Fundamento normativo en la etapa pre contractual

7.2.1 Effect of multimorbidity definition on HRQoL

The study reported in Chapter 3 is the first, to our knowledge, to compare number-

based and cluster-based definitions of multimorbidity using nationally representative data. This was achieved by determining the associations of multimorbidity with HRQoL, an important population health indicator. Based on a head-to-head comparison of a count and an alternative statistical approach to defining

multimorbidity, this result is consistent with previous studies and validated the use of a hierarchical clustering approach when the outcome of interest is HRQoL. Moreover, this work established that a simple count fails to identify whether specific conditions of interest drove the occurrence of poorer HRQoL. Researchers should exercise caution when selecting a definition of multimorbidity as it may significantly influence the observed association with study outcomes. These findings advanced the literature by assessing the underlying driver of health status (multimorbidity) at the

methodological level and confirmed that multimorbidity is a problem in the

Australian general population. Prior to this epidemiological analysis with a head-to- head comparison of health outcomes, the impact of multimorbidity in Australia was not well documented, particularly at the population level.

The count-based method does not account for the type of chronic conditions present, and thus this method can determine the overall influence of multimorbidity on

HRQoL but not the specific disease contributes to the associated HRQoL. The cluster- based method, hierarchical clustering in particular, could capture the common clusters; a finding supported by sensitivity analysis, including factor analysis, principal

component analysis and K-means clustering. Combining these findings validated the hierarchical clustering approach and proved it more useful and informative when HRQoL is the outcome of interest. However, future research is warranted to clearly describe the adopted definition of multimorbidity.

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7.2.2 Associations between multimorbidity and HSU for arthritis and CVD

After discussing multimorbidity in the general population, this thesis proceeded to address it in the Australian working population, which is a relatively healthier population and is central to the economic well-being of the country. To our

knowledge, the study described in Chapter 4 is the first to examine the associations

between multimorbidity and disease-specific healthcare service utilization in the workforce. Multimorbidity is known to increase the overall healthcare service

utilization in the primary healthcare setting and in the general population, particularly in the elderly. However, in contrast to the existing studies, this study revealed

multimorbidity also increased healthcare use in the working population, but single disease-specific healthcare use was not always positively associated with the existence of multimorbidity. Arthritis and CVD were the health conditions in the Australian workforce that showed higher healthcare utilization when co morbid with other chronic health conditions. This finding may inform future longitudinal research into when a higher burden of multimorbidity on HSU emerges for different

combinations of disorders. These findings can also inform workforce health

promotion interventions, and future research could focus on multimorbid employees living with arthritis or cardiovascular disease. Moreover, reforming health systems or policies to properly address these two health conditions may be beneficial, at least when focusing on the workforce.

7.2.3 Associations between multimorbidity and absenteeism, presenteeism and total LPT

In Chapter 5, this thesis explored the work attendance and productivity consequences

of multimorbidity in the workforce. The definition of LPT has differed between the productivity measures used in the literature including absenteeism, presenteeism and total LPT, which is considered the sum of absenteeism and presenteeism. This study obtained all three estimates from employees’ self-reported data over a 28-day period, consistent with common measurements of LPT in the health field. The results showed a strong, positive association between the presence of multimorbidity and LPT. Additionally, having more chronic conditions was associated with greater LPT. These findings were consistent with prior evidence and suggest the management of single

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health conditions in order to reduce health-related LPT may not, in fact, be tackling one of the strongest correlates of LPT- multimorbidity. Moreover, significant differences in LPT between men and women reporting multimorbidity were also identified. The female employees in in this public sector sample were more likely to report higher LPT when facing chronic conditions compared to their female

counterparts without chronic condition, while for male employees an association with LPT was not observed until four or more chronic conditions were reported.

7.2.4 A systematic review of COI studies on multimorbidity

The systematic review outlined in Chapter 6 was the first known attempt to compile

information on the economic burden of multimorbidity. It is difficult to compare results across studies when the “disease” of interest is multimorbidity because the included studies differ in their definition and measurement of multimorbidity, the health conditions included, and the samples and economic estimates used. Therefore, the discussion was limited to describing the results but not pooling them.

The main contribution of this review is the accumulation and summary of the available evidence on the costs of multimorbidity based on COI studies with

standardized and acceptable methodologies and how the different methodologies were used. Further examination of the definition resulted in two opposite outcomes:

exploring “multimorbidity” was not only with the count- methods even it was the most popular approach in COI studies, on the one hand, and reducing the comparisons between studies exactly due to different definitions of multimorbidity, on the other. This simple but important finding revealed multimorbidity cannot be managed without a clearer framework or better understanding of its definition.

This study also found the methodology used to derive costs differed markedly between studies. The average annual costs per patient with multimorbidity ranged from $49-$252,313. Using a cut-off of two or more conditions, the proportional increase in cost for multimorbid compared to non- multimorbid ranged from 100% to 1500% in the 17 available studies. The highest costs ($252,313) were found in a study

of children in the US 3. Using a cut-off of three or more conditions, the average costs

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studies providing a breakdown of costs, the largest proportion of costs was spent on inpatient care or prescriptions in studies with a non-societal perspective, whereas the

largest proportion was on social care costs in studies from a societal perspective 4.

These findings revealed that COI studies on multimorbidity are highly heterogeneous and that multimorbidity has been associated with a considerable economic burden. Referring to other COI reviews, the included studies were identified with good quality if the score was 7 and over (out of 10). Thirteen studies met this requirement and of them, only one study was with the best quality and scored 9. There were three studies conducted of children, with such studies rare due to the very low prevalence of multimorbidity amongst this age group. Those “extremely young” samples in multimorbidity were threatened by serious chronic conditions and some even died during the study, however, the results also showed the high costs for them in the following years, which was consistent with the findings from the other included studies.