# Adapting the log quadratic model to estimate age- and cause-specific mortality among neonates

## TL;DR

This paper adapts a statistical model to better estimate causes of neonatal deaths at different ages, using data from Nepal.

## Contribution

The study adapts the log quadratic model to estimate age- and cause-specific neonatal mortality using verbal autopsy data.

## Key findings

- The adapted model had lower cross-validation errors (0.9 and 1.1 per 1000 live births) for prematurity and birth asphyxia compared to a standard approach (7.4 and 7.8 per 1000 live births).
- Estimates for less common causes like congenital malformations and pneumonia had higher cross-validation errors.
- The model's performance was comparable to higher quality surveillance systems in China, showing its potential in low-resource areas.

## Abstract

Estimates for cause-specific mortality for neonates are generally available for all countries for neonates overall (0 to 28 days). However, cause-specific mortality is generally not being estimated at higher age resolution for neonates, despite evidence of heterogeneity in the causes of deaths during this period. We aimed to use the adapted log quadratic model in a setting where verbal autopsy was the primary means of determining cause of death.

We examined the timing and causes of death among a cohort of neonates in rural Nepal followed as part of the Nepal Oil Massage Study (NOMS). We adapted methods defined by Wilmoth et al (2012) and Guillot et al. (2022) to estimate age and cause-specific mortality among neonates. We used cross validation to estimate the accuracy of this model, holding out each three month period. We took the average cross validation across hold out as our measure of model performance and compared to a standard approach which did not account for the heterogeneity in cause-specific mortality rate within this age group.

There were 957 neonates in the NOMS cohort with known age and cause of death. We estimated an average cross-validation error of 0.9 per 1000 live births for mortality due to prematurity in the first week, and 1.1 for mortality due to birth asphyxia, compared to the standard approach, having error 7.4 and 7.8 per 1000 live births, respectively. Generally mortality rates for less common causes such as congenital malformations and pneumonia were estimated with higher cross-validation error.

The stability and precision of these estimates compare favorably with similar estimates developed with higher quality cause-specific mortality surveillance from China, demonstrating that reliably estimating causes of mortality at high resolution is possible for neonates in low resources areas.