Dataset Information

Phenotyping congenital anomalies in administrative hospital records.

ABSTRACT:

Background

Congenital anomalies are a major cause of co-morbidity in children. Diagnostic code lists are increasingly used to identify congenital anomalies in administrative health records. Evidence is lacking on comparability of these code lists.

Objectives

To compare prevalence of congenital anomalies and prognostic outcomes for children with congenital anomalies identified in administrative health records using three different code lists.

Methods

We developed national cohorts of singleton livebirths in England (n = 7 354 363, 2003-2014) and Scotland (n = 493 556, 2003-2011). Children with congenital anomalies were identified if congenital anomaly diagnosis was recorded at birth, during subsequent hospital admission or as cause of death before 2 years old. We used three code lists: the EUROCAT list for congenital anomaly surveillance in Europe; the Hardelid list developed to identify children with chronic conditions (including congenital anomalies) admitted to hospital in England; and the Feudtner list developed to indicate children with complex chronic conditions (including congenital anomalies) admitted to hospitals in the United States. We compared prevalence, and risks of postnatal hospital readmission and death according to each code list in England and Scotland.

Results

Prevalence of congenital anomalies was highest using the EUROCAT list (4.1% of livebirths in England, 3.7% in Scotland), followed by Hardelid (3.1% and 3.0% of livebirths, respectively) and Feudtner (1.8% and 1.5% of livebirths, respectively). 67.2%-73.3% of children with congenital anomalies in England and 65.2%-77.0% in Scotland had at least one postnatal hospital admission across the three code lists; mortality ranged between 42.6-75.4 and 41.5-88.7 deaths per 1000 births in England Scotland, respectively. The risk of these adverse outcomes was highest using Feudtner and lowest using EUROCAT code lists.

Conclusions

The prevalence of congenital anomalies varied by congenital anomaly code list, over time and between countries, reflecting in part differences in hospital coding practices and admission thresholds. As a minimum, researchers using administrative health data to study congenital anomalies should report sensitivity analyses using different code lists.

SUBMITTER: Zylbersztejn A

PROVIDER: S-EPMC7003968 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Background and objectiveImpact of appearance of congenital hand anomalies has not previously been reported. The purpose of this study was to describe the common perception about how different congenitally malformed hands look.MethodsWe developed a questionnaire in a game format to evaluate the appearance of different hands. Altogether 1450 (954 females) 4- to 84-year-old residents (296 children) of two European and one Asian (n = 102) country were asked to rate the appearance of different looking hands on a five-point pictorial Likert-type scale. Standardized photographs of the dorsal aspect of 17 different congenitally malformed non-operated hands and a normal hand were presented to respondents. Significance of age, gender, nationality, and profession of the respondents was assessed.ResultsThe respondents' ranking order of the hands was nearly consistent. The normal hand (mean = 4.43, standard deviation = 0.85, Md = 5) and clinodactyly (mean = 4.37, standard deviation = 0.86, Md = 5) were perceived to have the best appearance. Symbrachydactyly (mean = 1.42, standard deviation = 0.68, Md = 1) and radial club hand (mean = 1.40, standard deviation = 0.68, Md = 1) received the lowest scores. Adults rated the appearance of hands higher than children regarding 14 hands, females higher than men regarding 15 hands, and Europeans higher than Asians in 4 hands (p < 0.05, respectively). Europeans rated four-finger hand (mean = 3.21, standard deviation = 1.18, Md = 3) better looking than six-finger hand (mean = 2.92, standard deviation = 1.18, Md = 3, p < 0.005), whereas Asians gave higher scores to six-finger hand (mean = 2.66, standard deviation = 1.26, Md = 3) compared to four-finger hand (mean = 2.51, standard deviation = 1.14, Md = 2). Medical doctors and nurses gave higher scores compared to the other profession groups, school children, and high school students in five hands (p < 0.05).ConclusionsA normal hand is perceived distinctly better looking than most congenitally different hands. Different malformations' appearance was ranked very coherently in the same order despite of participants' age, gender, nationality, or profession. Asians seem to prefer an additional digit to a four-finger hand.

Project description:BACKGROUND:Administrative health records (AHRs) and electronic medical records (EMRs) are two key sources of population-based data for disease surveillance, but misclassification errors in the data can bias disease estimates. Methods that combine information from error-prone data sources can build on the strengths of AHRs and EMRs. We compared bias and error for four data-combining methods and applied them to estimate hypertension prevalence. METHODS:Our study included rule-based OR and AND methods that identify disease cases from either or both data sources, respectively, rule-based sensitivity-specificity adjusted (RSSA) method that corrects for inaccuracies using a deterministic rule, and probabilistic-based sensitivity-specificity adjusted (PSSA) method that corrects for error using a statistical model. Computer simulation was used to estimate relative bias (RB) and mean square error (MSE) under varying conditions of population disease prevalence, correlation amongst data sources, and amount of misclassification error. AHRs and EMRs for Manitoba, Canada were used to estimate hypertension prevalence using validated case definitions and multiple disease markers. RESULTS:The OR method had the lowest RB and MSE when population disease prevalence was 10%, and the RSSA method had the lowest RB and MSE when population prevalence increased to 20%. As the correlation between data sources increased, the OR method resulted in the lowest RB and MSE. Estimates of hypertension prevalence for AHRs and EMRs alone were 30.9% (95% CI: 30.6-31.2) and 24.9% (95% CI: 24.6-25.2), respectively. The estimates were 21.4% (95% CI: 21.1-21.7), for the AND method, 34.4% (95% CI: 34.1-34.8) for the OR method, 32.2% (95% CI: 31.8-32.6) for the RSSA method, and ranged from 34.3% (95% CI: 34.1-34.5) to 35.9% (95% CI, 35.7-36.1) for the PSSA method, depending on the statistical model. CONCLUSIONS:The OR and AND methods are influenced by correlation amongst the data sources, while the RSSA method is dependent on the accuracy of prior sensitivity and specificity estimates. The PSSA method performed well when population prevalence was high and average correlations amongst disease markers was low. This study will guide researchers to select a data-combining method that best suits their data characteristics.