NEW ZEALAND MEDICAL JOURNAL最新文献

Aims: For over a decade, New Zealand pursued a comprehensive reform of its outdated medicines legislation, culminating in the passage of the Therapeutic Products Act 2023 (TPA) in 2023. In a policy reversal, the Act was repealed by a new government in 2024. This study provides an analysis of this policy cycle to understand the drivers of the reform, its subsequent repeal and the implications for future health policy. We take a political economy perspective, foregrounding health policy instability and its consequences for patients, clinicians and Māori health interests.

Methods: We conducted a qualitative documentary policy analysis of 25 key government and stakeholder documents, including legislation, regulations, cabinet papers and select committee reports with their submissions. We employed a framework method for a systematic thematic analysis of the corpus to map and interpret the policy narratives.

Results: The impetus for the TPA was a consensus that the Medicines Act 1981 and its associated regulations from 1984 and 1985 were "no longer fit for purpose". The repeal was driven by an ideological shift, reframing the TPA as an unacceptable "regulatory burden". This has tangible consequences, including the loss of a pre-market approval framework for medical devices and the erasure of legislative provisions designed to protect and recognise Rongoā Māori (traditional Māori healing).

Conclusion: The TPA policy cycle is a case study in the fragility of evidence-based health reform. It demonstrates that without a durable, cross-party political consensus, long-term policy projects are highly vulnerable to being dismantled by short-term shifts in political ideology, with downstream harms from regulatory instability. It also illustrates how a targeted "micro‑reform" can generate outsized system‑level consequences.

Background: Computed tomography colonography (CTC) is an alternative to colonoscopy for the detection of polyps and colorectal cancer (CRC). One-third of CRCs arise via the sessile serrated pathway. Evidence supports using CTC to detect adenomas and CRC; however, its accuracy for sessile serrated lesions (SSLs) remains uncertain. This study aimed to determine the accuracy of CTC in detecting SSLs compared with colonoscopy.

Method: Electronic records identified all colonoscopy procedures where a histologically validated SSL was excised over a 11-month period. In those patients who had a CTC within 1 year prior to colonoscopy, the presence, size and location of SSLs were compared to determine the accuracy of CTC in SSL identification.

Results: A total of 4,346 procedures were performed (2,548 people, 2,082 [47.9%] male, mean age 59.6). A total of 2,204 SSLs were removed, representing 24% of all polypectomies. SSLs were predominantly located in the right colon (65.1%) and were typically (85%) <10mm in size. A total of 110 SSLs were obtained from 39 procedures with a prior CTC. Of these procedures, 12 (30.8%) had lesions identified on CTC; however, CTC only accurately identified 14.5% of the total SSLs. Five of 16 (32%) SSLs ≥10mm were correctly identified compared with 11 of 94 (11%) SSLs 1-9mm, (odds ratio 3.42, p=0.0495).

Conclusion: This study demonstrated that CTC has poor efficacy in detecting SSLs, irrespective of polyp size and location. Based on these findings, CTC as a substitute for colonoscopy is not advisable in patients at risk of SSLs.

Aotearoa New Zealand has one of the highest bowel cancer rates in the world. Bowel cancer incidence is increasing for Māori (the Indigenous people of Aotearoa), while trending downwards for non-Māori. Over half of Māori who get bowel cancer are diagnosed before the age of 60 years and are more likely than non-Māori to die within 2 years. Pacific people also experience bowel cancer inequities. In 2016, a national bowel screening programme for Aotearoa was announced, with an age range of 60-74 years. However, equity modelling showed that the proposed programme would disproportionately benefit non-Māori and that lowering the screening age for Māori and Pacific peoples to 50 years could achieve equal health gains. Over subsequent years, Māori cancer leaders advocated for policy change to lower the bowel screening age by 10 years for Māori. They used academic publications, presentations, letters, position statements, media stories and meetings with government leaders. Despite this advocacy, in 2020, the Government announced it was not going to lower the bowel screening age for Māori and Pacific peoples. The advocates persevered. They were supported in their efforts by new data that further confirmed the increasing bowel cancer incidence for Māori. In 2022, the Government committed to lowering the bowel cancer screening age to 50 for Māori and Pacific peoples. However, what followed was a tardy, phased rollout in only three regions. A year on, a new government embarked on a politically motivated agenda to reject ethnically targeted policies, with further significant equity changes to the programme announced. This paper summarises the lobbying efforts of cancer leaders and the government response, revealing structural and institutional racism, represented by inaction and active rejection of evidence-based advice. We describe the perseverance required to advocate for equity in the face of structural racism and the cost to Māori lives while inaction and racism persist.

Outpatient referrals for hospital specialist assessment are an increasing workload that carry significant risk if not attended to in a timely manner. This viewpoint discusses how decision support (including artificial intelligence and machine learning) may address this problem. Of the many possible approaches, we choose a combination of two that illustrate the breadth of available tools and how they combine to complement each other. To understand the issues and inform this discussion, a survey of general practitioners' views was conducted (Appendix 2), an audit of declined referrals was undertaken (Appendix 3) and draft decision trees were constructed (Appendix 4). To have data suitable for automated decision support, the current referral needs to change from free text to a structured format that ensures every patient has a complete minimum dataset. Regarding triaging decisions, at present there is human variability, but the decision support tools will need to be trained on a set of referrals that have an agreed gold-standard decision. In order to maintain patient safety throughout, the process needs to be incremental. We suggest that one way to assure patient safety is to combine simple decision trees with sophisticated contemporary machine learning.