Validity of hospital discharge records to identify pregestational diabetes in an Italian population.

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.

The reliability of hospital and pharmaceutical data to assess prevalent cases of chronic obstructive pulmonary disease.

Identifying chronic obstructive disease (COPD) cases is required to estimate COPD prevalence, to enroll COPD cohorts and to estimate air pollution health effects. Administrative health data are frequently used to identify COPD cases, though their validity has not been satisfactorily assessed. This paper aims to assess the contribution of pharmaceutical data in detecting COPD cases and to estimate the reliability of hospital/mortality databases in detecting COPD cases. Prevalent COPD cases among 35-plus-year-olds were estimated in four Italian areas in 2006 from hospital/mortality registries and adding pharmaceutical data. Age-specific and age-standardized prevalence rates were calculated in each area. Internal validity of COPD diagnoses from hospital and mortality databases was assessed. Pharmaceutical database was used to confirm the hospital/mortality COPD cases and to examine the selection and misclassification of hospitalized cases. Possible misclassification between COPD and asthma cases was estimated using hospital data. Prevalent COPD cases were 77,098 from hospital/mortality registries, 172,357 when respiratory prescriptions were added. Prevalence ranged from 4.0%-6.7%. Only 22.7% of pharmaceutical COPD cases were hospitalized or died and only 37.2% of hospital/mortality cases consumed respiratory medicines; this last proportion increased to 64.5% among the older cases with a principal diagnosis. COPD cases with a contemporary asthma diagnosis were 3.1%. We found that pharmaceutical data increases COPD prevalence estimates 2.2-2.5 times. Hospitalization does not necessarily indicate COPD severity, COPD as a principal diagnosis confirmed with medicine prescription more likely represented true cases. Misclassification affects asthma cases to greater extent than COPD cases.

Misclassification of breast cancer as cause of death in a service screening area.

OBJECTIVE: The aim of this study was to assess the misclassification of cause of death for breast cancer cases, and to evaluate the differential misclassification between cases detected in an organized screening program and cases found in current clinical practice. METHODS: All deaths occurring between 1999 and 2002 within breast cancer cases were linked to hospital discharge records. Death certificates and latest available hospital discharge notes were classified into various categories. We created a classification algorithm defining which combinations of categories (of death certificates and hospital discharge notes) suggested the probability of misclassification and the need for an in-depth diagnostic review. Questionable cases were reviewed by a team of experts in order to reach a consensus on cause of death. Based on our algorithmic classification and diagnostic review results, the agreement between original cause of death and that resulting from the assessment process was analyzed stratifying for every variable of interest. RESULTS: According to death certificates, breast cancer was the cause of death in 66.9% of subjects, and after assessment this figure changed to 65.7%. The misclassification rate was 4.3% and did not differ significantly between screen-detected (4.7%) and non-screen-detected (4.3%) cases. Higher misclassification rates in favor of false positivity (cause of death wrongly attributed to breast cancer in death certificates) was observed for subjects with multiple cancers (6.5% vs. 1.9%), with no admission in the year before death (4.6% vs. 2.4%) and with an unknown cancer stage (4.9% vs 2.4% or 2.3%). CONCLUSIONS: The cause of death misclassification rate is modest, causing a slight overestimate of deaths attributed to breast cancer, and is not affected by modality of diagnosis. The study confirmed the validity of using cause-specific mortality for service screening evaluation.