The Perfect Enemy | Obesity and adverse childhood experiences in relation to stress during the COVID-19 pandemic: an analysis of the Canadian Longitudinal Study on Aging | International Journal of Obesity - Nature.com
January 29, 2023

Obesity and adverse childhood experiences in relation to stress during the COVID-19 pandemic: an analysis of the Canadian Longitudinal Study on Aging | International Journal of Obesity – Nature.com

Obesity and adverse childhood experiences in relation to stress during the COVID-19 pandemic: an analysis of the Canadian Longitudinal Study on Aging | International Journal of Obesity  Nature.com

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Study design and participants

We conducted an analysis using longitudinal data collected as part of the Canadian Longitudinal Study on Aging (CLSA). The methodology of the CLSA has been published elsewhere [26]. Briefly, the CLSA is a national longitudinal study of adults aged 45–85 at the time of recruitment (2011–2015). At the time of recruitment, participants provided informed consent. Adults from the 10 Canadian provinces were recruited using population-based sampling strategies [26]. Participants were eligible for inclusion into the CLSA if they could complete interviews in English or French, were cognitively able participate on their own, were not in an institution, did not reside in a Canadian territory (The Northwest Territories, the Yukon and Nunavut) or on a Federal First Nations reserve, and were not a full-time member of the Canadian Armed forces. Ethics approval for this study was received from the Hamilton Integrated Research Ethics Board (HiREB).

The CLSA is comprised of the Tracking cohort and the Comprehensive cohort. Data for the Tracking cohort were collected using telephone interviews, whereas data for the Comprehensive cohort were collected via in-home interviews and clinical data collection site visits. Data are collected every three years and all participants will be followed for 20 years, or until death or loss-to-follow-up. Data for this analysis were collected at baseline in 2011–2015 and at follow-up 1 in 2015–2018. At the start of the COVID-19 pandemic, the CLSA COVID-19 Questionnaire Study was implemented, which collected longitudinal data from April 2020 to December 2020. Specific details about when data for this study were collected can be found in Table 1.

Table 1 Characteristics of participants from the Canadian Longitudinal Study on Aging (CLSA) COVID-19 Questionnaire study (n = 23,972), Canada.

Primary exposures

Obesity

Obesity was measured at CLSA follow-up 1 (2015–2018). For individuals in the Comprehensive cohort (n = 15,582), height and weight were measured by trained research assistants. These measurements were used to calculate body mass index (BMI) (kg/m2). For individuals in the Tracking cohort (n = 8390), height and weight were assessed using self-report, which were then used to calculate BMI. A correction factor developed by Statistics Canada was applied to the self-reported BMI to account for bias associated with self-report [27]. These correction equations were generated using the 2005 Canadian Community Health Survey with consideration of several sociodemographic variables separately for males and females [28]. Self-reported BMI was slightly underestimated compared to the corrected BMI, which is consistent with the literature [27]. BMI was categorized following World Health Organization standard cut-offs [29]: normal weight (≤24.9 kg/m2), overweight (25–29.9 kg/m2), obesity class I (30–34.9 kg/m2), obesity class II (35–39.9 kg/m2) and obesity class III (≥40 kg/m2). Obesity was further classified into 3 subgroups, as research has found variation in risk of health outcomes across the subtypes [30]. Underweight individuals were included in the normal weight category given the small sample size.

Adverse childhood experiences

To measure ACEs, at CLSA follow-up 1, participants were asked about 11 experiences before the age of 16 related to physical abuse, sexual abuse, emotional abuse, neglect, and exposure to intimate partner violence. Participants were also asked about three experiences before the age of 18 related to death of a parent, parental divorce/separation and living with a family member with mental health problems. These questions were adapted from the Childhood Experience of Violence Questionnaire and the National Longitudinal Study of Adolescent to Adult Health Wave III questionnaire [31, 32]. Based on responses to dichotomized yes/no questions, a cumulative score was created by summing the total number of ACEs participants reported [31]. Since only a small proportion of people reported 5 to 8 ACEs (4%), those reporting four or more were collapsed into one group. A cumulative ACEs score was used rather than subgroups by severity, as research has found this to be a better assessment of cumulative exposure, and has been found to be associated with health outcomes [33].

Measurement of outcomes (stress)

Stress was measured in two ways: (1) stressors and (2) the perceived consequences of the pandemic. These questions have previously been used in disaster research [18, 34,35,36] to study objective and subjective stress following a disaster such as the COVID-19 pandemic. The development of these questions were modified from gold-standard measurement tools [18].

Stressors

Stressors were measured at CLSA COVID-19 Questionnaire Study Exit Survey (September 2020-December 2020). Participants were asked, “Which of the following have you experienced during the COVID-19 pandemic?” where participants could select one or more of the following options: participant was ill, someone close to the participant was ill, someone close to the participant died, loss of income, unable to access necessary food and supplies, unable to access healthcare, unable to access usual prescriptions, increased conflict, separation from family, increased caregiving, unable to care for those who require assistance due to limitations, and breakdown in family relationships. The 12 stressors were classified into four domains for this analysis; (1) health (participant was ill, someone close to the participant was ill, someone close to the participant died), (2) resources (loss of income, unable to access necessary food and supplies, unable to access healthcare, unable to access usual prescriptions), (3) relationships (increased conflict, separation from family, breakdown in family relationships), and (4) caregiving (increased caregiving, unable to care for those who require assistance due to limitations). To create each domain, the total number of stressors within each category was summed. The range of values for each domain varied depending on how many stressors fell within the category. For instance, the health domain ranged from 0 to 3, whereas the resources domain ranged from 0 to 4. In addition, a cumulative stressor score was created by summing the total number of stressors participants experienced across all domains [37]. The cumulative stressor score ranged from 0 to 12.

Perceived consequences of the pandemic

As a subjective measure of perceived stress, participants were asked “Taking everything about COVID-19 into account, how would you describe the consequences of COVID-19 on you and your household?” during the CLSA COVID-19 Questionnaire Study Exit Survey (September 2020-December 2020) [18, 34, 35]. Response options were very negative, negative, neutral, positive, and very positive. Very few participants reported the consequences of the pandemic as very negative or very positive, so these categories were combined with negative and positive response options, respectively. The neutral category was further combined with the positive and very positive category to create a binary variable, since the objective of the analysis was to explore negative/very negative perceived consequences of the pandemic compared to all other perceptions.

Measurement of potential confounding variables

All remaining variables were measured at CLSA baseline (2011–2015), CLSA follow-up 1 (2015–2018), CLSA COVID-19 Baseline Survey (April 2020-June 2020) or the CLSA COVID-19 Exit Survey (September 2020-December 2020). These variables were chosen based on the framework proposed by van der Valk et al., identifying characteristics that are related to the association of stress and obesity [1]. Participant sex (male or female) and racial background (white or non-white) were collected at CLSA baseline. Participant age at CLSA COVID-19 Baseline Survey was categorized as 50–64 years, 65–74 years, and 75–96 years. Physical activity, household income, alcohol consumption and depression were measured at CLSA follow-up 1. The Physical Activity Scale for the Elderly (PASE) was used to assess level of physical activity for the previous seven days [38]. Based on the World Health Organization (WHO) guidelines [39], physical activity was dichotomized into ≤150 min/week of moderate-intensity or ≤75 min/week of vigorous-intensity versus >150 min/week of moderate-intensity or >75 min/week of vigorous-intensity. Household income was categorized into less than $50,000, $50,000 to less than $100,000, $100,000 to less than $150,000, and $150,000 or more, and alcohol consumption over the past 12 months was categorized as did not drink in the last 12 months, occasional drinker, and regular drinker (at least once a month). Depression was assessed using the Center for Epidemiologic Studies Short Depression (CESD) scale [40], where a score of ≥10 indicates risk for clinical depression.

Statistical analysis

All statistical analyses were completed using SAS 9.4. Statistical code is available upon request. The associations between both obesity and ACEs were independently evaluated in relation to the three primary outcomes, (1) the stressor domains, (2) total stressor score, and (3) the perceived consequences of the pandemic. PROC GENMOD was used to estimate relative risks (RRs) and 95% confidence intervals (CIs). For all outcomes, a log link function was used, however the distribution used varied for different outcomes. For the stressor domains, a Poisson distribution was assumed as this was a count variable. Although the total stressor variable was also a count variable, a negative binomial distribution was assumed given the overdispersion. Finally, a binomial distribution was assumed for the binary perceived consequences of the pandemic variable. All models were adjusted for potential confounders that were hypothesized a priori to be predictors of both the exposures and outcome variables. These included sex, age group, racial background, physical activity, household income, alcohol consumption and depression [1]. For the association between ACEs and stress, an additional model was run adding obesity to the fully adjusted model, given the potential mediating role of obesity. All variables had less than 5% of participants missing, and a complete case analysis was conducted. A sensitivity analysis was conducted to explore differences in associations by severity of ACEs. We explored the association between maltreatment ACEs and measures of stress, and family dysfunction ACEs and measures of stress.

For the association between obesity and measures of stress, interaction by both ACEs and sex were assessed separately on both the additive and multiplicative scales. In epidemiologic research, interaction is often only explored on the multiplicative scale, however, the assessment of interaction on the additive scale has significant public health importance as it can contribute to better allocation of resources and identification of high-risk subgroups [41]. STROBE guidelines recommend presenting the separate effects of exposures and modifiers, as well as joint effects to ensure readers can assess interaction on either scale [41]. To determine if the associations between obesity and measures of stress were modified by ACEs, a dichotomous ACEs variable was created. Individuals who reported no ACEs were categorized as none, and those who reported one or more ACES, were categorized as yes. Using the framework proposed by Knol and VanderWeele [41], interaction was tested on the additive scale using the relative excess risk due to interaction (RERI) and on the multiplicative scale using the ratio of relative risk (RRR). The 95% CI for the RERI were calculated using the delta method [41,42,43].