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

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

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Article number16
JournalPopulation Health Metrics
Volume13
Issue number1
DOIs
StatePublished - Jul 9 2015

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Propensity Score
China
Mortality
Online Systems
Age Groups
Geographic Locations
Internship and Residency
Information Systems
Cause of Death
Research Design
Logistic Models
Surveys and Questionnaires

Keywords

  • Mortality
  • Propensity scores
  • Surveillance
  • Under-reporting

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health
  • Epidemiology

Cite this

Propensity score weighting for addressing under-reporting in mortality surveillance : A proof-of-concept study using the nationally representative mortality data in China. / Guo, Kang; Yin, Peng; Wang, Lijun; Ji, Yibing; Li, Qingfeng; Bishai, David M; Liu, Shiwei; Liu, Yunning; Astell-Burt, Thomas; Feng, Xiaoqi; You, Jinling; Liu, Jiangmei; Zhou, Maigeng.

In: Population Health Metrics, Vol. 13, No. 1, 16, 09.07.2015.

Research output: Contribution to journalArticle

Guo, Kang ; Yin, Peng ; Wang, Lijun ; Ji, Yibing ; Li, Qingfeng ; Bishai, David M ; Liu, Shiwei ; Liu, Yunning ; Astell-Burt, Thomas ; Feng, Xiaoqi ; You, Jinling ; Liu, Jiangmei ; Zhou, Maigeng. / Propensity score weighting for addressing under-reporting in mortality surveillance : A proof-of-concept study using the nationally representative mortality data in China. In: Population Health Metrics. 2015 ; Vol. 13, No. 1.
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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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AU - Yin, Peng

AU - Wang, Lijun

AU - Ji, Yibing

AU - Li, Qingfeng

AU - Bishai, David M

AU - Liu, Shiwei

AU - Liu, Yunning

AU - Astell-Burt, Thomas

AU - Feng, Xiaoqi

AU - You, Jinling

AU - Liu, Jiangmei

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N2 - 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.

AB - 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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