Data

We used data from the Sax Institute’s 45 and Up Study, an Australian cohort involving 267,153 men and women aged 45 and over from New South Wales (NSW), Australia. Participants in the Study were randomly sampled from the database of Australia’s universal health insurance provider, Medicare Australia, with over-sampling by a factor of two, of individuals aged 80 years and over and people resident in rural areas. Around 10% of the entire NSW population aged 45 and over were included in the sample. Participants joined the Study by completing a baseline questionnaire (between Jan 2006 and April 2009) and giving signed consent for follow-up and linkage of their information to a range of health databases. The Study is described in detail elsewhere [33], and questionnaires can be viewed online [34].

Baseline survey data from the participants were linked to hospital data from the NSW Admitted Patient Data Collection (APDC, 1 July 2000 to 31 December 2013), data on date of death from the NSW Registry of Births, Deaths and Marriages (1 January 2006 to 31 December 2013), and data on causes of death from the Cause of Death Unit Record File (1 January 2006 to 31 December 2013). The APDC includes records of all hospitalisations in NSW, dates of admission and discharge and reasons for admission. Each record in APDC contains up to 51 diagnosis codes using the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification (ICD-10-AM) codes and up to 50 procedure codes using the Australian Classification of Health Interventions (ACHI) codes. The Cause of Death Unit Record File includes primary causes of death and up to 20 additional causes using ICD-10-AM. Data were linked probabilistically by the Centre for Health Record linkage using personal information (including full name, date of birth, sex and address). Over the relatively short follow-up period, a small but unknown number of participants are likely to have moved out of NSW. Although hospitalisations occurring in neighbouring states would not be captured, these are estimated to make up fewer than 2% of admissions in NSW residents. Hence, follow-up for hospitalisations is considered to be ~98% complete among those continuing to reside in NSW. Quality assurance data on the data linkage show false positive and negative rates of < 0.5% and < 0.1%, respectively.

Outcomes

The main outcome was a major CVD event: a composite endpoint of fatal or non-fatal major CVD, ascertained through first hospital admission for major CVD, or death due to CVD, following recruitment into the study. We defined major CVD as a sub-group of circulatory diseases that have a significant atherosclerotic or arteriovenous thromboembolic component, based on a combination of diagnosis codes from ICD-10-AM and CVD-related intervention procedure codes from the 5th to 7th editions of ACHI [35]. In addition, we separately examined two common CVD subtypes: myocardial infarction (ICD-10-AM codes: I21 and I22) and stroke (intracerebral haemorrhage, infarction or transient ischaemic attack, ICD-10-AM codes: I61, I63, I64, and G45). In a supplementary analysis we also report results for ischaemic heart disease combined (ICD-10-AM codes: I20–I25) for comparison with other published results. We ascertained outcomes using the primary diagnosis code field (and procedure fields) of the APDC, and the primary cause of death code field of the Cause of Death Unit Record File.

In order to distinguish primary from secondary CVD events, we analysed outcomes separately in those with and without prior history of CVD. Prior CVD was defined as self-reported heart disease, stroke, or blood clot (thrombosis) on the baseline questionnaire, and/or hospital admission for major CVD ascertained from the 51 diagnosis code fields and the 50 procedure code fields of APDC in the 6 years prior to entering the study.

For each analysis, participants contributed person-years from recruitment date to the outcome of interest (first major CVD/myocardial infarction/stroke/ischaemic heart disease hospital admission or death), death from any cause, or end of follow up (31 December 2013), whichever was the earliest.

Main exposure: socioeconomic position

Socioeconomic position was based on education attainment, as well as two supplementary socioeconomic exposures for comparison—annual household income and area-level disadvantage. Education attainment was used as the primary socioeconomic variable as it is an individual as opposed to area-based measure. In addition, unlike household income, education attainment is a stable indicator of SEP from relatively early in the life course, is unlikely to be subject to reverse causality (i.e. CVD outcomes impacting on SEP), and is considered to be reliably reported with little missing data.

Education attainment was self-reported in defined categories, which were grouped for the analysis: No qualifications (“no school certificate or other qualifications”); certificate/diploma/trade (“school or intermediate certificate or equivalent,” “higher school or leaving certificate or equivalent,” “trade/apprenticeship, e.g. hairdresser, chef,” “certificate/diploma, e.g., child care, technician”); and university degree (“university degree or higher”). Annual household income (from all sources, before tax) was self-reported in six defined brackets, which were grouped for analysis: < $20,000, $20,000- < $40,000, $40,000- < $70,000, ≥ $70,000 and missing. Area-level disadvantage was based on the Australian Bureau of Statistics Index of Relative Socio-economic Disadvantage (IRSD), a measure derived from Census data which summarises socioeconomic disadvantage in a particular area. [36] We categorised the IRSD into population-based quintiles using 2006 Australian Census data, and assigned it to individuals using their postcode of residence.

Analysis

All analyses were conducted separately in those with and without prior CVD. First, we calculated rates of major CVD in relation to education, separately in males and females. Rates were standardised by age to the 2006 NSW population, in 5-year age groups, using the direct method [37], and rates differences (RD) and rate ratios (RR) were calculated, comparing rates in the lowest education group to those in the highest.

Second, Cox regression was used to estimate hazard ratios (HRs) for each outcome (major CVD/myocardial infarction/stroke/ischaemic heart disease hospital admission or death) in relation to education, with age as the underlying time variable, as a measure of relative differences in outcomes according to SEP. Analyses were performed separately for three age groups (45–64, 65–79, and ≥ 80 years). Model 1 was adjusted for age (as the underlying time variable) and sex. Model 2 was adjusted for age, sex, region of birth (born in Australia/NZ and born in other countries) and region of residence (major cities, inner regional, and outer regional/remote/very remote). Model 3 was adjusted for the same factors as Model 2 and additionally adjusted for private health insurance (hospital/Department of Veterans Affairs concession card and no private health insurance). Participants with missing values for the main SEP measure were dropped from that analysis. Missing values for covariates were included in the models as separate categories. In supplementary analyses, we calculated age-adjusted rates and estimated HRs for major CVD in relation to income and area-based disadvantage (Model 1 only).

Two sets of sensitivity analyses were performed. In the first, we re-defined prior history of CVD to exclude self-reported blood clot (thrombosis) and re-ran the analyses for all outcomes; in the second analyses, we excluded transient ischaemic attack from the definition of stroke.

The proportional hazards assumption was verified using tests based on the Schoenfeld residuals for each model (significance level of 0.0001 was used due to the large sample size). Stratified forms of the models were used where covariates showed non-proportionality of hazards. Tests for linear trend were also performed for each model. All analyses were performed using Stata version 12.

Results

After excluding individuals with linkage errors (n = 196) and those aged less than 45 years at baseline (n = 8), the sample included 266,684 participants. Mean age was 63 years (Standard deviation (SD) =11), with 61% aged 45–64 years, 28% aged 65–79 years, and 10% aged 80+ years. Just over half of all participants (54%) were female. Of the total sample, 12% had no qualifications, 65% a certificate, diploma or trade and 23% a university degree, with education levels higher in the younger than older cohorts. One in five people (22%) reported a history of prior CVD, ranging from 12% in those aged 45–64, to 51% in those aged 80 or older. Further sample characteristics are shown in Table 1.

Table 1 Characteristics of study participants at baseline Full size table

There were a total of 38,255 major CVD events over 1,405,202 years of follow-up (median follow-up = 5.37 years), a rate of 27.2 per 1000 person-years. There were 18,207 primary major CVD events (i.e. events in people with no prior CVD) over 1,144,845 years, a rate of 15.9 per 1000 person-years, and 20,048 secondary events (i.e. events in people with prior CVD) over 260,357 years, a rate of 77.0 per 1000 person-years.

For both primary and secondary major CVD events, age-standardised rates decreased with increasing education, among both males and females (Fig. 1). For primary events, age-standardised rates for males ranged from 18.6 per 1000 person years among those with a university degree to 22.7 per 1000 person years among those with no school qualifications (RD = 4.07; RR = 1.22), with the corresponding rates in females being 10.4 and 15.3 per 1000 person years (RD = 4.86; RR = 1.47). Rate differences were notably higher for secondary events, with age-standardised rates for males ranging from 59.4 per 1000 person years among those with a university degree to 86.4 per 1000 person years among those with no qualifications (RD = 27.0; RR = 1.45); the corresponding rates in females were 36.0 and 50.9 per 1000 person years (RD = 14.9; RR = 1.41).

Fig. 1 Age-adjusted rates of major cardiovascular disease (CVD) events by education, in those with and without prior CVD Full size image

After adjusting for age and sex (Model 1), HRs increased with increasing education in each age group for both primary and secondary events, as indicated by the tests for trend (Table 2). In the 45–64 years age group, rates were around 50–60% higher among those with no qualifications than among those with a university degree, for both primary (HR = 1.62, 95% CI: 1.49–1.77) and secondary (HR = 1.49; 95% CI: 1.34–1.65) major CVD events. Additional adjustment for region of birth and region of residence (Model 2) and also private health insurance (Model 3) made little difference to the HR estimates (Additional file 1: Table S1). Similar but attenuated results were seen in the older age groups (65–79 and ≥ 80 years) (Table 2).

Table 2 Crude rates of major cardiovascular disease (CVD), myocardial infarction and stroke events and adjusted hazard ratios (HR), by education, in those with and without prior CVD Full size table

Analyses conducted for myocardial infarction only, which accounted for 17% of primary and 16% of secondary major CVD events, obtained similar patterns to analyses for total major CVD events but HRs were substantially higher, in all age groups (Table 2). In the 45–64 years age group, myocardial infarction rates were around two and half times higher among those with no qualifications than among those with a university degree for both primary (HR = 2.31, 95% CI: 1.87–2.85) and secondary (HR = 2.57; 95% CI: 1.90–3.47) events. Even in the older participants, rates were around 40% higher in the least compared to the most educated group (primary event HRs: 1.37, 95% CI: 1.05–1.80; and secondary events: HR = 1.38, 95% CI: 1.13–1.68). Analyses conducted for ischaemic heart disease events combined, which accounted for nearly half of all primary (44%) and secondary (47%) major CVD events, obtained similar results to analyses for total major CVD events (Additional file 2: Table S2).

Patterns for stroke, which accounted for 17% of both primary and secondary major CVD events, were also similar to those for all major CVD although trends were not significant for primary events among the two older age groups (65–79 and ≥ 80 years, Table 2). Hazard ratios were again highest in the 45–64 year age group, with a 50% higher risk of stroke among those with no qualifications compared to those with a university degree, for primary events (HR = 1.48, 95% CI: 1.16–1.87) and a nearly two-fold (100%) greater risk for secondary events (HR = 1.97, 95% CI: 1.42–2.74).

Hazard ratios for the two sets of sensitivity analyses — one excluding self-reported thrombosis from the definition of prior CVD (resulting in 928 events (2.5%) being re-classified as primary rather than secondary), and the other excluding transient ischaemic attack from the definition of the stroke outcome (resulting in 2289 (36%) fewer events) — did not differ materially from those for the main analyses.

Similar relationships between SEP and major CVD incidence were seen when rates were modelled according to annual household income, although SEP trends were not significant in the older age group (≥ 80 years); when area-level disadvantage was the measure of SEP, gradients were weak (45–64 years age group) or non-significant (65–79 and ≥ 80 years age groups) (Additional file 3: Figure S1 and Additional file 4: Table S3).