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Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Gini coefficient decomposition-based and mortality-rate-difference-based description of mortality causes in the

Feiling Ai¹, Xia Wan²

¹Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & School of Basic Medicine, Peking Union Medical College, Beijing, China.

|April 29, 2022

Abstract

Objectives:

Improved national Disease Surveillance Points systems (DSPs) in China have clarified mortality causes in the Chinese population. This study aimed to investigate the variations and drivers of multiple mortality causes.

Design:

This was a retrospective cross-sectional surveillance study.

Setting:

Original data in 1991 and 2000, and secondary data in 2010 and 2019 were collected from DSPs across China.

Participants:

Standardised mortality rates (SMRs) and crude mortality rates (CMRs) of the Chinese population in 1991, 2000, 2010 and 2019 were ascertained.

Main Outcome Measures:

Changes in the Gini coefficients (G), computed using SMR, were decomposed into reranking (R) and proportionality (P) to identify variations in communicable, maternal, neonatal and nutritional diseases (CMNN); non-communicable diseases (NCDs) and injury. The CMR difference (in %) was partitioned into the demographic structure and non-demographic factors using the mortality-rate-difference method.

Results:

From 1991 to 2019, the overall CMR increased from 591.327/100 000 to 674.505/100 000, whereas the SMR continually decreased. An increasing concentration of NCDs contributed to the increased all-cause G from 0.443 to 0.560 during 1991-2019. Between 1991 and 2019, compared with CMNN (R=0.054) and NCDs (R=0.037), the ranking of injury changed the most (R=0.174). The ranking of diabetes, falls and road traffic accidents increased markedly over time. The decreased SMR of NCDs (P=-0.013) was mainly due to low-ranking causes, whereas changes in CMNN (P=0.003) and injury (P=0.131) were due to high-ranking causes. All-cause CMR increased by 14.06% from 1991 to 2019 due to greater contributions from the demographic structure (68.46%) than the non-demographic factors (-54.40%). Demographic structural changes accounted more for CMR increases in males (70.52%) and urban populations (75.58%).

Conclusions:

Prevention and control measures targeting NCDs and specific causes are imperatively needed, and should be strengthened as the population ages, especially for males and rural populations.

Keywords:

EPIDEMIOLOGY PUBLIC HEALTH STATISTICS & RESEARCH METHODS

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