Prevalence and Clinical Attributes of Congenital Microcephaly - New York, 2013-2015.

Congenital Zika virus infection can cause microcephaly and other severe fetal neurological anomalies (1). To inform microcephaly surveillance efforts and assess ascertainment sources, the New York State Department of Health and the New York City Department of Health and Mental Hygiene sought to determine the prevalence of microcephaly in New York during 2013-2015, before known importation of Zika virus infections. Suspected newborn microcephaly diagnoses were identified from 1) reports submitted by birth hospitals in response to a request and 2) queries of a hospital administrative discharge database for newborn microcephaly diagnoses. Anthropometric measurements, maternal demographics, and pregnancy characteristics were abstracted from newborn records from both sources. Diagnoses were classified using microcephaly case definitions developed by CDC and the National Birth Defects Prevention Network (NBDPN) (2). During 2013-2015, 284 newborns in New York met the case definition for severe congenital microcephaly (prevalence = 4.2 per 10,000 live births). Most newborns with severe congenital microcephaly were identified by both sources; 263 (93%) were identified through hospital requests and 256 (90%) were identified through administrative discharge data. The proportions of newborns with severe congenital microcephaly who were black (30%) or Hispanic (31%) were higher than the observed proportions of black (15%) or Hispanic (23%) infants among New York live births. Fifty-eight percent of newborns with severe congenital microcephaly were born to mothers with pregnancy complications or who had in utero or perinatal infections or teratogenic exposures, genetic disorders, or family histories of birth defects.

Congenital Measles in a Premature 25-week Gestation Infant.

We describe a premature infant with congenital measles. Laboratory testing confirmed measles in the mother (polymerase chain reaction- and IgM-positive) and congenital measles in the infant (polymerase chain reaction-positive, culture-positive and IgM-positive). The infant never developed a rash, pneumonia, or neurologic complications. This case supports using compatible laboratory findings to diagnose congenital measles in infants without clinical manifestations of measles.

Evaluation of a health information exchange system for microcephaly case-finding - New York City, 2013-2015.

BACKGROUND: Birth defects surveillance in the United States is conducted principally by review of routine but lagged reporting to statewide congenital malformations registries of diagnoses by hospitals or other health care providers, a process that is not designed to rapidly detect changes in prevalence. Health information exchange (HIE) systems are well suited for rapid surveillance, but information is limited about their effectiveness at detecting birth defects. We evaluated HIE data to detect microcephaly diagnosed at birth during January 1, 2013-December 31, 2015 before known introduction of Zika virus in North America. METHODS: Data from an HIE system were queried for microcephaly diagnostic codes on day of birth or during the first two days after birth at three Bronx hospitals for births to New York City resident mothers. Suspected cases identified by HIE data were compared with microcephaly cases that had been identified through direct inquiry of hospital records and confirmed by chart abstraction in a previous study of the same cohort. RESULTS: Of 16,910 live births, 43 suspected microcephaly cases were identified through an HIE system compared to 67 confirmed cases that had been identified as part of the prior study. A total of 39 confirmed cases were found by both studies (sensitivity = 58.21%, 95% CI: 45.52-70.15%; positive predictive value = 90.70%, 95% CI: 77.86-97.41%; negative predictive value = 99.83%, 95% CI: 99.76-99.89% for HIE data). CONCLUSION: Despite limitations, HIE systems could be used for rapid newborn microcephaly surveillance, especially in the many jurisdictions where more labor-intensive approaches are not feasible. Future work is needed to improve electronic medical record documentation quality to improve sensitivity and reduce misclassification.