Lucia Borsari1, Carlotta Malagoli1, Paola Ballotari2, Gianfranco De Girolamo3, Karin Bonora3, Federica Violi1, Oreste Capelli3, Rossella Rodolfi4, Fausto Nicolini4, Marco Vinceti5. 1. Sezione di Sanità Pubblica, Dipartimento di Scienze Biomediche, Metaboliche e Neuroscienze, Università di Modena e Reggio Emilia, Modena, Italy. 2. Azienda Unità Sanitaria Locale di Reggio Emilia, Reggio Emilia, Italy; Arcispedale Santa Maria Nuova - IRCCS, Reggio Emilia, Italy. 3. Azienda Unità Sanitaria Locale di Modena, Modena, Italy. 4. Azienda Unità Sanitaria Locale di Reggio Emilia, Reggio Emilia, Italy. 5. Sezione di Sanità Pubblica, Dipartimento di Scienze Biomediche, Metaboliche e Neuroscienze, Università di Modena e Reggio Emilia, Modena, Italy. Electronic address: marco.vinceti@unimore.it.
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.
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.
Authors: Elisa Gualdani; Graziano Di Cianni; Marta Seghieri; Paolo Francesconi; Giuseppe Seghieri Journal: Acta Diabetol Date: 2021-04-09 Impact factor: 4.280
Authors: Ana López-de-Andrés; Napoleón Perez-Farinos; Valentín Hernández-Barrera; María A Palomar-Gallego; David Carabantes-Alarcón; José J Zamorano-León; Javier de Miguel-Diez; Rodrigo Jimenez-Garcia Journal: J Clin Med Date: 2020-02-21 Impact factor: 4.241