Obesity, defined as abnormal or excessive fat accumulation that may impair health(1), is a major public health concern worldwide. The obesity epidemic, although widespread, has been defined as complex in the Latin America and the Caribbean region, where both occurrence and trends vary within the territory and across social groups(2–Reference Jiwani, Carrillo-Larco and Hernández-Vásquez4). In Argentina, official reports show that the obesity prevalence in the urban adult population has risen from 14·6 % in 2005 to over 25 % in 2018.
Epidemiological research on obesity has traditionally focused on the role of individual-level behavioural factors (diet and physical activity, mainly). Subsequently, greater attention has been paid to social and contextual influences on obesity, since the notions of ‘obesogenic environments(Reference Kirk, Penney and McHugh5,Reference Townshend and Lake6) ’ and ‘nutrition transition(Reference Popkin, Adair and Ng7,Reference Popkin8) ’ have gradually consolidated.
In particular, the process of nutritional transition implies remarkable shifts in physical activity and diets of the populations with a rapid increase in the prevalence of overweight and obesity. These nutritional changes occur in the context of broader societal drivers (such as sociodemographic changes linked to urbanicity and a rapid economic development), which could mediate differential exposure to the causes of all forms of malnutrition(Reference Wells, Sawaya and Wibaek9). The Social Determinant of Health approach(Reference Solar and Irwin10) explains that attitudes, beliefs or behaviours represent only the most downstream determinants in the causal pathways influencing health, which are shaped by more upstream determinants; overall, they reflect the economic and social resources and opportunities for improving health(Reference Braveman, Egerter and Williams11). Specifically, it has been explained that the upstream determinants that influence obesogenic behaviours may simultaneously manifest as the form of tangible characteristics in the built or natural environments (which determine what is available), as well as less tangible features in our economic, political and sociocultural environments(Reference Lakerveld and Mackenbach12). Based on this framework, we assumed that upstream social factors underlie obesity-related behaviours. Additionally, from a multilevel perspective(Reference Merlo, Chaix and Ohlsson13), we recognise that certain contextual characteristics of the built and natural environments (defined at a higher level) could operate together with certain individual social features (related to structural mechanisms of social stratification such as income, education, occupation and sex)(Reference Solar and Irwin10) as socio-environmental factors related to obesity occurrence (defined at a lower level). Especially in developing countries, there is a lack of understanding about the simultaneous individual- and contextual-level factors that drive regional and other subnational disparities (e.g. by sexes and age groups) in obesity burden.
Overall, estimates indicate that the age patterns of obesity differ between men and women(Reference Ng, Fleming and Robinson14). Although the ways in which the nutrition transition affects the diets of age groups differently is not well described in the scientific literature, it is recognised that several dimensions of social disadvantage exert differential effects across the life course, impacting on food security and, in turn, on body composition or nutritional status(Reference Schrock, McClure and Snodgrass15). It has been suggested that, for example, the capacity to resist adverse societal influences could play a role in the result of malnutrition – including overweight(Reference Wells, Sawaya and Wibaek9), which could be important among more socially vulnerable population groups (possibly elderly or young women). However, the evidence on the mechanisms explaining age- and sex-related social disparities in obesity is still unclear. In the Latin American and the Caribbean region, the differentials by age groups deserve special attention given their ongoing and accelerated process of demographic ageing(Reference Palloni, Pinto-Aguirre and Pelaez16).
In Argentina, updated and further contextualised evidence is needed to identify socio-environmental determinants of obesity by specific population groups at a national scale. Most of the national population-level studies on adult obesity cover up to the year 2013(Reference Tumas, Rodríguez Junyent and Aballay17,Reference Linetzky, De Maio and Ferrante18) ; other works on lifestyles and sociodemographic factors related to obesity in this country, though interesting, do not have national representativeness(Reference Linetzky, De Maio and Ferrante18–Reference Pou, Del Pilar Díaz and De La Quintana21). Interestingly, some studies have identified diverse socio-environmental patterns and demographic factors accounting for the sex-specific geographical pattern on obesity-related chronic diseases in this country(Reference Pou, Tumas and Sánchez Soria22,Reference Pou, Diaz and Velazquez23) . Thus, areas with higher mortality risk of CVD in men have been directly associated with the smallest urban scale coupled with a higher level of poverty, in contrast to the effect observed for women(Reference Pou, Tumas and Sánchez Soria22). Besides, specific geographical patterns with disadvantageous socio-environmental features were identified in Argentina accounting for the differential burden of cancer mortality between sexes in this country(Reference Pou, Diaz and Velazquez23).
The National Survey of Chronic Diseases Risk Factors (CDRF) has been conducted every 4–5 years since 2005 in Argentina from a probabilistic population-based sample. This survey includes sociodemographic and health information from an urban target population aged 18 years or older living in Argentine towns with at least 5000 people. For the first time, the 2018 edition of this survey included anthropometric measurements (not self-reported data, as previously); the present study analyses the obesity scenario using this representative country dataset. To our knowledge, ours is the first research work that examines up-to-date information on obesity in this country to explain social disparities and contextual factors underlying obesity distribution among different population groups by sex and age. In this population-based study, we assessed the association of individual-level social characteristics and environmental factors, simultaneously, with the obesity occurrence, using a multilevel modelling strategy over the latest available CDRF survey, 2018.
Methods
Study design and data sources
This study is based on secondary data collected from the CDRF survey carried out in 2018 by the National Health Ministry of Argentina and the National Institute of Statistics and Census (known by its acronym in Spanish, INDEC). The CDRF is a nationally representative face-to-face survey conducted by trained interviewers, based on a rigorous probabilistic sampling design. The 2018 survey included anthropometric measurements of height and weight taken by trained health personnel. The instruments (portable electronic weighing scale and portable height measuring board) and the techniques used for the height and weight measurements follow the STEPS protocol of the WHO(24) endorsed by the Ministry of Health and Social Development of the Nation, Argentina.
The 2018 CDRF databases consist of a population-based sample of 29 224 persons aged 18 years and older living in towns of at least 5000 people of Argentina. The sampling design of the CDRF was probabilistic and multistage. At the first stage, sampling selection was based on the Master Urban Sample of Dwellings of the Argentine Republic (MUDAR). The MUDAR has a complex sample design and is used by the INDEC as a framework for the selection of private dwellings for all its national surveys. In the CDRF, sampling units were selected from the MUDAR list by means of a stratified probabilistic design (by sociodemographic variables) and a systematic sampling (proportional to the total number of occupied dwellings). For the definitive sample of dwellings in the CDRF, a systematic selection of segments of five contiguous dwellings (within the MUDAR list and cartography) was applied to obtain the final sample of dwellings. At the survey moment, the interviewer selected with equal probability a person aged 18 years or older, assisted by a random algorithm. The final size of the sample was 29 224 individuals for the application of the first part of the questionnaire (self-reported data), covering all jurisdictions in the country. For the second step that includes anthropometric measurements, a probabilistic subsample was made with 75 % of previously selected dwellings (n 16 577 individuals). More methodological details of the CDRF survey have been published in official reports of the National Health Ministry(25).
In our study, a subset of 16 410 persons with anthropometric measurements was extracted, after excluding 167 observations with incomplete data. For multilevel analyses, the individual-level dataset (n 16 410) was nested into the area-level information about environmental characteristics (n 24 geographical units, corresponding to administrative divisions). Two area-level variables were selected: the index of socially constructed recreational resources (SCRR) and the index of green spaces. The SCRR index is a score (calculated at provincial scale) that considers the availability of certain environmental conditions and cultural attractions such as urban aesthetics, urban heritage sites, cultural centres, shopping malls, sports centres, among others. The green space index (score) represents the availability of green spaces measured through land coverage by open green spaces or natural areas. Both indexes were part of the latest available Quality of Life Index database(Reference Velázquez and Tonon26), which incorporates data for Argentina at the county level from several sources (official reports, field studies and satellite imagery) into a Geographic Information System. This dataset and its methodological aspects were published elsewhere(Reference Pou, Diaz and Velazquez23,Reference Velázquez and Tonon26) .
Statistical analyses: multilevel modelling
Due to the hierarchical structure (spatial clustering) of the data (16 410 subjects nested into 24 geographical units), two-level logistic regression models were used to estimate the association between selected individual- and area-level covariables and obesity occurrence (dichotomous outcome, yes/no). Three age groups were defined as young (aged 18–44 years), middle-aged (aged 45–64 years) and older (65 years and older) considering the criteria of the INDEC and the MeSH descriptor of middle-aged provided by the US National Library of Medicine(27). Thus, the sex and age groups defined were set up as strata in multilevel model adjustments. Obesity was defined as having a BMI ≥ 30 (yes/no) following the WHO criterion(1). BMI was calculated by using measured anthropometric data.
The analyses were performed in sequential steps, from a variance component model (multilevel ‘empty’ model, without covariates) to an adjusted two-level model that included all the individual- and area-level variables. Model selection was based on the Akaike information criterion as well as the interpretability and comparability of results among the different population groups. Several individual-level variables that inform about sociodemographic characteristics of participants were considered in the models: marital status (married; divorced/widowed; single), household type (one-person household; a couple without children; multi-person household including a couple with children or other persons at home), education (highest level of education attained: incomplete primary education or lower; primary education; high school; higher education), income level (higher, intermediate or lower if self-reported incomes are at the highest, second-to-four or first quintile of the income distribution, respectively) and geographic location of residence (city of residence classified by population size as: small cities of 5000–149 999 people, or big/middle-sized cities of 150 000 or more people). Since previous findings have indicated a joint effect of poverty and urbanisation on health statistics in Argentina(Reference Palloni, Pinto-Aguirre and Pelaez16), additive interaction terms between income and geographic location were also considered. Finally, lifestyle-related variables such as physical activity (intense/intermediate/low), tobacco consumption (non-smoker/former smoker/smoker), and fruit and vegetable consumption (above or below the five portions/day recommendation by the Food Guide for the Argentine Population)(28) were included as adjustment variables. These data were gathered by a structured questionnaire administered by trained personnel. This instrument included questions about tobacco consumption (current consumption frequency and previous consumption of at least 100 cigarettes, among others) and the usual frequency of fruit or vegetable intake (times/week and daily portions by self-report), and a section with the International Physical Activity Questionnaire (IPAQ) for physical activity assessment(29).
The area-level variables included in the model were the aforementioned contextual indexes (continuous variables at the provincial scale) of SCRR and of green spaces; both were calculated as population size-weighted average of the indexes reported at the county level(Reference Velázquez and Tonon26). The highest values for these indexes reflect the best situation of the availability of SCRR or green spaces.
The linear predictor of the general mixed logistic model was
$$\matrix{
{logit\left[ {E{\mkern 1mu} \left( {{y_{ij}}} \right)} \right]{\mkern 1mu} = ({\beta _0} + {\xi _{\rm{j}}}) + {\beta _{\rm{1}}}{ \times _{{\rm{1}}ij}} + \ldots + {\beta _p}{x_{pij}} + {\delta _{0{\rm{1}}}}{w_{{\rm{1}}j}}} \cr
{ + \ldots + {\delta _{0{\rm{1}}}}{w_{lj,}}} \cr
} $$
where y ij is the response variable (obesity yes/no), x 1ij to x pij and w 1j to w lj are explanatory variables with fixed effects (linear coefficients), corresponding to the individual- or area-level covariates, respectively. The term ξj is a random intercept term representing the clustering variance structure and, therefore, accounting for the geographical variability in the estimation process. All models were adjusted by lifestyle-related variables. Thus, female-only and male-only models were fixed and the estimated association measures (OR) plotted. Then, six models were constructed for each sex and age group combination (young, middle-aged, and older men and women). All analyses were performed using Stata v14.
Results
This study examined the obesity occurrence among 16 410 adult people (58 % women and 42 % men) living in Argentina, using data from the CDRF survey. In 2018, over half (51·0 %) of the participants were young adults, and about 30·1 % and 18·9 % were middle-aged and older adults, respectively.
Table 1 presents the individual-level characteristics of the study participants, for the total sample and stratified according to age group and sex. As shown, about 46·8 % of the participants reported a low level of physical activity, reaching values of 54·7 % and 61·8 % in older men and women, respectively. The percentage of people with low fruit and vegetable intake was around 90 % in all age groups and both sexes. The percentage of smokers (21·8 % in whole sample) was higher in men than women, especially in those under 65 years of age. In older adults, the percentage of former smokers was more than double in males (45·4 %) compared with the female group (19·4 %). Most participants were married (49·5 %) and lived in a multi-person household (61·2 %). However, the distribution of subjects by marital status or household type shows differences between sexes. These differences were stronger in the elderly and were particularly related to the categories married or divorced (marital status variable), and one-person household (household-type variable). About 36 % of the total sample had completed primary education (with higher prevalence in men than women, especially in the 18–44 years age group), while 17·3 % reported having higher education. In all age groups, the percentage of people with higher education was higher in women than in men. Over half (59·9 %) of the participants had an intermediate income level. Particularly at higher-income levels, men always show higher values (%) compared to women, being the difference more noticeable in younger groups. Regarding geographical location, approximately 55–60 % of participants from all age group and both sexes lived in big/middle-sized cities (Table 1).
Individual-level characteristics by age group and sex among 16 410 adults. National Survey of Chronic Diseases Risk Factors, Argentina 2018
*The percentage distribution by categories of each variable was calculated on the total subjects (n as the 100 %) within each population group studied.
† Weight status by BMI: underweight (BMI de18·5); normal weight (BMI 18·5–24·9); pre-obesity (BMI 25·0–29·9); obesity (BMI ≥ 30).
‡ Income level: lower level (lowest quintile); intermediate level (middle quintiles); higher level (highest quintile).
§ Geographical location of residence: small cities (5000 up to 149 999 people); big cities or middle-sized cities (150 000 or more people).
A similar distribution across sex and age groups was found for area-level variables. In the whole sample, 33·9 % and 55·1 % lived in areas with high indexes (above the mean values) of the SCRR and green spaces, respectively. The mean (sd) score was 6·30 (0·76) for SCRR index and 1·65 (0·63) for green spaces, with maximum/minimum values of 4·96/9·00 and 0·67/2·94, respectively.
Participants (in the whole sample, regardless of sex) were homogeneously distributed across the categories of normal weight (30·6 %), pre-obesity (34·6 %) and obesity (33·3 %), with just 1·5 % of people with underweight (Table 1). Underweight was more prevalent in younger groups, and especially among young women (2·9 %). Over 65 % of the total participants (71·4 % in men and 65·2 % in women) had a BMI of 25 or more (pre-obesity or obesity) (Table 1). Figure 1 presents the weight status distribution by sex and age group. As this figure shows, the most frequent category of the weight status was pre-obesity (BMI 25–29·9) in men (all age groups) and obesity (BMI ≥ 30) in women (middle-aged and older age groups). In the whole sample, obesity prevalence was 33·3 %. Both in men and women, the higher values were concentrated in middle-aged groups (Table 1). However, there were differences in obesity prevalence between sexes, mainly among older adults (41·9 % v 36·9 % in women and men aged 65 years or more, respectively) (Table 1). Figure 2 depicts the adjusted OR of obesity estimated by the female- or male-only multilevel models (for all ages together). Middle-aged adults were 1·7 to 2 times more at risk of having obesity than young men or women. The elderly category also showed a similar OR (1·8) in women. Other individual-level characteristics such as living as a couple or in a multi-person household, and living in a small city with a lower-income level, showed opposite tendencies in women (direct association with obesity) and men (inverse association). A lower obesity risk of being divorced/widowed or single (v. married) was found. In both sexes, there was a social gradient of the education level (people who are more advantaged in terms of education had lower obesity risk than those who are less advantaged); however, the risk reduction at higher educational level was stronger in women than men. Furthermore, higher SCRR and green spaces indexes were inversely associated with obesity in both groups (Fig. 2).
Weight status distribution by age group based on a sample of 9450 women and 6960 men. National Survey of Chronic Diseases Risk Factors, Argentina 2018. Classification of weight status by measured BMI: underweight (BMI<18.5); normal weight (BMI 18.5–24.9); pre-obesity (BMI 25.0–29.9); and obesity (BMI≥30)
Association between socio-environmental factors and obesity in Argentina: OR and CI estimated by sex-specific models. Results from two-level logistic regression models with obesity (yes/no) as outcome, and provinces as clustering variable (random effect), adjusted by level of physical activity, tobacco, and fruit and vegetable consumption. National Survey of Chronic Diseases Risk Factors, Argentina, 2018. SCRR, SCRR, socially constructed recreational resources
Although Figure 2 shows that differences between sexes, without discrimination by age group, are slight, interesting results emerged when analyses were performed by sex and age groups. Crude and adjusted OR estimates by multilevel models stratified by age group among men and women separately are displayed in Tables 2 and 3. After controlling for lifestyle covariates, it was found that compared to married men, single (in all age groups) and divorced/widowed men (in the older and middle-aged groups) had lower risk of obesity (Table 2). In the female-only model (Table 3), the association between obesity occurrence and being single (lower risk compared to married) was significant among young and middle-aged women. There was a direct association between obesity and living in a multi-person household (v. one-person household) in middle-aged women (OR = 1·26, p = 0·042). Besides, a social gradient by education level was accentuated in the young women group (Table 3). In particular, higher education showed a significant inverse association with obesity in all the age groups for women, as well as in middle-aged men (Tables 2 and 3). The association of income level, coupled to geographical location (interaction term), with obesity was significant only for women (Table 3). Specifically, we observed that the income level factor is not independent of geographical location among young and middle-aged women; in these population groups, a lower-income level coupled with a highly urbanised context seems to be an unfavourable scenario related to the obesity outcome. An inverse association with obesity was observed for SCRR and green space indexes (lower risk as score increases), which was significant in young groups of both sexes (Tables 2 and 3).
Multilevel models examining the association of individual-level and contextual characteristics with obesity among men by age group. National Survey of Chronic Diseases Risk Factors, Argentina 2018
IL, income level; SCRR, socially constructed recreational resources.
*P < 0·05, **P < 0·01, or ***P < 0·001 as levels of significance.
† Two-level logistic regression model with provinces as clustering variable (random effect).
‡ OR crude.
§ OR adjusted by level of physical activity, tobacco consumption, and fruit and vegetable consumption.
Multilevel models examining the association of individual-level and contextual characteristics with obesity among women by age group. National Survey of Chronic Diseases Risk Factors, Argentina 2018
IL, income level; SCRR, socially constructed recreational resources.
*P < 0·05, **P < 0·01, or ***P < 0·001 as levels of significance.
† Two-level logistic regression model with provinces as clustering variable (random effect).
‡ OR crude.
§ OR adjusted by level of physical activity, tobacco consumption, and fruit and vegetable consumption.
Discussion
This study provides an updated and comprehensive picture of the obesity burden and its socio-environmental determinants in the adult population of Argentina, considering the multilevel structure of the national information. In our work, the 2018 obesity prevalence was about 33 %, with figures higher than 40 % for middle-aged groups (both sexes) and older women. For 2018, our findings indicate that the association of social and environmental factors with the obesity occurrence differ considerably across age and sex groups. In males, marital status emerges as an individual-level factor associated with obesity, and a high education level showed a significant association for the middle-aged group. In women, our findings suggest that the education level, having a multi-person household, and living in a highly urbanised context with a lower-income level are key factors associated with obesity occurrence, with differences by age groups. Contextual indexes of SCRR and green spaces were associated with obesity, specifically in the younger groups of both sexes.
The 2018 CDRF survey collected both self-reported and measured anthropometric data of the adult population of Argentina. Compared to previous statistics from the CDRF survey about self-reported BMI, increasing rates of obesity were observed since 2005(30). The 2018 CDRF report indicates that the prevalence of obesity obtained from measured data was about 7 % point higher than those based on self-reported BMI(30). The high levels of obesity estimated for Argentina in 2018 are consistent with the growing burden of obesity reported in the Latin American region(Reference Popkin and Reardon3,Reference Ng, Fleming and Robinson14) . The changes in the nutritional profile of the Latin American populations in the last decades have largely been attributed to the process of the nutrition transition(Reference Popkin and Reardon3), which occurred in parallel with several socio-economic and demographic shifts (e.g. changes linked to the urbanisation and globalisation processes) in most regions of the world(Reference Popkin8). Interestingly, a recent study in Argentina reports that sociodemographic factors (such as urbanisation and poverty level) play a major role in shaping diverse nutritional profiles across the territory, which configure a complex and heterogeneous socio-nutritional scenario(Reference Tumas, Rodríguez Junyent and Aballay17).
Overall, existing studies about the association between marital status and overweight indicate that the former appears to influence obesity more strongly among men than women(Reference Sobal, Rauschenbach and Frongillo31,Reference Manfredini, De Giorgi and Tiseo32) . The lower obesity risk in single men has been reported by other studies(Reference Aballay, Osella and Celi19,Reference Sobal, Hanson and Frongillo33,Reference Merino Ventosa and Urbanos-Garrido34) . This could be related to the lifestyle of married men that may lead to a more stable eating pattern, compared to unmarried people(Reference Sobal, Rauschenbach and Frongillo31). Besides, a larger body size is likely to be valued as a sign of physical dominance and prowess for men(Reference McLaren35), if we consider the expected social roles for men entering marriage in some social groups. It is important to note that the lower obesity risk observed in single men in our study was also present among single females, particularly in the younger groups. In this case, this finding may reflect certain body weight norms and expectations in our society linked to the female beauty ideal, more solid at certain stages of life. An exaggerated thin body ideal has been recognised as a distinctive characteristic of the Argentine population(Reference Forbes, Jung and Vaamonde36), especially among women.
We also found a significantly lower risk for the divorced/widowed category in the middle-aged and elderly men groups (v. married). This could be explained, in part, by the fact that marital disruption (i.e. being widowed or divorced) has been associated with poor physical health outcomes(Reference Manfredini, De Giorgi and Tiseo32), including weight loss linked to psychosocial frailty or high-risk alcohol or tobacco consumption. Additionally, it has been proposed that the marital role provides support and resources which may influence eating and physical activity habits(Reference Sobal, Rauschenbach and Frongillo31). Thus, the lower obesity risk observed for divorced/widowed men older than 44 years of age (v. married ones) may indirectly reflect their potential lack of social support as observed, for example, in eating habits. The importance of social support for health is well recognised(Reference Haslam, McMahon and Cruwys37), as well as the role of women as the ones mostly responsible for food preparation in families(Reference McLaren35). In Argentina, a national study on older adults highlights that women have a high physical and psychological burden associated with such activities as the care of elderly or family members(Reference Rodríguez López, Colantonio and Celton38).
Education is a recognised individual-level socio-economic factor related to obesity risk. Overall, there is a general agreement that the relationship between education and obesity is often more consistent among women(Reference Merino Ventosa and Urbanos-Garrido34,Reference Harrington and Elliott39,Reference Kim, Roesler and von dem Knesebeck40) . Additionally, we found a stronger educational gradient in the obesity burden among the younger group of Argentine women. This finding is in line with previous studies carried out in this country in 2005, which reported a lower obesity prevalence with better education level among women aged 20–49 years(Reference Zapata, Soruco and Carmuega41). Also in other Latin American countries, lower obesity prevalence was observed in women with higher education among this age group(Reference Curi-Quinto, Ortiz-Panozo and López de Romaña42,Reference Perez Ferrer, McMunn and Rivera Dommarco43) . Particularly from a longitudinal study, a reversal of the inverse association between education level and obesity risk during ageing was observed, especially in women. These authors propose that women with higher education put more effort than men into controlling their body weight, to fit themselves in the labour market and to reach a high social position; then, approaching old age, women try to get rid of the social pressures towards thinness(Reference Chung and Kim44). A sociocultural pressure on females to achieve the desired body image, especially among women of high socio-economic status suggested by other authors(Reference Wardle, Waller and Jarvis45), can explain, in part, our findings in Argentina.
Since education has a role as part of the complex phenomenon of socio-economic stratification, another aspect to considerer is that people with higher education may have greater job opportunities and, therefore, better access to physical activity facilities and healthy eating. Furthermore, education can be interpreted as a proxy for ‘health literacy’(Reference Harrington and Elliott39,Reference Kim, Roesler and von dem Knesebeck40) , which could improve an individual’s capacity to adequately address health-related issues, including overweight(Reference Kim, Roesler and von dem Knesebeck40,Reference Michou, Panagiotakos and Costarelli46) . These could be underlying mechanisms that explain, in part, the relationship observed between obesity and higher education, especially in middle-aged men and elderly women.
It has been highlighted that socio-economic inequalities within cities in developing countries are high and affect the social distribution of health outcomes(Reference Friel, Akerman and Hancock47). In Argentina, previous evidence indicates that the socio-economic patterning of chronic disease risk factors, including obesity, was modified by urbanicity(Reference Fleischer, Diez Roux and Alazraqui48), and that poverty and urban scale are associated contextual variables influencing the distribution of non-communicable diseases mortality(Reference Pou, Tumas and Sánchez Soria22). In our study, a higher risk of obesity was observed among women under 65 years of age living in big or middle-sized cities with a low family income level. Income generally reflects the availability of economic and material resources and, thus, it influences dietary quality(Reference Darmon and Drewnowski49). These results may reflect poor access to a healthy diet and low opportunities to adopt healthy behaviours, in women with a low-income level, especially if they live in urbanised areas. Interestingly, Oliveira et al. (Reference Oliveira, Marques and Dos Santos50) indicated that there are several aspects (physical, economic, political and sociocultural) of the environmental factors that would independently affect men and women, and that obesogenic influence of the environment may differ in small and medium-sized cities, when compared to large cities. According to the WHO, urban poverty and unhealthy living conditions themselves are social determinants of health that can affect disproportionately certain vulnerable subgroups such as women(51). Moreover, considering their potential family caregiver role(Reference Rodríguez López, Colantonio and Celton38), especially under unfavourable economic conditions, Argentine women may reduce their time for the care of their own health (impacting on their weight status). This could also explain, in part, the higher obesity risk in middle-aged women living in a multi-person household that we observed in our results.
Considering environmental factors, there is suggestive evidence about the relationship between green spaces and weight status or obesity-related health indicators. Several studies found evidence that these relationships varied by factors such as age and socio-economic status, although the mechanisms through which green spaces may influence health are not completely understood(Reference Lachowycz and Jones52). Evidence suggests that there is a link between green spaces and obesity, as the former would offer enhanced opportunities for physical activity(Reference Lachowycz and Jones52,Reference Villeneuve, Jerrett and Su53) , even within urbanised contexts(Reference Devarajan, Prabhakaran and Goenka54). However, the findings on this matter are not conclusive, especially in developing countries. In Argentina, agriculture and tourism are motors of regional economic development; agricultural and touristic activities usually take place in locations where natural resources are highly available. Thus, we additionally argue that the environmental indicator used here may represent proxy variables of socio-economic conditions at the macro-level.
From a gendered perspective, MacBride-Stewart et al. (Reference MacBride-Stewart, Gong and Antell55) identify key dimensions to consider in the study of the interconnections between health and nature, including accessibility, availability and usability of green spaces, as well as the boundaries (symbolic/material) that construct differential relationships between natural spaces, sex and health(Reference MacBride-Stewart, Gong and Antell55). Consistently, another work(Reference Sander, Ghosh and Hodson56) also indicates that the perception and use of green spaces, as well as green space attributes, can explain the different associations with BMI that the authors observed among age- and sex-specific adult groups. Given our finding of an association between area-level variables and obesity occurrence, those variables may also be relevant aspects to explain age group differences in Argentina.
Particularly, the role of the contextual conditions summarised in the SCRR index (such as urban aesthetics/urban heritage, cultural amenities, shopping malls and sports centres) is underexplored in obesity research. However, the notion of SCRR could be closely related to the idea of built environments, defined as the human-modified space in which people conduct their daily lives; their influence on obesity has been more extensively studied(Reference Drewnowski, Buszkiewicz and Aggarwal57). In general, there is consensus that the built environment plays a key role as a barrier or enabler to physical activity and as a mediator in access to healthy food(Reference Devarajan, Prabhakaran and Goenka54,Reference Tung, Cagney and Peek58) . Interestingly, an exhaustive review from the perspective of developing countries(Reference Oliveira, Marques and Dos Santos50) concludes that health-promoting built environments can have a profound influence not only on the population levels of physical activity but also on its well-being and equity – both socio-economic and age-sensitive. Thus, our findings of a lower obesity risk associated with contexts with greater SCCR in young people could be related to a mechanism mediated by the availability, perception and use of sports centres or other amenities that were part of the SCRR index. In a Brazilian study on obesity, the authors reinforce the idea that a favourable decrease in ‘obesogenic’ traits in the urban environment is possible when physical structures are planned to facilitate physical activity(Reference Oliveira, Marques and Dos Santos50). Alternatively, we propose that the distribution of the SCRR variable could depict a sort of ‘regionalisation’ (geographical division) underlying socio-economic and cultural characteristics of the populations or could represent different patterns of land use. Consistent evidence showed that a better mix of land use (residential, commercial, institutional, industrial, recreational and agricultural) is generally associated with less obesity burden(Reference Lakerveld and Mackenbach12), although there is a general lack of insight into the pathway by which land use mix impacts obesity. Further research would be useful in these regards to better understand the observed age differences.
A major strength of this research is its updated source of data and large sample size based on a rigorous probabilistic sampling design which ensures its national representativeness. Indeed, the matching between the age group distribution in our sample and in the national population projections for 2018 by the INDEC is notable. Besides, as far as we know, this is the first study that uses measured anthropometric data to identify social inequalities in obesity distribution from the most recent CDFR survey. Moreover, it provides an analytical example of how the multilevel epidemiological framework can be used in this field of study. However, there are limitations to consider. First, we know that our study was based on an urban setting and, thus, rural populations were not represented. However, about 91 % of the Argentine population is living in urban areas(Reference Pou, Tumas and Sánchez Soria22). Second, the reliability of income measures may be debatable in developing countries(Reference Howe, Galobardes and Matijasevich59), although several questions regarding household income were designed to minimise response bias in the CDRF. Also the use of BMI as a measure of obesity could be discussed, since this index should be preferably accompanied by other body composition measures in some specific population groups, such as athletes and the elderly. However, the WHO recognises that BMI provides the most useful population-level measure of overweight and obesity(1). Finally, considering that exposure measurement error may occur in the characterisation of green spaces in epidemiological studies(Reference Villeneuve, Jerrett and Su53), and that other unmeasured macro-level factors may affect conclusions, the interpretation of macro-contextual association measures should be conservative.
To conclude, our findings show high levels of obesity in Argentina in 2018 and an unequal distribution of their socio-environmental determinants. Specific social and environmental factors seem to operate differently according to sex and age groups in this country. Thus, comprehensive interventions against the obesity epidemic addressing gender inequalities and socio-environmental disadvantages at each stage of life are needed. Our results also showed a favourable relationship (inverse association) between obesity risk and contextual characteristics (such as availability of green spaces) among the younger groups. This target population could be considered in the design of interventions aimed at achieving healthy environments. Finally, further interdisciplinary research on sociocultural pathways linked to obesity within urban contexts in developing countries is needed.
Acknowledgements
Acknowledgements: We thank Gabriela Díaz Cortez for her language assistance. Financial support: This work was supported by the Science and Technical Department of the University of Córdoba (grant number 33820180100195CB01). Authorship: S. A. P., L. R. A. and G. A. V. contributed to the study conception and design. Data analysis was performed by S. A. P., L. R. A. and M. P. D. The first draft of the manuscript was written by S. A. P. L. R. A. and M. P. D. contributed to the writing of subsequent drafts of the manuscript. All authors contributed to the interpretation of results and have approved the final manuscript. Ethics of human subject participation: This study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving research study participants were approved by the Ethical Committee of the Hospital Nacional de Clínicas, University of Córdoba, Argentina. Written informed consent was obtained from all subjects.
Conflict of interest:
SAP, MPD, GAV and LRA confirm that there are no relevant financial or non-financial competing interests to report.
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