Methods
Data for this study was obtained from NPS MedicineWise’s dataset,
MedicineInsight. This is the largest and the most representative general
practice dataset available to researchers in Australia. In 2018, 671
(8.3%) of the 8,065 general practices in Australia had been recruited
by NPS MedicineWise (Busingye et al., 2019; MedicineInsight, 2018).
MedicineInsight uses a third-party tool that extracts, de-identifies and
securely transmits patient data each week to its secure data repository.
The extraction tool allows developing a longitudinal database of
patients in general practices. The data that MedicineInsight collects
from general practices include patient demographics, diagnoses,
pathology test results, prescribed medications and reasons for
encounter. However, specific patient identifiers, such as patient name,
address, and date of birth, are not included in this dataset
(MedicineInsight, 2018).
We performed ten sequential cross-sectional analyses of data on 1
September every year (census date) from 01 September 2009 to 01
September 2018. Patients with a recorded diagnosis of non-valvular AF
were included in each analysis if (i) they were aged 18 years or older
and not deceased on or before the census date, (ii) their recorded AF
diagnosis date was at least 4 months before the census date, (iii) they
had had three or more recorded general practice visits in the previous
two years and at least one of these visits was in the last six months,
and (iv) they had been registered in the general practice’s electronic
records at least one year before the census date.
We defined patients with AF as being prescribed an OAC (warfarin,
dabigatran, rivaroxaban or apixaban) or antiplatelet agent (clopidogrel,
ticagrelor, aspirin, ticlopidine, prasugrel, dipyridamole, abciximab,
eptifibatide or tirofiban) when they had at least one recorded
prescription, dated within 365 days before the census date. Aspirin is
available without a prescription, but we could only capture prescribed
data.
For most of our study period, guidelines recommended using the
CHA2DS2-VASc score (congestive heart
failure (1 point), hypertension (1 point), age ≥ 75 years (2 points),
diabetes mellitus (1 point), stroke/transient ischaemic attack (TIA) (2
points), vascular disease (1 point), age 65-74 (1 point) and sex female
(1 point)) for assessing stroke risk and treatment eligibility in
patients with AF (Steffel et al., 2018). Patients with AF were
stratified as low risk when CHA2DS2-VASc
was 0 and male or 1 and female, moderate risk with
CHA2DS2-VASc =1 and male, and high risk
with CHA2DS2-VASc ≥2 (Steffel et al.,
2018). The proportion of patients who were prescribed an OAC,
antiplatelets alone, or neither were calculated with 95% confidence
interval (CI) each year on 1 September from 01 September 2009 through 01
September 2018. Temporal trends were shown using graphs and a
Cochran-Armitage test for trend was used to determine if any observed
trends were statistically significant. Similarly, the proportion of
patients with moderate to high stroke risk
(CHA2DS2-VASc ≥1 and male or
CHA2DS2-VASc ≥2) or low stroke risk
(CHA2DS2-VASc =0 for male or
CHA2DS2-VASc =1 for female) who were
prescribed an OAC was calculated each year for each practice site. All
practice sites that contributed data at least for a year were included.
Prescribing rates were ranked into quintiles and used as an indicator of
general practice sites’ prescribing performance. The variation between
the highest- and lowest-prescribing practice quintiles each year was
calculated as a prescribing gap.
Socio-economic indexes for areas (SEIFA) quintile is an index developed
by the Australian Bureau of Statistics (ABS) and ranks areas in
Australia from 1 (most disadvantaged area) to 5 (most advantaged area)
(Australian Bureau of Statistics, 2018). The ABS categorise rurality
into five categories using the Accessibility/Remoteness Index of
Australia (ARIA) score. These categories are major cities (ARIA 0-0.20),
inner regional (0.21-2.40), outer regional (2.41-5.92), remote
(5.93-10.53), and very remote (10.54-15) (Australian Statistical
Geography Standard (ASGS), 2017); we collapsed remote and very remote
areas into one group. SAS software (SAS version 9.4, SAS Institute Inc.,
Cary, NC, USA) was used for all data analyses, and a two-sided p-value
<0.05 was considered statistically significant.
Ethics approval was obtained from the Tasmanian Health and Medical Human
Research Ethics Committee (H0017648). We also obtained approval to
conduct this study from the MedicineInsight independent Data Governance
Committee (2018-033). Patients were not identifiable, and individual
patient consent was waived for our ethics application.