Cases of a novel coronavirus were first reported in Wuhan, Hubei province, China, in December 2019 and have since spread across the world. Epidemiological studies have indicated human-to-human transmission in China and elsewhere. To aid the analysis and tracking of the COVID-19 epidemic we collected and curated individual-level data from national, provincial, and municipal health reports, as well as additional information from online reports. All data are geo-coded and, where available, include symptoms, key dates (date of onset, admission, and confirmation), and travel history. The generation of detailed, real-time, and robust data for emerging disease outbreaks is important and can help to generate robust evidence that will support and inform public health decision making.

Objectives. To examine the extent to which the phrases, “COVID-19” and “Chinese virus” were associated with anti-Asian sentiments. Methods. Data were collected from Twitter’s Application Programming Interface, which included the hashtags “#covid19” or “#chinesevirus.” We analyzed tweets from March 9 to 23, 2020, corresponding to the week before and the week after President Donald J. Trump’s tweet with the phrase, “Chinese Virus.” Our analysis focused on 1 273 141 hashtags. Results. One fifth (19.7%) of the 495 289 hashtags with #covid19 showed anti-Asian sentiment, compared with half (50.4%) of the 777 852 hashtags with #chinesevirus. When comparing the week before March 16, 2020, to the week after, there was a significantly greater increase in anti-Asian hashtags associated with #chinesevirus compared with #covid19 (P < .001). Conclusions. Our data provide new empirical evidence supporting recommendations to use the less-stigmatizing term “COVID-19,” instead of “Chinese virus.”

Importance: Abortion facility closures resulted in a substantial decrease in access to abortion care in the US.Objectives: To investigate the changes in travel time to the nearest abortion facility after the Dobbs v Jackson Women’s Health Organization (referred to hereafter as Dobbs) US Supreme Court decision.Design, Setting, and Participants: Repeated cross-sectional spatial analysis of travel time from each census tract in the contiguous US (n = 82 993) to the nearest abortion facility (n = 1134) listed in the Advancing New Standards in Reproductive Health database. Census tract boundaries and demographics were defined by the 2020 American Community Survey. The spatial analysis compared access during the pre-Dobbs period (January-December 2021) with the post-Dobbs period (September 2022) for the estimated 63 718 431 females aged 15 to 44 years (reproductive age for this analysis) in the US (excluding Alaska and Hawaii).Exposures: The Dobbs ruling and subsequent state laws restricting abortion procedures. The pre-Dobbs period measured abortion access to all facilities providing abortions in 2021. Post-Dobbs abortion access was measured by simulating the closure of all facilities in the 15 states with existing total or 6-week abortion bans in effect as of September 30, 2022.Main Outcomes and Measures: Median and mean changes in surface travel time (eg, car, public transportation) to an abortion facility in the post-Dobbs period compared with the pre-Dobbs period and the total percentage of females of reproductive age living more than 60 minutes from abortion facilities during the pre- and post-Dobbs periods.Results: Of 1134 abortion facilities in the US (at least 1 in every state; 8 in Alaska and Hawaii excluded), 749 were considered active during the pre-Dobbs period and 671 were considered active during a simulated post-Dobbs period. Median (IQR) and mean (SD) travel times to pre-Dobbs abortion facilities were estimated to be 10.9 (4.3-32.4) and 27.8 (42.0) minutes. Travel time to abortion facilities in the post-Dobbs period significantly increased (paired sample t test P <.001) to an estimated median (IQR) of 17.0 (4.9-124.5) minutes and a mean (SD) of and 100.4 (161.5) minutes. In the post-Dobbs period, an estimated 33.3% (sensitivity interval, 32.3%-34.8%) of females of reproductive age lived in a census tract more than 60 minutes from an abortion facility compared with 14.6.% (sensitivity interval, 13.0%-16.9%) of females of reproductive age in the pre-Dobbs period.Conclusions and Relevance: In this repeated cross-sectional spatial analysis, estimated travel time to abortion facilities in the US was significantly greater in the post-Dobbs period after accounting for the closure of abortion facilities in states with total or 6-week abortion bans compared with the pre-Dobbs period, during which all facilities providing abortions in 2021 were considered active.

The big data revolution presents an exciting frontier to expand public health research, broadening the scope of research and increasing the precision of answers. Despite these advances, scientists must be vigilant against also advancing potential harms toward marginalized communities. In this review, we provide examples in which big data applications have (unintentionally) perpetuated discriminatory practices, while also highlighting opportunities for big data applications to advance equity in public health. Here, big data is framed in the context of the five Vs (volume, velocity, veracity, variety, and value), and we propose a sixth V, virtuosity, which incorporates equity and justice frameworks. Analytic approaches to improving equity are presented using social computational big data, fairness in machine learning algorithms, medical claims data, and data augmentation as illustrations. Throughout, we emphasize the biasing influence of data absenteeism and positionality and conclude with recommendations for incorporating an equity lens into big data research.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: More than 3.5 million Americans live with autism spectrum disorder (ASD). Major challenges persist in diagnosing ASD as no medical test to diagnose these disorders exist. Digital phenotyping holds promise to helping guide in the clinical diagnoses and screening of ASD. Objective: To explore the feasibility of using the online social media platform Twitter to detect psychological and behavioral characteristics of self-identified persons with autism. Methods: Data from Twitter was retrieved from 152 self-identified users with autism or ASD and 182 randomly selected control users from March 22, 2012 to July 20, 2017. A comparative textual analysis of tweets about repetitive and obsessive-compulsive behavioral characteristics typically associated with autism was conducted between groups. Common emotional characteristics of persons with autism such as fear, paranoia and anxiety were also examined between groups through textual analysis. Timing of tweets was compared between users with autism and control users to identify patterns in communication. Results: Users with autism/ASD posted a significantly higher frequency of tweets related to the specific repetitive behavior of counting compared to control users (P<.001). Textual analysis of obsessive-compulsive behavioral characteristics such as fixate, excessive, and concern were significantly higher among users with autism compared to the control group (P<.001). Emotional terms related to fear, paranoia and anxiety were also tweeted at a significantly higher rate among users with autism compared to control users (P<.001). Users with autism posted a smaller proportion of tweets during time intervals of 00:00–05:59 (P<.001), 06:00–11:59 (P<.001), and 18:00-23.59 (P<.001), and a greater proportion of tweets from 12:00-17:59 (P<.001) compared to control users. Conclusions: Social media may be a valuable resource for observing unique psychological characteristics of self-identified persons with autism. Collecting and analyzing data from these digital platforms may afford opportunities to identify characteristics of autism and assist in the diagnosis or verification of autism disorders. This study highlights the feasibility of leveraging digital data for gaining new insights about various health conditions.