# CONTINUOUS QUALITY IMPROVEMENT—ADVANCING UNDERSTANDING OF DESIGN, APPLICATION, IMPACT AND EVALUATION OF CQI APPROACHES

EDITED BY: Ross Bailie, Sarah Larkins and Edward Broughton PUBLISHED IN: Frontiers in Public Health

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ISSN 1664-8714 ISBN 978-2-88945-377-1 DOI 10.3389/978-2-88945-377-1

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# **CONTINUOUS QUALITY IMPROVEMENT—ADVANCING UNDERSTANDING OF DESIGN, APPLICATION, IMPACT AND EVALUATION OF CQI APPROACHES**

Topic Editors: **Ross Bailie,** The University of Sydney, Australia **Sarah Larkins,** James Cook University, Australia **Edward Broughton,** University Research Co, United States

**Citation:** Bailie, R., Larkins, S., Broughton, E., eds. (2017). Continuous Quality Improvement— Advancing Understanding of Design, Application, Impact and Evaluation of CQI Approaches. Lausanne: Frontiers Media. doi: 10.3389/978-2-88945-377-1

# Table of Contents

*05 Editorial: Continuous Quality Improvement (CQI)—Advancing Understanding of Design, Application, Impact, and Evaluation of CQI Approaches*

Ross Bailie, Jodie Bailie, Sarah Larkins and Edward Broughton

#### **Variation and Priority Gaps in Care; Barriers and Enablers for Improvement**

*08 Determinants and Gaps in Preventive Care Delivery for Indigenous Australians: A Cross-sectional Analysis*

Christopher Bailie, Veronica Matthews, Jodie Bailie, Paul Burgess, Kerry Copley, Catherine Kennedy, Liz Moore, Sarah Larkins, Sandra Thompson and Ross Stewart Bailie

*19 Delivery of Eye and Vision Services in Aboriginal and Torres Strait Islander Primary Healthcare Centers*

Anthea M. Burnett, Anna Morse, Thomas Naduvilath, Andrea Boudville, Hugh R. Taylor and Ross Bailie

*27 Integrated Clinical Decision Support Systems Promote Absolute Cardiovascular Risk Assessment: An Important Primary Prevention Measure in Aboriginal and Torres Strait Islander Primary Health Care*

Veronica Matthews, Christopher P. Burgess, Christine Connors, Elizabeth Moore, David Peiris, David Scrimgeour, Sandra C. Thompson, Sarah Larkins and Ross Bailie

*36 System-Level Action Required for Wide-Scale Improvement in Quality of Primary Health Care: Synthesis of Feedback from an Interactive Process to Promote Dissemination and Use of Aggregated Quality of Care Data*

Jodie Bailie, Alison Laycock, Veronica Matthews and Ross Bailie

*48 Wide Variation in Absolute Cardiovascular Risk Assessment in Aboriginal and Torres Strait Islander People with Type 2 Diabetes*

Bhakti R. Vasant, Veronica Matthews, Christopher P. Burgess, Christine M. Connors and Ross S. Bailie

#### **Primary Healthcare Centre Systems**

*56 Exploring Systems That Support Good Clinical Care in Indigenous Primary Health-care Services: A Retrospective Analysis of Longitudinal Systems Assessment Tool Data from High-Improving Services*

Cindy Woods, Karen Carlisle, Sarah Larkins, Sandra Claire Thompson, Komla Tsey, Veronica Matthews and Ross Bailie

#### **Engaging at Multiple Levels of the System**

*73 Interactive Dissemination: Engaging Stakeholders in the Use of Aggregated Quality Improvement Data for System-Wide Change in Australian Indigenous Primary Health Care*

Alison Laycock, Jodie Bailie, Veronica Matthews and Ross Bailie

#### **Facilitation**

*81 Enabling Continuous Quality Improvement in Practice: The Role and Contribution of Facilitation*

Gillian Harvey and Elizabeth Lynch

#### **CQI in Health Professional Education**

*86 Improving Community Health Using an Outcome-Oriented CQI Approach to Community-Engaged Health Professions Education*

Amy Clithero, Simone Jacquelyn Ross, Lyn Middleton, Carole Reeve and Andre-Jacques Neusy

#### **CQI in Health Promotion and Beyond**

*92 Improving Health Promotion Using Quality Improvement Techniques in Australian Indigenous Primary Health Care*

Nikki Percival, Lynette O'Donoghue, Vivian Lin, Komla Tsey and Ross Stewart Bailie

# Editorial: Continuous Quality Improvement (CQI)—Advancing Understanding of Design, Application, Impact, and Evaluation of CQI Approaches

*Ross Bailie1 \*, Jodie Bailie1 , Sarah Larkins 2 and Edward Broughton3*

*1 The University of Sydney, The University Centre for Rural Health, Lismore, NSW, Australia, 2 James Cook University, College of Medicine and Dentistry, Townsville, QLD, Australia, 3University Research Co., LLC, Chevy Chase, MD, United States*

Keywords: primary health care, health systems research, continuous quality improvement, Aboriginal and Torres Strait Islander health, building block

#### **Editorial on the Research Topic**

#### **Continuous Quality Improvement (CQI)—Advancing Understanding of Design, Application, Impact, and Evaluation of CQI Approaches**

#### *Edited and Reviewed by:*

*Kai Ruggeri, University of Cambridge, United Kingdom*

*\*Correspondence: Ross Bailie<EMAIL_ADDRESS>
#### *Specialty section:*

*This article was submitted to Public Health Policy, a section of the journal Frontiers in Public Health*

*Received: 20 October 2017 Accepted: 03 November 2017 Published: 23 November 2017*

#### *Citation:*

*Bailie R, Bailie J, Larkins S and Broughton E (2017) Editorial: Continuous Quality Improvement (CQI)—Advancing Understanding of Design, Application, Impact, and Evaluation of CQI Approaches. Front. Public Health 5:306. doi: 10.3389/fpubh.2017.00306*

Continuous quality improvement (CQI) approaches are increasingly used to bridge gaps between the evidence base for best practice, what actually happens in practice, and achievement of better population health outcomes. Among a range of quality improvement strategies, CQI is characterized by iterative use of processes to identify quality problems, develop solutions, and implement and evaluate changes. Application of CQI in health care is evolving and evidence of their success continues to emerge (1–3).

Through the Research Topic, "Continuous Quality Improvement (CQI)—Advancing Understanding of Design, Application, Impact, and Evaluation of CQI approaches," we aimed to aggregate knowledge of useful approaches to tailoring CQI approaches for different contexts, and for implementation, scale-up and evaluation of CQI interventions/programs. This Research Topic has attracted seven original research reports and three "perspectives" papers. Thirty-six authors have contributed from eighteen research organizations, universities, and policy and service delivery organizations. All original research articles and one perspective paper come from the Australian Audit and Best Practice for Chronic Disease (ABCD) National Research Partnership ("ABCD Partnership") in Indigenous primary healthcare settings (4–6). To some extent, this reflects the interests and connections of two of the Topic Editors, who were lead investigators on the ABCD Partnership. This Partnership has made a prominent contribution to original research on CQI in primary healthcare internationally, with over 50 papers published in the peer-reviewed literature over the past 10 years.

As most articles in this Research Topic arise from the ABCD Partnership, a brief overview of the program provides a useful backdrop. The program originated in 2002 in the Top End of the Northern Territory in Australia, and built on substantial prior research and evaluation of CQI methods in Indigenous primary healthcare. With substantial growth and enthusiastic support from service providers and researchers around Australia, the ABCD Partnership has focused since 2010

**Abbreviations:** ABCD, audit and best practice for chronic disease; CQI, continuous quality improvement.

on exploring clinical performance variation, examining strategies for improving primary care, and working with health service staff, management and policy makers to enhance effective implementation of successful strategies (4). By the end of 2014, the ABCD Partnership had generated the largest and most comprehensive dataset on quality of care in Australian Indigenous primary healthcare settings. The Partnership's work is being extended through the Centre of Research Excellence in Integrated Quality Improvement (6).

Several research papers included in this Research Topic illustrate consistent findings of wide variation in adherence to clinical best-practice guidelines between health centers (Bailie et al.; Burnett et al.; Matthews et al.). The papers also show variation among different aspects of care, with relatively good delivery of some modes of care [Bailie et al.; (7)] and poor delivery of others—such as follow-up of abnormal clinical or laboratory findings. These findings are evident in eye care (Burnett et al.), general preventive clinical care (Bailie et al.), and in absolute cardiovascular risk assessment (Matthews et al.; Vasant et al.). The findings are consistent with other ABCD-related publications on diabetes care (8), preventive health (9), maternal care (10), child health (11), rheumatic heart disease (12), and sexual health (13).

Systems to support good clinical care are explored by Woods et al. in five primary healthcare centers that were identified through ABCD data as achieving substantially greater improvement than others over successive CQI cycles. Attention to understanding and improving systems was shown to be vital to the improvements in clinical care achieved by these health centers. Improved staffing and commitment to working in the community were standout aspects of health center systems that underpinned improvements in clinical care.

On a wider scale, engagement by primary healthcare services in the ABCD Partnership has enabled assessment of system functioning at district, regional, state, and national levels, as reflected in stakeholders' perceptions of barriers and enablers to addressing gaps in chronic illness care and child health, and identifying drivers for improvement (Bailie et al.). Primary drivers included staff capability, availability and use of information systems and decision support tools, embedding of CQI processes, and community engagement. We have also shown how consistent and sustained policy and infrastructure support for CQI enables large-scale and ongoing improvements in quality of care (3).

Commitment of the ABCD team to promoting effective use of CQI data is reflected in one "perspective" paper, which describes a theory-informed cyclical interactive dissemination strategy (Laycock et al.). Concurrent developmental evaluation provides a mechanism for learning and refinement over successive cycles (14).

The other two perspective articles (not specifically from the ABCD program) highlight the role of facilitation in CQI and the potential for application of CQI in health professional education. The emerging evidence on facilitation as a vital tool for effective CQI should guide resourcing and approaches to CQI (Harvey and Lynch). The approach builds on the humanistic principles of modern CQI methods—participation, engagement, shared decision-making, enabling others, and tailoring to context. The framework for CQI approaches to health professional education described by Clithero et al. directly addresses a critical need for innovative approaches to health workforce development that will strengthen community engagement and embed CQI principles into health system functioning. The scale and scope of need in workforce development is strongly evident in findings of the ABCD program.

Importantly, CQI methods are proving useful in assessing and potentially improving delivery of evidence-based health promotion practices (Percival et al.). Percival's experience in this field highlights the health facility and wider system challenges facing effective implementation of CQI methods. In health promotion these barriers include low priority given to health promotion in the face of heavy demands for acute clinical care. This work in health promotion complements other research on applying CQI to social determinants of health more broadly (15), including community food supply (16), housing (17), and education (18).

The publications in this special issue address many of the "building blocks" of high performing primary care described by Bodenheimer and colleagues in the US; namely, four foundational components (engaged leadership, data-driven improvement, empanelment, and team-based care) that are vital to facilitate the implementation of the other six elements (patient-team partnership, population management, continuity of care, prompt access to care, comprehensiveness, and care coordination) (19). They are also relevant to Australian based work on clinical microsystems and development of CQI tools for mainstream general practice, such as the Primary Care-Practice Improvement Tool (with similar components to the ABCD systems assessment tool) (20).

Continuous quality improvement is vital to improving health outcomes through system strengthening. We anticipate substantial future development of CQI methods. By late 2017, there had been over 20,000 views of this Research Topic, and many articles have already been cited in peer-review manuscripts. Further research on CQI in primary healthcare would be well guided by a systematic scoping review of literature summarizing empirical research on current knowledge in the field, and identifying key knowledge gaps.

### AUTHOR CONTRIBUTIONS

RB wrote the first draft. JB has revised content and structure. SL and EB reviewed and edited subsequent drafts. All authors have approved the final version of the manuscript for publication.

### ACKNOWLEDGMENTS

We would like to thank all of those who contributed to this Research Topic as authors, review editors, and colleagues.

## FUNDING

The National Health and Medical Research Council funded the ABCD National Research Partnership Project (grant number 545267) and the Centre for Research Excellence in Integrated Quality Improvement (grant number 1078927). In-kind and financial support was provided by the Lowitja Institute and a range of Community-Controlled and Government agencies.

## REFERENCES

improvement approach. *BMC Health Serv Res* (2013) 13(1):525. doi:10.1186/ 1472-6963-13-525

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. RB was the chief investigator on the ABCD National Research Partnership and is the chief investigator on the Centre of Research Excellence in Integrated Quality Improvement. All papers published in the Research Topic received peer review from members of the Frontiers in Public Health Policy panel of reviewers who were independent of named authors on any given article published in this volume, consistent with the journal policy on conflict-of-interest.

*Copyright © 2017 Bailie, Bailie, Larkins and Broughton. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Determinants and Gaps in Preventive Care Delivery for Indigenous Australians: A Cross-sectional Analysis

*Christopher Bailie1 , Veronica Matthews2 , Jodie Bailie2 , Paul Burgess2,3 , Kerry Copley4 , Catherine Kennedy5 , Liz Moore4 , Sarah Larkins6,7 , Sandra Thompson8 and Ross Stewart Bailie2,9 \**

*1School of Medicine, University of Queensland, Brisbane, QLD, Australia, 2Menzies School of Health Research, Charles Darwin University, Darwin, NT, Australia, 3Primary Health Care Branch, Top End Health Service, Northern Territory Government, Darwin, NT, Australia, 4Aboriginal Medical Services Alliance NT (AMSANT), Darwin, NT, Australia, 5Maari Ma Health Aboriginal Corporation, Broken Hill, NSW, Australia, 6College of Medicine and Dentistry, James Cook University, Townsville, QLD, Australia, 7Anton Breinl Research Centre for Health Systems Strengthening, Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, QLD, Australia, 8Western Australian Centre for Rural Health, School of Primary, Aboriginal and Rural Health Care, University of Western Australia, Geraldton, WA, Australia, 9School of Population Health, University of Queensland, Brisbane, QLD, Australia*

#### *Edited by:*

*Edward Broughton, University Research Co., LLC, USA*

#### *Reviewed by:*

*Elizabeth Querna Cliff, University of Michigan, USA Hugh J. S. Dawkins, Department of Health of Western Australia, Australia*

> *\*Correspondence: Ross Stewart Bailie<EMAIL_ADDRESS>
#### *Specialty section:*

*This article was submitted to Public Health Policy, a section of the journal Frontiers in Public Health*

*Received: 22 November 2015 Accepted: 15 February 2016 Published: 10 March 2016*

#### *Citation:*

*Bailie C, Matthews V, Bailie J, Burgess P, Copley K, Kennedy C, Moore L, Larkins S, Thompson S and Bailie RS (2016) Determinants and Gaps in Preventive Care Delivery for Indigenous Australians: A Cross-sectional Analysis. Front. Public Health 4:34. doi: 10.3389/fpubh.2016.00034*

Background: Potentially preventable chronic diseases are the greatest contributor to the health gap between Aboriginal and Torres Strait Islander peoples and non-Indigenous Australians. Preventive care is important for earlier detection and control of chronic disease, and a number of recent policy initiatives have aimed to enhance delivery of preventive care. We examined documented delivery of recommended preventive services for Indigenous peoples across Australia and investigated the influence of health center and client level factors on adherence to best practice guidelines.

Methods: Clinical audit data from 2012 to 2014 for 3,623 well adult clients (aged 15–54) of 101 health centers from four Australian states and territories were analyzed to determine adherence to delivery of 26 recommended preventive services classified into five different modes of care on the basis of the way in which they are delivered (e.g., basic measurement; laboratory tests and imaging; assessment and brief interventions, eye, ear, and oral checks; follow-up of abnormal findings). Summary statistics were used to describe the delivery of each service item across jurisdictions. Multilevel regression models were used to quantify the variation in service delivery attributable to health center and client level factors and to identify factors associated with higher quality care.

Results: Delivery of recommended preventive care varied widely between service items, with good delivery of most basic measurements but poor follow-up of abnormal findings. Health center characteristics were associated with most variation. Higher quality care was associated with Northern Territory location, urban services, and smaller service population size. Client factors associated with higher quality care included age between 25 and 34 years, female sex, and more regular attendance.

Conclusion: Wide variation in documented preventive care delivery, poor follow-up of abnormal findings, and system factors that influence quality of care should be addressed through continuous quality improvement approaches that engage stakeholders at multiple levels (including, for example, access to care in the community, appropriate decision support for practitioners, and financial incentives and context appropriate guidelines).

Keywords: preventive healthcare, Aboriginal and Torres Strait Islander, variation, indigenous, quality of care, adherence to best practice guidelines

#### INTRODUCTION

Worldwide, Indigenous populations experience poorer health than their non-Indigenous counterparts (1). The greatest contributor to the gap in health outcomes between Aboriginal and Torres Strait Islander1 peoples and the general Australian population is potentially preventable chronic disease (2). The critical role of preventive healthcare in curbing the impact of chronic disease is now widely recognized (3, 4). However, reported levels of delivery of recommended preventive care to Indigenous Australians still have much room for improvement (5–9).

Australians have access to Medicare, a universal health insurance scheme. Indigenous Australians access primary health care (PHC) through both private general practice and primary health services designed to meet the needs of Indigenous peoples. These include Aboriginal community-controlled health services (ACCHS) and government operated Indigenousspecific services (10).

Recently, a number of Australian Government policy initiatives have attempted to improve prevention for Indigenous Australians, including the Indigenous Chronic Disease Package (ICDP) from 2009 to 2013 (11). Preventive care priorities within the ICDP were: (a) smoking cessation; (b) increasing uptake of preventive health assessments and follow-up of abnormal findings; (c) workforce training to improve access to care and preventive health care delivery.

Preventive care for Indigenous Australians is incentivized by the Medicare Benefit Schedule (MBS) 715 annual "Health Assessment for Aboriginal and Torres Strait Islander People" that has achieved better identification of chronic disease and its risk factors as well as reduction in cardiovascular disease risk (12–14). Continuous quality improvement (CQI) in Indigenous PHC services has proved effective in improving preventive care delivery through enabling PHC services to identify and address barriers to preventive care (15).

The Audit and Best Practice for Chronic Disease National Research Partnership (ABCD NRP) and One21seventy have supported the use of CQI in Indigenous PHC by providing evidence-based clinical audit and systems assessment tools and through training and assistance with their use (16, 17). More than 270 health centers across Australia have used ABCD/ One21seventy tools and processes to improve their quality of care (18, 19). Data collected from over 170 health centers are available for research purposes through the ABCD NRP.

Obtaining reliable data on delivery of care in relation to best practice guidelines are critical in targeting strategies to improve performance (6). Data to date for individual preventive service items have shown wide variation in delivery between clients and health centers (5–8, 14, 20).

Overall measurement of service delivery may lack the detail needed to identify specific opportunities for improvement in quality of care (21). Understanding performance in relation to the mechanisms by which care processes are delivered (referred to as "modes of care"), for example: basic measurements, lifestyle interventions, laboratory tests, and so on, may allow better insight into higher level system changes needed to improve care quality (21).

The aim of this paper is to assess variation in delivery of preventive health care to Indigenous people using data collected through the ABCD NRP. Specifically, our objectives are to: (1) assess differences in delivery of recommended preventive services; (2) examine health center and client level factors associated with quality care; (3) examine how these factors vary across different modes of care.

### MATERIALS AND METHODS

#### Setting

ACCHS and government operated Indigenous health centers participating in the ABCD NRP were included in this study. These health centers vary in size from small services with 1–2 nursing staff to large services with a range of medical, nursing, and allied health professionals. They predominantly but not exclusively serve Indigenous clients.

#### Data Sources

As part of their routine CQI activities, participating health centers performed annual audits of client medical records to determine whether recommended preventive service items were documented as delivered in the previous 24 months. Audit inclusion criteria were: (1) age between 15 and 55 years; (2) resident in the community for at least six of the last 12 months; (3) no diagnosis of diabetes, hypertension, coronary heart disease, chronic heart failure, rheumatic heart disease or chronic kidney disease; (4) not pregnant or not less than 6 weeks postpartum at the time of audit; (5) attendance at the health center in the previous 24 months.

Health centers were encouraged to audit all client records if their eligible client population was less than 30. For eligible client populations greater than 30, health centers audited a random sample of records of at least 30 eligible clients.

Samples were stratified by age and gender. Data were available from 101 health centers spread across four Australian states and

1 In the interest of brevity Aboriginal and Torres Strait Islander people are hereafter respectfully referred to as "Indigenous."

territories over the period 2012–2014. Data from the most recent preventive services clinical audit from each health center were included in our analysis.

#### Measures

The One21seventy/ABCD NRP preventive services audit tool contains 26 items recommended for delivery at least every 24 months. We classified items into five modes of care: basic measurements; laboratory and imaging; eye, ear and oral checks; assessment and counseling for lifestyle risk factors; and follow-up of abnormal findings (**Table 1**). A service was recorded as delivered for each eligible client if there was clear record of delivery at least once within the previous 24 months.

Four follow-up items were included in the audit. These were follow-up for high blood pressure (BP), for protein on urinalysis, for high blood glucose level (BGL), and for abnormal lipid

#### TABLE 1 | Percentage delivery of preventive service items by jurisdiction.

*Number of client records and health centers are overall for each jurisdiction. The actual number of client records is lower for some service items that are recommended for restricted populations.*

*To calculate delivery we assumed that: a all adults had been sexually active; bthat adults had no family history of premature CHD or CKD; c that females were not at above average risk of breast cancer; for dthat all remote locations were trachoma endemic areas.*

*NAAT, nucleic acid amplification test; BP, blood pressure; CHD, chronic heart disease; CKD, chronic kidney disease.*

*\*National guide to a preventive health assessment for Aboriginal and Torres Strait Islander people (22).*

† *Standard treatment manual (23).*

‡ *Chronic disease guidelines (24).* profile. Definitions of adequate follow-up, respectively, were a documented plan for: (1) repeat BP measurement in 2–4 weeks; (2) testing albumin-creatinine ratio; (3) re-testing BGL; (4) re-testing lipid profile. Denominators for calculating indicators of follow-up were based on numbers of clients who had an abnormal result for the relevant clinical/laboratory investigation within the past 2 years.

Some guidelines restrict recommended delivery of some services to special populations, for example, based upon a family history or when a person is sexually active. In cases where audit data were insufficient to define these special populations we made the following assumptions in order to estimate delivery: (a) for items related to sexual and reproductive health, we assumed that all young adults had been sexually active [noting the high rate of sexual activity among young Australians (25)]; (b) for serum lipid testing, we assumed that no clients had a family history of premature coronary heart disease or chronic kidney disease; (c) for mammography, we assumed that no female clients were at above average risk of breast cancer; (d) for trachoma screening, we assumed that all remote health centers were located in trachoma endemic areas, and therefore that screening was recommended [noting the higher rates of trachoma in remote and very remote areas (26)].

Health center characteristics included, size of population served, governance (ACCHS or government operated), and location (urban, regional, or remote). Duration of CQI participation has been shown to be associated with improved quality of care (27) and was therefore included in the analysis to correct for potential bias associated with different health centers participating in CQI for different lengths of time. The clinical audit data included information on the following client characteristics; age, gender, Indigenous status, and whether or not the client had attended the health center in the previous 6 months.

#### Statistical Analysis

We used STATA version 13 software for statistical analysis. Using summary statistics, we described the mean delivery of each service item for each jurisdiction (**Table 1**).

For the purposes of the analysis, the data from South Australia (SA) and Western Australia (WA) health centers were pooled because (a) there were relatively small number of participating health centers (two from WA, six from SA); (b) service delivery levels were relatively similar for SA and WA compared to other jurisdictions; and (c) SA and WA had relatively less developed support structures for CQI processes compared to the Northern Territory (NT) and Queensland (QLD) over the period covered by this study.

Adherence to delivery of recommended service items was calculated for each mode of care by dividing the documented service items for that client by the total number of recommended service items in that mode of care. We also calculated adherence to delivery for an overall composite indicator that included all service items except those related to follow-up of abnormal findings (**Table 1**).

Aggregate scores for the composite indicator and each mode of care were converted into binary outcome indicators that categorized "higher" performance as being above the median (top 50%) of delivery for all clients across all health centers. "Lower performance" was categorized as being below or equal to the median.

We used multi-level mixed effects logistic regression models to quantify the variation in service delivery separately for the overall indicator and each mode of care. These models allowed for the hierarchical structure of the data (clients nested within health centers).

For each indicator, we calculated unadjusted odds ratios to measure the association between "higher" levels of delivery and each health center and client characteristic.

For the adjusted analysis, we used a stepwise modeling strategy starting with an "empty" model with no explanatory variables (Model A). Health center (Model B) and then client level variables (Model C) were then introduced into the empty model. Potential interactions were introduced into the final model (Model C) in a stepwise manner and their significance was tested. No interactions were found to make a meaningful difference, and none were included in the final analysis. The model was tested for sensitivity to alternate specifications (including alternative cut points for the outcome variable) during the model building process and was found to be generally robust.

The reduction in variance due to the stepwise introduction of the client and health center level variables in the models was determined by the proportional change in variance (PCV). The PCV provides an estimate of the extent to which these factors may explain differences in propensity for better delivery of health care (28).

We calculated median odds ratios (MORs) to interpret variance in the odds ratio scale. In the odds ratio scale, the MOR describes the increase in median probability of better delivery if a client was to move from one randomly picked health center to another (29). For a MOR equal to 1, there is no difference between health centers in their probability of adhering to the recommended service delivery. The greater the MOR, the greater the unexplained variability between health centers.

### Ethics Approvals

Ethics approval has been obtained for the ABCD NRP project research ethics committees in relevant jurisdictions of Australia. These include the Human Research Ethics Committee (HREC) of the Northern Territory Department of Health and Menzies School of Health Research (HREC-EC00153); Central Australian HREC (HREC-12-53); Queensland HREC Darling Downs Health Services District (HREC/11/QTDD/47); South Australian Aboriginal Health Research Ethics Committee (04-10-319); Curtin University HREC (HR140/2008); Western Australian Country Health Services Research Ethics Committee (2011/27); Western Australia Aboriginal Health Information and Ethics Committee (111-8/05); and University of Western Australia HREC (RA/4/1/5051).

## RESULTS

Of the 101 participating health centers, 93 were located in the NT and QLD. More than 90% from QLD and the NT were located in a regional or remote area compared to 50% for SA and WA (**Table 2**). Fifty percent of health centers had a population of fewer than 500 people, 84% were government operated.

Records of 3,623 clients from these health centers were audited from February 2012 to December 2014. The sample size for each health center varied between 7 and 68 client records. Of these, 50% were females and 90% were Indigenous (**Table 2**).

The proportion of eligible clients receiving recommended care ranged from 18 to 85% for individual preventive services (**Table 1**). The mean delivery of the composite indicator including all service items except follow-up was 58%. Most basic measurements were delivered at levels of approximately 70–80% (**Table 1**); although recording of body mass index (BMI), waist circumference, and urinalysis were relatively low (37–47%). Most recommended laboratory and imaging investigations were delivered to approximately 50% of clients, apart from mammography which was delivered to 18% of eligible females. Eyes, ears, and oral checks were recorded as delivered to 18–45% of eligible clients. Delivery of service items to do with assessment and brief intervention for lifestyle risk factors ranged from 46 to 72%. Follow-up of abnormal findings was relatively low (17–28%) except for follow-up of positive protein on urinalysis (61%).

The unadjusted logistic regression analysis for the composite overall indicator showed significant effects for all factors except governance (**Table 3**). The health center MOR for the empty model for the overall indicator was 4.02 (**Table 4**; Model A), meaning if a client were to move from one randomly picked health center to another with higher delivery, they would have a 4.02 times higher chance (in median) of higher delivery. For the adjusted analysis for the overall composite indicator, the reduction in health center level variance for the addition of health center factors (PCV) was 60% (**Table 4**; Model B). Health center factors associated with higher levels of delivery included urban location, smaller service population, and location in the NT. Client level factors associated with higher delivery included being aged 25–34 years compared to other age groups, female gender, and more recent health center attendance (**Table 4**; Model C).

The PCV in Model B for each mode of care (additional files 1–5) ranged between 14 and 67% for different models of care with follow-up being the lowest and basic measurements being the highest. Health center factors associated with higher delivery were similar across modes of care (**Table 5**). There was some variation across jurisdictions with NT health centers significantly associated with higher delivery for all modes of care except for lifestyle risk factors and follow-up. The pattern of client level effects was similar across modes of care except for gender, where females were more likely to receive basic measurements and assessment and intervention for lifestyle risk factors but less likely to have laboratory and imaging investigations. There was some variation in the effect of age group.

### DISCUSSION

Australia has lacked high quality systematically collected information on the quality of preventive care delivered in general practice and other primary health care settings. This study examining the most comprehensive dataset of its kind currently available in Australia provides the most extensive snapshot of delivery of

 **<** 0.01).

TABLE 4 | Adjusted multilevel logistic regression analysis of health center and client level factors on a composite indicator of guideline-scheduled preventive service items recommended for well adults (*N* **=** 3,623 clients, 101 health centers) (\**p* **<** 0.05; \*\**p* **<** 0.01) (see Table 1 for service items included in this indicator).

*Median odds ratio (MOR): odds of receiving above median service delivery if client was to change health center or jurisdiction; proportional change in variance (PCV): per cent variation explained in odds for better health care delivery by introduction of health center or client level factors.*

preventive care to Indigenous people across Australia to date. We discuss the key findings around three themes: (a) Variation between aspects of care and key opportunities for improvement; (b) follow-up of identified risk factors and abnormal clinical and laboratory findings; and (c) variation between health centers. Key considerations for policy, practice, and research arising from these findings are outlined in **Table 6**.

### Variation between Aspects of Care and Key Opportunities for Improvement

There is substantial variation in the delivery of recommended preventive services; from about 70 to 80% for most basic measurements to less than 20% for mammogram screening for breast cancer. While comparison with previously published research is limited by the comparability of data, the patterns of service delivery evident in our study are generally similar to those previously described, with generally good delivery of basic measurements and lower documented delivery of recommended laboratory investigations (5, 8, 20). While our analysis is consistent with previous findings of reasonable levels of assessment of lifestyle risk factors (5, 8, 20), we found higher levels of brief intervention to address these risk factors than previously reported, especially for alcohol and tobacco use.

Several service items including measurement of BMI and waist circumference stand out for being simple and quick to complete yet having low levels of delivery. This might reflect a lack of decision support within the clinic records, patient refusal or reluctance among some clinicians to engage clients in discussion of their weight. As the vast majority of clients present for reasons other than a preventive health assessment (8), the context of an acute presentation may detract from providing preventive care to people with no diagnosed chronic disease and isolated and non-urgent risk factors.

Low levels of documented delivery of some laboratory tests may relate to practical barriers to laboratory services in the

#### TABLE 5 | Adjusted multilevel logistic regression analyses of health center and client level factors on delivery of guideline-scheduled service items by mode of care showing only significant associations (see additional files for full model outputs) (\**p* **<** 0.05; \*\**p* **<** 0.01).

remote locations in which most services included in this study are operating. It may also reflect poor integration of pathology results into electronic health records.

The low level of mammogram screening may be due to limited access to medical imaging services in remote areas and reflects a wider problem of access to specialized services for Indigenous Australians (30). Mammography screening for remote communities relies on either mobile screening units with a limited number of visits, or patient travel to larger centers which is not currently funded in the NT (33).

Our findings that female gender, being aged 25–34 years and recent health center attendance are associated with higher delivery of preventive care are consistent with previous studies (5, 7). That better preventive care delivery was significantly higher for females compared to males is likely to reflect a number of factors, including differences in health-seeking behaviors, proactive women's health staff and that health centers tend to be predominantly female staffed so may be seen as "women's places." The relatively lower level of preventive care delivered to males represents a key opportunity for improvement.

### Follow-up of Risk Factors and Abnormal Findings

Documented evidence of follow-up of various identified risk factors and of abnormal clinical and laboratory findings was poor (notably follow-up for abnormal BP, BGL, and serum lipids) and arguably represents the most significant opportunity for improving early intervention for chronic diseases. Reasons for lack of follow-up are varied and are present at the patient, health

TABLE 6 | Key findings and considerations for policy, practice, and research.

center, and policy levels (31). Lack of knowledge of the reasons for follow-up among some patients and therefore low demand, as well as concerns regarding transport and the cost of follow-up services may contribute to low levels of follow-up (31). Follow-up is likely to be relatively more difficult for clients who attend the health center infrequently; more than one quarter of clients in our study had not attended in the previous 6 months and many Indigenous people have a high level of mobility.

### Variation between Health Centers

In contrast with previous findings that client level factors were responsible for the majority of inter-client variability in delivery (5), we found measured health center factors explained the majority of variation in delivery of preventive care. Urban location, smaller service population, and location in the NT were associated with higher quality care.

Our findings support a previous study showing that a smaller health center service population is associated with greater adherence to best practice guidelines. However, in contrast to another finding of that study that committee or board operated health centers performed better than their government run counterparts we found no difference in outcomes between the two (5). The association of health center location in the NT may reflect greater investment in PHC, a longer and stronger history of engagement in CQI, partnership between ACCHS and government operated services, support for guideline implementation, and possibly greater commitment to information management compared to other jurisdictions.

In contrast to previous research, we found that urban location was associated with better delivery compared to regional or remote (5). This finding in particular should be interpreted with caution given the relatively small number and selected nature of urban health centers included in our study. Excluding one especially high performing urban health center from our analysis caused the effect of remote location compared to urban location to be insignificant for overall delivery, although regional location was still significantly associated with poorer delivery. If this effect does hold for the wider population of health centers, it may in part reflect greater access to referral services in urban locations as remote practitioners may be reasonably reluctant to carry out services such as visual acuity testing and oral health checks where there are more limited options for referral for treatment. It may also reflect resourcing and funding advantages in urban areas or a strategic decision among larger urban health centers to invest in Medicare funded health assessments as an opportunity to increase service remuneration. There has been wide variation in uptake of adult health assessments between health centers (6, 34), with substantial increases in urban and regional locations in the last few years and relatively little change in remote locations (34).

There are various factors that influence the effectiveness of PHC centers, which can be complex and difficult to measure, and may account for the unexplained variation between health centers (35). These include community linkages, organizational culture, effectiveness of team structure and function, degree of staff turnover, availability of Aboriginal Health Practitioners, allied health professionals and other resources, and use of information technology systems for recall and reminders (27, 35, 36).

Limitations of this study mostly relate to the generalizability of findings. Health centers participated on a voluntary basis were not randomly selected, therefore, our data may not be representative of all health centers in each jurisdiction. Furthermore, data refer only to those people who have attended participating health centers within the last 24 months and therefore do not provide reliable population estimates. The age and gender stratified samples are designed to facilitate comparison between communities and over time. Estimates based on this sampling approach may differ from those based on unstratified samples.

It must be emphasized that our data are based on recorded delivery, which generally underestimate actual service delivery (37). However, under-recording is problematic in team based care and in areas of workforce turnover as well as potentially resulting in unnecessary servicing, so accurate recording is important for improving quality of care (5). Assumptions made regarding

#### REFERENCES

prevalence of sexual activity and trachoma are likely to have led to underestimation of actual levels of delivery in relation to best practice guidelines while those concerning lipid testing and mammography may have led to some over- or underestimation.

### CONCLUSION

We assessed the delivery of recommended preventive health care for clients of Indigenous health centers. Wide variation in delivery between service items, low levels of documented follow-up for abnormal clinical findings, and the importance of health center factors in determining adherence to best practice guidelines provide valuable insights for improving quality of preventive care for Indigenous Australians. Improvement may be achieved by addressing physical, social, and cultural barriers to accessing preventive care, and by strengthening systems for follow-up and completion of preventive health assessments. Further clarification of the impact of health center factors such as resourcing, team structure and function, and use of clinical information systems will give better insight into possible improvements. Addressing identified gaps in preventive screening according to best practice and prioritizing interventions to address these can be implemented through CQI approaches that engage stakeholders at multiple levels (15, 38).

### AUTHOR CONTRIBUTIONS

CB designed and conducted the analysis, and drafted the manuscript under the supervision of VM who has lead responsibility for ABCD NRP data management and analysis. All other authors contributed to design of audit tools and facilitation of quality improvement processes from which the data used in this study arise, and contributed to data interpretation and refinement of the manuscript.

### ACKNOWLEDGMENTS

The development of this manuscript would not have been possible without the active support, enthusiasm, and commitment of members of the ABCD National Research Partnership, especially the participating primary health care centers that have provided de-identified data.

### FUNDING

The ABCD National Research Partnership Project was supported by funding from the National Health and Medical Research Council (545267) and the Lowitja Institute, and by in-kind and financial support from a range of Community Controlled and Government agencies. RB's work is supported by an Australian Research Council Future Fellowship (100100087).

community health centres in Australia. *BMC Health Serv Res* (2011) **11**:139. doi:10.1186/1472-6963-11-139

**Conflict of Interest Statement:** The authors declare that the above research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2016 Bailie, Matthews, Bailie, Burgess, Copley, Kennedy, Moore, Larkins, Thompson and Bailie. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Delivery of Eye and Vision Services in Aboriginal and Torres Strait Islander Primary Healthcare Centers

*Anthea M. Burnett1,2,3 \*, Anna Morse1,2, Thomas Naduvilath1,2 , Andrea Boudville2,4 , Hugh R. Taylor2,4 and Ross Bailie5*

*1Brien Holden Vision Institute, Public Health, Sydney, NSW, Australia, 2Vision CRC, Sydney, NSW, Australia, 3School of Optometry and Vision Science, University of New South Wales, Kensington, NSW, Australia, 4 Indigenous Eye Health, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia, 5Menzies School of Health Research, Charles Darwin University, Brisbane, QLD, Australia*

Background: Routine eye and vision assessments are vital for the detection and subsequent management of vision loss, which is particularly important for Aboriginal and Torres Strait Islander people who face higher rates of vision loss than other Australians. In order to guide improvements, this paper will describe patterns, variations, and gaps in these eye and vision assessments for Aboriginal and Torres Strait Islander people.

#### *Edited by:*

*Diane Cooper, University of the Western Cape, South Africa*

#### *Reviewed by:*

*Anu Mary Oommen, Christian Medical College, India Ginny Marisa Sargent, Australian National University, Australia*

*\*Correspondence: Anthea M. Burnett<EMAIL_ADDRESS>
#### *Specialty section:*

*This article was submitted to Public Health Policy, a section of the journal Frontiers in Public Health*

*Received: 05 August 2016 Accepted: 05 December 2016 Published: 19 December 2016*

#### *Citation:*

*Burnett AM, Morse A, Naduvilath T, Boudville A, Taylor HR and Bailie R (2016) Delivery of Eye and Vision Services in Aboriginal and Torres Strait Islander Primary Healthcare Centers. Front. Public Health 4:276. doi: 10.3389/fpubh.2016.00276*

Methods: Clinical audits from 124 primary healthcare centers (sample size 15,175) from five Australian states and territories were conducted during 2005–2012. Main outcome measure was adherence to current guidelines for delivery of eye and vision assessments to adults with diabetes, those without a diagnosed major chronic disease and children attending primary healthcare centers.

Results: Overall delivery of recommended eye and vision assessments varied widely between health centers. Of the adults with diabetes, 46% had a visual acuity assessment recorded within the previous 12 months (health center range 0–88%) and 33% had a retinal examination recorded (health center range 0–73%). Of the adults with no diagnosed major chronic disease, 31% had a visual acuity assessment recorded within the previous 2 years (health center range 0–86%) and 13% had received an examination for trichiasis (health center range 0–40%). In children, 49% had a record of a vision assessment (health center range 0–97%) and 25% had a record of an examination for trachoma within the previous 12 months (health center range 0–100%).

Conclusion: There was considerable range and variation in the recorded delivery of scheduled eye and vision assessments across health centers. Sharing the successful strategies of the better-performing health centers to support focused improvements in key areas of need may increase overall rates of eye examinations, which is important for the timely detection, referral, and treatment of eye conditions affecting Aboriginal and Torres Strait Islander people, especially for those with diabetes.

Keywords: Aboriginal and Torres Strait Islander people, primary healthcare centers, delivery of health care, eye care, diabetes, quality of health care, quality indicators

## INTRODUCTION

Aboriginal and Torres Strait Islander Australians experience significantly higher rates of vision impairment than other Australians (1, 2). The largely avoidable, preventable, or treatable nature of the majority (94%) of these cases (2) indicates the need for improved early detection pathways, timely referral, and appropriate and accessible treatment. Additionally, as Aboriginal and Torres Strait Islander adults with diabetes, older than 40 years, form 72% of those requiring an eye examination in any year (3), understanding ways to further improve access and uptake of eye assessments for patients with diabetes is important, given their higher risk of preventable vision loss.

Eye health services for Aboriginal and Torres Strait Islander communities are typically provided by visiting practitioners; even more so in remote or very remote locations (4). However, there is still a shortage of optometric and ophthalmic services in many rural and remote areas (4, 5) and significantly lower rates of eye examinations (by optometrists or ophthalmologists) in areas with higher proportions of Aboriginal and Torres Strait Islander people (4, 6). Current policy recommendations for better Aboriginal and Torres Strait Islander eye care in the "Roadmap to Close the Gap for Vision" place emphasis on primary eye care as part of comprehensive primary health care to address barriers to eye care (7). Similarly, international eye care strategies highlight the key role of primary health care in preventing vision loss and blindness (8).

As primary healthcare (PHC) centers are the frontline of health service delivery, they can often be the first point of contact for Aboriginal and Torres Strait Islander adults with diabetes or vision/eye problems. Hence, PHC centers play a crucial role in eye care (9), especially for patients with diabetes (7). Basic eye and vision screening assessments are conducted during routine health assessments such as the Aboriginal and Torres Strait Islander Health Assessment (10). When linked with distinct eye care referral processes, regular screening can help to identify and refer eye problems earlier, preventing vision loss (11). Primary healthcare practitioners also play a case management role, supporting and coordinating patients' timely access to comprehensive eye examinations and specialist eye care, particularly for patients with chronic conditions such as diabetes (12). This process can help improve efficiency of eye care service delivery systems by identifying and referring cases needing comprehensive eye care, targeting visiting eye care services to patients who most need them, and detecting vision problems at earlier stages (particularly important in the case of diabetic retinopathy) (13).

To gain insight into primary eye care coverage in Aboriginal and Torres Strait Islander PHC centers and to establish a baseline for comparison with future studies, we undertook an exploratory analysis of datasets from clinical file audits of PHC centers participating in the quality improvement action research project – the Audit and Best practice for Chronic Disease (ABCD) project (14). This study describes patterns, variations, and gaps in eye and vision assessments and associations with geographic location of health center, patient age, gender, and health center attendance. We discuss the implications of the findings with a focus on identifying approaches that will drive improvements in primary eye care services for Aboriginal and Torres Strait Islander communities.

### MATERIALS AND METHODS

### Study Population and Data Collection

The data presented here were collected as part of a national quality improvement project – the ABCD project (15), between 2005 and 2012. One hundred and twenty-four Aboriginal and Torres Strait Islander PHC centers in five states/territories voluntarily performed annual audits of client medical records and provided de-identified audit data to the ABCD National Research Partnership to investigate variations in quality of care. The audits were conducted by trained members of the project team in conjunction with local PHC center staff using three standardized audit tools and protocols developed by the Menzies School of Health Research. These tools assess: (1) delivery of services to clients with Type 2 diabetes, (2) delivery of preventative health care, and (3) delivery of child health care. For each of these client cohort datasets, the delivery of eye and vision services according to existing best practice guidelines (**Table 1**) was assessed.

For the three client cohorts, the records of Aboriginal and Torres Strait Islander clients who met the following criteria were eligible for audit: (1) Aboriginal and Torres Strait Islander patients with a definite diagnosis of Type 2 diabetes aged 15 years and over, (2) Aboriginal and Torres Strait Islander adults with no diagnosed major chronic disease attending the PHC center, in the prior 24 months from the date of the audit, for an annual well-person's check, acute care, or a preventative service, and aged between 15 and 64 years, and (3) Aboriginal and Torres Strait Islander children aged 15 years and under. For each of these client cohorts, clients were required to be residents of the community for at least 6 months of 12 months prior to the audit (or in the case of an infant, half the infant's life) in order to be eligible. A random sample of 30 clinical records for each cohort was audited from participating centers (**Table 2**), where there were fewer than 30 eligible records identified, all eligible records were included. Eye and vision services were assessed as "delivered" if there was a record of the service being delivered within specific periods in line with best practice eye care guidelines.

### Statistical Analysis

Treating individual clients as the unit of analysis, our data had inherent multilevel, dependency structure as eye and vision care data collected at the individual level were clustered within health centers. Multistage logistic regression models were used to examine associations of specific factors (location, age, gender, and attendance), with delivery of eye and vision care services (**Tables 3**–**5**). The outcomes included eye examination, vision assessment, and examination for trichiasis and trachoma. The year of audit was added as a factor in the model to account for the variation over time. Association with outcomes was described using odds ratio and 95% confidence intervals. Variations in eye or vision assessments between health centers were described using violin plots. Level of statistical significance was set at 5%.

TABLE 1 | Recommended eye and vision assessments for Aboriginal and Torres Strait Islander Australians.

*VA, visual acuity; NT, the Northern Territory; QLD, Queensland.*

*NACCHO/RACGP – National guide to a preventive health assessment for Aboriginal and Torres Strait Islander people (16, 17).*

*NH&MRC – guidelines for the management of diabetic retinopathy (18, 19).*

*Medicare benefit schedule item 708 – Aboriginal and Torres Strait Islander Child Health Check (MBS item 708) (20).*

*Medicare benefit schedule item 715 – Medicare Health Assessment for Aboriginal and Torres Strait Islander people (MBS ITEM 715) (21).*

*a Communicable Diseases Network Australia (CDNA) guideline recommends screening by jurisdictional teams.*

Analysis was performed using STATA and SPSS (Version 22.0., IBM Corp., Armonk, NY, USA).

Ethics approval was obtained from research ethics committees in each jurisdiction [Human Research Ethics Committee of the Northern Territory Department of Health and Menzies School of Health Research (HREC-EC00153); Central Australian Human Research Ethics Committee (HREC-12-53)]; New South Wales Greater Western Area Health Service Human Research Committee (HREC/11/GWAHS/23); Queensland Human Research Ethics Committee Darling Downs Health Services District (HREC/11/ QTDD/47); South Australian Aboriginal Health Research Ethics Committee (04-10-319); Curtin University Human Research Ethics Committee (HR140/2008); Western Australian Country Health Services Research Ethics Committee (2011/27); Western Australia Aboriginal Health Information and Ethics Committee (111-8/05); University of Western Australia Human Research Ethics Committee (RA/4/1/5051).

#### RESULTS

The records of 15,175 Aboriginal and Torres Strait Islander clients were audited from 124 participating health centers across various locations (comprising city, regional, and remote jurisdictions) from five Australian states and territories (**Table 2**). Among the records of adults with diabetes, 46% (3,320/7,320) had a VA assessment recorded and 33% (2,381/7,300) had records of receiving a retinal examination within the previous 12 months. Of the records of the adults with no diagnosed major chronic disease, 31% had a VA assessment recorded within the last 2 years (236/759), while only 13% (380/2,829) of the audited records had received an examination for trichiasis. From the records of children, 49% (2,415/4,909) had a vision assessment recorded within the past 12 months (guidelines recommend an annual assessment for children aged ≥4 years in NT or ≥3 months in all other areas), 45% (2,085/4,632) had a record of an eye examination and 25% (223/893) had a record of an examination for trachoma (guidelines recommend an annual examination for children aged ≥4 years old in the NT, or if indicated in other states and territories).

There was significant variability in documented delivery of these assessments by state/territory and location (**Tables 3**–**5**). Participating PHC centers in New South Wales (NSW), the Northern Territory (NT), and Queensland (QLD) had relatively high recorded VA assessments and retinal examinations for adults with diabetes, while Western Australian (WA) health centers recorded higher rates of trichiasis examinations to adults with no diagnosed major chronic disease. Similarly, variation was observed in delivery between states among the audited child records, with participating PHC centers in NSW and South Australia (SA) delivering the most eye assessments and those in the NT recording the most trachoma examinations for children.

There was considerable variation in delivery of scheduled services to Aboriginal and Torres Strait Islander clients across health centers (**Figure 1**). The range between health centers for delivery of VA assessments to adults with type 2 diabetes was from 0 to 88%, while the delivery of retinal examinations ranged from 0 to 73%. For adults with no diagnosed major chronic disease, the range of documented delivery of visual acuity assessments was between 0 and 86%, with the range of trichiasis examinations delivered between 0 and 40%. Some centers provided vision and

#### TABLE 2 | Characteristics of the three cohorts audited.

*a Not all health centers participated in all three audits.*

*bOne participant was aged 68.5 years and was retained in analysis.*

TABLE 3 | Adjusted multilevel logistic regression analysis of patient characteristics and documented delivery of vision and eye health services for Aboriginal and Torres Strait Islander adults with diabetes in participating health centers between June 2005 and August 2012.

*VA, visual acuity, NSW, New South Wales, NT, Northern Territory, QLD, Queensland, WA, Western Australia, SA, South Australia.*

*Odds ratios significant at 0.05 level are shown in bold.*

*a Within the previous 12 months.* TABLE 4 | Adjusted multilevel logistic regression analysis of patient characteristics and documented delivery of vision and eye health services for Aboriginal and Torres Strait Islander adults in participating health centers between June 2005 and August 2012.

*VA, visual acuity; NSW, New South Wales; NT, Northern Territory; QLD, Queensland; WA, Western Australia; SA, South Australia.*

*Odds ratios significant at 0.05 level are shown in bold.*

*a Within the previous 2 years.*

TABLE 5 | Adjusted multilevel logistic regression analysis of patient characteristics and documented delivery of vision and eye health services for Aboriginal and Torres Strait Islander children in participating health centers between June 2005 and August 2012.

*Odds ratios significant at 0.05 level are shown in bold.*

*a Within the previous 12 months.*

eye assessments to 100% of eligible children – the delivery of trachoma and eye examinations to eligible children ranged from 0 to 100%, while the delivery of the vision assessment ranged from 0 to 97%.

Documented delivery of VA and retinal examinations increased with age for adults with diabetes, but not for documented delivery of VA and trichiasis assessments to adults with no major chronic disease (**Table 4**). There was no variability between genders for adults (**Tables 3** and **4**) or children (**Table 5**). Documented eye assessments were significantly more likely for patients who had visited the health center within the previous 12 months.

## DISCUSSION

Our investigation revealed significant variation in the documented delivery of eye and vision assessments to Aboriginal and Torres Strait Islander clients attending PHC centers. While some centers provided excellent levels of assessments, others provided low levels. This variation in performance presents an opportunity for improvement in the documented delivery of these assessments by examining the factors that underlie variation in delivering services, as has previously been conducted in Aboriginal and Torres Strait Islander communities for preventative and diabetes care (22, 23). The successful strategies of the better-performing health centers could then be shared with the under-performing, to inform and support focused improvements in key areas of need.

On average, clients with diabetes were more likely to have a VA assessment recorded (46%) than adults with no diagnosed major chronic disease (31%). This may reflect the stricter adherence to routine screening and regular monitoring for patients with diabetes encouraged by chronic disease management plans. It may also reflect practitioners' knowledge of the risk of increased vision loss for patients with diabetes or patients' reports of issues with their vision. Additionally, patients with greater engagement in their health care may be more likely to engage with eye care services.

In this study, only 33% of Aboriginal and Torres Strait Islander adults with diabetes had a documented retinal examination within the previous 12 months. This is consistent with a previous audit of patient files in the NT, which reported that 34% had a documented fundus examination in the prior 12 months (24). The National Indigenous Eye Health Survey conducted in 2008 also reported low frequencies (20%) of eye examinations for patients with diabetes (self-reported) (2). Understanding ways to further improve access and uptake of eye assessments for patients with diabetes is important, given their higher risk of preventable vision loss. As retinal examinations largely rely on services being provided by visiting or off-site eye practitioners, utilizing retinal photo-screening integrated with primary care, image grading, and reporting systems may increase rates of examinations. This strategy has been shown to improve screening outcomes for other Australians with diabetes (25, 26). As we were unable to determine which centers used retinal imaging in this dataset, we could not investigate whether retinal imaging influences examinations rates. This may be useful in future audits.

Australia is the only high-income country in the world where trachoma is endemic – trachoma currently occurs in remote and very remote Aboriginal communities in the NT, SA, and WA (27), with pockets of trachoma in Far West NSW and Far North QLD (2). Although trachoma examinations were recorded more frequently for NT children, 60% of children from the NT did not have a trachoma examination recorded, despite guidelines stating that all children aged 4 years or older should be examined for trachoma annually. In the other participating states, less than 10% of children in areas where trachoma was indicated had a recorded trachoma examination. The National Trachoma Surveillance and Reporting Unit (NTSRU) jurisdiction covers communities designated as being at-risk or potentially at-risk of trachoma and has reported trachoma screening rates ranging from of 63 to 92% (27). Our results are significantly less than this, and this would seem to reflect the failure to record in the clinical records the trachoma examinations conducted by the jurisdictional trachoma screening programs. Improved coordination between external trachoma screening services and PHC centers will enable PHC centers to continue to make a significant contribution toward closing the gap for vision in Australia (9).

Several limitations should be acknowledged when interpreting these data. First, the data only indicate recorded services provided. Given that some aspects of eye care may be accessed in other off-site settings (e.g., optometry and ophthalmology services), it is probable that the rates of retinal examinations may be higher than recorded in PHC records. Second, as these data are only from the Aboriginal and Torres Strait Islander health centers that agree to have their audit data included in the aggregate for the ABCD project, findings are not necessarily representative of all health centers across Australia. Third, the relatively small numbers, self-selection, and uneven distribution of participating health centers in some states/territories means that the data cannot be regarded as broadly representative for these jurisdictions and hence any comparison between state and territory data should only be considered as representative of the cluster of health services participating from each jurisdiction. The inclusion of the state/territory variables in the multivariate models was primarily to enhance model fit and adjust for state/territory level confounders. Finally, as the timeframe in which the adult audit data was collected (2005–2012) precedes the date (2013) from when adult vision and eye assessments became mandatory within Aboriginal and Torres Strait Islander health assessments (MBS715) (28), it is possible that reporting has since improved. A useful area for future analysis would be to determine whether mandatory inclusion of vision tests in adult health assessments has had a significant impact on rates of recorded eye assessments – these results provide an important baseline from which future improvements could be demonstrated. Furthermore, given that the ABCD program and associated audits exist for the purpose of supporting continuous quality improvement (CQI), another useful area for future study may be to track changes in recorded rates of eye and vision assessments over time.

This study has identified opportunities for PHC centers to increase the documented delivery of eye and vision assessments to Aboriginal and Torres Strait Islander clients. Establishing or strengthening systems for external eye practitioners to report back to PHC practitioners' results from retinal examinations or retinal photograph for people with diabetes may lead to notable improvements and represents a potential "quick win" to increase the rates of recorded retinal examinations. More importantly, it would also offer better patient care and coordination by informing PHC practitioners' of eye care history for the patients they oversee. Similarly, strengthening coordination with external trachoma screening programs may also allow PHC practitioners to better monitor trachoma endemicity in their community.

### CONCLUSION

Routine eye and vision assessments for Aboriginal and Torres Strait Islander adults and children attending PHC centers are currently not being recorded at the recommended levels with considerable variation between health centers. These results can represent a baseline, from which improvements in primary eye and vision assessments for Aboriginal and Torres Strait Islander Australians could be made and monitored.

These results also highlight the value of performing clinical audits to identify aspects of eye care and health centers that are being conducted relatively well, or need improvement. The successful strategies of the better-performing health centers could then be shared with the under-performing, to inform and support focused improvements in key areas of need.

### AUTHOR CONTRIBUTIONS

AB interpreted the data and drafted the manuscript. AM contributed to interpretation of the data, drafting, and critically revised the manuscript. TN analyzed and the data and contributed to interpretation. AB, HT, and RB critically reviewed and commented on the project design and on the manuscript. All authors read and approved the final manuscript.

### ACKNOWLEDGMENTS

This project was only possible with the contributions and partnership with the ABCD National Partnership Project team. We thank Veronica Matthews for her expertise in data analysis and reviewing the manuscript.

### FUNDING

The ABCD National Research Partnership Project is supported by funding from the National Health and Medical Research Council (ID No.545267) and the Lowitja Institute, and by in-kind and financial support from a range of Aboriginal Community Controlled and Government agencies. This study was funded by the Brien Holden Vision Institute and the Australian Government's CRC scheme through the Vision CRC.

### REFERENCES

*National Guide to a Preventative Health Assessment for Aboriginal and Torres Strait Islander People*. South Melbourne: The RACGP (2012).

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2016 Burnett, Morse, Naduvilath, Boudville, Taylor and Bailie. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Integrated Clinical Decision Support Systems Promote Absolute Cardiovascular Risk Assessment: An Important Primary Prevention Measure in Aboriginal and Torres Strait Islander Primary Health Care

*Veronica Matthews 1 \*, Christopher P. Burgess 2 , Christine Connors 2 , Elizabeth Moore3 , David Peiris 4 , David Scrimgeour 5 , Sandra C. Thompson6 , Sarah Larkins 7 and Ross Bailie1*

*1 The University of Sydney, University Centre for Rural Health – North Coast, Lismore, NSW, Australia, 2 Top End Health Service, Northern Territory Government, Darwin, NT, Australia, 3Aboriginal Medical Services Alliance Northern Territory, Alice Springs, NT, Australia, 4 The George Institute for Global Health, Sydney, NSW, Australia, 5Spinifex Health Service, Tjuntjuntjara, WA, Australia, 6Western Australian Centre for Rural Health, University of Western Australia, Geraldton, WA, Australia, 7College of Medicine and Dentistry, James Cook University, Townsville, QLD, Australia*

#### *Edited by:*

*Amelia Kekeletso Ranotsi, Maluti Adventist College, Lesotho*

#### *Reviewed by:*

*Jan Fizzell, NSW Health, Australia Colin MacDougall, Flinders University, Australia*

*\*Correspondence:*

*Veronica Matthews veronica.matthews@ sydney.edu.au*

#### *Specialty section:*

*This article was submitted to Public Health Policy, a section of the journal Frontiers in Public Health*

*Received: 09 March 2017 Accepted: 18 August 2017 Published: 04 September 2017*

#### *Citation:*

*Matthews V, Burgess CP, Connors C, Moore E, Peiris D, Scrimgeour D, Thompson SC, Larkins S and Bailie R (2017) Integrated Clinical Decision Support Systems Promote Absolute Cardiovascular Risk Assessment: An Important Primary Prevention Measure in Aboriginal and Torres Strait Islander Primary Health Care. Front. Public Health 5:233. doi: 10.3389/fpubh.2017.00233*

Background: Aboriginal and Torres Strait Islander Australians experience a greater burden of disease compared to non-Indigenous Australians. Around one-fifth of the health disparity is caused by cardiovascular disease (CVD). Despite the importance of absolute cardiovascular risk assessment (CVRA) as a screening and early intervention tool, few studies have reported its use within the Australian Indigenous primary health care (PHC) sector. This study utilizes data from a large-scale quality improvement program to examine variation in documented CVRA as a primary prevention strategy for individuals without prior CVD across four Australian jurisdictions. We also examine the proportion with elevated risk and follow-up actions recorded.

Methods: We undertook cross-sectional analysis of 2,052 client records from 97 PHC centers to assess CVRA in Indigenous adults aged ≥20 years with no recorded chronic disease diagnosis (2012–2014). Multilevel regression was used to quantify the variation in CVRA attributable to health center and client level factors. The main outcome measure was the proportion of eligible adults who had CVRA recorded. Secondary outcomes were the proportion of clients with elevated risk that had follow-up actions recorded.

Results: Approximately 23% (*n* = 478) of eligible clients had documented CVRA. Almost all assessments (99%) were conducted in the Northern Territory. Within this jurisdiction, there was wide variation between centers in the proportion of clients with documented CVRA (median 38%; range 0–86%). Regression analysis showed health center factors accounted for 48% of the variation. Centers with integrated clinical decision support systems were more likely to document CVRA (OR 21.1; 95% CI 5.4–82.4; *p* < 0.001). Eleven percent (*n* = 53) of clients were found with moderate/ high CVD risk, of whom almost one-third were under 35 years (*n* = 16). Documentation of follow-up varied with respect to the targeted risk factor. Fewer than 30% with abnormal blood lipid or glucose levels had follow-up management plans recorded.

Conclusion: There was wide variation in CVRA between jurisdictions and between PHC centers. Learnings from successful interventions to educate and support centers in CVRA provision should be shared with stakeholders more widely. Where risk has been identified, further improvement in follow-up management is required to prevent CVD onset and reduce future burden in Australia's Indigenous population.

Keywords: cardiovascular disease, risk assessment, Indigenous health, prevention, primary health care

#### INTRODUCTION

Health inequities between Aboriginal and Torres Strait Islander (respectfully referred to as Indigenous) and non-Indigenous Australians are well documented (1, 2) and are a legacy of colonization, disempowerment and ongoing racial, social and economic inequality (3). It has been estimated that continued inequality accounts for between one-third and one-half of the 10-year life expectancy gap between Indigenous and non-Indigenous people (2, 4), highlighting the importance of addressing the social determinants of health and ensuring equity of access to quality health care.

Highly preventable chronic diseases contribute most to the higher rate of poor health and premature death experienced by Aboriginal and Torres Strait Islander people. Cardiovascular disease (CVD), largely driven by the combined effect of several modifiable risk factors such as smoking and obesity, is the leading contributor accounting for one-fifth of the health gap (1). In addition to improving social and economic determinants of health, effective CVD prevention, through regular screening and early intervention, would make a significant contribution to reducing the health gap and disease burden within the Indigenous population (5).

Health promotion, prevention, and early treatment services are a key component of Australia's primary health care (PHC) system. Access to PHC for Aboriginal and Torres Strait Islander people is through community-controlled health centers, government-operated community health centers, and private general practitioners (GPs), with some variation across diverse geographies. Aboriginal and Torres Strait Islander community-controlled centers and some government centers operating in predominantly Indigenous communities offer models of comprehensive PHC providing access to doctors, nurses, allied health, social and emotional wellbeing professionals, and medical specialists. Service size, however, varies depending on remoteness, with visiting services a feature of remote locations.

A recent national initiative, "*Better cardiac care for Aboriginal and Torres Strait Islander people*," outlines priority action areas to address inequities in cardiovascular health service delivery between Indigenous and non-Indigenous people (6). Priority actions are staged across the disease continuum and include cardiovascular risk assessment (CVRA) as a key aspect of primary prevention, along with practitioner follow-up and intervention for those identified at risk, such as pharmacotherapy and ongoing culturally appropriate support to facilitate lifestyle modification (5).

Absolute CVRA is a screening and management process intended for use by PHC practitioners to calculate the probability of a cardiovascular event within 5 years, taking into account the synergistic effect of multiple risk factors that may be present (7). The risk calculator takes account of age, sex, systolic blood pressure, smoking status, levels of total and high-density lipoprotein cholesterol, and presence of diabetes (7).

Despite the importance of CVRA as a screening and early intervention tool, few studies have reported its use within the Indigenous PHC sector or in the broader Australian PHC setting. This reflects the lack of national and jurisdictional data on CVRA and PHC services in general (6). In 2012, the Northern Territory (NT) government implemented a large-scale strategy to strengthen chronic disease prevention in Indigenous communities that included regular CVRA data collection and reporting and the roll-out of an automated CVRA calculator within the electronic medical record system used by government health centers (8). In 2015, a similar calculator was introduced into the *Communicare* electronic medical record system used by many Aboriginal and Torres Strait Islander community-controlled health centers in the NT and other jurisdictions.

This study examines variation in documented CVRA for adults with no prior diagnosis of chronic disease as a primary prevention strategy in Indigenous PHC centers across four Australian jurisdictions (2012–2014). We also report on the proportion of Indigenous people found with elevated risk and the proportion that had subsequent follow-up actions documented.

#### MATERIALS AND METHODS

This is a retrospective cross-sectional study of preventive care clinical audits undertaken by 97 Indigenous PHC

**Abbreviations:** ABCD, Audit and Best Practice for Chronic Disease; CQI, continuous quality improvement; CVD, cardiovascular disease; CVRA, cardiovascular risk assessment; GP, general practitioner; nKPI, national Key Performance Indicator; NT, Northern Territory; PCV, proportional change in variance; PHC, primary health care; QLD, Queensland; SA, South Australia; WA, Western Australia.

centers participating in the Audit and Best Practice for Chronic Disease (ABCD) project. The ABCD project is a researchbased continuous quality improvement (CQI) initiative that has operated on a national scale since 2005, co-designing best practice clinical audit tools (covering different aspects of comprehensive PHC delivery) and processes with relevant stakeholders (9). The majority of participating services within the ABCD program are community-controlled or governmentoperated centers predominantly serving Indigenous communities. The preventive care CQI process was designed to enable participating health centers assess the level of adherence to best practice guidelines and assess organizational systems to support prevention and early detection of chronic disease. The audits are conducted by local staff trained in the use of ABCD tools and processes.

Clients included in a preventive care audit are those aged ≥15 years with no recorded diagnosis of diabetes, hypertension, coronary heart disease, rheumatic heart disease, or chronic kidney disease, who have been resident in the community for ≥6 months within the last year (10). Where the eligible population numbers 30 or less, the audit protocol recommends inclusion of all records. For 30 or more eligible clients, the protocol provides guidance on a sufficient number of randomly selected records to achieve 90–95% confidence of the sample representing the service population. Samples were stratified by age and gender.

Given the value of risk assessment as a primary prevention measure and high rates of premature CVD in Indigenous people, the audit tool incorporates assessment against best practice guidelines related to CVRA delivery. Current national guidelines recommend CVRA be provided to Indigenous adults aged between 35 and 74 years who are not known to have CVD or to be at clinically determined high risk (7). For NT Indigenous residents, the CVRA age criterion has been lowered to 20 years due to local prevalence of early onset CVD (11). Low, moderate, and high risks correspond to <10, 10–15, and >15% probability of a cardiovascular event within the next 5 years as determined by the type of calculator used in the assessment. As no CVRA algorithm has been validated in the Indigenous Australian population, current calculators underestimate the risk, failing to consider historical context and the consequential socioeconomic disadvantage and premature CVD prevalent within the population (12). Following the precedent set within New Zealand's CVRA guidelines for the Maori population, the NT PHC standard treatment (CARPA) manual included an upward risk adjustment of 5% on the Framingham algorithm for the NT Indigenous population (11, 13).

A subset of the preventive care audit data was used for this study to examine variation in documented CVRA as a primary prevention strategy for Indigenous adults with no previous documentation of chronic disease (**Figure 1**). We used the most recent preventive care audit from each of the 97 centers conducted between 2012 and 2014. While the audit tool includes clients with a diagnosis of dyslipidaemia, we excluded these records (*n* = 22) given it is a prominent risk factor for CVD and may influence clinical judgments with respect to a patient's

\*A preventive care audit excludes clients with a record of diabetes, hypertension, coronary heart disease, rheumatic heart disease, and chronic kidney disease.

background and calculating absolute risk (14). Almost 90% of clients with a record of dyslipidaemia did not have a record of CVRA within the last 24 months. Our "healthy cohort" criteria have therefore excluded individuals eligible for CVRA according to national guidelines, such as people with diabetes under the age of 60 years without microalbuminuria or people with stages 1 and 2 chronic kidney disease. De-identified clinic records of over 2,000 healthy Indigenous adults were included in the analysis. In addition to demographic information (age and sex), the audit recorded whether individuals received an adult health check and a CVRA within the last 24 months and the calculated CVD risk level. Other information collected included relevant risk factor documentation [yes/no for smoking status, body mass index, waist circumference, urinalysis, blood pressure, and blood glucose and lipid levels] and follow-up actions for abnormal findings. Health center factors such as location, population size, governance, and length of participation in the ABCD program were also recorded.

Our main outcome measure was the proportion of eligible clients who had documented CVRA. We used client and health center level information to determine independent factors associated with CVRA. Secondary outcomes for those identified at moderate/high risk were the proportion that had documented follow-up management plans and brief interventions. It was not possible to assess the level of pharmacotherapy intervention as this information was not captured within the audit tool.

Summary statistics was used to describe variation in CVRA across health centers and jurisdictions, the number of adults with elevated risk and documented level of follow-up. Cross-jurisdictional information has been aggregated where there were counts less than five. Given the hierarchical nature of the data (clients within health centers), multilevel mixed effects logistic regression models were used (health center variable was treated as a random effect with random intercept) to quantify the variation in CVRA attributable to health center and client level factors. Because a large majority of CVRA was recorded in NT centers, we restricted the regression analysis to this jurisdiction. As most centers were located in remote areas, we excluded location as a predictor variable. We calculated odds ratios to measure the unadjusted and adjusted associations between independent factors and CVRA (adjusting for year of audit). In a step-wise fashion, we included significant health center (Model A) then client variables (Model B) from the unadjusted analyses and measured the proportional change in variance to determine the amount of variation attributable to the different levels. Potential interactions were checked for significance. Statistical associations were considered significant if the *p*-value was <0.007 (Bonferroni correction). Analysis was completed using STATA software, version 14.

### RESULTS

The majority (92%) of PHC centers were located in remote or very remote areas, and 84% were government operated (**Table 1**). There were 2,052 eligible Indigenous clients aged between 20 and 75 years with almost equal numbers of males and females. Acute care was the primary reason for attendance for 49% of clients. There was wide variation across jurisdictions and health centers in the documentation of risk factors used to

*a Location based on the Australian Standard Geographical Classification system.*

*NT, Northern Territory; QLD, Queensland; SA, South Australia; WA, Western Australia.*

TABLE 2 | Primary health care (PHC) center documentation of risk factors used for cardiovascular risk assessment (median % and range).

*a A lipid profile includes total cholesterol, high-density lipoprotein, low-density lipoprotein, and triglycerides.*

*bMinimum level of risk factor documentation required for estimates using the Framingham algorithm.*

assess CVD risk (**Table 2**). NT centers had higher documentation of client smoking status, blood pressure, and lipid profile (essential measures for the Framingham algorithm) compared to other jurisdictions. Overall, 23% of eligible clients had CVRA documented and almost all of the assessments occurred in the NT (**Table 3**).

Due to the audit exclusion criteria and local CVRA guidelines, the NT cohort (*n* = 1,388) had a larger proportion (65%) of young adults (<35 years). Focusing on this jurisdiction, there was a clear trend of improvement in the mean proportion of PHC center clients documented as receiving CVRA; however, wide variation persisted across years (**Figure 2**). Health center factors accounted for 48% of the variation (**Table 4**, Model A). Clients were more likely to have documented CVRA if they attended government-operated centers (distinguished by the availability of automated CVRA calculators within their electronic patient information systems, Model A: OR 21.1; 95% CI 5.4–82.4; *p* < 0.001), and if they had an adult health check (Model A: OR 3.9; 95% CI 2.8–5.4; *p* < 0.001). There was no significant interaction between governance and provision of health checks. Client factors did not appreciably explain any further variation, although adults aged ≥45 years were more likely to have CVRA compared to the youngest age group (Model B: OR 2.0; 95% CI 1.3–3.2; *p* = 0.003).

Of the NT clients who received CVRA, 11% (*n* = 53; 95% CI: 8–14%) were found to be at moderate/high risk of a cardiovascular event in the next 5 years (**Table 5**). Thirty percent of adults with elevated risk were under 35 years (*n* = 16; **Table 5**) and 64% (*n* = 34) had ≥3 modifiable risk factors documented (**Table 6**). The main risk factors recorded were abnormal lipid levels (*n* = 43 at risk clients), being overweight (*n* = 38), and smoking (*n* = 33; **Table 6**). In terms of follow-up action documented, less than one-third of clients with abnormal blood lipid or glucose levels had a management plan recorded (with a scheduled repeat measurement), 82% (*n* = 27) of smokers and 71% (*n* = 27) of overweight clients had documentation of a brief intervention or referral (**Table 6**).

TABLE 3 | Documented delivery of cardiovascular risk assessment (CVRA) by primary health care (PHC) centers (mean % and total number), by jurisdiction (2012–2014).

#### DISCUSSION

This study provides original data on CVRA as an important CVD primary prevention activity for adults with no prior chronic disease diagnosis in the Aboriginal and Torres Strait Islander PHC sector. CVRA is particularly important in the generally healthy population as adults with known major chronic illnesses such as diabetes are at higher risk of CVD, regardless of the other risk measurements included in CVRA. Our finding that 23% of eligible clients had documented CVRA is lower than in similar studies reporting inadequate levels of screening (15, 16) due to our exclusion of clients with chronic disease, regardless of severity. The NT had substantially higher level documentation of CVRA than other jurisdictions and of those assessed at moderate/high risk, 30% were under the age of 35 years.

There have been similar findings within the Australian general practice sector where it has been reported that GPs: may not routinely calculate absolute risk for the general population; focus treatment on individual risk factors; and lack the data necessary for calculation of absolute risk (especially lipid screening) (17, 18). Lack of risk factor recording is also an issue in the current study, where in some jurisdictions, the minimal level of risk factor documentation in client records required for CVRA calculation (using the Framingham algorithm: smoking status, blood pressure, and lipid profile) was under 35%. Despite significant government TABLE 4 | Multilevel regression analysis—cardiovascular risk assessment (CVRA) in Northern Territory primary health care centers, 2012–2014 (*n* = 48 centers; 1,388 clients).

*PCV, proportional change in variance; significance level p* < *0.007.*

TABLE 5 | Number (%) of Northern Territory clients with documented cardiovascular risk assessment (CVRA) and risk level, by age group.

*a 95% confidence intervals calculated for population proportion of risk level estimates.*

funding for tackling smoking initiatives within the Indigenous community, there remains a high level of variation in recording of smoking status, particularly in Queensland. Participating health centers in Queensland were predominantly government-operated and not representative of the community-control sector. The latest national Key Performance Indicator (nKPI) report (December 2014) shows Aboriginal and Torres Strait Islander communitycontrolled health centers in Queensland documenting smoking status for 83% of their clientele (19).

TABLE 6 | Documentation of risk factors and follow-up interventions in Northern Territory clients with elevated risk (*n* = 53).

*a High BMI* ≥ *25; abnormal blood pressure: systolic pressure* ≥*140 mmHg and/or the diastolic pressure* ≥*90 mmHg; abnormal blood glucose:* ≥*5.5 mmol; abnormal blood lipids: low-density lipoprotein* >*2.0 mmol/L or high-density lipoprotein* <*1 mmol/L or triglycerides* >*1.5 mmol/L.*

*bFollow-up action—brief intervention or referral for relevant lifestyle modification (smoking cessation/weight management).*

*c Follow-up action—documented management plan including repeat test schedule to monitor levels and for high blood pressure and lipid readings, referral to doctor for assessment and potential medication control.*

Despite the recording of individual risk factors in the health records of many clients attending health centers in the NT, almost all CVRA (*n* = 439) occurred within the government sector, which had automated calculators available within their electronic patient information record systems. As manual calculation of CVRA can be cumbersome, this highlights the value of the automated calculator that was not available within the systems used by the community-control sector during the study period. Automation of CVRA also allowed the NT government to implement three monthly rapid CQI cycles on key performance indicators to continually identify screening and treatment gaps (8). There have also been dedicated educators and CQI facilitators coaching local teams in CVRA and assisting with assessments and identification of clients for recall while located off-site from the PHC centers. Sustained implementation of these higher level system supports differentiates the NT from other jurisdictions where large-scale CQI auditing for preventive care has taken place without improvement in CVRA. Other targeted interventions incorporating electronic decision support tools and CQI processes have also led to increased rates of CVRA coverage across PHC centre service populations in urban, rural and remote locations in Australia and New Zealand (20, 21). Learnings from these successful interventions have the potential to improve screening rates in other areas, and it is likely that coverage rates improved further beyond this study period due to the introduction of the automated calculator within communitycontrol PHC centers.

With the majority of clients in this study presenting for acute care, time and PHC center capacity may be a barrier to investigating CVD precursors in people with no diagnosis of a chronic condition (22). There was an association between CVRA and adult health checks suggesting that the checks are an important initiator of CVRA. Through a rebate system, the Australian Government has encouraged uptake of comprehensive health checks for Indigenous people that includes examination of physical, psychological, and social well-being [Medicare Benefits Schedule (MBS) item 715]. The use of health checks has increased over time and as at December 2014, 46% of regular Indigenous clients aged over 25 years in the NT had a health check within the last 24 months (44% nationally) (19). However, while the health check collects risk factor and biomedical information used to calculate cardiovascular risk, CVRA is not directly specified within the MBS 715 item descriptor. To more immediately address the CVD burden in the Indigenous population, a standalone rebate item for CVRA has been recommended to promote uptake rates beyond that of general health checks (8).

We found a lower proportion of Indigenous people assessed as having moderate/high CVD risk in comparison to that reported for the NT government sector in 2014 (8). This may be partly explained by differences in the study cohorts, with the preventive care audit excluding clients with a record of chronic disease. Based on this criteria, 65% of the NT clients in this study were under the age of 35 years and 15% were 45 years or older. Almost one-third of clients with moderate/high CVD risk were under 35 years, emphasizing the importance of targeted screening for adults younger than the current CVRA national guideline for Indigenous people.

Despite improvements in risk screening, evidence to practice gaps persist regarding follow-up treatment to reduce risk once identified (8, 20). Lack of follow-up has been attributed previously to barriers at various levels of the health system such as lack of time, staff capacity, availability of culturally appropriate referral services, and that practice incentives focus on assessment rather than follow-up (23). The lack of documentation of management plans for abnormal findings demonstrates the challenge of maintaining client continuity of care, particularly in remote contexts. Building client/health provider relationships is an important enabler for sustained engagement required to effect lifestyle change, where responsibility for care is shared between client and practitioner and where health centers work beyond traditional roles to influence social and economic determinants for individuals and their communities (24, 25). Lifestyle modifications such as improving nutrition and exercise and lowering rates of smoking will reduce the current and future burden of disease, not only with respect to CVD but other chronic conditions contributing to the health gap between Aboriginal and Torres Strait Islander and other Australians.

Large-scale examination of CVRA to Indigenous people has been made possible by the centers enrolled in the ABCD CQI program. However, the voluntary nature of their participation limits the generalizability of study findings, with the majority of centers from remote areas in the NT and the government sector in Queensland. In addition, as data are collected from client records, delivery of CVRA and associated follow-up for clients at risk may be underestimated due to poor documentation. However, accurate recording is an essential aspect of care quality and should be addressed as part of CQI processes.

The "*Better cardiac care for Aboriginal and Torres Strait Islander people*" strategy has increased attention on capturing CVRA data on a broad scale, initiating its introduction to the nKPI dataset (26) (although the age criteria begins at 35 years, some 10 years older than clients documented with elevated risk in the NT). A routine data source allows the assessment of "unwarranted" variation in the delivery of key service items, a necessary first step to examine potential health system factors that when leveraged, may enhance consistent, appropriate care on a broad-scale (27, 28). We demonstrate the value of CQI processes in systematically capturing, reporting and reviewing data on the variation in CVRA and follow-up. Systematic collection of data for primary prevention also emphasizes the need for a specific calculator to accurately predict CVD risk for this population.

### CONCLUSION

Our findings show that there is substantial room for improvement in CVRA and follow-up as an important primary prevention strategy within the Aboriginal and Torres Strait Islander population. Systematic CVRA provides an opportunity for the PHC sector to curb rates of early CVD onset as emphasized by the young "healthy" cohort of Aboriginal and Torres Strait Islander adults documented with elevated risk. Shared learnings from successful system interventions as demonstrated in the NT (integration of automated calculators, CQI processes, and dedicated staff support) have the potential to reduce unwarranted variation and increase rates of screening on a broad scale, enabling early intervention where necessary. Further work is required on improving follow-up of clients identified at risk and facilitating supportive health center–client/community relationships.

### ETHICS STATEMENT

Ethics approval was obtained from human research ethics committees (HRECs) in each jurisdiction: Northern Territory HREC-EC00153 & HREC-12-53; New South Wales HREC/11/ GWAHS/23; Queensland HREC/11/QTDD/47; South Australia Aboriginal Health Research Ethics Committee 04-10-319; Western Australia Curtin University HR140/2008; WA Country Health Services 2011/27; WA Aboriginal Health Information and Ethics Committee 111-8/05; and University of Western Australia RA/4/1/5051.

### AUTHOR CONTRIBUTIONS

RB and VM conceived and designed the study. VM analyzed the data, drafted, and revised the paper with important intellectual input from all authors (PB, CC, EM, DP, DS, ST, SL, and RB). RB played a lead role in design and development of the ABCD

#### REFERENCES

research project. CC, EM, DS, ST, and SL were co-investigators on the ABCD project. All authors read and approved the final manuscript.

#### ACKNOWLEDGMENTS

The development of this manuscript would not have been possible without the active support, enthusiasm, and commitment of staff in participating primary health care centers and members of the ABCD National Research Partnership and the Centre for Research Excellence in Integrated Quality Improvement. We would like to thank Jodie Bailie and Alison Laycock for their revision of the manuscript.

### FUNDING

The National Health and Medical Research Council has funded the ABCD National Research Partnership Project (# 545267) and the Centre for Research Excellence in Integrated Quality Improvement (# 1078927). In-kind and financial support has been provided by the Lowitja Institute and a range of communitycontrolled and government health agencies.

a cross-sectional analysis. *Front Public Health* (2016) 4:34. doi:10.3389/ fpubh.2016.00034

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2017 Matthews, Burgess, Connors, Moore, Peiris, Scrimgeour, Thompson, Larkins and Bailie. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# System-Level Action Required for Wide-Scale Improvement in Quality of Primary Health Care: Synthesis of Feedback from an Interactive Process to Promote Dissemination and Use of Aggregated Quality of Care Data

#### *Jodie Bailie\*, Alison Laycock, Veronica Matthews and Ross Bailie*

*Menzies School of Health Research, Charles Darwin University, Casuarina, NT, Australia*

#### *Edited by:*

*Edward Broughton, University Research Co., LLC, USA*

#### *Reviewed by:*

*Jason Richter, Army-Baylor University, USA Arnold Bosman, European Centre for Disease Prevention and Control (ECDC), Sweden*

#### *\*Correspondence:*

*Jodie Bailie<EMAIL_ADDRESS>
#### *Specialty section:*

*This article was submitted to Public Health Policy, a section of the journal Frontiers in Public Health*

*Received: 07 December 2015 Accepted: 20 April 2016 Published: 04 May 2016*

#### *Citation:*

*Bailie J, Laycock A, Matthews V and Bailie R (2016) System-Level Action Required for Wide-Scale Improvement in Quality of Primary Health Care: Synthesis of Feedback from an Interactive Process to Promote Dissemination and Use of Aggregated Quality of Care Data. Front. Public Health 4:86. doi: 10.3389/fpubh.2016.00086*

Introduction: There is an enduring gap between recommended practice and care that is actually delivered; and there is wide variation between primary health care (PHC) centers in delivery of care. Where aspects of care are not being done well across a range of PHC centers, this is likely due to inadequacies in the broader system. This paper aims to describe stakeholders' perceptions of the barriers and enablers to addressing gaps in Australian Aboriginal and Torres Strait Islander chronic illness care and child health, and to identify key drivers for improvement.

Methods: This paper draws on data collected as part of a large-scale continuous quality improvement project in Australian Indigenous PHC settings. We undertook a qualitative assessment of stakeholder feedback on the main barriers and enablers to addressing gaps in care for Aboriginal and Torres Strait Islander children and in chronic illness care. Themes on barriers and enablers were further analyzed to develop a "driver diagram," an improvement tool used to locate barriers and enablers within causal pathways (as primary and secondary drivers), enabling them to be targeted by tailored interventions.

Results: We identified 5 primary drivers and 11 secondary drivers of high-quality care, and associated strategies that have potential for wide-scale implementation to address barriers and enablers for improving care. Perceived barriers to addressing gaps in care included both health system and staff attributes. Primary drivers were: staff capability to deliver high-quality care; availability and use of clinical information systems and decision support tools; embedding of quality improvement processes and data-driven decision-making; appropriate and effective recruitment and retention of staff; and community capacity, engagement and mobilization for health. Suggested strategies included

**Abbreviations:** ABCD, audit and best practice for chronic disease; AHWs, Aboriginal and Torres Strait Islander health workers/practitioners; CQI, continuous quality improvement; ESP, engaging stakeholders in identifying priority evidence– practice gaps and strategies for improvement in primary health care; PHC, primary health care.

mechanisms for increasing clinical supervision and support, staff retention, reorientation of service delivery, use of information systems and community health literacy.

Conclusion: The findings identify areas of focus for development of barrier-driven, tailored interventions to improve health outcomes. They reinforce the importance of system-level action to improve health center performance and health outcomes, and of developing strategies to address system-wide challenges that can be adapted to local contexts.

Keywords: health system and staff attributes, primary care, Aboriginal and Torres Strait Islander health, tailored interventions, quality improvement, driver diagram, aggregated quality of care data

### INTRODUCTION

Despite efforts to promote best-practice clinical guideline use, adherence to guidelines remains variable between health centers and between health professionals (1–3). Interventions designed to address known barriers to care and based on evidence are more likely to produce the desired change in clinical care (4–7). Despite this knowledge, few interventions implemented are based on theory or a systematic assessment of barriers (3, 8, 9). Methods to identify barriers and to tailor interventions to address barriers need further development (4, 8, 10). In the context of major disparities in health outcomes between population groups – as between Aboriginal and Torres Strait Islander people and non-Indigenous Australians (11) – the importance of developing tailored interventions is even greater.

Large-scale improvement in the delivery of primary health care (PHC) requires change at multiple levels of the health system, not only at the local health center level (12, 13). The health system can be understood as consisting of "all organizations, people, and actions whose primary interest is to promote, restore, or maintain health" (14). Where aspects of care are not being done well across a range of PHC centers, this is likely due to inadequacies in the broader system. Ferlie and Shortell describe the health system as having four levels and argue that change is required at all four levels – those of the individual, the group or team, the overall organization, and the larger environment in which organizations are embedded – in order to improve care quality and outcomes (13). Taking a system-wide approach to continuous quality improvement (CQI) is associated with achieving large-scale improvements in health outcomes (15).

Gaps in the provision of care that may escape notice at a local level – for example, because of small numbers – can become noticeable when data are aggregated (16). In the context of limited availability of data on PHC system performance, we propose that aggregated CQI data can provide a useful source of evidence for identifying common and important gaps in care across health centers and for developing and implementing system-wide improvement efforts. A recent systematic review identified the need to seek perspectives from a range of stakeholders, such as policy and decision makers and service providers, on the health center and system attributes that lead to improved Aboriginal and Torres Strait Islander PHC outcomes (17). Reflections from a range of stakeholders in the health system can provide important insights on barriers, enablers, and strategies for improvement (17–20). The aim of this paper is, therefore, to describe stakeholders' perceptions of the main barriers and enablers to addressing identified priority gaps in chronic illness care and child health, and to identify drivers for improvement in Aboriginal and Torres Strait Islander PHC as reflected in these stakeholder perceptions.

We have developed and implemented an active dissemination strategy – "*Engaging Stakeholders in Identifying Priority Evidence-Practice Gaps and Strategies for Improvement in PHC*" *(the ESP Project)* – that aims to promote wide-scale improvement in quality of care by applying a system-wide approach to CQI (21–23). The ESP Project is designed to engage a wide range of stakeholders in a theory-driven approach to interpret aggregated CQI data on health system performance and to reflect on barriers, enablers, and strategies for improvement (see **Box 1**). The rationale for the ESP Project is that involving diverse stakeholders in a phased approach of using aggregated CQI data should stimulate discussion and information sharing, and enhance ownership of the development of interventions to address system gaps. The theoretical and conceptual base for the ESP Project is described in more detail in a separate publication (21). The focus of this paper is on addressing the specific aim as described above.

#### BOX 1 | Australian context and the ABCD National Research Partnership.

In Australia, a high-income country with a universal health insurance scheme (Medicare), Aboriginal and Torres Strait Islander populations experience inequitable access to health care and poorer health outcomes than for non-Indigenous Australians (11). Access to PHC for Aboriginal and Torres Strait Islander people is through private general practice, government managed and Aboriginal community-controlled health services.

Strengthening PHC is critical to closing the gap in health inequalities between Aboriginal and Torres Strait Islander people and other Australians. A wide-scale CQI project, the Audit and Best Practice in Chronic Disease (ABCD) National Research Partnership (2010–2014), has employed a systems approach to improving Aboriginal and Torres Strait Islander PHC delivery (24–27). PHC centers have used evidence-based best-practice clinical audit and system assessment tools to assess and reflect on system performance, typically on an annual basis. Available CQI tools cover various aspects of PHC (e.g., chronic illness care, child health, preventive, mental, and maternal health care). Audit tools are developed through a process of expert consensus that draws upon current evidence-based care guidelines [such as CARPA (28) and Queensland Chronic Disease Guidelines (29)] (30). Over 175 Aboriginal and Torres Strait Islander PHC centers using these CQI processes voluntarily provided de-identified CQI audit data to the ABCD National Research Partnership for analysis. The strongest engagement has been from health centers in the Australian jurisdictions of the Northern Territory and Queensland.

health.

### MATERIALS AND METHODS

The theoretical basis for the phased approach for the ESP Project draws on the methods outlined by French et al. (5) to develop interventions based on evidence and identified barriers and enablers, as outlined below and in **Figure 1** (21). We ran the ESP process separately for child health care and then for chronic illness care. We briefly describe each of the phases of the ESP project below by way of background. For the purpose of addressing the aim of this paper, we focus primarily on the analysis of the qualitative data derived from phase 2 of the ESP project for both child health and chronic illness care. Detailed reports on the process and findings from each of the phases of the ESP project for child health (22), chronic illness care (23), and the clinical audit tools and the audit process (25) have been published previously.

#### ESP Project Phases

#### Phase 1 – Identifying the Priority Evidence-Practice Gaps

In Phase 1, a consensus-driven approach was used to identify priority evidence-practice gaps for child health and for chronic illness care. We prepared separate reports for these two areas of care, using the most recent clinical audit data (2012–2013) to describe current delivery of guidelinescheduled care across health centers, and distributed them to a range of stakeholders.

For all phases, we aimed to include individuals and organizations representing diverse roles and geographical areas, identified as having an interest and experience in Aboriginal and Torres Strait Islander health delivery, management, policy, and research. They included health practitioners (e.g., doctors, nurses, allied health professionals, Aboriginal Health Workers), managers and policy-makers at various health system levels, researchers, and staff of peak bodies and support organizations that represent the interests of community-controlled health services and Aboriginal and Torres Strait Islander communities.

Aggregated CQI data were available from 123 health centers (6,523 patient records; 90 system assessments) for chronic illness care (23) and from 94 health centers (4,011 patient records; 62 systems assessments) for child health care (22). Preliminary evidence-practice gaps were determined with the assistance of clinical experts by identifying (a) areas of clinical care that were being delivered or recorded at a relatively low level by services; (b) aspects of care where there was more general wide variation in recorded delivery of care; and (c) components of the PHC center systems that were relatively poorly developed (23). Through the survey, we asked respondents to rate the relative importance of each preliminary priority evidence-practice gap identified in the report on a scale of 1–10; and the extent to which the listed priorities resonated with their experience. Open-ended questions were used to elicit explanatory information on reasons for scores and further comments.

#### Phase 2 – Identifying the Barriers and Enablers to Addressing the Identified Evidence-Practice Gaps

In Phase 2, trend data were presented for each of the priority evidence-practice gaps identified in Phase 1 by (a) calendar years and (b) audit cycles, to show trends in variation over time and across CQI cycles. We aggregated clinical audit and systems assessment data on adherence to best practice guidelines from 160 health centers (17,879 patient records; 390 systems assessments), over the period 2005–2013 for chronic illness care; and from 132 health centers (10,405 patient records; 265 systems assessments), 2007–2013 for child health.

Through the survey, we encouraged stakeholders to reflect on the influences underlying the data trends, and on their experience in PHC, to identify barriers and enablers to improving care. The survey tool for this phase drew on international and Australian literature on health system and staff attributes (or domains) relevant to implementation of change interventions and behavior change of health care professionals (5–7, 27, 31, 32) (Table S1 in Supplementary Material lists the attributes).

Respondents were asked to rate each attribute identified according to its relative importance in improving evidence-practice gaps on a five-point Likert scale of strongly agree, agree, disagree, strongly disagree, and "do not know/cannot say." Respondents were asked to relate their responses to providing best practice care as relevant to the priority evidence-practice gaps across the PHC system for Aboriginal and Torres Strait Islander people, rather than for any specific health center or service. Open-ended questions were also used to elicit explanatory data from stakeholders on aspects of the health system or health center environment, or staff attributes, which pose significant enablers or barriers to providing best practice care and strategies for improvement.

#### Phase 3 – Identifying the Strategies for Improvement to Address the Identified Evidence-Practice Gaps

In Phase 3, we presented a report on the barriers and enablers to addressing gaps in care identified in the previous phase. We asked respondents to comment on whether the report provided a fair reflection of the main barriers and enablers to improvement. Respondents were asked to suggest new or existing strategies to address the most common barriers and enablers.

#### Draft Final Report

In this step, we presented a draft final report on the whole process. We asked stakeholders to confirm that we had accurately reflected their feedback about strategies to address the gaps identified and whether they wished to provide additional comments that could be used to finalize the project report.

Respondents could enter data as individuals or on behalf of a group in order to encourage engagement of people who were less likely to provide individual responses. We used an iterative process to develop and refine reports through the project phases, making adjustments to content and presentation over time in response to stakeholder feedback.

Circulation lists for the ESP reports and surveys were based on networks developed over several years of the ABCD National Research Partnership. Invitations to participate were emailed by the project leader. We used a snowballing distribution technique, encouraging people to forward reports and surveys through their professional networks.

The child health ESP phases were undertaken in early 2014 and chronic illness care ESP phases in late 2014. A reminder email was sent 1 week before the closing date of surveys.

#### Data Synthesis for Identifying Common Barriers and Enablers, and Drivers for Improvement

Analysis of the difference in quantitative responses on barriers and enablers for individuals and groups showed similar response patterns. Individual and group responses were, therefore, analyzed together. Our primary interest was in the qualitative nature of responses.