Background:
In response to an epidemic increase in reported cases of early syphilis in the late 1980's, NYS health officials implemented an intervention program in mid-1990 which expanded serologic screening in non-traditional sites. Following this intervention, early syphilis morbidity has declined to its lowest level since the 1950's.

Objective:
To use the adaptive Kolmogorov-Zurbenko (KZA) algorithm, an iterative moving average filter for time series analysis, to assess the magnitude and time of the occurrence of shifts in syphilis surveillance data from 1975-1998 that may be attributable to the syphilis intervention.

Method:
Early syphilis cases reported from 1975 to 1998 among residents of NYS (outside New York City) were included. Data were homogenized prior to transformation on a log-linear scale to stabilize the variance. The KZA algorithm estimated the average daily case rate and identified change points in the baseline data.

Result:
A total of 17,618 early syphilis cases were reported during the study period. Use of the KZA algorithm identified three distinct time periods separated by abrupt changes. From 1975 – 1985, the annual rate remained constant. In December 1985, the annual rate increased 36% until January 1990, when the rate decreased 36%. The corresponding ratio of Primary and Secondary to Early syphilis was 1.28 (1975-85), 1.40 (1986-89), and 0.65 (1990-98).

Conclusion:
The KZA algorithm identified two temporal shifts in the annual syphilis case rate. The first, an increase corresponding to the crack cocaine epidemic and the second, a decrease associated with the implementation of the screening intervention.

Implications:
Researchers will become familiar with a time series model that identifies associations between temporal trends in STD surveillance data and program intervention while controlling for other data artifacts.