INTRODUCTION: Connections between environmental factors and mental health issues have been postulated in many different countries around the world. Previously undertaken research has shown many possible connections between these fields, especially in relation to air quality and extreme weather events. However, research on this subject is lacking in New Zealand, which is difficult to analyse as an overall nation due to its many micro-climates and regional differences.OBJECTIVES: The aim of this study and subsequent analysis is to explore the associations between environmental factors and poor mental health outcomes in New Zealand by region and predict the number of people with mental health-related illnesses corresponding to the environmental influence.METHODS: Data are collected from various public-available sources, e.g., Stats NZ and Coronial services of New Zealand, which comprised four environmental factors of our interest and two mental health indicators data ranging from 2016 up until 2020. The four environmental factors are air pollution, earthquakes, rainfall and temperature. Two mental health indicators include the number of people seen by District Health Boards (DHBs) for mental health reasons and the statistics on suicide deaths. The initial analysis is carried out on which regions were most affected by the chosen environmental factors. Further analysis using Auto-Regressive Integrated Moving Average(ARIMA) creates a model based on time series of environmental data to generate estimation for the next two years and mental health projected from the ridge regression.RESULTS: In our initial analysis, the environmental data was graphed along with mental health outcomes in regional charts to identify possible associations. Different regions of New Zealand demonstrate quite different relationships between the environmental data and mental health outcomes. The result of later analysis predicts that the suicide rate and DHB mental health visits may increase in Wellington, drop-in Hawke's Bay and slightly increase in Canterbury for the year 2021 and 2022 with different environmental factors considered.CONCLUSION: It is evident that the relationship between environmental and mental health factors is regional and not national due to the many micro-climates that exist around the nation. However, it was observed that not all factors displayed a good relationship between the regions. We conclude that our hypotheses were partially correct, in that increased air pollution was found to correlate to increased mental health-related DHB visits. Rainfall was also highly correlated to some mental health outcomes. Higher levels of rainfall reduced DHB visits and suicide rates in some areas of the country.
During 2010 and 2011, major earthquakes caused widespread damage and the deaths of 185 people in the city of Christchurch. Damaged school buildings resulted in state intervention which required amendment of the Education Act of 1989, and the development of ‘site sharing agreements’ in undamaged schools to cater for the needs of students whose schools had closed. An effective plan was also developed for student assessment through establishing an earthquake impaired derived grade process. Previous research into traditional explanations of educational inequalities in the United Kingdom, the United States of America, and New Zealand were reviewed through various processes within three educational inputs: the student, the school and the state. Research into the impacts of urban natural disasters on education and education inequalities found literature on post disaster education systems but nothing could be found that included performance data. The impacts of the Canterbury earthquakes on educational inequalities and achievement were analysed over 2009-2012. The baseline year was 2009, the year before the first earthquake, while 2012 is seen as the recovery year as no schools closed due to seismic events and there was no state intervention into the education of the region. National Certificate of Educational Achievement (NCEA) results levels 1-3 from thirty-four secondary schools in the greater Christchurch region were graphed and analysed. Regression analysis indicates; in 2009, educational inequalities existed with a strong positive relationship between a school’s decile rating and NCEA achievement. When schools were grouped into decile rankings (1-10) and their 2010 NCEA levels 1-3 results were compared with the previous year, the percentage of change indicates an overall lower NCEA achievement in 2010 across all deciles, but particularly in lower decile schools. By contrast, when 2011 NCEA results were compared with those of 2009, as a percentage of change, lower decile schools fared better. Non site sharing schools also achieved higher results than site sharing schools. State interventions, had however contributed towards student’s achieving national examinations and entry to university in 2011. When NCEA results for 2012 were compared to 2009 educational inequalities still exist, however in 2012 the positive relationship between decile rating and achievement is marginally weaker than in 2009. Human ethics approval was required to survey one Christchurch secondary school community of students (aged between 12 and 18), teachers and staff, parents and caregivers during October 2011. Participation was voluntary and without incentives, 154 completed questionnaires were received. The Canterbury earthquakes and aftershocks changed the lives of the research participants. This school community was displaced to another school due to the Christchurch earthquake on 22 February 2011. Research results are grouped under four geographical perspectives; spatial impacts, socio-economic impacts, displacement, and health and wellbeing. Further research possibilities include researching the lag effects from the Canterbury earthquakes on school age children.
The increase of the world's population located near areas prone to natural disasters has given rise to new ‘mega risks’; the rebuild after disasters will test the governments’ capabilities to provide appropriate responses to protect the people and businesses. During the aftermath of the Christchurch earthquakes (2010-2012) that destroyed much of the inner city, the government of New Zealand set up a new partnership between the public and private sector to rebuild the city’s infrastructure. The new alliance, called SCIRT, used traditional risk management methods in the many construction projects. And, in hindsight, this was seen as one of the causes for some of the unanticipated problems. This study investigated the risk management practices in the post-disaster recovery to produce a specific risk management model that can be used effectively during future post-disaster situations. The aim was to develop a risk management guideline for more integrated risk management and fill the gap that arises when the traditional risk management framework is used in post-disaster situations. The study used the SCIRT alliance as a case study. The findings of the study are based on time and financial data from 100 rebuild projects, and from surveying and interviewing risk management professionals connected to the infrastructure recovery programme. The study focussed on post-disaster risk management in construction as a whole. It took into consideration the changes that happened to the people, the work and the environment due to the disaster. System thinking, and system dynamics techniques have been used due to the complexity of the recovery and to minimise the effect of unforeseen consequences. Based on an extensive literature review, the following methods were used to produce the model. The analytical hierarchical process and the relative importance index have been used to identify the critical risks inside the recovery project. System theory methods and quantitative graph theory have been used to investigate the dynamics of risks between the different management levels. Qualitative comparative analysis has been used to explore the critical success factors. And finally, causal loop diagrams combined with the grounded theory approach has been used to develop the model itself. The study identified that inexperienced staff, low management competency, poor communication, scope uncertainty, and non-alignment of the timing of strategic decisions with schedule demands, were the key risk factors in recovery projects. Among the critical risk groups, it was found that at a strategic management level, financial risks attracted the highest level of interest, as the client needs to secure funding. At both alliance-management and alliance-execution levels, the safety and environmental risks were given top priority due to a combination of high levels of emotional, reputational and media stresses. Risks arising from a lack of resources combined with the high volume of work and the concern that the cost could go out of control, alongside the aforementioned funding issues encouraged the client to create the recovery alliance model with large reputable construction organisations to lock in the recovery cost, at a time when the scope was still uncertain. This study found that building trust between all parties, clearer communication and a constant interactive flow of information, established a more working environment. Competent and clear allocation of risk management responsibilities, cultural shift, risk prioritisation, and staff training were crucial factors. Finally, the post-disaster risk management (PDRM) model can be described as an integrated risk management model that considers how the changes which happened to the environment, the people and their work, caused them to think differently to ease the complexity of the recovery projects. The model should be used as a guideline for recovery systems, especially after an earthquake, looking in detail at all the attributes and the concepts, which influence the risk management for more effective PDRM. The PDRM model is represented in Causal Loops Diagrams (CLD) in Figure 8.31 and based on 10 principles (Figure 8.32) and 26 concepts (Table 8.1) with its attributes.
