Twitter, a popular social media outlet, has become a useful tool for the study of social behavior through user interactions called tweets. The location time, and message content of tweets provide invaluable social and demographic information for an applied comparison of social behaviors across the world. Our goal is to determine the density and sentiment surrounding tobacco and e-cigarette tweets and link prevalence of word choices to tobacco and e-cigarette use at various localities. All tweets with geo-spatial coordinates are salvaged from the twitter-feed, representing approximately 1% of the entire twitter-sphere. Pattern matching by tobacco and e-cigarette related keywords yield approximately 20,000 affiliated tweets per month from North America. The emotionally charged words that contribute to the positivity of various subsets of regional tweets are quantitatively measured using hedonometrics. We examined the density of these behavioral tweet indicators by region and tested the relationship between tweeted smoking sentiments and time-space-type coordinates over a 4-month span. For states with ≥600 tobacco related tweets (N=30), we find a strong positive correlation (Pearson’s r=0.54, p<0.01) between the relative tweet density per state and the average positivity of tobacco related tweets. However, state-to-state sentiment comparisons suggest the attitude toward tobacco use can vary. We also explore the relationship between the ratio of tobacco tweets per state-to-state smoking rate estimates. Our results illustrate significant variation in smoking sentiments by state and at varying regional scopes. It is anticipated that real-time analysis of nicotine and tobacco products using tweets will allow for more targeted forms of health policy planning and intervention. Regional density of nicotine and tobacco use related tweets yield insight to the prevalence of tobacco usage per capita. Sentiment analysis across the twitter-sphere can help illuminate hazardous health behavioral trends, which may lead to better targeting of health behavior interventions.

Objectives: We examined patient-specific predictors of high cost for endovascular (EVAR) and open (OPEN) repair of abdominal aortic aneurysm (AAA). Methods: Vascular Study Group of Northern New England data specific to Fletcher Allen Health Care were merged with cost data from the same source. We retrospectively analyzed 389 elective AAA repairs (230 EVAR, 159 OPEN) between 2003 and 2011 to determine clinical characteristics that contribute to membership in the upper quartile of cost (UQC) versus the remaining three quartiles. For the purpose of this exercise, it was assumed that clinical outcomes were equally good with EVAR versus OPEN repair. Results: Significant predictors of UQC for OPEN repair procedures were: history of treated chronic obstructive pulmonary disease (COPD), previous bypass surgery, transfer from hospital and age >70 (area under receiver operating curve [ROC] = 0.726). Predictors of UQC for EVAR were: presence of iliac aneurysm(s), coronary artery bypass graft surgery or percutaneous transluminal coronary angioplasty within the past 5 years, ejection fraction ≤30%, absence of beta blockers, creatinine ≥1.5mg/dL, and current use of tobacco (area under ROC = 0.784). The mean length of stay for EVAR and OPEN repair were 2.22 and 8.55 days, respectively. Costs for EVAR and OPEN repair were $32,656 (standard error of the mean [SEM] $591) and $28,183 (SEM $1,571), respectively. Conclusions: Certain risk factors at the individual patient level are predictive of UQC. Under such circumstances, it is our expectation that such algorithms may be used to select the most cost-efficient treatment.

Cost data from two academic medical centers were examined to determine patient characteristics and/or clinical events that are predictive of high cost hospitalizations after elective endovascular (EVAR) and open (OAAA) abdominal aortic aneurysm repair. Elements of patient selection, operative performance, and postoperative complications were examined for their influence on cost. Vascular Quality Initiative (VQI) data for 465 EVAR and 431 OAAA were linked to cost data at two centers. High-cost cases were defined as those in the upper quartile of cost for each procedure at each center. VQI data elements were then examined for their relative risk of predicting a high-cost outcome. Total cost of hospitalization for AAA repair was the cost measure evaluated. Categoric variables were tested by χ2 and continuous variables by two-sample t-test. The cost of OAAA (mean, $28,183; range, $12,557-$266,615) and EVAR (mean, $32,6546; range, $11,926-$60,894) at center A were compared with OAAA (mean, $27,744; range, $7139-$583,701) and EVAR (mean, $26,634; range, $5372-$302,111) at center B. Factors linked to high cost are reported in the Table. Markers of adverse intraoperative performance and postoperative complications were better predictors of high-cost hospitalizations than preoperative patient characteristics in both OAAA and EVAR patients. Future efforts to optimize costs in all AAA repairs should focus on improving intraoperative performance. This strategy differs from other quality efforts where risk-adjusted models using preoperative patient characteristics were developed to aid patient selection. The total cost of EVAR (and potential applicability of this technology at a given center) is significantly affected by the structure of local stent graft contracting.TableCost data from two academic medical centersEVAR patientOAAA patientCenter A factorsPCenter B factorsPCenter APCenter BPIliac aneurysm.01Transferred.01Red EF.04CHF.03CABG/PTCA <5 years.04AAA diameter.04COPD – meds.02β-Blockers (protective).04Reduced EF.04Prior bypass.01COPD-all.01Elevated creat.02Transfer.048Age.002EVAR proceduralOAAA proceduralCover Int Iliac.006Graft vendor.007Anesthesia.05Exposure<.0001Other art proc.002OR coiling.03Exposure.002EBL.03EBL<.0001Unplanned ext.01Clamp position.0001IVF.0004IVF<.0001Art inj reg ext.01EBL.004Proc time<.0001Proc time<.0001EBL.0007IVF.0003IVF.002Proc time.0004Proc time.0001Dysrhythmia.01Return OR.01Dysrhythmia<.0001Dysrhythmia<.0001Resp comp.02ICU stay.049Resp comp<.0001Resp comp<.0001Renal failure.04Renal failure.008Renal failure.0005AAA, Abdominal aortic aneurysm; CABG, coronary artery bypass grafting; COPD, chronic obstructive pulmonary disease; EBL, estimated blood loss; EF, ejection fraction; EVAR, endovascular aneurysm repair; ICU, intensive care unit; OAAA, open abdominal aortic aneurysm repair; OR, operating room; PTCA, percutaneous transluminal angioplasty. Open table in a new tab