Skip to main content
Download PDF
Download ePub

Explaining gender inequalities in overweight people: a Blinder-Oaxaca decomposition analysis in northern Sweden

International Journal for Equity in Health volume 22, Article number: 159 (2023) Cite this article

Abstract

Background

Being overweight and obesity are considered serious public health concerns worldwide. At the population level, factors contributing to overweight as well as the differences in overweight between men and women in terms of prevalence or associated factors are relatively well-known. What is less known is what explains the inequalities in overweight between men and women. In this study, we examined the contribution of material, behavioural, and psychosocial factors in explaining the gender differences in overweight among adults in northern Sweden.

Methods

This study was based on the 2018 Swedish Health on Equal Terms survey, which was carried out in Sweden’s four northernmost regions. The analytical sample consisted of 20,855 participants (47% men) aged 20–84 years. Overweight (including obesity) was the outcome, and the selected explanatory variables were grouped according to three theoretical perspectives: material, behavioural and psychosocial. Descriptive statistics and Blinder-Oaxaca decomposition were applied for analysing the data.

Results

Our study showed that the prevalence of overweight was 64% and 52% among men and women, respectively. It, therefore, revealed a gender gap in overweight people of 11.7% points with explanatory factors accounting for 39% of that gap. This gender gap in overweight people was mostly explained by behavioural variables (19.3%), followed by the materialistic variables and age accounting for 16.2% and 3.1%, respectively. Specifically, having low education, being in the lowest income quintile, alcohol drinking and snus usage contributed to explain 8.4%, 8.9%, 2.8% and 6.3% of the gender difference, respectively.

Conclusions

We found a considerable gender inequality in overweight between men and women. The findings highlight that future overweight prevention initiatives would benefit from targeting the uncovered contributing factors to reduce gender inequalities in overweight people.

Background

Obesity is one of the most serious public health challenges of the 21st century, placing adults at increased risk of morbidity and mortality. In 2015, high body mass index (BMI) caused an estimated 4 million deaths and 40 million disability-adjusted life years (DALY) among adults worldwide [1]. Excess weight lowers the quality of life, increases medical costs, strains the healthcare system and results in productivity losses [2]. In addition, high BMI is recognised as the main risk factor for several chronic conditions such as type 2 diabetes, cardiovascular diseases, osteoarthritis, hepatitis and some cancers [3, 4].

In the last three decades, overweight population has grown dramatically [5]. According to a World Health Organization report, there were more than 1.9 billion overweight adults in 2016 globally [5]. In the case of the European region, many countries have an overweight prevalence of around 50% [6, 7]. For instance, in Sweden, despite increasing calls for action, there has also been a rising trend in the prevalence of overweight and obese people. Between 1995 and 2017, the prevalence of obesity and overweight increased by 86% and 23%, respectively, while severe obesity increased by 153% [8]. The rise in overweight and obesity rates is increasingly considered to be the result of growing social inequalities and changes in lifestyle behaviour such as dietary habits, smoking and alcohol consumption [9].

The prevalence of overweight and obesity appears to differ by gender, as significant sex differences in overweight and obesity across countries are consistently shown in the literature [1, 10]. According to a review of 68 countries by Wells et al., there were 3 obese women for every 2 obese men [11]. Other studies also reinforce the finding that women frequently show a higher rate of obesity [12,13,14]. Gender differences in diet, physical exercise and lifestyle factors are potentially generating these gender inequalities in overweight/obesity risk [15,16,17,18]. These gender variations may be due to differences in socioeconomic indicators like income and education between men and women [19].

In Sweden, the prevalence of overweight and obesity is higher among men compared to women [8]. Hence, it is worth investigating why these differences in overweight between men and women exist in order to (i) provide direction for research and preventive policy and (ii) to reduce inequalities in excess weight between men and women.

Previous research in the field has mainly focused on identification of factors associated with overweight and obesity among men and women separately [20, 21]. To our knowledge, very little is known about what might explain the gender gap. This study, therefore, sought (i) to estimate the gender gap of overweight people as well as (ii) to explain inequalities in overweight between men and women in northern Sweden.

Methods

Study design and data collection

This study used the most recent secondary data from the 2018 Health on Equal Terms (HET) cross-sectional survey carried out in the four northernmost regions (Västernorrland, Jämtland, Västerbotten and Norrbotten) of northern Sweden. The survey represents the regionally expanded sample of the national HET survey, which is implemented as a collaboration between the Swedish National Public Health Agency and the individual regions, with the purpose of monitoring the health and living conditions of the population. All residents aged 20–84 years in the aforementioned four regions were identified as the target population, and a random sample stratified by age, gender, region and municipality was selected. The survey gathered information from 23,487 respondents who answered either the postal or web questionnaire representing a 58.6% response rate. The survey data was linked to individual-level register data on, for example, income and education, through the Swedish Personal Identity Number.

Measures

The outcome variable in this study was whether an individual was or was not overweight (including obese). BMI was computed from self-reported weight and height data and calculated as weight (kg) divided by height squared (m2). BMI categories were calculated in accordance with World Health Organization guidelines [22]. Overweight was defined as having a BMI ≥ 25 kg/m2. In our analysis, we considered both overweight and obese categories within the same group. Gender was used as the exposure variable, and participants were grouped into men and women based on the registered data. Explanatory variables with a plausible link to overweight were broadly categorised into three theoretical perspectives: materialistic, behavioural and psychosocial (adapted from the conceptual framework on social determinants of health) [23].

The age of the participants was categorised into 20–60 years (reference) and 61–84 years. The material variables included education and income. Education was categorised into high (three or more years of tertiary education, reference), medium (up to two years of tertiary education) and low (less than three years of secondary education). Income was based on the individual disposable income and coded into five quintiles, with the first quintile (reference) being the highest one. Disposable income is defined here as the amount of money (Swedish Krona) available to be spent or saved at discretion, after deducting taxes and social security charges.

Behavioural variables included smoking, use of snus (the Swedish moist tobacco product), risky alcohol drinking, vegetable and fruit intake and physical inactivity. Smoking was coded as current smoker or not while snus usage was coded as yes/no. Risky alcohol drinking was captured by Audit-C [24], which comprises the following three questions : ‘How often did you drink alcohol?’ with response options ranging from 0 (never) to 4 (four times a week or more often), ‘How many ‘glasses’ did you drink on a typical day when you drank alcohol?’ with response options ranging from 0 (1–2) to 4 (10 or more), and ‘How often did you drink six ‘glasses’ or more at a time in the past 12 months?’ with response options ranging from 0 (never) to 4 (daily or almost daily). The maximum total score for Audit-C is 12 and is calculated by summing up the scores for all items. The cut-off point for risky drinking was determined as > 5 for men, and > 4 for women [24]. Vegetable and fruit consumption was operationalised using the questions: ‘How often do you eat vegetables and root vegetables?’ and ‘How often do you eat fruits and berries?’ The two variables were added and dichotomized into daily (if both vegetables and fruits were consumed each day) and not daily. Physical inactivity was captured by the following question ‘How much do you sit during a normal day, not counting sleep?’ and was coded as ≤ 6hr/day and > 6hr/day. Finally, stress was added as part of the psychosocial model and measured using the following question ‘Do you feel stressed at present?’ with response options as not at all, to some extent, quite a lot, and very much. These answers were further dichotomised into No for ‘not at all’ with all other answers deemed as Yes.

Data analysis

Descriptive statistical analysis was done to summarise the proportion of overweight people in total and by sex across the explanatory variables. Then, the Blinder-Oaxaca decomposition analysis was conducted to estimate and decompose the disparity in overweight between men and women. The Blinder-Oaxaca decomposition approach allows for a comparison between two groups (men and women) of variables [25, 26], producing an explained component that corresponds to the contribution to the inequality by the differences in the included variables and a residual component, which corresponds to what cannot be explained. The significance level was set at a p-value of less than 0.05. The variance inflation factor (VIF) was used to check for multicollinearity (mean VIF value was 1.1). A complete case analysis was applied to handle missing data which accounted for 7% of the collected data. Therefore, 20,855 (93%) of the observations (9,721 men and 11,134 women) were included in the analysis.

Ethics consideration

All participants provided informed consent for the use of the data for research purposes. The study was approved by the Swedish Ethical Review Authority (reference number 2020–00204 and 2015/134–31).

Results

Descriptive statistics

Table 1 summarises the study sample’s characteristics by gender. Most participants were in the age group below 60 years and had attained less than 3 years of secondary education. Men were more likely than women to report using alcohol and snus. Additionally, most men did not regularly eat fruits and vegetables and were physically sedentary for more than six hours every day. In contrast, women reported smoking and stress at higher levels than men.

The prevalence of overweight was 64% and 52% among men and women, respectively. As education level increased, the prevalence of overweight declined. Overweight among men steadily decreased along income quintiles, but a similar trend was not seen among women. More than half of both men and women who did not regularly consume vegetable and fruit and were physically inactive for > 6 h/day were overweight (Table 1).

Table 1 Distribution of background characteristics of participants and prevalence of overweight by risk factors
Full size table

Decomposition analysis

Overall, the men-women inequality in overweight people was estimated to be 11.7% points (Table 2). Of this difference, 39% was explained by the selected variables while the rest remained unexplained. Regarding age, to be in the 61–84 years group explained 3.1% of the gender gap in overweight people. Among the materialistic factors, low education and being in the 5th quintile explained 8.4% and 8.9% of the inequality in overweight people, respectively. All the included behavioral factors contributed to explaining the gap in overweight people. Snus and vegetable and fruit intake contributed to the inequality by 6.3% and 6.7%, respectively. However, stress did not play any role in explaining the inequality (Table 2). Figure 1 depicts the material, behavioural and psychosocial contributions to explaining the gender inequality in overweight people.

Table 2 Decomposition of the difference in overweight between men and women in northern Sweden
Full size table
Fig. 1
figure 1

Relative (%) contributions of the groups of variables used in the decomposition analysis

Full size image

Discussion

This study estimated the gender inequality of overweight people in northern Sweden, using data from the most recent HET survey. It revealed a gap of 11.7% points with material, behavioural and psychosocial factors accounting for 39% of the gap. Education, income, snus use and diet accounted for most of the explained inequality (Table 2).

Our study showed that age was an important contributor to explaining the gender gap in overweight people, but the degree of inequality varied across both age groups. Men over the age of 60 exhibited a higher prevalence of overweight in comparison to women. The observed gap could be explained by a decreased physical activity and poorer diet among older men, maybe due to poor cooking skills and low motivation to change eating habits [27]. Similar results have been found in other studies that linked age to being overweight [7, 10, 28].

Correspondingly, education had a relevant role in explaining the gender inequality in overweight people. In this study, men with low and medium education were more likely than women to be overweight. Individual’s education attainment generally ensures acquisition of health promoting behaviours. Hence, highly educated women might be better aware of the consequences of obesity and more likely to engage in preventive health behaviours like regular exercise than men [29].

Furthermore, our study showed that overweight affects men and women with different income in a distinct degree. Generally, men were more likely to be overweight than women across income quintiles with the largest gender inequality in overweight people found within the lowest quintile. Socioeconomic differences in diet/nutrition and leisure-time physical activity might explain the above observed gender gap. Women might be more likely than men to spend their income on healthier diets and weight reduction activities over obesogenic foods and drinks such as alcohol. Prior studies also reinforce the aforementioned finding [27, 30]. Overweight and obese people, on the other hand, frequently experience less favourable material circumstances, including lower incomes and less opportunities for employment due to discrimination [31]. Thus, their ability to buy healthy food could be greatly impacted by their lower income, which ultimately leads to weight gain [32].

Similarly, our study showed that men snus users were more likely than women to be overweight. Use of snus might be a marker of other unhealthy behaviours, such as alcohol and food habits. Though several studies have explored the association between snus use and weight, but the findings are not conclusive. Some reported results similar to ours [33, 34], while other studies found no differences in mean BMI [35, 36]. Since little is known about the metabolic effects of snus use, more research is needed to explore the possible role of snus in overweight.

We also found certain gender differences in alcohol use that potentially generated gender inequalities in overweight people. Alcohol provides calories in addition to those obtained from other foods, which can lead to a positive energy balance and weight gain. The findings were consistent with prior studies, where heavy drinking was associated with a higher BMI [33, 37,38,39].

Moreover, our study showed that particularly diet and less physical activity contributed to the gender gap in overweight people. The consumption of a healthy diet, like vegetables and fruits, reduces weight gain, which was observed primarily among women. It is also possible that women who eat healthier foods are more likely to engage in health promoting behaviours like exercise and avoiding alcoholic beverages [37]. On the other hand, decreased energy expenditure from physical inactivity results in weight gain, which was seen mainly among men in our study. This can be supported by several studies that have shown dietary behaviour and exercise to be significantly associated with BMI [30, 34, 40, 41].

Methodological considerations

To our knowledge, very little is known about the factors explaining the gender inequalities in overweight and obesity globally, as the majority of previous studies conducted separate analyses for men and women. The large population-based sample, which was linked with registered data on gender, age, education and income, can be considered strengths of this study. Hence, reported bias on such key register-based variables is expected to be reduced.

This study also has some limitations that need to be taken into account when interpreting the results. The study used a cross-sectional design, which implies that the study identified factors that contribute to inequality in overweight people but cannot provide causal inference. Second, weight and height were self-reported which could possibly result in biased estimates of the prevalence of overweight in the general population because of potential issues of social desirability. However, our rates of overweight were comparable to those found in another study conducted in Sweden, using objective measurements of body weight and height [8]. Likewise, the data on tobacco smoking, snus use, and alcohol habits were self-reported, which may have led to underreporting, the extent of this bias is however impossible to estimate. Another drawback of the study is that it only explained 39% of the disparity in overweight, leaving many other potential explanatory factors unknown. Future studies should be specifically designed to disentangle the gender inequality in terms of being overweight.

Conclusions

We found a high prevalence of overweight in both sexes and a considerable inequality in overweight between men and women in northern Sweden. Our study also revealed that low education and income, as well as poor lifestyle factors, were the most important factors explaining the gender differences in overweight prevalence. The findings indicate that future overweight prevention initiatives would benefit from targeting the uncovered contributing factors to reduce the gender inequalities in overweight people.

Data Availability

The data that support the findings of this study are available from Norrbotten, Västerbotten, Västernorrland and Jämtland/Härjedalen regions, but these data are not publicly available. However, data are available from the corresponding author upon reasonable request and with permission of the four regions.

Abbreviations

BMI:

Body mass index

DALY:

Disability adjusted life year

HET:

Health on Equal Terms

VIF:

Variance inflation factor

References

  1. GBD 2015 Obesity Collaborators. Health effects of overweight and obesity in 195 countries over 25 years. N Engl J Med. 2017;377(1):13–27. https://doi.org/10.1056/NEJMoa1614362.

    Article  Google Scholar 

  2. Chu DT, Nguyet NTM, Dinh TC, Lien NVT, Nguyen KH, Ngoc VTN, et al. An update on physical health and economic consequences of overweight and obesity. Diabetes Metab Syndr Clin Res Rev. 2018;12(6):1095–100. https://doi.org/10.1016/j.dsx.2018.05.004.

    Article  Google Scholar 

  3. Fruh SM, Obesity. Risk factors, complications, and strategies for sustainable long-term weight management. J Am Assoc Nurse Pract. 2017;29(S1):3–14. https://doi.org/10.1002/2327-6924.12510.

    Article  Google Scholar 

  4. Finer N. Medical consequences of obesity. Med (Baltim). 2015;43(2):88–93. https://doi.org/10.1016/j.mpmed.2014.11.003.

    Article  Google Scholar 

  5. World Health Organization. World Health Organization obesity and overweight fact sheet. 2016. 2019.

  6. Brandt L, Erixon F. The prevalence and growth of obesity and obesity related illnesses in Europe. Geneva Eur Cent Int Polit Econ ECIPE. 2013.

  7. Gallus S, Lugo A, Murisic B, Bosetti C, Boffetta P, La Vecchia C. Overweight and obesity in 16 european countries. Eur J Nutr. 2015;54(5):679–89. https://doi.org/10.1007/s00394-014-0746-4.

    Article  PubMed  Google Scholar 

  8. Hemmingsson E, Ekblom Ö, Kallings LV, Andersson G, Wallin P, Söderling J, et al. Prevalence and time trends of overweight, obesity and severe obesity in 447,925 swedish adults, 1995–2017. Scand J Public Health. 2020. https://doi.org/10.1177/1403494820914802.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Molarius A, Lindén-Boström M, Granström F, Karlsson J. Obesity continues to increase in the majority of the population in mid-Sweden—a 12-year follow-up. Eur J Public Health. 2016;26(4):622–7. https://doi.org/10.1093/eurpub/ckw042.

    Article  PubMed  Google Scholar 

  10. Chooi YC, Ding C, Magkos F. The epidemiology of obesity. Metabolism. 2019;92:6–10. https://doi.org/10.1016/j.metabol.2018.09.005Get.

    Article  CAS  PubMed  Google Scholar 

  11. Wells JCK, Marphatia AA, Cole TJ, McCoy D. Associations of economic and gender inequality with global obesity prevalence: understanding the female excess. Soc Sci Med 1982. 2012;75(3):482–90. https://doi.org/10.1016/j.socscimed.2012.03.029.

    Article  Google Scholar 

  12. Ogden CL, Fakhouri TH, Carroll MD, Hales CM, Fryar CD, Li X, et al. Prevalence of obesity among adults, by household income and education—United States, 2011–2014. Morb Mortal Wkly Rep. 2017;66(50):1369. https://doi.org/10.15585/mmwr.mm6650a1.

    Article  Google Scholar 

  13. Dahly DL, Gordon-Larsen P, Popkin BM, Kaufman JS, Adair LS. Associations between multiple indicators of socioeconomic status and obesity in young adult Filipinos vary by gender, urbanicity, and indicator used. J Nutr. 2010;140(2):366–70. https://doi.org/10.3945/jn.109.114207.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Muhammad T, Boro B, Kumar M, Srivastava S. Gender differences in the association of obesity-related measures with multi-morbidity among older adults in India: evidence from LASI, Wave-1. BMC Geriatr. 2022;22(1):1–12. https://doi.org/10.1186/s12877-022-02869-z.

    Article  Google Scholar 

  15. Hajizadeh M, Campbell MK, Sarma S. Socioeconomic inequalities in adult obesity risk in Canada: trends and decomposition analyses. Eur J Health Econ HEPAC Health Econ Prev Care. 2014;15(2):203–21. https://doi.org/10.1007/s10198-013-0469-0.

    Article  Google Scholar 

  16. Kanter R, Caballero B. Global gender disparities in obesity: a review. Adv Nutr. 2012;3(4):491–8. https://doi.org/10.3945/an.112.002063.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Garawi F, Devries K, Thorogood N, Uauy R. Global differences between women and men in the prevalence of obesity: is there an association with gender inequality? Eur J Clin Nutr. 2014;68(10):1101–6. https://doi.org/10.1038/ejcn.2014.86.

    Article  CAS  PubMed  Google Scholar 

  18. Costa-Font J, Gil J. What lies behind socio-economic inequalities in obesity in Spain? A decomposition approach. Food Policy. 2008;33(1):61–73. https://doi.org/10.1016/j.foodpol.2007.05.005.

    Article  Google Scholar 

  19. Quezada AD, Lozada-Tequeanes AL. Time trends and sex differences in associations between socioeconomic status indicators and overweight-obesity in Mexico (2006–2012). BMC Public Health. 2015;15:1–10. https://doi.org/10.1186/s12889-015-2608-2.

    Article  Google Scholar 

  20. Tekalegn Y. Determinants of Overweight or Obesity among Men Aged 20–59 Years: A Case-Control Study Based on the 2016 Ethiopian Demographic and Health Survey. J Obes. 2021;2021. https://doi.org/10.1155/2021/6627328.

  21. Sarma H, Saquib N, Hasan MM, Saquib J, Rahman AS, Khan JR, et al. Determinants of overweight or obesity among ever-married adult women in Bangladesh. BMC Obes. 2016;3:1–11. https://doi.org/10.1186/s40608-016-0093-5.

    Article  Google Scholar 

  22. Consultation WHO. Obesity: preventing and managing the global epidemic. World Health Organ Tech Rep Ser. 2000;894:1–253.

    Google Scholar 

  23. Solar O, Irwin A. A conceptual framework for action on the social determinants of health. WHO Doc Prod Serv. 2010. https://doi.org/10.13016/17cr-aqb9.

    Article  Google Scholar 

  24. Reinert DF, Allen JP. The alcohol use disorders identification test: an update of research findings. Alcohol Clin Exp Res. 2007;31(2):185–99. https://doi.org/10.1111/j.1530-0277.2006.00295.x.

    Article  PubMed  Google Scholar 

  25. Blinder AS. Wage discrimination: reduced form and structural estimates. J Hum Resour. 1973;436–55. https://doi.org/10.2307/144855.

  26. Oaxaca R. Male-female wage differentials in urban labor markets. Int Econ Rev. 1973;693–709. https://doi.org/10.2307/2525981.

  27. Atkins JL, Ramsay SE, Whincup PH, Morris RW, Lennon LT, Wannamethee SG. Diet quality in older age: the influence of childhood and adult socio-economic circumstances. Br J Nutr. 2015;113(9):1441–52. https://doi.org/10.1017/S0007114515000604.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Panagiotakos DB, Pitsavos C, Chrysohoou C, Risvas G, Kontogianni MD, Zampelas A, et al. Epidemiology of overweight and obesity in a greek adult population: the ATTICA Study. Wiley Online Library; 2004. https://doi.org/10.1038/oby.2004.239.

  29. Laaksonen M, Sarlio-Lähteenkorva S, Lahelma E. Multiple dimensions of socioeconomic position and obesity among employees: the Helsinki Health Study. Obes Res. 2004;12(11):1851–8. https://doi.org/10.1038/oby.2004.230.

    Article  PubMed  Google Scholar 

  30. Castetbon K, Vernay M, Malon A, Salanave B, Deschamps V, Roudier C, et al. Dietary intake, physical activity and nutritional status in adults: the french nutrition and health survey (ENNS, 2006–2007). Br J Nutr. 2009;102(5):733–43. https://doi.org/10.1017/S0007114509274745.

    Article  CAS  PubMed  Google Scholar 

  31. Lissner L, Johansson SE, Qvist J, Rössner S, Wolk A. Social mapping of the obesity epidemic in Sweden. Int J Obes Relat Metab Disord J Int Assoc Study Obes. 2000;24(6):801–5. https://doi.org/10.1038/sj.ijo.0801237.

    Article  CAS  Google Scholar 

  32. Courtney MG, Carroll A. Sex differences in overweight and obesity among mexican Americans in the National Health and Nutrition Examination Survey: a comparison of measures. SSM-Popul Health. 2022;101297. https://doi.org/10.1016/j.ssmph.2022.101297.

  33. Norberg M, Stenlund H, Lindahl B, Boman K, Weinehall L. Contribution of swedish moist snuff to the metabolic syndrome: a wolf in sheep’s clothing? Scand J Public Health. 2006;34(6):576–83. https://doi.org/10.1080/14034940600665143.

    Article  PubMed  Google Scholar 

  34. Hansson J, Galanti MR, Magnusson C, Hergens MP. Weight gain and incident obesity among male snus users. BMC Public Health. 2011;11(1):1–8. https://doi.org/10.1186/1471-2458-11-371.

    Article  Google Scholar 

  35. Eliasson M, Asplund K, Nasic S, Rodu B. Influence of smoking and snus on the prevalence and incidence of type 2 diabetes amongst men: the northern Sweden MONICA study. J Intern Med. 2004;256(2):101–10. https://doi.org/10.1111/j.1365-2796.2004.01344.x.

    Article  CAS  PubMed  Google Scholar 

  36. Bolinder G, Norén A, de Faire U, Wahren J. Smokeless tobacco use and atherosclerosis: an ultrasonographic investigation of carotid intima media thickness in healthy middle-aged men. Atherosclerosis. 1997;132(1):95–103. https://doi.org/10.1016/S0021-9150(97)00075-0.

    Article  CAS  PubMed  Google Scholar 

  37. Yeomans MR. Alcohol, appetite and energy balance: is alcohol intake a risk factor for obesity? Physiol Behav. 2010;100(1):82–9. https://doi.org/10.1016/j.physbeh.2010.01.012.

    Article  CAS  PubMed  Google Scholar 

  38. Kim O, Jeon HO. Relationship between obesity, alcohol consumption, and physical activity of male office workers in South Korea. Nurs Health Sci. 2011;13(4):457–62. https://doi.org/10.1111/j.1442-2018.2011.00639.x.

    Article  PubMed  Google Scholar 

  39. Rodu B, Stegmayr B, Nasic S, Cole P, Asplund K. The influence of smoking and smokeless tobacco use on weight amongst men. J Intern Med. 2004;255(1):102–7. https://doi.org/10.1046/j.0954-6820.2003.01244.x.

    Article  CAS  PubMed  Google Scholar 

  40. Bullock VE, Griffiths P, Sherar LB, Clemes SA. Sitting time and obesity in a sample of adults from Europe and the USA. Ann Hum Biol. 2017;44(3):230–6. https://doi.org/10.1080/03014460.2016.1232749.

    Article  PubMed  Google Scholar 

  41. Gómez-Cabello A, Pedrero-Chamizo R, Olivares PR, Hernández-Perera R, Rodríguez-Marroyo JA, Mata E, et al. Sitting time increases the overweight and obesity risk independently of walking time in elderly people from Spain. Maturitas. 2012;73(4):337–43. https://doi.org/10.1016/j.maturitas.2012.09.001.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The Authors would like to acknowledge the four regions of Norrland (Jämtland/Härjedalen, Västernorrland, Västerbotten, and Norrbotten) for giving us access to data from the Health on Equal Terms survey.

Funding

Not applicable.

Open access funding provided by Umea University.

Author information

Authors and Affiliations

  1. Department of Epidemiology and Global Health, Umeå University, Umeå, SE -901 87, Sweden

    Fethi Mohammed Yusuf, Miguel San Sebastián & Masoud Vaezghasemi

Authors
  1. Fethi Mohammed Yusuf
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Miguel San Sebastián
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Masoud Vaezghasemi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

FMY conceived the topic, carried out the statistical analyses and drafted the manuscript. MSS and MV oversaw the analyses and interpretation of the results and revised the manuscript. All authors read and agreed on the final manuscript.

Corresponding author

Correspondence to Fethi Mohammed Yusuf.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

The study was approved by the Swedish Ethical Review Authority (reference number 2020–00204 and 2015/134–31). Administrative permission to access and use the data for research purposes was granted by each of the four Regions owning and managing the data (Norrbotten, Västerbotten, Västernorrland and Jämtland/Härjedalen). All participants provided informed consent for the use of the data for research purposes. The need for additional consent for this specific study was waived by the Swedish Ethical Review Authority. All methods were performed in accordance with the relevant guidelines and regulations.

Consent for publication

Not applicable.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yusuf, F.M., San Sebastián, M. & Vaezghasemi, M. Explaining gender inequalities in overweight people: a Blinder-Oaxaca decomposition analysis in northern Sweden. Int J Equity Health 22, 159 (2023). https://doi.org/10.1186/s12939-023-01973-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12939-023-01973-9

Keywords

International Journal for Equity in Health

ISSN: 1475-9276

Contact us