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

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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.

Authors


  •   Guo, Kang (external author)
  •   Yin, Peng (external author)
  •   Wang, Lijun (external author)
  •   Ji, Yibing (external author)
  •   Li, Qingfeng (external author)
  •   Bishai, David (external author)
  •   Liu, Shiwei (external author)
  •   Liu, Yunning (external author)
  •   Astell-Burt, Thomas E
  •   Feng, Xiaoqi
  •   You, Jinling (external author)
  •   Liu, Jiangmei (external author)
  •   Zhou, Maigeng (external author)

Publication Date


  • 2015

Citation


  • Guo, K., Yin, P., Wang, L., Ji, Y., Li, Q., Bishai, D., Liu, S., Liu, Y., Astell-Burt, T., Feng, X., You, J., Liu, J. & Zhou, M. (2015). Propensity score weighting for addressing under-reporting in mortality surveillance: a proof-of-concept study using the nationally representative mortality data in China. Population Health Metrics, 13 (16), 1-11.

Scopus Eid


  • 2-s2.0-84936744169

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2586&context=sspapers

Ro Metadata Url


  • http://ro.uow.edu.au/sspapers/1587

Number Of Pages


  • 10

Start Page


  • 1

End Page


  • 11

Volume


  • 13

Issue


  • 16

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.

Authors


  •   Guo, Kang (external author)
  •   Yin, Peng (external author)
  •   Wang, Lijun (external author)
  •   Ji, Yibing (external author)
  •   Li, Qingfeng (external author)
  •   Bishai, David (external author)
  •   Liu, Shiwei (external author)
  •   Liu, Yunning (external author)
  •   Astell-Burt, Thomas E
  •   Feng, Xiaoqi
  •   You, Jinling (external author)
  •   Liu, Jiangmei (external author)
  •   Zhou, Maigeng (external author)

Publication Date


  • 2015

Citation


  • Guo, K., Yin, P., Wang, L., Ji, Y., Li, Q., Bishai, D., Liu, S., Liu, Y., Astell-Burt, T., Feng, X., You, J., Liu, J. & Zhou, M. (2015). Propensity score weighting for addressing under-reporting in mortality surveillance: a proof-of-concept study using the nationally representative mortality data in China. Population Health Metrics, 13 (16), 1-11.

Scopus Eid


  • 2-s2.0-84936744169

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2586&context=sspapers

Ro Metadata Url


  • http://ro.uow.edu.au/sspapers/1587

Number Of Pages


  • 10

Start Page


  • 1

End Page


  • 11

Volume


  • 13

Issue


  • 16