[Research Summary] Three ways data from the Canadian Longitudinal Study on Aging can be used by the Community-Based Seniors’ Services sector

Three ways data from the Canadian Longitudinal Study on Aging can be used by the Community-Based Seniors’ Services sector

There is an abundance of data on seniors available through the Canadian Longitudinal Study on Aging (CLSA). The CLSA is the largest long-term study on aging in Canada ever conducted – following over 50,000 individuals between the ages of 45 to 85 over a 20-year period to capture the transitions, trajectories, and profiles of healthy aging.

One of the central objectives of the CLSA was to generate knowledge that would improve supports, services, and programs for older adults. Yet this data source remains largely untapped by the community-based seniors’ services (CBSS) sector – a key player in the delivery of programs and services for seniors.

Through a Health Systems Impact Fellowship, I spent this past year working with the Healthy Aging team at United Way BC to bridge this data gap. The overarching aim of my fellowship was to learn how data from the CLSA can be analyzed to support the CBSS sector.

I spent many hours sifting through the data and meeting with people in the CBSS sector – listening to their ideas and concerns.

One of the main learnings from this endeavour is that the CLSA is useful for informing the higher-level direction of the CBSS sector – but perhaps less so for the design and delivery of programs and services in particular communities. Although the CLSA has a large sample size, it was never collected with the intention of being used at the community level. The data can be used for provincial and national estimates, but there simply aren’t enough participants in each community for the data to provide reliable estimates on the number of seniors who are socially isolated in a particular community, for example.

This is a limitation for senior-serving organizations who often want community-specific data to inform the design and delivery of their programs and services. Other places to look for community-level data include the Canadian Census, Vital Signs, or Health Authorities.

Despite this limitation, if we consider the CBSS sector as a whole, there are numerous ways that data from the CLSA can be very useful. Some examples that arose in my work are:

1.    To characterize seniors’ experiences and needs in the core service areas of the CBSS sector.

A wide range of data are collected through interviews and in-person visits on topics that directly relate to the core service areas of the CBSS sector, including:

·       Nutritional supports

·       Health and wellness

·       Physical activity

·       Educational, cultural, and recreational programs

·       Transportation, and

·       Housing.

The CLSA can be used to better understand seniors’ experiences and needs in these areas. For example, I analyzed data from the CLSA that aligned with the Provincial Working Group on Seniors’ Transportation focus area of driving cessation. A few learnings from the CLSA that related to driving cessation were that:

·       Women were less likely to hold a drivers’ licenses compared to men (54% of women aged 85+ had a drivers’ license compared to 87% of men aged 85+)

·       People who lived in rural settings retained their driver’s license longer than those in urban counterparts (77% of people in rural settings aged 85+ had had a drivers’ license compared to 66% aged 85+ in urban settings).

·       Driving cessation is a topic that is largely avoided with health care professionals, with only 1 in 6 older adults aged 65+ having spoken with a medical professional about driving.

These data points provided evidence to inform the Provincial Working Groups’ advocacy efforts in this topic area.

2.    To explore the profiles and needs of sub-populations.

An important caveat to note about the CLSA is that the sample skews healthier and wealthier. This means that results from the CLSA should be interpreted with some caution, especially in the context of the CBSS sector which often targets services to seniors who are socially isolated or have lower incomes.

With this limitation in mind, the CLSA still has much to offer. The large sample size provides the opportunity to understand the profiles of needs of sub-populations – for example, those with lower incomes (n=2,670 had an annual household income <$20,000 at baseline), immigrants (n=8,164 at baseline), and people who identify as 2SLGBTQ+ (n=1,156 at baseline).

Smaller scale surveys often don’t have a large enough sample sizes to separate out the data for these populations. With a baseline sample size of 50,000, it is possible in the CLSA.

Here are a few examples of studies that have focused their analyses on sub-populations:

·       Examining individual and geographic factors associated with social isolation and loneliness using Canadian Longitudinal Study on Aging (CLSA) data.

·       Successful Aging among Immigrant and Canadian-Born Older Adults: Findings from the Canadian Longitudinal Study on Aging (CLSA).

·       Physical and mental health inequalities among aging lesbian, gay, and bisexual Canadians: cross-sectional results from the Canadian Longitudinal Study on Aging (CLSA).

The CLSA could be tapped to explore other topics and sub-populations that may be of interest to people working in the CBSS sector.

3.    To inform the target population for the CBSS sector.

A third way data from the CLSA can be used is to inform the target population of the CBSS sector. For example, analyses I conducted on the transportation data available in the CLSA showed that the population groups that are most likely to experience unmet transportation needs were women, people with lower household incomes and education levels, and those who live alone and in rural settings. Further, people who had an unmet travel need were three times as likely to report feeling socially isolated, compared to those without an unmet travel need.

Cross-referencing this type of evidence with the local knowledge of people working in community could be used to inform the design of delivery of transportation services, and which populations may benefit the most from these services.  

Data collection over a 20-year period also means that the CLSA will be able to answer questions about the transitions and trajectories of aging – potentially informing how people’s needs for services and programming changes with age.

Conclusion

The CLSA is a large dataset that has potential to inform the CBSS sector. In this post I’ve outlined three potential ways the data could be used – there is no doubt many others.

Applications for CLSA are processed through academic institutions. Therefore, if you are interested in using the CLSA data, the first step would be to search for a researcher at an academic institution to collaborate with. Fortunately, there is no shortage researchers who are currently working with data from the CLSA.

For more information about the CLSA and research currently underway, visit: https://www.clsa-elcv.ca/