Racial/Ethnic disparities in exposure to neighborhood violence and lung cancer risk in Chicago.

BACKGROUND: Despite the lower prevalence and frequency of smoking, Black adults are disproportionately affected by lung cancer. Exposure to chronic stress generates heightened immune responses, which creates a cell environment conducive to lung cancer development. Residents in poor and segregated neighborhoods are exposed to increased neighborhood violence, and chronic exposure to violence may have downstream physiological stress responses, which may explain racial disparities in lung cancer in predominantly Black urban communities. METHODS: We utilized retrospective electronic medical records of patients who underwent a screening or diagnostic test for lung cancer at an academic medical center in Chicago to examine the associations between lung cancer diagnosis and individual characteristics (age, gender, race/ethnicity, and smoking status) and neighborhood-level homicide rate. We then used a synthetic population to estimate the neighborhood-level lung cancer risk to understand spatial clusters of increased homicide rates and lung cancer risk. RESULTS: Older age and former/current smoking status were associated with increased odds of lung cancer diagnosis. Hispanic patients were more likely than White patients to be diagnosed with lung cancer, but there was no statistical difference between Black and White patients in lung cancer diagnosis. The odds of being diagnosed with lung cancer were significantly higher for patients living in areas with the third and fourth quartiles of homicide rates compared to the second quartile of homicide rates. Furthermore, significant spatial clusters of increased lung cancer risk and homicide rates were observed on Chicago's South and West sides. CONCLUSIONS: Neighborhood violence was associated with an increased risk of lung cancer. Black residents in Chicago are disproportionately exposed to neighborhood violence, which may partially explain the existing racial disparity in lung cancer. Incorporating neighborhood violence exposure into lung cancer risk models may help identify high-risk individuals who could benefit from lung cancer screening.

An Investigation of Age-Differentiated Conversations About Electronic Nicotine Delivery Systems on Reddit.

Introduction: This study analyzes age-differentiated Reddit conversations about ENDS. Methods: This study combines 2 methods to (1) predict Reddit users' age into 2 categories (13-20 years [underage] and 21-54 years [of legal age]) using a machine learning algorithm and (2) qualitatively code ENDS-related Reddit posts within the 2 groups. The 25 posts with the highest karma score (number of upvotes minus number of downvotes) for each keyword search (i.e., query) and each predicted age group were qualitatively coded. Results: Of 9, the top 3 topics that emerged were flavor restriction policies, Tobacco 21 policies, and use. Opposition to flavor restriction policies was a prominent subcategory for both groups but was more common in the 21-54 group. The 13-20 group was more likely to discuss opposition to minimum age laws as well as access to flavored ENDS products. The 21-54 group commonly mentioned general vaping use behavior. Conclusions: Users predicted to be in the underage group posted about different ENDS-related topics on Reddit than users predicted to be in the of-legal-age group.

Incorporation of near-real-time hospital occupancy data to improve hospitalization forecast accuracy during the COVID-19 pandemic.

Public health decision makers rely on hospitalization forecasts to inform COVID-19 pandemic planning and resource allocation. Hospitalization forecasts are most relevant when they are accurate, made available quickly, and updated frequently. We rapidly adapted an agent-based model (ABM) to provide weekly 30-day hospitalization forecasts (i.e., demand for intensive care unit [ICU] beds and non-ICU beds) by state and region in North Carolina for public health decision makers. The ABM was based on a synthetic population of North Carolina residents and included movement of agents (i.e., patients) among North Carolina hospitals, nursing homes, and the community. We assigned SARS-CoV-2 infection to agents using county-level compartmental models and determined agents' COVID-19 severity and probability of hospitalization using synthetic population characteristics (e.g., age, comorbidities). We generated weekly 30-day hospitalization forecasts during May-December 2020 and evaluated the impact of major model updates on statewide forecast accuracy under a SARS-CoV-2 effective reproduction number range of 1.0-1.2. Of the 21 forecasts included in the assessment, the average mean absolute percentage error (MAPE) was 7.8% for non-ICU beds and 23.6% for ICU beds. Among the major model updates, integration of near-real-time hospital occupancy data into the model had the largest impact on improving forecast accuracy, reducing the average MAPE for non-ICU beds from 6.6% to 3.9% and for ICU beds from 33.4% to 6.5%. Our results suggest that future pandemic hospitalization forecasting efforts should prioritize early inclusion of hospital occupancy data to maximize accuracy.

Lessons learned from the rapid development of a statewide simulation model for predicting COVID-19's impact on healthcare resources and capacity.

The first case of COVID-19 was detected in North Carolina (NC) on March 3, 2020. By the end of April, the number of confirmed cases had soared to over 10,000. NC health systems faced intense strain to support surging intensive care unit admissions and avert hospital capacity and resource saturation. Forecasting techniques can be used to provide public health decision makers with reliable data needed to better prepare for and respond to public health crises. Hospitalization forecasts in particular play an important role in informing pandemic planning and resource allocation. These forecasts are only relevant, however, when they are accurate, made available quickly, and updated frequently. To support the pressing need for reliable COVID-19 data, RTI adapted a previously developed geospatially explicit healthcare facility network model to predict COVID-19's impact on healthcare resources and capacity in NC. The model adaptation was an iterative process requiring constant evolution to meet stakeholder needs and inform epidemic progression in NC. Here we describe key steps taken, challenges faced, and lessons learned from adapting and implementing our COVID-19 model and coordinating with university, state, and federal partners to combat the COVID-19 epidemic in NC.

Subgroups of failure after surgery for pelvic organ prolapse and associations with quality of life outcomes: a longitudinal cluster analysis.

BACKGROUND: Treatment outcomes after pelvic organ prolapse surgery are often presented as dichotomous "success or failure" based on anatomic and symptom criteria. However, clinical experience suggests that some women with outcome "failures" are asymptomatic and perceive their surgery to be successful and that other women have anatomic resolution but continue to report symptoms. Characterizing failure types could be a useful step to clarify definitions of success, understand mechanisms of failure, and identify individuals who may benefit from specific therapies. OBJECTIVE: This study aimed to identify clusters of women with similar failure patterns over time and assess associations among clusters and the Pelvic Organ Prolapse Distress Inventory, Short-Form Six-Dimension health index, Patient Global Impression of Improvement, patient satisfaction item questionnaire, and quality-adjusted life-year. STUDY DESIGN: Outcomes were evaluated for up to 5 years in a cohort of participants (N=709) with stage ≥2 pelvic organ prolapse who underwent surgical pelvic organ prolapse repair and had sufficient follow-up in 1 of 4 multicenter surgical trials conducted by the Eunice Kennedy Shriver National Institute of Child Health and Human Development Pelvic Floor Disorders Network. Surgical success was defined as a composite measure requiring anatomic success (Pelvic Organ Prolapse Quantification system points Ba, Bp, and C of ≤0), subjective success (absence of bothersome vaginal bulge symptoms), and absence of retreatment for pelvic organ prolapse. Participants who experienced surgical failure and attended ≥4 visits from baseline to 60 months after surgery were longitudinally clustered, accounting for similar trajectories in Ba, Bp, and C and degree of vaginal bulge bother; moreover, missing data were imputed. Participants with surgical success were grouped into a separate cluster. RESULTS: Surgical failure was reported in 276 of 709 women (39%) included in the analysis. Failures clustered into the following 4 mutually exclusive subgroups: (1) asymptomatic intermittent anterior wall failures, (2) symptomatic intermittent anterior wall failures, (3) asymptomatic intermittent anterior and posterior wall failures, and (4) symptomatic all-compartment failures. Each cluster had different bulge symptoms, anatomy, and retreatment associations with quality of life outcomes. Asymptomatic intermittent anterior wall failures (n=150) were similar to surgical successes with Ba values that averaged around -1 cm but fluctuated between anatomic success (Ba≤0) and failure (Ba>0) over time. Symptomatic intermittent anterior wall failures (n=82) were anatomically similar to asymptomatic intermittent anterior failures, but women in this cluster persistently reported bothersome bulge symptoms and the lowest quality of life, Short-Form Six-Dimension health index scores, and perceived success. Women with asymptomatic intermittent anterior and posterior wall failures (n=28) had the most severe preoperative pelvic organ prolapse but the lowest symptomatic failure rate and retreatment rate. Participants with symptomatic all-compartment failures (n=16) had symptomatic and anatomic failure early after surgery and the highest retreatment of any cluster. CONCLUSION: In particular, the following 4 clusters of pelvic organ prolapse surgical failure were identified in participants up to 5 years after pelvic organ prolapse surgery: asymptomatic intermittent anterior wall failures, symptomatic intermittent anterior wall failures, asymptomatic intermittent anterior and posterior wall failures, and symptomatic all-compartment failures. These groups provide granularity about the nature of surgical failures after pelvic organ prolapse surgery. Future work is planned for predicting these distinct outcomes using patient characteristics that can be used for counseling women individually.

Correction: Predicting Age Groups of Reddit Users Based on Posting Behavior and Metadata: Classification Model Development and Validation.

[This corrects the article DOI: 10.2196/25807.].

Predicting Age Groups of Reddit Users Based on Posting Behavior and Metadata: Classification Model Development and Validation.

BACKGROUND: Social media are important for monitoring perceptions of public health issues and for educating target audiences about health; however, limited information about the demographics of social media users makes it challenging to identify conversations among target audiences and limits how well social media can be used for public health surveillance and education outreach efforts. Certain social media platforms provide demographic information on followers of a user account, if given, but they are not always disclosed, and researchers have developed machine learning algorithms to predict social media users' demographic characteristics, mainly for Twitter. To date, there has been limited research on predicting the demographic characteristics of Reddit users. OBJECTIVE: We aimed to develop a machine learning algorithm that predicts the age segment of Reddit users, as either adolescents or adults, based on publicly available data. METHODS: This study was conducted between January and September 2020 using publicly available Reddit posts as input data. We manually labeled Reddit users' age by identifying and reviewing public posts in which Reddit users self-reported their age. We then collected sample posts, comments, and metadata for the labeled user accounts and created variables to capture linguistic patterns, posting behavior, and account details that would distinguish the adolescent age group (aged 13 to 20 years) from the adult age group (aged 21 to 54 years). We split the data into training (n=1660) and test sets (n=415) and performed 5-fold cross validation on the training set to select hyperparameters and perform feature selection. We ran multiple classification algorithms and tested the performance of the models (precision, recall, F1 score) in predicting the age segments of the users in the labeled data. To evaluate associations between each feature and the outcome, we calculated means and confidence intervals and compared the two age groups, with 2-sample t tests, for each transformed model feature. RESULTS: The gradient boosted trees classifier performed the best, with an F1 score of 0.78. The test set precision and recall scores were 0.79 and 0.89, respectively, for the adolescent group (n=254) and 0.78 and 0.63, respectively, for the adult group (n=161). The most important feature in the model was the number of sentences per comment (permutation score: mean 0.100, SD 0.004). Members of the adolescent age group tended to have created accounts more recently, have higher proportions of submissions and comments in the r/teenagers subreddit, and post more in subreddits with higher subscriber counts than those in the adult group. CONCLUSIONS: We created a Reddit age prediction algorithm with competitive accuracy using publicly available data, suggesting machine learning methods can help public health agencies identify age-related target audiences on Reddit. Our results also suggest that there are characteristics of Reddit users' posting behavior, linguistic patterns, and account features that distinguish adolescents from adults.