Topics

Abstract

Objectives: To determine the proportion of Australian adolescent girls who experience menstrual pain (dysmenorrhea); to assess associations of dysmenorrhea and period pain severity with adolescents missing regular activities because of their periods.

Study design: Prospective, population‐based cohort study; analysis of Longitudinal Study of Australian Children (LSAC) survey data.

Setting, participants: Female adolescents in the nationally representative cross‐sequential sample of Australian children recruited in 2004 for the Kinder cohort (aged 4–5 years at enrolment). Survey data from waves 6 (mean age 14 years), wave 7 (16 years) and wave 8 (18 years) were analysed.

Main outcome measures: Severity of period pain during the preceding three months (very, quite, a little, or not at all painful); number of activity types missed because of periods; relationship between missing activities and period pain severity.

Results: Of the 1835 participating female members of the LSAC Kinder cohort at waves 6 to 8, 1600 (87%) responded to questions about menstruation during at least one of waves 6 to 8 of data collection. At wave 6 (14 years), 227 of 644 respondents (35%) reported dysmenorrhea, 675 of 1341 (50%) at wave 6 (16 years), and 518 of 1115 (46%) at wave 8 (18 years). Of the 366 participants who reported period pain severity at all three waves, 137 reported no dysmenorrhea at all three waves (37%), 66 reported dysmenorrhea at all three waves (18%), 89 reported increasing period pain over time (24%), and 38 reported declining pain (10%). At wave 6, 223 of 647 participants reported missing at least one activity because of their periods (34%), 454 of 1341 at wave 7 (34%), and 344 of 1111 at wave 8 (31%). Of the participants who experienced very painful periods, 72% (wave 6), 63% (wave 7), and 65% (wave 8) missed at least one activity type because of their periods, as did 45% (wave 6), 36% (wave 7), and 40% (wave 8) of those who experienced quite painful periods.

Conclusions: A large proportion of adolescent girls in Australia experience period pain that affects their engagement in regular activities, including school attendance. Recognising adolescent period pain is important not only for enhancing their immediate quality of life with appropriate support and interventions, but also as part of early screening for chronic health conditions such as endometriosis.

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1 Deakin University, Melbourne, VIC
2 Monash University, Melbourne, VIC
3 Deakin University, Geelong, VIC
4 University Hospital Geelong, Geelong, VIC
5 Endo Help Foundation, Point Lonsdale, VIC

Correspondence: lauren.cameron@deakin.edu.au

Open access:

Open access publishing facilitated by Deakin University, as part of the Wiley – Deakin University agreement via the Council of Australian University Librarians.

Acknowledgements:

The study was funded by the Endo Help Foundation (https://endohelp.com.au), a not‐for‐profit advocacy organisation. This article uses unit record data from the Longitudinal Study of Australian Children (LSAC), conducted by the Australian Government Department of Social Services (DSS) (doi: 10.26193/QR4L6Q). The findings and views reported in this article, however, are those of the authors and should not be attributed to the Australian government, DSS, or any of contractors or partners of DSS.

Competing interests:

No relevant disclosures.

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2. Armour M, Ferfolja T, Curry C, et al. The prevalence and educational impact of pelvic and menstrual pain in Australia: a national online survey of 4202 young women aged 13–25 years. J Pediatr Adolesc Gynecol 2020; 33: 511‐518.
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15. Australian Institute of Health and Welfare. Chronic pain in Australia (cat. no. PHE 267). 7 May 2020. https://www.aihw.gov.au/reports/chronic‐disease/chronic‐pain‐in‐australia/summary (viewed Nov 2022).
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18. Finning K, Ukoumunne OC, Ford T, et al. The association between child and adolescent depression and poor attendance at school: a systematic review and meta‐analysis. J Affect Disord 2019; 245: 928‐938.
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20. Belcher BR, Zink J, Azad A, et al. The roles of physical activity, exercise, and fitness in promoting resilience during adolescence: effects on mental well‐being and brain development. Biol Psychiatry Cogn Neurosci Neuroimaging 2021; 6: 225‐237.

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Research letter

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Lifestyle advice from general practitioners and changes in health‐related behaviour in Australia: secondary analysis of 2020–21 National Health Survey data

Loai Albarqouni, Hannah Greenwood, Caroline Dowsett and Paul P Glasziou

Med J Aust || doi: 10.5694/mja2.52285
Published online: 6 May 2024

ARTICLE
AUTHORS
REFERENCES

Topics

General medicine

Health services administration

Statistics, epidemiology and research design

Lifestyle factors — smoking, alcohol consumption, inadequate dietary levels of fruit and vegetables — are major risk factors for chronic medical conditions.1 The importance of clinicians encouraging people to modify their lifestyles is emphasised in many guidelines.2 A study that included 4716 American adults found that patient‐reported lifestyle advice from their doctors was associated with corresponding behavioural changes (weight reduction, increased physical activity).3 How often Australian general practitioners provide their patients with lifestyle advice and whether such advice is effective are unknown.

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Institute for Evidence‐Based Healthcare, Bond University, Gold Coast, QLD

Correspondence: lalbarqo@bond.edu.au

Open access:

Open access publishing facilitated by Bond University, as part of the Wiley ‐ Bond University agreement via the Council of Australian University Librarians.

Data sharing:

The data we analysed for this report are publicly available.

Acknowledgements:

This study was supported by a National Health and Medical Research Council Investigator Grant (2008379). The funder played no role in the planning, writing, or publication of this study.

Competing interests:

No relevant disclosures.

1. GBD 2019 Risk Factors Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 2020; 396: 1223‐1249.
2. US Preventive Services Task Force; Mangione CM, Barry MJ, Nicholson WK, et al. Behavioral counseling interventions to promote a healthy diet and physical activity for cardiovascular disease prevention in adults without cardiovascular disease risk factors: US Preventive Services Task Force recommendation statement. JAMA 2022; 328: 367‐374.
3. Williams AR, Wilson‐Genderson M, Thomson MD. A cross‐sectional analysis of associations between lifestyle advice and behavior changes in patients with hypertension or diabetes: NHANES 2015–2018. Prev Med 2021; 145: 106426.
4. Australian Bureau of Statistics. Data items. In: National Health Survey: first results methodology, 2020–21. 21 Mar 2022. https://www.abs.gov.au/methodologies/national‐health‐survey‐methodology/2020‐21 (viewed Oct 2023).
5. Henry JA, Jebb SA, Aveyard P, et al. Lifestyle advice for hypertension or diabetes: trend analysis from 2002 to 2017 in England. Br J Gen Pract 2022; 72: e269‐e275.
6. Albarqouni L, Montori V, Jørgensen KJ, et al. Applying the time needed to treat to NICE guidelines on lifestyle interventions. BMJ Evid Based Med 2023; 28: 354‐355.

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Symptomatic cancer diagnosis in general practice: a critical perspective of current guidelines and risk assessment tools

Brent Venning and Jon D Emery

Med J Aust || doi: 10.5694/mja2.52287
Published online: 6 May 2024

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AUTHORS
REFERENCES

Topics

Neoplasms

General medicine

On 2 November 2023, Cancer Australia unveiled the Australian Cancer Plan, emphasising a strategic commitment to “maximising cancer prevention and early detection”.1,2 This initiative holds particular significance for general practice, as most cancer diagnoses originate from symptomatic presentations to primary care, even when screening programs are available.3 For context, full‐time general practitioners on average diagnose up to 12 non‐cutaneous cancers annually, but they see patients consulting with symptoms associated with cancer almost daily.3,4

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Centre for Cancer Research, University of Melbourne, Melbourne, VIC

Correspondence: brent.venning@unimelb.edu.au

Open access:

Open access publishing facilitated by The University of Melbourne, as part of the Wiley ‐ The University of Melbourne agreement via the Council of Australian University Librarians.

Acknowledgements:

Brent Venning is supported by the Melbourne Academic Centre for Health (MACH) through the MACH‐Track program and by a University of Melbourne Research Training Program Scholarship. Jon Emery is supported by a National Health and Medical Research Council Investigator Grant (APP1195302) and is an Associate Director of the CanTest Collaborative (funded by Cancer Research UK, C8640/A23385).

Competing interests:

No relevant disclosures.

1. Cancer Australia. Australian Cancer Plan [website]. Canberra: Commonwealth of Australia, 2021. https://www.canceraustralia.gov.au/australian‐cancer‐plan (viewed Nov 2023).
2. Chaji D, Boltong A, Der Vartanian C, et al. Setting the policy agenda for cancer control reform: Australia's first national cancer control plan. Med J Aust 2023; 219: 451‐454. https://www.mja.com.au/journal/2023/219/10/setting‐policy‐agenda‐cancer‐control‐reform‐australias‐first‐national‐cancer
3. Emery JD. The challenges of early diagnosis of cancer in general practice. Med J Aust 2015; 203: 391‐393. https://www.mja.com.au/journal/2015/203/10/challenges‐early‐diagnosis‐cancer‐general‐practice
4. Hamilton W. Cancer diagnosis in primary care. Br J Gen Pract 2010; 60: 121‐128.
5. Rubin G, Berendsen A, Crawford SM, et al. The expanding role of primary care in cancer control. Lancet Oncol 2015; 16: 1231‐1272.
6. Koo MM, Hamilton W, Walter FM, et al. Symptom signatures and diagnostic timeliness in cancer patients: a review of current evidence. Neoplasia 2018; 20: 165‐174.
7. Stapley S, Peters TJ, Neal RD, et al. The risk of pancreatic cancer in symptomatic patients in primary care: a large case‐control study using electronic records. Br J Cancer 2012; 106: 1940‐1944.
8. Jensen H, Tørring ML, Olesen F, et al. Cancer suspicion in general practice, urgent referral and time to diagnosis: a population‐based GP survey and registry study. BMC Cancer 2014; 14: 636.
9. McGarvey N, Gitlin M, Fadli E, Chung KC. Increased healthcare costs by later stage cancer diagnosis. BMC Health Serv Res 2022; 22: 1155.
10. Emery JD, Shaw K, Williams B, et al. The role of primary care in early detection and follow‐up of cancer. Nat Rev Clin Oncol 2014; 11: 38‐48.
11. Renzi C, Kaushal A, Emery J, et al. Comorbid chronic diseases and cancer diagnosis: disease‐specific effects and underlying mechanisms. Nat Rev Clin Oncol 2019; 16: 746‐761.
12. Brownlee S, Chalkidou K, Doust J, et al. Evidence for overuse of medical services around the world. Lancet 2017; 390: 156‐168.
13. Lyratzopoulos G, Vedsted P, Singh H. Understanding missed opportunities for more timely diagnosis of cancer in symptomatic patients after presentation. Br J Cancer 2015; 112 (Suppl): S84‐S91.
14. Banks J, Hollinghurst S, Bigwood L, et al. Preferences for cancer investigation: a vignette‐based study of primary‐care attendees. Lancet Oncol 2014; 15: 232‐240.
15. Venning B, Pearce A, Lourenco RDA, et al. Patient preferences for investigating cancer‐related symptoms in australian general practice: a discrete choice experiment. Br J Gen Pract 2024; https://doi.org/10.3399/BJGP.2023.0583 [Epub ahead of print].
16. Cancer Australia. Optimal Care Pathways. Canberra: Commonwealth of Australia, 2024. https://www.canceraustralia.gov.au/optimal‐cancer‐care‐pathways (viewed Apr 2024).
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23. Hamilton W, Green T, Martins T, et al. Evaluation of risk assessment tools for suspected cancer in general practice: a cohort study. Br J Gen Pract 2013; 63: e30‐e36.
24. Price S, Spencer A, Medina‐Lara A, Hamilton W. Availability and use of cancer decision‐support tools: a cross‐sectional survey of UK primary care. Br J Gen Pract 2019; 69: e437‐e443.
25. Hamilton W, Mounce L, Abel GA, et al. Protocol for a pragmatic cluster randomised controlled trial assessing the clinical effectiveness and cost‐effectiveness of Electronic RIsk‐assessment for CAncer for patients in general practice (ERICA). BMJ Open 2023; 13: e065232.
26. Royal Australian College of General Practitioners. Electronic clinical decision support in general practice [website]. Melbourne: RACGP, 2024. https://www.racgp.org.au/advocacy/position‐statements/view‐all‐position‐statements/clinical‐and‐practice‐management/electronic‐clinical‐decision‐support (viewed Mar 2024).
27. Chima S, Reece JC, Milley K, et al. Decision support tools to improve cancer diagnostic decision making in primary care: a systematic review. Br J Gen Pract 2019; 69: e809‐e818.
28. Hunter B, Biezen R, Alexander K, et al. Future Health Today: codesign of an electronic chronic disease quality improvement tool for use in general practice using a service design approach. BMJ Open 2020; 10: e040228.
29. Davies D, Morgan M, de Wet C. Supporting quality and safety in general practice: Response rates to computer decision support. Aust J Gen Pract 2022; 51: 884‐892.

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Research

How should artificial intelligence be used in Australian health care? Recommendations from a citizens’ jury

Stacy M Carter, Yves Saint James Aquino, Lucy Carolan, Emma Frost, Chris Degeling, Wendy A Rogers, Ian A Scott, Katy JL Bell, Belinda Fabrianesi and Farah Magrabi

Med J Aust 2024; 220 (8): . || doi: 10.5694/mja2.52283
Published online: 6 May 2024

ARTICLE
AUTHORS
REFERENCES

Topics

Information science

Health services administration

Social determinants of health

Abstract

Objective: To support a diverse sample of Australians to make recommendations about the use of artificial intelligence (AI) technology in health care.

Study design: Citizens’ jury, deliberating the question: “Under which circumstances, if any, should artificial intelligence be used in Australian health systems to detect or diagnose disease?”

Setting, participants: Thirty Australian adults recruited by Sortition Foundation using random invitation and stratified selection to reflect population proportions by gender, age, ancestry, highest level of education, and residential location (state/territory; urban, regional, rural). The jury process took 18 days (16 March – 2 April 2023): fifteen days online and three days face‐to‐face in Sydney, where the jurors, both in small groups and together, were informed about and discussed the question, and developed recommendations with reasons. Jurors received extensive information: a printed handbook, online documents, and recorded presentations by four expert speakers. Jurors asked questions and received answers from the experts during the online period of the process, and during the first day of the face‐to‐face meeting.

Main outcome measures: Jury recommendations, with reasons.

Results: The jurors recommended an overarching, independently governed charter and framework for health care AI. The other nine recommendation categories concerned balancing benefits and harms; fairness and bias; patients’ rights and choices; clinical governance and training; technical governance and standards; data governance and use; open source software; AI evaluation and assessment; and education and communication.

Conclusions: The deliberative process supported a nationally representative sample of citizens to construct recommendations about how AI in health care should be developed, used, and governed. Recommendations derived using such methods could guide clinicians, policy makers, AI researchers and developers, and health service users to develop approaches that ensure trustworthy and responsible use of this technology.

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1 University of Wollongong, Wollongong, NSW
2 Australian Centre for Health Engagement, Evidence and Values, University of Wollongong, Wollongong, NSW
3 Macquarie University, Sydney, NSW
4 University of Queensland, Brisbane, QLD
5 Princess Alexandra Hospital, Brisbane, QLD
6 University of Sydney, Sydney, NSW
7 Australian Institute for Health Innovation, Macquarie University, Sydney, NSW

Correspondence: stacyc@uow.edu.au

Open access:

Open access publishing facilitated by University of Wollongong, as part of the Wiley – University of Wollongong agreement via the Council of Australian University Librarians.

Data sharing:

Individual deidentified participant data will be partially shared. The ethics approval for the study stipulated that transcripts of recordings of the jurors’ deliberations would remain confidential because of the risk of individual identification. Our study did not involve data dictionaries. Extensive information about the study protocol, and data generated for and in the study (including descriptions of the process, the expert witness videos, questions generated by the jury, and answers provided by the experts) are available at https://uow.info/TAWSYN_JURY.

Acknowledgements:

This study was supported by the National Health and Medical Research Council (1181960).

Competing interests:

No relevant disclosures.

1. Australian Department of Industry, Science and Resources. Supporting responsible AI: discussion paper. Government's interim response. 17 Jan 2024. https://consult.industry.gov.au/supporting‐responsible‐ai (viewed Mar 2024).
2. Lyell D, Wang Y, Coiera E, Magrabi F. More than algorithms: an analysis of safety events involving ML‐enabled medical devices reported to the FDA. J Am Med Inform Assoc 2023; 30: 1227‐1226.
3. Wadie J. A roadmap for artificial intelligence in healthcare for Australia news]. Australian Alliance for Artificial Intelligence in Healthcare, 1 Dec 2021. https://aihealthalliance.org/2021/12/01/a‐roadmap‐for‐ai‐in‐healthcare‐for‐australia (viewed Oct 2023).
4. Coiera EW, Verspoor K, Hansen DP. We need to chat about artificial intelligence. Med J Aust 2023; 219: 98‐100. https://www.mja.com.au/journal/2023/219/3/we‐need‐chat‐about‐artificial‐intelligence
5. Pearce C, McLeod A, Rinehart N, et al. Artificial intelligence and the clinical world: a view from the front line. Med J Aust 2019; 210 (6 Suppl): S38‐S40. https://www.mja.com.au/journal/2019/210/6/artificial‐intelligence‐and‐clinical‐world‐view‐front‐line
6. Moodie C. Australian Medical Association calls for national regulations around AI in health care. ABC News (Australia), 28 May 2023. https://www.abc.net.au/news/2023‐05‐28/ama‐calls‐for‐national‐regulations‐for‐ai‐in‐health/102381314 (viewed Oct 2023).
7. Mathews DJH, Balatbat CA, Dzau VJ. Governance of emerging technologies in health and medicine: creating a new framework. N Engl J Med 2022; 386: 2239‐2242.
8. Degeling C, Carter SM, Rychetnik L. Which public and why deliberate? A scoping review of public deliberation in public health and health policy research. Soc Sci Med 2015; 131: 114‐121.
9. Flanigan B, Gölz P, Gupta A, et al. Fair algorithms for selecting citizens’ assemblies. Nature 2021; 596: 548‐552.
10. Australian Centre for Health Engagement Evidence and Values. Artificial intelligence in health: community jury. Undated. https://uow.info/TAWSYN_JURY (viewed Oct 2023).
11. White K, Hunter N, Greaves K. Facilitating deliberation: a practical guide. Melbourne: MosaicLab, 2022.
12. Brown J, Isaacs D. The world café: shaping our futures through conversations that matter. San Francisco: Berrett–Koehler, 2005.
13. Aquino YSJ, Carter SM, Houssami N, et al. Practical, epistemic and normative implications of algorithmic bias in healthcare artificial intelligence: a qualitative study of multidisciplinary expert perspectives. J Med Ethics 2023: jme‐2022‐108850.
14. Thomas R, Sims R, Degeling C, et al. CJCheck Stage 1: development and testing of a checklist for reporting community juries: Delphi process and analysis of studies published in 1996–2015. Health Expect 2016; 20: 626‐637.
15. Australian Bureau of Statistics. Snapshot of Australia, 2021. 28 June 2022. https://www.abs.gov.au/statistics/people/people‐and‐communities/snapshot‐australia/2021 (viewed Oct 2023).
16. Australian Bureau of Statistics. Education and training: census, 2021. 28 June 2022. https://www.abs.gov.au/statistics/people/education/education‐and‐training‐census/latest‐release (viewed Oct 2023).
17. Australian Bureau of Statistics. National state and territory population, December 2023. 28 June 2022. https://www.abs.gov.au/statistics/people/population/national‐state‐and‐territory‐population/dec‐2021 (viewed Oct 2023).
18. Australian Institute of Health and Welfare. Profile of Australia's population. 6 June 2023. https://www.aihw.gov.au/reports/australias‐health/profile‐of‐australias‐population (viewed Oct 2023).
19. van der Veer SN, Riste L, Cheraghi‐Sohi S, et al. Trading off accuracy and explainability in AI decision‐making: findings from 2 citizens’ juries. J Am Med Inform Assoc 2021; 28: 2128‐2138.
20. Forum for Ethical AI. Democratising decisions about technology: a toolkit. 24 Oct 2019. https://www.thersa.org/reports/democratising‐decisions‐technology‐toolkit (viewed Oct 2023).
21. Ada Lovelace Institute. The Citizens’ Biometrics Council London: report with recommendations and findings of a public deliberation on biometrics technology, policy and governance. 30 Mar 2021. https://www.adalovelaceinstitute.org/report/citizens‐biometrics‐council (viewed Oct 2023).
22. European Commission. Proposal for a regulation of the European parliament and the council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending certain union legislative acts (COM/2021/206 final). 21 Apr 2021. https://eur‐lex.europa.eu/legal‐content/EN/TXT/?uri=CELEX:52021PC0206 (viewed Oct 2023).
23. Therapeutic Goods Administration. Regulation of software based medical devices. 28 Sept 2023. https://www.tga.gov.au/how‐we‐regulate/manufacturing/medical‐devices/manufacturer‐guidance‐specific‐types‐medical‐devices/regulation‐software‐based‐medical‐devices (viewed Oct 2023).
24. Street J, Duszynski K, Krawczyk S, Braunack‐Mayer A. The use of citizens' juries in health policy decision‐making: a systematic review. Soc Sci Med 2014; 109: 1‐9.

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Perspectives

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Towards national paediatric clinical practice guidelines

Mike Starr

Med J Aust || doi: 10.5694/mja2.52272
Published online: 15 April 2024

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AUTHORS
REFERENCES

Topics

General medicine

Pediatric medicine

Clinical practice guidelines (CPGs) are intended to improve the quality of clinical care by promoting evidence‐based care, reducing inappropriate variation, and producing optimal outcomes for patients.

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1 Royal Children's Hospital, Melbourne, VIC
2 University of Melbourne, Melbourne, VIC

Correspondence: mike.starr@rch.org.au

Open access:

Open access publishing facilitated by The University of Melbourne, as part of the Wiley ‐ The University of Melbourne agreement via the Council of Australian University Librarians.

Acknowledgements:

The PIC CPG work is funded in part by Clinical Excellence Queensland, the NSW Agency for Clinical Innovation, Safer Care Victoria, RCH Melbourne, and the RCH Foundation. Kate Harding, Matthew O'Meara and Mike South have had an integral role in the work of the CPG committee and PIC steering committee.

Competing interests:

No relevant disclosures.

1. Cohn SL, Gautam B, Preddy JS, et al. Barriers to the use of paediatric clinical practice guidelines in rural and regional New South Wales Australia. Aust J Rural Health 2016; 24, 23‐28.
2. Braithwaite J, Hibbert PD, Jaffe A, et al. Quality of health care for children in Australia, 2012‐2013. JAMA 2018; 319: 1113‐1124.
3. Duggan A, Koff E, Marshall V. Clinical variation: why it matters. Med J Aust 2016; 205: S3‐S4. https://www.mja.com.au/journal/2016/205/10/clinical‐variation‐why‐it‐matters
4. Royal Children's Hospital Melbourne. Clinical Practice Guidelines: CPG information. https://www.rch.org.au/clinicalguide/about_rch_cpgs/CPG_information/ (viewed July 2023).
5. Rea CJ, Alvarez FJ, Tieder JS. The silent crisis of pediatric clinical practice guidelines. JAMA Pediatr 2021; 175: 1201‐1202.
6. Flores G, Lee M, Bauchner H. Pediatricians’ attitudes, beliefs, and practices regarding clinical practice guidelines: a national survey. Pediatrics 2000; 105: 496‐501.

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Ethics and law

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Mainstreaming genomic testing: pre‐test counselling and informed consent

Michaela Cormack, Kathryn B Irving, Fiona Cunningham and Andrew P Fennell

Med J Aust || doi: 10.5694/mja2.52254
Published online: 8 April 2024

ARTICLE
AUTHORS
REFERENCES

Topics

Medical genetics

Ethics and law

General medicine

Health services administration

Nervous system diseases

There is unprecedented, increasing demand for genomic testing in Australia.1,2 Recent developments in paediatric neurology alone include Medical Benefits Schedule, industry and research sponsored testing for monogenic causes of epilepsy, neuromuscular disorders, and syndromic intellectual disability, among others. To be ethically and legally valid, patients must undergo pre‐test counselling before they consent to genomic testing.

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1 Monash Health, Melbourne, VIC
2 Royal Children's Hospital, Melbourne, VIC
3 University of Melbourne, Melbourne, VIC
4 Monash University, Melbourne, VIC

Correspondence: michaela.cormack@monashhealth.org

Consent for publication:

Consent for publication of the case study described and discussed in this article was obtained from both parents on behalf of the child.

Open access:

Open access publishing facilitated by Monash University, as part of the Wiley ‐ Monash University agreement via the Council of Australian University Librarians.

Competing interests:

No relevant disclosures.

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Medicare‐funded reproductive genetic carrier screening in Australia has arrived: are we ready?

Alice P Rogers, Lara Fitzgerald, Jan Liebelt and Christopher Barnett

Med J Aust || doi: 10.5694/mja2.52261
Published online: 8 April 2024

ARTICLE
AUTHORS
REFERENCES

Topics

Women's health

Medical genetics

Health occupations

Reproductive genetic carrier screening (RGCS) is a preventive health strategy performed to identify healthy couples and individuals who are at increased chance of having a child affected by a serious, childhood onset autosomal recessive or X‐linked genetic condition (Box 1).

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1 SA Clinical Genetics Service, Women's and Children's Hospital, Adelaide, SA
2 University of Adelaide, Adelaide, SA
3 Repromed (Adelaide Fertility Centre), Adelaide, SA

Correspondence: christopher.barnett@sa.gov.au

Open access:

Open access publishing facilitated by The University of Adelaide, as part of the Wiley ‐ The University of Adelaide agreement via the Council of Australian University Librarians.

Competing interests:

Jan Liebelt and Lara Fitzgerald are employed by Repromed, whose laboratory offers preconception RGCS. They also provide genetic counselling services.

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7. Archibald AD, Smith MJ, Burgess T, et al. Reproductive genetic carrier screening for cystic fibrosis, fragile X syndrome, and spinal muscular atrophy in Australia: outcomes of 12,000 tests. Genet Med 2018; 20: 513‐523.
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The 2023 report of the MJA–Lancet Countdown on health and climate change: sustainability needed in Australia's health care sector

Paul J Beggs, Stefan Trueck, Martina K Linnenluecke, Hilary Bambrick, Anthony G Capon, Ivan C Hanigan, Nicolas Borchers Arriagada, Troy J Cross, Sharon Friel, Donna Green, Maddie Heenan, Ollie Jay, Harry Kennard, Arunima Malik, Celia McMichael, Mark Stevenson, Sotiris Vardoulakis, Tran N Dang, Gail Garvey, Raymond Lovett, Veronica Matthews, Dung Phung, Alistair J Woodward, Marina B Romanello and Ying Zhang

Med J Aust || doi: 10.5694/mja2.52245
Published online: 25 March 2024

ARTICLE
AUTHORS
REFERENCES

Topics

Environment and public health

Mental disorders

Health services administration

Statistics, epidemiology and research design

Summary

The MJA–Lancet Countdown on health and climate change in Australia was established in 2017 and produced its first national assessment in 2018 and annual updates in 2019, 2020, 2021 and 2022. It examines five broad domains: health hazards, exposures and impacts; adaptation, planning and resilience for health; mitigation actions and health co‐benefits; economics and finance; and public and political engagement. In this, the sixth report of the MJA–Lancet Countdown, we track progress on an extensive suite of indicators across these five domains, accessing and presenting the latest data and further refining and developing our analyses.
Our results highlight the health and economic costs of inaction on health and climate change. A series of major flood events across the four eastern states of Australia in 2022 was the main contributor to insured losses from climate‐related catastrophes of $7.168 billion — the highest amount on record. The floods also directly caused 23 deaths and resulted in the displacement of tens of thousands of people.
High red meat and processed meat consumption and insufficient consumption of fruit and vegetables accounted for about half of the 87 166 diet‐related deaths in Australia in 2021. Correction of this imbalance would both save lives and reduce the heavy carbon footprint associated with meat production.
We find signs of progress on health and climate change. Importantly, the Australian Government released Australia's first National Health and Climate Strategy, and the Government of Western Australia is preparing a Health Sector Adaptation Plan. We also find increasing action on, and engagement with, health and climate change at a community level, with the number of electric vehicle sales almost doubling in 2022 compared with 2021, and with a 65% increase in coverage of health and climate change in the media in 2022 compared with 2021.
Overall, the urgency of substantial enhancements in Australia's mitigation and adaptation responses to the enormous health and climate change challenge cannot be overstated. Australia's energy system, and its health care sector, currently emit an unreasonable and unjust proportion of greenhouse gases into the atmosphere. As the Lancet Countdown enters its second and most critical phase in the leadup to 2030, the depth and breadth of our assessment of health and climate change will be augmented to increasingly examine Australia in its regional context, and to better measure and track key issues in Australia such as mental health and Aboriginal and Torres Strait Islander health and wellbeing.

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1 Macquarie University, Sydney, NSW
2 University of Technology Sydney, Sydney, NSW
3 National Centre for Epidemiology and Population Health, Australian National University, Canberra, ACT
4 Monash Sustainable Development Institute, Monash University, Melbourne, VIC
5 Curtin University, Perth, WA
6 Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS
7 University of Sydney, Sydney, NSW
8 Australian National University, Canberra, ACT
9 Climate Change Research Centre and ARC Centre of Excellence for Climate Extremes, UNSW, Sydney, NSW
10 Australian Prevention Partnership Centre, Sax Institute, Sydney, NSW
11 The George Institute for Global Health, Sydney, NSW
12 Thermal Ergonomics Laboratory, University of Sydney, Sydney, NSW
13 Center on Global Energy Policy, Columbia University, New York, NY, USA
14 University of Melbourne, Melbourne, VIC
15 Transport, Health and Urban Design (THUD) Research Lab, University of Melbourne, Melbourne, VIC
16 University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
17 University of Queensland, Brisbane, QLD
18 Australian Institute of Aboriginal and Torres Strait Islander Studies, Canberra, ACT
19 University Centre for Rural Health, University of Sydney, Sydney, NSW
20 University of Auckland, Auckland, NZ
21 Institute for Global Health, University College London, London, UK

Correspondence: paul.beggs@mq.edu.au

Open access:

Open access publishing facilitated by Macquarie University, as part of the Wiley ‐ Macquarie University agreement via the Council of Australian University Librarians.

Acknowledgements:

We thank Robert Fawcett, Justin Peter, and John Nairn (all from the Australian Bureau of Meteorology) for indicators 1.1 Exposure of vulnerable populations to heatwaves and 2.2 Climate information for health. Ivan Hanigan's access to data for indicators 1.4 and 3.3 was supported by the Centre for Air Pollution, Energy, and Health Research Data Platform funded by the NHMRC Centre for Safe Air (https://www.car‐cre.org.au/; https://cardat.github.io), which received funding from the Australian Research Data Commons for the Integrated National Air Pollution and Health Data project (https://doi.org/10.47486/PS022), and he acknowledges the HEAL (Healthy Environments And Lives) National Research Network, which receives funding from the NHMRC (grant no. 2008937). The Bushfires indicator was generated with support from NASA Applied Sciences Program (grant no. 80NSSC21K0507) and we thank Yang Liu and Qiao Zhu (Emory University) as well as Yun Hang (now at University of Texas Health Science Center) for the Australian data used for this indicator (1.3). We also thank Fay Johnston for assistance with this indicator. We thank Carole Dalin (University College London) for assistance with indicator 3.5 Emissions from agricultural production and consumption. We thank the Lancet Countdown for providing the results for indicators 3.6 Diet and health co‐benefits, and 3.7 Health care sector emissions. We thank the NHMRC for providing the data for indicator 5.4 Health and climate change research funding.

Competing interests:

No relevant disclosures.

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