Overweight and obesity are more prevalent among urban than rural populations in Africa, yet evidence on these disparities specifically among men remains limited. This study examined the urban-rural disparities and associated factors in overweight/obesity among Ghanaian men aged 20–49.

We analysed data from the 2022 Ghana Demographic and Health Survey, with a total sample of 4,797 men aged 20–49 with valid BMI measurements. Overweight/obesity was defined as a BMI ≥ 25 kg/m². Data were analysed using Stata version 17.0. A multivariate nonlinear decomposition model assessed the contributions of sociodemographic characteristics to urban–rural disparities in overweight/obesity.

The prevalence of overweight/obesity was significantly higher among urban men (27.48%) than rural men (12.03%). Differences in sociodemographic characteristics explained 87% of the observed urban–rural disparities, with differences in wealth index explaining over half (77%) of the urban-rural differences in overweight/obesity. Factors associated with overweight/obesity included age, wealth index, marital status, and alcohol use, while smoking was protective. Among urban men, the odds of overweight/obesity were higher in those aged 30–34 years (AOR = 2.56; 95% CI: 1.37–4.79), 35–39 years (AOR = 2.86; 95% CI: 1.54–5.30), 40–44 years (AOR = 2.80; 95% CI: 1.56–5.03), and 45–49 years (AOR = 4.47; 95% CI: 2.20–9.07). Wealth was a strong predictor across settings; urban men in the richest quintile had an AOR of 17.37 (95% CI: 5.41–55.83), compared to 5.35 (95% CI: 2.76–10.34) among rural men. Alcohol use was significantly associated with overweight/obesity among urban men (AOR = 1.49; 95% CI: 1.09–2.04), while smoking showed a protective association in rural men (AOR = 0.33; 95% CI: 0.13–0.86).

This study shows that urban–rural disparities in overweight/obesity among men are largely driven by differences in wealth index. Socioeconomic differences should be considered when designing interventions to reduce overweight and obesity among men. Targeted strategies promoting healthy eating and physical activity, particularly among men in urban settings, are essential to address these disparities.

According to the International Fund for Agricultural Development [1], approximately 70% of individuals who are overweight or obese live in low- and middle-income countries (LMICs), challenging the earlier notion that overweight and obesity are primarily issues of high-income countries. It is projected that by 2035, 79% of overweight and obese adults will reside in developing countries [2]. The targets for reducing overnutrition among men in Africa remain unmet, as the prevalence of obesity and overweight continues to rise steadily, currently reaching 7.8% and 27.7%, respectively [3]. Overweight and obesity are associated with various non-communicable diseases (NCDs), including diabetes, cardiovascular diseases, and several forms of cancer [4]. Previous research suggests a greater burden of NCDs among younger individuals and men in sub-Saharan Africa [5]. If the rising trend of overweight and obesity among men is not addressed, it will further exacerbate the NCD burden in this population [6, 7].

Historically, LMICs have faced high levels of undernutrition; however, the recent surge in overnutrition has doubled the burden of malnutrition [8,9,10]. This shift has created significant challenges for public healthcare systems, which have traditionally focused their resources on combating undernutrition and infectious diseases but must now also address diseases related to overnutrition [10, 11]. The increasing trend in overweight and obesity rates in developing countries has largely been attributed to the nutrition transition associated with westernisation, urbanisation, technological advancements, food processing, the globalisation of food markets, and rising disposable income [12,13,14]. Traditional lifestyles, characterised by energy-demanding work and the consumption of unprocessed, low-fat diets, are rapidly being replaced by obesogenic alternatives, such as sedentary occupations, reduced physical activity, and the consumption of calorie-dense foods high in fats and sugars and low in fibre [15, 16].

Overweight and obesity are becoming increasingly prevalent in urban settings within LMICs, where people typically experience better livelihoods and improved living conditions compared to rural areas [17,18,19,20,21]. A recent systematic review and meta-analysis of observational studies found that urban dwellers had higher prevalence rates of overweight (27.6%) and obesity (17.3%), compared to rural dwellers at 18.2% and 11.0%, respectively [22]. Similarly, the most recent Ghana Demographic and Health Survey report indicates that men aged 20–49 years living in urban areas have a higher prevalence of overweight (21.7%) and obesity (5%) compared to their rural counterparts, where the prevalence rates are 10.5% and 1.5%, respectively [23].

Despite the growing body of research on overweight and obesity in LMICs, little attention has been paid to this condition among men. Previous studies in Africa have primarily focused on urban-rural disparities in overweight and obesity among women of reproductive age and children [24,25,26,27]. However, there remains a gap in the literature regarding how these nuances play out among African men. Given the steady rise in overweight and obesity among urban men compared to rural men in LMICs, it is crucial to understand the factors driving these urban-rural disparities. The present study examined urban-rural disparities in overweight/obesity and the factors contributing to these disparities among men aged 20–49 in Ghana.

We used secondary data from the 2022 Ghana Demographic and Health Survey (GDHS). The GDHS is a nationally representative survey designed to estimate demographic and health indicators. The 2022 GDHS collected data from approximately 18,540 households across all 16 regions of Ghana, interviewing 7,044 men aged 15–59 years [23]. Approval for access to and use of the dataset was obtained from ICF International via the DHS website (http://www.dhsprogram.com). The male recode (MR) file was merged with the household member recode (PR) file.

The GDHS employs a multistage sampling design. In the first stage, clusters were selected from a master sampling frame derived from the Ghana National Population and Housing Census data. These clusters were randomly selected using probability proportional to size. Following the selection of clusters, households were chosen through systematic random sampling [23]. Detailed information on the study design used in the GDHS can be accessed via https://dhsprogram.com/pubs/pdf/FR387/FR387.pdf.

Outcome variable

The primary outcome variable of this study was overweight/obesity, which was derived from the body mass index (BMI). We classified men as overweight or obese if their BMI was ≥ 25.0 kg/m² [28]. Overweight/obesity was treated as a binary outcome, with overweight/obesity coded as 1, and normal weight and underweight coded as 0.

Equity stratifer

The respondents’ place of residence was used as an equity stratifier. In the DHS questionnaire, respondents indicated their place of residence, categorised as either rural or urban. For the final analysis, these responses were recoded as 0 = rural and 1 = urban. Place of residence has been used in previous studies as an equity stratifier for overweight and obesity [25, 26].

We included 12 covariates based on their availability in the 2022 GDHS dataset and their association with overweight/obesity in previous studies [29,30,31,32,33,34]. All covariates were treated as categorical variables and coded as follows: age (20–24, 25–29, 30–34, 35–39, 40–44, 45–49 years); place of residence (urban, rural); educational attainment (no formal education, primary, secondary, tertiary); occupation (unemployed, professional/technical/managerial, agricultural, manual/other); current employment status (no, yes); marital status (never in union, currently in union, cohabiting, previously in union); region (coastal, forest, savannah); household size (< 5, ≥ 5 members); wealth index (poorest, poorer, middle, richer, richest); tobacco use (no, yes); alcohol consumption in the past month (no, yes); and media exposure (no, yes). Media exposure was defined as exposure to at least one form of media (television, radio, or newspapers) at least once a week. The wealth index was derived by the DHS Programme using principal component analysis of household assets and dwelling characteristics. Urban and rural residences were classified based on Ghana Statistical Service administrative definitions, as applied in the GDHS (Fig. 1).

Flowchart: Inclusion and exclusion criteria

Full size image

Statistical analyses were performed using Stata version 17 (StataCorp, College Station, TX, USA). We restricted our analysis to men aged 20–49 years, as the most recent GDHS indicates that the prevalence of overweight and obesity is highest among men within this age category [35]. Prior to the data analysis, the completeness of data on men’s anthropometric measurements was assessed, and cases with missing data were excluded. The data were weighted to ensure an accurate representation of the study population and to adjust for variations in the probability of sample selection.

Descriptive statistics were summarised as weighted frequencies and percentages. A chi-square test was conducted to show the distribution of overweight/obesity across background characteristics. Bivariate logistic regression was then performed to identify factors associated with overweight/obesity among urban and rural men. Independent variables that were statistically significant at the bivariate level were included in the multivariable logistic regression model to assess the strength of their association with overweight/obesity. Bivariate logistic regression results were presented as Crude Odds Ratios (COR), while multivariable logistic regression results were presented as Adjusted Odds Ratios (AOR). Variables with p-values < 0.05 at a 95% confidence interval were considered statistically significant.

For the final analysis, a multivariate non-linear decomposition analysis was conducted to examine urban–rural disparities in overweight/obesity [36]. This method, widely used in social research, decomposes group differences in average predictions derived from multivariate models. It leverages regression model outputs to separate the overall group difference in a statistic, such as the mean or proportion, into two key components: one attributed to compositional differences between groups (i.e., variations in characteristics or endowments), and another attributed to differences in the effects of these characteristics [36]. We applied this technique to assess the relative contribution of each covariate to urban–rural disparities in overweight/obesity among men.

Multicollinearity was assessed using the variance inflation factor (VIF), with no evidence detected (mean VIF = 1.34; maximum = 2.29; minimum = 1.03). The Stata “svy” command was used to account for the complex sampling structure.

The dataset analysed in this study is freely available upon request at https://dhsprogram.com/data/dataset/Ghana_Standard-DHS_2022.cfm?flag=0.

Table 1 presents the background characteristics of the study participants. Of the 4,797 men who participated, the majority (21.43%) were aged 20–24 years, and slightly more than half (56.46%) resided in urban areas. Most respondents were currently in a union (48.65%) and lived in the forest zone (42.06%). More than half (58.87%) had attained secondary-level education, and 47.08% were employed in professional, technical, or managerial roles. Additionally, 93.27% reported that they were currently working. The wealth distribution showed that most participants belonged to the richest wealth quintile (25.07%), and 59.32% lived in households with fewer than five members. Furthermore, the majority (79.21%) reported exposure to media. In terms of health behaviours, 5.36% of respondents currently smoked tobacco, and 35.49% reported alcohol consumption in the past month.

Table 2 presents the distribution of overweight/obesity by participant characteristics. The overall prevalence of overweight/obesity among the respondents was 20.76%. Overweight/obesity was more prevalent among urban men (27.48%) than rural men (12.03%). The highest prevalence was observed among men aged 45–49 years (34.00%) and those in the richest wealth quintile (38.00%). Similarly, overweight/obesity was notably higher among men with tertiary education (30.93%), those engaged in manual or other occupations (24.76%), and men currently in a union (27.13%). In terms of regions, the Coastal zone recorded the highest prevalence (27.67%) compared to the Forest and Savannah zones. Additionally, men living in households with fewer than five members (22.85%) and those with media exposure (22.39%) exhibited higher prevalence rates. Furthermore, overweight/obesity was more common among men who reported alcohol consumption in the past month (24.81%), but lower among current tobacco smokers (15.05%).

Table 3 presents the bivariable and multivariable logistic regression analysis of factors associated with overweight/obesity, stratified by residence. Among urban men, age was positively associated with overweight/obesity. Compared to those aged 20–24, higher odds were observed in the 30–34 (AOR = 2.56; 95% CI: 1.37–4.79), 35–39 (AOR = 2.86; 95% CI: 1.54–5.30), 40–44 (AOR = 2.80; 95% CI: 1.56–5.03), and 45–49 (AOR = 4.47; 95% CI: 2.20–9.07) age groups. Rural men showed a similar trend, with significant associations in age groups 25–29 (AOR = 2.13; 95% CI: 1.07–4.25), 35–39 (AOR = 2.13; 95% CI: 1.04–4.39), and 45–49 (AOR = 3.09; 95% CI: 1.58–6.05). Wealth index was a significant predictor in both settings. In urban areas, men in the middle (AOR = 3.53; 95% CI: 1.07–11.61), richer (AOR = 10.40; 95% CI: 3.27–33.04), and richest (AOR = 17.37; 95% CI: 5.41–55.83) quintiles had higher odds than those in the poorest. Among rural men, odds were similarly elevated in the poorer (AOR = 1.89; 95% CI: 1.20–2.98), middle (AOR = 2.41; 95% CI: 1.39–4.18), richer (AOR = 3.75; 95% CI: 2.03–6.92), and richest (AOR = 5.35; 95% CI: 2.76–10.34) groups. Marital status was associated with overweight/obesity in rural areas: currently in union (AOR = 3.15; 95% CI: 1.81–5.47) and previously in union (AOR = 3.72; 95% CI: 1.70–8.13) men had higher odds than never-married men. In urban settings, alcohol use was a significant risk factor (AOR = 1.49; 95% CI: 1.09–2.04), while smoking had a protective association in rural men (AOR = 0.33; 95% CI: 0.13–0.86).

We found that differences in sociodemographic characteristics accounted for approximately 87% of the urban–rural disparity in overweight/obesity (Table 4). If disparities in sociodemographic characteristics were eliminated, more than half of the inequality in overweight/obesity would be reduced. The wealth index of the respondent explained approximately 77% of the overall urban–rural difference in overweight/obesity. Surprisingly, those in the poorest wealth category explained about 47% of the urban-rural disparity in overweight/obesity among men.

This study examined urban-rural disparities in overweight/obesity among men aged 20–49 in Ghana. The prevalence of overweight/obesity was higher among urban men (29.17%) than rural men (12.86%). Our findings show that sociodemographic differences accounted for approximately 85% of the urban–rural disparities in overweight/obesity. Differences in the wealth index explained more than three-quarters (77%) of the disparity among men. Notably, men in the lowest wealth index category accounted for approximately 47% of the overall urban–rural disparity, even though this association was not evident in the regression analysis. This finding aligns with previous research indicating that the rise in overweight/obesity among those in the poorest wealth index in LMICs is linked to food insecurity [37,38,39]. The main mechanism through which food insecurity leads to overweight and obesity among poorer populations in LMICs is through the affordability and consumption of calorie-dense processed foods. Other contributing mechanisms include poor dietary diversity and lack of access to high-quality and nutritious foods [37]. These findings emphasise the urgent need for targeted interventions to mitigate the impact of food insecurity on overweight and obesity. Importantly, reducing wealth disparities could eliminate over half of the urban–rural differences in overweight and obesity among men.

Our finding on the higher prevalence of overweight/obesity among urban men compared to rural men corroborates previous studies [22, 40,41,42]. This trend may be attributed to the ongoing nutritional transition occurring in many low-resource settings such as Ghana. Many young men tend to consume energy-dense, nutrient-poor fast foods [43, 44] instead of traditional, nutrient-rich foods. In addition, physical activity levels have declined, while sedentary lifestyles have increased, largely due to the nature of modern employment [45]. The clinical implication is that overweight and obesity significantly increase the risk of NCDs such as diabetes and hypertension [46], which in turn escalates healthcare costs at both individual and health system levels. Therefore, comprehensive public health strategies are needed to reduce overweight/obesity prevalence among young men in Ghana. These strategies should include promoting healthy eating habits, encouraging physical activity at the individual level, and implementing policies that improve access to nutritious foods across the population.

We also found that age was significantly associated with higher odds of overweight/obesity among men. The odds increased with age, peaking among urban men aged 45–49. Among rural men, however, the odds did not follow a consistent upward trend but were also highest among those aged 45–49. This finding is consistent with previous studies showing that overweight and obesity increase with age among adults [47,48,49,50]. Ageing is associated with hormonal changes, decreased metabolism, and reduced physical activity, which contribute to weight gain [51,52,53]. These findings highlight the need for continuous weight monitoring as individuals age to prevent obesity-related conditions such as cardiovascular diseases, type 2 diabetes, and musculoskeletal disorders.

Additionally, we found that men in the richest wealth quintile, in both urban and rural settings, had increased odds of being overweight or obese compared to those in the poorest quintile. This finding is consistent with other studies showing a strong association between higher wealth status and overweight/obesity [54, 55]. Higher wealth status is often linked to sedentary lifestyles and unhealthy dietary habits, characterised by greater consumption of processed and high-calorie foods with low nutritional value [56, 57]. Thus, health strategies must address the complex interplay of factors contributing to overweight/obesity, including education on healthy diets, promotion of physical activity, and policies aimed at improving access to nutritious foods.

Marital status was also associated with increased odds of overweight/obesity. Previously married men and those currently in a union had higher odds of being overweight or obese. Divorce or separation may cause stress, leading to poor eating habits such as overeating as a coping mechanism [58]. Evidence suggests that divorced men tend to consume more processed foods [59]. The observed association may also be due to divorced men lacking the time or skills to prepare nutritious meals. However, our findings contradict other studies that report divorce/separation is associated with a reduced risk of overweight/obesity [60, 61]. For men currently in a union, the increased odds of overweight/obesity may be attributed to societal norms in Ghana, where weight gain after marriage is culturally perceived as a sign of good living [62].

Furthermore, we found that smoking was protective against overweight/obesity among rural men. Nicotine suppresses appetite by acting on the brain, extending inter-meal intervals, and reducing food intake, thereby contributing to weight loss [63, 64]. Although smoking may be protective against overweight/obesity, it remains a significant risk factor for other NCDs such as cancer [65]. Therefore, efforts to reduce smoking must continue despite this association.

Lastly, alcohol consumption was significantly associated with higher odds of overweight/obesity among urban men. This finding is in line with previous research indicating that alcohol intake, particularly beer, contributes to weight gain and obesity [66, 67]. However, the relationship between alcohol use and overweight/obesity is complex and inconsistent across studies [68]. The association observed in our study may be influenced by the type of alcohol predominantly consumed by urban men. Beer consumption is common among this group, contributing to higher caloric intake and potentially promoting weight gain. Moreover, alcohol consumption has been linked to poor dietary choices, including increased intake of high-fat and high-calorie foods [69, 70], which may further exacerbate the risk of overweight and obesity.

This study contributes valuable knowledge on urban–rural disparities in overweight and obesity among men. We used a nationally representative dataset, enhancing the generalisability of our findings to the broader Ghanaian context. However, our analysis was limited by the types of explanatory variables available for control in the multivariable logistic regression model. For instance, there were no data on respondents’ physical activity, eating habits, or sedentary behaviour. Additionally, the cross-sectional design of the Demographic and Health Survey (DHS) precludes the establishment of causal relationships. Our analysis also relied on media exposure as a variable without information on its duration, which may have limited our ability to capture its true influence. Lastly, variables such as alcohol consumption and smoking were self-reported, which may have introduced recall bias or social desirability bias, potentially affecting the accuracy and validity of the data.

COR:

Crude odds ratio

AOR:

Adjusted odds ratio

DHS:

Demographic and health survey

LMICs:

Low-middle-income countries

NCDs:

Non-Communicable Diseases

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PT and SS conceptualised the study: PT, SS, BOA and JS analyzed the data; PT, SS, BOA and BN: wrote the main manuscript text: PT and BTM prepared Tables 1, 2, 3 and 4. All authors reviewed the final manuscript.

Correspondence to Prince Tsekpetse.

We did not need to seek ethical clearance for this study because the DHS dataset is publicly available. The dataset was obtained from the DHS Program after applying and getting approval for usage. All ethical guidelines that pertain to using secondary datasets in research publications have been strictly adhered to. Detailed information about how we used the DHS data and the ethical standards we followed is available at this link: http://goo.gl/ny8T6X.

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The authors declare no competing interests.

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