Israel Medical Association Journal

Background: Allergic contact dermatitis (ACD), a prevalent skin disorder marked by delayed hypersensitivity reactions to specific allergens, is commonly diagnosed through patch testing. Previous studies have indicated lower rates of positive patch tests in summer months compared to winter months.

Objectives: To investigate whether there is a difference in the proportion of positive patch test results between summer and winter months.

Methods: A retrospective study was performed on 1128 patients, with 14 individuals undergoing two tests each, resulting in a total of 1142 patch tests. The tests were conducted at a major tertiary referral center between 2016 and 2020. The data set encompassed patient demographics and comprehensive patch test results.

Results: Of the 1142 tests conducted, 808 (70.8%) yielded a positive response. The most frequently administered test series was the European standard series, conducted for 1135 (99.3%) of the tests, with 559/1135 (49.2%) showing positive results, followed by the cosmetics series (394/1120, 35.1%) and fragrances series (61/118, 51.7%). No statistically significant difference was observed in the proportion of positive patch tests between summer and winter months (313/419, 74.7% vs. 175/245, 71.4%, respectively; P-value = 0.35). There was no statistically significant difference in the rate of testing each specific series between the summer and winter months, except for the fragrances series.

Conclusions: We found no significant difference in the positive patch test rates between the summer and winter months. Therefore, patch testing can be reliably conducted during the summer without an increased risk of false-negative results.

Background: Syndromic surveillance systems have been developed for early detection of bioterrorist attacks, but few validation studies exist for these systems and their efficacy has been questioned.

Objectives: To assess the capabilities of a syndromic surveillance system based on community clinics in conjunction with the WSARE[1] algorithm in identifying early signals of a localized unusual influenza outbreak.

Methods: This retrospective study used data on a documented influenza B outbreak in an elementary school in central Israel. The WSARE algorithm for anomalous pattern detection was applied to individual records of daily patient visits to clinics of one of the four health management organizations in the country.

Results: Two successive significant anomalies were detected in the HMO’s[2] data set that could signal the influenza outbreak. If data were available for analysis in real time, the first anomaly could be detected on day 3 of the outbreak, 1 day after the school principal reported the outbreak to the public health authorities.

Conclusions: Early detection is difficult in this type of fast-developing institutionalized outbreak. However, the information derived from WSARE could help define the outbreak in terms of time, place and the population at risk.