Addressing the challenge of health measurement

• Main Author: Oberhauser, Cornelia
• Format: Others
• Published: Ludwig-Maximilians-Universität München 2014
• Subjects: Medizinische Fakultät
• Online Access: http://edoc.ub.uni-muenchen.de/16802/1/Oberhauser_Cornelia.pdf; http://nbn-resolving.de/urn:nbn:de:bvb:19-168023

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.