Objectives: The objectives of this thesis were to: 1) develop and validate a coding algorithm to identify true cases of neonatal bacterial sepsis, and 2) apply the algorithm to calculate incidence rates and estimate temporal trends of neonatal bacterial sepsis.
Methods: For Objective 1, the reference cohorts were assembled among neonates born in 2012-2017 using patient-level health care encounter data. Any neonates who met both the Diagnostic Criterion Ⅰ (microbiological confirmation) and Criterion Ⅱ (sepsis-related antibiotic administration) were included in the true-positive cohort. Potential coding algorithms were developed based on different combinations of ICD-10-CA codes on the hospitalization discharge abstract. For Objective 2, the coding algorithm with the most optimal characteristics was applied to provincial data to calculate incidence rates in Ontario during 2003-2017. Recent temporal trends were estimated by Poisson regression analysis.
Results: In Objective 1, since all true-positive cases identified were born at preterm gestation, the study population in Objective 2 was limited to preterm infants. The final coding algorithm selected had sensitivity of 75.3% (95% CI, 66.8%-83.7%), specificity of 98.2% (95% CI, 97.8%-98.6%) and PPV of 50.0% (95% CI, 42.1%-58.0%). Using this algorithm, the annual incidence declined over time from 50.2 (95% CI, 45.4-55.4) per 1000 preterm infants in 2003 to 27.5 (95% CI, 20.4-36.9) per 1000 preterm infants in 2017. The trend over time was statistically significant with P-value <0.0001. Significant variation in bacterial sepsis incidence rates was noted across infant sex and gestational age.
Conclusion: The coding algorithm developed in this study could not accurately identify neonates with bacterial sepsis from within health administrative database using the data available to us now. For the purpose of demonstrating the application of the algorithm, we carried out Objective 2; however, it is important to cautiously interpret the provincial rates given the the poor performance of the case-finding algorithm.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/39679 |
| Date | 01 October 2019 |
| Creators | Yao, Chunhe |
| Contributors | Hawken, Steven, Fell, Deshayne |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
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