How can we value what is not measured: the need for better data on women's health and primary care

In this viewpoint we describe the women’s health gap and the challenges to assessing the cost of this gap when using the secondary care–focussed national health data collections currently available in Aotearoa New Zealand. Conditions which are chronic, disabling but typically non-fatal, such as migraine, that are largely managed in primary care are not sufficiently visible in these data collections, leaving the burden and inequities in outcomes inadequately measured or unmeasured. Full article available to subscribers Routine or administrative datasets currently used in Aotearoa New Zealand fail to collect comprehensive information about health conditions that are predominantly managed in primary care settings. Such health conditions are therefore under-prioritised in health strategies, planning and funding and also disproportionately affect women. These conditions include those that only occur in women (e.g., endometriosis, menopause, premenstrual syndrome) and those that are more prevalent or carry a higher burden in women than in men (e.g., mood disorders, migraine). For both categories, lack of data, monitoring and research into these conditions perpetuates a cycle of neglect, in that being unable to accurately quantify the burden and cost of these diseases means they are under-prioritised for research and funding,1 obscuring data and health gaps. To illustrate the importance of obtaining better primary care data for women’s health, we use the example of migraine disease, the most disabling neurological condition in Australasia2 that affects at least twice as many women as men. In Aotearoa New Zealand, migraine affects a similar proportion of NZ European, Māori and Pacific people, but Māori are more likely to report recent symptoms and Māori and Pacific people are less likely to have a migraine diagnosis.3 Globally, despite its high prevalence of around 15%, migraine is also acknowledged to be under-researched, under-diagnosed, under-treated and underfunded.1,4 For example, analysis of health research funding from the United States of America National Institutes of Health that adjusted allocation of funds across diseases to be commensurate with disease burden found that diseases affecting women were disproportionately underfunded, with migraine (along with myalgic encephalomyelitis/chronic fatigue syndrome) the most severely underfunded in 2019.1 In this viewpoint we describe the women’s health gap and the challenges to assessing the cost of this gap when using the secondary care–focussed national health data collections currently available in Aotearoa New Zealand. Conditions that are chronic, disabling but typically non-fatal, such as migraine, that are largely managed in primary care are not sufficiently visible in these data collections, leaving the burden and inequities in outcomes inadequately measured or unmeasured. While we acknowledge that such conditions likely also disproportionately impact people assigned female at birth who do not identify as women, we use the term “women” throughout to reinforce the gendered nature of these issues. On average, women live longer than men, but spend 25% more time in poor health, experiencing pain and disability.5 The social impact of this health gap is loss of women’s quality of life. The economic impact is the reduced ability of women to participate fully and productively in the workforce and to undertake unpaid labour, such as caregiving, volunteering and domestic work. The World Economic Forum in 2024 estimated that addressing the women’s health gap could “potentially boost the global economy by [US]$1 trillion annually by 2040.”5 They listed 10 women’s health conditions that contributed to more than half of this economic impact. Number three on this list was migraine (behind premenstrual syndrome and depressive disorders)—number four if menopause was included. If migraine in women could be addressed, this had the potential to contribute US$80 billion to the global economy. What about the Aotearoa New Zealand economy? That is unknown. To estimate the cost of a disease to society we need a lot of high-quality data. We need to know how many people have the disease, by age, sex and ethnicity. We need to know what kind of healthcare people with the disease use (e.g., visiting general practitioners [GPs], emergency departments [EDs], hospitals, specialist [outpatient] appointments; medications both prescribed and over the counter; complementary therapies; supplements; diagnostic and monitoring tests) and how much these services cost. We need to know the impact of the disease on people’s ability to work, whether it causes unemployment, underemployment, early retirement, time off work or time at work with poor performance. We need to cost this impact, to measure “lost productivity”, and ideally even put a price on the pain and disability that people experience because of the disease. Lots of countries have estimated the cost of migraine to their economy (e.g., Alberta, Canada estimated additional healthcare costs of migraine to be more than CA$1 billion in 2022;6 Spain estimated the total cost, including lost productivity, to be between €10 million and €14 million in 2020;7 the United Kingdom [UK] estimated migraine cost the public economy £12 billion in 20228). This has not been done in Aotearoa New Zealand, although our scoping review9 of international studies made recommendations on best practice and ways to reduce bias and avoid either overinflating or understating results. However, a major barrier to completing a costing study in Aotearoa New Zealand is lack of data. When we reviewed what would be needed, we found major deficiencies in the data on healthcare use and nothing substantive that could be used to estimate lost productivity.10 One of the reasons for this lack of health data is that most cases of migraine are managed in primary care. This is also true for many of the conditions that significantly affect women (e.g., long COVID, premenstrual syndrome, endometriosis, mood disorders) and contribute to the women’s health gap. Primary care data include health information collected at points of contact between patients and primary care services, including patient demographics, symptoms, test results, prescriptions and other treatments, referrals and diagnoses. However, primary care information systems are not set up to consistently and accurately record diagnostic information. In addition, primary care is privatised and there is no mechanism or incentive for data from individual private practices, or the primary health organisations (PHOs) that manage government funding for large groups of practices, to be appropriately collated and shared for monitoring and research. Health New Zealand – Te Whatu Ora is working with PHOs and general practices on the development of a national primary care dataset, but at this stage this is purely administrative, to count bookings and encounters, and will collect no clinical information.11 In the UK, databases of primary care electronic health records cover most practices and are linked to other datasets (e.g., death records, hospitalisations, immunisation) and accessible to bona fide researchers.12 This is possible in part because primary care services in the UK are publicly funded. Aotearoa New Zealand keeps good records on hospitalisations, but conditions such as migraine do not usually lead to hospital admission (nor should they—a hospitalisation for migraine could rightly be seen as a failure of primary care,13 otherwise known as an “ambulatory sensitive hospitalisation”). Four of the five current health targets14 are based on secondary care data (faster cancer treatment, shorter stays in EDs, shorter wait times for first specialist assessment, shorter wait times for elective [hospital] treatment), and the fifth is based on registry data (improved immunisation for tamariki). Many other indicators of health system performance included in the Government Policy Statement on Health 2024–202715 are based on registries, mortality data and hospitalisations. Aotearoa New Zealand has national registries for cancer incidence, cancer screening, diabetes and immunisations, but neither these nor the other datasets are useful for monitoring the women’s health gap caused by non-fatal, high-disability health conditions. Useful information on these conditions could be gained from EDs and specialist outpatient hospital clinics. Since 2021, all ED clinicians have been asked to report SNOMED codes on “presenting complaint” (what the patient is noted as attending the ED for, e.g., vomiting or headache), diagnosis (what the clinician who assessed the patient noted as the underlying disease or cause for the presentation, e.g., migraine) and any procedure, although this practice has not yet been fully implemented nationally. For outpatient clinics, we can see how many people attend but not what they attend for. In addition, these collections do not record specialist visits in the private sector, although around one-fifth of specialist medical workforce hours are spent in private practice.16 Similarly, national collections of laboratory and imaging tests may record the timing and completion of a test but not what the test was done for and what the result was. In some instances, it is not even possible to distinguish the type of test performed. Even if a disease has a definitive diagnostic test, these records can only identify people who were sent for and had a test done. However, in the case of migraine and many other conditions affecting women, the diagnosis rests on the pattern and experience of symptoms and there is no specific diagnostic test. This creates a cycle of invisibility and neglect (Figure 1). There is no definitive imaging or blood test, which makes research more challenging and the impact of the disease harder to measure and easy to under-estimate. Lack of research means slow progress in understanding the pathogenesis and pathophysiology of the disease and the development of new treatments. Lack of knowledge about impact means less incentive to fund any new treatments or tests that do arise, when health services and research funding are allocated by apparent need. Lack of funding means that the impact of new treatments is hard to measure, as these are poorly accessible, and this will exacerbate inequities in outcomes, as access is dependent on the capacity of patients to self-fund. And because poorer and unequal outcomes are not captured in data and research that inform policy and planning, there is no incentive to improve or correct existing data collections. These issues may also reinforce existing inequities across groups by leading to the (mis)perception that groups with poorer service access, often Māori and Pacific people, have lower disease risk. One data source that can sometimes be used to identify and monitor health conditions in a population is use of medications. In Aotearoa New Zealand, the pharmaceutical collection is the main source of such data: a national data collection of all funded pharmaceuticals dispensed in the community. This data collection presents some limitations. If a medicine is prescribed but the prescription is not filled, this is not counted, and there are equity concerns about access to medications, with Māori more likely to face cost barriers to collecting a prescription (and seeing a GP).17 If a prescription medicine is dispensed but it is not funded by Pharmac and instead paid for out-of-pocket by the patient, this is not counted. Medicines administered in hospital settings are not counted. While the New Zealand ePrescription Service now captures most prescribing and dispensing activity in real time and is used to populate the Medicines Data Repository (MDR), these data are not easily available. None of these collections capture medicines bought over the counter, such as non-steroidal anti-inflammatory drugs, which can be purchased from pharmacies, supermarkets and other stores. Besides these limitations, none of these (potential) sources of pharmaceutical data collect information on the reason for the prescription nor can they identify people with health conditions for which there are no drug treatments. Using migraine as a case in point, when migraine attacks are frequent or severe, preventive medications can be prescribed. However, the only funded migraine preventives in Aotearoa New Zealand are medications developed for and used to treat other conditions, namely hypertension/heart disease, epilepsy and mood disorders. For example, we can see from the pharmaceutical collection how many people are dispensed amitriptyline, a tricyclic antidepressant as well as a first-line migraine preventive medication, but not whether this was prescribed to treat migraine, depression, neuropathic pain, another condition or a combination of conditions. New migraine preventive medications are specific to migraine, so will not be used for any other condition, but as these are not funded, they will only appear in the New Zealand ePrescription Service or MDR but not the pharmaceutical collection, which is the main source of pharmaceutical information available to researchers. Even if primary care data were widely available for research and evaluation in Aotearoa New Zealand, they may have limited value at a population level without significant investment into quality improvement, particularly in diagnostic coding. Hospitals employ trained clinical coders who scour hospital notes for diagnoses and procedures, the recording of which is used for planning and funding of services. EDs, outpatient services and primary care are funded differently and are not reliant on this level of coding, so do not have dedicated coders reviewing their consultations. Artificial intelligence–based approaches to support coding practices are being developed and tested but have not yet been implemented in Aotearoa New Zealand to our knowledge. We have demonstrated the potential limitations of primary care data in a recent study18 using an extract of electronic health records from 2014 from ProCare, the largest PHO in the Auckland Region, covering 170 general practices and around 820,000 patients. For this study, electronic health records from 374,071 adults were able to be extracted and these were searched for any mention of migraine, including within free-text entries. Only 3.8% of adults had migraine recorded (5.3% of women and 2.0% of men). Globally, and from Aotearoa New Zealand survey data, the prevalence of migraine is around 14–15%.19 The primary care records identified barely a quarter of the expected number of patients with migraine and even fewer of the expected number of Māori or Pacific people with migraine. Electronic recording of diagnoses may have improved in primary care since 2014 but the improvement would need to be substantial for these datasets to be anything close to a robust source of information about the burden and cost of migraine at a population level. In other countries, administrative data have been used to identify people with migraine with reasonable accuracy,20 but this depends on access to, and adequate diagnostic coding within, both primary and secondary care datasets. Even high-quality administrative data have limitations. Again, using an illustrative example from our review of migraine data, relying on administrative data to identify people with migraine will only reveal those who seek healthcare for migraine, who are correctly diagnosed with migraine and who have this diagnosis recorded in their health record. This would miss a large proportion of people with migraine, because of the high rates of undiagnosed and untreated migraine in the community, and those who are missed will differ from those who are included. But we will not know about those who are missing and how they are different without a benchmark that includes all people with migraine, many of whom do not approach or receive a diagnosis from a health professional, despite significant levels of pain and disability. In Aotearoa New Zealand, according to 2023/2024 New Zealand Health Survey data, around half of those with recent migraine symptoms do not have a migraine diagnosis, but this proportion is higher in Māori and Pacific people.3 This overall rate is consistent with large international population-based surveys that use questionnaires based on the International Classification of Headache Disorders criteria to identify people with migraine.21,22 These surveys typically find high rates of migraine-related disability (58%)21,23 that would merit medical treatment but only around 20–33% of these consult a health professional and receive an accurate diagnosis.23–25 Reasons for under-diagnosis include stigma and barriers to healthcare, including cost and a fear of not being taken seriously.24,26,27 Under-diagnosis can be more common in minoritised groups (e.g., Black American, Indigenous people), with contributing factors such as discrimination and lack of trust in health services.28 For chronic diseases that do not show up in hospitalisation data or other national health data collections because people are not seeking healthcare or receiving a correct diagnosis, survey data can plug the gap and add information not collected from administrative sources. Surveys, such as the New Zealand Health Survey, that are of a reasonable size, are population-based and representative are essential to assess differences by ethnicity and other characteristics. Monitoring of Māori health and addressing causes for inequities in health outcomes is an obligation under Te Tiriti o Waitangi, which includes appropriate and robust data collection on health conditions that affect Māori. Questions about migraine were included in the 2023/2024 New Zealand Health Survey, a high-quality, population-based survey that has provided valuable estimates of migraine prevalence by age, gender, ethnicity and disability status.3 The survey not only asked about migraine diagnosed by a doctor but also about recent experiences of migraine symptoms, so as to identify people likely to have undiagnosed migraine disease. Some other chronic conditions with poor visibility in secondary care data are also asked about in the New Zealand Health Survey 2024/2025, including long COVID, chronic pain, mental health conditions and arthritis. For conditions with a lower prevalence (e.g., Ehlers-Danlos syndrome), affecting primarily one part of the population (e.g., dementia in older people) or if diagnosis is relatively complex (e.g., endometriosis, polyendocrine metabolic ovarian syndrome), this type of survey may not be the best way to collect information about prevalence and impact. For low-prevalence conditions, a very large sample size would be needed to generate robust estimates and measure impacts on Māori. For conditions affecting a slice of the population, more targeted surveys may be more appropriate, with different sampling methodology. Diagnostically complex conditions are difficult to include in broad population surveys, where brevity and timing are prioritised to maximise participant engagement. These examples all reinforce the importance of robust and accessible primary care data. However, reliable estimates of prevalence based on survey (and administrative) data are reliant on reliable denominators of population estimates and projections from the census. The Government is currently considering replacing a full field enumeration survey supplemented with administrative data with an administrative data–first approach supplemented by a survey of 3% of the population. Marrying administrative data with its known limitations to a survey, which will also be subject to bias, is unlikely to provide a comprehensive and reliable picture of the Aotearoa New Zealand population, particularly for small groups. It could serve to entrench the biases already present in administrative data and make gaps in the data impossible to discover without a robust baseline. The Global Burden of Disease (GBD) study provided a source for migraine prevalence in Aotearoa New Zealand when few other estimates existed.29 GBD data have been the backbone of global research highlighting the high disability burden of migraine and headache disorders and call for more effective treatment and prevention.30–32 However, the only local data source for migraine estimates for Aotearoa New Zealand is a publication from the Dunedin Multidisciplinary Health and Development Study, reporting on migraine prevalence in a birth cohort at age 26.33 This means that results for Aotearoa New Zealand, which are routinely presented by gender and age (from 0 to 70+ years) are largely derived from modelling based on international datasets.10 A one-off publication of estimates of disease burden for Māori using GBD 2021 data34 would have had minimal local data to draw on for migraine, and no estimates have been developed for any other ethnic groups in Aotearoa New Zealand. Decisions about healthcare funding and delivery in Aotearoa New Zealand and monitoring of health outcomes are largely driven by data from national collections on secondary care. These datasets do not provide adequate data on many chronic conditions that predominantly affect women and cause a substantial social and economic burden from lost health and productivity. For illustrative purposes, we have focussed on migraine and the “women’s health gap” but there are many people who do not identify with binary genders who also experience migraine. Accurate recording of affirmed gender is needed to uncover the burden of disease in transgender and non-binary people. The example of migraine is also intended to highlight the broader issues around recognition, monitoring and treatment of these often-invisible health conditions. Other conditions with an outsized impact on women, such as endometriosis, perimenopause/menopause and long COVID, confront the same data limitations. Without robust and comprehensive data on healthcare use and productivity, we can only guess at the actual cost of these chronic conditions and the existence of unequal impact across minoritised populations. This restricts our ability to plan and make a case for improved health services and access to treatment and support. Improved diagnostic information in existing collections is needed as well as access to high-quality, integrated primary care data. National surveys are invaluable but insufficient for measuring the prevalence and impact of all health conditions that are predominantly managed in primary care, across all population groups. The potential of primary care research has been demonstrated through a regional research database in Otago/Southland35 but requires dedicated funding and commitment to infrastructure development and ethical and Māori data governance principles.36 Improved diagnostic coding is needed for primary care and other datasets, such as the national collections of outpatient and ED visits, to be useful for monitoring the women’s health gap. Careful consideration must be given to the proposed replacement of Aotearoa New Zealand’s census, as this risks undermining the utility of existing and future surveys for collecting this type of information and jettisoning the ability of services, researchers, policymakers and communities to assess and monitor Māori health outcomes. View Figure 1. Decisions about healthcare funding and delivery in Aotearoa New Zealand, as well as the monitoring of health service performance and outcomes, are driven by readily available data, in particular from administrative health datasets. Most of these national health data collections are generated through the delivery of secondary health services. Further, apart from hospitalisation and mortality collections, these lack diagnostic coding, rendering the burden and cost of chronic health conditions predominantly managed in primary care largely invisible. Many of these types of health conditions disproportionately affect women, who, despite their longer life expectancy, spend 25% more time in poor health than men, according to international research on the women’s health gap. This gap is driven by conditions occurring only in women (e.g., premenstrual syndrome, endometriosis, polyendocrine metabolic ovarian syndrome) or with higher burden in women (e.g., anxiety, depression, migraine). Addressing this gap could add US$1 trillion to the global economy. In Aotearoa New Zealand, major improvements in national health data collections are urgently needed to assess the cost of the women’s health gap and the burden of chronic diseases that have high social and economic impact but are undetectable or difficult to survey in our existing administrative datasets. We illustrate these issues using the example of migraine disease, the most disabling neurological condition in Australasia that also affects at least twice as many women as men. Fiona Imlach: Senior Research Fellow, Department of Public Health, University of Otago, Wellington, New Zealand. Natalia Boven: Research Fellow, COMPASS Research Centre, The University of Auckland, Auckland, New Zealand. Vanessa Selak: Associate Professor, Department of Epidemiology & Biostatistics, The University of Auckland, Auckland, New Zealand. Fiona Imlach: Senior Research Fellow, Department of Public Health, University of Otago Wellington, 23A Mein St, Newtown, Wellington 6021. None. 1) Mirin AA. Gender Disparity in the Funding of Diseases by the U.S. National Institutes of Health. J Womens Health (Larchmt). 2021 Jul;30(7):956-963. doi: 10.1089/jwh.2020.8682. 2) Peres MFP, Sacco S, Pozo-Rosich P, et al. Migraine is the most disabling neurological disease among children and adolescents, and second after stroke among adults: A call to action. Cephalalgia. 2024 Aug;44(8):3331024241267309. doi: 10.1177/03331024241267309. 3) Ministry of Health – Manatū Hauora. Migraine 2023/24: New Zealand Health Survey [Internet]. Wellington, New Zealand: 2026 Jan 22 [cited 2026 Jul 6]. Available from: https://www.health.govt.nz/publications/migraine-202324-new-zealand-health-survey 4) Martin VT, Feoktistov A, Solomon GD. A rational approach to migraine diagnosis and management in primary care. Ann Med. 2021 Dec;53(1):1979-1990. doi: 10.1080/07853890.2021.1995626. 5) World Economic Forum, McKinsey Health Institute. Closing the Women’s Health Gap: A $1 Trillion Opportunity to Improve Lives and Economies [Internet]. New York, United States of America: World Economic Forum; 2024 Jan [cited 2026 Jul 6]. Available from: https://www3.weforum.org/docs/WEF_Closing_the_Women%E2%80%99s_Health_Gap_2024.pdf 6) Nguyen PU, Luu H, So H, et al. The Healthcare Cost of Migraine: A Retrospective Cohort Study from Alberta, Canada. Can J Neurol Sci. 2025 Mar 3:1-11. doi: 10.1017/cjn.2025.40. 7) Fernández-Ferro J, Ordás-Bandera C, Rejas-Gutiérrez J, et al. The economic burden of migraine: a nationwide cost-of-illness approach from the year 2020 European Health Survey in Spain. Neurologia (Engl Ed). 2025 Jul-Aug;40(6):533-547. doi: 10.1016/j.nrleng.2025.06.002. 8) Martins R, Large S, Russell R, et al. The Hidden Economic Consequences of Migraine to the UK Government: Burden-of-Disease Analysis Using a Fiscal Framework. J Health Econ Outcomes Res. 2023 Oct 3;10(2):72-81. doi: 10.36469/001c.87790. 9) Imlach F, Irurzun-Lopez M, Tsaregorodtseva S, et al. Methodologies and Data Used in Migraine Cost-of-Illness Studies: A Scoping Review for the New Zealand Context. J R Soc N Z. 2026 Aug 5;56(4). doi: 10.1002/snz2.70073. 10) Imlach F, Tsaregorodtseva S, Irurzun-Lopez M, et al. What’s Needed for a Migraine Cost-Of-Illness Study in Aotearoa New Zealand: Review of Data Sources and Gaps. J R Soc N Z 2026 May 18;56:e70055. doi: 10.1002/snz2.70055. 11) Health New Zealand – Te Whatu Ora. National Primary Care Dataset [Internet]. Welington, New Zealand: 2026 Feb 5 [cited 2026 May 4]. Available from: https://www.healthnz.govt.nz/about-us/what-we-do/planning-and-performance/primary-care-tactical-action-plan/national-primary-care-dataset-and-new-primary-care-health-target 12) Edwards L, Pickett J, Ashcroft DM, et al. UK research data resources based on primary care electronic health records: review and summary for potential users. BJGP Open. 2023 Sep 19;7(3):BJGPO.2023.0057. doi: 10.3399/BJGPO.2023.0057. 13) Oliveira WS, Dos Santos ERR, Peixoto PM, et al. Migraine as a primary care-sensitive condition: Building pathways to accessible care. Cephalalgia. 2026 Feb;46(2):3331024261418704. doi: 10.1177/03331024261418704. 14) Health New Zealand – Te Whatu Ora. Health targets [Internet]. Wellington, New Zealand: 2025 Dec 2 [2026 May 4]. Available from: https://www.healthnz.govt.nz/about-us/what-we-do/planning-and-performance/health-targets 15) Ministry of Health – Manatū Hauora. Government Policy Statement on Health 2024–2027 [Internet]. Wellington, New Zealand: 2024 Jun 30 [cited 2026 Jul 6]. Available from: https://www.health.govt.nz/publications/government-policy-statement-on-health-2024-2027 16) Keene L. A spreading problem: Changes in the distribution of medical specialists between public and private work - 2022 to 2024 [Internet]. Wellington, New Zealand: Association of Salaried Medical Specialists – Toi Mata Hauora; 2025 May [cited 2026 Jul 6]. Available from: https://asms.org.nz/wp-content/uploads/2025/05/A-Spreading-Problem-FINAL.pdf?utm_source=nz.vnexplorer.net&utm_campaign=vnexplorer.net 17) Jeffreys M, Ellison-Loschmann L, Irurzun-Lopez M, et al. Financial barriers to primary health care in Aotearoa New Zealand. Fam Pract. 2024 Dec 2;41(6):995-1001. doi: 10.1093/fampra/cmad096. 18) Mugridge O, Choi YC, Wells S, et al. Frequency of migraine recorded in primary care and hospital records in Aotearoa New Zealand: A cross-sectional study. SN Compr Clin Med 2026 Feb 16;8:35. doi: 10.1007/s42399-026-02263-5. 19) Husøy AK, Steiner TJ. GBD 2023: over twice the health loss from headache disorders among females compared with males, and one fifth of the loss is attributable to medication overuse. J Headache Pain. 2025 Oct 21;26(1):227. doi: 10.1186/s10194-025-02192-z. 20) Albanese CM, Bondy SJ, Lay C, et al. Use of Health Administrative Data to Identify Migraine in Individuals With a Recognized Pregnancy: A Validation Study in Ontario, Canada. Epidemiology. 2025 Sep 1;36(5):599-605. doi: 10.1097/EDE.0000000000001890. 21) Lipton RB, Nicholson RA, Reed ML, et al. Diagnosis, consultation, treatment, and impact of migraine in the US: Results of the OVERCOME (US) study. Headache. 2022 Feb;62(2):122-140. doi: 10.1111/head.14259. 22) Adams AM, Buse DC, Leroux E, et al. Chronic Migraine Epidemiology and Outcomes - International (CaMEO-I) Study: Methods and multi-country baseline findings for diagnosis rates and care. Cephalalgia. 2023 Jun;43(6):3331024231180611. doi: 10.1177/03331024231180611. 23) Lanteri-Minet M, Leroux E, Katsarava Z, et al. Characterizing barriers to care in migraine: multicountry results from the Chronic Migraine Epidemiology and Outcomes - International (CaMEO-I) study. J Headache Pain. 2024 Aug 19;25(1):134. doi: 10.1186/s10194-024-01834-y. 24) Ailani J, Okonkwo R, Johnston E, et al. Reasons for patient reluctance to take preventive medications for migraine: Results of the OVERCOME (US) study. Headache. 2026 Apr;66(4):846-858. doi: 10.1111/head.70014. 25) Buse DC, Armand CE, Charleston L 4th, et al. Barriers to care in episodic and chronic migraine: Results from the Chronic Migraine Epidemiology and Outcomes Study. Headache. 2021 Apr;61(4):628-641. doi: 10.1111/head.14103. 26) Casas-Limón J, Quintas S, López-Bravo A, et al. Unravelling Migraine Stigma: A Comprehensive Review of Its Impact and Strategies for Change. J Clin Med. 2024 Sep 3;13(17):5222. doi: 10.3390/jcm13175222. 27) Shapiro RE, Muenzel EJ, Nicholson RA, et al. Factors and Reasons Associated with Hesitating to Seek Care for Migraine: Results of the OVERCOME (US) Study. Neurol Ther. 2025 Feb;14(1):135-155. doi: 10.1007/s40120-024-00668-9. 28) White CS. Unique Considerations in Episodic Migraine: Underserved Populations. Curr Pain Headache Rep. 2023 Oct;27(10):503-509. doi: 10.1007/s11916-023-01152-z. 29) Migraine Foundation Aotearoa New Zealand. New Zealand Health Survey data - a decade to be released [Internet]. 2022 Oct 31 [cited 2026 Jul 6]. Available from: https://migrainefoundation.org.nz/new-zealand-health-survey-data-a-decade-to-be-released/ 30) GBD 2023 Headache Collaborators. Global, regional, and national burden of headache disorders, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023. Lancet Neurol. 2025 Dec;24(12):1005-1015. doi: 10.1016/S1474-4422(25)00402-8. 31) Peres MFP, Sacco S, Pozo-Rosich P, et al. Migraine is the most disabling neurological disease among children and adolescents, and second after stroke among adults: A call to action. Cephalalgia. 2024 Aug;44(8):3331024241267309. doi: 10.1177/03331024241267309. 32) Steiner TJ, Stovner LJ, Jensen R, et al. Migraine remains second among the world's causes of disability, and first among young women: findings from GBD2019. J Headache Pain. 2020 Dec 2;21(1):137. doi: 10.1186/s10194-020-01208-0. 33) Waldie KE, Poulton R. The burden of illness associated with headache disorders among young adults in a representative cohort study. Headache. 2002 Jul-Aug;42(7):612-619. doi: 10.1046/j.1526-4610.2002.02148.x. 34) Ministry of Health – Manatū Hauora. Māori Years of Life Lost: Estimates from the Global Burden of Disease Study, 2021 [Internet]. Wellington, New Zealand: 2025 Dec 12 [cited 2026 Jul 6]. Available from: https://www.health.govt.nz/publications/maori-years-of-life-lost-estimates-from-the-global-burden-of-disease-study-2021 35) Leitch S, Pigden A, Ryde A, et al. The Southern Primary Care Research Network 3 years on - reflections from the end of the beginning. J Prim Health Care. 2025 Sep 19;17(3):269-275. doi: 10.1071/HC25020. Leitch S, Wang A, Moerenhout T, et al. Our health (data) is our wealth: improving primary healthcare data access for research in Aotearoa New Zealand. N Z Med J. 2026 Jun 12;139(1636):102-109. doi: 10.26635/6965.7318. Sign in to view your account and access the latest publications by the NZMJ. Don't have an account? Let's get started with creating an account. Already have an account? Become a member to enjoy unlimited digital access and support the ongoing publication of the New Zealand Medical Journal. The New Zealand Medical Journal is fully available to individual subscribers and does not incur a subscription fee. 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