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A Pilot Study of Valley Fever Tweets
- Nana Li, Gondy Leroy, Fariba Donovan, John Galgiani, Katherine Ellingson
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 41 / Issue S1 / October 2020
- Published online by Cambridge University Press:
- 02 November 2020, p. s101
- Print publication:
- October 2020
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- Article
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Background: Twitter is used by officials to distribute public health messages and by the public to post information about ongoing afflictions. Because tweets originate from geographically and socially diverse sources, scholars have used this social media data to analyze the spread of diseases like flu [Alessio Signorini 2011], asthma [Philip Harber 2019] and mental health disorders [Chandler McClellan, 2017]. To our knowledge, no Twitter analysis has been performed for Valley fever. Valley fever is a fungal infection caused by the Coccidioides organism, mostly found in Arizona and California. Objective: We analyzed tweets concerning Valley fever to evaluate content, location, and timing. Methods: We collected tweets using the Twitter search application programming interface using the terms “Valley fever,” “valleyfever,” “cocci” or “‘Valleyfever” from August 6 to 16, 2019, and again from October 20 to 29, 2019. In total, 2,117 Tweets were retrieved. Tweets not focused on Valley fever were filtered out, including a tweet about “Rift valley fever” and tweets where “valley” and “fever” were separate and not one phrase. We excluded tweets not written in English. In total, 1,533 tweets remained; we grouped them into 3 categories: original tweets, hereafter labeled “normal” (N = 497), retweets (N = 811), and replies (N = 225). We converted all terms to lowercase, removed white space and punctuation, and tokenized the tweets. Informal messaging conventions (eg, hashtag, @user, RT, links) and stop words were removed, and terms were lemmatized. Finally, we analyzed the frequency of tweets by season, state, and co-occurring terms. Results: Tweet frequency was 228.5 per week in summer and 113.4 per week in the fall. Users tweeted from 40 different states; the most common were California (N = 401; 10.1 per 100,00 population) and Arizona (N = 216, 30.1 per 100,000 population), New York (N = 49), Florida (N = 21), and Washington, DC (N = 14). Term frequency analysis showed that for normal tweets, the 5 most frequent terms were “awareness,” “Arizona,” “disease,” “California,” and “people.” For retweets, the most common terms were “Gunner” (a dog name), “vet,” “prayer,” “cough,” and “family.” For replies, they were “dog,” “lung,” “vet,” “day,” and “result.” Several symptoms were mentioned: “cough” (normal: 8, retweets: 104, and replies: 7), “sick” (normal: 21, retweets: 42, replies: 7), “rash” (normal: 2, retweets: 6, replies: 1), and “headache” (normal: 1, retweets: 3, replies: 0). Conclusions: Valley fever tweets are potentially sufficient to track disease intensity, especially in Arizona and California. Data collection over longer intervals is needed to understand the utility of Twitter in this context.
Disclosures: None
Funding: None
The Role of Understaffing in Central Venous Catheter-Associated Bloodstream Infection
- Scott K. Fridkin, Suzanne M. Pear, Theresa H. Williamson, John N. Galgiani, William R. Jarvis
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 17 / Issue 3 / March 1996
- Published online by Cambridge University Press:
- 02 January 2015, pp. 150-158
- Print publication:
- March 1996
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Objective:
To determine risk factors for central venous catheter-associated bloodstream infections (CVC-BSI) during a protracted outbreak.
Design:Case-control and cohort studies of surgical intensive care unit (SICU) patients.
Setting:A university-affiliated Veterans Affairs medical center.
Patients:Case-control study: all patients who developed a CVC-BSI during the outbreak period (January 1992 through September 1993) and randomly selected controls. Cohort study: all SICU patients during the study period (January 1991 through September 1993).
Measurements:CVC-BSI or site infection rates, SICU patient clinical data, and average monthly SICU patient-to-nurse ratio.
Results:When analyzed by hospital location and site, only CVC-BSI in the SICU had increased significantly in the outbreak period compared to the previous year (January 1991 through December 1991: pre-outbreak period). In SICU patients, CVC-BSI were associated with receipt of total parenteral nutrition [TPN]; odds ratio, 16; 95% confidence inter val, 4 to 73). When we controlled for TPN use, CVC-BSI were associated with increasing severity of illness and days on assisted ventilation. SICU patients in the out-break period had shorter SICU and hospital stays, were younger, and had similar mortality rates, but received more TPN compared with patients in the pre-outbreak period. Furthermore, the patient-to-nurse ratio significantly increased in the outbreak compared with the pre-outbreak period. When we controlled for TPN use, assisted ventilation, and the period of hospitalization, the patient-to-nurse ratio was an independent risk factor for CVC-BSI in SICU patients.
Conclusions:Nursing staff reductions below a critical level, during a period of increased TPN use, may have contributed to the increase in CVC-BSI in the SICU by making adequate catheter care difficult. During healthcare reforms and hospital downsizing, the effect of staffing reductions on patient outcome (ie, nosocomial infection) needs to be critically assessed.