Spatial Distribution and Epidemiological Characterization of Congenital Syphilis in Monterrey, Mexico, 2016–2024

Congenital syphilis is a preventable infectious disease that is rising in low- and middle-income countries. The CDC classifies congenital syphilis into four clinical scenarios to guide diagnosis and treatment: Scenario 1, proven or highly probable congenital syphilis; Scenario 2, possible congenital syphilis; Scenario 3, less likely congenital syphilis; and Scenario 4, unlikely congenital syphilis. Geolocation is crucial for analyzing the distribution of infectious diseases by pinpointing high-prevalence areas and enabling targeted interventions. This retrospective observational study analyzed Scenario 1 and 2 cases from a tertiary hospital in Monterrey, Mexico (2016 to 2024). Geocoding was performed by converting descriptive location data, such as patients' postal codes, into geographic coordinates (latitude and longitude) using Python v. 3.10.13 (Python Software Foundation, Wilmington, DE). These coordinates were then used in spatial analysis and visualized through kernel density mapping to identify high-incidence zones. Logistic regression identified associations with geographic, socioeconomic, and clinical factors, and odds ratios quantified risks. Data analysis was performed with SPSS version 26 (IBM Corp., Armonk, NY). A total of 167 Scenario 1 and 2 cases were identified, with the highest incidence in 2023 (82.3%, n = 51). The mean maternal age was 23 years; 56.3% of maternal syphilis cases were diagnosed postpartum, 49.7% of cases were late latent syphilis, and 48.5% of patients received no treatment. Higher prevalence was observed in densely populated areas such as Monterrey. This study highlights the utility of geolocation and kernel mapping for designing prevention strategies tailored to high-risk regions. Strengthening prenatal care, early diagnostic testing, and timely treatment is crucial for reducing congenital syphilis rates.