Solidarity and strife after the Atlanta spa shootings: A mixed methods study characterizing Twitter discussions by qualitative analysis and machine learning.

Background: On March 16, 2021, a white man shot and killed eight victims, six of whom were Asian women at Atlanta-area spa and massage parlors. The aims of the study were to: (1) qualitatively summarize themes of tweets related to race, ethnicity, and racism immediately following the Atlanta spa shootings, and (2) examine temporal trends in expressions hate speech and solidarity before and after the Atlanta spa shootings using a new methodology for hate speech analysis. Methods: A random 1% sample of publicly available tweets was collected from January to April 2021. The analytic sample included 708,933 tweets using race-related keywords. This sample was analyzed for hate speech using a newly developed method for combining faceted item response theory with deep learning to measure a continuum of hate speech, from solidarity race-related speech to use of violent, racist language. A qualitative content analysis was conducted on random samples of 1,000 tweets referencing Asians before the Atlanta spa shootings from January to March 15, 2021 and 2,000 tweets referencing Asians after the shooting from March 17 to 28 to capture the immediate reactions and discussions following the shootings. Results: Qualitative themes that emerged included solidarity (4% before the shootings vs. 17% after), condemnation of the shootings (9% after), racism (10% before vs. 18% after), role of racist language during the pandemic (2 vs. 6%), intersectional vulnerabilities (4 vs. 6%), relationship between Asian and Black struggles against racism (5 vs. 7%), and discussions not related (74 vs. 37%). The quantitative hate speech model showed a decrease in the proportion of tweets referencing Asians that expressed racism (from 1.4% 7 days prior to the event from to 1.0% in the 3 days after). The percent of tweets referencing Asians that expressed solidarity speech increased by 20% (from 22.7 to 27.2% during the same time period) (p < 0.001) and returned to its earlier rate within about 2 weeks. Discussion: Our analysis highlights some complexities of discrimination and the importance of nuanced evaluation of online speech. Findings suggest the importance of tracking hate and solidarity speech. By understanding the conversations emerging from social media, we may learn about possible ways to produce solidarity promoting messages and dampen hate messages.

Using Convolutional Neural Networks to Derive Neighborhood Built Environments from Google Street View Images and Examine Their Associations with Health Outcomes.

Built environment neighborhood characteristics are difficult to measure and assess on a large scale. Consequently, there is a lack of sufficient data that can help us investigate neighborhood characteristics as structural determinants of health on a national level. The objective of this study is to utilize publicly available Google Street View images as a data source for characterizing built environments and to examine the influence of built environments on chronic diseases and health behaviors in the United States. Data were collected by processing 164 million Google Street View images from November 2019 across the United States. Convolutional Neural Networks, a class of multi-layer deep neural networks, were used to extract features of the built environment. Validation analyses found accuracies of 82% or higher across neighborhood characteristics. In regression analyses controlling for census tract sociodemographics, we find that single-lane roads (an indicator of lower urban development) were linked with chronic conditions and worse mental health. Walkability and urbanicity indicators such as crosswalks, sidewalks, and two or more cars were associated with better health, including reduction in depression, obesity, high blood pressure, and high cholesterol. Street signs and streetlights were also found to be associated with decreased chronic conditions. Chain link fence (physical disorder indicator) was generally associated with poorer mental health. Living in neighborhoods with a built environment that supports social interaction and physical activity can lead to positive health outcomes. Computer vision models using manually annotated Google Street View images as a training dataset were able to accurately identify neighborhood built environment characteristics. These methods increases the feasibility, scale, and efficiency of neighborhood studies on health.

Procalcitonin-Guided Antibiotic Prescribing for Acute Exacerbations of Chronic Obstructive Pulmonary Disease in the Emergency Department.

PURPOSE: Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) can be caused by viral, bacterial, or environmental factors. Recent studies have suggested that procalcitonin serum levels may help reduce unnecessary antibiotic use without statistically significant differences in rates of treatment failure for AECOPD. The purpose of this quality improvement project was to create a procalcitonin-based algorithm to aid emergency department (ED) clinicians in the management of patients with AECOPD who do not require hospitalization and to evaluate its efficacy and practicality. The primary outcome of this project was the rate of antibiotic prescriptions before and after the initiation of the algorithm. METHODS: This study used an observational, retrospective, pre-and posteducation/intervention design. Clinicians were educated individually on the use of procalcitonin, and a copy of the algorithm was made available to each clinician and posted in the ED. Patients who were discharged from the ED with a diagnosis of an AECOPD were identified using International Classification of Diseases, Tenth Revision codes. Patient charts were reviewed from November 2018 to March 2019 for the preimplementation period and November 2019 to March 2020 for the postimplementation period. The rate of antibiotic prescriptions and the number of procalcitonin tests ordered before and after the introduction of the algorithm were analyzed. In addition, information on COPD Global Initiative for Chronic Obstructive Lung Disease (GOLD) grouping and 30-, 60-, and 90-day reexacerbation rates were collected. It was estimated that a sample size of 146 patients (73 patients/group) would provide 80% power to detect a between-group difference of 10% in the percentage of patients who were prescribed antibiotics. Categorical variables were expressed using estimates of their frequency and percentages. Percentages were compared using Fisher exact tests. For all tests, the significance level was set at 0.05. RESULTS: Seventy-three patients were included in the preintervention group, and 77 patients were included in the postintervention group. Patients in the preintervention and postintervention groups had similar representation in GOLD categories: 52% and 51% for D, 17.8% and 23.4% for C, 21.9% and 16.8% for B, and 8.2% and 7.8% for A, respectively. The rate of antibiotic prescriptions decreased by 20% after implementation from 83.6% before to 63.6% after implementation (P = .01). The differences in reexacerbation rates between the preintervention and postintervention groups were similar: 19.2% vs 23.4% at 30 days, 12.3% vs 11.7% at 60 days, and 4.1% vs 9.1% at 90 days, respectively. Prior to education and introduction of the procalcitonin algorithm, procalcitonin was ordered for 1.4% of AECOPD cases. Postimplementation, procalcitonin was ordered for 28.6% of AECOPD cases and used in clinical decision making 81.8% of the time. CONCLUSIONS: In this study of the implementation of a treatment algorithm for patients with mild and moderate AECOPD who present to the ED, procalcitonin was shown to reduce the rate of antibiotic prescriptions without an observable difference in reexacerbation rates 30, 60, and 90 days after presentation.

Progress and push-back: How the killings of Ahmaud Arbery, Breonna Taylor, and George Floyd impacted public discourse on race and racism on Twitter.

This study examined whether killings of George Floyd, Ahmaud Arbery, and Breonna Taylor by current or former law enforcement officers in 2020 were followed by shifts in public sentiment toward Black people. Methods: Google searches for the names "Ahmaud Arbery," "Breonna Taylor," and "George Floyd" were obtained from the Google Health Application Programming Interface (API). Using the Twitter API, we collected a 1% random sample of publicly available U.S. race-related tweets from November 2019-September 2020 (N = 3,380,616). Sentiment analysis was performed using Support Vector Machines, a supervised machine learning model. A qualitative content analysis was conducted on a random sample of 3,000 tweets to understand themes in discussions of race and racism and inform interpretation of the quantitative trends. Results: The highest rate of Google searches for any of the three names was for George Floyd during the week of May 31 to June 6, the week after his murder. The percent of tweets referencing Black people that were negative decreased by 32% (from 49.33% in November 4-9 to 33.66% in June 1-7) (p < 0.001), but this decline was temporary, lasting just a few weeks. Themes that emerged during the content analysis included discussion of race or racism in positive (14%) or negative (38%) tones, call for action related to racism (18%), and counter movement/arguments against racism-related changes (6%). Conclusion: Although there was a sharp decline in negative Black sentiment and increased public awareness of structural racism and desire for long-lasting social change, these shifts were transitory and returned to baseline after several weeks. Findings suggest that negative attitudes towards Black people remain deeply entrenched.

Exploring U.S. Shifts in Anti-Asian Sentiment with the Emergence of COVID-19.

Background: Anecdotal reports suggest a rise in anti-Asian racial attitudes and discrimination in response to COVID-19. Racism can have significant social, economic, and health impacts, but there has been little systematic investigation of increases in anti-Asian prejudice. Methods: We utilized Twitter's Streaming Application Programming Interface (API) to collect 3,377,295 U.S. race-related tweets from November 2019-June 2020. Sentiment analysis was performed using support vector machine (SVM), a supervised machine learning model. Accuracy for identifying negative sentiments, comparing the machine learning model to manually labeled tweets was 91%. We investigated changes in racial sentiment before and following the emergence of COVID-19. Results: The proportion of negative tweets referencing Asians increased by 68.4% (from 9.79% in November to 16.49% in March). In contrast, the proportion of negative tweets referencing other racial/ethnic minorities (Blacks and Latinx) remained relatively stable during this time period, declining less than 1% for tweets referencing Blacks and increasing by 2% for tweets referencing Latinx. Common themes that emerged during the content analysis of a random subsample of 3300 tweets included: racism and blame (20%), anti-racism (20%), and daily life impact (27%). Conclusion: Social media data can be used to provide timely information to investigate shifts in area-level racial sentiment.

Patterns and predictors of enactment of state childhood obesity legislation in the United States: 2006-2009.

OBJECTIVES: We developed a content review for state policies related to childhood obesity, and we have quantitatively described the predictors of enactment. METHODS: We collected an inventory of 2006 through 2009 state legislation on 27 childhood obesity topics from legislative databases. We coded each bill for general information, topic content, and other appropriate components. We conducted a general descriptive analysis and 3 multilevel analyses using bill- and state-level characteristics to predict bill enactment. RESULTS: Common topics in the 27% of the bills that were enacted were community physical activity access, physical education, and school food policy. Committee and bipartisan sponsorship and having term limits significantly predicted enactment in at least 1 model. Bills with safe routes to school or health and nutrition content were twice as likely to be enacted. Bills containing product and menu labeling or soda and snack taxes were significantly less likely to be enacted. CONCLUSIONS: Bipartisan and committee support and term limits are important in bill enactment. Advocacy efforts can be tailored to increase awareness and sense of priority among policymakers.

It's all in the lens: differences in views on obesity prevention between advocates and policy makers.

BACKGROUND: Intervention strategies to reduce obesity include policy and environmental changes that are designed to provide opportunities, support, and cues to help people develop healthier behaviors. Policy changes at the state level are one way to influence access, social norms, and opportunities for better nutrition and increased physical activity among the population. METHODS: Ten states were selected for a broad variance in obesity rates and number of enacted obesity prevention policies during the years of 2006-2009. Within the selected states, a purely qualitative study of attitudes of childhood obesity policy using semistructured telephone interviews was conducted. Interviews were conducted with state policy makers who serve on public health committees. A set of six states that had more than eight childhood obesity policies enacted were selected for subsequent qualitative interviews with a convenience sample of well-established advocates. RESULTS: Policy makers in states where there was more childhood obesity policy action believed in the evidence behind obesity policy proposals. Policy makers also varied in the perception of obesity as a constituent priority. The major differences between advocates and policy makers included a disconnect in information dissemination, opposition, and effectiveness of these policies. CONCLUSIONS: The findings from this study show differences in perceptions among policy makers in states with a greater number of obesity prevention bills enacted. There are differences among policy makers and advocates regarding the role and effectiveness of state policy on obesity prevention. This presents an opportunity for researchers and practitioners to improve communication and translation of evidence to policy makers, particularly in states with low legislation.

State legislation savvy: a primer and tools for online legislative research in the United States.

We describe sources and methods for state legislative research and provide access to the State Legislative Search Guide tool. State legislation creates and regulates chronic disease prevention interventions both directly through programs targeted to reduce the chronic disease burden and legislation affecting environments such as parks and trails that support health behaviors. Researching state legislation helps advocates, policy makers, researchers, and practitioners make informed recommendations to improve chronic disease prevention policies. Several online sources exist for state legislative information, including subscription databases that cover all 50 US states, single-state subscription databases, and public domain state legislative databases administered by each state. The State Legislative Search Guide, in full-length and condensed versions, uses free public domain databases to facilitate comparison of state legislation for all US states. Links to both versions are provided in the article. Legislative research tips on creating search phrases, searching bill content, bill tracking, and selecting databases and also a table of major subscription databases are provided.