National movement patterns during the COVID-19 pandemic in New Zealand: the unexplored role of neighbourhood deprivation

Abstract

BackgroundThe COVID-19 pandemic has asked unprecedented questions of governments around the world. Policy responses have disrupted usual patterns of movement in society, locally and globally, with resultant impacts on national economies and human well-being. These interventions have primarily centred on enforcing lockdowns and introducing social distancing recommendations, leading to questions of trust and competency around the role of institutions and the administrative apparatus of state. This study demonstrates the unequal societal impacts in population movement during a national ‘lockdown’.MethodsWe use nationwide mobile phone movement data to quantify the effect of an enforced lockdown on population mobility by neighbourhood deprivation using an ecological study design. We then derive a mobility index using anonymised aggregated population counts for each neighbourhood (2253 Census Statistical Areas; mean population n=2086) of national hourly mobile phone location data (7.45 million records, 1 March 2020–20 July 2020) for New Zealand (NZ).ResultsCurtailing movement has highlighted and exacerbated underlying social and spatial inequalities. Our analysis reveals the unequal movements during ‘lockdown’ by neighbourhood socioeconomic status in NZ.ConclusionIn understanding inequalities in neighbourhood movements, we are contributing critical new evidence to the policy debate about the impact(s) and efficacy of national, regional or local lockdowns which have sparked such controversy.

Pandemics and emergent digital inequalities

“…A complete list of alert‐level changes and restrictions can be found online 5 . This situation is similar to other countries that have also imposed restrictions on movement or travel, at different points in time for different purposes, impeding the free flow of people and goods, with evident inequalities in neighbourhood movement by deprivation status (Campbell et al, 2021). A further example is the map of the exposure locations of interest in Auckland (Figure 1b), which is also an internet or smartphone‐based resource.…”

Community vulnerability and mobility: What matters most in spatio-temporal modeling of the COVID-19 pandemic?

“…Among the studies we surveyed, authors were concerned with the risk factors for contracting and dying of COVID-19 ( Fan et al, 2020 ; Guha et al, 2020 ), disease prevalence over time and across various regions ( Carroll and Prentice, 2021 ), mitigation strategies for disease control ( Lyu and Wehby, 2020 ), financial and employment impacts of the pandemic ( Moen et al, 2020 ), social isolation and its effect on mental health ( Douglas et al, 2020 ; Substance Abuse and Mental Health Services Administration, 2020 ), and community philanthropy and cross-sector collaborative response ( Finchum-Mason et al, 2020 ). A comprehensive review of these disparate bodies of literature is beyond the scope of this manuscript; nonetheless, we note the breadth of these evolving literatures to set the stage for challenging a common methodological practice that persists across them – namely, employing a variety of measures as proxies for socio-demographic characteristics to explain COVID-19's spread ( Campbell et al, 2021 ; Daras et al, 2021 ).…”

“…Note that one of the factors that comprises PVI is a measure of social distancing to account for human behavior, a feature we suspect is useful for modeling COVID-19. Finally, we supplement the composite measures with a key variable noted in recent COVID-19 literature – mobility – to see whether these patterns have similar or greater associations with the outcomes of interest ( Badr et al, 2020 ; Campbell et al, 2021 ). We expect, as other scholars have suggested, that modeling human behavior is necessary for understanding COVID-19 disease transmission.…”

Reconsidering movement and exposure: Towards a more dynamic health geography

“…Other uses of the approach presented here would be to validate or assess the extent to which simulation models align with the real GPS data from smartphones (Sallah et al., 2017). Our next step is to expand the size of the cohorts we track over time to see the degree to which these initial findings presented here apply to a wider range of the population (Campbell, Marek, et al., 2021). It is fair to say that much research in this area is in its infancy.…”