health care providers with the benefit of laparoscopic surgery for the patient is vital. Objective: We aimed to provide an overview of potential contamination routes and possible risks for health care providers; we also aimed to propose research questions based on current literature and expert opinions about performing laparoscopic surgery on patients with COVID-19. Methods: We performed a scoping review, adding five additional questions concerning possible contaminating routes. A systematic search was performed on the PubMed, CINAHL, and Embase databases, adding results from gray literature as well. The search not only included COVID-19 but was extended to virus contamination in general. We excluded society and professional association statements about COVID-19 if they did not add new insights to the available literature. Results: The initial search provided 2007 records, after which 267 full-text papers were considered. Finally, we used 84 papers, of which 14 discussed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Eight papers discussed the added value of performing intubation in a low-pressure operating room, mainly based on the SARS outbreak experience in 2003. Thirteen papers elaborated on the risks of intubation for health care providers and SARS-CoV-2, and 19 papers discussed this situation with other viruses. They conclude that there is significant evidence that intubation and extubation is a high-risk aerosol-producing procedure. No papers were found on the risk of SARS-CoV-2 and surgical smoke, although 25 papers did provide conflicting evidence on the infection risk of human papillomavirus, hepatitis B, polio, and rabies. No papers were found discussing tissue extraction or the deflation risk of the pneumoperitoneum after laparoscopic surgery. Conclusions: There seems to be consensus in the literature that intubation and extubation are high-risk procedures for health care providers and that maximum protective equipment is needed. On the other hand, minimal evidence is available of the actual risk of contamination of health care providers during laparoscopy itself, nor of operating room pressure, surgical smoke, tissue extraction, or CO2 deflation. However, new studies are being published daily from current experiences, and society statements are continuously updated. There seems to be no reason to abandon laparoscopic surgery in favor of open surgery. However, the risks should not be underestimated, surgery should be performed on patients with COVID-19 only when necessary, and health care providers should use logic and common sense to protect themselves and others by performing surgery in a safe and protected environment.

  • Source: Pixabay; Copyright: Betshy; URL: https://pixabay.com/photos/cellular-address-book-point-2866230/; License: Licensed by JMIR.

    Perspective of Medical Students on the COVID-19 Pandemic: Survey of Nine Medical Schools in Uganda

    Abstract:

    Background: The coronavirus disease (COVID-19) pandemic is a global public health concern affecting over 5 million people and posing a great burden on health care systems worldwide. Objective: The aim of this study is to determine the knowledge, attitude, and practices of medical students in Uganda on the COVID-19 pandemic. Methods: We conducted an online, descriptive cross-sectional study in mid-April 2020, using WhatsApp Messenger. Medical students in 9 of the 10 medical schools in Uganda were approached through convenience sampling. Bloom’s cut-off of 80% was used to determine good knowledge (≥12 out of 15), positive attitude (≥20 out of 25), and good practice (≥12 out of 15). Results: The data of 741 first- to fifth-year medical students, consisting of 468 (63%) males with a mean age of 24 (SD 4) years, were analyzed. The majority (n=626, 84%) were pursuing Bachelor of Medicine and Bachelor of Surgery degrees. Overall, 671 (91%) had good knowledge, 550 (74%) had a positive attitude, and 426 (57%) had good practices. Knowledge was associated with the 4th year of study (adjusted odds ratio [aOR] 4.1, 95% CI 1.6-10.3; P<.001). Attitude was associated with the female sex (aOR 0.7, 95% CI 0.5-1; P=.04) and TV or radio shows (aOR 1.1, 95% CI 0.6-2.1; P=.01). Practices were associated with the ≥24 years age category (aOR 1.5, 95% CI 1.1-2.1; P=.02) and online courses (aOR 1.8, 95% CI 1.1-3.2; P=.03). In total, 592 (80%) medical students were willing to participate in frontline care if called upon. Conclusions: Medical students in Uganda have sufficient knowledge of COVID-19 and will be a large reservoir for health care response when the need arises.

  • Source: Pexels; Copyright: Vlada Karpovich; URL: https://www.pexels.com/photo/woman-working-at-home-with-her-laptop-4050296/; License: Licensed by JMIR.

    Modeling COVID-19 Latent Prevalence to Assess a Public Health Intervention at a State and Regional Scale: Retrospective Cohort Study

    Abstract:

    Background: Emergence of the coronavirus disease (COVID-19) caught the world off guard and unprepared, initiating a global pandemic. In the absence of evidence, individual communities had to take timely action to reduce the rate of disease spread and avoid overburdening their health care systems. Although a few predictive models have been published to guide these decisions, most have not taken into account spatial differences and have included assumptions that do not match the local realities. Access to reliable information that is adapted to local context is critical for policy makers to make informed decisions during a rapidly evolving pandemic. Objective: The goal of this study was to develop an adapted susceptible-infected-removed (SIR) model to predict the trajectory of the COVID-19 pandemic in North Carolina and the Charlotte Metropolitan Region, and to incorporate the effect of a public health intervention to reduce disease spread while accounting for unique regional features and imperfect detection. Methods: Three SIR models were fit to infection prevalence data from North Carolina and the greater Charlotte Region and then rigorously compared. One of these models (SIR-int) accounted for a stay-at-home intervention and imperfect detection of COVID-19 cases. We computed longitudinal total estimates of the susceptible, infected, and removed compartments of both populations, along with other pandemic characteristics such as the basic reproduction number. Results: Prior to March 26, disease spread was rapid at the pandemic onset with the Charlotte Region doubling time of 2.56 days (95% CI 2.11-3.25) and in North Carolina 2.94 days (95% CI 2.33-4.00). Subsequently, disease spread significantly slowed with doubling times increased in the Charlotte Region to 4.70 days (95% CI 3.77-6.22) and in North Carolina to 4.01 days (95% CI 3.43-4.83). Reflecting spatial differences, this deceleration favored the greater Charlotte Region compared to North Carolina as a whole. A comparison of the efficacy of intervention, defined as 1 – the hazard ratio of infection, gave 0.25 for North Carolina and 0.43 for the Charlotte Region. In addition, early in the pandemic, the initial basic SIR model had good fit to the data; however, as the pandemic and local conditions evolved, the SIR-int model emerged as the model with better fit. Conclusions: Using local data and continuous attention to model adaptation, our findings have enabled policy makers, public health officials, and health systems to proactively plan capacity and evaluate the impact of a public health intervention. Our SIR-int model for estimated latent prevalence was reasonably flexible, highly accurate, and demonstrated efficacy of a stay-at-home order at both the state and regional level. Our results highlight the importance of incorporating local context into pandemic forecast modeling, as well as the need to remain vigilant and informed by the data as we enter into a critical period of the outbreak.

  • Distribution of patients at risk for complications related to coronavirus disease (COVID-19) in the United States. Source: Figure 1 from https://publichealth.jmir.org/2020/2/e19606; Copyright: the authors; License: Creative Commons Attribution (CC-BY).

    Distribution of Patients at Risk for Complications Related to COVID-19 in the United States: Model Development Study

    Abstract:

    Background: Coronavirus disease (COVID-19) has spread exponentially across the United States. Older adults with underlying health conditions are at an especially high risk of developing life-threatening complications if infected. Most intensive care unit (ICU) admissions and non-ICU hospitalizations have been among patients with at least one underlying health condition. Objective: The aim of this study was to develop a model to estimate the risk status of the patients of a nationwide pharmacy chain in the United States, and to identify the geographic distribution of patients who have the highest risk of severe COVID-19 complications. Methods: A risk model was developed using a training test split approach to identify patients who are at high risk of developing serious complications from COVID-19. Adult patients (aged ≥18 years) were identified from the Walgreens pharmacy electronic data warehouse. Patients were considered eligible to contribute data to the model if they had at least one prescription filled at a Walgreens location between October 27, 2019, and March 25, 2020. Risk parameters included age, whether the patient is being treated for a serious or chronic condition, and urban density classification. Parameters were differentially weighted based on their association with severe complications, as reported in earlier cases. An at-risk rate per 1000 people was calculated at the county level, and ArcMap was used to depict the rate of patients at high risk for severe complications from COVID-19. Real-time COVID-19 cases captured by the Johns Hopkins University Center for Systems Science and Engineering (CSSE) were layered in the risk map to show where cases exist relative to the high-risk populations. Results: Of the 30,100,826 adults included in this study, the average age is 50 years, 15% have at least one specialty medication, and the average patient has 2 to 3 comorbidities. Nearly 28% of patients have the greatest risk score, and an additional 34.64% of patients are considered high-risk, with scores ranging from 8 to 10. Age accounts for 53% of a patient’s total risk, followed by the number of comorbidities (29%); inferred chronic obstructive pulmonary disease, hypertension, or diabetes (15%); and urban density classification (5%). Conclusions: This risk model utilizes data from approximately 10% of the US population. Currently, this is the most comprehensive US model to estimate and depict the county-level prognosis of COVID-19 infection. This study shows that there are counties across the United States whose residents are at high risk of developing severe complications from COVID-19. Our county-level risk estimates may be used alongside other data sets to improve the accuracy of anticipated health care resource needs. The interactive map can also aid in proactive planning and preparations among employers that are deemed critical, such as pharmacies and grocery stores, to prevent the spread of COVID-19 within their facilities.

  • Untitled. Source: Freepik; Copyright: KamranAydinov; URL: https://www.freepik.com/free-photo/front-view-young-man-white-shirt-blue-gloves-along-with-blue-mask-blue-space_8251793.htm; License: Licensed by JMIR.

    Mining Physicians’ Opinions on Social Media to Obtain Insights Into COVID-19: Mixed Methods Analysis

    Abstract:

    Background: The coronavirus disease (COVID-19) pandemic is considered to be the most daunting public health challenge in decades. With no effective treatments and with time needed to develop a vaccine, alternative approaches are being used to control this pandemic. Objective: The objective of this paper was to identify topics, opinions, and recommendations about the COVID-19 pandemic discussed by medical professionals on the Twitter social medial platform. Methods: Using a mixed methods approach blending the capabilities of social media analytics and qualitative analysis, we analyzed COVID-19–related tweets posted by medical professionals and examined their content. We used qualitative analysis to explore the collected data to identify relevant tweets and uncover important concepts about the pandemic using qualitative coding. Unsupervised and supervised machine learning techniques and text analysis were used to identify topics and opinions. Results: Data were collected from 119 medical professionals on Twitter about the coronavirus pandemic. A total of 10,096 English tweets were collected from the identified medical professionals between December 1, 2019 and April 1, 2020. We identified eight topics, namely actions and recommendations, fighting misinformation, information and knowledge, the health care system, symptoms and illness, immunity, testing, and infection and transmission. The tweets mainly focused on needed actions and recommendations (2827/10,096, 28%) to control the pandemic. Many tweets warned about misleading information (2019/10,096, 20%) that could lead to infection of more people with the virus. Other tweets discussed general knowledge and information (911/10,096, 9%) about the virus as well as concerns about the health care systems and workers (909/10,096, 9%). The remaining tweets discussed information about symptoms associated with COVID-19 (810/10,096, 8%), immunity (707/10,096, 7%), testing (605/10,096, 6%), and virus infection and transmission (503/10,096, 5%). Conclusions: Our findings indicate that Twitter and social media platforms can help identify important and useful knowledge shared by medical professionals during a pandemic.

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  • Self-reported symptoms of SARS-CoV-2 infection in a non-hospitalized population: data from the large Italian web-based EPICOVID19 cross-sectional survey

    Date Submitted: Jun 27, 2020

    Open Peer Review Period: Jun 27, 2020 - Jul 11, 2020

    Background: Understanding the occurrence of Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2)-like symptoms in a large non-hospitalized population, when the epidemic peak was occurring in I...

    Background: Understanding the occurrence of Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2)-like symptoms in a large non-hospitalized population, when the epidemic peak was occurring in Italy, is of paramount importance but data are scarce. Objective: Aims of this study were to evaluate the association of self-reported symptoms with SARS-CoV-2 nasopharyngeal swab (NPS) test in non-hospitalized individuals and to estimate the occurrence of COVID-19-like symptoms in a larger non-tested population. Methods: This is an Italian countrywide self-administered cross-sectional web-based survey on voluntary adults who completed an anonymous questionnaire in the period 13-21 April 2020. The associations between symptoms potentially related to SARS-CoV-2 infection and NPS results were calculated as adjusted odds ratios with 95% confidence intervals (aOR, 95%CI) by means of multiple logistic regression analysis controlling for age, sex, education, smoking habits, and the number of co-morbidities. Thereafter, for each symptom and for their combination, we calculated sensitivity, specificity, accuracy and AUC in a ROC analysis to estimate the occurrence of COVID-19-like infections in the non-tested population. Results: A total of 171,310 responded to the survey (59.9% females, mean age 47.4 years). Out of the 4,785 respondents with known NPS test result, 4,392 were not hospitalized. Among them, the NPS positive respondents (n=856) most frequently reported myalgia (61.6%), olfactory and/or taste disorders (OTDs, 59.2%), cough (54.4%), and fever (51.9%) whereas 7.7% were asymptomatic. Multiple regression analysis showed that OTDs (aOR 10.3, [95%CI 8.4-12.7]), fever (2.5, 95%CI 2.0-3.1), myalgia (1.5, 95%CI 1.2-1.8), and cough (1.3, 95%CI 1.0-1.6) were associated with NPS positivity. Having two to four of these symptoms increased the aOR from 7.4 (95%CI, 5.6-9.7) to 35.5 (95%CI, 24.6-52.2). The combination of the four symptoms showed an AUC of 0.810 (95%CI 0.795-0.825) in classifying NPS-P, and was applied to the non-hospitalized and non-tested sample (n=165,782). We found that from 4.4% to 12.1% of respondents had experienced symptoms suggestive of COVID-19 infection. Conclusions: Our results suggest that self-reported symptoms may be reliable indicators of SARS-CoV-2 infection in a pandemic context. A not negligible part (up to 12.1%) of the symptomatic respondents were left undiagnosed and potentially contributed to the spread of the infection.

  • Hospital Epidemics Tracker (HEpiTracker): Description and pilot study of a mobile app to track COVID-19 epidemics in hospital workers

    Date Submitted: Jun 21, 2020

    Open Peer Review Period: Jun 20, 2020 - Jul 4, 2020

    Background: Hospital workers are the professional group more frequently and severely affected by the COVID-19 pandemic. Innovative tools are required to measure in real-time the symptoms compatible wi...

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