Maternal, infant, and environmental risk factors for sudden unexpected infant deaths: results from a large, administrative cohort.

OBJECTIVES

Many studies of sudden unexpected infant death (SUID) have focused on individual domains of risk factors (maternal, infant, and environmental), resulting in limited capture of this multifactorial outcome. The objective of this study was to examine the geographic distribution of SUID in San Diego County, and assess maternal, infant, and environmental risk factors from a large, administrative research platform.

STUDY DESIGN

Births in California between 2005 and 2017 were linked to hospital discharge summaries and death files. From this retrospective birth cohort, cases of SUID were identified from infant death files in San Diego County. We estimated adjusted hazard ratios (aHRs) for infant, maternal, and environmental factors and SUID in multivariable Cox regression analysis. Models were adjusted for maternal sociodemographic characteristics and prenatal nicotine exposure.

RESULTS

There were 211 (44/100,000 live births; absolute risk 0.04%) infants with a SUID among 484,905 live births. There was heterogeneity in geographic distribution of cases. Multiparity (0.05%; aHR 1.4, 95% confidence interval (CI) 1.1, 1.9), maternal depression (0.11%; aHR 1.8, 95% CI 1.0, 3.4), substance-related diagnoses (0.27%; aHR 2.3, 95% CI 1.3, 3.8), cannabis-related diagnosis (0.35%; aHR 2.7, 95% CI 1.5, 5.0), prenatal nicotine use (0.23%; aHR 2.5, 95% CI 1.5, 4.2), preexisting hypertension (0.11%; aHR 2.3, 95% CI 1.2, 4.3), preterm delivery (0.09%; aHR 2.1, 95% CI 1.5, 3.0), infant with a major malformation (0.09%; aHR 2.0, 95% CI 1.1, 3.6), respiratory distress syndrome (0.12%; aHR 2.6, 95% CI 1.5, 4.6), and select environmental factors were all associated with SUID.

CONCLUSIONS

Multiple risk factors were confirmed and expanded upon, and the geographic distribution for SUID in San Diego County was identified. Through this approach, prevention efforts can be targeted to geographies that would benefit the most.