https://orcid.org/0000-0001-5113-6475
Figures
Abstract
There is evidence for the influence of socioeconomic status (SES) on healthy behaviours but the effect of social mobility (SM) is not yet well known. This study aims to analyse the influence of origin and destination SES (O-SES and D-SES) and SM on healthy behaviours and co-occurrence, from an integrated gender and age perspective. Data were obtained from the controls of MCC-Spain between 2008–2013 (3,606 participants). Healthy behaviours considered: healthy diet, moderate alcohol consumption, non-smoking and physical activity. SM was categorized as stable high, upward, stable medium, downward or stable low. Binary and multinomial logistic regression models were adjusted. Those aged <65, with a low O-SES, D-SES and stable low SM are less likely to have healthy behaviours in the case of both women (physically active: OR = 0.65 CI = 0.45–0.94, OR = 0.71 CI = 0.52–0.98, OR = 0.61 CI = 0.41–0.91) and men (non-smokers: OR = 0.44 CI = 0.26–0.76, OR = 0.54 CI = 0.35–0.83, OR = 0.41 CI 0.24–0.72; physically active: OR = 0.57 CI = 0.35–0.92, OR = 0.64 CI = 0.44–0.95, OR = 0.53 CI = 0.23–0.87). However, for those aged ≥65, this probability is higher in women with a low O-SES and D-SES (non-smoker: OR = 8.09 CI = 4.18–15.67, OR = 4.14 CI = 2.28–7.52; moderate alcohol consumption: OR = 3.00 CI = 1.45–6.24, OR = 2.83 CI = 1.49–5.37) and in men with a stable low SM (physically active: OR = 1.52 CI = 1.02–1.26). In the case of men, the same behaviour pattern is observed in those with a low O-SES as those with upward mobility, with a higher probability of co-occurring behaviours (three-to-four behaviours: OR = 2.00 CI = 1.22–3.29; OR = 3.13 CI = 1.31–7.48). The relationship of O-SES, D-SES and SM with healthy behaviours is complex and differs according to age and gender.
Citation: Pinto-Carbó M, Peiró-Pérez R, Molina-Barceló A, Vanaclocha-Espi M, Alguacil J, Castaño-Vinyals G, et al. (2021) Social mobility and healthy behaviours from a gender perspective in the Spanish multicase-control study (MCC-Spain). PLoS ONE 16(5): e0251447. https://doi.org/10.1371/journal.pone.0251447
Editor: Thomas Behrens, Ruhr University Bochum, GERMANY
Received: January 21, 2021; Accepted: April 26, 2021; Published: May 12, 2021
Copyright: © 2021 Pinto-Carbó et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The database was registered with the Spanish data protection agency, under number 210267217118. Data cannot be shared publicly because of the confidential nature of the data imposed by the national legislation (BOE no. 171, announcement 24716, on 16th July 2009). However, the data used in this analysis and other data from the MCC-Spain study is available upon request to the Barcelona Institute for Global Health (ISGlobal) institution (contact via: del Rosselló street, 132, Barcelona, 08036, Spain. Telephone: +34 932 27 18 06 E-mail: info@isglobal.org) or to the MCC-Spain Steering Committee through the study coordinators: Manolis Kogevinas (manolis.kogevinas@isglobal.org) and Marina Pollán (mpollan@isciii.es).
Funding: This research was supported by the “Acción Transversal del Cancer”, approved by the Spanish Council of Ministers on 11th October 2007, by the Instituto de Salud Carlos III-FEDER [grant number:PI08/1770, PI08/0533, PI08/1359, PS09/00773-Cantabria, PS09/01286-León, PS09/01903-Valencia, PS09/02078-Huelva, PS09/ 01662-Granada, PI11/01403, PI11/01889-FEDER, PI11/00226, PI11/01810, PI11/02213, PI12/00488, PI12/00265, PI12/01270, PI12/00715, PI12/00150, PI14/01219, PI14/0613, PI15/00069, PI15/00914, PI15/01032, PI11/01810, PI14/01219, PI11/02213, PIE16/00049, PI17/01179, PI17-00092], by the Fundación Marqués de Valdecilla [grant number: API 10/09], by the ICGC International Cancer Genome Consortium CLL (The ICGC CLL-Genome Project is funded by Spanish Ministerio de Economía y Competitividad (MINECO) through the Instituto de Salud Carlos III (ISCIII)), by the Red Temática de Investigación del Cáncer (RTICC) del ISCIII [grant number: RD12/0036/0036], by the Junta de Castilla y León [grant number: LE22A10-2], by the Consejería de Salud of the Junta de Andalucía [grant number: PI-0571-2009, PI-0306-2011, salud201200057018tra], by the Conselleria de Sanitat of the Generalitat Valenciana [grant number: AP_061/10], by the Recercaixa [grant number: 2010ACUP00310], by the Regional Government of the Basque Country, by the Consejería de Sanidad de la Región de Murcia, by the European Commission [grant number: FOOD-CT-2006-036224-HIWATE], by the Spanish Association Against Cancer (AECC) Scientific Foundation [grant number: GCTRA18022MORE], by the Catalan Government-Agency for Management of University and Research Grants (AGAUR) [grant number: 2014SGR647, 2014SGR850 and 2017SGR723], by the Fundación Caja de Ahorros de Asturias and by the University of Oviedo. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Social determinants of health are the conditions in which people are born, grow up, work and live that affect their values, preferences and access to resources, and therefore their health behaviours and health outcomes [1]. Thus, health behaviours are socially determined and result in health inequalities, with gender and socioeconomic status (SES) being key social determinants [2]. Some of the most important healthy behaviours for management of chronic diseases include a diet rich in fruits and vegetables, the absence of smoking, responsible alcohol consumption and physical activity [3].
It has been observed that populations with a lower SES have more unhealthy behaviours [4–8]. Gender inequalities in health-related behaviours have also been found, with alcohol consumption being the most common unhealthy behaviour in men and physical inactivity in women [9]. Moreover, taking into account that the worst health indicators are seen in women with a lower SES, it is important to explore how the intersection between these two axes of inequality also determines health behaviours [10].
Health behaviours usually appear cumulatively, with a co-occurrence of two or more risk behaviours being the most common scenario [5, 9]. The bault’s cross-sectional study concluded that, the accumulation of risk behaviours was similar between both genders although the greatest predisposition is observed in men with a low educational level [11]. Therefore, the intersection between gender and educational level is also important when studying the co-occurrence of health behaviours.
There are several studies that show the influence of SES on health behaviours and health outcomes [11, 12], but the effect of social mobility is not yet well known. Social mobility can be understood from an intragenerational perspective, meaning, the change in an individual’s SES throughout his or her lifecourse or from an intergenerational perspective, meaning, changes in SES between two generations (progenitors-children) [13]. This study is framed from an intergenerational social mobility perspective, since it considers the SES of the parents and the participants.
Campos-Matos’s study [14] shows that the upwardly mobile population, that is, those who ascend the social scale, present better health than the downwardly mobile population, that is, those who descend it. Regarding health behaviours, another study observed that people with stable low, meaning they remain in a low social position, or downward mobility are more likely to have unhealthy behaviours [5]. Gugushvili’s study [15] shows a differential mobility pattern according to gender, with women being more influenced by their origin SES than men. These findings also coincide with other studies [16]. Considering Van Eijck’s sociological theories regarding the impact of social mobility on behaviours [17], the results of aforementioned studies [15, 16] coincide with the socialization hypothesis, which puts forward that primary socialization has a stronger impact on healthy behaviours than secondary socialization. Therefore, our hypothesis is that origin SES influences health behaviours to a greater extent than destination SES, depending on gender and age, with different results according to the type of behaviour studied.
SES can be measured by income, occupation or educational level [18]. To analyse intergenerational social mobility, this study used the origin SES obtained through the self-referred position of the progenitors and the destination SES obtained through the current educational level of participants, used as an approximation of their status.
This study aims to analyse the influence of origin and destination SES and social mobility on healthy behaviours, as well as co-occurrence, from an integrated gender and age perspective.
Materials and methods
Design and study population
We performed a cross-sectional design using data from men and women recruited as population-based controls in the Multicase Control study in Spain (MCC-Spain). The MCC-Spain is a case-control study on several types of cancers, namely breast, prostate, colorectal, gastric and chronic lymphocytic leukemia, carried out in 10 Spanish regions (Madrid, Catalonia, Valencian Community, Andalucia, Navarra, Castilla León, Murcia, Asturias, Basque Country, and Cantabria). Its main characteristics have been described elsewhere [19]. For the present study, only population without cancer (controls) was included. Selected controls were recruited between September 2008 and December 2013, and were randomly selected from the administrative records of primary care health centres located within the catchment areas [19]. These controls were frequency-matched by age, sex and region, ensuring that in each region there was at least one control of the same sex and 5-year interval for each case. All controls had to be between 20–85 years of age, to have resided in the catchment area for at least 6 months before the selection and to be able to answer the epidemiological questionnaire [19].
The sample of this study consisted of 4,098 participants. Participants that were missing information on any of the study variables were excluded from this analysis (492 excluded). Therefore, the study sample included 3,606 participants aged between 24 and 85 (1,762 women and 1,844 men).
Data collection
Information on sociodemographic characteristics, tobacco habits and physical activity was collected from the epidemiological MCC-Spain questionnaire, developed by MCC-Spain researchers. Food intake and alcohol consumption were evaluated using a self-administered, semi-quantitative food frequency questionnaire (FFQ) [20] validated in Spain to include regional products.
All these data were collected in a cross-sectional manner when the controls accepted participation in the study.
Ethics statement
Participants who agreed to partake in the study signed an informed consent form. The MCC-Spain study protocol was approved by the Clinical Research Ethics Committee of the Municipal Health Care Institute (IMAS) in accordance with conformity to the principles of the Declaration of Helsinki. The database was registered with the Spanish data protection agency, under number 210267217118 [19].
Study variables
Origin SES.
We use the SES of progenitors, obtained through a self-referred question. This variable is categorized as low, medium or high.
Destination SES.
The participants’ current educational level is used as an indicator of the destination SES [18]. This variable is categorized as low (less than or equal to primary school level), medium (secondary studies) or high (university studies).
Social mobility (intergenerational).
Social mobility was categorized into five groups, taking into account the origin and destination SES: 1) “stable high", which includes participants that have a high origin SES and high destination SES; 2) "stable medium", which includes participants that have a medium origin SES and medium destination SES; 3)"stable low", which includes participants that have a low origin SES and low destination SES; 4) "upward", which includes participants that have a low origin SES and medium/high destination SES, or that have medium origin SES and high destination SES; and 5) "downward", which includes participants that have a high origin SES but have medium/low destination SES, or that have a medium origin SES but now have low destination SES.
Healthy behaviours.
We studied four healthy behaviours: 1) healthy diet, considered as the consumption of ≥400 g/d of fruits and vegetables [21]. Intake from previous years was evaluated.; 2) tobacco consumption, grouping the responses into smokers (former and current smokers) and non-smokers (never smoked before); 3) alcohol consumption, measured as the number of glasses consumed per day and categorized in Standard Drinking Units (SDU). According to the recommendations of the Dietary Guidelines for Americans [22], moderate consumption is considered as ≤one SDU/day for women and ≤ two SDU/day for men; 4) physical activity, evaluated using metabolic equivalents (METs), establishing adequate physical activity as a weekly energy expenditure in physical activities higher than 8 METs hour per week [23], that is to say, consuming 8 times the energy consumed at rest. Physical activity referred to a long period of time (approximately 10 years).
Co-occurrence of healthy behaviours.
This refers to the coexistence of behaviours and was categorized into: none, one, two, three and four healthy behaviours. Three groups were established for the analysis: none-to-one, two and three-to-four. These categories allowed us to compare extreme groups, i.e., few healthy behaviours (none-to-one) versus many healthy behaviours (three-to-four).
Statistical analysis
Information on age, origin and destination SES, social mobility, healthy behaviours and co-occurrence was obtained for the entire sample and expressed in frequencies and percentages.
Logistic regression models were used to study the association between origin and destination SES and social mobility (explanatory variables) for each of the healthy behaviours studied (outcome variables). These models were first calculated for total sample (adjusted by age, gender, interaction age*gender and region) and stratified by age and gender groups later on (adjusted by region). Age, gender and region were considered as confounding factors. The sample was stratified by gender as gender roles can lead to inequalities in health behaviours. The age stratification used two categories: under 65 and over 65. The population aged under 65 was born in the 50s, and therefore achieved social and economic independence around the 70s when political, social and economic changes took place in Spain towards greater democratization which improved gender and social equity. This social transformation may have reduced social and gender inequalities, and may also have influenced the health behaviour patterns of the population.
Multinomial logistic regression models were used to study the association between origin and destination SES and social mobility (explanatory variables) with the co-occurrence of healthy behaviours (outcome variables) (category of reference: none-to-one behaviours). All models were adjusted by region and stratified by gender and age to control their role as confounding factors. Results were presented in terms of the odds ratio and a 95% confidence interval. Statistical analysis was performed using SPSS Statistics V21.
Results
In Table 1, the characteristics of study participants can be seen. In both genders, the most represented social mobility category is stable low, and the least represented category is upward. The most frequent healthy behaviour in women is moderate alcohol consumption and the least frequent is being physically active. In men, the most frequent is moderate alcohol consumption and the least frequent is being a non-smoker. Both genders are most likely to present co-occurrence of three-to-four behaviours (Table 1).
In the logistic regression models for the entire sample (Table 2) it is observed that the population with low origin and destination SES (OR = 1.23 CI = 1.01–1.50, OR = 1.40 CI = 1.11–1.76), as well as those with stable medium mobility (OR = 1.38 CI = 1.09–1.75), are more likely to be non-smoker. In addition, this table shows that, regardless of origin and destination SES and social mobility, sex influences all healthy behaviors (p <0.05), that age in turn impacts healthy diet and physical activity and finally, that the interaction between sex and age has an impact on be non-smoker.
Stratifying by sex and age, as detailed in Table 3, women under 65 with a low origin and destination SES and stable low mobility are more likely to be non-smokers (OR = 1.83 CI = 1.26–2.66, OR = 1.63 CI = 1.18–2.24, OR = 1.69 CI = 1.14–2.51) and less likely to be physically active (OR = 0.65 CI = 0.45–0.94, OR = 0.71 CI = 0.52–0.98, OR = 0.61 CI = 0.41–0.91). Women over 65 with a low origin and destination SES are more likely to be non-smokers (OR = 8.09 CI = 4.18–15.67, OR = 4.14 CI = 2.28–7.52) and those with a low origin and destination SES and stable low mobility consume moderate amounts of alcohol (OR = 3.00 CI = 1.45–6.24, OR = 2.83 CI = 1.49–5.37 OR = 3.97 CI = 1.81–8.70).
For men, those under 65 with a low origin and destination SES and stable low mobility are less likely to be non-smokers (OR = 0.44 CI = 0.26–0.76, OR = 0.54 CI = 0.35–0.83, OR = 0.41 CI = 0.24–0.72) and be physically active (OR = 0.57 CI = 0.35–0.92, OR = 0.64 CI = 0.44–0.95 OR = 0.53 CI = 0.23–0.87). Those with a low origin SES and upward mobility are less likely to consume moderate amounts of alcohol (OR = 0.33 CI = 0.20–0.55, OR = 0.35 CI = 0.19–0.66). Men over 65 with stable low mobility are more likely to be physically active (OR = 1.52 CI = 1.02–1.26) (Table 3).
On the other hand, as shown in the multinomial regression models (Table 4), women over 65 with a low origin and destination SES and stable low mobility are more likely to have three-to-four behaviours (OR = 6.54 CI = 2.38–17.97, OR = 3.92 CI = 1.69–9.09, OR = 6.19 CI = 2.09–18.34) vs. none-to-one than those with a high origin and destination SES and stable high mobility.
For men, those under 65 with a low origin SES and upward or stable low mobility are less likely to have three-to-four behaviours (OR = 0.36 CI = 0.19–0.66, OR = 0.27 CI = 0.13–0.59, OR = 0.39 CI = 0.21–0.73) vs. none-to-one than those with a high origin SES and stable high mobility. Men over 65 with a low origin SES and upward or stable low mobility are more likely to have three-to-four healthy behaviours (OR = 2.00 CI = 1.22–3.29, OR = 3.13 CI = 1.31–7.48, OR = 2.04 CI = 1.23–3.39).
Discussion
This study shows that there are gender and age inequalities in terms of the impact of origin and destination SES and social mobility on the prevalence and co-occurrence of healthy behaviours, with different results depending on the type of behaviour. Those aged under 65 are less likely to have healthy behaviours if they have stable low social mobility, and the opposite is true in women and men over 65. Moreover, the same age trend was observed in the co-occurrence of healthy behaviours in men with a low origin SES and those with upward social mobility.
Some studies have observed that the absence of tobacco use and moderate alcohol consumption occur more frequently in women than men [24, 25]. From a gender perspective, these inequalities can be interpreted by the fact that, in traditional societies with a clear gender division of labour, both behaviours were only socially accepted in the productive, public and power spheres, which were historically occupied by men [26, 27]. The results of this study are in line with these studies [24, 25] and could indicate that traditional gender stereotypes are still evident in the population.
In this study, it is observed that women with low SES, regardless of their origin and destination, show healthy smoking and alcohol consumption behaviours. This is in line with others studies [27]. These results can also be explained by the influence of traditional gender roles, which are mainly present in women from low social groups. In addition, this connection is stronger in older women and could be due to the social changes that occurred in Spain during the 70s. In this decade, women were gradually incorporated into education and the labour market, which reduced socioeconomic and gender inequalities not only between men and women, but also between women themselves. As a consequence of this social transformation, women acquired behaviours associated with the traditional male role, such as the consumption of tobacco and alcohol, and social and gender inequalities in health behaviours reduced [28]. However, this increased equity may have had a lower impact on the older women in our sample, who seems to maintain traditional gender roles.
In contrast to women, some studies show that younger men with low SES are less likely to be non-smokers [29, 30], in line with our results. Traditionally speaking, tobacco consumption was associated with male behaviour as a symbol of power. However, this stereotype began to change in the 80s due to public health policies against this unhealthy behaviour [31]. From this point, the number of non-smokers gradually increased, but consumption remained unchanged in men with a low SES.
It should be noted that this study shows the same trend of healthy behaviour in relation to alcohol in both younger men with low origin SES and those with upward mobility. Taking this into account, we could suppose that men with upward mobility maintain the behaviours of the origin SES. This finding is in line with the socialization hypothesis, confirming the strong impact of primary socialization on men’s healthy behaviour patterns [17].
There is possibly a negative relationship between women with downward mobility and healthy diet, regardless of age. Bonaccio et al’s study [32] shows that the increase in the price of healthy food decreases access to these types of products, especially in the low-income population. Considering that 91.91% of the women in our sample with downward mobility have gone from a medium to a low SES, the results seem to indicate that the process of social mobility leads to the incorporation of lifestyle habits from the achieved SES, coinciding with the results of another study [33].
Most published studies observe that men and women with a low SES are less physically active, regardless of age [34–36]. This is in line with our results for younger women and men, but not for older men. This may be due to the fact that occupation is acting as a confounder. Manual occupations such as masonry or agriculture are typical of the population with a low educational level, which implies physical activity and, possibly, later retirement [37].
Regarding the co-occurrence of behaviours, studies have reported that men accumulate more risk behaviours than women [9, 11, 38], in line with our results showing that women accumulate a greater number of healthy behaviours. These differences can again be explained by the impact of traditional gender roles, which influence men and women’s different conception of health and, therefore, the related behaviours [39]. Traditionally speaking, women’s behaviours are influenced by the role of the female caregiver, which predisposes them to take care of their health and that of their relatives while men tend to have fewer healthy behaviours due to the influence of masculine stereotypes [40].
Inequalities in the co-occurrence of healthy behaviours were observed in both genders and age groups by origin and destination SES and social mobility. Older women are more likely to have co-occurrence if they have a low SES, independently of it is origin or destination SES. This could be explained by the fact that, as observed in the analysis of each healthy behaviour, older women with a low SES are more likely to be non-smokers and consume moderate amounts of alcohol, which may be due to the impact traditional gender roles have had on older women.
In the case of men, the same behaviour pattern is observed in those with a low origin SES as those with upward mobility, regardless of age. These results once again confirm the socialization hypothesis [17].
This study provides an approach to the analysis of the impact of origin and destination SES and social mobility on healthy behaviours due to the intersection of age and gender. The results reinforce the idea that health and health behaviours are socially determined and strongly linked to the specific values, norms and social resources of each social strata. The study on social mobility has provided a complex vision of the influence of origin and destination SES on the assumption of healthy behaviours. It has shown that in men, healthy behaviours from the low social position of origin are maintained when there is social mobility.
One of the limitations of this study could be the social desirability bias, meaning that participants respond according to what is considered socially correct or expected, overestimating healthy behaviours in all social strata. However, the fact that the questionnaire is administered by qualified interviewers who are continuously learning and that the FFQ questionnaire was validated in Spain reduces this type of bias [19, 41].
On the other hand, an effect of reverse causality could be present, common in studies with a cross-sectional design. Taking other studies [14] into account, the association between health and SES is bidirectional, since healthy behaviours could also influence the population’s social mobility.
It is important to take into account the possible confounding factors behind SES and healthy behaviours. In this article, only the confounding factors of sex, age and region are controlled through adjustment or stratification, but other possible confounding factors could affect our results (such as occupation, marital status, Body Mass Index etc.). Despite this, they provide an approximation of how origin and destination SES and social mobility impact healthy behaviours.
Despite the fact that the sample was randomly selected in each of the regions taking part in the study and that this aspect reduced selection bias, age may be represented unequally between men and women. To overcome this limitation, the sample was stratified by age and sex.
Although the results obtained coincide with the findings of recently published studies on this topic, it would be advisable to replicate this study in younger cohorts.
Conclusion
This study shows that the impact of origin and destination SES and social mobility on the prevalence and co-occurrence of healthy behaviours differs by gender and age. These differences justify the importance of pursing a health-focused approach in all policies based on the principle of health promotion [42, 43], as well as interventions adapted to the needs of each social group in order to improve equity in health [44].
References
- 1.
World Health Organization (WHO. About social determinants of health [Internet]. WHO. Available at: http://www.who.int/social_determinants/sdh_definition/en/
- 2. Marmot M, Allen J, Bell R, Bloomer E, Goldblatt P. WHO European review of social determinants of health and the health divide. 2012; 380:1011–29.
- 3.
World Health Organization (WHO). The World Health Report. 2002, Geneva, Switzerland.
- 4. Dike Van de Mheen H, Stronks K, Mackenbach J. A lifecourse perspective on socio-economic inequalities in health: the influence of childhood socio-economic conditions and selection processes. Sociol Health Illn. 1998; 5(20):754–77.
- 5. Elhakeem A, Hardy R, Bann D, Caleyachetty R, Cosco TD, Hayhoe RP, et al. Intergenerational social mobility and leisure-time physical activity in adulthood: a systematic review. J Epidemiol Community Health. 2017;71(7):673–80. pmid:27979970
- 6. Falkstedt D, Möller J, Zeebari Z, Engström K. Prevalence, co-occurrence, and clustering of health-risk behaviors among people with different socio-economic trajectories: A population-based study. Prev Med. 2016;93:64–9. pmid:27663427
- 7. Osler M, Godtfredsen NS, Prescott E. Childhood social circumstances and health behaviour in midlife: the Metropolit 1953 Danish male birth cohort. Int J Epidemiol. 2008;37(6):1367–74. pmid:18550623
- 8. Park J-B, Han K, Park Y-G, Ko Y. Association between socioeconomic status and oral health behaviors: The 2008–2010 Korea national health and nutrition examination survey. Exp Ther Med. 2016;12(4):2657–64. pmid:27703514
- 9. Gómez Gutiérrez LF, Lucumí Cuesta DI, Girón Vargas SL, Espinosa García G. Conglomeración de factores de riesgo de comportamiento asociados a enfermedades crónicas en adultos jóvenes de dos localidades de Bogotá, Colombia: importancia de las diferencias de género. Rev Esp Salud Pública. 2004;78(4):493–504. pmid:15384263
- 10. Borrell C, García-Calvente M, Martí-Boscà J. La salud pública desde la perspectiva de género y clase social. Gac Sanit. 2004;4(18):02–6.
- 11. Thebault J-L, Ringa V, Panjo H, Bloy G, Falcoff H, Rigal L. Accumulation of unhealthy behaviors: Marked social inequalities in men and women. Prev Med Rep. 2018;12:1–5. pmid:30116703
- 12. Meader N, King K, Moe-Byrne T, Wright K, Graham H, Petticrew M, et al. A systematic review on the clustering and co-occurrence of multiple risk behaviours. BMC Public Health. 2016;16(1):657. pmid:27473458
- 13. Jonsson F, San Sebastian M, Hammarström A, Gustafsson PE. Intragenerational social mobility and functional somatic symptoms in a northern Swedish context; alayses of diagonal reference models. Journal of Equity in Health. 2017; 16:1. pmid:28057005
- 14. Campos-Matos I, Kawachi I. Social mobility and health in European countries: Does welfare regime type matter? Soc Sci Med. 2015;142:241–48. pmid:26318213
- 15. Gugushvili A, Zhao Y, Bukodi E. Intergenerational educational mobility and smoking: a study of 20 European countries using diagonal reference models. Public Health. 2020;181:94–101. pmid:31981813
- 16. Sidorchuk A, Goodman A, Koupil I. Social class, social mobility and alcohol-related disorders in Swedish men and women: A study of four generations. Karriker-Jaffe KJ, editor. PLOS ONE. 2018;13(2). pmid:29444095
- 17. Van Eijck K. Socialization, education, and lifestyle: How social mobility increases the cultural heterogeneity of status groups. Poetics. 1999;26:309–28.
- 18. Domingo-Salvany A, Bacigalupe A, Carrasco JM, Espelt A, Ferrando J, Borrell C. Propuestas de clase social neoweberiana y neomarxista a partir de la Clasificación Nacional de Ocupaciones 2011. Gac Sanit. 2013;27(3):263–72. pmid:23394892
- 19. Castaño-Vinyals G, Aragonés N, Pérez-Gómez B, Martín V, Llorca J, Moreno V, et al. Population-based multicase-control study in common tumors in Spain (MCC-Spain): rationale and study design. Gac Sanit. 2015;29(4):308–15. pmid:25613680
- 20. Martin-Moreno JM, Boyle P, Gorgojo L, Maisonneuve P, Fernandez-Rodriguez JC, Salvini S, et al. Development and Validation of a Food Frequency Questionnaire in Spain. Int J Epidemiol. 1993;22(3):512–9. pmid:8359969
- 21.
Dapcich V, Castell GS, Barba LR, Rodrigo CP, de Bilbao A, Bartrina JA, et al. Guía de la alimentación saludable. Madrid: Sociedad Española de Nutrición Comunitaria (SENC); 2004 p. 105.
- 22.
2015–2020 Dietary Guidelines for Americans. United States of America: 28. U.S Department of Health and Human Services and U.S. Department of Agriculture; 2015 p. 144.
- 23. Garber CE, Blissmer B, Deschenes MR, Franklin BA, Lamonte MJ, Lee I-M, et al. Quantity and Quality of Exercise for Developing and Maintaining Cardiorespiratory, Musculoskeletal, and Neuromotor Fitness in Apparently Healthy Adults: Guidance for Prescribing Exercise. Med Sci Sports Exerc. 2011;43(7):1334–59. pmid:21694556
- 24. Erol A, Karpyak VM. Sex and gender-related differences in alcohol use and its consequences: Contemporary knowledge and future research considerations. Drug Alcohol Depend. 2015;156:1–13. pmid:26371405
- 25. Gallus S, Muttarak R, Martínez-Sáncehz JM, Zuccaro P, Colombo P, La Vecchia C. Smoking prevalence and smoking attributable mortality in Italy, 2010. Preventive Medecine. 2011; 52: 434–38.
- 26. Bilal U, Beltrán P, Fernández E, Navas-Acien A, Bolumar F, Franco M. Gender equality and smoking: a theory-driven approach to smoking gender differences in Spain. Tob Control. 2016;25(3):295–300. pmid:25701858
- 27. Dixon J, Banwell C. Theory driven research designs for explaining behavioural health risk transitions: The case of smoking. Soc Sci Med. 2009;68(12):2206–14. pmid:19394742
- 28.
Velasco S. Recomendaciones para la práctica clínica con enfoque de género. Madrid: Dirección General de la Agencia de Calidad del Sistema Nacional de Salud. Ministerio de Sanidad y Consumo; 2008 p. 57.
- 29. Garrett BE, Martell BN, Caraballo RS, King BA. Socioeconomic Differences in Cigarette Smoking Among Sociodemographic Groups. Prev Chronic Dis. 2019;16. pmid:31198164
- 30. Yaya S, Bishwajit G, Shah V, Ekholuenetale M. Socioeconomic Disparities in Smoking Behavior and Early Smoking Initiation Among Men in Malawi. Tob Use Insights. 2017;10. pmid:28839446
- 31.
Nguyen L, Rosenqvist G, Pekurinen M. Demand for Tobacco in Europe: An Econometric Analysis of 11 Countries for the PPACTE Project. Helsinki, Finland: National Institute for Health and Welfare; 2012.
- 32. Bonaccio M, Bes-Rastrollo M, de Gaetano G, Iacoviello L. Challenges to the Mediterranean diet at a time of economic crisis. Nutr Metab Cardiovasc Dis. 2016;26(12):1057–63. pmid:27524802
- 33. Assumpção DD, Senicato C, Fisberg RM, Canesqui AM, Barros MB de A. Há diferenças na qualidade da dieta de trabalhadoras remuneradas e donas de casa? Rev Saúde Pública. 2018;52:47. pmid:29723387
- 34. González S, Sarmiento OL, Lozano O, Ramírez A, Grijalba C. Niveles de actividad física de la población colombiana: desigualdades por sexo y condición socioeconómica. Biomédica. 2014;34(3).
- 35. Mathieu RA, Powell-Wiley TM, Ayers CR, McGuire DK, Khera A, Das SR, et al. Physical activity participation, health perceptions, and cardiovascular disease mortality in a multiethnic population: The Dallas Heart Study. Am Heart J. 2012;163(6):1037–40. pmid:22709758
- 36. Demarest S, Van Oyen H, Roskam AJ, Cox B, Regidor E, Mackenbach JP et al. Educational inequalities in leisure-time physical activity in 15 European countries. Eur. J. Public Health. 2014; 24 (2): 199–204. pmid:23748597
- 37. Regidor E. La clasificación de clase social de Goldthorpe: marco de referencia para la propuesta de medición de la clase social del grupo de trabajo de la Sociedad Española de Epidemiología. Rev Esp Salud Pública. 2001;75(1).
- 38. Kino S, Bernabé E, Sabbah W. Socioeconomic inequality in clusters of health-related behaviours in Europe: latent class analysis of a cross-sectional European survey. Public Health. 2017; 17:497. pmid:28535751
- 39. Guibert W, Prendes M, González R, Valdés E. Influencia en la salud del rol de género. Rev Cuba Gen Integr. 1999;1(15):7–13.
- 40. Courtenay WH. Constructions of masculinity and their infuence on men’s well-being: a theory of gender and health. Soc Sci. 2000;17.
- 41.
Bradburn N, Sudman S, Wansink B. Asking questions. The definitive guide to questionnaire design-For market research, political polls, and social and health questionnaire. Revised edition. Rev. Ed. San Francisco: Wiley Imprint; 2004. 446 p.
- 42.
Carta de Ottawa para el Fomento de la Salud. Canadá: Organización Mundial de la Salud; 1986.
- 43.
Declaración de Alma-Ata. Conferencia Internacional sobre Atención Primaria de Salud; 1978 p. 3.
- 44. Candlish J, Teare MD, Cohen J, Bywater T. Statistical design and analysis in trials of proportionate interventions: a systematic review. Trials. 2019;20(1):151. pmid:30819224