Coding errors in an analysis of the impact of pay-for-performance on the care for long-term cardiovascular disease: a case study.

Abstract

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.