Background Evidence for a cross-sectional relationship between income and health is strong but is probably biased by substantial confounding. Longitudinal data with repeated income and health measures on the same individuals can be analysed to control completely for time-invariant confounding, giving a more accurate estimate of the impact of short-term changes in income on health.
Methods 4 years of annual data (2002–2005) from the New Zealand longitudinal Survey of Family, Income and Employment were used to investigate the relationship between annual household income and self-rated health (SRH) using a fixed-effects ordinal logistic regression model. Possible effect modification of the income–SRH relationship by poverty and baseline health was tested with interactions.
Results An increase in income of $10 000 over the past year increased the odds of reporting better SRH by 1% (OR 1.01, 95% CI 1.00 to 1.02). Poor baseline health significantly modified the association between income and SRH. A $10 000 increase in income increased the odds of better SRH by 10% for those with two or more chronic conditions. Poverty or deprivation did not modify the income–health association.
Conclusions The overall small, positive, but statistically non-significant, income–health effect size is consistent with similar analyses from other longitudinal studies. Despite the overwhelming consensus that income matters for health over the medium and long-term, evidence free of time-invariant confounding for the short-run association remains elusive. However, measurement error in income and health has probably biased estimates towards the null.
- longitudinal studies
- logistic models
- health surveys
- longitudinal data analysis
- self-rated health
- social epidemiology
Statistics from Altmetric.com
If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.
Statistics New Zealand Security Statement: Access to the data used in this study was provided by Statistics New Zealand in the Statistics New Zealand Data Laboratory (Wellington), a secure environment designed to give effect to the confidentiality provisions of the Statistics Act, 1975. The results in this study and any errors contained therein are those of the authors, not Statistics New Zealand.
Funding This research was supported by the Health Research Council of New Zealand and was completed as part of a PhD thesis within the Health Inequalities Research Programme, University of Otago. Publication was supported by the University of Otago Research Committee, by means of the University of Otago Postgraduate Publishing Bursary.
Competing interests None.
Ethics approval This study was conducted with the approval of Statistics New Zealand, the official national government agency that obtains official statistics, collected the data and administered the survey. Access to the data used in this study was provided by Statistics New Zealand in the Statistics New Zealand Data Laboratory (Wellington), a secure environment designed to give effect to the confidentiality provisions of the Statistics Act, 1975. The results in this study and any errors contained therein are those of the authors, not Statistics New Zealand.
Provenance and peer review Not commissioned; externally peer reviewed.