Identification of Unusual Patterns or Clusters of Microcephaly in Pitalito, Huila (Colombia), up to Epidemiological Period X of 2023
DOI:
https://doi.org/10.33610/28059611.183Keywords:
Cluster, microcephaly, zika, prevalenceAbstract
Background. Since 2010, the National Institute of Health (NIH) has been monitoring congenital defects in Colombia and tracking potential clusters. In 2023, a possible cluster of microcephaly was detected in Pitalito, Huila, prompting an investigation into its potential association with Zika virus infection during pregnancy.
Methodology. A retrospective descriptive study was conducted from 2018 to 2023 to identify congenital defect clusters using the unusual pattern detection model. Individual microcephaly notification records submitted to the Public Health Surveillance System under the congenital defects event were reviewed, and a field epidemiological investigation was carried out.
Results. The historical average of microcephaly cases in Pitalito, Huila, from 2018 to 2022 was 1 case per year In 2023 a significant increase in microcephaly cases was identified during epidemiological period X, with an observed count of 11 cases. Upon reviewing medical records, following the microcephaly case analysis manual (1), a total of 32 microcephaly cases were found, of which 11 had been reported through passive surveillance. The mothers of these 11 affected infants tested negative for STORCH infections, including syphilis, cytomegalovirus, rubella, and herpes simplex (types 1 and 2). No Zika virus cases were detected in Pitalito in 2023.
Conclusion. The microcephaly surveillance training conducted in 2022 contributed to the increased identification of cases in Pitalito. During active institutional surveillance at a tertiary-level healthcare facility, more cases of congenital defects, including microcephaly, were identified that had not been detected through passive surveillance. It is essential to maintain training for territorial health authorities on congenital defects and to recommend active case finding when clusters are identified.
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