Primary care

Objective To identify barriers and facilitators to the timely detection and optimal management of chronic wet cough (CWC) in children from the perspectives of carers, across multiple, diverse First Nations Australian communities.Study TypeQualitative participatory action research study.Setting and ParticipantsEight Australian communities across three states and territories. Semi-structured interviews and focus groups with 167 parents and carers of Aboriginal children were conducted between May 2021 and July 2023.Main Outcome MeasuresBarriers to and facilitators of detection and management of CWC in Aboriginal children in a primary care setting.ResultsKey barriers identified included inadequate health promotion, the common perception that CWC is normal and high turnover among health staff, leading to mistrust of newly onboarded health staff. The overarching facilitator identified was knowledge dissemination through health promotion, delivered in person by Aboriginal health staff using culturally secure resources, supplemented by posters placed in the community and the use of radio and social media. Individual communities expressed varied preferences regarding specific health promotion methods and designs. Communities valued staff who had spent considerable time within the community, allowing for the development of trust and rapport.ConclusionThe barriers and facilitators to detecting and managing CWC in children were generally consistent across the settings we studied. Implementation approaches to facilitate care require tailoring to local settings. Further, to reduce identified barriers for the detection and management of CWC in children, the incorrect perception that CWC is normal must be addressed and the potential harms of staff turnover need to be recognised.

Gloria T. Y. Lau, Pam Laird, Robyn Aitken, Melanie Barwick, Liam Bedford, Emily R. Bowden, Jamie Everingham, Angela Fuery, Reece Griffin, Neha Jain, Mina Kinghorn, Renae Knapp, Gabrielle B. McCallum, Peter Morris, Richard Norman, Peter C. Richmond, Aarti Saiganesh, Slade Sibosado, Cameron Taylor, Maree Toombs, Roz Walker, Joan Wilson, Anne B. Chang, André Schultz

Objectives To quantify cardiovascular disease (CVD) risk, and implications for targeting preventive pharmacotherapy, using the 2023 guideline-recommended Australian CVD risk algorithm, and compare this to the previous (2012) algorithm.Study TypeApplication and comparison of two risk prediction algorithms.Setting and ParticipantsData from 115,873 people aged 45–74 years without existing CVD, who had a clinical encounter between September 2020 and August 2022, recorded within MedicineInsight, a longitudinal primary care database covering 8% of Australian general practices.Main Outcome MeasuresCVD risk distribution and risk categorisation into low-, intermediate- and high-risk groups under the 2023 and 2012 algorithms. Cohen's kappa and Bland–Altman plots were used to assess agreement and concordance.ResultsUsing the 2023 CVD risk algorithm and revised thresholds, 9.7% of participants were at high CVD risk (≥ 10% 5-year risk or clinically determined high risk); 26.4% were at intermediate CVD risk (5% to < 10% 5-year risk) and 63.9% were at low CVD risk (< 5% 5-year risk). Corresponding 2012 figures for CVD risk were 17.6% high (> 15% 5-year risk or clinically determined high risk), 11.6% intermediate (10%–15% 5-year risk) and 70.8% low (< 10% 5-year risk). Differences in proportions at high risk were largely driven by changes to clinically determined criteria for high risk. Overall, there was moderate-to-substantial agreement (linear-weighted kappa = 0.62) and concordance (Kendall's tau-b = 0.74) between algorithms.ConclusionProportions estimated at low risk and not routinely recommended pharmacotherapy align with international standards and were similar between guidelines. Although fewer people would be recommended pharmacotherapy due to being high risk under the updated versus previous guidelines, this likely reflects more accurate updated CVD risk estimation for the contemporary Australian population. The 2023 guidelines include a discretionary step (untested in our study) allowing adjustment based on additional factors. To ensure continued reduction of CVD burden across the population, we emphasise the use of this reclassification step by clinicians and consideration of the benefits of pharmacotherapy for those at intermediate risk.

Nina Lazarevic, Meghana Bhat, Grace Joshy, Danielle C. Butler, Mark Woodward, Anushka Patel, Rod T. Jackson, Garry Jennings, Rosemary Wyber, Ellie Paige, Emily Banks

Objective To examine changes in rates of primary care patients seeing multiple prescribers and characteristics of patients who ceased seeing multiple prescribers for monitored medicines after voluntary implementation of the Victorian prescription drug monitoring program (PDMP). Study Design Controlled interrupted time series analysis of primary care electronic medical records. Setting A total of 562 general practices across three Victorian healthcare networks (Monash Health, Peninsula Health, Eastern Health). Patients People prescribed at least one PDMP-monitored medicine (e.g., opioids, benzodiazepines) and/or non-monitored psychotropic medicines (e.g., antidepressants, antipsychotics) between 1 January 2017 and 30 June 2023. Intervention Voluntary (1 April 2019) and mandatory (1 April 2020) implementation of the Victorian PDMP. Main Outcome Measures Changes in the monthly rate of people seeing multiple prescribers (defined as four or more prescribers) following PDMP implementation for monitored medicines, with non-monitored medicines used as a control; characteristics of people who ceased seeing multiple prescribers for monitored medicines following PDMP implementation. Results Following voluntary PDMP implementation (1 April 2019), there was a significant reduction in the differential step and trend changes in the rates of seeing multiple prescribers between people prescribed monitored and non-monitored medicines (differential step change: β, −3.55 [95% confidence interval (CI), −5.08 to −2.03]; differential trend change: β, −0.29 [95% CI, −0.46 to −0.12]). Following mandatory PDMP implementation (1 April 2020), there was no significant step change difference. However, there was an increase in the differential trend change in the rate of seeing multiple prescribers between those prescribed monitored and non-monitored medicines (differential trend change: β, 0.21 [95% CI, 0.05–0.37]; p=0.009). Logistic regression revealed that older age (95% CI, 1.39–1.75), male gender (95% CI, 1.09–1.25), metropolitan residence (95% CI, 1.04 and 1.23) and substance use disorder diagnosis (95% CI, 1.07–1.28) were associated with significantly higher odds of seeing multiple prescribers before PDMP implementation. Conclusions Implementation of the PDMP was associated with meaningful reductions in people accessing monitored medicines from four or more prescribers.

Louisa Picco, Monica Jung, Grant Russell, Samanta Lalic, Mahbod A. Fini, Dan I. Lubman, Rachelle Buchbinder, Ting Xia, Suzanne Nielsen

Objectives To examine two decades of Australian expenditure trends across components of primary health and to assess whether recent expenditure changes have been equitably distributed. Study Type Descriptive modelling using standardised framework for classifying primary care expenditure. Setting Australian public and private health expenditure data (2002–03 to 2022–23) were disaggregated into: broad primary health care services (Tier A); direct primary care, predominantly funding general practice (Tier B); and funding for enhanced primary care for people with greater needs (Tier C). Distributional analysis was conducted across geographies. Participants No individual participants; analysis used aggregated health expenditure data across 327 Statistical Area Level 3 geographies. Main Outcome Measures Proportions of total and public expenditure allocated to each tier; equity in public Tier B and Tier C spending across areas, assessed using standardised slope indices. Results The share of total health spending allocated to primary care declined over the period. Tier A spending declined from 36.3% to 33.0% of total health spending; Tier B fell more sharply from 8.0% to 5.5%; and Tier C remained flat at 0.7%. Public spending trends were similar, but declines were more muted, with Tier C unchanged at 1.0%. Public spending on Tier B was 13% higher in the most disadvantaged areas than in the most advantaged areas in 2013–14; by 2023–24, this declined to 7%. Public Tier C spending remained progressive at 35% higher in the most disadvantaged areas, but decreased from 51% over the decade. Exploratory multivariate analyses suggested that Tier C spending was more redistributive than Tier B after accounting for need. Conclusions Data indicate that primary care has declined as a funding priority in relative terms in Australia, and investment in high-value care has remained stagnant and appears increasingly less redistributive. These patterns may have implications for health equity.

Rafal Chomik, Shona M. Bates, Michael Wright

Gin S. Malhi, Erica Bell, Kinga Szymaniak, Philip M. Boyce, Jeffrey C. L. Looi

Katharine A. Wallis, Joanna Moncrieff