Analysed the associations between damp, cold homes and the health of occupants using nationally representative survey datasets and linked data in the Stats NZ Integrated Data Infrastructure (IDI). This work used propensity score matching to estimate health effects while controlling for other factors, such as poverty, which can complicate such analysis.
For Professor Chris Cunningham, Research Centre for Hauora and Health, Massey University, in collaboration with Michele Morris.
This work involved:
- extracting, linking, and cleaning data from the General Social Survey, Te Kupenga, the Census, and other IDI datasets, within the Stats NZ datalab
- a descriptive analysis of the relationships between demographics, poverty, house condition, and physical and mental health, before controlling for other factors
- using the econometric technique, propensity score matching, to estimate the direct associations between house condition and health while controlling for confounding factors (including poverty, age, gender, smoking, region, and more)
- using SQL and R to extract, clean, and analyse the data
- writing a report on the findings in preparation for academic publication.