Data sources
Eligible women were all those with a first primary breast cancer diagnosed and recorded in any of four population-based regional breast cancer registries (Auckland, Waikato, Wellington, and Christchurch) [58] in New Zealand between 1 Jan 2007 and 31 Dec 2016. These registers include all women diagnosed with breast cancer in their defined areas, and together cover about 70% of all breast cancer registrations in New Zealand. Using an anonymised National Health Index number, data were linked to several national data bases: the Pharmaceutical Collection (PHARMS), a national database containing dispensing information and medication identifiers from pharmacists for subsidised dispensings [59]; the National Minimum Dataset, relating to all day patients and inpatients discharged from both public and private hospitals; and the National Mortality Collection, with information about all certified deaths [60]. Women were excluded if their records did not link to at least one dispensing from the pharmaceutical collection (n = 14) or if their date of death was on or before their recorded date of breast cancer diagnosis (n = 3). The final cohort for analyses was comprised of 14,976 women. This study was approved by the Central Health and Disability Ethics Committee (Ref: 19/CEN/4).
Exposure And Outcome Data
In the PHARMS database, medications dispensed any time after breast cancer diagnosis were determined using the therapeutic group ID, a PHARMAC identifier for each group of Anatomical, Therapeutic, and Chemical properties [59]. All statins dispensed to women in our cohort (atorvastatin, pravastatin, and simvastatin) were included. For each dispensing, we calculated the number of daily defined doses by multiplying the number of tablets dispensed by the dose per tablet in mg, and dividing by the daily defined dose in mg from the World Health Organisation database [61].
Deaths were determined from the underlying cause of death in the regional breast cancer registries and National Mortality Collection, with ICD codes C50.0 to C50.9 classified as deaths from breast cancer.
Confounders
Demographic and clinical information came from the regional breast cancer registries, and covariates considered included date of diagnosis, age, ethnic group [62, 63], socioeconomic deprivation (NZDep) [64], urban/rural status [65], public/private status of the treatment facility, register, stage [66], grade [67], mode of detection (screen detected vs symptomatic), lymphovascular invasion, and molecular subtype (as defined previously [68], including Luminal A, Luminal B, Luminal B HER2+, HER2 + non-luminal, and triple negative). Other post-diagnostic medications included beta blockers, angiotensin converting enzyme inhibitors (ACEIs), angiotensin receptor blockers (ARBs), diuretics, metformin, tamoxifen, and aromatase inhibitors. Comorbidities adjusted for included any cardiac condition (angina, arrhythmia, congestive heart failure, hypertension, myocardial infarction, ‘other cardiac conditions’, and valve disease) as yes/no, diabetes, stroke, chronic obstructive pulmonary disorder, peripheral vascular disease, and renal disease. We defined comorbidities as any of the above conditions appearing in a patient’s linked hospital record (inpatient admissions) in the 5-year period before their breast cancer diagnosis.
Statistical Analyses
Comparisons by statin use at baseline (date of diagnosis of breast cancer) were conducted using the chi-square test. We used Cox proportional hazard models to assess hazard ratios (HRs) of breast cancer-specific mortality associated with any post-diagnostic statin use vs non-use. Death registrations and Pharmaceutical Collection coverage were complete to the end of 2017, so we followed patients from their breast cancer diagnosis until death or 31 December 2017. Women with no death recorded prior to 31 December 2017 were assumed to be alive as at 31 December 2017. Medication use was conceptualised as a time-varying covariate (with all women considered nonusers at baseline), such that time before the first dispensing was counted as ‘nonuser’ time, and time from the first dispensing to end of follow up was counted as ‘user’ time [69]. Models were adjusted in a systematic fashion, with the first adjustment including demographic and breast cancer clinical data, and the second adding other medication use and comorbidities.
Analyses were conducted considering statin use as a binary variable (user/nonuser), and also by splitting statin use into seven categories based on the number of daily defined doses (DDDs: categorised as 1–90 DDDs, 91–181 DDDs, 182–272 DDDs, 273–364 DDDs, 365–729 DDDs, 730–1094 DDDs, or 1095 or more DDDs, corresponding to the equivalent of 0–3 months, 3–6 months, 6–9 months, 9 months-1 year, 1–2 years, 2–3 years, and 3 + years of statin use respectively). Dose analyses were conducted using a time varying approach, such that women spent time in the lowest category before moving into the next dose category.
As dispensings toward the end of life may reflect changes in morbidity (including cancer recurrence/progression) or in health care related to end of life care [70, 71], we also conducted analyses lagging medication times [72]. In these analyses, patients are initially considered nonusers and then users after a lag period has elapsed after their first medication dispensing. Using this approach, dispensings toward the end of life are removed by the lag; for example, a 6-month lag will ignore dispensings in the 6 months prior to death/last follow up and classify these women are as medication nonusers as opposed to users. To appropriately account for different periods in which end of life care may be administered, we also considered lag periods of 1 year and 2 years. In these analyses, all medications were modelled in the same fashion (for example, if statins were lagged by 6-months, all other medications were as well).
To evaluate the effect of the competing risk of death from other causes, the proportional subhazards model was also used [73]. For this analysis, all deaths apart from breast cancer deaths were treated as competing events.
We stratified the main analysis by estrogen receptor (ER) status to explore the relationship between statin use and tumours expressing different ER profiles (ER + vs ER-). We also stratified the main analysis by triple negative status (cancers that were ER-, PR-, and HER2- vs cancers not fulfilling these criteria). In all of these analyses, patients with an ‘unknown’ ER, PR, or HER2 status were excluded where appropriate.
To investigate the effect of statin use in a more homogenous group and one in which the mechanisms of estrogen production [74] (and therefore cholesterol levels [75]) may differ, another analysis was conducted restricting the cohort to postmenopausal women only.
To examine the effect of statin use in early-stage patients only, an analysis was carried out restricted to patients with stage 1, stage 2, or stage 3a cancers. In this analysis, patients with an ‘unknown’ stage were excluded. An analysis was also carried out in late-stage (stage 3b, stage 3b, and stage 4 cancers) patients.
In order to address the selection bias inherent in analysing both incident and prevalent users together [76–79], an analysis was carried out splitting these users into different categories. Incident/new statin users were defined as women who did not have a statin dispensing in the year prior to breast cancer diagnosis, while prevalent users were defined as those who did have a statin dispensing in the year prior to breast cancer diagnosis.
In order to compare statin users to patients using a different medication for a similar indication, a further analysis was carried out comparing statin users to statin nonusers who were dispensed aspirin. In this analysis, statin nonusers who used aspirin were followed from their first post-diagnostic aspirin dispensing until death or 31 December 2017.
We also conducted an analysis with breast cancer recurrence (BCR) as the outcome. In this analysis, we defined a BCR as either a local/regional recurrence or distant metastasis and restricted the cohort to patients with early-stage breast cancer as above. Recurrences were determined from the breast cancer registry data through patient’s routine clinical records, and women were followed from their breast cancer diagnosis until BCR, death, last follow up date, or end of Pharmaceutical Collection coverage (31 December 2017), whichever came first. These analyses examined risk of BCR associated with statin use vs non-use, and the same analyses were also carried out separately for local and distant recurrences.
Results are reported as HRs and their 95% confidence intervals (CIs), with the two-sided significance level set at 0.05. Statistical analyses were conducted in STATA 17.0 (StataCorp, College Station, TX).