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Control charts for chronic disease surveillance: testing algorithm sensitivity to changes in data coding.

Naomi C Hamm1, Depeng Jiang2, Ruth Ann Marrie2,3

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Summary
This summary is machine-generated.

Control charts assess administrative health data algorithm stability for juvenile diabetes. No incidence trend differences were found, but prevalence trends varied between algorithms with wider control limits.

Keywords:
Administrative health dataChronic disease surveillanceControl chartsInternational classification of diseases codes

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Area of Science:

  • Health Informatics
  • Epidemiology
  • Biostatistics

Background:

  • Administrative health data algorithms may be unstable over time.
  • Control charts can evaluate data variations impacting disease trend stability.
  • Observed-expected control charts were used to assess juvenile diabetes algorithms.

Purpose of the Study:

  • To compare the stability of incidence and prevalence trends for juvenile diabetes algorithms.
  • To assess the impact of data variations on algorithm performance over time.

Main Methods:

  • Applied 18 juvenile diabetes algorithms to Manitoba administrative health data (1975-2018).
  • Modeled incidence and prevalence trends using negative binomial regression and GEE.
  • Used control charts with limits at predicted case count ±0.8*SD to assess stability.

Main Results:

  • Proportion of out-of-control observations ranged from 0.57-0.76 for incidence and 0.45-0.83 for prevalence.
  • No significant differences in out-of-control observations across algorithms for incidence or prevalence with standard limits.
  • Sensitivity analysis with relaxed limits (±2*SD) showed fewer out-of-control years but revealed prevalence stability differences among algorithms.

Conclusions:

  • No differences in incidence trend stability were found across juvenile diabetes algorithms.
  • Select algorithms showed differing prevalence trend stability when using wider control limits.
  • Control charts are valuable for assessing the temporal stability of health administrative data algorithms.