Validity of hospital discharge records to identify pregestational diabetes in an Italian population. (January 2017)

• Record Type: Journal Article
• Authors: Borsari, Lucia; Malagoli, Carlotta; Ballotari, Paola; De Girolamo, Gianfranco; Bonora, Karin; Violi, Federica; Capelli, Oreste; Rodolfi, Rossella; Nicolini, Fausto; Vinceti, Marco
• Is Part Of: Diabetes research and clinical practice. Volume 123(2017)
• Page Start: 106
• Page End: 111
• Publication Date: 2017-01
• Subjects: Pregestational diabetes -- Pregnancy -- Routinely collected data -- Registers; Diabetes -- Periodicals; Diabetes Mellitus -- Periodicals; 616.462
• DOI: 10.1016/j.diabres.2016.11.023
• Languages: English
• ISSNs: 0168-8227

Abstract

Highlights: This is the first Italian study evaluating the accuracy of HD codes to identify PGDM. Hospital discharge coding showed sensitivity 99.3%, but low PPV (46.4%). PGDM prevalence calculated through HD coding appears to be largely overestimated. Abstract: Aims: In recent years, the prevalence of pregestational diabetes (PGDM) and the concern about the possibility of adverse pregnancy outcomes in affected women have been increasing. Routinely collected health data represent a timely and cost-efficient approach in PGDM epidemiological research. This study aims to evaluate the reliability of hospital discharge (HD) coding to identify a population-based cohort of pregnant women with PGDM and to assess trends in prevalence in two provinces of Northern Italy. Methods: We selected all deliveries occurred in the period 1997–2010 with ICD-9-CM codes for PGDM in HD record and we matched up to 5 controls from mothers without diabetes. We used Diabetes Registers (DRs) as the gold standard for validation analysis. Results: We selected 3800 women, 653 with diabetes and 3147 without diabetes. The agreement between HD records and DRs was 90.7%, with K = 0.58. We detected 350 false positives and only 1 false negative. Sensitivity was 99.3%, specificity 90.0%, positive predictive value 46.4% and negative predictive value 99.9%. Of the false positives, 48.6% had gestational diabetes and 2.3% impaired glucose tolerance. After the validation process, PGDM prevalence decreased from 4.4 to 2.0 per 1000 deliveries. Conclusions: Our results show that HD facilitate detection of almost all PGDM cases, but they also include a large number of false positives, mainly due to gestational diabetes. This misclassification causes a large overestimation of PGMD prevalence. Our findings require accuracy evaluation of ICD-9-CM codes, before they can be widely applied to epidemiological research and public health surveillance related to PGDM.