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Updated: Feb 28, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
Published on: February 25, 2013
[Spatial analysis of syphilis and gonorrhea infections in a Public Health Service in Madrid]
Irene G M Wijers1,2, Amaya Sánchez Gómez1, Jose Antonio Taveira Jiménez1
1Sección de Epidemiología. Servicio de Salud Pública Área 7. Subdirección de Epidemiologia. Dirección General de Salud Pública. Comunidad de Madrid. España.
Objective:
Sexually transmitted diseases are a significant public health problem. Within the Madrid Autonomous Region, the districts with the highest syphilis and gonorrhea incidences are part of the same Public Health Service (Servicio de Salud Pública del Área 7, SSPA 7). The objective of this study was to identify, by spatial analysis, clusters of syphilis and gonorrhea infections in this SSPA in Madrid.
Methods:
All confirmed syphilis and gonorrhea cases registered in SSPA 7 in Madrid were selected. Moran's I was calculated in order to identify the existence of spatial autocorrelation and a cluster analysis was performed. Clusters and cumulative incidences (CI) per health zone were mapped.
Results:
The district with most cases was Centro (CI: 67.5 and 160.7 per 100.000 inhabitants for syphilis and gonorrhea, respectively) with the highest CI (120.0 and 322.6 per 100.000 inhabitants) in the Justicia health zone.91.6% of all syphilis cases and 89.6% of gonorrhea cases were among men who have sex with men (MSM). Moran's I was 0.54 and 0.55 (p=0.001) for syphilis and gonorrhea, respectively. For syphilis, a cluster was identified including the six health zones of the Centro district, with a relative risk (RR)of 6.66 (p=0.001). For gonorrhea, a cluster was found including the Centro district, three health zones of the Chamberí district and one of Latina (RR 5.05; p=0.001).
Conclusions:
Centro was the district with most cases of syphilis and gonorrhea and the most affected population were MSM. For both infections, clusters were found with an important overlap. By identifying the most vulnerable health zones and populations, these results can help to design public health measures for preventing sexually transmitted diseases.
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Statistical Methods for Analyzing Epidemiological Data
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