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Construction of an area-deprivation index for 2869 counties in China: a census-based approach
  1. Zhicheng Wang1,2,3,
  2. Kit Yee Chan4,5,
  3. Adrienne N Poon4,6,
  4. Kirsten Homma6,7,
  5. Yan Guo1
  1. 1 Department of Health Policy and Management, School of Public Health, Peking University, Beijing, China
  2. 2 Vanke School of Public Health, Tsinghua University, Beijing, China
  3. 3 Research Centre for Public Health, School of Medicine, Tsinghua University, Beijing, China
  4. 4 Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
  5. 5 Nossal Institute for Global Health, University of Melbourne, Melbourne, Australia
  6. 6 Department of Medicine, School of Medicine & Health Sciences, George Washington University, Washington, DC, USA
  7. 7 Department of Medicine, New York Presbyterian - Columbia University, New York, NY, USA
  1. Correspondence to Kit Yee Chan, Centre for Global Health, Usher Institute, The University of Edinburgh, Edinburgh, UK; k.chan{at} and Yan Guo, Department of Health Policy and Management, School of Public Health, Peking University, Beijing, China; guoyan{at}


Background A paucity of data has made it challenging to construct a deprivation index at the lowest administrative, or county, level in China. An index is required to guide health equity monitoring and resource allocation to regions of greatest need. This study used China’s 2010 census data to construct a county-level area-deprivation index (CADI).

Methods Data for 2869 counties from China’s 2010 census were used to generate a CADI. Eleven indicators across four domains of deprivation were selected for principal component analysis with standardisation of the first principal component. Sensitivity analysis was used to test whether the population size and weighting method affected the index’s robustness. Deprived counties identified by the CADI were then compared with China’s official list of poverty-stricken counties.

Results The first principal component explained 60.38% of the total variation in the deprivation indicators. The CADI ranged from the least deprived value of −2.71 to the most deprived value of 2.92, with SD of 1. The CADI was found to be robust against county-level population size and different weighting methods. When compared with the official list of poverty-stricken counties in China, the deprived counties identified by the CADI were found to be even more deprived.

Conclusion Constructing a robust area-deprivation index for China at the county level based on population census data is feasible. The CADI is a potential policy tool to identify China’s most deprived areas. In the future, it may support health equity monitoring and comparison at the national and subnational levels.

  • Health inequalities
  • geography
  • health policy
  • deprivation
  • social inequalities

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  • Twitter Zhicheng Wang @wangzc61.

  • Contributors ZW, YG and KYC conceived the study. ZW was primarily responsible for the literature search, data collection and analysis. ZW wrote the first and successive drafts of the manuscript. All authors (ZW, KYC, AP, KH and YG) contributed to the analysis and interpretation of the data and draft revisions. All authors read and approved the final manuscript. KYC and YG had access to the data in the study and had final responsibility for the decision to submit for publication.

  • Funding We would like to thank the China Scholarship Council for the scholarship (201606010306) to ZW. The authors have not declared a specific grant for this research from any funding agency in the public, commercial, or not-for-profit sectors.

  • Map disclaimer The depiction of boundaries on the map(s) in this article does not imply the expression of any opinion whatsoever on the part of BMJ (or any member of its group) concerning the legal status of any country, territory, jurisdiction or area or of its authorities. The map(s) are provided without any warranty of any kind, either express or implied.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement The data that support the findings of this study are available from the corresponding author, YG, KYC, upon reasonable request.