Related Experiment Video
Updated: Feb 2, 2026
Determining Soil-transmitted Helminth Infection Status and Physical Fitness of School-aged Children
Published on: August 22, 2012
Use of physician billing claims to identify infections in children
Jeremiah Hwee1,2, Lillian Sung3,4,5, Jeffrey C Kwong1,2,6,7,8
1Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
Insights
Administrative billing claims are a reasonably accurate tool for identifying pediatric infections, especially respiratory ones. This study validates their use in large-scale health data analysis for children under 18.
Area of Science:
- Health Informatics
- Pediatric Epidemiology
- Data Validation
Background:
- Medical records offer detailed patient information but have limited accessibility.
- Administrative data provide broad population coverage but have limited scope.
- Validating administrative data for pediatric infection identification is crucial but understudied.
Purpose of the Study:
- To determine the criterion validity of physician billing claims in administrative data for identifying infectious disease syndromes in pediatric populations.
- To compare the accuracy of administrative data against electronic medical record diagnoses as a reference standard.
Main Methods:
- A random sample of pediatric patients (<18 years) from Ontario, Canada, was drawn from the Electronic Medical Record Administrative data Linked Database (EMRALD).
- Physician diagnoses from electronic medical records (EMR) served as the reference standard.
- Criterion validity of physician billing claims was assessed for various infection categories (respiratory, skin, gastrointestinal, urinary tract, otitis externa) from 2012 to 2014.
Main Results:
- Overall sensitivity for identifying infections was 0.74, specificity was 0.95, positive predictive value (PPV) was 0.87, and negative predictive value (NPV) was 0.88.
- Respiratory infections showed high performance metrics (sensitivity 0.77, specificity 0.96, PPV 0.85, NPV 0.94).
- Performance was highest when patients presented with a single health complaint; other infection types had lower sensitivity.
Conclusions:
- Linked administrative billing codes are reasonably accurate for identifying infections in pediatric populations when using EMR data as the reference standard.
- Administrative data can be a valuable tool for large-scale epidemiological studies of pediatric infections.
- Further research may refine the use of administrative data by considering patient visit context.
Abstract:
While medical records have detailed information, they are limited in reach to the availability and accessibility of those records. On the other hand, administrative data while limited in scope, have a much further reach in coverage of an entire population. However, few studies have validated the use of administrative data for identifying infections in pediatric populations. Pediatric patients from Ontario, Canada aged <18 years were randomly sampled from the Electronic Medical Record Administrative data Linked Database (EMRALD). Using physician diagnoses from the electronic medical record (EMR) as the reference standard, we determined the criterion validity of physician billing claims in administrative data for identifying infectious disease syndromes from 2012 to 2014. Diagnosis codes were assessed by infection category (respiratory, skin and soft tissue, gastrointestinal, urinary tract and otitis externa) and for all infections combined. Sensitivity analyses assessed the performance if patients had more than one reason to visit the physician. We analysed 2,139 patients and found 33.3% of all visits were for an infection, and respiratory infections accounted for 67.6% of the infections. When we combined all infection categories, sensitivity was 0.74 (95% CI 0.70-0.77), specificity was 0.95 (95% CI 0.93-0.96), positive predictive value (PPV) was 0.87 (95% CI 0.84-0.90), and negative predictive value (NPV) was 0.88 (95% CI 0.86-0.89). For respiratory infections, sensitivity was 0.77 (95% CI 0.73-0.81), specificity was 0.96 (95% CI 0.95-0.97), PPV was 0.85 (95% CI 0.81-0.88), and NPV was 0.94 (95% CI 0.92-0.95). Similar performance was observed for skin and soft tissue, gastrointestinal, urinary tract, and otitis externa infections, but with lower sensitivity. Performance measures were highest when the patient visited the physician with only one health complaint. We found when using linked EMR data as the reference standard, administrative billing codes are reasonably accurate in identifying infections in a pediatric population.
Related Concept Videos
01:11Testing a Claim about Mean: Known Population SD
Estimating a population mean requires the samples to be distributed normally. The data should be collected from the randomly selected samples having no sampling bias. The sample size needed to be higher than 30, and most importantly, the population standard deviation should be already known.
In most realistic situations, the population standard deviation is often unknown, but in rare circumstances, when it...
01:24Testing a Claim about Population Proportion
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
01:19Testing a Claim about Standard Deviation
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
01:21Testing a Claim about Mean: Unknown Population SD
Estimating a population mean requires the samples to be approximately normally distributed. The data should be collected from the randomly selected samples having no sampling bias. There is no specific requirement for sample size. But if the sample size is less than 30, and we don't know the population standard deviation, a different approach is used;...
01:29Drug Dosing: Infants and Children
01:14Identifying Statistically Significant Differences: The F-Test