Addressing the challenge of health measurement

Assessing the health of populations is important for various
reasons, especially for health policy purposes. Therefore, there
exists a substantial need for health comparisons between
populations, including the comparison of individuals, groups of
persons, or even populations from different countries, at one point
in time and over time. Two fundamentally different approaches exist
to assess the health of populations. The first approach relies on
indirect measures of health, which are based on mortality and
morbidity statistics, and which are therefore only available at the
population level. The second approach relies on direct measures of
health, which are collected – based on health surveys – at the
individual level. Based on the needs for comparisons, indirect
measures appear to be less appropriate, as they are only available
at the population level, but not at the individual or group level.
Direct measures, however, are originally obtained at the individual
level, and can then be aggregated to any group level, even to the
population level. Therefore, direct measures seem to be more
appropriate for these comparison purposes. The open question is
then how to compare overall health based on data collected within
health surveys. At first glance, a single general health question
seems to be appealing. However, studies have shown that this kind
of question is not appropriate to compare health over time, nor
across populations. Qualitative studies found that respondents even
consider very different aspects of health when responding to such a
question. A more appropriate approach seems to be the use of data
on several domains of health, as for example mobility, self-care
and pain. Anyway, measuring health based on a set of domains is an
extremely frequent approach. It provides more comprehensive
information and can therefore be used for a wider range of possible
applications. However, three open questions must be addressed when
measuring health based on a set of domains. First, a parsimonious
set of domains must be selected. Second, health measurement based
on this set of domains must be operationalized in a standardized
way. Third, this information must be aggregated into a summary
measure of health, thereby taking into account that categorical
responses to survey questions could be differently interpreted by
respondents, and are not necessarily directly comparable. These
open questions are addressed in this doctoral thesis. The overall
objective of this doctoral thesis is to develop a valid, reliable
and sensitive metric of health – based on data collected on a set
of domains – that permits to monitor the health of populations over
time, and which provides the basis for the comparisons of health
across different populations. To achieve this aim two psychometric
studies were carried out, entitled “Towards a Minimal Generic Set
of Domains” and “Development of a metric of health”. In the first
study a minimal generic set of domains suitable for measuring
health both in the general population and in clinical populations
was identified, and contrasted to the domains of the World Health
Survey (WHS). The eight domains of the WHS – mobility, self-care,
pain and discomfort, cognition, interpersonal activities, vision,
sleep and energy, and affect – were used as a reference, as this
set – developed by the World Health Organization (WHO) – so far
constitutes the most advanced proposal of what to measure for
international health comparisons. To propose the domains for the
minimal generic set, two different regression methodologies –
Random Forest and Group Lasso – were applied for the sake of
robustness to three different data sources, two national general
population surveys and one large international clinical study: the
German National Health Interview and Examination Survey 1998, the
United States National Health and Nutrition Examination Survey
2007/2008, and the ICF Core Set studies. A domain was selected when
it was sufficiently explanatory for self-perceived health. Based on
the analyses the following set of domains, systematically named
based on their respective categories within the International
Classification of Functioning, Disability and Health (ICF), was
proposed as a minimal generic set: b130 Energy and drive functions
b152 Emotional functions b280 Sensation of pain d230 Carrying out
daily routine d450 Walking d455 Moving around d850 Remunerative
employment Based on this set, four of the eight domains of the WHS
were confirmed both in the general and in clinical populations:
mobility, pain and discomfort, sleep and energy, and affect. The
other WHS domains not represented in the proposed minimal generic
set are vision, which was only confirmed with data of the general
population, self-care and interpersonal activities, which were only
confirmed with data of the clinical population and cognition, which
could not be confirmed at all. The ICF categories of `carrying out
daily routine´ and `remunerative employment´ also fulfilled the
inclusion criteria, though not directly related to any of the eight
WHS domains. This minimal generic set can be used as the starting
point to address one of the most important challenges in health
measurement, namely the comparability of data across studies and
countries. It also represents the first step for developing a
common metric of health to link information from the general
population to information about sub-populations, such as clinical
and institutional populations, e.g. persons living in nursing
homes. In the second study a sound psychometric measure was
developed based on information collected on the domains of the
minimal generic set: energy and drive functions, emotional
functions, sensation of pain, carrying out daily routine, mobility
and remunerative employment. It was demonstrated that this metric
can be used to assess the health of populations and also to monitor
health over time. To develop this metric of health, data from two
successive waves of the English Longitudinal Study of Ageing (ELSA)
was used. A specific Item Response Theory (IRT) model, the Partial
Credit Model (PCM), was applied on 12 items representing the 6
domains from the minimal generic set. All three IRT model
assumptions – unidimensionality, local independency and
monotonicity – were examined and found to be fulfilled. The
developed metric showed sound psychometric properties: high
internal consistency reliability, high construct validity and high
sensitivity to change. Therefore, it can be considered an
appropriate measure of population health. Furthermore, it was
demonstrated how the health of populations can be compared based on
this metric, for subgroups of populations, and over time. Finally,
it was outlined how this metric can be used as the basis for
comparing health across different populations, as for example from
two different countries. The developed health metric can be seen as
the starting point for a wide range of health comparisons, between
individuals, groups of persons and populations as a whole, and both
at one point in time and over time. It opens up a wide range of
possible applications for both health care providers and health
policy, and both in clinical settings and in the general
population.