Adopting an improvement science approach to inform ongoing implementation of the Mana Ake initiative in Canterbury

Recognising the value of data and analytics saw the integration of an improvement science approach to inform service-delivery, within a cross-sector mental health initiative — Mana Ake. An increased prevalence of wellbeing concerns resulted in a holistic, locally-informed, collaborative initiative for tamariki. Internal review of mixed-methods data has allowed for a truly data-informed iterative approach to service improvement. Consequently, evaluation has influenced the responsiveness of the initiative to optimally meet the needs of the communities it supports. A collaborative initiative between health, education, and wider communities. Evaluation domains include: tamariki, whānau, schools/kura, communities, system.

More information: www.Manaake.health.nz

Linked data is vital: evaluating the impact of prehospital care on mortality following major trauma

The potentially fatal or severe consequences of many injuries can be reduced through an optimally structured prehospital trauma system that can provide timely and appropriate care. This retrospective cohort study linked data from St John and Wellington Free Ambulances, the NZ Trauma Registry, Ministry of Health (Mortality, Hospital discharge and NHI) and Coronial post-mortem reports. The project’s aim is to help identify opportunities to optimise the delivery of Emergency Medical Services (EMS) care in NZ. The collaborative multidisciplinary team includes researchers from St John, Universities of Auckland and Otago. More details about this project are in our protocol paper https://pubmed.ncbi.nlm.nih.gov/33514568/.

Growth monitoring Aotearoa: Scoping a national system for tamariki and rangatahi

In New Zealand (NZ) three in ten 2-14 year- olds are affected by overweight/obesity with ethnic disparities. The existing data used to monitor growth of 0-19-year-olds are fragmented and mostly aggregated at the national level. Thus, NZ lacks group-specific information on prevalence, trends, and determinants of healthy growth for this group, crucial information if equitable improvements are to be achieved. We will map and assess diverse national sources of anthropometric data of 0-19-year-olds (last 2 decades) to establish what would be required and which sources would be suitable to be included in a national monitoring platform. For more information: t.castro@auckland.ac.nz

Precision Driven health: Partnership model for health data science

Precision Driven Health is New Zealand’s health data science partnership. For the past five years, we have been forming collaborations between New Zealand’s health IT sector, health providers and universities, aimed at improving health outcomes through data science. Our work seeks to personalise health by integrating new data sources, developing predictive models, optimise decision making and empower people with new tools. We will share some lessons learned from over 100 projects, including the challenges of working with personal information and ensuring equity is improved by design.

Using risk-adjusted Days Alive and Out of Hospital (DAOH) to compare health outcomes across NZ after surgery

DAOH scores can be an effective way to measure health outcomes by collapsing many negative outcomes after surgery, such as death or readmission, into one number. This study used routine data from the Ministry of Health and applied novel risk adjustment methods to illustrate how DAOH scores can detect differences between patients, different types of operations and DHBs in NZ.

Using this data, we can identify important areas of difference, for example between hospitals, that can be further audited to improve outcomes after surgery or to investigate optimal patient pathways for recovery.

Occupational Exposures and Ischaemic Heart Disease (IHD): results from the entire New Zealand population using the Integrated Data Infrastructure (IDI)

Common occupational exposures have been associated with IHD, but evidence is conflicting. For the whole NZ adult working population at the time of the 2013 census, data were extracted from the Statistics NZ IDI, on occupation and incident IHD from 2013 to 2018. The number of working hours was extracted from the census, and exposure to sedentary work, loud noise, and night shift was assessed through NZ job exposure matrices. This study suggests occupational exposure to high levels of noise and night shifts increase IHD risk while there was no evidence of association with sedentary work and long working hours.

Association between Occupation and Ischaemic Heart Disease (IHD) by sex and ethnicity in the whole New Zealand population using the Integrated Data Infrastructure (IDI)

Associations between IHD and occupations are poorly understood. This whole-population study aimed to identify occupations associated with increased IHD risk in NZ by sex and ethnicity. A cohort of workers was constructed for the whole NZ adult working population using the Statistics NZ IDI. Occupation was obtained from census data and incident IHD was determined using hospitalisation, prescription, and death records from 2013 to 2018. This study confirmed an increased IHD risk for several occupations previously identified as being high risk for IHD and has also identified some potentially new occupational groups. Important sex and ethnic differences were also observed.

Sharing our experiences from COVID-19 and Te Pokapū Hātepe o Aotearoa, the New Zealand Algorithm Hub

In support of the response to COVID-19, New Zealand initiated and rapidly delivered a solution for national algorithm management. During this project, we stood up a national instance of a machine learning platform, developed a governance process tailored to our local context in Aotearoa, deployed 30 models, and went live with a website (www.algorithmhub.co.nz) to support user engagement and interaction. The underlying technology solution has evolved over the last five years through our research partnership focused on the development and delivery of data science in healthcare; it was built in response to the need for tooling to deploy, manage and monitor algorithms safely and effectively in a healthcare context. This talk will share the key lessons learned from our COVID-19 experience and discuss the technology, user engagement and governance processes that made this successful.

Epidemiology for non-epidemiologists

Comparing population health outcomes for different places, population groups or time periods is complicated by access to data, different population sizes and population age structures. There are robust epidemiological methods for doing these calculations, but they are not always straightforward, especially when depicting the precision estimates in the results. Mātau is a tool that embeds various robust epidemiological methods, population data and imports outcome data to create a point and click way to calculate levels of health outcomes and compare populations. Already in use in Aotearoa and overseas, it is quick, easy to learn and all results in table and graph format.