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Data & Statistics
STD EpiQuery

Diseases reported to the Bureau of Sexually Transmitted Disease Control are now available on STD EpiQuery. EpiQuery is an interactive, user-friendly system designed to guide users through basic data analyses.

According to Article 11 of the New York City Health Code, health care providers diagnosing sexually transmitted diseases (STDs) and clinical laboratories licensed to perform laboratory testing for New York City (NYC) residents are required to report certain STDs to the Health Department. Seven sexually transmitted diseases have been designated as ‘notifiable’ or reportable to the Bureau of STD Control.

  • Chlamydia (Chlamydia trachomatis)
  • Lymphogranuloma venereum (L-serovars of Chlamydia trachomatis)
  • Gonorrhea (Neisseria gonorrhoeae)
  • Syphilis (Treponema pallidum) - all stages, including congenital syphilis
  • Chancroid [PDF] (Haemophilus ducreyi)
  • Granuloma inguinale [PDF] (Klebsiella granulomatis)
  • Neonatal Herpes (Herpes simplex virus infection in infants aged 60 days and younger) – reportable since April 2006

The Bureau of STD Control manages the data derived from these reports. Data are cleaned, analyzed and reported as part of public health surveillance. Detailed STD data are published in quarterly reports.

The data in EpiQuery include all cases of STD reported among NYC residents, beginning in 2000. Reported cases and case rates (per 100,000 population) are available by select demographic (age group, sex, race/ethnicity) and geographic (citywide, borough, neighborhood) characteristics.

Population denominators

Rate calculations use the NYC Health Department’s neighborhood population estimates, modified from US Census Bureau vintage population estimates, 2000 onwards, available on EpiQuery. Case rates for the most recent year of data use the previous year’s population estimates until the current year’s estimates become available; a note on the EpiQuery page indicates where the previous year’s population was used in calculating case rates.

For diseases affecting infants (congenital syphilis and neonatal herpes), incidence is reported per 100,000 live births, using the annual number of live births in NYC from the Bureau of Vital Statistics in the most recent completed year as the denominator (also available on EpiQuery).

Borough-level data

Borough of residence is assigned to each case using patient borough and zip code of residence at the time of diagnosis. Zip code 10463 crosses Manhattan and the Bronx. For calculation of borough-specific case rates only, the population of zip code 10463 has been attributed to the Bronx, as STD cases with this zip code and a reported borough are generally from the Bronx. Similarly, zip code 11370 crosses Queens and the Bronx. The population of zip code 11370 has been attributed to Queens, as STD cases with this zip code and a reported borough are generally from Queens.

Race and ethnicity data

The ordered selection rules used to define race/ethnicity of cases first classifies as “Hispanic” any person who reports Hispanic ethnicity, regardless of race. Those of other or unknown ethnicity are then classified by race as non-Hispanic Asian, non-Hispanic white, non-Hispanic black, non-Hispanic Native American/Alaskan Native, non-Hispanic Other (includes Native Hawaiian/Pacific Islander and multiple races), or non-Hispanic Unknown race.

The group of non-Hispanic persons with multiple races is evenly distributed across five other non-Hispanic groups: White, Black, Asian, American Indian, Native Hawaiian/Pacific Islander.

Data limitations

Under-ascertainment of cases
For some STDs, there are a large number of infections that are not diagnosed because they frequently lack symptoms, such as chlamydia. Asymptomatic persons may not be screened, and thus diseases may exist at higher levels in the population than notifiable disease data indicate. In some instances, considerable differences in numbers and rates of infection between subgroups may be largely attributable to screening and testing practices, rather than real differences in disease burden. For example, there are national recommendations that young women be screened for chlamydia annually, while there are no similar recommendations for young men. Consequently, the number of reported cases of female chlamydia in NYC is substantially higher among women than men, although the gender difference in actual disease rates is likely to be much smaller.

Under-reporting by providers and clinical laboratories
All diseases are subject to under-reporting. While chlamydia, gonorrhea, syphilis, chancroid, lymphogranuloma venereum, granuloma inguinale, and neonatal herpes are all notifiable diseases in NYC, providers or laboratories may fail to report detected infections. This may be particularly true when providers do not perform diagnostic testing and instead treat presumptively (i.e., based on symptoms); in these instances no laboratory tests will be generated and sent to the Health Department.

Missing race/ethnicity data
Patient race/ethnicity is recorded for the majority of cases diagnosed in Bureau of STD Control clinics. However, private sector providers diagnose the majority of citywide STD cases, and race/ethnicity is missing for many cases reported to the Health Department from private sector providers, either because a case report with that information was not submitted or a submitted case report lacked information on race/ethnicity. Data tables provide these case counts.

Missing age data
Patient date of birth is recorded for the majority of cases diagnosed in Bureau of STD Control clinics. However, private sector providers diagnose the majority of citywide STD cases, and birth date or age information is missing for some cases reported to the NYC DOHMH from private sector providers, either because a case report with that information was not submitted or a submitted case report lacked information on date of birth or age. Data tables provide these case counts.

Artifactual changes in neighborhood case counts and rates
Case reports lacking patient address are attributed to provider address, so if a provider does a substantial amount of testing for patients for whom address is missing, this may artificially elevate the disease rate in that provider’s neighborhood .  Examples of where this may occur are neighborhoods with jails or detention centers.