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Updated: May 20, 2025
A Neonatal Imaging Model of Gram-Negative Bacterial Sepsis
Published on: August 12, 2020
An Application for Spatial Frailty Models: An Exploration with Data on Fungal Sepsis in Neonates
Palaniyandi Paramasivam1,2, Nagaraj Jaganathasamy3, Srinivasan Ramalingam4
1Department of Statistics, ICMR-National Institute for Research in Tuberculosis, Chennai 600 031, Tamil Nadu, India.
Background:
Globally, neonatal fungal sepsis (NFS) is a leading cause of neonatal mortality, particularly among vulnerable populations in neonatal intensive care units (NICU). The use of spatial frailty models with a Bayesian approach to identify hotspots and risk factors for neonatal deaths due to fungal sepsis has not been explored before.
Methods:
A cohort of 80 neonates admitted to the NICU at a Government Hospital in Tamil Nadu, India and diagnosed with fungal sepsis through blood cultures between 2018-2020 was considered for this study. Bayesian spatial frailty models using parametric distributions, such as Log-logistic, Log-normal, and Weibull proportional hazard (PH) models, were employed to identify associated risk factors for NFS deaths and hotspot areas using the R version 4.1.3 software and QGIS version 3.26 (Quantum Geographic Information System).
Results:
The spatial parametric frailty models were found to be good models for analyzing NFS data. Abnormal levels of activated thromboplastin carried a significantly higher risk of death in neonates across all PH models (Log-logistic, Hazard Ratio (HR), 95% Credible Interval (CI): 22.12, (5.40, 208.08); Log-normal: 20.87, (5.29, 123.23); Weibull: 18.49, (5.60, 93.41). The presence of hemorrhage also carried a risk of death for the Log-normal (1.65, (1.05, 2.75)) and Weibull models (1.75, (1.07, 3.12)). Villivakkam, Tiruvallur, and Poonamallee blocks were identified as high-risk areas.
Conclusions:
The spatial parametric frailty models proved their effectiveness in identifying these risk factors and quantifying their association with mortality. The findings from this study underline the importance of the early detection and management of risk factors to improve survival outcomes in neonates with fungal sepsis.
Insights
Neonatal fungal sepsis (NFS) is a major cause of death. Bayesian spatial frailty models identified abnormal thromboplastin and hemorrhage as key risk factors, highlighting areas needing targeted interventions for improved infant survival.
Area of Science:
- Neonatal intensive care
- Epidemiology
- Biostatistics
Background:
- Neonatal fungal sepsis (NFS) is a significant global cause of neonatal mortality, especially in neonatal intensive care units (NICUs).
- Limited research exists on applying spatial frailty models with a Bayesian approach to identify NFS hotspots and mortality risk factors.
Purpose of the Study:
- To explore the utility of Bayesian spatial frailty models in identifying risk factors and geographical hotspots for neonatal mortality due to fungal sepsis.
- To analyze data from a cohort of neonates diagnosed with fungal sepsis in Tamil Nadu, India.
Main Methods:
- Utilized Bayesian spatial frailty models (Log-logistic, Log-normal, Weibull proportional hazard models) for analyzing a cohort of 80 neonates with fungal sepsis (2018-2020).
- Employed R version 4.1.3 and QGIS version 3.26 for risk factor identification and hotspot mapping.
- Assessed the effectiveness of parametric distributions within spatial frailty models.
Main Results:
- Abnormal activated thromboplastin levels significantly increased mortality risk across all models (HRs ranging from 18.49 to 22.12).
- Hemorrhage was also identified as a risk factor in Log-normal and Weibull models (HRs 1.65 and 1.75, respectively).
- Villivakkam, Tiruvallur, and Poonamallee blocks were identified as high-risk areas for NFS mortality.
Conclusions:
- Bayesian spatial frailty models are effective for identifying NFS mortality risk factors and quantifying their impact.
- Early detection and management of identified risk factors are crucial for improving survival rates in neonates with fungal sepsis.
- The study highlights the need for targeted interventions in identified high-risk geographical areas.
