assessing quality of diabetes care and its variation in aboriginal community health centres in...

DIABETES/METABOLISM RESEARCH AND REVIEWS R E S E A R C H A R T I C L EDiabetes Metab Res Rev 2010; 26: 464–473.Published online 25 January 2010 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/dmrr.1062

Assessing quality of diabetes care and its variationin Aboriginal community health centres in Australia

Damin Si1,2* Ross Bailie1

Michelle Dowden1

Catherine Kennedy3

Rhonda Cox4

Lynette O’Donoghue1

Helen Liddle1 Ru Kwedza5

Christine Connors6

Sandra Thompson4,7

Hugh Burke3 Alex Brown8

Tarun Weeramanthri9

1Menzies School of Health Research,Charles Darwin University, NT 0810,Australia 2School of Medicine,University of Queensland, QLD 4029,Australia 3Maari Ma HealthAboriginal Corporation, NSW 2880,Australia 4Faculty of Health Sciences,Curtin University of Technology, WA6845, Australia 5QueenslandDepartment of Health, Cairns QLD4870, Australia 6Northern TerritoryDepartment of Health and Families,NT 0810, Australia 7AboriginalHealth Council of Western Australia,WA 6000, Australia, 8Baker IDIHeart and Diabetes Institute (AliceSprings), NT 0870, Australia9Western Australia Department ofHealth, WA 6004, Australia

*Correspondence to: Damin Si,Level 8, Health Sciences Building,University of Queensland, RoyalBrisbane and Women’s Hospital,QLD 4029, Australia. E-mail:[email protected]

Received: 7 April 2009Revised: 1 December 2009Accepted: 10 December 2009

Abstract

Background Examining variation in diabetes care across regions/organiza-tions provides insight into underlying factors related to quality of care. Theaims of this study were to assess quality of diabetes care and its variationamong Aboriginal community health centres in Australia, and to estimatepartitioning of variation attributable to health centre and individual patientcharacteristics.

Methods During 2005–2009, clinical medical audits were conducted in62 Aboriginal community health centres from four states/territories. Mainoutcome measures include adherence to guidelines-scheduled processes ofdiabetes care, treatment and medication adjustment, and control of HbA1c,blood pressure, total cholesterol and albumin/creatinine ratio (ACR).

Results Wide variation was observed across different categories of diabetescare measures and across centres: (1) overall adherence to delivery of servicesaveraged 57% (range 22–83% across centres); (2) medication adjustmentrates after elevated HbA1c: 26% (0–72%); and (3) proportions of patientswith HbA1c < 7% : 27% (0–55%); with blood pressure <130/80 mmHg:36%(0–59%). Health centre level characteristics accounted for 36% of thetotal variation in adherence to process measures, and 3–11% of the totalvariation in patient intermediate outcomes; the remaining, substantial amountof variation in each measure was attributable to patient level characteristics.

Conclusions Deficiencies in a range of quality of care measures providemultiple opportunities for improvement. The majority of variation in qualityof diabetes care appears to be attributable to patient level characteristics.Further understanding of factors affecting variation in the care of individualsshould assist clinicians, managers and policy makers to develop strategiesto improve quality of diabetes care in Aboriginal communities. Copyright 2010 John Wiley & Sons, Ltd.

Keywords diabetes mellitus type 2; quality of care; variation of care; Aboriginalpopulations

Introduction

Using the general definition of quality of health care [1], quality of diabetescare can be defined as ‘the degree to which health services for individu-als and populations with diabetes increase the likelihood of desired healthoutcomes and are consistent with current professional knowledge’. Donabe-dian’s conceptual framework has been widely accepted to measure qualitywith regard to structure, process and outcomes [2]. Simply stated, struc-tural quality refers to health system characteristics (e.g. facilities, money,

Copyright 2010 John Wiley & Sons, Ltd.

Diabetes Care for Aboriginal Australians 465

and human resources), process quality refers to practition-ers’ and patients’ activities in health care, and outcomesrefer to the effects of care on the health status of patientsand populations.

In the area of diabetes care, the past 15 years have seenconsiderable progress in producing evidence, knowledgeand clinical guidelines to support high quality diabetescare [3–8]. However, in the real world there is a large gapbetween recommended diabetes care and the care patientsactually receive. A national survey in the United Statesshowed that only 7% of adults with diagnosed diabetesachieved recommended goals of HbA1c level (<7.0%),blood pressure (<130/80 mmHg) and total cholesterollevel (<5.18 mmol/L) simultaneously [9]. In Australia, itwas reported that only 27% of people with diabetes hadan HbA1c test every 6 months [10].

Diabetes is a major contributor to the 17-year gap inlife expectancy between Indigenous and other Australians.Despite this, there is a lack of data at the national level onthe quality of diabetes care among Indigenous Australians.Several regional studies conducted in Australia havereported that most Indigenous people with diabetes didnot achieve adequate glycaemic control [11–13]. Forexample, of Indigenous patients with HbA1c tested in theprevious year, less than one-third had an HbA1c < 7.0%.

Studies from the USA and UK have reported substantialvariation in quality of diabetes care at the state andorganization levels [14,15]. Examining variation acrossregions/organizations is of interest to policy makersand service providers, as it provides insight into healthcare quality issues relative to resource allocation, access,clinical performance and patient outcomes. Studyingvariation of care also provides opportunities to understandunderlying causes of inadequate care, which in turncan contribute to development of targeted strategies forimprovements. In Australia, variation in quality of carehas been reported in hospital settings [16]. However,little is known about variation in diabetes care in primarycare settings.

The Audit and Best-practice for Chronic DiseaseExtension (ABCDE) project is a quality improvementproject which aims to improve quality of care in a rangeof priority aspects of Indigenous primary health care,including prevention and management of chronic disease,maternal health and child health [17]. The project iscurrently supporting 62 Indigenous community healthcentres from four states/territories [Northern Territory(NT), New South Wales (NSW), Western Australia (WA)and Queensland (QLD)]. The project provides a uniqueopportunity to improve understanding of the standard ofdiabetes care in Australian Indigenous primary health caresettings, and, most importantly, to develop and implementstrategies to improve patient outcomes. The aims of thisstudy were to assess the quality of diabetes care and itsvariation among Indigenous community health centresand to estimate partitioning of variation attributable tohealth centre and individual patient characteristics.

Materials and methods

Study setting and sample selection

Sixty two Indigenous community health centres partic-ipating in the ABCDE project completed baseline datacollection in relation to diabetes care during 2005–2009(health centres had staggered enrolment to the project).Geographic distribution of these health centres is shownin Figure 1, with 24 centres from the Top End of the NT,9 from Central Australia (CA – also part of the NT), 5from the Far West NSW, 7 from the WA and 17 fromthe North QLD. Participating health centres in the NT,WA and North QLD had service populations comprisedlargely of Aboriginal people, whereas in Far West NSW theservice populations were approximately half Indigenousand half non-Indigenous (Table 1). While the Australianhealth system provides some incentive payments for pro-cess measures and for achieving practice accreditationstatus, there are currently no outcomes based incentivepayments.

For the 62 participating Indigenous community healthcentres, community members who met all of the followingcriteria were eligible for the study: (1) a definite diagnosisof type 2 diabetes according to health centre records;(2) aged 16 years or older and (3) lived in the communityfor 6 months or more during the past 12 months. For the34 communities where more than 30 eligible people wereidentified, a random sample of 30 records was drawnin each health centre. In the remaining 28 communitieswhere there were fewer than 30 eligible people identified,records of all eligible people were included. Based on thissampling approach, we yielded a total sample of 1593records for this study. Taking into account the designeffect of 1.84 (for the outcome measure – HbA1c level),we had an effective sample size of 866 (1593/1.84). Withthis sample size, we had a statistical power of 95% tobe sure that a sample mean observed in this study waswithin 5% of the true population mean [18].

Measurements and data collection

Audit of clinical medical records was used to assessthe quality of diabetes care in participating healthcentres in terms of processes of care and intermediatepatient outcomes. Details of the audit form, auditprotocol and audit procedures can be accessed atwww.abcdproject.org.au.

Processes of diabetes care

1. Adherence to delivery of guidelines-scheduled dia-betes services: this study measured 13 service items,which the clinical guidelines at present use across thestates/territories recommended for delivery at regularintervals for all people with diabetes. The 13 servicescover six areas (Table 2): basic measurements (three

Copyright 2010 John Wiley & Sons, Ltd. Diabetes Metab Res Rev 2010; 26: 464–473.DOI: 10.1002/dmrr

466 D. Si et al.

Figure 1. Geographic distribution of participating Aboriginal community health care centres in the study. This figure is available incolour online at wileyonlinelibrary.com

items), eye and feet examinations (three), labora-tory investigations (three), counselling/advice (two),and immunizations (two). The overall adherence todelivery of scheduled services for each patient was cal-culated by dividing the sum of services delivered by 13(the total number of scheduled services), and express-ing this as a percentage. For example, if there weresix services assessed as delivered for a patient withinthe appropriate period preceding the audit, the overalladherence to delivery of services for the patient was46% (6/13). A mean of the adherence to delivery bydiabetes patients in a given health centre representedan overall adherence to delivery in the health centre.

2. Treatment and medication adjustment: we recordedmedical regimens for patients at the time of the audit.Detailed information was collected for all hypogly-caemic, antihypertensive (including Angiotensin Con-verting Enzyme (ACE) inhibitors), lipid-lowering andanti-platelet medicines.

We also assessed whether, within the 12 months priorto the audit, the adjustment of medication prescriptionwas made among patients whose HbA1c and bloodpressure levels were above treatment goals. ‘Abovetreatment goal’ was defined as HbA1c > 7.0% and/orblood pressure >130/80 mmHg. The medical regimenswere considered as adjusted if the dose of a medicinewas increased, an additional agent added or a medicinesubstituted by another one.

As a patient might have several results of HbA1c andblood pressure within the previous 12-month period,we assessed whether there was evidence of medicationadjustment after identification of any result which was‘above treatment goals’.

Patient intermediate outcomesWe used the most recent values of HbA1c, blood pressure,total cholesterol and albumin/creatinine ratio (ACR)within 12 months prior to the audit. We also presentedthese results in a dichotomous format using cut-pointsspecified below: HbA1c level <7.0% or <8.0%; bloodpressure <130/80 mmHg or <140/90 mmHg; and totalcholesterol <4.0 mmol/L or <5.5 mmol/L. The ACR levelwas presented in a categorical format (≤3.4; >3.4 and≤34; >34).

Statistical analysis

Means, proportions and medians were used to sum-marize data as appropriate. Our data had inherentmultilevel, dependency structure, as diabetes care datacollected at the individual patient level were clusteredwithin health centres, which in turn were clusteredwithin jurisdictions. Multilevel random effects regres-sion models (linear or logistic) [19] were used toassess differences in quality of diabetes care betweenregions.



Table 1. Characteristics of participating health centres and diabetes patients

CharacteristicNT

Top EndNT CentralAustralia

Far WestNSW WA North QLD Total

Community health centres 24 9 5 7 17 62Health service governance

Government funded/operated 10 (42%) 5 (56%) 0 (0%) 1 (14%) 17 (100%) 33 (53%)Managed by local or regional

Indigenous committee or board14 (58%) 4 (44%) 5 (100%) 6 (86%) 0 (0%) 29 (47%)

General practice accreditation statusCurrently accredited 9 (38%) 3 (33%) 0 (0%) 2 (29%) 5 (29%) 19 (31%)Accreditation scheduled/in

progress2 (8%) 1 (11%) 1 (20%) 3 (43%) 10 (59%) 17 (27%)

No accreditation 13 (54%) 5 (56%) 4 (80%) 2 (28%) 2(12%) 26 (42%)Sizes of populations served

≤500 9 (37%) 4 (44%) 1 (20%) 0 (0%) 6 (35%) 20 (32%)501–999 6 (25%) 4 (45%) 3 (60%) 0 (0%) 4 (24%) 17 (28%)≥1000 9 (38%) 1 (11%) 1 (20%) 7 (100%) 7 (41%) 25 (40%)

Participating patients with diabetes 538 262 137 210 446 1593Median age (inter-quartile range),years

51 (42, 60) 47 (38, 57) 57 (48, 69) 50 (43, 58) 51 (43, 61) 51 (42, 61)∗

Proportion of males 34% 40% 47% 49% 41% 40%∗

Aboriginal statusAboriginal 93% 98% 42% 95% 89% 88%∗Non-Aboriginal 5% 1% 53% 4% 4% 8%∗Not stated 2% 1% 5% 1% 7% 4%

Median duration of diabetesa

(inter-quartile range), years4.4 (2.0, 7.6) 5.9 (3.4, 10.0) 5.3 (2.0, 9.3) 6.0 (2.7, 9.3) 6.7(3.8,10.8) 5.8(2.9, 9.4)∗

Documented as a current smoker 36% 24% 37% 30% 31% 32%∗Diagnosis of hypertension in medicalrecords

50% 48% 73% 63% 49% 53%∗

Diagnosis of hyperlipidemia inmedical records

33% 37% 40% 31% 44% 37%

Diagnosis of renal disease in medicalrecords

38% 37% 19% 14% 25% 29%∗

Any microvascular complicationsb 13% 16% 18% 14% 18% 16%Any macrovascular complicationsc 3% 2% 0% 3% 4% 4%

Numbers of medical co-morbid conditionsd

0 28% 28% 17% 28% 29% 27%1–2 55% 52% 66% 59% 51% 54%3+ 17% 20% 17% 13% 20% 19%

Attended health centres in last6 months

92% 93% 86% 81% 91% 90%∗

Attended health centres in last12 months

96% 99% 93% 95% 96% 96%∗

Reasons for last attendance during previous 12 monthsChronic illness care 54% 58% 75% 53% 52% 56%∗Acute care 33% 29% 11% 22% 25% 27%∗Sexual health, immunization or

others13% 13% 14% 25% 23% 17%∗

aExcluded 332 patients (21% of the total sample) whose dates of diagnosis of diabetes were not documented in medical records.bMicrovascular complications include any retinopathy, neuropathy, foot ulcer or amputation noted in medical records.cMacrovascular complications include any Acute Myocardial Infarction (AMI) or Cerebrovascular Accident (CVA) noted in medical records.dMedical co-morbid conditions include hypertension, hyperlipidemia, renal disease, or any micro/macrovascular complications mentioned above.∗p < 0.01 for comparison between regions.

We used multilevel random effects models to quantifythe amount of variation in diabetes care attributable tohealth centre or individual patient level characteristics.Adherence to delivery of services, medication adjustmentand intermediate patient outcomes were treated asdependent variables in the models respectively. Weconstructed a two-level (patient and health centre levels)random intercept model with no explanatory variables(also known as an empty model) [20]. In the contextof multilevel modelling, the empty model provides anestimate of the basic partition of the variability in the

data between the two levels. Based on the model, anintra-class correlation coefficient (’rho’ in Stata [19])between two randomly drawn individuals in a givenhealth centre was estimated. The intra-class correlationcoefficient can also be interpreted as the fraction oftotal variability in the dependent variable that is dueto health centre level characteristics. The remainingamount of variation was derived from the patientlevel.

The study was approved by the Human Research EthicsCommittees in the Top End of the NT, CA, Far West


468 D. Si et al.

Table 2. Adherence to delivery of scheduled services at participating centres

NTTop End

NT CentralAustralia

Far WestNSWb WA North QLD Total

ScheduledService item interval (months) Percentage of patients receiving each service (range between health centres)

Basic measurementsBP 6 86 88 77 78 82 83

(54–100) (70–100) (50–100) (53–100) (48–100) (48–100)Weight 6 72 66 40 61 67 65∗

(19–100) (27–93) (12–73) (37–93) (26–97) (12–100)Waist circumference 6 48 46 64 22 23 39∗

(8–100) (15–80) (50–76) (0–80) (0–57) (0–100)Eye and feet examinations

Visual acuity 12 45 41 64 35 42 44(10–83) (17–71) (30–94) (13–73) (3–77) (3–94)

Dilated eye examinationsa 24 36 37 64 37 40 40∗(5–76) (14–60) (43–77) (13–60) (0–55) (0–77)

Feet examinations done 12 53 41 58 36 52 49(19–100) (7–76) (43–76) (0–83) (18–93) (0–100)

Laboratory investigationsHbA1c 6 70 73 73 54 67 66

(30–100) (47–93) (53–100) (37–87) (19–94) (19–100)Lipids 12 74 76 81 44 74 71∗

(38–100) (53–93) (53–100) (13–90) (12–100) (12–100)ACR 12 64 65 56 44 69 62∗

(35–100) (23–90) (43–76) (13–87) (10–97) (10–100)Counselling/advice

Nutrition 12 46 57 63 36 56 51∗(3–83) (29–90) (43–88) (10–90) (22–97) (3–97)

Physical activity 12 45 58 64 32 51 49∗(0–93) (35–90) (47–88) (3–70) (15–93) (0–93)

ImmunizationsFlu vaccination 12 69 70 45 36 62 61∗

(23–95) (21–100) (23–57) (10–70) (11–97) (10–100)Pneumococcal vaccination 5 years 78 86 29 30 60 64∗

(47–100) (40–100) (10–40) (0–93) (23–90) (0–100)Overall adherence 60 62 60 42 57 57∗

(25–83) (40–82) (44–74) (22–82) (26–81) (22–83)

aConducted by an ophthalmologist, retinal camera or optometrist.bAdditional comparison of service delivery between Indigenous and non-Indigenous patients in Far West NSW were conducted. Identified statisticallysignificant differences included ACR tests (47% versus 63%, p = 0.033) and pneumococcal vaccination (38% versus 19%, p = 0.025).∗p < 0.01 for comparison between regions.

NSW, WA and North QLD, and each service signed aparticipation agreement.

Results

Characteristics of patientsand community health centres

Records of 1593 people with type 2 diabetes wereincluded in the study (Table 1). The median age ofpatients was 51 years and 40% were men. Participantsfrom the NT, WA and North QLD centres werepredominantly Aboriginal (89–98%). However, only 42%of the participants from the Far West NSW centreswere Aboriginal. Thirty-two percent of participants weredocumented as current smokers, 53% as having adiagnosis of hypertension and 37% as having a diagnosisof hyperlipidemia. Seventy-three percent of participantshad one or more medical co-morbid conditions. Ninety-percent of patients had a record of health centre

attendance within the previous 6 months. Patients fromFar West NSW and WA tended to have lower documentedprevalence of renal disease and lower attendance rates inthe previous 6 months.

Of 62 participating health centres, 47% were man-aged by a local or regional Aboriginal committee (board),with the remainder being government funded/operated.Thirty-one percent of the centres had current generalpractice accreditation status. Forty-percent served com-munities with populations of 1000 or more.

Adherence to delivery of scheduledservices

Overall, 57% of guidelines-scheduled diabetes serviceswere delivered to patients, with wide variation betweenhealth centres (range 22–83%) (Table 2). There wassubstantial variation in delivery across different categoriesof services, different regions and different health centreswithin the same region. Adherence was relatively high



Table 3. Pharmacological treatment and medication adjustment among patients

NT Top EndNT CentralAustralia


Percentage (range between health centres)

TreatmentAny insulin use 9 5 22 11 26 15–232/1593∗

(0–35) (0–10) (7–33) (0–27) (7–60) (0–60)Oral hypoglycaemic agents only 70 81 55 67 55 66–1047/1593∗

(56–100) (71–90) (40–67) (50–90) (33–86) (33–100)Hypertension on treatment 89 91 80 84 85 87–732/846

(50–100) (80–100) (62–91) (47–95) (47–100) (47–100)Renal disease on ACE inhibitors 83 84 69 62 76 79–370/467

(45–100) (74–100) (40–100) (0–100) (33–100) (0–100)Hyperlipidemia on statins 75 68 69 83 81 76–449/590

(25–100) (44–100) (50–89) (50–100) (38–100) (25–100)Coronary heart disease on aspirin 63 67 Not applicable Not applicable 58 60–18/30

(0–75) (0–100) (0–100) (0–100)Aspirin use among diabetes patients 63 74 36 41 47 55–648/1187∗without cardiovascular events butwith one or more othercardiovascular risk factorsa inaddition to hyperglycaemia

(22–96) (40–100) (26–60) (27–59) (16–73) (16–100)

Medication adjustmentHypoglycaemic medications/insulin 25 16 40 13 46 26∗adjusted when HbA1c > 7% (0–72) (0–30) (20–71) (0–31) (30–68) (0–72)Antihypertensive medications 13 8 17 14 13 13adjusted when blood pressure>130/80 mmHg

(0–67) (0–22) (0–29) (0–26) (5–30) (0–67)

aOther cardiovascular risks refer to hypertension, hyperlipidemia and smoking.bAdditional comparison in treatment and medication adjustment between Indigenous and non-Indigenous patients in Far West NSW showedstatistically significant differences as follows: oral hypoglycaemic agents only (50% versus 58%, p = 0.021) and use of aspirin as primary preventionof cardiovascular disease (49% versus 25%, p = 0.009).∗p < 0.01 for comparison between regions.

for delivery of basic measurements such as BP andweight checks (around 60–80%), followed by delivery oflaboratory investigations and immunizations (60–70%).Less attention was paid to delivery of basic measurementssuch as waist circumference checks, eye and feetexaminations, and counselling services (40–50%). Thehighest adherence was delivery of BP checks (83%),and the lowest was measurement of waist circumference(39%).

Further comparison of service delivery betweenIndigenous and non-Indigenous patients in Far West NSWshowed the former were less likely to have ACR tests, butwere more likely to have pneumococcal vaccination inaccordance with guidelines (Table 2).

Pharmacological treatmentand medication adjustment

At the time of audit, 81% of patients were takingmedications for glycaemic control (Table 3). While 15% ofpatients used insulin (with or without oral hypoglycaemicagents), another 66% used oral hypoglycaemic agentsonly. Eighty-seven percent of patients with hypertensionwere on antihypertensive treatments, 79% of patientswith renal disease were on ACE inhibitors, and 76% ofpatients with hyperlipidemia were on statins. Variation intreatment was more evident between health centres thanthat between regions.

Twenty-six percent of patients had a record oftheir hypoglycaemic medications/insulin being adjustedafter identification of elevated HbA1c over the previous12 months (Table 3). Large variation was observedbetween health centres (range 0–72%). Patients fromhealth centres in Far West NSW and North QLD weremore likely to have their medications adjusted for HbA1c

control than patients in other regions. Antihypertensivemedication adjustment rates were similar across regions,but again showed variation between health centres (range0–67%).

Intermediate patient outcomes

Of patients who had measurements recorded withinthe previous 12 months, about a quarter had the mostrecent HbA1c < 7.0%, about one-third had the mostrecent blood pressure <130/80 mmHg, 29% had themost recent total cholesterol <4.0 mmol/L and 38%had ACR equal to or less than 3.4 (Table 4). Patientsfrom health centres in Far West NSW tended to havebetter HbA1c control; further analysis revealed that thiswas mostly due to relatively better control of HbA1c

among the non-Indigenous patients who accounted forabout half of the sample in Far West NSW. Qualityof HbA1c control among Indigenous patients in farwest NSW (e.g. the mean HbA1c level of 8.5%) wassimilar to the levels of control in other regions. Patients


470 D. Si et al.

Table 4. Intermediate patient outcomes for diabetes care

NT TopEnd

NT CentralAustralia


Intermediate outcomea Mean, proportion or median (range between health centres)

HbA1cMean HbA1c levels (%) 8.7 9.0 8.0 9.0 9.0 8.8∗

(7.6–10.1) (7.3–11.0) (7.5–8.4) (7.7–10.1) (7.1–10.0) (7.1–11.0)HbA1c < 8% 44% 46% 61% 40% 46% 46%∗

(11–65%) (13–68%) (50–71%) (16–61%) (29–85%) (11–85%)HbA1c < 7% 27% 27% 34% 22% 25% 27%

(0–50%) (8–55%) (17–53%) (8–43%) (0–53%) (0–55%)Blood pressure

Mean systolic blood pressure (mmHg) 128 127 138 136 127 130∗(117–140) (120–137) (132–144) (128–147) (110–139) (110–147)

Mean diastolic blood pressure (mmHg) 78 78 80 82 77 78∗(73–86) (75–84) (75–83) (77–85) (66–84) (66–86)

Blood pressure <140/90 mmHg 67% 72% 48% 55% 69% 65%∗(45–86%) (50–89%) (35–55%) (24–83%) (54–87%) (24–89%)

Blood pressure <130/80 mmHg 37% 43% 19% 29% 40% 36%∗(15–59%) (25–59%) (0–34%) (12–41%) (24–56%) (0–59%)

Total cholesterolMean total cholesterol levels (mmol/L) 4.6 4.4 5.1 4.8 4.8 4.7

(3.6–5.5) (4.1–4.7) (4.8–5.7) (4.3–6.0) (3.9–5.4) (3.6–6.0)Total cholesterol <5.5 mmol/L 78% 88% 64% 76% 79% 79%∗

(33–100%) (67–100%) (58–75%) (38–100%) (50–100%) (33–100%)Total cholesterol <4.0 mmol/L 30% 34% 19% 22% 28% 29%

(0–71%) (0–48%) (5–32%) (0–32%) (0–67%) (0–71%)ACR

Median ACR levels 8.5 6.8 2.1 7.3 5.3 6.2∗(0.8–47.0) (2.8–15.8) (1.9–3.2) (1.3–23.0) (3.2–46.0) (0.8–47.0)

ACR ≤3.4 29% 35% 59% 41% 43% 38%∗(0–60%) (19–60%) (53–69%) (14–86%) (0–73%) (0–86%)

3.4 < ACR ≤ 34 40% 46% 27% 38% 35% 38%(20–71%) (33–57%) (15–38%) (14–75%) (10–56%) (10–75%)

ACR >34 31% 19% 14% 21% 22% 24%∗(11–61%) (0–33%) (0–24%) (0–29%) (7–67%) (0–67%)

aThe most recent readings in the previous 12 months were used.bAdditional comparison of intermediate outcomes between Indigenous and non-Indigenous patients in Far West NSW showed the following statisticallysignificant differences: mean HbA1c levels (8.5 versus 7.5, p = 0.009), proportions of patients with HbA1c < 8% (49% versus 74%, p = 0.009), andmean diastolic blood pressure levels (83 versus 77, p = 0.018).∗p < 0.01 for comparison between regions.

from health centres in NT Central Australia were morelikely to have better blood pressure control. There wassubstantial variation in patient intermediate outcomesbetween health centres. For example, proportions ofpatients with HbA1c < 7.0% ranged from 0% to 55%across health centres.

Variation in quality of care explainedby health centre and individual patientlevel characteristics

Most of the variation in diabetes care was at thepatient level, which accounted for 64–97% of thetotal variation in the measures (Table 5). The healthcentre component contributed a considerable amount ofvariation in adherence to scheduled services (36%), amoderate amount of variation in medication adjustment(11–22%) and a relatively small amount of variation inintermediate patient outcomes (3–11%).

Discussion

This study provides insight into the quality of diabetescare in a range of Indigenous primary health care settings.Information on this scope of diabetes care indicators hasnot been previously available on a wide scale in Australia.Of people with diabetes in our study, about 60–80%had HbA1c, blood pressure and blood lipid tests withinthe previous 6-month (HbA1c and blood pressure) or 12-month period (blood lipids). Although around 80–90%of patients were taking medications for the controlof hyperglycaemia, hypertension and hyperlipidemia,respectively, rates of medication adjustment for patientswith elevated HbA1c and blood pressure were relativelylow (10–30%), due to which, only about one-third ofpatients achieved good control of HbA1c, blood pressureand total cholesterol. In addition, this study revealedlarge variation in diabetes care across health centres,and demonstrated that patient level characteristicscontributed substantially to variation in processes ofcare and even more substantially to variation in patient



outcomes. This finding should encourage practitioners,managers and policy makers to strengthen their efforts todeliver care and to manage services in a way that mosteffectively meets the varying needs of individual patientsas a way of achieving population level outcomes.

Quality of diabetes care varied between regions inthis study. For example, overall delivery of scheduleddiabetes services was relatively low in WA comparedwith other jurisdictions. Participating health centresin WA predominantly served large communities (withpopulations ≥1000), while those in the other regionsmainly served smaller communities. Large communitiesare more likely to have multiple service providers(e.g. general practitioners in addition to the Aboriginalcommunity-controlled primary care services) and lesswell defined service populations. Challenges associatedwith sharing of clinical information and coordination ofcare between multiple service providers were likely tocontribute to the relatively low delivery of diabetes care.

With regard to medication adjustment rates, healthcentres in Far West NSW and North QLD performedrelatively well. One possible explanation of this differenceis that Far West NSW health centres had residentdoctors which should increase opportunities for timelymedication adjustment, whereas many health centresin the NT were served by visiting doctors andconsequently had less potential for close medicalsupervision. This sort of cross-region comparison indicatesthat regional level policies may have an importantrole in enhancing clinical performance (e.g. throughsupporting mechanisms for effective information sharingand care coordination between multiple service providersor addressing workforce shortage and staffing profileissues).

Adherence to delivery of guidelines-scheduled servicesamong participating Aboriginal patients was higher thanregional and national data reported earlier (e.g. thosestudies showed about 40–60% of patients with diabeteshad HbA1c and total cholesterol tested and blood pressure

Table 5. Percent of total variance attributable to health centrelevel and individual patient level factors

Partition of variation

Diabetes care measure(as dependent variables in models)

Healthcentrelevel

factor (%)

Patientlevel

factor (%)

Process measuresOverall adherence to scheduled services 36 64Hypoglycaemic medication/insulin adjustment 22 78Antihypertensive medication adjustment 11 89

Intermediate patient outcomesLast HbA1c levels 4 96Last HbA1c < 7.0% 4 96Last systolic blood pressure values 6 94Last diastolic blood pressure values 5 95Last blood pressure <130/80 mmHg 4 96Last cholesterol values 3 97Last cholesterol <4.0 mmol/L 7 93ACR ≤3.4 11 89

checked annually) [21,22]. However, the current levelof metabolic control in Aboriginal participants wasrelatively poor when compared with general Australiansor Indigenous patients in countries such as New Zealand.The national population-based AusDiab study found thatfor Australian adults with known type 2 diabetes 57%had HbA1c < 7% [23]. In New Zealand, data from thenational primary care diabetes registers showed that 60%of Maori patients and 56% of Pacific Island patients hadHbA1c < 8.0% (the corresponding figure in our study was46%) [24].

It was likely that low rates of medication adjustmentconstituted the major barrier to translating favourablelevels of service delivery (e.g. regular HbA1c testing andblood pressure checking) into adequate cardio-metaboliccontrol among participating Aboriginal patients. Failure ofhealth care providers to initiate or intensify therapy whenevidence-based treatment goals are not achieved has beendescribed in the literature as ‘clinical inertia’ [25]. Studieshave demonstrated widespread of clinical inertia in bloodpressure, lipid and glycaemia management in practice[26–28]. Reasons given for clinical inertia includedoctors overestimating the care actually provided, doctors’concerns about side effects and patient non-adherence,lack of appropriate training, and lack of systems in practiceto facilitate identification of therapeutic problems [25].Targeted strategies need to be developed to addressclinical inertia and improve medication adjustment.Otherwise, enormous resources and efforts put intodelivery of diabetes care processes (testing, checking andscreening) will only bring limited benefits to patients.

To our knowledge, this is the first study to report thewide variation in processes and outcomes of diabetescare in Australian Indigenous primary care settings.Previous studies have investigated state, health carefacility, clinician and individual client level characteristicsas determinants of variation in quality of primary care[14,15,29–31].

Studies of team culture and climate, and nurse–doctorcomposition of the clinical team have shown no or limitedassociation with quality of care [32,33]. An observationalstudy suggests larger clinical teams and practices, whichare more likely to respond to financial incentives deliverhigher quality clinical care [34].

Multilevel statistical modelling studies [29,30] indicatethat individual client level characteristics are responsiblefor a large proportion of variation in selected indicatorsof diabetes care, with less variation explained by clinicianand facility factors. Our study confirms that patientlevel factors contribute substantially to variation in arange of diabetes care measures. Importantly, the largecontribution of patient level factors does not imply thatwe should hold patients themselves accountable fordiabetes care outcomes. On the contrary, this suggeststhat strategies which strengthen patient-centred care[35], which enhance interaction between providers andpatients [36], and which improve patient health literacy[37] are vitally important to improve patient outcomes.


472 D. Si et al.

Our study is subject to a number of limitations. First,health centres were not randomly selected and theirparticipation in the project was on a voluntary basis.This might lead to selection bias and data on diabetescare may not be representative for regions involved.However, a relatively large number of health centres frommultiple states/territories provide first hand informationon diabetes care and patterns of variation in diverseIndigenous primary care settings, in an area where thereis little data available at the national level. Second, werelied on clinical medical records to retrieve diabetescare data, which may underestimate service delivery dueto under-documentation. However, delivering services topatients with chronic illness is not a one-off practice,but a periodic and ongoing process, involving multipleservice providers. Accurate and clear documentation ofservices delivered is important in ensuring continuity andcoordination of service delivery. From this point of view,documentation is a critical aspect of quality. Third, clinicalaudits were carried out by multiple data abstractors andinter-rater reliability was not formally assessed in thisstudy. However, reliability of medical record audits wassatisfactory when using similar audit forms in our previousstudy [38].

In conclusion, the major findings of this study pro-vide a broad picture of quality of diabetes care inAboriginal primary health care settings. There waswide variation in delivery of guidelines-scheduled ser-vices, medication adjustment and patient intermediateoutcomes across health centres. Further understand-ing of system factors and patient level factors thatunderpin variation of care should assist clinicians,health managers and policy makers to develop strate-gies to improve quality of diabetes care in Aboriginalcommunities.

Acknowledgements

This project would not be possible without the active support,enthusiasm and commitment of staff and management of theparticipating health services and the contribution made by thewider ABCDE project team. The ABCDE project is supported byfunding from the Cooperative Research Centre for AboriginalHealth and the Commission for Safety and Quality in HealthCare. The work of a number of people with key roles inthe project is supported by their employing organizations,including state and territory governments, and community-controlled health organizations. Damin Si’s work is supported bya National Health and Medical Research Council (NHMRC)Capacity Building in Population Health Grant and NHMRCPostdoctoral Fellowship. Ross Bailie’s work is supported by anNHMRC Research Fellowship.

Competing interests

The authors declare that they have no conflict of interest.

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