10  Chronic diseases

As well as the measures for physical and mental health, the HWR survey records whether a respondent has ever been told by a health professional that have any one of 19 separate so-called “chronic” conditions. The purpose of this section is to explore the extent to which the presence of any one or more of the 19 chronic diseases are associated with a lower probability of an individual being in the labour force. A secondary objective is to assess the overall impact on the labour supply of older workers from particular chronic diseases. This will depend not only on the lower probability that an individual sufferer will be in the workforce but, in addition, the prevalence of the condition in the population.

10.1  Extent of chronic disease

A summary of the chronic diseases, their prevalence rates amongst this sub-population of 55 to 70 year-olds and the distribution of the self-rated health status for each illness is given in Table 10-2. Of those reporting no disease, nearly three-quarters of respondents rate their health as excellent or very good. However, in the presence of any one or more of the 19 conditions, this proportion drops sharply to under 40%. Stroke together with liver and kidney disorders are the conditions that lead to the lowest health rankings based on those reporting fair and poor health. In addition, 16% of respondents suffering from chronic liver conditions rate their health as poor, the highest proportion of any condition.

Although correlation is not considered, Appendix Table C.23 has the correlation matrix for the chronic diseases. The largest is 30% between asthma and respiratory conditions, the second largest is 21% between high blood pressure and heart conditions and the third largest is 18% between high blood pressure and diabetes.

Heavy drinking and smoking are associated with a much lower proportion of respondents reporting excellent or very good health, compared with those who have no chronic disease.

The effect of a chronic disease on the distribution of self-rated health status is only one measure of its importance. Prevalence as well as severity matters. So while stroke and kidney and liver conditions are severe in their impact on health status, their prevalence is low relative to skin cancer, heart conditions, asthma, high blood pressure, arthritis and hearing loss. Figure 18 summarises data on prevalence by ethnicity. It is notable in all but four of the 19 conditions that Māori respondents report higher rates of prevalence. The two largest differences are for blood pressure and diabetes.

Figure 19 summaries data on prevalence by work status. The retired group reports a greater prevalence of all diseases relative to the working group (with the exception of hepatitis, where the sample size is very small). Again, arthritis, high blood pressure and heart conditions are the most prevalent conditions and the ones where the difference between the retired and working groups is most marked.

Figure 18: Disease prevalence by ethnicity

Source:  Health, Work and Retirement Survey

Figure 19: Disease prevalence by work status

Source:  Health, Work and Retirement Survey

Of those reporting no chronic conditions, 80% are working. However, in the presence of any one or more of the diseases listed (Table 10-3) the proportion working falls to 65%. The three most significant conditions seemingly associated with reduced labour force participation are liver conditions, arthritis and heart trouble. However, a truer measure of the importance of diseases on labour force participation requires that the fall in participation be weighted by the prevalence; for example, skin cancer is (0.67-0.80)*10%= -13. When this is done, arthritis, high blood pressure and heart trouble are the three largest prevalence-weighted conditions reducing aggregate labour force participation amongst those aged 55 to 70. Note, however, the estimates of prevalence made by this method have not controlled for the influence of other factors. We return to the question of prevalence in Section 10.3.

To this point the results have been based on simple bivariate comparisons. However, it is important to hold other factors constant and this was done in a logit model for the presence of any disease. Formally we fitted the following:

Pr(respondent has any chronic disease) = α + β^oH^o + β^a (age) + Σβ_kZ_k + ε (18)

where H^o is the overall health score, and Z_k a series of control variables.

The results are presented in Figure 20. As overall health status improves the likelihood of reporting the presence of any chronic condition falls markedly. However, at any given overall health score, the likelihood increases with age.