Estimating quality of life: A spatial microsimulation model of well-being in Aotearoa New Zealand.

Quality of life is a complex concept characterised by several dualities, it has many definitions depending on the field of research and an abundance of diverse objective and subjective measures. The latter often represents the extent of perceived (dis)satisfaction with various domains of life experienced by individuals or groups, and research is increasingly focusing on subjective measures of well-being to better understand personal drivers related to quality of life. A better understanding of these factors at a local level has potential to shed light on an often-overlooked aspect of the mental health landscape in Aotearoa New Zealand. Individual-level data on adults (15+ years) is sourced from the New Zealand Attitudes and Values Study 2018 (N = 47,949) and aggregate-level data from the Census 2018 (N = 3,775,854). Matching constraint variables include sex, age, ethnicity, highest qualification, and labour force status. Outcome variables include personal and national well-being scores from 0 to 10 (extremely dissatisfied-extremely satisfied). Spatial microsimulation is used to create a synthetic population based on the above data. Results show lower mean national well-being scores than personal well-being scores, with spatial variations that broadly reflect patterns of socioeconomic deprivation. Low mean values for both personal and national well-being scores are seen in rural areas of high socioeconomic deprivation, particularly those with large Māori populations. High mean values are associated with areas of low deprivation. Additionally, high national well-being scores are associated with areas of agricultural activity, particularly in the South Island. Consideration should be given to factors that influence responses in such topics however, including demographic profiles as well as economic and social conditions of individuals and their surrounding communities. This study demonstrates that spatial microsimulation can be used as a powerful tool to understand population well-being. It can help support future planning and resource allocation, aiding in achieving health equity.