Effects of a Tobacco-Free Work Site Policy on Employee Tobacco Attitudes and Behaviors, Travis County, Texas, 2010-2012.

BACKGROUND: The adoption of tobacco-free policies in behavioral health settings is an important step in reducing staff tobacco use as well as the high rates of tobacco use among people with mental illness and behavioral disorders. Studies have demonstrated the importance of staff support when implementing tobacco-free workplace policies, but there is limited research examining tobacco use prevalence among staff and staff attitude before and after policy adoption. COMMUNITY CONTEXT: Integral Care, a local authority for behavioral health and developmental disabilities in Austin, Texas, and Austin Public Health embarked on a comprehensive planning process before implementing a 100% tobacco-free campus policy. The objectives were 1) assess staff tobacco use and attitudes toward a tobacco-free policy, 2) communicate policy to staff, 3) provide staff education and training, and 4) provide cessation resources. METHODS: Integral Care and Austin Public Health conducted a web-based employee survey 6 months before and 6 and 12 months after implementation of the policy to measure tobacco use prevalence and attitudes among employees. OUTCOME: Employees had significant improvements in tobacco use prevalence and attitudes toward the tobacco-free policy from pre-implementation to post-implementation. Tobacco use prevalence among staff decreased from 27.6% to 13.8%, and support for the policy increased from 60.6% to 80.3% at 12 months post-implementation. INTERPRETATION: Adoption of 100% tobacco-free campus policies in behavioral health settings can result in significant reductions in staff tobacco use. Leadership should provide staff with education, training, and cessation support before adoption of tobacco-free work site policies to ensure success.

Evaluating real-time syndromic surveillance signals from ambulatory care data in four states.

OBJECTIVES: We evaluated a real-time ambulatory care-based syndromic surveillance system in four metropolitan areas of the United States. METHODS: Health-care organizations and health departments in California, Massachusetts, Minnesota, and Texas participated during 2007-2008. Syndromes were defined using International Classification of Diseases, Ninth Revision diagnostic codes in electronic medical records. Health-care organizations transmitted daily counts of new episodes of illness by syndrome, date, and patient zip code. A space-time permutation scan statistic was used to detect unusual clustering. Health departments followed up on e-mailed alerts. Distinct sets of related alerts ("signals") were compared with known outbreaks or clusters found using traditional surveillance. RESULTS: The 62 alerts generated corresponded to 17 distinct signals of a potential outbreak. The signals had a median of eight cases (range: 3-106), seven zip code areas (range: 1-88), and seven days (range: 3-14). Two signals resulted from true clusters of varicella; six were plausible but unconfirmed indications of disease clusters, six were considered spurious, and three were not investigated. The median investigation time per signal by health departments was 50 minutes (range: 0-8 hours). Traditional surveillance picked up 124 clusters of illness in the same period, with a median of six ill per cluster (range: 2-75). None was related to syndromic signals. CONCLUSIONS: The system was able to detect two true clusters of illness, but none was of public health interest. Possibly due to limited population coverage, the system did not detect any of 124 known clusters, many of which were small. The number of false alarms was reasonable.

Caregivers and families in medical special needs shelters: an experience during Hurricane Rita.

INTRODUCTION: Local public health departments may assume responsibility for sheltering and providing care for medically needy populations displaced by disasters. In addition, medical special needs shelters will inevitably house persons not requiring medical assistance. The presence of nonpatients may help or hinder shelter operations. This analysis examines the composition, demographics, and medical requirements of a population in a special needs shelter. METHODS: Frequencies and ratios were used to describe persons residing in a medical special needs shelter. All data were obtained from registration records from the city of Austin's medical special needs shelter, established in response to Hurricane Rita in 2005. RESULTS: The medically needy accounted for 58.4 percent of the shelter population. For every 100 patients, the shelter housed 71.2 nonpatients. The most common nonpatients in the shelter were family caregivers (13.1 percent), followed by dependent children (8.0 percent). Most professional caregivers were associated with some type of group facility. CONCLUSIONS: Sheltering a medically needy population means caring not only for patients but also for their accompanying caregivers, family, and dependents. Non-medically needy persons utilize shelter capacity and require different resources. Shelter staffing plans should not rely heavily on assistance from accompanying caregivers; instead, they should assume a substantial proportion of shelter capacity will be dedicated to non-medically needy persons.