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Population Health Metrics
Published in: Population Health Metrics 1/2015

Open Access 01-12-2015 | Research

Propensity score weighting for addressing under-reporting in mortality surveillance: a proof-of-concept study using the nationally representative mortality data in China

Authors: Kang Guo, Peng Yin, Lijun Wang, Yibing Ji, Qingfeng Li, David Bishai, Shiwei Liu, Yunning Liu, Thomas Astell-Burt, Xiaoqi Feng, Jinling You, Jiangmei Liu, Maigeng Zhou

Published in: Population Health Metrics | Issue 1/2015

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Abstract

Background

National mortality data are obtained routinely by the Disease Surveillance Points system (DSPs) in China and under-reporting is a big challenge in mortality surveillance.

Methods

We carried out an under-reporting field survey in all 161 DSP sites to collect death cases during 2009–2011, using a multi-stage stratified sampling. To identify under-reporting, death data were matched between field survey system and the routine online surveillance system by an automatic computer checking followed by a thorough manual verification. We used a propensity score (PS) weighting method based on a logistic regression to calculate the under-reporting rate in different groups classified by age, gender, urban/rural residency, geographic locations and other mortality related variables. For comparison purposes, we also calculated the under-reporting rate by using capture-mark-recapture (CMR) method.

Results

There were no significant differences between the field survey system and routine online surveillance system in terms of age group, causes of death, highest level of diagnosis and diagnostic basis. The overall under-reporting rate in the DSPs was 12.9 % (95%CI 11.2 %, 14.6 %) based on PS. The under-reporting rate was higher in the west (18.8 %, 95%CI 16.5 %, 21.0 %) than the east (10.1 %, 95%CI 8.6 %, 11.3 %) and central regions (11.2 %, 95%CI 9.6 %, 12.7 %). Among all age groups, the under-reporting rate was highest in the 0–5 year group (23.7 %, 95%CI 16.1 %, 35.5 %) and lowest in the 65 years and above group (12.4 %, 95%CI 10.9 %, 13.6 %). The under-reporting rates in each group by PS were similar to the results calculated by the CMR methods.

Conclusions

The mortality data from the DSP system in China needs to be adjusted. Compared to the commonly used CMR method in the estimation of under-reporting rate, the results of propensity score weighting method are similar but more flexible when calculating the under-reporting rates in different groups. Propensity score weighting is suitable to adjust DSP data and can be used to address under-reporting in mortality surveillance in China.
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Metadata
Title
Propensity score weighting for addressing under-reporting in mortality surveillance: a proof-of-concept study using the nationally representative mortality data in China
Authors
Kang Guo
Peng Yin
Lijun Wang
Yibing Ji
Qingfeng Li
David Bishai
Shiwei Liu
Yunning Liu
Thomas Astell-Burt
Xiaoqi Feng
Jinling You
Jiangmei Liu
Maigeng Zhou
Publication date
01-12-2015
Publisher
BioMed Central
Published in
Population Health Metrics / Issue 1/2015
Electronic ISSN: 1478-7954
DOI
https://doi.org/10.1186/s12963-015-0051-3

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