Using administrative data to define diabetes cases in children and youth

Cummings, Elizabeth A.; Dodds, Linda; Cooke, Charmaine; Wang, Y.; Spencer, Andrew; Dunbar, Margaret; Karlovic, Zlatko; MacDonald, Noni E.; Kephart, George

doi:10.1016/s1499-2671(09)33116-0

Cited by 2 publications

(2 citation statements)

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“…An algorithm for identifying diabetes in the adult population (≥20 years of age) has been validated within health administrative data and is currently used by the Canadian Chronic Disease Surveillance System (CCDSS) 9. However, algorithms validated in adults have been previously shown to be less accurate in children 3,10,11. Therefore, to assess disease burden, health care utilization and outcomes in children with diabetes, it is essential to develop and validate a pediatric-specific algorithm with excellent validity parameters.…”

Section: Introductionmentioning

confidence: 99%

<p>Identifying pediatric diabetes cases from health administrative data: a population-based validation study in Quebec, Canada</p>

Nakhla

Simard

Dubé

et al. 2019

CLEP

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BackgroundType 1 diabetes is one of the most common chronic diseases in childhood with a worldwide incidence that is increasing by 3–5% per year. The incidence of type 2 diabetes, traditionally viewed as an adult disease, is increasing at alarming rates in children, paralleling the rise in childhood obesity. As the rates of diabetes increase in children, accurate population-based assessment of disease burden is important for those implementing strategies for health services delivery. Health administrative data are a powerful tool that can be used to track disease burden, health services use, and health outcomes. Case validation is essential in ensuring accurate disease identification using administrative databases.AimThe aim of our study was to define and validate a pediatric diabetes case ascertainment algorithm (including any form of childhood-onset diabetes) using health administrative data.Research design and methodsWe conducted a two-stage method using linked health administrative data and data extracted from charts. In stage 1, we linked chart data from a large urban region to health administrative data and compared the diagnostic accuracy of various algorithms. We selected those that performed the best to be validated in stage 2. In stage 2, the most accurate algorithms were validated with chart data within two other geographic areas in the province of Quebec.ResultsAccurate identification of diabetes in children (ages ≤15 years) required four physician claims or one hospitalization (with International Classification of Disease codes within 1 year (sensitivity 91.2%, 95% confidence interval [CI] 89.2–92.9]; positive predictive value [PPV] 93.5%, 95% CI 91.7–95.0) or using only four physician claims in 2 years (sensitivity 90.4%, 95% CI 88.3–92.2; PPV 93.2%, 95% CI 91.7–95.0). Separating the physician claims by 30 days increased the PPV of all algorithms tested.ConclusionPatients with child-onset diabetes can be accurately identified within health administrative databases providing a valid source of information for health care resource planning and evaluation.

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Section: Introductionmentioning

confidence: 99%

<p>Identifying pediatric diabetes cases from health administrative data: a population-based validation study in Quebec, Canada</p>

Nakhla

Simard

Dubé

et al. 2019

CLEP

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“…We adopted the latter exclusion, because our primary interest was in data coded using International Classification of Disease (ICD) codes. Table 1 summarizes the 18 algorithms we identified from the literature to include in this study [23][24][25][26][27][28][29]. Six algorithms were validated in Manitoba, Canada; three were validated in British Columbia, Canada; 13 were validated in Ontario, Canada; 16 were validated in Quebec, Canada; and one was validated in Nova Scotia, Canada.…”

Section: Selection Of Algorithmsmentioning

confidence: 99%

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

et al. 2022

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Background Algorithms used to identify disease cases in administrative health data may be sensitive to changes in the data over time. Control charts can be used to assess how variations in administrative health data impact the stability of estimated trends in incidence and prevalence for administrative data algorithms. We compared the stability of incidence and prevalence trends for multiple juvenile diabetes algorithms using observed-expected control charts. Methods Eighteen validated algorithms for juvenile diabetes were applied to administrative health data from Manitoba, Canada between 1975 and 2018. Trends in disease incidence and prevalence for each algorithm were modelled using negative binomial regression and generalized estimating equations; model-predicted case counts were plotted against observed counts. Control limits were set as predicted case count ±0.8*standard deviation. Differences in the frequency of out-of-control observations for each algorithm were assessed using McNemar’s test with Holm-Bonferroni adjustment. Results The proportion of out-of-control observations for incidence and prevalence ranged from 0.57 to 0.76 and 0.45 to 0.83, respectively. McNemar’s test revealed no difference in the frequency of out-of-control observations across algorithms. A sensitivity analysis with relaxed control limits (2*standard deviation) detected fewer out-of-control years (incidence 0.19 to 0.33; prevalence 0.07 to 0.52), but differences in stability across some algorithms for prevalence. Conclusions Our study using control charts to compare stability of trends in incidence and prevalence for juvenile diabetes algorithms found no differences for disease incidence. Differences were observed between select algorithms for disease prevalence when using wider control limits.

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Using administrative data to define diabetes cases in children and youth

Cited by 2 publications

References 0 publications

<p>Identifying pediatric diabetes cases from health administrative data: a population-based validation study in Quebec, Canada</p>

<p>Identifying pediatric diabetes cases from health administrative data: a population-based validation study in Quebec, Canada</p>

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

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