Related Experiment Videos
Coding errors in an analysis of the impact of pay-for-performance on the care for long-term cardiovascular disease: a
Simon de Lusignan1, Benjamin Sun1, Christopher Pearce2
1Clinical Informatics and Health Outcomes Research Group, Department of Health Care Management and Policy, University of Surrey, Guildford GU2 7XH, UK.
Insights
Code selection significantly impacts research findings from routine data. Standardizing the publication of code ranges is crucial for consistent and precise health research results.
Area of Science:
- Health Informatics
- Clinical Research Methodology
- Data Science in Healthcare
Background:
- Standardized methods for publishing code ranges in routine data research are lacking.
- Inconsistent code selection can affect the reported prevalence and precision of study outcomes.
- Pay-for-performance (P4P) schemes and data analysis teams may use different coding strategies.
Purpose of the Study:
- To investigate how variations in code selection influence the reported prevalence and precision of results in research utilizing routine data.
- To compare code ranges used in P4P schemes with those used by informatics teams for case identification.
- To assess the impact of code selection on the prevalence and blood pressure of individuals with hypertension.
Main Methods:
- Utilized routinely collected primary care data from the Quality Improvement in Chronic Kidney Disease (QICKD) trial.
- Compared code ranges for identifying chronic conditions (hypertension, stroke, coronary heart disease) used in P4P, the QICKD trial, and the informatics team's case study.
- Estimated positive predictive values (PPV) for identified chronic conditions and compared prevalence and blood pressure (BP) in hypertension cohorts.
Main Results:
- Significant inconsistencies were found between selected codes for long-term cardiovascular conditions and those specified in P4P or the QICKD trial.
- Hypertension prevalence varied: 10.3% using case study codes versus 11.8% using P4P codes.
- Mean blood pressure differences were observed between case study and P4P populations (e.g., 138.3/79.4 mmHg vs. 137.3/79.1 mmHg), with statistical significance (p < 0.001).
Conclusions:
- The case study analysis lacked precision due to inconsistent code selection.
- Individuals excluded based on specific codes had lower blood pressure, indicating a potential bias.
- Mandating the publication of code ranges used in studies based on routine data is essential for transparency and comparability.
Objective:
There is no standard method of publishing the code ranges in research using routine data. We report how code selection affects the reported prevalence and precision of results.
Design:
We compared code ranges used to report the impact of pay-for-performance (P4P), with those specified in the P4P scheme, and those used by our informatics team to identify cases. We estimated the positive predictive values (PPV) of people with chronic conditions who were included in the study population, and compared the prevalence and blood pressure (BP) of people with hypertension (HT).
Setting:
Routinely collected primary care data from the quality improvement in chronic kidney disease (QICKD-ISRCTN56023731) trial.
Main Outcome Measures:
The case study population represented roughly 85% of those in the HT P4P group (PPV = 0.842; 95%CI = 0.840-0.844; p < 0.001). We also found differences in the prevalence of stroke (PPV = 0.694; 95%CI = 0.687- 0.700) and coronary heart disease (PPV = 0.166; 95%CI = 0.162-0.170), where the paper restricted itself to myocardial infarction codes.
Results:
We found that the long-term cardiovascular conditions and codes selected for these conditions were inconsistent with those in P4P or the QICKD trial. The prevalence of HT based on the case study codes was 10.3%, compared with 11.8% using the P4P codes; the mean BP was 138.3 mmHg (standard deviation (SD) 15.84 mmHg)/79.4 mmHg (SD 10.3 mmHg) and 137.3 mmHg (SD 15.31)/79.1 mmHg (SD 9.93 mmHg) for the case study and P4P populations, respectively (p < 0.001).
Conclusion:
The case study lacked precision, and excluded cases had a lower BP. Publishing code ranges made this comparison possible and should be mandated for publications based on routine data.