The burden of misclassification of antepartum stillbirth in Nepal

BackgroundGlobally, every year 1.1 million antepartum stillbirths occur with 98% of these deaths taking place in countries where the health system is poor. In this paper we examine the burden of misclassification of antepartum stillbirth in hospitals of Nepal and factors associated with misclassification.MethodA prospective observational study was conducted in 12 hospitals of Nepal for a period of 6 months. If fetal heart sounds (FHS) were detected at admission and during the intrapartum period, the antepartum stillbirth (fetal death ≥22 weeks prior labour) recorded in patient’s case note was recategorised as misclassified antepartum stillbirth. We further compared sociodemographic, obstetric and neonatal characteristics of misclassified and correctly classified antepartum stillbirths using bivariate and multivariate analysis.ResultA total of 41 061 women were enrolled in the study and 39 562 of the participants’ FHS were taken at admission. Of the total participants whose FHS were taken at admission, 94.8% had normal FHS, 4.7% had abnormal FHS and 0.6% had no FHS at admission. Of the total 119 recorded antepartum stillbirths, 29 (24.4%) had FHS at admission and during labour and therefore categorised as misclassified antepartum stillbirths. Multivariate analysis performed to adjust the risk of association revealed that complications during pregnancy resulted in a threefold risk of misclassification (adjusted OR-3.35, 95% CI 1.95 to 5.76).ConclusionAlmost 25% of the recorded antepartum stillbirths were misclassified. Improving quality of data is crucial to improving accountability and quality of care. As the interventions to reduce antepartum stillbirth differ, accurate measurement of antepartum stillbirth is critical.Trial registration numberISRCTN30829654.

A Global Multilayer Cloud Identification with POLDER/PARASOL

The detection of multilayer cloud situations is important for satellite retrieval algorithms and for many climate-related applications. In this paper, the authors describe an algorithm based on the exploitation of the Polarization and Directionality of the Earth’s Reflectance (POLDER) observations to identify monolayered and multilayered cloudy situations along with a confidence index. The authors’ reference comes from the synergy of the active instruments of the A-Train satellite constellation. The algorithm is based upon a decision tree that uses a metric from information theory and a series of tests on POLDER level-2 products. The authors obtain a multilayer flag as the final result of a tree classification, which takes discrete values between 0 and 100. Values closest to 0 (100) indicate a higher confidence in the monolayer (multilayer) character. This indicator can be used as it is or with a threshold level that minimizes the risk of misclassification, as a binary index to distinguish between monolayer and multilayer clouds. For almost fully covered and optically thick enough cloud scenes, the risk of misclassification ranges from 29% to 34% over the period 2006–10, and the average confidences in the estimated monolayer and multilayer characters of the cloud scenes are 74.0% and 58.2%, respectively. With the binary distinction, POLDER provides a climatology of the mono–multilayer cloud character that exhibits some interesting features. Comparisons with the performance of the Moderate Resolution Imaging Spectroradiometer (MODIS) multilayer flag are given.

Differentiating Incident from Recurrent Stroke Using Administrative Data: The Impact of Varying Lengths of Look-Back Periods on the Risk of Misclassification

Background/Aims: Administrative data are widely used to monitor epidemiological trends in stroke and outcomes; yet there is scant empirical guidance on how to best differentiate incident from recurrent stroke. Methods: We identified all hospital admissions in New South Wales, Australia, with a principal stroke diagnosis from July 1, 2013 to June 30, 2014, linked to 12 years of previous admissions. We calculated the proportion of cases identified with a prior stroke to determine the number of years of look-back required to minimise misclassification of incident and recurrent strokes. Results: Using the maximum available look-back period of 12 years, 1,171 out of 8,364 eligible stroke cases (14.0%) had a stroke history. A 1-year look-back period identified only 25.1% of these patients and 1 in 10 stroke cases were misclassified as incident. With a 10-year clearance period, less than 1 in 100 stroke cases were misclassified as incident. The risk of misclassification was lower in patients younger than 65 years and in those with haemorrhagic stroke. Conclusion: Hospital administrative data sets linked to prior admissions can be used to distinguish recurrent from incident stroke. The risk of misclassifying recurrent stroke cases as incident events is negligible with a look-back period of 10 years.

The validity and reliability of the diagnosis of hyperkinetic disorders in the Danish Psychiatric Central Research Registry

ObjectiveTo validate the diagnosis of hyperkinetic disorders (HD) in the Danish Psychiatric Central Research Registry (DPCRR) for children and adolescents aged 4 to 15 given in the years 1995 to 2005.MethodFrom a total of 4568 participants, a representative random subsample of n = 387 patients were used to validate the diagnosis. Patient files were systematically scored for the presence of ICD-10 criteria for HD and oppositional defiant disorder/conduct disorder (ODD/CD; F91). Further to this, an inter-rater reliability study was also conducted, whereby two experienced child and adolescent psychiatrists who were blind to patients discharge diagnoses, rated a random subsample of n = 101 participants.ResultsInformation was available for 372 out of 387 patients. Out of n = 372 available files, n = 324 (86.8%) were evaluated to fulfil diagnostic criteria for HD. Due to missing information it was not possible to reach a conclusion for 5.1% of the cases, 3.8% of the diagnoses were registration errors, and in 4.3% of the files the diagnosis had to be rejected. Inter-rater agreement was high (κ = 0.83, z = 10.9, P < .001). The validity of hyperkinetic disorders, unspecified (F90.9) was lower and comorbid CD/ODD were under-diagnosed in the sample. All participants fulfilling HD criteria also fulfilled DSM-5-criteria for ADHD.ConclusionThe risk of misclassification of patients with HD in the DPCRR is relatively low, with the exception of the diagnosis of hyperkinetic disorders, unspecified (F90.9).