Translate 
Abstract
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. 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). Routinely collected primary care data from the quality improvement in chronic kidney disease (QICKD-ISRCTN56023731) trial. 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. 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). 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.
Highlights
We compared the code ranges used in the case study with those used by the P4P program and those used by a clinical informatics group to identify people with these conditions from routinely collected data
We explored a single P4P year, 2009–2010, to see if blood pressure (BP) were different between case study, P4P indicator populations and the quality improvement in chronic kidney disease (QICKD) trial population with HT
We report the mean and standard deviation (SD), and use an independent samples t-test to compare the mean systolic and diastolic BP for the people identified by the case study codes with the BP of the people in the two groups
Summary
We compared the code ranges used in the case study with those used by the P4P program and those used by a clinical informatics group to identify people with these conditions from routinely collected data.First, we identified the reference terminology used in the case study and a justification for the code ranges used within the reference terminology. We compared the code ranges used in the case study with those used by the P4P program and those used by a clinical informatics group to identify people with these conditions from routinely collected data. We compared the use of disease, symptom, procedure, and drug codes that we found with the codes used in the case study. We identified the long-term cardiovascular conditions in the P4P scheme that correspond to the conditions described in the case study. We extracted the inclusion and exclusion code ranges for each condition category from the P4P business rules, which are available online.[8] We compared the codes used in the case study with those used by our clinical informatics team to detect these same long-term conditions from routinely collected data. The routine data were a convenience sample, those extracted for the quality improvement in chronic kidney disease (QICKD) trial.[9,10]
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
