Worldwide cohort studies to support healthy ageing research: data availabilities and gaps

Background: Population ageing is a transforming demographic force. To support evidence-based efforts for promoting healthy ageing, a summary of data availabilities and gaps to study ageing is needed. Method: Through a multifaceted search strategy, we identified relevant cohort studies worldwide to studying ageing and provided a summary of available pertinent measurements. Following the World Health Organization ’ s definition of healthy ageing, we extracted information on intrinsic capacity domains and sociodemo-graphic, social, and environmental factors. Results: We identified 287 cohort studies. South America, the Middle East, and Africa had a limited number of cohort studies to study ageing compared to Europe, Oceania, Asia, and North America. Data availabilities of different measures varied substantially by location and study aim. Using the information collected, we developed a web-based Healthy Ageing Toolkit to facilitate healthy ageing research. Conclusions: The comprehensive summary of data availability enables timely evidence to contribute to the United Nations Decades of Healthy Ageing goals of promoting healthy ageing for all. Highlighted gaps guide strategies for increased data collection in regions with limited cohort studies. Comprehensive data, encompassing intrinsic capacity and various sociodemographic, social, and environmental factors, is crucial for advancing our understanding of healthy ageing and its underlying pathways.


Introduction
Those aged 65 and over make up a growing proportion of populations worldwide (He et al., 2016).Global population ageing is a critical public health consideration with significant social and economic implications (World Health Organization, 2017).Increased longevity is not necessarily synonymous with ageing in good health (Beard et al., 2016(Beard et al., , 2017)).Various factors influence the maintenance of health of older adults, corresponding to a lifelong process of maintaining physical, mental, and social well-being (Beard and Bloom, 2015).Thus, a meaningful public health response to population ageing must tackle the broader determinants of health, including social and environmental factors (Beard and Bloom, 2015).These broader determinants are reflected in the multidimensional concept of "healthy ageing" proposed by the World Health Organisation (WHO) in 2015 as "the process of developing and maintaining the functional ability that enables wellbeing in older age" (Beard et al., 2016).Functional ability is determined by an individual's intrinsic capacity (their physical and mental capabilities), the home, community, and social environments, and the interactions between the two (Beard et al., 2016).
Over recent decades, many ageing cohort studies have been established globally to study ageing and inform policy.Cohort studies are essential for understanding the role of social and environmental factors that shape individuals' ageing trajectories across the life-course.Yet, availability of longitudinal data with relevant measurements consistent with functional ability (i.e., intrinsic capacity and social and environmental factors) of the WHO healthy ageing framework is unclear.Summaries of existing ageing cohort studies and their available measurements are not easily available, even though some cohort studies have published articles on their methodology and/or have a website containing relevant information.Growing efforts to take advantage of these valuable resources requires an overview of all ageing cohorts.
In response to the United Nations' (World Health Organization, 2017) calls for guidance on data collection, we aimed to identify and systematise ageing cohorts across the globe; a summary of key characteristics of available studies and healthy ageing domains collected are presented.From the results of this study, we provided a web-based inventory, Healthy Ageing Toolkit, which is also described in this paper.

Study selection
We used a multifaceted approach to identify relevant ageing cohort studies-a type of study that follows up a group of individuals who share some characteristics.Online cohort directories and repositories were first consulted, as these consolidate internationally harmonised and large-scale cohort studies, including Maelstrom Research, International Research Network on Dementia Prevention (IRNDP), the Neurodegenerative Disease Research (JPND), and Gateway to Global Ageing Data.Additional resources were scouted through keyword searches on electronic journal databases, including Cohort Profiles in the International Journal of Epidemiology, intended to raise awareness of the existence of cohort studies.As other journals also publish Cohort Profiles, we additionally searched in PubMed.A list of previous review papers on availability of cohorts studies was also hand searched (Bell et al., 2015;Erten-Lyons et al., 2012;Joly et al., 2012;Kingston and Jagger, 2018;Kogevinas et al., 2020;Pansieri et al., 2020;Seematter-Bagnoud and Santos-Eggimann, 2006).We conducted our search for European and Oceanian cohorts in January 2019 with an updated search in January 2022.We search for cohorts from other regions in January 2022.
European, Oceanian, Asian, Middle Eastern, American, and African cohort studies of ageing, with participants comprising the older population (aged 60 or 65+) at baseline were included in this study and the Healthy Ageing Toolkit.Additionally, studies involving mid-age (aged 45-65), or younger (aged 18-45) participants and birth cohorts were also incorporated if ageing was a primary focus of data collection or if study objectives evolved over follow-up time with the ageing of the cohort.Newly established ongoing cohort studies with only one wave of data currently available and those with partial follow-up were also included.
Cohort studies exclusively involving selected participant groups were excluded, including patients in clinical care, those with specific health conditions or on specific medications, pregnancy, or randomised clinical trials were not considered for inclusion.However, if a study aimed to investigate incidence of a specific disease as the cohort aged, it was included.

Data extraction
We extracted key characteristics of included studies, including region, country, specific aim of the cohort study, sample size, baseline age distribution, study duration, and number of follow-up waves.Additional information collected from each cohort was organized into four main categories: sociodemographic, social factors, environmental factors, and intrinsic capacity domains (Table 1).Selection of the variables and measurements extracted in this study was based on recent research on how the WHO healthy ageing, functional ability, and intrinsic capacity might be operationalised (Beard et al., 2019;Cesari et al., 2018).We also extracted data on diseases, injury, and disability (Table 1).We categorized cohort studies into those with explicit aim to study ageing if their primary focus was on addressing general research questions related ageing, as opposed to studies focused primarily on specific conditions such as dementia, cardiovascular disease.
Given that there is no consensus on a standardised measure of intrinsic capacity domains and metrics (López-Ortiz et al., 2022), we extracted information on sleep as a potential indicator of psychological functioning (Beard et al., 2019).Information on pain and incontinence were also considered, given their high prevalence at older ages (Batmani et al., 2021;Blyth, 2008;Wainwright et al., 2022).We also extracted information on availability of self-rated health, which has been shown to be a risk factor for health trajectories (Cesari et al., 2018;López-Ortiz et al., 2022).In addition, while diseases, injuries, and/or disability could determine the intrinsic capacity of an individual, intrinsic capacity could also explain or predict an individual's health characteristics.Thus, it is important to have a summary of the availability of these measures in included ageing cohort studies.
To maximise information on each cohort study, we systematically assessed a) websites (if available), b) design papers (if available), c) reports (if available), and/or d) articles using relevant cohort study data.Information was collected by MD, TS, and YS.SKS and YS randomly double-checked collected information of the 25% of the included cohort studies to assess accuracy.

Synthesis of results
We plotted the distribution of ageing cohort studies availabilities by specific regions using the Mercator projection.We provided a descriptive summary of available sociodemographic, social, and environmental factors, and intrinsic capacity domains by different regions.We also provided a descriptive summary of available measures stratified by whether cohort studies had an explicit aim to study ageing.

Results
We identified 287 cohort studies worldwide, including 82 cohort studies in Europe, 25 in Oceania, 68 in Asia, 8 in the Middle East, 90 in North America, 8 in South America, and 7 in Africa-three multiregional cohort studies counted in Asia, North America, the Middle East, and Africa (Fig. 1 and Table S1).While most nations (or areas) had at least one cohort study relevant for healthy ageing research, many had none, particularly countries in the Middle East, South America, and Africa (Fig. 1 and Figure S1).The US had the highest number of cohort studies worldwide (n = 74), followed by the UK (n = 24), China (n = 21), and   Table 1 Sociodemographic, social and physical environmental factors and intrinsic capacity domains and subdomains collected in ageing cohort studies.Australia (n = 21) (Figure S1).
Among those available cohort studies, 64.3% of cohort studies (184 out of 287) had an explicit aim to study ageing, including 65% of cohorts in Europe, 60% in Oceania, 66% in Asia, 75% in the Middle East, 64% in North America, 71% in South America, and 28% in Africa.The sample size ranged from 67 to 1,319,475; the earliest cohort study started in 1905 in Europe;; and depending on the study design, the age distributions of participants differed (Table 2).The number of follow-up waves varied between geographical regions.North American cohort studies had the highest average of number of follow-up waves of 7.8; the corresponding average number of follow-up waves were 6 for Oceania, 5 for Europe, and ranged between 3 and 4 for Asia, the Middle East, South America, and Africa.Three North American and three European cohort studies reported annual follow-ups; one Asian cohort study had biannual follow-ups, and four Asian and two North America cohort studies had more than biannual follow-ups, ranging between every 3 to every 5 years (Table S1).Fig. 2 summarises data availability on sociodemographic, social, and environmental factors.While around 80% of Oceanian and North American cohort studies collected data on ethnicity, less than 50% of those from other nations collected this data.Except for Oceanian cohort studies (where 80% collected data on birthplace), less than 40% of cohort studies from all other nations collected this data.Most studies collected data on commonly used indicators of socioeconomic conditions, including education, occupation, and income.However, a limited number of cohort studies collected data on wealth and owning property.Around 50% of European, Oceanian, and African cohort studies and less than 30% of Asian, Middle Eastern, and North/ South American ones collected data on housing type.Data on neighborhood conditions were collected in less than one-third of included studies.While marital status was collected almost universally, data on social support, social network, and social participation were collected in 50-70% of cohort studies (Fig. 2).
Fig. 3 provides a summary of data collected on intrinsic capacity domains.While most cohort studies had at least one relevant measure of physical functioning (ranging from 71% in African to 96% of Oceanian cohort studies), fewer than 50% of measures were objective (e.g., gait speed, grip strength, or balance).At least one sensory functioning measure (i.e., visual acuity, auditory acuity, and self-reported general vision and hearing) was collected in 88% of Oceanian cohort studies, followed by 73.2% of European, 71% of South American, 57% of  African, 56% of Asian, and around 50% of North American and the Middle Eastern cohort studies.Over 80% of cohort studies collected at least one relevant cognitive functioning measure using objective and/or self-reported measures.Most studies had at least one measure related to psychological functioning, particularly depression, followed by emotions, anxiety, and self-esteem.Pain and sleep measures were collected in 50-80% of cohort studies.Incontinence was collected in fewer than 40% of cohort studies; no African cohort study collected data on incontinence.Except in the Middle Eastern (50%) and African (43%) cohorts, self-rated health was collected in more than 70% of cohort studies across other nations.Injury, disability, and diseases data collection is shown in Fig. 4. Data collection on falls and fractures ranged from less than one-fifth in African cohort studies to two-thirds of European, Oceanian, and Middle Eastern cohort studies.Most cohort studies collected at least one disability measure, with a focus on general disability in European, Oceanian, and Asian cohort studies and a focus on activities of daily living (ADL) in North/South American and African cohort studies; general disability refers to generic measures other than ADL and instrumental activities of daily living (IADL  collected data on different types of diseases (>86%); detailed information on specific disease measures is provided in Fig. 4. Cohort studies with an explicit aim to study ageing were more likely to collect data on intrinsic capacity domains compared to those without an explicit aim to study ageing (Figure S2 and Figure S3).Using the results from our search, we developed the Healthy Ageing Toolkit (https://www.healthyageing-toolkit.cepar.edu.au/)(Khalatbari-Soltani and Blyth, 2023).This free cohort repository includes 287 ageing cohort studies across the globe and their available measures to provide a resource for studying healthy ageing.The Toolkit has an online search dashboard that researchers can use to easily and quickly find a list of cohort studies with their measures of interest or in their region of interest.

Discussion
Our results highlight the data availability from 287 cohort studies useful for studying healthy ageing and its social and environmental drivers.Notably, our results also highlight that many geographical areas, nations, and countries had limited or no data.For instance, South America, the Middle East, and Africa had fewer than 10 ageing cohort studies, much lower compared to Europe, Oceania, Asia, and North America.Synthesising available data highlights the need to focus more on collecting various socioeconomic indicators and environmental factors.Cohort studies that explicitly studied ageing collected at least one measure of physical, sensory, cognitive, and psychological functioning and injury and disability.

Strengths and limitations
Strengths include a comprehensive strategy to identify all ageing cohort studies across the globe, reliance on multiple sources to collect relevant information for each cohort study, inclusion of all intrinsic capacity domains as well as some measures of health characteristics (e. g., diseases, disability, and injuries).Of note, while some eligible studies were possibly missed in our review, the paper and the Toolkit reflect most data sources available to support healthy ageing studies and inform healthy ageing strategies.This paper and the Healthy Ageing Toolkit Several limitations should also be considered.Despite reliance on multiple sources of publicly available information, we did not crossvalidate our results through contacting principal investigators of cohort studies.Given the review's large scope, we collected information on a limited number of measures for each domain based on previous literature (Beard et al., 2019;Cesari et al., 2018) and the comparability of cohort studies for measurement methods used needs further research.For those measures included in this review, the most accurate information was obtained when cohort questionnaires were publicly available.We acknowledge the importance of information that we did not extract.This includes whether cohort studies were representative of their defined population; whether they have been linked to routinely collected data (e.g., hospital records, death registry, cancer registry); and whether they collected information on other important risk factors of healthy ageing across the life-course, such as childhood socioeconomic conditions and adverse life events.Finally, since our focus was on the main aim of the cohort studies, our categorizations regarding whether these studies had an explicit aim to study ageing or not might be limited.Given that cohort studies often have broader aims than their specific/primary focus and the main aim may change over the follow-up time.However, we did not extract such detailed information.

Sociodemographic, social, and environmental factors and intrinsic capacity domains
Ethnicity and birthplace are potentially influential in shaping health and healthy ageing across the life-course (Bhopal and Europe, 2016).

Europe
Oceania Asia Middle East North America South America Africa  However, most regions lacked data collection of this data.Therefore, collecting data on ethnicity and relevant factors (e.g., birthplace, language proficiency, visa type) should be a focus going forward.Socioeconomic conditions-general position in the social system--are important drivers of inequities in healthy ageing across the lifecourse needing explicit consideration (Chatelan and Khalatbari-Soltani, 2022;Wagg et al., 2021).Ageing is taking place alongside social and structural changes that will affect the lives of older people, and is strongly affected by the cumulative effect of socioeconomic inequities across the life-course (Khalatbari-Soltani et al., 2020;Sadana et al., 2016;Si et al., 2023).Most studies recorded at least one socioeconomic indicator, most commonly educational level and occupational position, followed by income level.In contrast, wealth and its relevant measures, such as owning a property, were not as commonly collected.Wealth is one of the most important socioeconomic indicators at older ages, as it reflects the accumulation of material resources in later life and is also interrelated with early life education and occupational position (Galobardes et al., 2006).To be able to holistically understand how key socioeconomic indicators relate to inequities in healthy ageing, consideration of several indicators across the life-course is essential and cannot be used interchangeably (Khalatbari-Soltani and Blyth, 2022).
Notably, the physical and social environment that an individual inhabits also plays an important role in older people's health (Beard et al., 2016;World Health Organization, 2017) and is closely tied to socioeconomic condition (Khalatbari-Soltani et al., 2020;Krause and Borawski-Clark, 1995).Being socially connected is not only influential for psychological well-being but also has a significant and positive influence on physical well-being (Holt-Lunstad et al., 2015).Supportive age friendly environments can promote dignity, autonomy, functioning, and continued personal growth (Berkman et al., 2000;Clarke and Nieuwenhuijsen, 2009;Wells and Miranda, 2013).We identified a greater focus on collecting data on social support, participation, and networks than physical environmental factors (e.g., housing type and neighbourhood condition).Fewer than 50% of studies included a housing type measure and fewer than 30% had any data on neighbourhood conditions.We recognise that by using the geocoded residential addresses of the cohort's participants (if available), researchers can have access to link data on environmental factors, including air pollution, road exposure, and greenspaces; however, collecting data on dwelling and community features (e.g., housing tenure and tenure type, dwelling structure, housing conditions) could provide detail information necessary to examine the impact of environmental factors on healthy ageing that are not possible through data linkage.
Physical, sensory, cognitive, and psychological functioning are all key components of intrinsic capacity, driving healthy ageing (Beard et al., 2019;Cesari et al., 2018).Maintaining physical functioning capabilities into older ages is essential for many aspects of healthy ageing (Lowsky et al., 2014), including reducing incident falls, risk of frailty, and disability.Over 70% of studies had at least one relevant measure of physical functioning.However, a notable number only focused on self-reported measures rather than objective assessments of physical functioning, such as gait speed, grip strength, and balance.These measures can provide complementary and more detailed information on physical functioning of participants and its impact on healthy ageing trajectories.
Despite its importance (Heft and Robinson, 2017), sensory functioning was the most overlooked domain of intrinsic capacity, included in only about half of all cohorts, predominantly those that explicitly studied healthy ageing.This is in agreement a scoping review of 53 studies on measurements of intrinsic capacity among older adults (López-Ortiz et al., 2022).
Poor cognitive and psychological functioning can diminish the quality of life across the life-course, and exacerbate loneliness and loss of social contact, particularly among older adults (Domènech-Abella et al., 2019;Harada et al., 2013;Landeiro et al., 2017).Most cohort studies (>80% of cohort studies in all regions except the Middle East) had at least one measure of cognitive functioning.Furthermore, almost all studies collected data on emotions, depression, and anxiety symptoms.
The inclusion of disability measures varied substantially across different regions; while European, Oceanian, and Asian cohort studies focused mainly on a general disability measure, North/South American and African cohort studies focused mainly on ADL.Considering falls and fractures, overall, there is a need for more data collection on these injury related factors, particularly in African and North American cohort studies.Finally, morbidities in older age represent a critical health risk, due to its impacts on overall functioning, quality of life, and mortality.Hypertension, diabetes, cardiovascular disease, stroke, and arthritis were the most frequently recorded conditions across studies, reflecting their high prevalence e among older adults.Additionally, heterogeneity is apparent within the range of diseases collected.

Healthy Ageing Toolkit
Finding cohort studies with relevant sets of measurements on intrinsic capacity and social and environmental factors could be challenging and time-consuming steps for epidemiological research.The online search dashboard of the Toolkit facilitates this by enabling researchers to identify studies with measurements of interest across different countries and population groups.The Toolkit might also facilitate cross-national comparison studies of healthy ageing-an important current knowledge gap.Population ageing is a global phenomenon, and much can be learned about ways to promote healthy ageing through cross-national comparisons.We plan to keep updating the Toolkit by a) adding newly established ageing cohorts and b) providing an option for principal investigators of cohort studies to contact us in case of an update to their data collections.

Conclusion
This study provides a comprehensive summary of currently available ageing cohort studies and provided insight into data availability and collection gaps, which are crucial to progress toward healthy ageing.The gaps highlighted in this study can underpin efforts to increase data collection in some geographical regions (the Middle East, South America, and Africa) and countries with limited number of ageing cohort studies.Our results also highlight the need for further emphasis on intersectionality by collecting data on demographic variables, socioeconomic indicators, and social and environmental factors in studies of ageing.Our open access Healthy Ageing Toolkit not only helps researchers find relevant cohorts to conduct studies into healthy ageing, but it also provides evidence on data collection gaps to measure healthy ageing that needs to be addressed, further supporting the global, regional, and national monitoring of actions, programmes, and policies.critically reviewing and revising the draft.All authors read and approved the final manuscript before submission and agreed with the decision to submit the manuscript.

Declaration of Competing Interest
None declared.

Fig. 3 .
Fig. 3. Intrinsic capacity domains measured in ageing cohort by regions.The dark grey shading graphically represents the percentages of cohorts with at least one measurement available of the domain specified.The light grey shading graphically represents the percentage of cohorts with measured variables.
extend those existing consortia and projects such as the internationallyfocused ATHLOS (Ageing Trajectories of Health: Longitudinal Opportunities and Synergies), IALSA (Integrative Analysis of Longitudinal Studies on Aging), Gateway to Global Ageing Data, Europe and USfocused CHANCES (Health and Ageing: Network of Cohorts in Europe and the United States), UK-focused HALCyon (Healthy Ageing across the Life Course), and Australian-focused DYNOPTA (Dynamic Analyses to Optimise Ageing).The Healthy Ageing Toolkit offers a valuable resource to promote data utilisation across different ageing cohorts by facilitating finding cohort studies with relevant measures of interest in regions of interest.

Fig. 4 .
Fig. 4. Injury, disability, and diseases measured in ageing cohorts by regions.The dark grey shading graphically represents the percentages of cohorts with at least one measurement available of the domain specified.The light grey shading graphically represents the percentage of cohorts with measured variables.

Table 2
Summary of ageing cohorts characteristics.
).Most cohort studies Fig. 2. Demographic, socioeconomic, environmental and social factors measured in ageing cohorts by regions.The grey shading graphically represents the percentage of cohorts with measured variables.