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Integrated model of secondary fracture prevention in primary care (INTERCEPT): protocol for a cluster randomised controlled multicentre trial

BMC Primary Care volume 25, Article number: 349 (2024) Cite this article

Abstract

Background

Osteoporotic fractures signal severely compromised bone strength and are associated with a greatly increased risk of refracture. Despite the availability of effective and safe medications that reduce fracture risk, 70–80% of patients are inadequately investigated or treated for osteoporosis following an initial fracture, constituting a significant ‘osteoporosis care gap’. Optimal methods of bridging this gap with primary care at the forefront of secondary fracture prevention remain undetermined. This protocol describes a cluster randomised controlled trial to evaluate the effectiveness of a novel integrated model of secondary fracture prevention and management in primary care.

Methods

The cluster randomised controlled trial involves multiple branches of a community-based radiology provider (CRP), a hospital-based secondary fracture prevention program (SFPP) and numerous primary care practices in metropolitan Sydney that refer to either the CRP or SFPP. Using natural language processing tools, patients diagnosed with a potential osteoporotic fracture will be identified by automatically screening radiology reports generated at the CRP or SFPP. The primary care practices that these patients attend will be randomised (1:1) to either the intervention or usual care. The intervention consists of (i) electronic and fax alerts informing the practice/primary care physician that their patient has been diagnosed with a potential osteoporotic fracture; (ii) provision of osteoporosis management guidelines and (iii) follow-up surveys at 4 weeks and 6 months. Practices in the usual care (control) group will receive no alerts and provide usual care. The primary outcome is the proportion of patients undergoing a bone density scan and/or filling a prescription for osteo-protective pharmacotherapy within 3 months of the initial diagnostic imaging report. Secondary outcomes are the proportion of patients: (i) undergoing an osteoporosis-related blood test within 3 months of the initial diagnostic imaging report; (ii) initiated on a chronic disease management plan within 3 months of the diagnostic report, and (iii) filling a second prescription for osteo-protective pharmacotherapy within 9 months post initial diagnostic imaging report. Outcomes will be obtained through de-identified linked data from Medical Benefits Schedule and Pharmaceutical Benefits Scheme held by the Australian Institute of Health and Welfare.

Discussion

This is the first randomised trial to integrate case-detection of potential osteoporotic fractures in a hospital and community setting with direct alerts to the patient’s primary care provider. This study will determine whether such an intervention is effective in improving investigation and/or treatment rates of osteoporosis in patients with a potential osteoporotic fracture.

Trial registration

This study is registered with the Australian New Zealand Clinical Trials Registry (ANZCTR): ACTRN12623000658617p.

Peer Review reports

Background

Osteoporosis is a common chronic condition characterised by loss of bone mass and strength, resulting in fractures following trauma that is normally insufficient to cause such injuries (referred to as ‘minimal trauma’ or ‘fragility’ fractures). It is a major public health burden affecting over 200 million people globally, leading to over 8.9 million osteoporotic fractures annually [1, 2]. One in three women and one in five men over the age of 50 will experience an osteoporotic fracture during their remaining lifetime [3]. Osteoporotic fractures lead to increased morbidity and are associated with significant mortality [4]. Up to 20% of patients die within the first year following a hip fracture, while vertebral fractures are associated with an eight-fold increase in age-adjusted mortality [5, 6].

Any osteoporotic fracture significantly increases the risk of subsequent fractures, with 60–80% of these secondary fractures occurring within two years of the initial fracture [4, 7]. First fragility fractures are therefore sentinel events that should trigger investigation and, if indicated, treatment to reduce the risk of secondary fracture. In those with undiagnosed and untreated osteoporotic fractures, there is a two to threefold higher relative risk of subsequent fracture, resulting in lengthy hospital stays and considerable healthcare expenditure [4]. However, studies from USA [8,9,10], Canada [11], Denmark [12] and Australia [13] have consistently shown that fewer than 20% of patients who experience an osteoporotic fracture are initiated on appropriate post-fracture pharmacotherapy. This widespread failure in managing a common disease and its debilitating complications exists despite the availability of effective, safe and subsidised diagnostics and treatments [4, 5, 7, 14,15,16].

In the past two decades, systematic and coordinated models of care in secondary fracture prevention, known as Secondary Fracture Prevention Programs (SFPP) or Fracture Liaison Services (FLS) have been established in numerous hospital settings globally [17,18,19,20]. These services may follow an organised process of identification, investigation, initiation of treatment, improvement and integration, often supported by a dedicated coordinator and appropriately trained personnel [21, 22]. While such hospital-based services are effective in reducing secondary fracture incidence and healthcare costs [20, 23,24,25,26], these services have limitations.

Firstly, hospital-based SFPP typically look after patients with non-vertebral fractures (e.g. hip or major long bone fractures) as these almost always present to hospital emergency departments. In contrast, vertebral fractures are often asymptomatic, diagnosed on a radiological examination as an incidental finding to the referral problem, do not present to hospitals and are therefore missed by the service. However, vertebral fractures constitute a large proportion of osteoporotic fracture burden, lead to significant morbidity and carry the greatest risk of re-fracture [5]. An additional hiatus on the osteoporosis management pathway lies between a radiologist identifying a vertebral fracture and appropriate follow-up by the referring physician. It is common for such incidental fractures to be inadvertently overlooked, causing a gap in the clinical pathway that could otherwise lead to the investigation and management of osteoporosis. A study of over 17,000 general practice patients at risk for osteoporosis found that while 30% had vertebral fractures documented on X-ray, only 3.8% were treated for osteoporosis [13].

Secondly, according to a 2019 Australian analysis, almost all hospital-based SFPP lacked effective information technology (IT) systems for the identification of patients with sentinel fractures [27]. Most identified patients through manual searches of hospital records or referrals from other departments and none included community-based radiology in their case-finding activities, despite substantial private imaging throughput. It is likely that similar limitations exist in other jurisdictions. Optimal models of care would include automated systems that search hospital and other health record systems to maximise case-finding. Blaker et al. recently showed that the use of such electronic tools increases the number of patients identified with likely osteoporotic fractures by up to five-fold [28]. Thus, with improved IT systems, there is potential to substantially improve identification of patients with potential osteoporotic fractures.

Thirdly, the impact of many hospital-based SFPP is further limited by their low capacity and insufficient resources. Most programs would be overwhelmed if they were to identify and manage an additional 50–60% of patients, a level of coverage that has been achieved by very few services [19, 29].

Finally, similar to services abroad, Australian SFPP do not have meaningful engagement with primary care. An analysis found that none of the 29 surveyed hospital-based programs had integrated operations with local primary care physicians, both separately from or through Primary Health Networks [27]. A lack of “safety net mechanism” to monitor patients between the suspected sentinel fracture and follow-up care, and poor bi-directional communication contributed to the disjointed integration with primary care [30].

In summary, hospital-based SFPP alone cannot effectively manage the burden of osteoporotic fractures in the general population. While improvements could be made to their operational efficiency, the present system of predominantly specialist care does not close the secondary fracture prevention gap, and active engagement and integration with primary healthcare into secondary fracture prevention is required. There is growing consensus that most patients with osteoporosis should be managed by their primary care physician and not in capacity-limited and costly hospital-based specialist services [30, 31]. Best practice osteoporosis management in primary care should involve assessment of a patient’s bone health and fracture risk through clinical risk factor analysis and bone density scanning, exclusion of secondary causes (e.g. through blood tests) and treatment initiation as appropriate [32]. However, the quality of such care varies significantly among primary care physicians, andmost patients with incident osteoporotic fractures are neither diagnosed with, nor treated for their osteoporotic bone disease [8,9,10,11,12,13].

Given the capacity for primary care physicians to competently manage osteoporosis, we propose an integrated model of osteoporosis management where primary care is the hub of osteoporosis management, while hospital-based SFPP and community radiology practices (CRPs) are mechanisms of identification of patients with potential osteoporotic fractures, and referral to and from primary care. This model integrates Natural Language Processing (NLP) search tools with electronic methods of communication with primary care, and proposes a potentially highly streamlined process for fracture identification and management. The INTegrated model of sEcondaRy fraCturE PrevenTion in primary care (INTERCEPT) trial evaluates the effectiveness of the implementation of such a model on improving management of patients with potential osteoporotic fractures. INTERCEPT is a cluster randomised controlled trial, with randomisation by the primary care practice of patients identified as having a potential osteoporotic fracture.

Aims and hypotheses

INTERCEPT aims to assess the extent to which the intervention changes primary care physician behaviour in regards to the investigation and management of patients with potential osteoporotic fractures.

The primary hypothesis is that compared to the usual care (control) group, there will be a higher proportion of patients with a potential osteoporotic fracture attending primary care practices randomised to the intervention group who undergo a bone density scan and/or fill a prescription for osteo-protective pharmacotherapy within three months of an initial diagnostic imaging report.

The secondary hypotheses are that compared to the usual care group, there will be a higher proportion of patients with a potential osteoporotic fracture attending primary care practices randomised to the intervention group who: (1) undertake a blood test related to bone health within three months of the initial diagnostic imaging report; (2) have a chronic disease management plan initiated within three months of the initial diagnostic imaging report, or (3) fill a second prescription for osteo-protective pharmacotherapy within 9 months of the initial diagnostic imaging report.

Study design and methods

The model

The Model of osteoporosis care underlying the current trial integrates secondary osteoporotic fracture prevention across several health sectors, as summarised in Fig. 1. There are three interrelated components where primary care is the hub of osteoporosis investigation and management, while hospital-based SFPP and community-based radiology practices are mechanisms of identification and referral to and from primary care. Within this Model, most cases are seen and managed by their primary care physician. Only complex cases that require specialist management will be referred to and managed by a hospital-based service. The basic premise of the Model is that the primary care physician is made aware of a patient’s potential osteoporotic fracture and then encouraged to investigate and manage it in accordance with best-practice guidelines.

Fig. 1
figure 1

Model of integrated osteoporosis management. Legend: SFPP: Secondary Fracture Prevention Programs. ‘Cases’ refer to patients with a potential osteoporotic fracture

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Study design

Setting and population

Study patients of interest (cases) are those who have been diagnosed with a potential osteoporotic fracture either through a study performed at the study CRP, or by coming to the attention of the FLS at the hospital, and are the unit of analysis. The CRP has five participating branches in Eastern Sydney and four in South-Western Sydney and the participating SFPP is a dedicated Fracture Liaison Service (FLS) situated at a tertiary hospital in Western Sydney.

The intervention is targeted at the primary care physician who referred the patient for the imaging examination through the CRP, or the specified primary care physician of the patient identified through the FLS. Primary care practises are predominantly based in Western Sydney, South-Western Sydney and Eastern Sydney Local Health Districts, which serve a combined population of around 3 million people.

Case finding strategies

Electronic artificial intelligence tools utilising NLP and clinical coding technology have been installed at the CRP and hospital-based FLS. The tool searches all X-ray, computerised tomography (CT) and magnetic resonance imaging (MRI) reports for cases where the report suggests the presence of a fracture.

Case managers embedded in the clinical environment of the radiology practice and FLS review the list of cases generated by the electronic search tool at regular intervals and review the radiology report to remove ‘false positives’. Cases are further triaged by applying the study inclusion and exclusion criteria (Table 1). Complex and/or major fractures referred to the hospital-based specialist service are retained and managed by the FLS, and thus ineligible for the study (Appendix 1).

Table 1 Inclusion and exclusion criteria for cases
Full size table

Details of all remaining cases deemed eligible for the study are transferred from the clinical environment of the radiology practice and FLS into the Research Electronic Data Capture (REDCap) database. The case manager is then responsible for ensuring the accuracy of patient and primary care information within REDCap and utilises an electronic randomisation tool to randomise the primary care practice of the case into either the intervention or usual care group. This process is summarised in Fig. 2.

Fig. 2
figure 2

Study design and randomisation. Legend: FLS – fracture liaison service, REDCap - Research Electronic Data Capture

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The intervention

For all patients undergoing diagnostic imaging, the referring primary care physician receives a diagnostic report from the community-based radiology practice or a hospital discharge report. These reports are generated and delivered to the referring primary care physician independent of the study, as part of usual care. As part of the intervention, the case manager sends the referring primary care physician an alert letter via fax and/or Medical Objects, a commercial electronic secure messaging service to which about 80% of practices in Sydney are subscribed, within 1–2 weeks. The purpose of this letter is to (1) alert or remind the primary care physician that their patient has been diagnosed with a potential osteoporotic fracture; (2) encourage review of the case using a management flowchart based upon the Royal Australian College of General Practitioners (RACGP) guidelines; and (3) provide a link to the Australian National SOS Fracture Alliance (SOSFA) website with access to Best Practice Guidelines and templates for Chronic Disease Management Plans [32, 33].

After 4 weeks, the primary care physician receives a Fracture Management Survey via fax; while the physician is asked to return the survey to the case manager, it is not used as outcome data but serves as an additional alert and checklist for best practice osteoporosis management. Another fax reminder survey is sent if there is no response after 4 weeks. The case manager reviews the returned survey and confirms whether actions taken were in accordance with national osteoporosis guidelines, and, if not, the case manager contacts the primary care physician in writing seeking further clarification and offer specialist input if required. For primary care physicians who report in their survey that they had commenced patients on osteo-protective pharmacotherapy, the case manager sends a follow-up fax survey about patient adherence to the treatment after six months. Again, this survey is part of the intervention and not a primary data source.

The usual care for osteoporosis is the active control in this study, whereby primary care physicians receive a diagnostic report from community-based radiology practices or hospitals where their patients have had a radiological examination but do not receive any alert letters from the study team.

No individuals presenting to a participating primary care practice (intervention or usual care group) during the study duration are restricted in any way in terms of the care and treatment they receive from their primary care physician or other healthcare professionals.

The study design was informed by a Feasibility Study undertaken in 2021–2022 to test different modalities of communication with the primary care physicians regarding their patient’s recent fracture and to assess the intervention’s acceptability. The Feasibility Study showed that the most appropriate method of sending initial alerts and guidelines was via fax and/or Medical Objects, and that overall feedback from primary care physicians and patient consumer representatives was positive. The intervention will run for 14 months, followed by a 9 month follow-up window period to allow patients to fill a second prescription of osteo-protective pharmacotherapy (Fig. 3).

Fig. 3
figure 3

Study timeline

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Randomisation and blinding

Primary care practices are randomised 1:1 to the intervention or usual care arms of the study using stratified block randomisation in SAS v9.4 implemented in the REDCap Study database. Randomisation is stratified by source of referral (hospital-based FLS vs. CRP), and within the latter by location of the referring primary care practice (Western Sydney vs. Eastern Sydney) and the size of referring primary care practice (single, 2–5, or ≥ 6 primary care physicians)– a total of seven strata. Random permuted blocks of 4 and 6 are used for group allocation within each stratum.

Primary care practices are randomised as cases diagnosed with a potential osteoporotic fracture are identified. Once randomisation is performed, the group allocation is automatically stored within the database so that if the same primary care physician or practice is entered in the future, it will automatically be allocated to the same group. If a primary care physician is associated with multiple practices, only cases from their first randomised practice will be included in the study.

The randomisation scheme has been generated by an independent statistician not involved in data collection or analysis. Primary care physicians, practices and cases will be blinded to their group allocation, as will the investigators, apart from the case manager and data analysts.

Outcomes and data sources

While the intervention is targeted at primary care physicians, it is not always feasible to directly measure physician behaviour such as referral for investigations and prescribing. Thus, except for chronic disease management plans, which are submitted by the primary care physician and remunerated by the government, all other outcomes for this study use patient behaviour, such as attending for an investigation or filling of a prescription. These activities are captured by Medicare Benefits Schedule (MBS) and Pharmaceutical Benefits Scheme (PBS) item numbers [34, 35]. In Australia, all patients with a minimal trauma fracture are eligible for BMD testing and certain forms of osteo-protective pharmacotherapy.

The primary outcome is the proportion of patients with a potential osteoporotic fracture who undertake a bone density scan and/or fill a prescription for osteo-protective pharmacotherapy within three months of an initial diagnostic imaging report. For this study, the definition of bone density scan is a dual-energy X-ray absorptiometry (DXA) scan. The definition of osteo-protective pharmacotherapy includes:

  • bisphosphonates (alendronate, risedronate, zoledronic acid).

  • denosumab.

  • raloxifene.

  • hormone replacement therapy (including oestrogen replacement in females and testosterone replacement in males).

  • osteo-anabolic therapies such as teriparatide or romosozumab.

For this study, osteo-protective pharmacotherapy does not include cholecalciferol, calcium, vitamin K2 or other dietary supplements.

The secondary outcomes are the proportion of patients with a potential osteoporotic fracture:

  • who undergo a blood test related to bone health (Vitamin D, creatinine or calcium/magnesium/phosphate) within three months of the initial diagnostic imaging report.

  • for whom a chronic disease management plan was submitted within three months of the initial diagnostic imaging report.

  • who filled a second prescription for osteo-protective pharmacotherapy within nine months of the initial diagnostic imaging report.

Outcome data will be assessed using linked MBS and PBS data provided by the Australian institute of Health and Welfare (AIHW). Identifiable data will be provided to AIHW for linkage, and individual level de-identified data will be returned; this will include patient, primary care physician and primary care physician practice identifiers which have been scrambled to maintain confidentiality, group allocation, stratum, patient age and sex.

Statistical methods

Baseline characteristics of the intervention and usual care groups will be reported. The analysis will compare outcomes between intervention and usual care groups post intervention, using a mixed effects logistic regression model to adjust for correlation of outcomes within primary care physicians and practices. The model will include allocation group (intervention or usual care), stratification variables (FLS versus CRP, primary care location and size of practice), sex and age group of patients. Fracture site is included as a covariable (vertebral, hip/pelvis, upper limb, lower limb, chest, other). Separate analyses will be undertaken for each outcome. Results will be presented as the odds ratios (odds of the outcome in the intervention versus usual care group), with 95% confidence intervals. Outcomes will be assessed according to the intention-to-treat principle.

Based on information collected in the feasibility phase of INTERCEPT, we anticipate a sample size of approximately 600 patients per group (1200 patients in total). For a 5% significance level, assuming an average of 3 patients per primary care practice, a coefficient of variation of 0.9 for primary care practice sample size, and an intra-class correlation coefficient of 0.05 to 0.5 (for correlation of patients within primary care physicians/ practices), the study will have at least 80% power to detect a difference in outcomes between groups of: 9–13% for an outcome of 50% in the usual care group; 9–12% for an outcome of 30% in the usual care group and 6–9% for an outcome of 10% in the usual care group. If the average number of patients per primary care practice is larger than 3, then the detectable effect sizes will be slightly larger. If the average number of patients per primary care practice is less than 3, the detectable effect sizes will be slightly smaller. The estimates of detectable difference between groups will be around 2% higher for a power of 90%.

Ethics

INTERCEPT adheres to the National Health and Medical Research Council (NHMRC) ethical guidelines for human research. The study applied for a waiver of individual patient and primary care physician consent as: (1) involvement in the research carries no more than low risk to participants, (2) the benefits justify any risk of harm associated with not seeking consent, (3) examining change in primary care physician behaviour in response to the intervention precludes patient and physician knowledge of study inclusion and, (4) there is adequate protection of the confidentiality of data. Approval for this study has been obtained by the Sydney Local Health District Human Research Ethics Committee (reference number 2023/ETH00731) and the Australian Health and Institute Ethics Committee (reference number EO2023/5/1431). A waiver of consent was granted to access health information without individual patient consent at the CRP and FLS using the NLP search tool. This is in accordance with the State Privacy Commissioner’s Guidelines for research under the NSW Health Records & Information Privacy Act (HRIPA) 2002 and Section 95 A of the Privacy Act 1988 and the National Statement on Ethical Conduct in Human Research (2007) updated 2018, Chap. 2.3: Qualifying or waiving conditions for consent [36,37,38].

Data storage and management

Patient and primary care identifiable data will be stored in REDCap data management software hosted by the Hunter Medical Research Institute (Fig. 4). Identifiable data containing allocation status and clinical variables will be securely transferred from REDCap using AIHW Secure Messaging (ASM), to AIHW. AIHW will then use a Master Code Sheet and linkage keys to link with MBS/PBS datasets and return de-identified linked datasets, using ASM, to the Secure Unified Research Environment (SURE) at the Sax Institute. Only the study team will have access to the SURE facility through username and password. They may login remotely and analyse data, without datasets ever leaving SURE at the Sax Institute.

Fig. 4
figure 4

Collection, storage and exchange of patient and primary care physician data. Legend: SFPP - secondary fracture prevention program, NLP - natural language processing, CRP - community radiology practice, DOB - date of birth, SURE - Secure Unified research Environment

Full size image

Discussion

The osteoporosis care gap will continue to widen over time, and effective strategies aimed at the primary care level are urgently needed. A previous systematic review and meta-analysis of interventions targeting osteoporosis treatment in high-risk patients in the primary care setting have found increased incidence of treatment initiation of 20% and bone mineral density testing and/or treatment initiation of 40% following interventions [39]. These interventions were multifaceted and could be summarised as a combination of verbal and written patient education, alert letters to the primary care physician, telephone follow-up of patients, and organising screening blood tests and bone mineral density testing.

To our knowledge, our study is the first to utilise artificial intelligence-based NLP technology for case detection and to send an alert directly to the primary care physician, bypassing the patient. This is beneficial for several reasons. Firstly, the utilisation of NLP in the hospital and community-based radiology settings leads to a higher rate of fracture detection than traditional methods of manual detection and referrals [28]. Secondly, a direct alert to the primary care physician allows for clear communication and eliminates the risk of the primary care physician overlooking the diagnosis of an incidental fracture on a scan requested for an unrelated reason, or not receiving a discharge summary from the hospital. Finally, there is no ambiguity about the intent of the alert: primary care physicians are encouraged to review osteoporosis guidelines for the patient and expected to initiate treatment if deemed appropriate.

There are limitations of this study. Accurate analysis of outcome data is contingent on the supply of MBS and PBS data from AIHW. Assessment of patient behaviour, i.e. undertaking an investigation, or filling of a prescription issued by the primary care physician, are indirect measures of physician behaviour. It is known that most patients fill prescriptions that have been prescribed by their physician, and thus assessing patient behaviour to measure the effect of the intervention on physician behaviour, is justified [40]. Initiation of chronic disease management plans are the only direct measure of physician behaviour, although these can be non-specific to osteoporosis and may be initiated for other chronic diseases.

There is the possibility that patients may visit different primary care physicians in a different practice between their diagnostic scan and subsequent visits, thus leading to a contamination or attenuation of effect of the intervention. However, this was not observed during the Feasibility Study. While the Medical Objects or fax alert is delivered to the primary care physician, it is also not possible to determine whether they have read it, unless they return the survey. Furthermore, although care is taken to exclude patients with major comorbidities, non-osteoporotic fractures and who are already on appropriate osteoporosis therapy, access to this information is not always available, and ineligible patients may be incorrectly included in the study. Due to the large number of patients in the study and randomisation, we expect that only a small proportion of patients are potentially ineligible, and that they would be evenly distributed between the intervention and usual care groups.

We expect that the study will produce findings of relevance for policy makers, primary care physicians, and specialists involved in the management of osteoporosis in Australia. We will have sufficient data to determine the impact the intervention has on improving the rates of detection and treatment of osteoporosis in the described cohort. Study findings will be published in academic journals, presented at relevant research conferences and policy impact labs to key health stakeholders involved in osteoporosis management and care in Australia to identify opportunities for improvement, sustainability and scale-up of the model.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

AIHW:

Australian Institute of Health and Welfare

ASM:

AIHW Secure Messaging

CRP:

Community Radiology Practice

CT:

Computed Tomography

DXA:

Dual-energy X-ray absorptiometry

INTERCEPT:

Integrated model of secondary fracture prevention in primary care

IT:

Information Technology

FLS:

Fracture Liaison Service

MBS:

Medicare Benefits Schedule

MRI:

Magnetic Resonance Imaging

NHMRC:

National Health and Medical Research Council

NLP:

Natural Language Processing

PBS:

Pharmaceutical Benefits Scheme

RACGP:

Royal Australian College of General Practitioners

REDCap:

Research Electronic Data Capture

SFPP:

Secondary Fracture Prevention Program

SOSFA:

National Alliance for Secondary Fracture Prevention (SOS Fracture Alliance)

SURE:

Secure Unified Research Environment (SURE)

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Acknowledgements

We thank all the general practitioners and general practice staff who support our study by reading the alerts, answering phone calls from case managers and completing fracture surveys. We thank case managers in identifying cases from Westmead Hospital- Minh Nguyen, Victoria Deacon, Nick Green and Dr. Divia Mohandas. We thank Kerrin Palazzi from Hunter Medical Research Institute for the design of the REDCap randomisation and data storage tool. Finally, this project would not have been possible without the support of Spectrum Medical Imaging and Westmead Hospital for providing the cases for identification.

Funding

This study is funded by an NHMRC Investigator Grant (APP1196062).

Author information

Authors and Affiliations

  1. Bone Research Program, ANZAC Research Institute, The University of Sydney at Concord Campus, Concord, NSW, Australia

    Mawson Wang & Markus J. Seibel

  2. Department of Endocrinology and Metabolism, Concord Hospital, Sydney Local Health District, Concord, NSW, Australia

    Mawson Wang & Markus J. Seibel

  3. Sax Institute, Glebe, NSW, Australia

    Alice Knight, Anastassia Demeshko & Catherine D’Este

  4. Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia

    Christian M. Girgis

  5. Department of Diabetes and Endocrinology, Westmead Hospital, Westmead, NSW, Australia

    Christian M. Girgis

  6. Centre for Primary Health Care and Equity, UNSW, Sydney, NSW, Australia

    Patrick Bolton

  7. South East Sydney Local Health District, Sydney, NSW, Australia

    Patrick Bolton

  8. Spectrum Medical Imaging, Sydney, NSW, Australia

    Anurina Das, Andrew Auwyang, Brett Williams & Daniel Moses

  9. School of Medicine and Public Health, The University of Newcastle, Newcastle, NSW, Australia

    Catherine D’Este

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  1. Mawson Wang
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Contributions

MW, AK, AD, CD, CG, PB, MS conceived the study design and protocol. MW, CG, PB, MS provided clinical oversight of the study. CD provided biostatistical advice on the study. MW, AK, AD, MS coordinated ethics approvals. BW, DM conceived the design of the primary care alert model and were involved in the overall running of the intervention. AD, AA were case managers involved in the randomisation and overall running of the intervention. All authors reviewed and accepted the manuscript in its final form.

Corresponding author

Correspondence to Mawson Wang.

Ethics declarations

Ethics approval and consent to participate

Ethics has been approved by the Sydney Local Health District Human Research Ethics Committee (reference number 2023/ETH00731) and the Australian Health and Institute Ethics Committee (reference number EO2023/5/1431). As described in the Ethics section, a waiver of consent was approved by all ethics committees for participation in this study.

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Not applicable.

Competing interests

The authors declare no competing interests.

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Wang, M., Knight, A., Demeshko, A. et al. Integrated model of secondary fracture prevention in primary care (INTERCEPT): protocol for a cluster randomised controlled multicentre trial. BMC Prim. Care 25, 349 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12875-024-02601-3

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Keywords

BMC Primary Care

ISSN: 2731-4553

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