The purpose of the present pilot study was to provide a preliminary estimate of the minimum detectable difference (MDD) and minimum clinically important difference (MCID) of the six-minute walk test (6MWT) and daily activity in outpatients with chronic heart failure (CHF). Methods: A convenience sample of 22 adults with stable New York Heart Association Functional Class II and III CHF performed two baseline 6MWTs separated by 30 minutes of rest. Subjects then wore a triaxial accelerometer for 7 days to monitor daily activity. After 7 weeks of usual care, subjects again wore the accelerometer for 7 days and then returned to the clinic to complete the Global Rating of Change Scale (GRS) with regard to their heart disease and perform another set of 6MWTs. For the 6MWT, the MDD was calculated using the two baseline 6MWT distances. For daily activity, the MDD was calculated using two methods: (1) day-today test-retest reliability during baseline monitoring, and (2) baseline to follow-up test-retest reliability in those who reported no change on the GRS. The MCID for the 6MWT and daily activity was calculated using the mean and 95% confidence interval (CI 95%) for those subjects who reported 'improvement' on the GRS. Results: The MDD at the CI 95% for the 6MWT was 32.4 meters. The MCID for the 6MWT was 30.1 (CI 95% 20.8, 39.4) meters. The MDD for daily activity was 5,909 vector magnitude units (VMU•hr.-1) The MCID for daily activity was 1,337 VMU•hr.-1 There was good alignment of the MDD and MCID for the 6MWT, suggesting that clinically meaningful change is approximately 32 meters. However, the calculated MCID was substantially less than measurement error as represented by the MDD, indicating that the MCID was underestimated in this sample or that daily activity may be robust to change in overall disease status.

This investigation showed that a young child can transmit Mycobacterium tuberculosis to a large number of contacts. Children with tuberculosis, especially cavitary or laryngeal tuberculosis, should be considered potentially infectious, and screening of their contacts for infection with M. tuberculosis or active tuberculosis may be required.

The objective of this study was to examine food selectivity in children with autism spectrum disorders longitudinally. Additionally explored were the stability of the relationship between food selectivity and sensory over-responsivity from time 1 to time 2 and the association between food selectivity and restricted and repetitive behavior at time 2. A total of 52 parents of children with autism were surveyed approximately 20 months after completing an initial questionnaire. First and second surveys each contained identical parent-response item to categorize food selectivity level and a scale to measure sensory over-responsivity. A new scale to measure restricted and repetitive behaviors was added at time 2. Results comparing time 1 to time 2 indicated no change in food selectivity level and a stable, significant relationship between food selectivity and sensory over-responsivity. The measure of restrictive and repetitive behavior (time 2) was found to significantly predict membership in the severe food selectivity group. However, when sensory over-responsivity and both restricted and repetitive behaviors were included in the regression model, only sensory over-responsivity significantly predicted severe food selectivity. These results support conclusions about the chronicity of food selectivity in young children with autism and the consistent relationship between food selectivity and sensory over-responsivity.

Surveillance of health care-associated infections and antimicrobial resistance is an important aspect of prevention. In 2004, the Centers for Disease Control and Prevention had 3 national health care surveillance systems. During 2004-2005, these will be combined into a single Internet-based system, the National Healthcare Safety Network (NHSN). The NHSN will feature a number of enhancements, and ultimately, all US hospitals and other health care facilities will be encouraged to participate. Health care surveillance using standard methods has been very useful and is cited as a model for prevention. However, alternative approaches may improve health care surveillance by reducing complexity, decreasing the burden of data collection, and improving accuracy. These alternative approaches include adopting simpler methods and more-objective definitions, using sampling and estimation, substituting information in computer databases for manually collected data, and increasing surveillance for process measures with known prevention efficacy. Maintaining successful features of standard systems, adopting alternate surveillance approaches, and exploiting new technologies, such as the Internet, will make health care surveillance an even better tool for prevention.

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