Rural-urban variation in COVID-19 vaccination uptake in Aotearoa New Zealand: Examining the national roll-out.

This study aimed to understand rural-urban differences in the uptake of COVID-19 vaccinations during the peak period of the national vaccination roll-out in Aotearoa New Zealand (NZ). Using a linked national dataset of health service users aged 12+ years and COVID-19 immunization records, age-standardized rates of vaccination uptake were calculated at fortnightly intervals, between June and December 2021, by rurality, ethnicity, and region. Rate ratios were calculated for each rurality category with the most urban areas (U1) used as the reference. Overall, rural vaccination rates lagged behind urban rates, despite early rapid rural uptake. By December 2021, a rural-urban gradient developed, with age-standardized coverage for R3 areas (most rural) at 77%, R2 81%, R1 83%, U2 85%, and U1 (most urban) 89%. Age-based assessments illustrate the rural-urban vaccination uptake gap was widest for those aged 12-44 years, with older people (65+) having broadly consistent levels of uptake regardless of rurality. Variations from national trends are observable by ethnicity. Early in the roll-out, Indigenous Maori residing in R3 areas had a higher uptake than Maori in U1, and Pacific peoples in R1 had a higher uptake than those in U1. The extent of differences in rural-urban vaccine uptake also varied by region.

Equity of travel to access surgery and radiation therapy for lung cancer in New Zealand.

PURPOSE: Centralisation of lung cancer treatment can improve outcomes, but may result in differential access to care for those who do not reside within treatment centres. METHODS: We used national-level cancer registration and health care access data and used Geographic Information Systems (GIS) methods to determine the distance and time to access first relevant surgery and first radiation therapy among all New Zealanders diagnosed with lung cancer (2007-2019; N = 27,869), and compared these outcomes between ethnic groups. We also explored the likelihood of being treated at a high-, medium-, or low-volume hospital. Analysis involved both descriptive and adjusted logistic regression modelling. RESULTS: We found that Maori tend to need to travel further (with longer travel times) to access both surgery (median travel distance: Maori 57 km, European 34 km) and radiation therapy (Maori 75 km, European 35 km) than Europeans. Maori have greater odds of living more than 200 km away from both surgery (adjusted odds ratio [aOR] 1.83, 95% CI 1.49-2.25) and radiation therapy (aOR 1.41, 95% CI 1.25-1.60). CONCLUSIONS: Centralisation of care may often improve treatment outcomes, but it also makes accessing treatment even more difficult for populations who are more likely to live rurally and in deprivation, such as Maori.

Inequities in COVID-19 Omicron infections and hospitalisations for Maori and Pacific people in Te Manawa Taki Midland region, New Zealand.

COVID-19 impacts population health equity. While mRNA vaccines protect against serious illness and death, little New Zealand (NZ) data exist about the impact of Omicron - and the effectiveness of vaccination - on different population groups. We aim to examine the impact of Omicron on Maori, Pacific, and Other ethnicities and how this interacts with age and vaccination status in the Te Manawa Taki Midland region of NZ. Daily COVID-19 infection and hospitalisation rates (1 February 2022 to 29 June 2022) were calculated for Maori, Pacific, and Other ethnicities for six age bands. A multivariate logistic regression model quantified the effects of ethnicity, age, and vaccination on hospitalisation rates. Per-capita Omicron cases were highest and occurred earliest among Pacific (9 per 1,000) and Maori (5 per 1,000) people and were highest among 12-24-year-olds (7 per 1,000). Hospitalisation was significantly more likely for Maori people (odds ratio (OR) = 2.03), Pacific people (OR = 1.75), over 75-year-olds (OR = 39.22), and unvaccinated people (OR = 4.64). Length of hospitalisation is strongly related to age. COVID-19 vaccination reduces hospitalisations for older individuals and Maori and Pacific populations. Omicron inequitably impacted Maori and Pacific people through higher per-capita infection and hospitalisation rates. Older people are more likely to be hospitalised and for longer.

Improving spatial data in health geographics: a practical approach for testing data to measure children's physical activity and food environments using Google Street View.

BACKGROUND: Geographic information systems (GIS) are often used to examine the association between both physical activity and nutrition environments, and children's health. It is often assumed that geospatial datasets are accurate and complete. Furthermore, GIS datasets regularly lack metadata on the temporal specificity. Data is usually provided 'as is', and therefore may be unsuitable for retrospective or longitudinal studies of health outcomes. In this paper we outline a practical approach to both fill gaps in geospatial datasets, and to test their temporal validity. This approach is applied to both district council and open-source datasets in the Taranaki region of Aotearoa New Zealand. METHODS: We used the 'streetview' python script to download historic Google Street View (GSV) images taken between 2012 and 2016 across specific locations in the Taranaki region. Images were reviewed and relevant features were incorporated into GIS datasets. RESULTS: A total of 5166 coordinates with environmental features missing from council datasets were identified. The temporal validity of 402 (49%) environmental features was able to be confirmed from council dataset considered to be 'complete'. A total of 664 (55%) food outlets were identified and temporally validated. CONCLUSIONS: Our research indicates that geospatial datasets are not always complete or temporally valid. We have outlined an approach to test the sensitivity and specificity of GIS datasets using GSV images. A substantial number of features were identified, highlighting the limitations of many GIS datasets.

Developing the geographic classification for health, a rural-urban classification for New Zealand health research and policy: A research protocol.

INTRODUCTION: Rural-urban health inequities, exacerbated by deprivation and ethnicity, have been clearly described in the international literature. To date, the same inequities have not been as clearly demonstrated in Aotearoa New Zealand despite the lower socioeconomic status and higher proportion of Maori living in rural towns. This is ascribed by many health practitioners, academics and other informed stakeholders to be the result of the definitions of 'rural' used to produce statistics. AIMS: To outline a protocol to produce a 'fit-for-health purpose' rural-urban classification for analysing national health data. The classification will be designed to determine the magnitude of health inequities that have been obscured by use of inappropriate rural-urban taxonomies. METHODS: This protocol paper outlines our proposed mixed-methods approach to developing a novel Geographic Classification for Health. In phase 1, an agreed set of community attributes will be used to modify the new Statistics New Zealand Urban Accessibility Classification into a more appropriate classification of rurality for health contexts. The Geographic Classification for Health will then be further developed in an iterative process with stakeholders including rural health researchers and members of the National Rural Health Advisory Group, who have a comprehensive 'on the ground' understanding of Aotearoa New Zealand's rural communities and their attendant health services. This protocol also proposes validating the Geographic Classification for Health using general practice enrolment data. In phase 2, the resulting Geographic Classification for Health will be applied to routinely collected data from the Ministry of Health. This will enable current levels of rural-urban inequity in health service access and outcomes to be accurately assessed and give an indication of the extent to which older classifications were masking inequities.

Spatial equity and realised access to healthcare - a geospatial analysis of general practitioner enrolments in Waikato, New Zealand.

INTRODUCTION: Geographic measures of accessibility can quantify inequitable distributions of health care. Although closest distance measures are often used in Aotearoa New Zealand these may not reflect patient use of health care. This research examines patterns of patient enrolment in general practitioner (GP) services from a geospatial perspective. METHODS: Patient enrolment records (n=137 596) from one primary health organisation were examined and geographic information systems used to determine whether patients enrolled with their closest GP service. A binomial logistic regression was performed to examine factors associated with the bypass of GP services closer to patients' homes. RESULTS: Overall 68.1% of patients in the sample bypassed the GP service closest to their home, while rates of GP bypass varied across the Waikato region and between rural and urban areas. A binary logistic regression analysis revealed that rurality of patient residence, patient ethnicity, patient age, area-level socioeconomic deprivation, sex, distance to the closest GP clinic, clinic after-hours availability, Māori service provider status, GP and nurse full time equivalent hours, and clinic fees were statistically significant predictors of increased closest-GP bypass. While residents of major urban areas had high rates of GP bypass, this was followed by patients living in rural areas - patients living more than 20 km from the closest GP service had exceptionally high rates of GP bypass. CONCLUSION: This study suggests that most patients in the Waikato region do not enrol with the GP service closest to their home and it outlines several factors, including rurality of residence, associated with the GP bypass. Closest distance accessibility measures may be inappropriate in mixed urban-rural settings, and researchers should consider other approaches to quantifying spatial equity. Health services should also be designed to better reflect the realities of the populations they serve.

Framework for examining the spatial equity and sustainability of general practitioner services.

OBJECTIVE: To propose a framework for examining both the spatial equity and sustainability of GP services. DESIGN: A conceptual discussion based on a systematic literature review of spatial equity definitions and methods. SETTING: Improving the spatial equity of health services is a key step in achieving health equity. Health systems should contribute to achieving health equity and maintain equitable services into the future. The GP services are a key component of primary health care, which often aims to promote health equity. Despite the importance of spatially equitable and sustainable GP services, a framework for analysis has not yet been established. MAIN OUTCOME MEASURE: Examples of how the proposed framework could be implemented are provided from the New Zealand health care context. RESULT: The framework entails three steps: (i) defining spatial equity and sustainability; (ii) estimating current and future distributions of health services and needs; and (iii) quantifying spatial equity and sustainability. In step (i), a needs-based distribution is the most common definition of spatial equity, while sustainability is the ability to provide ongoing equitable access. Step (ii) depends on current and future estimates of access and need within a well-defined geographical area. In step (iii), spatial equity and sustainability should be quantified through measures, such as the Gini coefficient. Current and future levels of spatial equity should then be compared to assess the sustainability of equitable GP services. CONCLUSION: This article outlines a novel conceptual framework for examining the spatial equitability and sustainability of GP services.

Avoiding double counting: the effect of bundling hospital events in administrative datasets for the interpretation of rural-urban differences in Aotearoa New Zealand.

BACKGROUND AND OBJECTIVES: All publicly funded hospital discharges in Aotearoa New Zealand are recorded in the National Minimum Dataset (NMDS). Movement of patients between hospitals (and occasionally within the same hospital) results in separate records (discharge events) within the NMDS and if these consecutive health records are not accounted for hospitalization (encounters) rates might be overestimated. The aim of this study was to determine the impact of four different methods to bundle multiple discharge events in the NMDS into encounters on the relative comparison of rural and urban Ambulatory Sensitive Hospitalization (ASH) rates. METHODS: NMDS discharge events with an admission date between July 1, 2015, and December 31, 2019, were bundled into encounters using either using a) no method, b) an "admission flag", c) a "discharge flag", or d) a date-based method. ASH incidence rate ratios (IRRs), the mean total length of stay and the percentage of interhospital transfers were estimated for each bundling method. These outcomes were compared across 4 categories of the Geographic Classification for Health. RESULTS: Compared with no bundling, using the date-based method resulted in an 8.3% reduction (150 less hospitalizations per 100,000 person years) in the estimated incidence rate for ASH in the most rural (R2-3) regions. There was no difference in the interpretation of the rural-urban IRR for any bundling methodology. Length of stay was longer for all bundling methods used. For patients that live in the most rural regions, using a date-based method identified up to twice as many interhospital transfers (5.7% vs 12.4%) compared to using admission flags. CONCLUSION: Consecutive events within hospital discharge datasets should be bundled into encounters to estimate incidence. This reduces the overestimation of incidence rates and the undercounting of interhospital transfers and total length of stay.

Rural-urban variation in the utilisation of publicly funded healthcare services: an age-stratified population-level observational study.

AIM: To compare age-stratified public health service utilisation in Aotearoa New Zealand across the rural-urban spectrum. METHODS: Routinely collected hospitalisation, allied health, emergency department and specialist outpatient data (2014-2018), along with Census denominators, were used to calculate utilisation rates for residents in the two urban and three rural categories in the Geographic Classification for Health. RESULTS: Relative to their urban peers, rural Maori and rural non-Maori had lower all-cause, cardiovascular, mental health and ambulatory sensitive (ASH) hospitalisation rates. The age-standardised ASH rate ratios (major cities as the reference, 95% CIs) across the three rural categories were for Maori 0.79 (0.78, 0.80), 0.83 (0.82, 0.85) and 0.80 (0.77, 0.83), and for non-Maori 0.87 (0.86, 0.88), 0.80 (0.78, 0.81) and 0.50 (0.47, 0.53). Residents of the most remote communities had the lowest rates of specialist outpatient and emergency department attendance, an effect that was accentuated for Maori. Allied health service utilisation by those in rural areas was higher than that seen in the major cities. CONCLUSIONS: The large rural-urban variation in health service utilisation demonstrated here is previously unrecognised and in contrast to comparable international data. New Zealand's most remote communities have the lowest rates of health service utilisation despite high amenable mortality rates. This raises questions about geographic equity in health service design and delivery and warrants further in-depth research.

Access to and Timeliness of Lung Cancer Surgery, Radiation Therapy, and Systemic Therapy in New Zealand: A Universal Health Care Context.

PURPOSE: Lung cancer is the biggest cancer killer of indigenous peoples worldwide, including Maori people in New Zealand. There is some evidence of disparities in access to lung cancer treatment between Maori and non-Maori patients, but an examination of the depth and breadth of these disparities is needed. Here, we use national-level data to examine disparities in access to surgery, radiation therapy and systemic therapy between Maori and European patients, as well as timing of treatment relative to diagnosis. METHODS: We included all lung cancer registrations across New Zealand from 2007 to 2019 (N = 27,869) and compared access with treatment and the timing of treatment using national-level inpatient, outpatient, and pharmaceutical records. RESULTS: Maori patients with lung cancer appeared less likely to access surgery than European patients (Maori, 14%; European, 20%; adjusted odds ratio [adj OR], 0.82 [95% CI, 0.73 to 0.92]), including curative surgery (Maori, 10%; European, 16%; adj OR, 0.72 [95% CI, 0.62 to 0.84]). These differences were only partially explained by stage and comorbidity. There were no differences in access to radiation therapy or systemic therapy once adjusted for confounding by age. Although it appeared that there was a longer time from diagnosis to radiation therapy for Maori patients compared with European patients, this difference was small and requires further investigation. CONCLUSION: Our observation of differences in surgery rates between Maori and European patients with lung cancer who were not explained by stage of disease, tumor type, or comorbidity suggests that Maori patients who may be good candidates for surgery are missing out on this treatment to a greater extent than their European counterparts.

Unmasking hidden disparities: a comparative observational study examining the impact of different rurality classifications for health research in Aotearoa New Zealand.

OBJECTIVES: Examine the impact of two generic-urban-rural experimental profile (UREP) and urban accessibility (UA)-and one purposely built-geographic classification for health (GCH)-rurality classification systems on the identification of rural-urban health disparities in Aotearoa New Zealand (NZ). DESIGN: A comparative observational study. SETTING: NZ; the most recent 5 years of available data on mortality events (2013-2017), hospitalisations and non-admitted hospital patient events (both 2015-2019). PARTICIPANTS: Numerator data included deaths (n=156 521), hospitalisations (n=13 020 042) and selected non-admitted patient events (n=44 596 471) for the total NZ population during the study period. Annual denominators, by 5-year age group, sex, ethnicity (Maori, non-Maori) and rurality, were estimated from Census 2013 and Census 2018. PRIMARY AND SECONDARY OUTCOME MEASURES: Primary measures were the unadjusted rural incidence rates for 17 health outcome and service utilisation indicators, using each rurality classification. Secondary measures were the age-sex-adjusted rural and urban incidence rate ratios (IRRs) for the same indicators and rurality classifications. RESULTS: Total population rural rates of all indicators examined were substantially higher using the GCH compared with the UREP, and for all except paediatric hospitalisations when the UA was applied. All-cause rural mortality rates using the GCH, UA and UREP were 82, 67 and 50 per 10 000 person-years, respectively. Rural-urban all-cause mortality IRRs were higher using the GCH (1.21, 95% CI 1.19 to 1.22), compared with the UA (0.92, 95% CI 0.91 to 0.94) and UREP (0.67, 95% CI 0.66 to 0.68). Age-sex-adjusted rural and urban IRRs were also higher using the GCH than the UREP for all outcomes, and higher than the UA for 13 of the 17 outcomes. A similar pattern was observed for Maori with higher rural rates for all outcomes using the GCH compared with the UREP, and 11 of the 17 outcomes using the UA. For Maori, rural-urban all-cause mortality IRRs for Maori were higher using the GCH (1.34, 95% CI 1.29 to 1.38), compared with the UA (1.23, 95% CI 1.19 to 1.27) and UREP (1.15, 95% CI 1.10 to 1.19). CONCLUSIONS: Substantial variation in rural health outcome and service utilisation rates were identified with different classifications. Rural rates using the GCH are substantially higher than the UREP. Generic classifications substantially underestimated rural-urban mortality IRRs for the total and Maori populations.

Defining catchment boundaries and their populations for Aotearoa New Zealand's rural hospitals.

Introduction There is considerable variation in the structure and resources of New Zealand (NZ) rural hospitals; however, these have not been recently quantified and their effects on healthcare outcomes are poorly understood. Importantly, there is no standardised description of each rural hospital's catchment boundary and the characteristics of the population living within this area. Aim To define and describe a catchment population for each of New Zealand's rural hospitals. Methods An exploratory approach to developing catchments was employed. Geographic Information Systems were used to develop drive-time-based geographic catchments, and administrative health data (National Minimum Data Set and Primary Health Organisation Data Set) informed service utilisation-based catchments. Catchments were defined at both the Statistical Area 2 (SA2) and domicile levels, and linked to census-based population data, the Geographic Classification for Health, and the area-level New Zealand Index of Socioeconomic Deprivation (NZDep2018). Results Our results highlight considerable heterogeneity in the size (max: 57 564, min: 5226) and characteristics of populations served by rural hospitals. Substantial differences in the age structure, ethnic composition, socio-economic profile, 'remoteness' and projected future populations, are noted. Discussion In providing a standardised description of each rural hospital's catchment boundary and its population characteristics, the considerable heterogeneity of the communities served by rural hospitals, both in size, rurality and socio-demographic characteristics, is highlighted. The findings provide a platform on which to build further research regarding NZ's rural hospitals and inform the delivery of high-quality, cost-effective and equitable health care for people living in rural NZ.

Comparison of urban and rural mortality rates across the lifespan in Aotearoa/New Zealand: a population-level study.

BACKGROUND: Previous studies undertaken in New Zealand using generic rurality classifications have concluded that life expectancy and age-standardised mortality rates are similar for urban and rural populations. METHODS: Administrative mortality (2014-2018) and census data (2013 and 2018) were used to estimate age-stratified sex-adjusted mortality rate ratios (aMRRs) for a range of mortality outcomes across the rural-urban spectrum (using major urban centres as the reference) for the total population and separately for Maori and non-Maori. Rural was defined according to the recently developed Geographic Classification for Health. RESULTS: Mortality rates were higher overall in rural areas. This was most pronounced in the youngest age group (<30 years) in the most remote communities (eg, all-cause, amenable and injury-related aMRRs (95% CIs) were 2.1 (1.7 to 2.6), 2.5 (1.9 to 3.2) and 3.0 (2.3 to 3.9) respectively. The rural:urban differences attenuated markedly with increasing age; for some outcomes in those aged 75 years or more, estimated aMRRs were <1.0. Similar patterns were observed for Maori and non-Maori. CONCLUSION: This is the first time that a consistent pattern of higher mortality rates for rural populations has been observed in New Zealand. A purpose-built urban-rural classification and age stratification were important factors in unmasking these disparities.

Emergency presentation prior to lung cancer diagnosis: A national-level examination of disparities and survival outcomes.

OBJECTIVES: A recent multinational investigation of emergency presentation within 30 days of cancer diagnosis, conducted within the International Cancer Benchmarking Programme (ICBP), observed that New Zealand had the highest rate of emergency presentation prior to lung cancer diagnosis compared to other similar countries. Here we use national-level health data to further investigate these trends, focussing on ethnic disparities in emergency presentation prior to lung cancer diagnosis. We have also compared survival outcomes between those who had an emergency presentation in the preceding 30 days to those who did not. MATERIALS AND METHODS: Our study included all lung cancer registrations between 2007 and 2019 on the New Zealand Cancer Registry (N = 27,869), linked to national hospitalisation and primary healthcare data. We used descriptive (crude and age-standardised proportions) and logistic regression (crude and adjusted odds ratios) analyses to examine primary care access prior to cancer diagnosis, emergency hospitalisation up to and including 30 days prior to diagnosis, and one-year mortality post-diagnosis, both for the total population and between ethnicities. Regression models adjusted for age, sex, deprivation, rurality, comorbidity, tumour type and stage. RESULTS: We found stark disparities by ethnic group, with 62% of Pacific peoples and 54% of Maori having an emergency presentation within 30 days prior to diagnosis, compared to 47% of Europeans. These disparities remained after adjusting for multiple covariates including comorbidity and deprivation (adj. OR: Maori 1.21, 95% CI 1.13-1.30; Pacific 1.50, 95% CI 1.31-1.71). Emergency presentation was associated with substantially poorer survival outcomes across ethnic groups (e.g. 1-year mortality for Maori: no emergency presentation 50%, emergency presentation 79%; adj. OR 2.40, 95% CI 2.10-2.74). CONCLUSIONS: These observations reinforce the need for improvements in the early detection of lung cancer, particularly for Maori and Pacific populations, with a view to preventing diagnosis of these cancers in an emergency setting.

Structural disadvantage for priority populations: the spatial inequity of COVID-19 vaccination services in Aotearoa.

AIM: To examine the spatial equity, and associated health equity implications, of the geographic distribution of COVID-19 vaccination services in Aotearoa New Zealand. METHOD: The distribution of Aotearoa's population was mapped, and the enhanced two-step floating catchment method (E2SFCA) applied to estimate spatial access to vaccination services. The Gini coefficient and spatial autocorrelation measures assessed the spatial equity of vaccination services. Additional statistics included an analysis of spatial accessibility for priority populations, and by District Health Board (DHB) region. RESULTS: Spatial accessibility to vaccination services varies across Aotearoa, and appears to be better in major cities than rural regions. A Gini coefficient of 0.426 confirms that spatial accessibility scores are not shared equally across the vaccine-eligible population. Furthermore, priority populations including Maori, Pasifika, and older people have statistically significantly lower spatial access to vaccination services. This is also true for people living in rural areas. Spatial access to vaccination services also varies significantly by DHB region as does the Gini coefficient, and the proportion of DHB priority population groups living in areas with poor access to vaccination services. A strong and significant positive correlation was identified between average spatial accessibility and the Maori vaccination rate ratio of DHBs. CONCLUSION: COVID-19 vaccination services in Aotearoa are not equitably distributed. Priority populations, with the most pressing need to receive COVID-19 vaccinations, have the worst access to vaccination services.

Selecting Health Need Indicators for Spatial Equity Analysis in the New Zealand Primary Care Context.

PURPOSE: To examine potential indicators of health need for primary care in spatial equity research, and evidence of the Inverse Care Law in the Waikato region of New Zealand. METHODS: A cross-sectional analysis of 7 health need indicators (ambulatory sensitive hospitalizations; cancer rate; mortality rate; New Zealand index of multiple deprivation-health domain; age; New Zealand index of deprivation; smoking rate) that were identified through a systematic review was carried out. Values of indicators were mapped and analyzed using geographic information systems (GIS). Spearman's correlations were calculated between indicators, and clusters of high need were identified through spatial autocorrelation. The impact of incorporating indicator-based weightings into an accessibility model was tested using analysis of variance and Spearman's correlations. General practice service spatial equity was assessed by comparing clusters of high access versus need, and quantified through the Gini coefficient. FINDINGS: Ambulatory sensitive hospitalization (ASH) rates were significantly correlated with all indicators. Health needs were significantly clustered, but incorporating indicator weightings into the spatial accessibility analysis did not impact accessibility scores. A misalignment of access and need, and a Gini coefficient of 0.281 suggest that services are not equitably distributed. CONCLUSION: ASH rates seem a robust indicator of health need. However, data access issues may restrict their use. Area-level socioeconomic deprivation measures incorporate some social determinants of health, and they have potential for wider use. High need clusters vary spatially according to the indicator used. GIS techniques can identify "hot-spots" of need, but these can be masked in accessibility models.

Equity of travel required to access first definitive surgery for liver or stomach cancer in New Zealand.

In New Zealand, there are known disparities between the Indigenous Maori and the majority non-Indigenous European populations in access to cancer treatment, with resulting disparities in cancer survival. There is international evidence of ethnic disparities in the distance travelled to access cancer treatment; and as such, the aim of this paper was to examine the distance and time travelled to access surgical care between Maori and European liver and stomach cancer patients. We used national-level data and Geographic Information Systems (GIS) analysis to describe the distance travelled by patients to receive their first primary surgery for liver or stomach cancer, as well as the estimated time to travel this distance by road, and the surgical volume of hospitals performing these procedures. All cases of liver (ICD-10-AM 3rd edition code: C22) and stomach (C16) cancer that occurred in New Zealand (2007-2019) were drawn from the New Zealand Cancer Registry (liver cancer: 866 Maori, 2,460 European; stomach cancer: 953 Maori, 3,192 European), and linked to national inpatient hospitalisation records to examine access to surgery. We found that Maori on average travel 120km for liver cancer surgery, compared to around 60km for Europeans, while a substantial minority of both Maori and European liver cancer patients must travel more than 200km for their first primary liver surgery, and this situation appears worse for Maori (36% vs 29%; adj. OR 1.48, 95% CI 1.09-2.01). No such disparities were observed for stomach cancer. This contrast between cancers is likely driven by the centralisation of liver cancer surgery relative to stomach cancer. In order to support Maori to access liver cancer care, we recommend that additional support is provided to Maori patients (including prospective financial support), and that efforts are made to remotely provide those clinical services that can be decentralised.

Mortality outcomes and inequities experienced by rural Maori in Aotearoa New Zealand.

Background: Previous research identified inequities in all-cause mortality between Maori and non-Maori populations. Unlike comparable jurisdictions, mortality rates in rural areas have not been shown to be higher than those in urban areas for either population. This paper uses contemporary mortality data to examine Maori and non-Maori mortality rates in rural and urban areas. Methods: A population-level observational study using deidentified routinely collected all-cause mortality, amenable mortality and census data. For each level of the Geographic Classification for Health (GCH), Maori and non-Maori age-sex standardised all-cause mortality and amenable mortality incident rates, Maori:Non-Maori standardised incident rate ratios and Maori rural:urban standardised incident rate ratios were calculated. Age and deprivation stratified rates and rate ratios were also calculated. Findings: Compared to non-Maori, Maori experience excess all-cause (SIRR 1.87 urban; 1.95 rural) and amenable mortality (SIRR 2.45 urban; 2.34 rural) and in all five levels of the GCH. Rural Maori experience greater all-cause (SIRR 1.07) and amenable (SIRR 1.13) mortality than their urban peers. Maori and non-Maori all-cause and amenable mortality rates increased as rurality increased. Interpretation: The excess Maori all-cause mortality across the rural: urban spectrum is consistent with existing literature documenting other Maori health inequities. A similar but more pronounced pattern of inequities is observed for amenable mortality that reflects ethnic differences in access to, and quality of, health care. The excess all-cause and amenable mortality experienced by rural Maori, compared to their urban counterparts, suggests that there are additional challenges associated with living rurally. Funding: This work was funded by the Health Research Council of New Zealand (HRC19/488).

Defining rural in Aotearoa New Zealand: a novel geographic classification for health purposes.

AIM: Describe the first specifically designed and validated five-level rurality classification for health purposes in New Zealand that is both data-driven and incorporates heuristic understandings of rurality. METHOD: Our approach involved: (1) defining the purpose and parameters of a proposed five-level Geographic Classification for Health (GCH); (2) developing a quantitative framework; (3) undertaking co-design with the National Rural Health Advisory Group (NRHAG), and extensive consultation with key stakeholders; (4) testing the validity of the five-level GCH and comparing it to previous Statistics New Zealand (Stats NZ) rurality classifications; and (5) describing rural populations and identifying differences in all-cause mortality using the GCH and previous Stats NZ rurality classifications. RESULTS: The GCH is a technically robust and heuristically valid rurality classification for health purposes. It identifies a rural population that is different to the population defined by generic Stats NZ classifications. When applied to New Zealand's Mortality Collection, the GCH estimates a rural mortality rate 21% higher than for residents of urban areas. These rural-urban disparities are masked by the generic Stats NZ classifications. CONCLUSION: The development of the five-level GCH embraces both the technical and heuristic aspects of rurality. The GCH offers the opportunity to develop a body of New Zealand rural health literature founded on a robust conceptualisation of rurality.

Will access to COVID-19 vaccine in Aotearoa be equitable for priority populations?

AIM: This research examines the equity implications of the geographic distribution of COVID-19 vaccine delivery locations in Aotearoa New Zealand under five potential scenarios: (1) stadium mega-clinics; (2) Community Based Assessment Centres; (3) GP clinics; (4) community pharmacies; and (5) schools. METHOD: We mapped the distribution of Aotearoa New Zealand's population and the location of potential vaccine delivery facilities under each scenario. Geostatistical techniques identified population clusters for Maori, Pacific peoples and people aged 65 years and over. We calculated travel times between all potential facilities and each Statistical Area 1 in the country. Descriptive statistics indicate the size and proportion of populations that could face significant travel barriers when accessing COVID-19 vaccinations. RESULTS: Several areas with significant travel times to potential vaccine delivery sites were also communities identified as having an elevated risk of COVID-19 disease and severity. All potential scenarios for vaccine delivery, with the exception of schools, resulted in travel barriers for a substantial proportion of the population. Overall, these travel time barriers disproportionately burden Maori, older communities and people living in areas of high socioeconomic deprivation. CONCLUSION: The equitable delivery of COVID-19 vaccines is key to an elimination strategy. However, if current health services and facilities are used without well-designed and supported outreach services, then access to vaccination is likely to be inequitable.