Overview of Available Functioning Data in Switzerland: Supporting the Use of Functioning as a Health Indicator Alongside Mortality and Morbidity

Introduction

The world’s population is rapidly ageing, creating unprecedent challenges for society and health systems. By 2050, the number of people aged 60 and above is projected to double to 2.1 billion [1]. Europe is at the forefront of this demographic transition, with more than 30% of its citizens expected to be 65 years of age or older by 2100 [2]. As life expectancy increases, so does the prevalence of non-communicable diseases (NCDs). Switzerland follows the trend, with the Swiss living longer and at the same time experiencing NCDs to a degree not seen in the past. By 2050, 30% of the population will likely be 65 or older [3] and about 50% of those will have at least one chronic illness [4]. The increasing number of people living with chronic conditions is driving a higher demand for healthcare services, especially rehabilitation and long-term care (LTC) [5, 6], putting pressure on resource-constrained systems.

According to the United Nations’ Decade of Healthy Ageing [1], health spending should not just extend life but also improve health and wellbeing. To achieve those goals, policymakers generally assess morbidity and mortality as key health indicators, based on the World Health Organization’s (WHO) International Classification of Diseases [7] and use these statistics for planning and setting priorities in health systems. But mortality and morbidity alone do not capture the lived experience of health [8], i.e., the extent to which health limitations and disability are present in a population, and the impact they have on people’s everyday life.

The concept of functioning operationalizes health from a biopsychosocial approach that fully acknowledges the context of a person or population. The definition of human functioning was introduced by WHO in its International Classification of Functioning, Disability and Health (ICF) [9]. Functioning is a universal experience and measured on a continuum from none to extreme problems. It consists of the components of body functions and structures, activities and participation, that is, the set of all functions and structures of the human body and all of the behaviors, actions, tasks that a person performs [9]. This understanding of functioning goes beyond the activities and instrumental activities of daily living (ADL and IADL), which are commonly used in gerontological research [10] to measure a person’s functional status. ADL and IADL are part of functioning and coded in the ICF within the activities and participation component. Functioning, however, includes more domains than the usual ADL and IADL, and goes beyond them for also including body functions and structures components. It considers further important aspects related to the overall functioning of a person or population, for example, body functions such as energy and drive functions, emotional functions, and sensation of pain which are known to have a high impact on functioning [11]. Moreover, functioning is understood as the result of the interaction between the health condition and the context of a person - environmental and personal factors [9]. The health condition and the context of a person are not part of functioning, but rather determinants of it. They are critical to understanding functioning because people with the same health condition may experience, depending on their context, very different levels of functioning in their daily lives.

Using a functioning indicator requires the construction of a functioning metric based on information about the extent of problems that respondents experience in a range of functioning domains. And it is conditional on the data necessary for constructing a corresponding metric: either a functioning measurement instrument or a selection of item/question set(s) that assess relevant functioning information targeting the intensity of functioning problems by means of ordered response options. Psychometric analysis is then needed to confirm the measurement properties of the functioning metric. Based on a functioning metric, individual’s functioning levels can be expressed in terms of a score (ideally interval-scaled). Further, the functioning of populations can be expressed in terms of a functioning indicator, i.e., a summary statistic of individual functioning scores, such as mean or median [12]. WHO has a long tradition of collecting functioning data to develop metrics of functioning that enable measurement and comparison of the health of populations [13–16]. And since 2014, WHO has introduced a standard valid and reliable survey to collect information on the functioning and disability of populations called “Model Disability Survey” (MDS) [12], whose brief version [17] can be easily added to existing household surveys.

Although information on functioning is already collected in Switzerland through population health surveys, household surveys and cohort studies, the potential to generate a functioning metric and indicator has not been fully exploited yet. We argue in the present study that this is so for two main reasons: first, functioning is not yet fully recognized as the third health indicator that complements mortality and morbidity, and second, there is no overview of either the functioning information collected or the comparability of this data across different sources. This study aims to identify official sources that routinely collect functioning data for the general and the ageing population in Switzerland and to provide an overview of the existing data and its comparability. Additionally, it aims to assess the extent to which data across sources is suitable for developing a functioning metric and indicator that can be used to estimate the need for health services, such as rehabilitation or LTC. This approach makes use of existing data without the need to change current collection tools. The overview is expected to facilitate future efforts to build a functioning metric using Swiss data and to inform the design of future data collection by ensuring that the minimum information needed to construct a functioning metric and indicator is collected in a standardized manner.

Methods

Identification of Data Sources

An iterative search was performed to identify official sources used to routinely collect functioning data of the general and the ageing populations in Switzerland. The search was conducted in October and November 2022 through PubMed, and official websites such as the Swiss Federal Statistical Office (Bundesamt für Statistik, FSO) [18], the Federal Office of Public Health (Bundesamt für Gesundheit, FOPH) [19]. For the literature search on PubMed, key terms such as “functioning” OR “disability” AND “cohort study” OR “longitudinal data” OR “health data” AND “elderly” OR “ageing population” AND “Switzerland” OR “swiss” were used. The criteria for including or excluding a data source are described in Table 1.

Table 1. Inclusion and exclusion criteria for data sources selection and for the extraction of items of interest. (Overview of available functioning data in Switzerland: supporting the use of functioning as a health indicator alongside mortality and morbidity, Switzerland, 2022–2024).

Mapping Process

Prior to the mapping process, the ICF Research Branch, which maintains a collection of linking tables from various research studies available upon request, was contacted regarding existing tables of the identified data sources. If they had already been linked to the ICF, they were checked for new content or updates (questionnaires, modules, items) since the last linking. Generally, if different versions or data collection periods (so-called “waves”) of a data source were available, the most recent version/wave was used for the mapping and content analysis. The sources or updates were then linked by pairs of professionals trained in the ICF linking rules [20]. The linking process included the following steps.

Step 1 Extraction of Items

A list of items (i.e., questions) of interest was developed for each data source. For each item, the item identifier, exact wording and instructions, response options, perspective adopted (descriptive, appraisal, or need or dependency), categorization of response options (intensity, frequency, duration, confirmation or agreement, or qualitative attributes) [20] were independently screened and extracted by two investigators (referred to as “linkers”) according to the specific inclusion and exclusion criteria shown in Table 1. Corresponding inclusion and exclusion criteria were designed to extract potentially useful items to generate a functioning metric and relevant determinants of functioning in the population of the respective survey. The two linkers compared and discussed the items each extracted until they reached consensus on the items to include in the linking process. A third investigator was consulted to resolve continued disagreement on individual items to include in the final item list to link to the ICF.

Step 2 ICF Linking

The ICF linking of the extracted items from step 1 was conducted using ICF linking rules [20]. As with the item extraction, two linkers independently extracted the adopted perspective, main and additional concept(s) contained in the items selected in step 1 and linked them to the most accurate ICF categories. Again, a third ICF-trained investigator was consulted to resolve any discrepancies. Note that an item may be coded with several ICF categories, depending on the number of concepts or response options that belong to different components or domains.

Content Analysis

The final linking tables were compared in terms of the reported ICF categories using the full MDS as a reference. As the MDS was developed based on the ICF and is the current standard recommended by the WHO to measure disability and functioning of populations [17], we considered it suitable to provide a reference list of essential ICF categories for comparison. The full MDS [21] was used for the functioning components (FC), which are the components that are used to develop a potential functioning metric. To compare the environmental factors (EF), the determinants of functioning, the brief MDS [22] was used as a reference list of essential and relevant ICF categories. The linking table of the full MDS was available through the ICF Research Branch. For the brief MDS linking, items were extracted from the full MDS and recoded where necessary (Supplementary Table S1).

Results

Identification of Sources

Figure 1 shows the results of the process of the selection of the data sources. Out of 21 screened data sources, four were included: the Swiss part of the Survey of Health, Ageing, and Retirement in Europe (SHARE) [23], the Swiss Health Survey (Schweizerische Gesundheitsbefragung, SHS) [24], the Lausanne cohort 65+ (Lc65+) [25], and the Swiss Household Panel (SHP) [26]. A complete list of all searched institutions/websites and data sources can be found in the Supplementary Tables S2, S3.

Figure 1. Flowchart of the identification and selection of the data sources. (Overview of available functioning data in Switzerland: supporting the use of functioning as a health indicator alongside mortality and morbidity, Switzerland, 2022–2024) Notes: *The numbers shown correspond to the identified sources that were selected for detailed examination and screening according to our criteria, they do not correspond to the results retrieved from the full search performed; the results of the full search strategy are shown in Supplementary Tables S2, S3 of the Supplementary File 1.

Table 2 shows a comparison of the characteristics of these data sources and gives detailed information about which questionnaires were used in this study. The questionnaires of SHARE and SHP were available in English versions. The questionnaires of SHS and Lc65+ were translated from French to English, as they were not available in English.

Table 2. Overview of the comparison of the characteristics of the included data sources. (Overview of available functioning data in Switzerland: supporting the use of functioning as a health indicator alongside mortality and morbidity, Switzerland, 2022–2024).

Mapping Process

Because of the length of the included sources and considering the lessons learned after screening the first tool, the planned linking process was adjusted. One reviewer (BM) would pre-select modules and items independently before step 1, after which the extraction and linking would be performed. The extraction (step 1) and linking (step 2) of items were performed by MS and JK for SHARE, and by MS and BM for the remaining tools. Discrepancies in both steps were discussed with a third linker (BM for SHARE and JH for the other tools).

Moreover, while extracting the items of interest (i.e., items to include for the linking process), the investigators found that the inclusion criterion that items of FC should describe the expression or extent of a problem was open to interpretation. To ensure data quality, the linking results underwent a final review and consistency check by BM, JH, and CS. The final linking tables can be found in the Supplementary Table S4 of the Supplementary File 2.

Previous data collection waves of the SHS and the SHP had been linked to the ICF and were available through the ICF Research Branch. Before step 1, previously linked items from SHS 2012 and SHP wave 18 were merged with new items from SHS 2017 and SHP wave 22, respectively. The merging included eliminating items present in previous sources but no longer in the most recent versions. For previously linked items, the reviewers checked the concepts, perspective adopted, categorization of response options and the ICF categories and revised them when necessary. Revisions included adding missing information or specification of ICF categories to the most accurate category.

In total, the four sources contained 71 items (out of 863 items identified in total) about FC, of which around 70% were coded as body functions, and 30% as activities and participation; and 61 items about EF. Most items linked across sources have a descriptive perspective and confirmation or agreement as response option. Items had various timeframes, ranging from “in the last 12 months” (Lc65+) to “at the time of the interview” (all sources).

Content Analysis: Results for the Functioning Components

Table 3 shows an overview of the ICF categories covered in the identified sources, using the MDS as a reference. SHARE and Lc65+ are the sources with the most categories in common with the MDS. The SHP is the source with the least categories in common with the MDS, and the other sources; the SHP only covers five body functions and two activities and participation categories, and these items, d850 Remunerative employment and d920 Recreation and leisure, are not present in the other sources.

Table 3. Comparison of International Classification of Functioning, Disability and Health categories covered in the identified data sources that reflect the expression or extent of a functioning problem, using the World Health Organization’s Model Disability Survey as a reference. Categories that belong to the brief Model Disability Survey are in bold. Categories covered in all sources are highlighted in dark grey. Categories covered by 3 sources are highlighted in light grey. (Overview of available functioning data in Switzerland: supporting the use of functioning as a health indicator alongside mortality and morbidity, Switzerland, 2022–2024).

Four ICF categories are covered across all sources (Table 3, highlighted in dark grey) and they all belong to body functions: b134 Sleep functions, b1300 Energy level, b152 Emotional functions, and b280 Sensation of pain. These categories belong to both the full MDS and the brief MDS (Table 3, bold). Regarding activities and participation, no ICF categories are covered across all sources. However, b140 Attention functions, d3 Communication, d450 Walking and d4500 Walking short distances, d5 Self-care, d530 Toileting, d550 Eating, d640 Doing housework, and d860 Basic economic transactions are included in all the data sources, except for the SHP (Table 3, highlighted in light grey) while b455 Exercise tolerance functions is included in SHS, Lc65+, and SHP. Of those, walking, self-care, toileting, doing housework belong also to the brief MDS. Categories of the full and brief MDS that none of the sources cover include, for example, handling stress and other psychological demands.

Table 4 compares the wording of the items and their response options for the categories common to the MDS in all sources. Overall, Lc65+ has more items covering the common domains than the other sources, followed by SHS. Most items use frequency as response option, while Lc65+ and SHS focus more on response options that assess intensity.

Table 4. Comparison of the wording of the items and their response options for the International Classification of Functioning, Disability and Health categories of the functioning components common to the Survey of Health, Ageing and Retirement in Europe; Swiss Health Survey; Lausanne cohort 65+, and Swiss Household Panel, using the Model Disability Survey as a reference. (Overview of available functioning data in Switzerland: supporting the use of functioning as a health indicator alongside mortality and morbidity, Switzerland, 2022–2024).

Table 4 shows that the wording of the items is different across sources and that the types of response options are mixed. Commonly assessed topics include insomnia and difficulty falling asleep for the category b134 Sleep functions; lack or plenty of energy for the category b1300 Energy level; feelings about sadness, depression or anxiety for the category b152 Emotional functions; and back pain for the category b280 Sensation of pain. Except for SHP which used a continuous scale (0“Never” – 10 “Always”), all other sources used a 4-point response scale (with variations of “Always, Often, Sometimes, Rarely, Never”). The response options for assessing intensity of problems vary greatly, also within the same tool (in Lc65+: “Not at all; A little bit; Medium; Many; Very much” and “No, none; Yes, but light; Yes, a lot”), depending on the formulation of the question. The category with the highest number of items with intensity of problems as response option is b280 Sensation of pain, while for b152 Emotional functions there is no item with intensity as an option. b152 Emotional functions and b1300 Energy level are assessed more often with response options that reflect frequency.

A similar comparison for the common categories in three tools can be found in the Supplementary Table S5.

Content Analysis: Results for the Determinants of Functioning

All the sources include questions on the EF. Most items reflect a descriptive perspective and confirmative response option (Supplementary Table S6). Exceptions are items coded as e3 Support and relationships, which often reflect a need or dependency perspective. Table 5 provides an overview of the commonly covered ICF categories in each tool, with the brief MDS as a reference. SHARE and SHS are the tools with the most categories in common with the brief MDS. The Lc65+ is the tool with fewest categories in common with the brief MDS, and with the other tools.

Table 5. Comparison of International Classification of Functioning, Disability and Health categories of environmental factors covered in the Brief Model Disability Survey and in the data sources. Categories covered by 3 sources are highlighted in light grey. (Overview of available functioning data in Switzerland: supporting the use of functioning as a health indicator alongside mortality and morbidity, Switzerland, 2022–2024).

Most tools (SHARE, SHS, and SHP) overlap in items belonging to the chapter e3 Support and relationships. The chapter e4 Attitudes is only covered by the SHS, while Lc65+ focuses only on products and technology, but not on support and relationships.

Discussion

This study identified and examined the content and comparability of existing official sources that collect functioning data in Switzerland, using the current WHO functioning and disability survey as a reference framework. In total, we identified four sources that routinely collect data on functioning: SHARE, SHS, Lc65+, and SHP. They all share several conceptually equivalent items about specific functioning categories, namely four body functions – sleep functions, energy and drive, emotional functions, and sensation of pain – while three of them (SHARE, SHS, and Lc65+) have nine categories in common: attention functions, communication, walking, walking short distances, self-care, toileting, eating, doing housework and economic transactions. Across sources, the response options for assessing frequency of problems are similar while response options assessing intensity of problems vary greatly, also within the same tool. Regarding the determinants of functioning, all the sources collect information about EF, namely about support and relationships.

To our knowledge, this is the first study to compare functioning data across official population-based data sources in Switzerland. Different studies about content mappings and analyses using the ICF as a framework exist in different contexts. However, comparisons between them are generally difficult due to the specific goals shaping each analysis’s execution and interpretation. Instead of focusing on the mapping and comparison of specific instruments and/or conditions or sub-populations in clinical settings [34–36], our study focused on population data sources. We applied the ICF linking rules to compare population-based data from different sources similarly to previous work [37], but our comparison focused on the availability of functioning information suitable for the construction of a functioning metric and indicator.

Our findings show that Switzerland has readily available routinely collected functioning data that can serve as the basis for creating metrics of functioning. To do that, it is important to have a suitable collection of items that reflect relevant categories of functioning and ask about the extent or presence of problems, as functioning is understood as a matter of degree ranging from no to extreme problems. Different approaches to developing such metrics are possible. One approach involves using items from one setting (data source) and creating a corresponding metric – a particular functioning metric. This metric is independent of the development data and can be applied in similar settings. Another approach involves selecting items that are common across tools and constructing a common metric – a common functioning metric. Our study demonstrates that functioning data is available in Switzerland for creating particular functioning metrics and indicators but that a higher level of overlap across sources in terms of ICF categories and response options focusing on intensity of problems, and not on frequency, would be needed to create a common functioning metric.

Evidence on the feasibility of the construction of functioning metrics and corresponding overall scores is broad. In the general population, a common functioning metric has been developed and used to compare the health of the English and the Americans by challenging the standard practice of counting the prevalence of chronic diseases to determine how healthy a population is [38]. Importantly for policymakers, the English have better health than Americans if prevalence of disease is used but the advantage disappears almost completely when a functioning metric is used [38]. Functioning has also been used to study global patterns of ageing [39] and to build and model healthy ageing trajectories and gather a better understanding of their determinants [40]. In these studies, psychometric analyses according to Item Response Theory and Rasch Measurement Models were performed to estimate the functioning metrics and scores. Moreover, it has been demonstrated that a valid and reliable functioning metric and overall scores can be built even using a minimal selection of functioning domains [11, 41]. The so-called minimal ICF set requires information about the level of problems in energy and drive functions, emotional functions, pain, carrying out daily routine, walking and moving around [11]; variables that are available in three of the four sources examined in our study. Some of these categories pertain to ADL, which are traditionally used to measure functional status in older persons, but importantly, also to body functions such as pain, fatigue, and emotional functions.

Although common ICF categories have been identified across sources, there are several other considerations for a successful development of a common functioning metric. From a psychometric analysis perspective, successful equating across sources depends, for example, on the proportion of common items, the location of the items on the continuum, the quality of the data (missing values, homogeneity), the fit of the items, and the targeting of the scales [42]. From a data characteristics perspective, as functioning is understood as a matter of degree (ranging from low to high levels) data about the presence or intensity of problems is essential. For example, the frequency with which an activity is performed does not necessarily indicate that a person does or does not have a problem in functioning. Therefore, building functioning metrics is conditional on having items that can identify people with mild to severe problems. The variability in the wording of items and in response options for the selected items in our study, as well as items targeting frequency rather than intensity of problems, both within and across tools, is challenging when building a common metric.

If the use of a functioning indicator for Switzerland is planned, two minor changes to the structure of the current data collection (similar to how socio-demographic variables are standardized [43]) might be considered to achieve a comparable minimum set of functioning items across surveys. First, converting frequency response options into intensity and adhering to consistent descriptors for intensity levels, such as “Not at all,” “A little bit,” “Medium,” “Many,” and “Very much,” is an essential step for measuring functioning on an intensity-of-problems continuum. Secondly, it is necessary to transform the response options from confirmation (the problem is present or not) to intensity, ideally using more than two options, to allow for the expression of mild and moderate functioning problems [44]. These minor changes would facilitate meaningful cross-tools comparisons and the development of functioning metrics. Lastly, given that Switzerland has plans to establish a large population-based cohort [45], it would be important to ensure that at least the domains of the minimal ICF generic set [11] are covered to be able to generate a common functioning metric that makes it possible to compare general and specific populations.

Limitations

This study has several limitations. First, although we performed an iterative search, we may have missed Swiss data sources that fit our criteria. Moreover, potentially important sources might have been excluded due to the lack of access to the data at the time of the search. Secondly, the identified sources are developed and conducted by different actors in Switzerland, and have distinct purposes, reflected in their scope, topics and target population, which limits direct comparisons. Thirdly, we only included and mapped the most recent waves of the tools’ questionnaires, so we may have missed common items across tools from previous waves. Finally, although previous linking tables from the ICF Research Branch have been checked by the linkers, relying on them may have introduced inconsistencies in the mapping process.

Conclusion

In conclusion, using the ICF linking rules and the MDS as reference framework, this study provides an overview of the currently available population-based functioning data in Switzerland across different data sources. Our results can inform the development of functioning metrics as well as of a potential common Swiss functioning metric and indicator based on official data collected directly and routinely from individuals of the general and ageing populations. This benefits researchers and policymakers as a reliable and informative indicator would become available to complement mortality and morbidity data, to support both the estimation of rehabilitation and LTC needs, and to inform the development and monitoring of policies and programs without the need to implement new data collection tools.

Author’s Note

The paper is part of the cumulative PhD thesis of BM.

Author Contributions

BM contributed to the study design, conducted the analyses, interpreted the results and drafted the manuscript. MS and JK conducted analyses and reviewed the manuscript. CF and JB reviewed the manuscript. JH and CS contributed to the study design, conducted and supervised the analysis, interpreted the results and reviewed the manuscript. All authors contributed to the article and approved the submitted version.

Funding

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Conflict of Interest

The authors declare that they do not have any conflicts of interest.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.ssph-journal.org/articles/10.3389/ijph.2024.1607366/full#supplementary-material

References