|Year : 2022 | Volume
| Issue : 4 | Page : 148-157
Prevalence of obesity and elevated body mass index along a progression of rurality: A cross-sectional study – The Canadian Longitudinal Study on Aging
Samuel Quan1, Verena Menec2, Megan O'Connell3, Denise Cloutier4, Nancy Newall5, Robert Tate2, Philip St. John1
1 Department of Internal Medicine, Section of Geriatric Medicine, Max Rady College of Medicine, University of Manitoba, Manitoba, Canada
2 Department of Community Health Sciences, University of Manitoba, Manitoba, Canada
3 Department of Psychology, University of Saskatchewan, Saskatoon, Canada
4 Department of Geography, University of Victoria, Victoria, Canada
5 Department of Psychology, Brandon University, Brandon, Manitoba, Canada
|Date of Submission||02-Sep-2021|
|Date of Decision||14-Jan-2022|
|Date of Acceptance||23-Jan-2022|
|Date of Web Publication||07-Oct-2022|
MD, MPH, CCFP, FRCPC Philip St. John
Department of Internal Medicine, Section of Geriatric Medicine, Max Rady College of Medicine, University of Manitoba, Manitoba
Source of Support: None, Conflict of Interest: None
Introduction: Obesity is an important public health concern, and large studies of rural–urban differences in prevalence of obesity are lacking. Our purpose is to compare body mass index (BMI) and obesity in Canada using an expanded definition of rurality.
Methods: A cross-sectional analysis of self-reported BMI across diverse communities of Canadians aged 45–85 years was conducted using data from the Canadian Longitudinal Study on Aging (CLSA), a national sample representative of community-dwelling residents. Rurality was identified in the CLSA based on residential postal codes, which were divided into 4 categories: urban, peri-urban, mixed and rural. Logistic regression models were constructed to calculate adjusted odds ratios (aORs) with 95% confidence intervals (95% CIs) between obesity (BMI ≥30 kg/m2 from self-reported weight and height) and rurality, adjusting for age, sex, province, marital status, number of residents in household and household income.
Results: Twenty-one thousand one hundred and twenty-six Canadian residents aged 45–85 years, surveyed during 2010–2015, were included. 26.8% were obese. Obesity was less prevalent amongst urban (25.2%) than rural (30.3%, P < 0.0001), mixed (28.7%, P < 0.0001) or peri-urban communities (28.1%, P < 0.0001). When compared to urban areas, the aOR (95% CI) for obesity was 1.09 (1.00–1.20) in rural regions and 1.20 (1.08–1.35) in peri-urban settings. In areas of mixed urban and rural residence, the aOR was 1.12 (0.99–1.27).
Conclusion: One in four Canadian adults were obese. Living in a non-urban setting is an independent risk factor for obesity. Rural–urban health disparities could underlie rural–urban differences, but further research is needed.
Introduction: L'obésité est un important problème de santé publique et des études de grande envergure sur les différences de prévalence de l'obésité entre les régions rurales et urbaines font défaut. Notre objectif est de comparer l'indice de masse corporelle (IMC) et l'obésité au Canada en utilisant une définition élargie de la ruralité.
Méthodes: Une analyse transversale de l'IMC autodéclaré dans diverses communautés de Canadiens âgés de 45 à 85 ans a été réalisée à l'aide des données de l'Étude longitudinale canadienne sur le vieillissement (ELCV); un échantillon national représentatif des résidents vivant en communauté. Dans l'ELCV, la ruralité a été identifiée à partir des codes postaux résidentiels, qui ont été divisés en 4 catégories: urbain, périurbain, mixte et rural. Des modèles de régression logistique ont été construits pour calculer les rapports de cotes ajustés (RCa) avec des intervalles de confiance à 95% (95% IC) entre l'obésité (IMC ≥30 kg/m2 à partir du poids et de la taille autodéclarés) et la ruralité, en tenant compte de l'âge, du sexe, de la province, de l'état civil, du nombre de résidents dans le ménage et du revenu du ménage.
Résultats: 21 126 résidents canadiens âgés de 45 à 85 ans, interrogés au cours de la période 2010-2015, ont été inclus. 26,8% étaient obèses. L'obésité était moins répandue dans les communautés urbaines (25,2%) que rurales (30,3%, P < 0,0001), mixtes (28,7%, P < 0,0001) ou périurbaines (28,1%, P < 0,0001). Par rapport aux zones urbaines, le RCa (95% IC) pour l'obésité était de 1,09 (1,00, 1,20) dans les régions rurales, et de 1,20 (1,08, 1,35) dans les milieux périurbains. Dans les zones de résidence mixte urbaine et rurale, le RCa était de 1,12 (0,99, 1,27).
Conclusion: Un adulte canadien sur quatre était obèse. Le fait de vivre dans un milieu non urbain est un facteur de risque indépendant d'obésité. Les disparités en matière de santé entre les régions rurales et urbaines pourraient être à l'origine de ces différences, mais des recherches supplémentaires sont nécessaires.
Mots-clés: Rural, Obésité, Indice de masse corporelle, ELCV
Keywords: Body mass index, Canadian Longitudinal Study on Aging, obesity, rural
|How to cite this article:|
Quan S, Menec V, O'Connell M, Cloutier D, Newall N, Tate R, John PS. Prevalence of obesity and elevated body mass index along a progression of rurality: A cross-sectional study – The Canadian Longitudinal Study on Aging. Can J Rural Med 2022;27:148-57
|How to cite this URL:|
Quan S, Menec V, O'Connell M, Cloutier D, Newall N, Tate R, John PS. Prevalence of obesity and elevated body mass index along a progression of rurality: A cross-sectional study – The Canadian Longitudinal Study on Aging. Can J Rural Med [serial online] 2022 [cited 2022 Nov 29];27:148-57. Available from: https://www.cjrm.ca/text.asp?2022/27/4/148/357855
| Introduction|| |
There are many discrepancies in health status between urban and rural residents.,,, Some studies show that rural communities have higher rates of comorbidities and mortality. Rural areas have lower availability and accessibility of healthcare services., Overall health behaviours are different, with higher rates of smoking and sedentary activity found amongst residents in rural areas. Socioeconomic factors also affect rural residents, as lower income, lower levels of educational attainment and higher unemployment rates are found amongst some rural areas., However, there is also significant heterogeneity in rural health research, which is partly due to variations in methodology, setting and the population.
Many challenges exist in comparing rural–urban health status. First, studies vary in how 'rural' and 'urban' are defined. Second, many socioeconomic discrepancies exist across countries, making rural–urban comparisons difficult across societies. Third, rural areas and urban areas are heterogeneous in terms of health status and access to health services. Examining rurality using expanded definitions or categories rather than a strict 'urban–rural' binary categorisation may lessen, but not eliminate this difficulty. Furthermore, many studies focused their analyses in small geographic regions, and there are relatively few representative epidemiological studies including both large urban and rural populations. Therefore, large representative studies of rurality are important to continuously evaluate the presence of health discrepancies. It is important to continuously update findings, as socioeconomic factors and health services change continuously over time.
Obesity is a prevalent risk factor associated with an increase in morbidity and mortality., Obesity is also associated with a diverse set of health complications, including cardiovascular disease, non-alcoholic fatty liver disease, osteoarthritis and various solid organ malignancies.,, Body mass index (BMI), a metric of weight (in kilogram) divided by height (in metres squared), is commonly used to define underweight (BMI <18.5 kg/m2), overweight (BMI 25–29 kg/m2) and obesity (BMI ≥30 kg/m2)., Obesity is associated with a higher all-cause mortality, with higher risks found amongst higher classes of obesity (i.e. higher BMI).
The prevalence and percentage of the population with self-reported obesity are increasing in Canada. Canada has the fourth highest prevalence of obesity amongst the Organisation for Economic Co-operation and Development countries. Direct and indirect costs of obesity are increasing, and obesity was estimated to cost Canadians $4.6 billion in the year 2008 alone. Weight and obesity have been identified as a priority health concern amongst rural communities. Over the last 30 years, BMI is rising faster in rural settings compared to urban areas in many countries. In countries with emerging economies, rural areas contribute more to rising BMI than urban settings. Obesity is a major public health concern and is associated with socioeconomic inequalities.,, Therefore, it is important to understand how the social determinants of health and rurality relate to the prevalence of obesity.,,,
We conducted a study comparing BMI and the presence of obesity using an expanded definition of rurality. To address many of the previous limitations of urban–rural health studies, we used a large, nationally representative, population-based sample that includes a diverse range of rural and urban communities to address the following two objectives.
- We examined obesity and mean BMI (using self-reported weight and height) along a progression of rurality (urban, mixed, peri-urban and rural areas)
- We determined predictors of obesity and BMI in Canada.
| Methods|| |
Study design, population and data sources
We used a cross-sectional design to investigate the association between rurality and BMI. Data from the Canadian Longitudinal Study on Aging (CLSA) were used for this study. The CLSA is a large, multi-faceted, prospective cohort study of community-dwelling Canadian residents aged 45–85 years at the time of recruitment between 2010 and 2015.,,, The sampling frame is intended to be as representative of the general population as far as possible. CLSA participants were first recruited from Statistics Canada's Canadian Community Health Survey version 4.2 on Healthy Aging. CLSA then supplemented their initial cohort with a nationally representative sample using provincial healthcare registration databases and random digit dialling to obtain data through telephone interviews.,,, This general cohort, termed the CLSA 'tracking cohort', consisted of 21,241 study participants. For this study, we used data gathered from the initial recruitment and baseline interview of the CLSA tracking cohort. The CLSA is ongoing and will follow all participants aged ≥45 years over the next two decades.
Inclusion criteria for the CLSA tracking cohort included: community dwelling adults aged 45–85 years at the time of recruitment, understood English and/or French and resided within a Canadian province. Individuals with cognitive impairment at baseline, resided on a First Nations reserve, who were full-time members of the Canadian Armed Forces or who were not permanent Canadian residents or citizens were excluded. Patients who reported being pregnant, did not know whether they were pregnant or who declined to report their pregnancy status were excluded from our regression analyses. All participants in the CLSA provided informed consent.
BMI was calculated from self-reported weight and height data obtained through computer-assisted telephone interviews., Participants were asked 'how tall you are you without shoes on?' for height and 'how much do you weigh? (specified afterwards if the reported weight was in pounds or kilograms)' for weight. Self-reported height was rounded up to the nearest inch when recorded, and weight was recorded exactly as reported. We then converted the data from the CLSA telephone survey to metric units and calculated self-reported BMI.
To ensure reproducibility of our results, we used two different outcome metrics. We classified our outcomes as: (1) a dichotomous variable for the presence of obesity (based on self-reported BMI ≥30 kg/m2) versus no obesity (BMI <30 kg/m2) or (2) BMI as a continuous variable. We defined obesity and weight classes based on BMI on the same scale used by the World Health Organization.
Independent study variables of interest
Area of residence, or rurality, was classified based on CLSA and the Canadian Census definitions. We classified the nature of rural–urban communities into four categories, ranging from most rural to most urban: 'rural' (rural), 'mixed' (postal code link to dissemination area), 'peri-urban' (urban fringe, urban population outside census metropolitan areas and census agglomerations and secondary core) and 'urban' (urban core).
Confounding variables included were as follows: biological sex, age (at the time of recruitment), province of residence, education status, marital status, number of other household residents and household income. These variables were all self-reported. The variables were included in our model because sex, age and socioeconomic factors (e.g. education, marital status, income and household living arrangements) were associated with obesity.,, These definitions and methods were consistent with our analyses of other outcomes within this data set.,,
Bivariate and multivariate analyses were performed. Demographics and socioeconomic variables were compared between areas of residence with either Chi-squared tests or analysis of variance where appropriate. Inflation weights were used when mean BMI was calculated and when BMI was categorised. These weights were provided and calculated by the CLSA to create prevalence estimates that represent the Canadian population. Adjusted odds ratios with 95% confidence intervals (95% CIs) were calculated using multivariate logistic regression with the presence of obesity (BMI ≥30 vs. <30 kg/m2) as the outcome variable. To ensure the robustness of our results, multivariate linear regression was performed to investigate associations with increased BMI as a continuous outcome variable. Following CLSA protocol, inflation weights were used to portray descriptive statistics (as these weights were designed to be more representative of the general Canadian population) and analytic weights were used for all regression models (as analytic weights were better suited to evaluate the relationship between variables in regression models). We used inflation and analytic weights included in the Baseline Tracking (TRM) Dataset version 3.6. The following regression models were constructed: Model 1 – socioeconomic variables were not included, Model 2 – socioeconomic variables except household income were included and Model 3 – all socioeconomic variables, including household income, were included. Age, sex and province of residence were included as confounder variables in all three adjusted models. Analyses for interactions between variables of interest and rural residence were assessed. Statistical analyses were performed using the software SAS (SAS Analytics in Cary, North Carolina, United States of America).
The study adhered to the Declaration of Helsinki and these analyses were approved by the University of Manitoba Bannatyne Campus Research Ethics Board.
| Results|| |
[Table 1] shows the definition of rurality that we used in this study. A total of 21,241 Canadian community-dwelling residents between the ages of 45 and 85 were identified by the CLSA tracking cohort [Table 2]. Only a very small portion (n = 115, 0.5%) of individuals were excluded due to either pregnancy or insufficient data to calculate BMI. After excluding these 115 individuals, a total of 4681 (22.2%) rural, 2624 (12.4%) peri-urban, 2116 (10.0%) mixed and 11,705 (55.4%) urban residents were included in our study.
|Table 1: Definition of rurality adapted from the Canadian Longitudinal Study on Aging survey.|
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|Table 2: Demographic and socioeconomic characteristics of a nationally representative sample of community-dwelling Canadians as stratified by rurality*|
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Over a quarter of Canadians were obese as calculated from self-reported height and weight, and obesity was less common in urban areas. Obesity was present amongst 30.3% of rural, 28.7% of mixed, 28.1% of peri-urban and 25.2% of urban residents (P < 0.0001). The mean self-reported BMIs in our weighted sample were as follows: rural 28.2 kg/m2, mixed 28.0 kg/m2, peri-urban 27.8 kg/m2 and urban 27.4 kg/m2 (P < 0.0001 for comparisons across geographic areas). Levels of education, household income, marital status and number of household residents [Table 2] differed between urban, peri-urban, mixed and rural communities (P < 0.0001).
Rurality was found to be independently associated with obesity [Table 3]. Higher odds of obesity were seen amongst rural and peri-urban residents compared to urban residents, even after adjusting for sociodemographic and socioeconomic variables. Residents of mixed districts were also at higher odds of obesity compared to urban residents in unadjusted and most adjusted models; however, after household income was incorporated into our multivariate logistic regression model, the association became non-significant.
|Table 3: Logistic regression analysis examining the relationship between obesity (BMI ≥30) and rurality*|
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Using linear regression, urban residents had a lower average BMI than rural, peri-urban and mixed communities [Table 4]. Although these associations decreased in magnitude when sociodemographic and socioeconomic variables were adjusted for, a statistically significant positive correlation between rurality and BMI remained [Table 4].
|Table 4: Linear regression analysis examining the relationship between BMI and rurality*|
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In model checking, we noted statistically significant interactions on the outcome of obesity for a rural residence and age and rural residence and sex. However, the effect size was small and of borderline significance. The interaction terms did not alter the main effect associations that we observed. We therefore presented the regression models without interaction terms.
| Discussion|| |
There was a modest, but statistically significant, independent association between urban residence and lower odds of obesity (based on self-reported height and weight) amongst community-dwelling Canadians aged 45–85 years. Even after age, sex, province of residence, education, marital status, number of household residents and household income were adjusted for, both lower odds of obesity and lower mean BMI were witnessed in urban settings compared to rural, peri-urban and mixed communities. The magnitude of associations decreased with adjustment of some socioeconomic factors, suggesting that income, education, living arrangement and marital status may have explained some (but not all) of the urban–rural differences in BMI and obesity.
Other Canadian studies also reported a lower prevalence of obesity and lower BMI within urban settings. A recently published study by Forbes et al. found similar results within Atlantic Canada in a slightly younger population. Their study included available data collected during 2009 to 2015 from 17,054 of 31,173 possible participants. Study participants were aged 35 to 69 and resided within Canada's four Atlantic provinces. Forbes et al. found that urban residents had modestly lower BMIs than rural residents (mean BMIs were 28.1 in urban vs. 28.5 kg/m2 in rural areas, P < 0.001). Forbes et al. found that mean BMIs were lower amongst urban residents than rural residents, even after age, sex, ethnicity, education and health behaviours, such as smoking and alcohol use, were adjusted for using multiple linear regression. Another study by Hajizadeh et al. found that obesity (based on adjusted self-reported BMI ≥30 kg/m2) was more prevalent amongst rural areas of Canada between the fiscal years of 2000–2009. Even when demographic factors, health behaviours (e.g. diet, exercise and smoking) and a variety of socioeconomic variables were accounted for, rurality remained a modest and independent risk factor for obesity.
A lower prevalence of obesity in urban settings could have several explanations. A study in the United States found that urban residents had increased leisure-time physical activity, lower levels of sedentary behaviour, more fruit consumption and less consumption of sweetened beverages compared to rural residents. These health behaviours might be due to differences in infrastructure or geography, as rural residents might have more limited access to nutritious foods or have to rely more heavily on vehicles for transportation rather than walking or biking. Our study was not able to account for these variables, and therefore, it is unknown how much these health behaviours may have contributed to the differences seen. Mental health may also impact health behaviours and obesity. Future Canadian studies on a change in BMI and rurality should include health behaviours, mental health and psychiatric comorbidities when examining BMI and rurality. Future studies that examine urban–rural differences in health more fully are important as the risk factors and socioeconomics effects may change differentially between urban and rural regions over time.
Our study had strengths and limitations. We used a large and nationally representative sample that was inclusive of multiple rural and urban regions. We used an expanded definition of urbanicity and rurality, rather than a strict urban–rural dichotomy, which accounted for some of the heterogeneity between rural communities. We were able to adjust for socioeconomic variables, such as income and household living arrangements, which is not always possible when conducting research using large population-based datasets (e.g. hospital administrative data or physician claims databases). Regarding study limitations, obesity was defined solely based on a single BMI cut-off of 30 kg/m2, and we were unable to account for other metrics of obesity (such as waist circumference). Other nuanced factors that were limited by this definition, such as body composition (e.g. extensive muscle mass) or ethnicity, may also affect BMI interpretation in select subpopulations. Second, we used self-reported measurements of weight and height, which have inaccuracies. A Canadian study found that individuals tend to overestimate height and underestimate weight, especially amongst overweight and obese individuals. This suggests that Canadians would likely have even higher BMIs than our reported findings. However, it is not clear how this misclassification is related to rural residence. Third, this cross-sectional study was only able to capture a single moment in time. Many variables are dynamic in nature, including weight, health behaviours, socioeconomic circumstance and area of residence. Future studies would benefit from examining changes in BMI over time. Fourth, the clinical interpretation of BMI amongst older adults differs from that of younger adults, especially amongst the older adult population. Other competing risks of mortality and disabilities can contribute to an individual's overall health and frailty. Fifth, the CLSA does not collect data on some important subgroups who reside in Canada, such as those residing on First Nation reserves, active armed forces personnel, non-permanent residents or recent immigrants. We therefore caution generalising results to these individuals or communities. Further studies that explore specific Canadian subgroup populations that the CLSA is unable to capture would be beneficial. Sixth, minor rank-order differences in BMI or obesity were observed between the three rural settings when our three models were directly compared. This might have been due to the adjustment of confounders. However, in all cases, obesity had the lowest prevalence in urban settings. Finally, while we indeed found differences in BMI and in obesity rates between settings, the implications regarding the magnitude of these differences for clinical and policy decisions are not clear.
| Conclusion|| |
Our study demonstrated that a substantial proportion of Canada's population is obese. BMIs were modestly lower in urban settings than rural, peri-urban and mixed communities. Although absolute differences in prevalence of obesity or mean BMI appeared small, individually, the effects could be magnified on a population scale. Furthermore, significant differences were seen even after adjusting for age, sex, province of residence and socioeconomic factors, which suggest the independent effects of rurality. Early interventions are needed to address the underlying social determinants that contribute to obesity. Health systems need to prepare in advance for an increasing burden of obesity-related complications. Increasing accessibility and access to healthcare, public health and social resources (e.g. parks, physical activity programmes and availability of nutritious foods) are needed. Longitudinal research studies of rural–urban differences in obesity-related interventions will be important to help guide policy and management. These studies and interventions should consider a diverse spectrum of communities.
Acknowledgements: This research was made possible using the data collected by the CLSA. Funding for the CLSA is provided by the Government of Canada through the Canadian Institutes of Health Research under grant reference: LSA 94473 and the Canada Foundation for Innovation. This research has been conducted using the CLSA dataset, CLSA Baseline Tracking Dataset 3.6, under Application Number 2002009. The CLSA is led by Drs. Parminder Raina, Christina Wolfson and Susan Kirkland. We thank all participants of the CLSA.
Financial support and sponsorship: The contents of this study were developed under a Catalyst Grant (50240) from the Canadian Institutes of Health Research (CIHR).
Conflicts of interest: There are no conflicts of interest.
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[Table 1], [Table 2], [Table 3], [Table 4]