This work depends on detailed mathematical modelling of complex systems and population dynamics.
As reported in issue 3 of Pūtaiao, our researchers Drs Rachelle Binny and Audrey Lustig applied their predator-modelling skills to Covid-19 virus epidemiology, working with mathematicians and modellers at Te Pūnaha Matatini: the Centre for Complex Systems and Networks (TPM), University of Canterbury, and the University of Auckland to assess likely patterns of disease spread into the country. Their contributions to this national team were crucial in informing Government decisions about the ensuing national and regional lockdowns.
Since then, the team has applied their modelling skills to other aspects of epidemiology, including – in a recent paper in the New Zealand Medical Journal – questions of Covid disease outcome by ethnicity.
That work, led by Nic Steyn (TPM and University of Auckland) and Prof. Mike Plank (TPM and University of Canterbury), combined existing demographic and health data for ethnic groups in New Zealand with international data on Covid-19 infection fatality rates (IFR) for different age groups. Care was taken to control for differences by ethnic group in unmet healthcare needs, life expectancy, and underlying health issues such as heart disease, diabetes, and high blood pressure.
As an initial guide to the potential scale of Covid-19 health inequity in New Zealand, the results were striking. The IFR for Māori was estimated to be 50% higher than that of non-Māori – and could be even higher, depending on the relative contributions of age and underlying health conditions to mortality risk. Additional risk factors for accelerated disease transmission, which were not factored into the study, include crowded, substandard housing, jobs or workplaces with higher health risks, and multi-generational living arrangements – these, again, are disproportionally experienced among Māori and Pacific people.
With the global pandemic likely to stretch long into 2021, this work continues our vital contribution to future national-scale disease incidence and impact modelling, especially in matters of equitable access to healthcare.
