title: Analysis on health information extracted from an urban professional population in Beijing.
abstract: BACKGROUND
  The assembled data from a population could provide information on health trends within the population. The aim of this research was to extract and know basic health information from an urban professional population in Beijing.
  
  
  METHODS
  Data analysis was carried out in a population who underwent a routine medical check-up and aged > 20 years, including 30 058 individuals. General information, data from physical examinations and blood samples were collected in the same method. The health status was separated into three groups by the criteria generated in this study, i.e., people with common chronic diseases, people in a sub-clinic situation, and healthy people. The proportion of both common diseases suffered and health risk distribution of different age groups were also analyzed.
  
  
  RESULTS
  The proportion of people with common chronic diseases, in the sub-clinic group and in the healthy group was 28.6%, 67.8% and 3.6% respectively. There were significant differences in the health situation in different age groups. Hypertension was on the top of list of self-reported diseases. The proportion of chronic diseases increased significantly in people after 35 years of age. Meanwhile, the proportion of sub-clinic conditions was decreasing at the same rate. The complex risk factors to health in this population were metabolic disturbances (61.3%), risk for tumor (2.7%), abnormal results of morphological examination (8.2%) and abnormal results of lab tests of serum (27.8%).
  
  
  CONCLUSIONS
  Health information could be extracted from a complex data set from the heath check-ups of the general population. The information should be applied to support prevention and control chronic diseases as well as for directing intervention for patients with risk factors for disease.
authors.affiliation: Beijing Hospital, Beijing 100730, China. tmzhang126@126.com
authors.forename: Tie-Mei
authors.initials: TM
authors.lastname: Zhang
authors.forename: Ling
authors.initials: L
authors.lastname: Zhu
authors.forename: Sen-Lin
authors.initials: SL
authors.lastname: Luo
authors.forename: Yi-Wen
authors.initials: YW
authors.lastname: Han
authors.forename: Yan
authors.initials: Y
authors.lastname: Zhang
authors.forename: Shu-Wen
authors.initials: SW
authors.lastname: Yang
authors.forename: An-Nan
authors.initials: AN
authors.lastname: Liu
authors.forename: Lan-Jun
authors.initials: LJ
authors.lastname: Ma
authors.forename: Yan-Yan
authors.initials: YY
authors.lastname: Zhao
authors.forename: Yan
authors.initials: Y
authors.lastname: Zhang
authors.forename: Bin
authors.initials: B
authors.lastname: Liu
authors.forename: Hong-Bo
authors.initials: HB
authors.lastname: Jia
authors.forename: Yun-Jie
authors.initials: YJ
authors.lastname: Liu
mesh_terms: D000328:Adult; D017677:Age Distribution; D000368:Aged; D000369:Aged, 80 and over; D002681:China; D005260:Female; D006304:Health Status; D006801:Humans; D008297:Male; D008875:Middle Aged; D017678:Sex Distribution; D014505:Urban Population; D055815:Young Adult