1 Introduction

rcprd contains a number of functions which extract specific variables, namely:

• BMI: extract_BMI
• Cholesterol/high-density lipoprotein (HDL) ratio: extract_cholhdl_ratio
• Diabetes status: extract_diabetes
• Smoking status: extract_smoking

The algorithms underpinning the extraction of these variables are given in section 2. A summary of the unit measurements recorded for these variables is given in section 3.

2 Algorithms for variable extraction

3 Summary of units of measurement for test data

In this section we report the different units of measurement that the test data for the above variables may be recorded in. The unit of measurement is denoted with the numunitid variable in the observation file, which has a corresponding lookup file in the CPRD data. We queried the observation data for a large cohort of individuals aged 18 - 85 between 2005 - 2020 using the code lists provided within inst/codelists directory of rcprd.

list.files(system.file("codelists", package = "rcprd"))
#> [1] "edh_bmi_medcodeid.csv"           "edh_chol_medcodeid.csv"         
#> [3] "edh_cholhdl_ratio_medcodeid.csv" "edh_hdl_medcodeid.csv"          
#> [5] "edh_sbp_medcodeid.csv"           "height_medcodeid.csv"           
#> [7] "weight_medcodeid.csv"

The test data was searched separately using each code list, and the resulting unit measurements that were recorded in more than 0.01% of the query are presented. The results of this were fed into the programs for deriving each of those variables (see section 2). When defining a variable, the aim is to convert all measurements to be on the scame scale/unit of measurement. For example, height measurements in metres should be converted to the same scale as those recorded in centimeters. However, for some records, the unit of measurement might be something odd, or missing, meaning it is unclear how to convert onto the desired scale. For observations with such unit measurements, we do not exclude these observations, as they may be correct measurements with a mis-recorded unit measurement. Instead, when extracting variables, and converting relevant unit measurements, we define a minimum and maximum value, and exclude observations that do not fit into this range. As will be seen below, the proportion of observations with unclear unit measurement is small (with the exception of cholesterol/hdl ratio, which is a special case).

The most common is ‘NA’ (78.95%). The second most common is ‘ratio’ (12.49%) then 1/1 (2.33%). The confusion about unit of measurement is likely due to the fact that this ratio has no unit of measurement, because total cholesterol and high-density lipoprotein have the same unit of measurement. All measurements are therefore assumed to be in the same unit of measurement (ratio).

The majority of unit measurements are mmol/L (96.35%) or NA (3.54%). All observations are therefore assumed to be recorded in mmol/L.

The majority of unit measurements are mmol/L (94.21%) or NA (5.67%). All observations are therefore assumed to be recorded in mmol/L.

The majority of unit measurements are kg/m2 (39.58%), kg/mA2 (1.26%) or NA (58.08%). All observations are therefore assumed to be recorded in kg/m2.

The majority of unit measurements are kg (98.67%) or NA (1.24%). Most observations are therefore assumed to be recorded in kg, however we also know from that for numunitid \(\in {1691, 2318, 2997 or 6265}\), this refers to stone. Observations with these units of measurements are therefore converted to kg.

The majority of unit measurements are cm (96.82%), m (1.7%), metres (0.02%) or NA (.37%). All observations with numunit not corresponding to metres, will be assumed to be in centimetres, and converted to metres to enable estimation of BMI.

While there is not a unique algorithm for SBP, we still present the results from the database query for this variable. All measurement are in mm/Hg.