Appendix D
Figure D1 provides the formulas used to estimate the different component costs of ill health. More explanation of the methods behind these formulas can be found in the main body of the paper.
Figure D1 – Summary of formula used to calculate cost of ill health using SoFIE
Hospital inpatient costs
Where: ndirect is the total number of people in the sample for direct costs (aged 17 and over).
i = 1,…, n identifies the ith person in the sample.
wi is the adjusted longitudinal weight for the ith person.
The cost weight is zero if a person has no hospital appointments.
Absenteeism costs = Absenteeism Component 1 + Absenteeism Component 2
Where:
ndirect is the total number of labour force participants in the sample for indirect costs (participating, working age, non-students).
payft is the average full-time hourly rate from the NZ Income Survey ($19.95).
hdaily is the average daily hours worked in the reference period.
losdays is the length of stay across all hospital inpatient appointments (in days). This is zero if a person has no hospital inpatient appointments.
And:
hweekly is the average weekly hours worked in the reference period.
illness is an indicator of whether an illness has stopped activity for at least a week in the last 12 months. For each person this is equal to 1 if the response is yes and 0 otherwise.
hmonthly is the average monthly hours worked in the reference period.
prod% is the proportion of hours worked at reduced productivity, calculated using Methods 1 to 3.
prodlevel is the level of productivity for those hours worked at reduced productivity, based on Assumptions 1 to 3.
Where:
hours is the additional hours a person would work annually in the absence of ill health. Results are from a regression model. It is non‐zero for those affected and zero for all other people.
Where:
j = 1,…, n identifies the health state.
workhealth is the marginal effect for each health state, evaluated at the sample means. This is zero if the health state is excellent (or not significantly different from excellent). Results are from a logistic regression.
health is the weighted number of people in the equivalent health state.
