scholarly journals Data Quality Assessment on Congenital Anomalies in Ontario, Canada

2020 ◽  
Vol 8 ◽  
Author(s):  
Qun Miao ◽  
Aideen M. Moore ◽  
Shelley D. Dougan
Keyword(s):  
Data Quality ◽  
Congenital Anomalies ◽  
Kappa Statistics ◽  
Discharge Abstract Database ◽  
Ontario Health Insurance Plan ◽  
Orofacial Clefts ◽  
Great Vessels ◽  
Icd 10 ◽  
Future Data ◽  
Birth Records

Background: Congenital anomalies (CAs) are a major cause of infant morbidity and mortality in Canada. Reliably identifying CAs is essential for CA surveillance and research. The main objective of this study was to assess the agreement of eight sentinel anomalies including: neural tube defects (NTD), orofacial clefts, limb deficiency defects (LDD), Down syndrome (DS), tetralogy of Fallot (TOF), gastroschisis (GS), hypoplastic left heart syndrome (HLHS) and transposition of great vessels (TGA) captured in the BORN Information System (BIS) database and the Canadian Institute for Health Information (CIHI) Discharge Abstract Database (DAD).Methods: Live birth and stillbirth records between the BIS and CIHI-DAD in the fiscal years of 2012–2013 to 2015–2016 were linked using 10 digit infant Ontario Health Insurance Plan (OHIP) numbers. Percent agreement and Kappa statistics were performed to assess the reliability (agreement) of CAs identified in the linked BIS and CIHI-DAD birth records. Then, further investigations were conducted on those CA cases identified in the CIHI-DAD only.Results: Kappa coefficients of the eight selected CAs between BIS (“Confirmed” or “Suspected” cases) and CIHI-DAD were 0.96 (95% CI: 0.93–0.98) for GS; 0.81 (95% CI: 0.78–0.83) for Orofacial clefts; 0.75 (95% CI: 0.72–0.77) for DS; 0.71 (95% CI: 0.65–0.77) for TOF; 0.62 (95% CI: 0.55–0.68) for TGA; 0.59 (95% CI: 0.49–0.68) for HLHS, 0.53 (95% CI: 0.46–0.60) for NTD-all; and 0.30 (95% CI: 0.23–0.37) for LDD.Conclusions: The degree of agreement varied among sentinel CAs identified between the BIS and CIHI. The potential reasons for discrepancies include incompleteness of capturing CAs using existing picklist values, especially for certain sub-types, incomplete neonatal special care data in the BIS, and differences between clinical diagnosis in the BIS and ICD-10-CA classification in the DAD. A future data abstraction study will be conducted to investigate the potential reasons for discrepancies of CA capture between two databases. This project helps quantify the quality of CA data collection in the BIS, enhances understanding of CA prevalence in Ontario and provides direction for future data quality improvement activities.

scholarly journals Improving the Coding Completeness of Hypertension in Inpatient Administrative Health Data Using Machine Learning Methods

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Adam D'Souza ◽  
Zhiyang Liang ◽  
Tyler Williamson ◽  
Tony Smith ◽  
Hude Quan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Quality ◽  
Chart Review ◽  
Age Groups ◽  
Learning Approaches ◽  
Age Group ◽  
Discharge Abstract Database ◽  
Ulcer Disease ◽  
Diagnosis Codes ◽  
Icd 10

IntroductionThe Discharge Abstract Database (DAD) associates ICD-10-CA diagnosis codes with inpatient care episodes at acute-care facilities. Codes are assigned by human coders, based on chart review. Coding guidelines stipulate mandatory coding of major and fatal conditions but only optional coding of secondary conditions, which results in undercoding for many conditions. Objectives and ApproachThis research evaluates machine learning approaches for identifying and completing records with missing codes, to improve data quality. The Alberta Hospital DAD for 2013-14 was used in this study. We assumed that the existing ICD-10-CA codes in the DAD are correct, and used them as training examples. Several ML classifiers, including logistic regression and random forest, were used to develop models to assess the coding probability, using existing codes and demographic information. 3300 chart-review records were used as the reference standard. We focused on hypertension-related codes. Validity of raw diagnosis codes in the DAD was used as the baseline. ResultsA record is deemed to have a missing hypertension diagnosis code if the predicted probability is high, but without the diagnosis codes having been assigned by the coders. In the baseline, the original hypertension codes have high PPV (ranging from 0.902 for the age group 35-54 to 1.000 for the age group 18-34) but low sensitivity (ranging from 0.200 for the age group 18-34 to 0.565 for the age group 75+). The most successful models that we have tested so far have provided improvements of 2-6% in the sensitivity, while maintaining the PPV. More improvement is generally seen for the younger age groups. Initial experiments indicate greater improvements in sensitivity may be possible for other conditions, such as peptic ulcer disease and cerebrovascular disease. Conclusion/ImplicationsMachine learning approaches can be useful and cost-effective for improving data quality in DAD. While the improvements in sensitivity relative to the baseline are modest at present, further experiments with different models and feature sets are warranted. Experiments with other conditions may also be fruitful.

scholarly journals Data Linkage and Data Quality Assessment for Congenital Anomalies Surveillance in Ontario

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Qun (Grace) Miao ◽  
Shelley Miao ◽  
Shelley Dougan
Keyword(s):  
Data Quality ◽  
Quality Assessment ◽  
Congenital Anomalies ◽  
Data Linkage ◽  
Health Agency ◽  
Lookup Table ◽  
Fetal Anomaly ◽  
Fetal Anomalies ◽  
Data Quality Assessment ◽  
Icd 10

IntroductionBORN Ontario is collaborating with the Public Health Agency of Canada (PHAC) to enhance the surveillance of congenital anomalies (CA) in Ontario and participate in the national CA Surveillance Enhancement Initiative. Since 2013, BORN has provided Ontario CA cases and the birth population data to the PHAC annually. Objectives and ApproachThe objectives include a description of CA data linkage methodology and a data quality assessment. Suspected and confirmed fetal anomalies were ascertained from regional sites entering data in the BORN Information System’s (BIS) Antenatal Specialty (AS) and the Prenatal Screening Follow-up (PSFU) encounters. Newborn anomalies are identified from aggregate infant data ascertained from the Birth Child, Postpartum Child and Neonatal Care encounters. Both fetal and newborn anomalies are collected in the BIS using an extensive pick list, allowing for precise and accurate ascertainment. Once entered, pick list values are converted to ICD-10-CA codes or ranges using a lookup table. ResultsA few pick list values for minor congenital anomalies are not mapped to ICD-10-CA codes in the BIS. In this year’s cohort (CY 2016), 13 pick list values did not map to ICD-10-CA codes. This impacted 127 of 5,346 records (2.4%, one infant may have multiple records). In these cases, the CA chosen from the pick list did not have a corresponding ICD-10-CA code. Among 447 PSFU fetal anomaly records for singletons (one fetus may have multiple records), 16 records did not have a corresponding ICD-10 code. Of the AS fetal anomaly records for singletons, 109 of 3,302 records (3.3%, one fetus may have multiple records) had fetal anomalies identified in the pick list that did not have a corresponding ICD-10-CA code. Conclusion/ImplicationsBORN’s CA pick list values were developed and enhanced by clinical experts. There is a discrepancy between clinical diagnosis and the ICD-10-CA classification for certain sub-types of CA posing a challenge for mapping. To enhance data quality, BORN will continue to improve matching of pick list values with ICD-10-CA classification.

An Atlas of Congential Anomalies of the Heart and Great Vessels

PEDIATRICS ◽  
1954 ◽  
Vol 14 (6) ◽  
pp. 685-685
Keyword(s):  
Mayo Clinic ◽  
Congenital Anomalies ◽  
Right Heart Catheterization ◽  
Congenital Defects ◽  
Heart Catheterization ◽  
Right Heart ◽  
Dye Dilution ◽  
Physiological Information ◽  
Great Vessels

An Atlas of Congenital Anomalies of the Heart and Great Vessels presents a pictorial outline of congenital defects of the heart and great vessels selected from the records of the Mayo Clinic. In most instances brief clinical resumes, radiographic, and electrocardiographic data are also included. In rarer instances certain physiological information available from right heart catheterization and dye-dilution technics is also presented. The preface deservedly gives credit to the photographic and art staffs of the Mayo Clinic and Foundation, since their contribution is primarily responsible for making this volume unique and valuable.

Congenital Anomalies of the Heart and Great Vessels

1948 ◽  
Vol 4 (4) ◽  
pp. 327-360 ◽  
Author(s):  
T. J. Dry ◽  
J. E. Edwards ◽  
R. L. Parker ◽  
H. B. Burchell ◽  
H. M. Rogers ◽  
...  
Keyword(s):  
Congenital Anomalies ◽  
Great Vessels

scholarly journals 819 Orofacial Clefts and Associated Congenital Anomalies: The 18 Years Experience of a Multidisciplinary Group in a Tertiary Hospital in Portugal

2010 ◽  
Vol 68 ◽  
pp. 412-412
Author(s):  
L Correia-Costa ◽  
J Miranda ◽  
V Mendonça ◽  
R Furfuro ◽  
A Bessa-Monteiro ◽  
...  
Keyword(s):  
Congenital Anomalies ◽  
Tertiary Hospital ◽  
Orofacial Clefts ◽  
Multidisciplinary Group

scholarly journals A Nationwide Registry-Based Study on Mortality Due to Rare Congenital Anomalies

2018 ◽  
Vol 15 (8) ◽  
pp. 1715 ◽  
Author(s):  
Verónica Alonso-Ferreira ◽  
Germán Sánchez-Díaz ◽  
Ana Villaverde-Hueso ◽  
Manuel Posada de la Paz ◽  
Eva Bermejo-Sánchez
Keyword(s):  
Congenital Anomalies ◽  
Time Trends ◽  
Mortality Rates ◽  
Population Based ◽  
Geographical Differences ◽  
Disease Epidemiology ◽  
Risk Of Death ◽  
Health Authorities ◽  
Icd 10 ◽  
Related Mortality

This study aimed to analyse population-based mortality attributed to rare congenital anomalies (CAs) and assess the associated time trends and geographical differences in Spain. Data on CA-related deaths were sourced from annual mortality databases kept by the National Statistics Institute of Spain (1999–2013). Based on the ICD-10, only CAs corresponding to rare diseases definition were included in this study. Annual age-adjusted mortality rates were calculated and time trends were evaluated by joinpoint regression analysis. Geographical differences were assessed using standardised mortality ratios and cluster detection. A total of 13,660 rare-CA-related deaths (53.4% males) were identified in the study period. Annual age-adjusted mortality rates decreased by an average of −5.2% (−5.5% males, −4.8% females, p < 0.001). Geographical analysis showed a higher risk of rare-CA-related mortality in regions largely located in the south of the country. Despite their limitations, mortality statistics are essential and useful tools for enhancing knowledge of rare disease epidemiology and, by extension, for designing and targeting public health actions. Monitoring rare-CA-related mortality in Spain has shown a 15-year decline and geographical differences in the risk of death, all of which might well be taken into account by the health authorities in order to ensure equality and equity, and to adopt appropriate preventive measures.

Congenital Anomalies of the Great Vessels

2017 ◽  
pp. 663-718
Author(s):  
Gerhard Ziemer ◽  
Renate Kaulitz
Keyword(s):  
Congenital Anomalies ◽  
Great Vessels

Congenital anomalies of the heart and great vessels

1991 ◽  
pp. 118-132
Author(s):  
Martin Kaltenbach ◽  
Ronald E. Vlietstra
Keyword(s):  
Congenital Anomalies ◽  
Great Vessels
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