title: Area disease estimation based on sentinel hospital records.
abstract: BACKGROUND
  Population health attributes (such as disease incidence and prevalence) are often estimated using sentinel hospital records, which are subject to multiple sources of uncertainty. When applied to these health attributes, commonly used biased estimation techniques can lead to false conclusions and ineffective disease intervention and control. Although some estimators can account for measurement error (in the form of white noise, usually after de-trending), most mainstream health statistics techniques cannot generate unbiased and minimum error variance estimates when the available data are biased.
  
  
  RESULTS
  A new technique, called the Biased Sample Hospital-based Area Disease Estimation (B-SHADE), is introduced that generates space-time population disease estimates using biased hospital records. The effectiveness of the technique is empirically evaluated in terms of hospital records of disease incidence (for hand-foot-mouth disease and fever syndrome cases) in Shanghai (China) during a two-year period. The B-SHADE technique uses a weighted summation of sentinel hospital records to derive unbiased and minimum error variance estimates of area incidence. The calculation of these weights is the outcome of a process that combines: the available space-time information; a rigorous assessment of both, the horizontal relationships between hospital records and the vertical links between each hospital's records and the overall disease situation in the region. In this way, the representativeness of the sentinel hospital records was improved, the possible biases of these records were corrected, and the generated area incidence estimates were best linear unbiased estimates (BLUE). Using the same hospital records, the performance of the B-SHADE technique was compared against two mainstream estimators.
  
  
  CONCLUSIONS
  The B-SHADE technique involves a hospital network-based model that blends the optimal estimation features of the Block Kriging method and the sample bias correction efficiency of the ratio estimator method. In this way, B-SHADE can overcome the limitations of both methods: Block Kriging's inadequacy concerning the correction of sample bias and spatial clustering; and the ratio estimator's limitation as regards error minimization. The generality of the B-SHADE technique is further demonstrated by the fact that it reduces to Block Kriging in the case of unbiased samples; to ratio estimator if there is no correlation between hospitals; and to simple statistic if the hospital records are neither biased nor space-time correlated. In addition to the theoretical advantages of the B-SHADE technique over the two other methods above, two real world case studies (hand-foot-mouth disease and fever syndrome cases) demonstrated its empirical superiority, as well.
authors: State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China. wangjf@igsnrr.ac.cn
authors: Jin-Feng
authors: JF
authors: Wang
authors: Ben Y
authors: BY
authors: Reis
authors: Mao-Gui
authors: MG
authors: Hu
authors: George
authors: G
authors: Christakos
authors: Wei-Zhong
authors: WZ
authors: Yang
authors: Qiao
authors: Q
authors: Sun
authors: Zhong-Jie
authors: ZJ
authors: Li
authors: Xiao-Zhou
authors: XZ
authors: Li
authors: Sheng-Jie
authors: SJ
authors: Lai
authors: Hong-Yan
authors: HY
authors: Chen
authors: Dao-Chen
authors: DC
authors: Wang
mesh_terms: D000465:Algorithms; D002681:China / Q000453:epidemiology; D004812:Epidemiologic Methods; D005334:Fever / Q000453:epidemiology; D006232:Hand, Foot and Mouth Disease / Q000453:epidemiology; D006753:Hospital Records / Q000706:statistics & numerical data*; D006801:Humans; D015994:Incidence; D018571:Sentinel Surveillance