Cross-sectional analysis of educational inequalities in primary prevention statin use in UK Biobank

Objective Identify whether participants with lower education are less likely to report taking statins for primary cardiovascular prevention than those with higher education, but an equivalent increase in underlying cardiovascular risk. Methods Using data from a large prospective cohort study, UK Biobank, we calculated a QRISK3 cardiovascular risk score for 472 097 eligible participants with complete data on self-reported educational attainment and statin use (55% female participants; mean age 56 years). We used logistic regression to explore the association between (i) QRISK3 score and (ii) educational attainment on self-reported statin use. We then stratified the association between QRISK3 score and statin use, by educational attainment to test for interactions. Results There was evidence of an interaction between QRISK3 score and educational attainment. Per unit increase in QRISK3 score, more educated individuals were more likely to report taking statins. In women with ≤7 years of schooling, a one unit increase in QRISK3 score was associated with a 7% higher odds of statin use (OR 1.07, 95% CI 1.07 to 1.07). In women with ≥20 years of schooling, a one unit increase in QRISK3 score was associated with an 14% higher odds of statin use (OR 1.14, 95% CI 1.14 to 1.15). Comparable ORs in men were 1.04 (95% CI 1.04 to 1.05) for ≤7 years of schooling and 1.08 (95% CI 1.08, 1.08) for ≥20 years of schooling. Conclusion Per unit increase in QRISK3 score, individuals with lower educational attainment were less likely to report using statins, likely contributing to health inequalities.


UK Biobank
All UK Biobank participants are linked to mortality records, hospital episode statistics (HES) or Scottish morbidity and mortality records (referred to jointly throughout as hospital admissions data), with data available from 1997 in England, 1998 in Wales and1981 in Scotland, with the most recent entry recorded in this analysis in May 2017. A subset of participants (approximately 230,000) have linked primary care and prescribing data.

Treatments
Use of drugs at baseline (antihypertensives, corticosteroids and atypical antipsychotics) were defined by selfreported medication use to clinic nurses at baseline. Individuals were coded as using medication if they reported any medication included in the QRISK3 score. In the QRISK3 derivation cohort individuals were required to have at least two prescriptions representing long term use. It was not possible to ascertain the number of prescriptions in UK Biobank; however, UK Biobank participants were asked to record regular treatments, rather than short term medication or over the counter medication. All treatment codes used to define these variables in UK Biobank are available in Supplementary Table 2.

Behavioral, lifestyle and biological factors
Ethnicity Ethnicity was reported by participants to study nurses at UK baseline assessment centres. Ethnicity was categorised according to the categories used in the QRISK3 algorithm.

Townsend deprivation index
Townsend deprivation index of current location was recorded by UK Biobank at baseline .

BMI
Height (m) and weight (kg) were measured by UK Biobank study nurses ate baseline assessment centres which were used to calculate BMI (kg/m 2 ).

Smoking
Smoking status (never, former or current) was determined by self-reported data at baseline assessment centres.
The number of cigarettes smoked per day in current smokers was reported at baseline assessment centres and categorised according to QRISK3 categories of light (1-9/day), moderate (10-19/day) and heavy smokers (≥20/day). BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s)

Biological factors
Systolic blood pressure The mean from two resting automated measures of systolic blood pressure, measured using an Omron HEM-7105IT digital blood pressure monitor, was used in the QRISK3 score.

Systolic blood pressure variability
In the absence of repeated measures of systolic blood pressure on UK biobank a measure of systolic blood pressure variability was derived from the standard deviation of the two recorded measurements of systolic blood pressure at the baseline assessment centre.

Total cholesterol:HDL cholesterol ratio
Non-fasting measures of total serum cholesterol and high-density lipoprotein (HDL)-cholesterol were measured using enzymatic assays (Backman Coulter AU5800) and the ratio of the two values was calculated. UK Biobank corrected serum data for laboratory dilution effects and were excluded if they did not pass UK Biobank quality control.
Coronary heart disease in a first degree relative under 60 years of age A measure of family history of cardiovascular disease was ascertained from reported heart disease in mothers, fathers and siblings of UK Biobank participants, however age of diagnosis, nor type of cardiovascular disease, could not be determined.

Incident cardiovascular disease
The validity of QRISK3 scores was assessed by evaluating the association between QRISK3 and incident cardiovascular disease (CVD) (see statistical analyses in main text). Incident CVD was defined using hospital admissions data. All cardiovascular subtypes were combined to define cases, and cases were any individual with an ICD10 I code or G45, or an ICD9 code between 3900-4599 recorded (see sTable 3). The follow up period was defined as any event following date of baseline assessment centre (between 2006 and 2010) until the most recent date available in the linked hospital inpatient data (May 2017).