6.2  Conclusions and limitations

The indirect costs of ill health alone are estimated to be between $4.127 billion and $11.563 billion. To put these figures into context, at the top end of the estimate this is close to the total budget for Vote Health (of around $9.917 billion in 2004/05). The indirect costs covered are not the only indirect costs of ill health. As an example, there will be indirect costs as a result of people caring for sick dependants. These are not included in these estimates.

Alongside the indirect costs there are the direct costs of ill health. One small part of the direct costs, hospital inpatient costs, can be estimated from SoFIE, but this is just one part of a much larger cost. Vote Health is known to have been $9.917 billion in 2004/05. In addition to this cost there are health care costs and treatments that are privately funded.

Focusing on just absenteeism and presenteeism, together these components from SoFIE are estimated to be between $0.929 billion and $8.365 billion. This large range is owing to the varying measures of presenteeism that were employed. The only other known source of data in the New Zealand context is recent research by Southern Cross Medical Care. They estimate these costs to be over $2 billion (Southern Cross Medical Care Society, 2009), within the range of estimates from SoFIE. In line with the Southern Cross results, the SoFIE results suggest that presenteeism is the biggest of the two cost components, although the gap between the two estimates from the Southern Cross research is not as great as for the SoFIE estimates, in part indicating the under coverage of the absenteeism estimates in SoFIE (the Southern Cross estimates are $1.260 billion for presenteeism compared with $0.98 billion for absenteeism). Like the results in the Southern Cross research, results from SoFIE indicate that it is those who take days off sick who are most likely to experience presenteeism. The large estimate of presenteeism is also in line with that found in some US studies (DeVol and Bedroussian, 2007; Newton, 2000, in DeVol and Bedroussian, 2007). The output lost owing to presenteesim alone is thought to be immense. DeVol and Bedroussian estimated that 79% of the indirect costs are a result of individual presenteeism, compared with between 18% to 71% from this analysis.[57]

Looking at the lost hours by qualification level indicates that the lost hours are spread across different qualification levels, rather than being congregated on just a few qualification levels. This indicates that using the average hourly full-time rate to evaluate the hours lost is justified. In future work the costs could be broken down by occupation to better determine if this is the case. The average hourly rate for different occupations could then be used to evaluate the cost rather than the overall average hourly rate.

It is interesting to consider who bears the indirect costs. The employee will bear the costs if they do not work or work less, while if a person is entitled to sick pay, the absenteeism and presenteeism costs are mostly borne by employers. Employers may also have to incur additional costs to hire temporary workers (eg, relief teachers). As a result, the estimates of the cost of ill health owing to not working or working less rest on an assumption that there would be demand for these additional hours to be worked, so the costs result in a loss in GDP rather than just a cost to the individual.

Another interesting finding of this research is a significant relationship between deferring going to the doctors or collecting a prescription owing to affordability in the past 12 months and absenteeism, with those deferring going to the doctors or collecting a prescription significantly more likely to experience absenteeism. Further, those who have deferred going to the doctors in the past 12 months owing to affordability are more likely to have suffered from presenteeism. However, it should be remembered that these results do not imply causality and that the results are based on models that do not control for endogeneity so may be subject to endogeneity bias. However, they do identify an area where further work could be undertaken.

In interpreting the results of this study, it is important to keep in mind a number of limitations, in terms of both the methodology behind its estimates and its implications. This study is a preliminary analysis of some of the costs of ill health. As discussed throughout this paper, there are data limitations owing to the nature of information available in SoFIE. For instance, SoFIE does not ask respondents questions that directly measure the extent of absenteeism and presenteeism, and thus these estimates are based on other related questions in SoFIE and assumptions about the responses to these questions. One component of absenteeism assumes that those who are participating in the labour force who responded that illness or a health problem stopped them from doing their usual activities for more than a week, have taken a week off work. However, this method does not account for those who may have taken less than a week at a time off work during the year. It also does not account for time taken off of more than a week. Other data sources suggest that the methodology used to estimate absenteeism from SoFIE misses a large amount of sick leave. A similar methodology limitation results in the wide range of the presenteeism estimate. This range reflects the several ways in which presenteeism can be measured based on information from SoFIE.

While this study does give an indication of the potential loss in GDP resulting from some of the costs of ill health, value-for-money conclusions cannot be drawn from these estimates for the following reasons. First, this research does not comment on the extent to which ill health is amenable to policy interventions. For instance, some of the estimated components rest on an assumption that it would be possible to improve the health status of those with less than excellent health. In the case of some types of illness, this would not be possible. Second, even if it were possible for all working age non-students to achieve excellent health status, this study does not provide an estimate of how much it would cost to achieve this, and therefore, does not offer any information about the value-for-money of policy interventions aimed at improving health status. Moreover, it is important to note that health care funding is driven by an individual's needs rather than an individual's labour market contributions. This study in no way suggests a policy link between an individual's publicly-provided health care and labour market contribution. Finally, this study does not comment on the effectiveness or the economic value of the current stock of health care interventions. Thus, it would be inappropriate to draw policy conclusions about health care interventions and spending based on this research.