Results of the analysis show that there is an increase in mean VAS for men aged 15-44 years, and a decrease in mean VAS for women aged 65-74 years and 75 years and more. The increase in mean VAS for men aged 15-44 years could be explained by a decrease in the severity effect, which offsets the increase in the prevalence effect. In particular, osteoarthritis increases prevalence and decreases severity; and nervous problems-depression increase its prevalence and severity. The decrease in mean VAS for women aged 65-74 years and 75 years and more could be explained by an increase in the prevalence effect, which does not offset the decrease in the severity effect. The increase in prevalence is especially notorious for osteoarthritis, nervous problems-depression and non common conditions.
For the three age and sex groups with statistically significant changes in mean VAS, there has been an increase in the prevalence effect and a decrease in the severity effect. Following the paradox of health, despite the increase in the prevalence of chronic conditions, from 1994 to 2006 there has been an improvement in health status for men aged 15-44 years, both measured as self-perceived health (VAS) and as life expectancy. The improvement in their self-perceived health is due to a decrease in the severity effect that offsets the increase in the prevalence effect of chronic conditions. For women aged 65-74 years and 75 years and more, although there is an improvement of their life expectancy, the prevalence of chronic conditions increases, and their self-perceived health decrease due to an increase in the prevalence effect, which offsets the decrease in the severity effect. Differences among younger men and older women may result from the positive and statistically significant effect of labour status on the prevalence and the severity effect for men aged 15-44 years. These results are consistent with previous research on male and female differences on self-assessed health and chronic conditions .
As previously mentioned, several factors could help us to explain the increase in the prevalence effect and the decrease in the severity effect of chronic conditions in self-reported health: the decrease in mortality due to acute diseases; the increase in the awareness of bodily symptoms; the varying idealized states of health and the willingness of an individual to acknowledge sickness; the changes over time in diagnosing illness (people are screened more often, and thresholds are lower); the commercialization of health; the increase in the expectation of being cured; the phenomenon of adaptation to illness and even the conscious misreporting of morbidity to achieve other goals (labour participation and government benefits) [6, 19–21].
In particular, it is worth mentioning the considerable increase in the prevalence of some chronic conditions over time, like nervous problems-depression, that could be related to the increase in the availability of drugs for their treatment, and cataracts, which could be explained by the increase in their diagnosis and treatment by means of surgery. On the contrary, osteoarthritis has decreased, probably due to the fact that the questionnaire in 2006 included other related conditions, such as backache and osteoporosis. Bronchitis has also decreased among men, consistent with development of the smoking epidemic in the last century. Also, as this is not panel data, the increase in the diagnosis of chronic conditions could be at the expense of adding less severe individuals to the sample in the most recent cohorts.
The analysis performed is an application of the Blinder - Oaxaca decomposition to health data. The Blinder - Oaxaca decomposition is widely used to identify and quantify the separate contributions of group differences in measurable characteristics, such as education, experience, marital status, etc., to racial and gender gaps in outcomes. Although this methodology has been extensively applied in labour economics to analyzed wage differentials [13, 14, 22], its application to health is much less frequent [23–26]. Regarding the use of a linear approximation, the qualitative similarity of the obtained results reassures us that the influence of the linearity assumption on the results of the paper is not that significant.
The work is not absent of limitations. The main one would be the use of self-declared information, both regarding health status and chronic conditions, although a good correlation of subjective health indicators and final outcome indicators, and even healthcare expenditure, is known [27, 28]. Considerable attention has been devoted to the reliability of self-assessed health status and the scope for contamination by measurement error; and there is evidence of reporting bias [29–31]. Different groups (according to age, gender, education, income, language, or personal experience of illness) appear to interpret the questions within their own specific context and therefore use different reference points when responding to the same question, which may invalidate comparisons and measures of health inequality. Whilst the evidence shows mixed results depending on the variable analyzed, the methodology used in this study makes it possible to control for age and sex bias in self-reported health, and the year variable reflects the cohort effect. Additionally, one of the advantages of using VAS as the dependent variable is that we do not carry the effect of changes in cut-points that have been previously described in the literature [30, 31], or at least they are minimized since we are using a 0 to 100 points scale.
There is some evidence about misreporting of chronic conditions by respondents' level of education and labour status [20, 32]. The effect is minimized here, since the model used controls for education level and labour status. Finally, it has been argued that health interviews have an inherent selection bias, because of the death of some individuals before the interview. The probability of declaring a good or bad health status is conditioned to being alive, so there could be a selective truncation of the distribution function .
Also related with the use of health interviews is the comparability of the surveys. We used 1994 survey because it is the oldest one carried out in Catalonia that includes the EQ-5D instrument, and 2006 as it is the most recent. However, the 2006 questionnaire includes many more chronic conditions that the one in 1994. For that reason, we decided to include common conditions in the model, and to create a variable "non common conditions and others", to collapse the rest of the information. Regarding age groups, the ones used are common for the analysis of mortality data, separating younger and older adults, as well as younger and very old; those groups have distinctive epidemiological characteristics, and also different mean VAS, as shown.