The Socioeconomic Distressed Communities Index Predicts 90-Day Mortality Among Intracranial Tumor Patients.

BACKGROUND: Socioeconomic status (SES) is a major determinant of quality of life and outcomes. However, SES remains difficult to measure comprehensively. Distress communities index (DCI), a composite of 7 socioeconomic factors, has been increasingly recognized for its correlation with poor outcomes. As a result, the objective of the present study is to determine the predictive value of the DCI on outcomes following intracranial tumor surgery. METHODS: A single institution, retrospective review was conducted to identify adult intracranial tumor patients undergoing resection (2016-2021). Patient ZIP codes were matched to DCI and stratified by DCI quartiles (low:0-24.9, low-intermediate:25-49.9, intermediate-high:50-74.9, high:75-100). Univariate followed by multivariate regressions assessed the effects of DCI on postoperative outcomes. Receiver operating curves were generated for significant outcomes. RESULTS: A total of 2389 patients were included: 1015 patients (42.5%) resided in low distress communities, 689 (28.8%) in low-intermediate distress communities, 445 (18.6%) in intermediate-high distress communities, and 240 (10.0%) in high distress communities. On multivariate analysis, risk of fracture (adjusted odds ratio = 1.60, 95% confidence interval 1.26-2.05, P < 0.001) and 90-day mortality (adjusted odds ratio = 1.58, 95% confidence interval 1.21-2.06, P < 0.001) increased with increasing DCI quartile. Good predictive accuracy was observed for both models, with receiver operating curves of 0.746 (95% CI 0.720-0.766) for fracture and 0.743 (95% CI 0.714-0.772) for 90-day mortality. CONCLUSIONS: Intracranial tumor patients from distressed communities are at increased risk for adverse events and death in the postoperative period. DCI may be a useful, holistic measure of SES that can help risk stratifying patients and should be considered when building healthcare pathways.

Whether the weather will help us weather the COVID-19 pandemic: Using machine learning to measure twitter users' perceptions.

OBJECTIVE: The potential ability for weather to affect SARS-CoV-2 transmission has been an area of controversial discussion during the COVID-19 pandemic. Individuals' perceptions of the impact of weather can inform their adherence to public health guidelines; however, there is no measure of their perceptions. We quantified Twitter users' perceptions of the effect of weather and analyzed how they evolved with respect to real-world events and time. MATERIALS AND METHODS: We collected 166,005 English tweets posted between January 23 and June 22, 2020 and employed machine learning/natural language processing techniques to filter for relevant tweets, classify them by the type of effect they claimed, and identify topics of discussion. RESULTS: We identified 28,555 relevant tweets and estimate that 40.4 % indicate uncertainty about weather's impact, 33.5 % indicate no effect, and 26.1 % indicate some effect. We tracked changes in these proportions over time. Topic modeling revealed major latent areas of discussion. DISCUSSION: There is no consensus among the public for weather's potential impact. Earlier months were characterized by tweets that were uncertain of weather's effect or claimed no effect; later, the portion of tweets claiming some effect of weather increased. Tweets claiming no effect of weather comprised the largest class by June. Major topics of discussion included comparisons to influenza's seasonality, President Trump's comments on weather's effect, and social distancing. CONCLUSION: We exhibit a research approach that is effective in measuring population perceptions and identifying misconceptions, which can inform public health communications.