New Zealanders living with a family member who has a long-term health condition: cross sectional analysis of integrated Census and administrative data

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Lisa Underwood
Nick Bowden
Andrea Teng
Ofa Dewes
Lukas Marek
Barry Milne

Abstract

Background
Living with a family member who has a long-term health condition (HC) is associated with poorer health and well-being outcomes, but the number and socio-demographics of people and families impacted by a family member who has an HC is unknown. 


Methods
Using the Integrated Data Infrastructure (IDI), a collection of linked administrative datasets for the full New Zealand (NZ) population, we identified n= 1,043,172 families using 2013 NZ Census data, and used health data over the previous 5-years to ascertain whether people in these families (n=3,137,517) had cancer, chronic obstructive pulmonary disease, coronary health disease, diabetes, dementia, gout, stroke, traumatic brain injury (TBI) or a mental health/behavioural condition (MHBC). 


Results
Over 60% of families included at least one person with a HC. The most common HCs were MHBCs (39.4% of families), diabetes (16.0%), and TBI (13.9%). A high proportion of multi-generation families (73.9%) included a member with a HC. Two-thirds (67.7%) of Pacific Peoples either had a HC themselves or were living with a family member who had a HC, compared with 62.3% of Europeans and 62.5% of Māori (the indigenous peoples of NZ). At the highest level of socioeconomic deprivation, 57.6% of children lived with a family member who had a HC. 


Conclusions
Three in five NZ families were living with a HC, with differences in the proportion affected according to family composition, socio-economic status and an individuals’ ethnicity. This suggests that there are a substantial number of people at risk of the associated poor outcomes.

Article Details

How to Cite
Underwood, L., Bowden, N., Teng, A., Dewes, O., Marek, L. and Milne, B. (2024) “New Zealanders living with a family member who has a long-term health condition: cross sectional analysis of integrated Census and administrative data”, International Journal of Population Data Science, 9(5). doi: 10.23889/ijpds.v9i5.2764.

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