Timing of Entry to Care by Newly Diagnosed HIV Cases Before and After the 2010 New York State HIV Testing Law

2015 ◽  
Vol 68 ◽  
pp. S54-S58 ◽  
Author(s):  
Daniel E. Gordon ◽  
Fuqin Bian ◽  
Bridget J. Anderson ◽  
Lou C. Smith
2014 ◽  
Vol 34 (2) ◽  
pp. 403-423 ◽  
Author(s):  
Erika G. Martin ◽  
Roderick H. MacDonald ◽  
Lou C. Smith ◽  
Daniel E. Gordon ◽  
James M. Tesoriero ◽  
...  

2008 ◽  
Vol 98 (4) ◽  
pp. 728-735 ◽  
Author(s):  
James M. Tesoriero ◽  
Haven B. Battles ◽  
Karyn Heavner ◽  
Shu-Yin John Leung ◽  
Chris Nemeth ◽  
...  

1997 ◽  
Vol 41 ◽  
pp. 207-207
Author(s):  
Lori B. Nizel ◽  
Debbie Indyk ◽  
Ian R. Holzman

2020 ◽  
Author(s):  
Li Sun ◽  
Xinyi Lu ◽  
Zidian Xie ◽  
Dongmei Li

BACKGROUND Flavored electronic cigarettes (e-cigarettes) have become popular in recent years, especially among youth and young adults. To address the epidemic of e-cigarettes, New York State approved a ban on sales of most flavored vaping products other than tobacco and menthol flavors on September 17, 2019. OBJECTIVE This study aimed to examine the public responses on social media to the policy on flavored e-cigarettes in New York State. METHODS Twitter posts (tweets) related to e-cigarettes and the New York State policy on flavored e-cigarettes were collected using Twitter streaming API from June 2019 to December 2019. Tweets from New York State, and other states that did not have a flavored e-cigarettes policy were extracted. Sentiment analysis was applied to analyze the proportion of negative and positive tweets about e-cigarettes or the flavor policy. Topic modeling was applied to e-cigarettes related datasets to identify the most frequent topics before and after the announcement of the New York State policy on flavored e-cigarettes. RESULTS Our results showed that average number of tweets related to e-cigarettes and the New York State policy on flavored e-cigarettes increased in both New York State and other states after the NY flavor policy was announced. Sentiment analysis revealed that after the announcement of the New York State flavor policy, in both New York State and other states, the proportion of negative tweets on e-cigarettes increased, from 34.07% to 44.58% and from 32.48% to 44.40% respectively, while positive tweets decreased significantly, from 39.03% to 32.86% and from 42.78% to 33.93% respectively. The majority of tweets about the New York State flavor policy were negative in both New York State (from 88.78% to 83.46%) and other states (from 78.43% to 81.54%) while New York State had a higher proportion of negative tweets than other states. Topic modeling results demonstrated that teenage vaping and health problems were the most discussed topic associated with e-cigarettes. CONCLUSIONS Public attitudes toward e-cigarettes became more negative on Twitter after the New York State announced the policy on flavored e-cigarettes. Twitter users in other states that did not have such a policy on flavored e-cigarettes paid close attention to New York State flavor policy. This study provides some valuable information about the potential impact of the flavored e-cigarettes policy in New York State on public attitudes towards the flavored e-cigarettes.


2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Rebecca Schnall ◽  
Nan Liu

Study Objectives.In response to the 2010 New York State HIV testing law, we sought to understand the contextual factors that influence HIV testing rates in the emergency department (ED).Methods.We analyzed electronic health record logs from 97,655 patients seen in three EDs in New York City. We used logistic regression to assess whether time of day, day of the week, and season significantly affected HIV testing rates.Results.During our study period, 97,655 patients were evaluated and offered an HIV test. Of these, 7,763 (7.9%) agreed to be tested. Patients arriving between 6 a.m. and 7:59 p.m. were significantly (P<0.001) more likely to be tested for HIV, followed by patients arriving between 8:00 p.m. and 9:59 p.m. (P<0.01) and followed by patients arriving between 5–5:59 a.m. and 10–10:59 p.m. (P<0.05) compared to patients arriving at midnight. Seasonal variation was also observed, where patients seen in July, August, and September (P<0.001) were more likely to agree to be tested for HIV compared to patients seen in January, while patients seen in April and May (P<0.001) were less likely to agree to be tested for HIV.Conclusion.Time of day and season affect HIV testing rates in the ED, along with other factors such as patient acuity and completion of other blood work during the ED visit. These findings provide useful information for improving the implementation of an HIV testing program in the ED.


Sign in / Sign up

Export Citation Format

Share Document