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Pandemics and emergent digital inequalities
Abstract
Over the course of the COVID-19 pandemic, it has become increasingly apparent that a public health crisis exposes underlying inequalities in society. This commentary focuses on emergent digital inequalities in Aotearoa New Zealand, noting the accelerated use of digital tools and technologies such as smartphone applications, online maps and vaccination booking websites during the pandemic. It argues that there is a need to address both the underlying population inequalities and the associated digital inequalities that have arisen during the pandemic.
... 1. Demographic details beyond geographic location were not ascertained meaning we were unable to test the representativeness of the sample. However, it is known that online surveys have some inherent bias, requiring access to the internet and digital literacy and due to the use of social media and GeoNet convenience sampling (Campbell, 2021;Field, 2020;Zhang et al. 2020). ...
Instrumental records from coastal gauges, DART: Deep-ocean Assessment and Reporting of Tsunamis buoys, meteorological stations and post-event damage surveys are being used to shape our understanding of the physical processes associated with the eruption and associated tsunami, such as pressure changes, acoustic signals and tsunami wave arrival times. However, there is a need for more holistic understanding of how people in Aotearoa New Zealand understand, and experience tsunami hazard, in order to improve warnings and increase preparedness. On 15 January 2022, social media and news reports described booming and explosion noises throughout Aotearoa New Zealand and the Pacific, caused by atmospheric pressure waves from the eruption of Hunga-Tonga Hunga Ha’Apai volcano, Tonga. In response to this historic and complex event, a team of tsunami, volcano, and social scientists at GNS Science designed a survey to broaden our documentation of the event beyond the physical processes, and to promote public engagement with science. We sought to understand the spatial and temporal distribution of sound and tsunami observations using crowdsourced geospatial data, allowing the public to participate and report experiences. The survey was launched online from 21 January to 13 February 2022. Thirty-nine questions were posed to the public, asking about location, time, water colour, smells, damage to the built/natural environments and number of surges/booms observed. Respondents provided images and videos displaying impacted coastlines, flooded campgrounds, coastal erosion, unusual currents and audio of booms. This report summarises the results of this survey based on the anonymised responses from over 2,000 individuals.
The COVID-19 pandemic continues to have unprecedented impacts on people and places globally, challenging the ability of both government and citizens to respond. The use of Nonpharmaceutical Interventions (NPIs) has been widespread in attempting to curtail the spread of COVID-19. However, there were important distinctions in how governments chose to use the public health tools at their disposal. We focus on the experience of Aotearoa New Zealand (NZ), a jurisdiction that has been featured as arguably one of the most successful and stringent in attempts to eliminate COVID-19 from the population. We also explore and examine the social and spatial patterns that exist and have widened within NZ as the pandemic has unfolded, exposing the underlying social structures and fractures that now exist and persist in many countries. Initially, a geographic approach was absent, but as the pandemic progressed, this became a key aspect of the public health response. This chapter explores a range of data sources from the ongoing pandemic in NZ. We conclude by discussing an emerging syndemic, concluding the chapter with policy implications and potential future research directions.
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 Māori, 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 Māori, 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.
Background: Accounting for the co-occurrence of multiple environmental influences is a more accurate reflection of population exposure than considering isolated influences, aiding in understanding the complex interactions between environments, behaviour and health. This study examines how environmental ‘goods’ such as green spaces and environmental ‘bads’ such as alcohol outlets co-occur to develop a nationwide area-level Healthy Location Index (HLI) for New Zealand.
Methods: Nationwide data were collected, processed, and geocoded on a comprehensive range of environmental exposures. Health-constraining ‘bads’ were represented by: i) fast-food outlets, ii) takeaway outlets, iii) dairy outlets and convenience stores, iv) alcohol outlets, v) and gaming venues. Health-promoting ‘goods’ were represented by: i) green spaces, ii) blue spaces, iii) physical activity facilities, iv) fruit and vegetable outlets, and v) supermarkets. The HLI was developed based on ranked access to environmental domains. The HLI was then used to investigate socio-spatial patterning by area-level deprivation and rural/urban classification.
Results: Results showed environmental ‘goods’ and ‘bads’ co-occurred together and were patterned by area-level deprivation. The novel HLI shows that the most deprived areas of New Zealand often have the most environmental ‘bads’ and less access to environmental ‘goods’.
Conclusions: The index, that is now publicly available, is able to capture both inter-regional and local variations in accessibility to health-promoting and health-constraining environments and their combination. Results in this study further reinforce the need to embrace the multidimensional nature of neighbourhood and place not only when designing health-promoting places, but also when studying the effect of existing built environments on population health.
COVID-19 can affect the entire population, but it poses an increased risk for particular population groups. Socioeconomic and demographic factors as well as long-term health conditions can make populations vulnerable to adverse health outcomes and mortality related to COVID-19. This study uses geospatial methods to visualise metrics of vulnerability to COVID-19 in New Zealand. Based on Ministry of Health guidelines, nationwide data on risk factors included age, ethnicity, population density, socioeconomic deprivation, smoking, long-term health conditions (cancer, cardiovascular conditions, diabetes, renal conditions, and respiratory illnesses), and health service awareness. Data were sourced from the Census (2018), the New Zealand Deprivation Index (NZDep2018), and the National Minimum Dataset (2011–2016). Factor analysis and bivariate mapping were used to identify areas of high vulnerability. Results demonstrate the unequal social and spatial vulnerabilities to COVID-19 across New Zealand. While some major cities were highlighted many areas also occured outside of the major cities in smaller communities, which also typically have less access to healthcare and fewer resources. This study has generated data that may help mitigate potential inequality in our response to the COVID-19 pandemic, or indeed for future pandemics.
Background: The 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 wellbeing. 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’.
Methods: We 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 derived a mobility index using anonymised aggregated population counts for each neighbourhood (2,253 Census Statistical Areas; mean population n=2,086) of national hourly mobile phone location data (7.45 million records, 01/03/20–20/07/20) for New Zealand (NZ).
Results: Curtailing movement has highlighted and exacerbated underlying social and spatial inequalities. Our analysis reveals the unequal movements during ‘lockdown’ by neighbourhood socioeconomic status in New Zealand.
Conclusion: In 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.
The covid-19 pandemic has exposed the longstanding structural drivers of health inequities, such as precarious and adverse working conditions, growing economic disparities, and anti-democratic political processes and institutions. These important determinants of health have interlinked with class, ethnicity, gender, education level, and other factors during covid-19 to exacerbate existing social vulnerabilities in society.
The New Zealand Government should be commended for including the nation's wellbeing as a measure of success. However, health inequity in New Zealand is persistent. The scarcity of tangible reductions in inequity between Māori and non-Māori populations raises questions about the effectiveness of policies to date. To address health inequity, New Zealand might need to revisit deep-rooted historical, cultural, and systemic issues.
The spatial locations of food retailers are considered to be an influential aspect of population consumption patterns. Such contextual relationships are often related to socio‐economic deprivation, with disparities in accessibility having important implications. This study used Geographic Information Systems and an Enhanced Two‐Step Floating Catchment Area model of spatial accessibility to further understand such relationships within urban areas of New Zealand. Findings, while mixed, indicate that there is generally increased accessibility to all food retailers in highly deprived areas. Understanding these socio–spatial relationships in local environments has important implications for policy initiatives, health outcomes and sustainable development.
This paper presents applied geographical research based on a spatial microsimulation model, SimAlba, aimed at estimating geographically sensitive health variables in Scotland. SimAlba has been developed in order to answer a variety of “what-if” policy questions pertaining to health policy in Scotland. Using the SimAlba model, it is possible to simulate the distributions of previously unknown variables at the small area level such as smoking, alcohol consumption, mental well-being, and obesity. The SimAlba microdataset has been created by combining Scottish Health Survey and Census data using a deterministic reweighting spatial microsimulation algorithm developed for this purpose. The paper presents SimAlba outputs for Scotland’s largest city, Glasgow, and examines the spatial distribution of the simulated variables for small geographical areas in Glasgow as well as the effects on individuals of different policy scenario outcomes. In simulating previously unknown spatial data, a wealth of new perspectives can be examined and explored. This paper explores a small set of those potential avenues of research and shows the power of spatial microsimulation modeling in an urban context.
The main purpose of this thesis is to explore social and spatial inequalities of ill-health in Scotland using a spatial microsimulation modelling approach.
The complex questions of what socio-economic or geographical factors may influence the health of individuals are explored in this PhD, using a variety of statistical methods. Using data from the Scottish Health Survey and the UK Census of Population a Spatial Microsimulation model was designed and constructed to undertake this task. The Spatial Microsimulation Model developed allowed the exploration of simulated health and socio-economic data at small area (micro) level as well as modelling of `what-if' policy scenarios. The study is focused on Scotland.
The Research begins with a general introduction to what the areas of study will be, with a series of substantive research questions being forwarded for examination. The literature relevant to the field of study is then carefully critiqued and examined to ensure the originality of this research and the gaps which exist in the field of health inequalities research. An examination of the data and methods used as well as the more technical details of Microsimulation modelling are also discussed at chapter length which forms the basis for proceeding with the research questions.
The complex task of building a Spatial Microsimulation Model, the challenges involved and the inner workings of the model are discussed along with methods to assess the accuracy of the model.
The subsequent chapters then focus on the results of the analysis performed. These chapters deal with the research questions posed at the beginning as well as the `what-if' policy scenarios.
The study then concludes with directions for future research as well as some key points that have been drawn out over the course of the three year PhD project.
Objective: To detect spatial clusters of high infant mortality rates in New Zealand for Māori and non-Māori populations and verify if these clusters are stable over a certain time period (1995–2008) and similar between the two populations.
Method: We applied the Kulldorff's spatial scan statistics on data collected by New Zealand Ministry of Health (1995 to 2008) at the territorial local authorities (TLA) level. Kappa coefficient was used to assess the concordance between clusters obtained for Māori and non-Māori populations. T-test analyses were conducted to identify associations between spatial clusters and two predictors (population density and deprivation score).
Results: There are some significant spatial clusters of infant mortality in New Zealand for both Māori and Non-Māori. The concordance of the cluster locations between the two populations is strong (kappa=0.77). Unsurprisingly, infant mortality clusters for both Māori and Non-Māori are associated with the deprivation score. The population density predictor is only significantly and positively associated with clusters obtained for the non-Māori population. After controlling for deprivation the presence of spatial clusters is all but eliminated.
Conclusions: Infant mortality patterns are geographically similar for both Māori and Non-Māori. However, there are differences geographically between the two populations after accounting for deprivation.
Implications: Health services that can affect infant mortality should be aware of the geographical differences across NZ. Deprivation is an important factor in explaining infant mortality rates and policies that ameliorate its effects should be pursued, as it is the major determinant of the geographical pattern of infant mortality in NZ.
Background
Immunisation is a safe and effective way of protecting children and adults against harmful diseases. However, immunisation coverage of children is declining in some parts of New Zealand.
Aim
Use a nationwide sample to first, examine the socioeconomic and demographic determinants of immunisation coverage and spatial variation in these determinants. Second, it investigates change in immunisation coverage in New Zealand over time.
Methods
Individual immunisation records were obtained from the National Immunisation Register (NIR) (2005–2017; 4,482,499 events). We calculated the average immunisation coverage by year and milestone age for census area units (CAU) and then examined the immunisation coverage by selected socioeconomic and demographic determinants. Finally, local variations in the association between immunisation coverage and selected determinants were investigated using geographically weighted regression.
Results
Findings showed a decrease of immunisation rates in recent years in CAUs with high immunisation coverage in the least deprived areas and increasing immunisation rates in more deprived areas. Nearly all explanatory variables exhibited a spatial variation in their association with immunisation coverage. For instance, the strongest negative effect of area-level deprivation is observed in the northern part of the South Island, the central-southern part of the North Island, around Auckland, and in Northland.
Conclusion
Our findings show that childhood immunisation coverage varies by socioeconomic and demographic factors across CAUs. We also identify important spatial variation and changes over time in recent years. This evidence can be used to improve immunisation related policy in New Zealand.
Background
We examined the association between area-level deprivation and dental ambulatory sensitive hospitalizations (ASH) and considered the moderating effect of community water fluoridation (CWF). The hypothesis was that higher levels of deprivation are associated with higher dental ASH rates and that CWF will moderate this association such that children living in the most deprived areas have greater health gain from CWF.
Methods
Dental ASH conditions (dental caries and diseases of pulp/periapical tissues), age, gender and home address identifier (meshblock) were extracted from pooled cross-sectional data (Q3, 2011 to Q2, 2017) on children aged 0–4 and 5–12 years from the National Minimum Dataset, New Zealand (NZ) Ministry of Health. CWF was obtained for 2011 and 2016 from the NZ Institute of Environmental Science and Research. Dental ASH rates for children aged 0–4 and 5–12 years (/1000) were calculated for census area units (CAUs). Multilevel negative binomial models investigated associations between area-level deprivation, dental ASH rate and moderation by CWF status.
Results
Relative to CWF (2011 and 2016), no CWF (2011 and 2016) was associated with increased dental ASH rates in children aged 0–4 [incidence rate ratio (IRR) = 1.171 (95% confidence interval 1.064, 1.288)] and aged 5–12 years [IRR = 1.181 (1.084, 1.286)]. An interaction between area-level deprivation and CWF showed that the association between CWF and dental ASH rates was greatest within the most deprived quintile of children aged 0–4 years [IRR = 1.316 (1.052, 1.645)].
Conclusions
CWF was associated with a reduced dental ASH rate for children aged 0–4 and 5–12 years. Children living in the most deprived areas showed the greatest effect of CWF on dental ASH rates, indicating that the greater health gain from CWF occurred for those with the highest socio-economic disadvantage. Variation in CWF contributes to structural inequities in oral-health outcomes for children.
This short paper aims to discuss the role and the importance of the Census in New Zealand. It also discusses some of the multitude of uses of Census data in various contexts to help us understand the demographic and socio-economic landscape of New Zealand. Debates internationally on the nature, scale and frequency of a national Census, or even whether a Census is necessary at all, mean that we cannot take this valuable resource for granted. With this in mind, we as geographers have a wealth of data to explore and analyse that we should take full advantage of. What gets counted counts!
The previous decade has given rise to the importance of Geographic Information Systems (GIS) in explaining inequalities in health outcomes between groups based on their spatial location and social background. The GeoHealth Laboratory, based at the University of Canterbury, is a joint venture with the Health and Disability Intelligence unit within the Ministry of Health (MoH). The aims of this relationship are to add analytical capacity to MoH data collections and increase academic outputs of geospatial health research in New Zealand. GeoHealth research has often been a joint venture between Laboratory staff and students as well as collaboration with local and international researchers. These partnerships along with widely varied research interests have resulted in a large contribution of spatial health research in the field of health geography. This article reports on research undertaken by the GeoHealth Laboratory that has focused on access to neighbourhood determinants of health. An overview of key neighbourhoods and health research areas are outlined within the over-arching themes of indices of access to neighbourhood factors, access to undesireable neighbourhood destinations, health promoting neighbourhood factors, access to and utilisation of health services, and complementary data collection and research groups within New Zealand.
Bridging the global digital divide: A platform to advance digital development in low-and middle-income countries
- G Ingram