Despite the strength of the studies conducted in this thesis and the aforementioned implications regarding HRQoL, health care service use, productivity loss and the related financial burdens, particularly in the working population, the limitations must also be considered. For example, the analyses conducted in this thesis were restricted to self-reported information. Further, the data source used did not link to medical records. Therefore, the results may be under- or over-estimates due to recall bias. For example, conditions such as mental disorders, are likely under-reported due to stigma, and behavioural disorders or diabetes often present as “silent” conditions, which could
not be recognised by patients 12. That said, the samples used in this thesis were large.
For example, the NSMHWB2007 included approximately 8,800 Australians, the NHS
2011-12 included 20,250Australians, and even the state- level data pH@W 2013
included more than 3,000 Australian employees. The validity of self- reported chronic
conditions has been indicated in different contexts 13-17. Moreover, self- reported data
are cost-effective and convenient for gathering information in population-based
surveys 18. These studies also progressed beyond the typical samples used in
multimorbidity studies, which are often restricted to older or clinical populations.
This thesis did not account for the severity of health conditions, which could prove
useful in clinical settings 19, 20. However, national prevalence surveys of community-
dwelling populations tend to use a simple count method, rather than comorbidity
measures, which weight conditions by their severity 21. There are two reasons for this:
i) the weights of functional disease burden change by disease coding systems, and thus the scoring algorithms used to generate weights need regularly updating; and ii) the considerable costs of non-count methods are generally not feasible when
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further limitation is that the studies in Chapters 3, 4 and 5 excluded acute conditions
6
. Including more health conditions is more likely to provide a more comprehensive understanding of an individual’s health status, however, acute conditions were not considered in this thesis, because of the high probability of their temporarily impact
on health status only 22 and therefore irrelevant to long-term health care planning.
In addition to the shared limitations of the studies, each study has its own specific limitations. In Chapter 3, the data used were derived from a survey focused on mental health and well-being. So the assessments of chronic physical conditions were
relatively brief, although consistent with many population prevalence surveys 23. In
Chapter 4, the GP visits may be underestimated because some chronic health conditions are not serious and could be self- managed on a daily basis by patients themselves without any health care visits, especially in just a short period of one year. In Chapter 5, the survey used did not limit the measurement of productivity loss to the pre-specified health conditions. Therefore, employees may have reported on LPT due to other health problems. Hence, the impact of multimorbidity on productivity loss may have been underestimated. Further, recall bias may have been introduced as the rate of productivity loss was captured through the employees’ self-reported responses. However, the employees are in a better position than researchers to recognize,
evaluate and rate their overall work performance based on self-reported evaluation. Moreover, previous research has shown that employees’ self-reported days lost are
consistent with employers’ reported days lost 24
.
Another limitation worthy of note is that, this study obtained cross-sectional data in 2013. Therefore, the direction of causality cannot be explored, and the results may only reflect short-term (four-week) employee behaviour, and the associations of multimorbidity with that behaviour. This methodology reduced the potential for recall
bias of the self-reported questionnaire 25, as employees’ absenteeism or presenteeism
behaviour may change over time. For example, based on our study, men may not currently be willing to take days off, but they may be more willing to do so at a later time, and after several years, they may ask for even more days off. However, this claim cannot be proven with cross-sectional data and requires further investigation using longitudinal data. For this reason, we presented the results as they were, in
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contrast to one study, which annualized the same duration measures to reflect an
entire work year 26.
It is critical to note the increasing length of recall time may reduce the accuracy of estimation of the impact of health problems on their productivity by respondents
themselves 25. However, if the focus of survey is on the frequency of productivity loss
due to illness, the potential bias may be reduced 27. Finally, unlike the pathway to
estimating the lost work time which did not distinguish the different types of lost time, the used pathway in Chapter 5 did not account for workers coming in early or leaving late on other days. Additionally, we surveyed a sample of TSS employees un-
representative of the total workforce. Moreover, pH@W used a self- report, short and
simple measure to identify health conditions, which wascommonly used in large
population health surveys such as NHS 28 and NSMHWB 23.
In Chapter 6, the results of this review were limited by the nature of the studies identified. The main limitation of this review was its inability to include all relevant studies. Costs were estimated in 16 countries or regions from 1996 to 2013. The large number of abstracts derived from the databases improved the sensitivity of our search strategy. The absence of a MeSH term for multimorbidity was a clear limitation. There was no published checklist for the quality of COI studies. Therefore, we adopted the modified British Medical Journal Checklist for authors and peer
reviewers of economic submissions 29, which could help the BMJ editors improve the
efficiency of the editorial process 30. The limitation of its scoring method was lack of
weighting. Therefore, there was possible the scores were more likely affected by some items than others. Further works must be performed in these areas. However, adding multimorbidity-related terms from previous studies to our search strategy helped mitigate this limitation. We included papers published in English only, which restricted our sample.
Moreover, the utility of COI studies in aiding policy decision making has been
debated, 31, 32 and its inability to prioritize resources has been criticized 33, 34. COI
studies serve a different purpose than health economic evaluations (e.g., cost-benefit, cost-effectiveness, and cost-utility analyses), which aim to describe the economic burden of a health condition on society which could potentially be avoided if the
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condition is eradicated 35-37. COI studies can provide useful information as long as
they adhere to standardized and acceptable methodologies 38, 39. Furthermore, the
results of COI studies have been used by organizations such as the World Bank and the World Health Organization to estimate public, private and total national health
expenditure on a global scale 40. Different stakeholders can utilize COI studies for
different purposes 41. For example, for resource allocation purposes, governments
may obtain the financial impact of a health condition on public budgets; whereas pharmaceutical companies are more interested in health condition with high
management and direct research costs 41. However, caution is warranted when using
COI studies. COI studies should be adopted in combination with other thorough
economic evaluations in order to get optimal resource allocation 42.