New Zealand, a geographically remote Pacific island with easily sealable borders, implemented a nationwide ‘lockdown’ of all non-essential services to curb the spread of COVID-19. Here, we generate 649 SARS-CoV-2 genome sequences from infected patients in New Zealand with samples collected during the ‘first wave’, representing 56% of all confirmed cases in this time period. Despite its remoteness, the viruses imported into New Zealand represented nearly all of the genomic diversity sequenced from the global virus population. These data helped to quantify the effectiveness of public health interventions. For example, the effective reproductive number, R_e of New Zealand’s largest cluster decreased from 7 to 0.2 within the first week of lockdown. Similarly, only 19% of virus introductions into New Zealand resulted in ongoing transmission of more than one additional case. Overall, these results demonstrate the utility of genomic pathogen surveillance to inform public health and disease mitigation.

There is limited evidence as to how COVID-19 infection fatality rates (IFR) may vary by ethnicity. We combine demographic and health data for ethnic groupings in Aotearoa New Zealand with international data on IFR for different age groups to estimate inequities in IFR by ethnicity. We find that, if age is the dominant factor determining IFR, estimated IFR for Māori is around 50% higher than non-Māori. If underlying health conditions are more important than age per se, then estimated IFR for Māori is more than 2.5 times that of New Zealand European, and estimated IFR for Pasifika is almost double that of New Zealand European. IFRs for Māori and Pasifika are likely to be increased above these estimates by racism within the healthcare system and other inequities not reflected in official data. IFR does not account for differences among ethnicities in COVID-19 incidence, which could be higher in Māori and Pasifika as a result of crowded housing and higher inter- generational contact rates. These factors should be included in future disease incidence modelling. The communities at the highest risk will be those with elderly populations, and Māori and Pasifika communities, where the compounded effects of underlying health conditions, socioeconomic disadvantage, and structural racism result in imbricated risk of contracting COVID-19, becoming unwell, and death.

The Statistics New Zealand Integrated Data Infrastructure (IDI) is a collection of de‐identified whole‐population administrative datasets. Researchers are increasingly utilising the IDI to answer pressing social and policy research questions. Our work provides an overview of the IDI, associated issues for Māori (the Indigenous peoples of New Zealand), and steps to realise Māori data aspirations. We first introduce the IDI including what it is and how it was developed. We then move to an overview of Māori Data Sovereignty. We consider the main issues with the IDI for Māori including technical issues and problems with ethnic identifiers, deficit‐framed work, community involvement, consent, social licence, further data linkage, offshore access, and barriers to access for Māori. We finish with a set of recommendations around how to improve the IDI for Māori, making sure that Māori can get the most out of administrative data for our communities. These include the need to build data researcher capacity and capability for Māori; work with hapori Māori to increase utilisation; change accountability mechanisms, including greater co‐governance of data; adequately fund alternatives; or potentially even abolishing the IDI and starting again.

Identifying with more than one ethnicity is becoming increasingly common in New Zealand. It is therefore important that the methods of categorising ethnicity used by government agencies and researchers effectively account for diverse ethnic identities. The aim of this research was to assess the impact of using different methods of categorising ethnicity on understanding outcomes in New Zealand, using Census 2013 information about tobacco smoking among the population aged 15+ years as an illustration. Prioritised, total response and single/combination categorisation methods were compared. Prioritised and total response categorisation generally produced similar prevalence estimates and patterns for smoking status, although estimates diverged somewhat for Māori ethnic groups and to a lesser extent for Pacific Peoples and Asian ethnic groups. Single/combination categorisation indicated that combination ethnic groups tend to have smoking outcomes between those of the constituent groups. Categorisation method had a considerable impact on the size of some ethnic groups. Prioritised ethnicity categorisation may be inappropriate for use by government agencies and for policy‐relevant research principally concerned with children, young people, Māori or Pacific Peoples. Single/combination categorisation is useful for understanding the associations between ethnicity and outcomes, however previous work suggests this method should be used with caution.

Aotearoa New Zealand implemented a Covid-19 elimination strategy in 2020 and 2021, which enabled a large majority of the population to be vaccinated before being exposed to the virus. This strategy delivered one of the lowest pandemic mortality rates in the world. However, quantitative estimates of the population-level health benefits of vaccination are lacking. Here, we use a validated mathematical model of Covid-19 in New Zealand to investigate counterfactual scenarios with differing levels of vaccine coverage in different age and ethnicity groups. The model builds on earlier research by adding age- and time-dependent case ascertainment, the effect of antiviral medications, improved hospitalisation rate estimates, and the impact of relaxing control measures. The model was used for scenario analysis and policy advice for the New Zealand Government in 2022 and 2023. We compare the number of Covid-19 hospitalisations, deaths, and years of life lost in each counterfactual scenario to a baseline scenario that is fitted to epidemiological data between January 2022 and June 2023. Our results estimate that vaccines saved 6650 (95% credible interval [4424, 10180]) lives, and prevented 74500 [51000, 115400] years of life lost and 45100 [34400, 55600] hospitalisations during this 18-month period. Making the same comparison before the benefit of antiviral medications is accounted for, the estimated number of lives saved by vaccines increases to 7604 [5080, 11942]. Due to inequities in the vaccine rollout, vaccination rates among Māori were lower than in people of European ethnicity. Our results show that, if vaccination rates had been equitable, an estimated 11%-26% of the 292 Māori Covid-19 deaths that were recorded in this time period could have been prevented. We conclude that Covid-19 vaccination greatly reduced health burden in New Zealand and that equity needs to be a key focus of future vaccination programmes.