Socioeconomic Determinants Associated With Overweight and Obesity in Adults in Southern Tehran: A Cross‐Sectional Study

ABSTRACT

Background and Aims

Overweight and obesity represent major public health challenges shaped by social, economic, behavioral, and environmental determinants. This study aimed to identify socioeconomic factors associated with overweight and obesity among adults living in southern Tehran in order to inform targeted, context‐specific prevention strategies.

Methods

This cross‐sectional study included 800 adults aged 18–64 years selected through stratified random sampling from 28 comprehensive health service centers affiliated with Tehran University of Medical Sciences. Data were collected via structured telephone interviews using a validated questionnaire. Anthropometric measures were self‐reported. Statistical analyses included χ ² tests and binary multiple logistic regression. Odds ratios (ORs) with 95% confidence intervals (CIs) were estimated, using a two‐sided significance level of 0.05.

Results

Overweight and obesity were significantly associated with age, gender, marital status, educational attainment, employment status, economic status, place of residence, physical activity, and active transportation. Higher economic status was associated with increased odds of overweight/obesity (high: AOR = 2.04; moderate: AOR = 1.52). In contrast, higher levels of physical activity and active transportation showed inverse association with overweight and obesity. No significant associations were observed with private vehicle ownership, access to parks and green spaces, housing conditions, or access to physician and nutritionist services.

Conclusion

Socioeconomic determinants play a pivotal role in shaping overweight and obesity among adults in southern Tehran. Modifiable factors, particularly physical activity and active transportation, should be prioritized in obesity prevention strategies. Public health policies aimed at enhancing neighborhood safety, improving walking and cycling infrastructure, and increasing awareness of lifestyle‐related risks may contribute to reducing obesity prevalence in socioeconomically diverse urban settings. Importantly, effective and sustainable implementation of these measures requires strong political commitment and intersectoral collaboration across health, transportation, urban planning, and community development sectors.

1. Introduction

Overweight and obesity are among the most pressing global public health challenges, contributing substantially to morbidity, mortality, and healthcare costs worldwide [1, 2]. International estimates indicate that more than one‐third of adults are classified as obese, and when overweight individuals are included, the combined prevalence approaches 50% [3, 4]. In Iran, findings from the 2021 national STEPS survey show that 24.96% of adults are obese and 38.05% are overweight, with comparable rates reported in Tehran [5].

Obesity is associated with more than 50 chronic diseases, including cardiovascular diseases, diabetes, respiratory disorders, neurological diseases, gastrointestinal disorders, and several types of cancers [6, 7]. According to the World Health Organization, elevated body mass index (BMI) was responsible for approximately 5 million deaths globally in 2019 [8]. The economic burden of obesity is similarly profound: global costs attributable to overweight and obesity were estimated $1.96 trillion in 2020 and are projected to increase markedly in the coming decades [8, 9]. In Iran, each one‐unit increase in BMI has been associated with a 2.31% rise in public healthcare expenditure [10]. Beyond individual health outcomes, overweight and obesity exert wide‐ranging effects on social, economic, and healthcare systems [7, 11].

The etiology of overweight and obesity is multifactorial and involves biological, behavioral, cultural, social, and economic determinants [12, 13]. While non‐modifiable factors include age, sex, and genetic predisposition, modifiable factors such as dietary practices, physical inactivity, and lifestyle behaviors play a central role in its development [14, 15, 16]. Increasing evidence also highlights the influence of broader structural determinants, including food environments, urban design, socioeconomic conditions, and access to health services [12, 17]. Studies consistently demonstrate that socioeconomic disparities shape obesity risk in both high‐ and middle‐income settings, although the direction and magnitude of these associations vary across contexts [16, 17, 18, 19, 20]. Given the high prevalence of overweight and obesity in the Middle East and Iran, particularly in Tehran [21, 22], a context‐specific understanding of these determinants is essential.

This study is informed by a social determinants of health (SDH) framework, which conceptualizes overweight and obesity as outcomes of complex interactions between: (a) structural determinants, including education, economic status, employment, and residential context; (b) intermediary determinants, such as physical activity, transportation behaviors, access to services, and social support; and (c) individual factors, such as biological and demographic characteristics [23]. This framework allows for a more comprehensive understanding of obesity beyond individual behaviors, situating it within broader social and environmental contexts.

Accordingly, the aim of this study was to examine the socioeconomic, behavioral, and demographic determinants of overweight and obesity among adults aged 18–64 years residing in southern Tehran and covered by Tehran University of Medical Sciences. By identifying key drivers of excess weight within this urban setting, the findings are intended to support the development of evidence‐based and context‐specific preventive interventions.

2. Materials and Methods

2.1. Study Design

This cross‐sectional study was conducted among adults aged 18–64 years residing in southern Tehran and covered by primary healthcare centers affiliated with Tehran University of Medical Sciences (TUMS). The study was designed and reported in accordance with the STROBE Reporting Guidelines for observational studies.

2.2. Conceptual and Theoretical Framework

This study was guided by the SDH Framework, which conceptualizes overweight and obesity as outcomes shaped by multiple interacting levels of influence. These include: (a) structural determinants such as socioeconomic status, educational attainment, employment status, and neighborhood characteristics; (b) intermediary determinants including physical activity, transportation behaviors, dietary behaviors, social support, and access to healthcare services; and (c) individual‐level determinants encompassing demographic characteristics (age, sex, marital status) and biological conditions.

The SDH framework informed the selection of study variables, the design of the questionnaire, and the development of the statistical analysis strategy.

2.3. Sampling and Recruitment Procedures

The study employed a multistage sampling design and was conducted across all 28 active comprehensive health service centers in southern Tehran. The target population consisted of 1,530,122 individuals registered in the Integrated Health System.

Participants were randomly selected from SIB system records, and their contact information was extracted accordingly. Stratified random sampling was used, with each health service center considered a separate stratum. A total of 800 participants (400 men and 400 women) were selected, with the sample size in each stratum determined proportionally to the population covered by that center. To account for potential non‐response, an additional 25% of the required sample was randomly selected as a reserve list.

Data were collected through structured telephone interviews conducted by trained interviewers using standardized scripts. Interviewers explained the study objectives and obtained verbal informed consent prior to administering the questionnaire. Participants who declined to participate or did not respond after three contact attempts made on different days and at different times were replaced with individuals from the reserve list. A detailed participant flow diagram is presented in Figure 1. In addition, the completed STROBE checklist for cross‐sectional studies is provided as Supporting Information S2.

Figure 1.

Flow diagram of participant selection, exclusions, and final analytical sample (n = 800).

2.4. Inclusion and Exclusion Criteria

Inclusion criteria were as follows:

• 1.Aged 18–64 years
• 2.Women who were not pregnant and not within 6 months postpartum
• 3.Residence in the study catchment areas for at least 1 year

Exclusion criteria included:

• 1.Pregnant women
• 2.Individuals with medical conditions known to severely affect body weight (e.g., cancer, renal failure)

2.5. Data Collection Instrument

Data were collected using a validated, researcher‐developed questionnaire comprising 56 items across four main domains: social, economic, behavioral, and demographic factors. These domains encompassed 12 subdomains aligned with structural, intermediary, and individual‐level determinants. The complete questionnaire is provided in Supporting Information S1.

• 1.Social factors included five subdomains: (a) active and inactive transportation behaviors and perceived barriers (14 items), (b) access to community facilities (4 items), (c) access to obesity‐related health services (4 items), (d) social support, assessed using the Zimet Perceived Social Support Questionnaire (12 items) [24], (e) place of residence (1 item)
• 2.Economic factors included household income sufficiency, economic status, and homeownership (3 items).
• 3.Behavioral factors included tobacco use (3 items), dietary behaviors (5 items), and physical activity (2 items). Dietary assessment focused on general eating behaviors and did not include detailed quantitative measures such as caloric intake or food‐frequency quantities.
• 4.Demographic and anthropometric data included demographic characteristics (6 items) and anthropometric measures (height, weight, and waist circumference; 3 items). Anthropometric data (height and weight) were primarily obtained from the SIB electronic health records and were additionally self‐reported by participants during telephone interviews.

Reporting of the study followed the STROBE checklist for cross‐sectional studies, which is provided in Supporting Information S2.

2.6. Body Mass Index (BMI) Calculation

Body mass index (BMI) was calculated using the standard formula:

$$\text{BMI}=\text{weight}\left(\text{kg}\right)/\text{height}\left({\mathrm{m}}^2\right)$$

Based on WHO cut‐off points, BMI was categorized as follows: < 25 kg/m²= normal, 25–29.9 kg/m²= overweight, ≥ 30 kg/m²= obesity. For analytical purposes, BMI was dichotomized into a binary outcome variable: 0 for normal weight and 1 for overweight/obesity (BMI ≥ 25).

2.7. Validity and Reliability

Content validity was assessed by a panel of 15 experts using the Content Validity Ratio (CVR; range: 0.64–1.00) and Content Validity Index (CVI; range: 0.81–1.00). All values met acceptable thresholds according to Lawshe's table [25].

Face validity was evaluated by 10 healthcare service users using the Item Impact Method, with scores ranging from 3.52 to 4.90.

Reliability was assessed using the test–retest method, yielding Kappa coefficients ≥ 0.70 (range: 0.70–0.94). Internal consistency was evaluated using Cronbach's alpha, with a mean value of 0.74 across applicable domains. Overall, the questionnaire demonstrated acceptable validity and reliability.

2.8. Handling of Missing Data

No missing data were observed, as all questionnaires were completed in full by trained interviewers during telephone interviews.

2.9. Statistical Analysis

Statistical analyses were conducted using SPSS software (version 26.0). Descriptive statistics, including frequencies, percentages, and means, were used to summarize study variables. χ ² tests and binary multiple logistic regression analyses were performed to examine associations between independent variables and overweight/obesity.

A two‐sided p value < 0.05 was considered statistically significant. The dependent variable (overweight/obesity) was dichotomized, with absence coded as 0 and presence coded as 1. Dummy variables were created for categorical predictors, with reference categories indicated by a dash (–) in the logistic regression tables. All variables were entered into the regression model using the Enter method to control for potential confounders.

Model goodness‐of‐fit was assessed using the Hosmer–Lemeshow test. Interaction effects were not examined due to sample size limitations required for reliable interaction analysis. All analyses were performed using SPSS software.

2.10. Ethical Considerations

Ethical approval was obtained from the Ethics Committee of Tehran University of Medical Sciences (IR.TUMS.SPH.REC.1402.152). Verbal informed consent was obtained from all participants, as approved by the ethics committee. All data were anonymized prior to analysis to ensure confidentiality.

3. Results

A total of 800 adults aged 18–64 years participated in the study. The participant selection process, including exclusions and the final analytic sample, is illustrated in Figure 1. Overall, 36.4% of participants were classified as overweight and 23.1% as obese, indicating a substantial burden of excess body weight in the study population.

3.1. Sociodemographic Characteristics (Individual‐Level Determinants)

The prevalence of overweight and obesity differed significantly across key sociodemographic characteristics (Table 1). Higher prevalence rates were observed among older adults, particularly those aged 40–49 years, as well as among women and married participants. Educational attainment showed a clear inverse relationship with excess body weight, with individuals who had not completed high school experiencing a markedly higher prevalence compared with those holding higher education degrees.

Table 1.

Sociodemographic characteristics of study participants (n = 800).

Variable	Category	Female n (%)	Male n (%)	Total n (%)
Age (years)	18–29	126 (31.5)	109 (27.3)	235 (29.4)
	30–39	91 (22.8)	86 (21.5)	177 (22.1)
	40–49	110 (27.5)	83 (20.8)	193 (24.1)
	50–64	73 (18.3)	122 (30.5)	195 (24.4)
Marital status	Single	65 (16.3)	93 (23.3)	158 (19.8)
	Married	313 (78.3)	304 (76.0)	617 (77.1)
	Divorced	12 (3.0)	1 (0.3)	13 (1.6)
	Widowed	10 (2.5)	2 (0.5)	12 (1.5)
Body mass index (kg/m2)	Underweight (< 18.5)	9 (2.3)	4 (1.0)	13 (1.6)
	Normal weight (18.5–24.9)	144 (36.0)	167 (41.8)	311 (38.9)
	Overweight (25.0–29.9)	140 (35.0)	151 (37.8)	291 (36.4)
	Obese (≥ 30)	107 (26.8)	78 (19.5)	185 (23.1)
Educational level	Illiterate	23 (5.8)	4 (1.0)	27 (3.4)
	Below diploma	100 (25.0)	80 (20.0)	180 (22.5)
	Diploma	171 (42.8)	177 (44.3)	348 (43.5)
	Associate/bachelor degree	97 (24.3)	120 (30.0)	217 (27.1)
	Postgraduate degree	9 (2.3)	19 (4.8)	28 (3.5)
Employment status	Government employee	22 (5.5)	96 (24.0)	118 (14.7)
	Private sector employee	18 (4.5)	23 (5.8)	41 (5.1)
	Worker	12 (3.0)	48 (12.0)	60 (7.5)
	Housewife	265 (66.3)	0 (0.0)	265 (33.1)
	Self‐employed	13 (3.3)	135 (33.9)	148 (18.6)
	Retired	6 (1.5)	42 (10.5)	48 (6.0)
	Student	64 (16.0)	53 (13.3)	117 (14.6)
	Unemployed	0 (0.0)	3 (0.4)	3 (0.4)
Ethnicity	Turk	168 (42.0)	171 (42.8)	339 (42.4)
	Fars	152 (38.0)	145 (36.3)	297 (37.1)
	Lur	35 (8.8)	38 (9.5)	73 (9.1)
	Kurd	15 (3.8)	23 (5.8)	38 (4.8)
	Arab	3 (0.8)	3 (0.8)	6 (0.8)
	Baluch	0 (0.0)	2 (0.5)	2 (0.3)
	Gilaki	18 (4.5)	12 (3.0)	30 (3.8)
	Foreign nationals	9 (2.3)	6 (1.5)	15 (1.9)

After adjustment for potential confounders, participants with less than a high school education had significantly higher odds of overweight/obesity compared with those with higher educational attainment (Table 2). Gender differences were also evident, with women exhibiting a higher combined prevalence of overweight and obesity than men. The mean body mass index of the study population was 26.75 ± 4.68 kg/m², corresponding to the overweight category (Table 1).

Table 2.

Crude and adjusted associations between social and economic factors and overweight/obesity among adults in southern Tehran.

Variable	Category	Overweight/obese n (%)	Unadjusted odds ratio (95% CI)	Adjusted odds ratio (95% CI)
Active transportation	Low	150 (67.9)	1.33 (0.89–2.00)	1.47 (0.94–2.30)
	Moderate	209 (53.9)	0.73 (0.51–1.05)	1.01 (0.68–1.49)
	High	117 (61.3)	—	—
Access to public transport	Low	41 (56.2)	1.08 (0.63–1.82)	1.41 (0.79–2.51)
	Moderate	104 (56.5)	1.01 (0.74–1.61)	1.21 (0.80–1.85)
	High	206 (65.8)	1.61 (1.14–2.29)	1.70 (1.17–2.48)
	Very high	125 (54.3)	—	—
Place of residence	District 10	119 (70.8)	—	—
	District 11	92 (54.8)	0.50 (0.31–0.78)	0.55 (0.34–0.88)
	District 16	70 (56.5)	0.53 (0.32–0.87)	0.68 (0.41–1.16)
	District 17	76 (54.3)	0.49 (0.30–0.78)	0.60 (0.36–1.00)
	District 19 (urban)	93 (60.4)	0.63 (0.39–0.99)	0.75 (0.45–1.22)
	District 19 (rural)	26 (56.5)	0.53 (0.27–1.04)	0.67 (0.32–1.38)
Economic status	Low	41 (49.4)	—	—
	Moderate	370 (59.3)	1.49 (0.94–2.36)	1.52 (0.93–2.48)
	High	65 (69.9)	2.37 (1.28–4.41)	2.04 (1.06–3.94)
Employment status	Employee	99 (62.3)	—	—
	Worker	30 (50.0)	0.61 (0.31–0.78)	0.83 (0.43–1.57)
	Housewife	184 (69.4)	1.37 (1.32–1.87)	1.18 (0.67–2.07)
	Self‐employed 1a	60 (65.9)	1.17 (0.68–2.01)	1.20 (0.69–2.10)
	Self‐employed 2b	33 (57.9)	0.83 (0.45–1.54)	0.73 (0.38–1.39)
	Retired	38 (79.2)	2.30 (1.07–4.95)	1.84 (0.80–4.24)
	Student	30 (25.6)	0.21 (0.12–0.35)	0.45 (0.23–0.84)
	Unemployed	2 (66.7)	1.21 (0.10–13.65)	1.04 (0.09–12.03)

^aSelf‐employed (1): contractor, lawyer, factory owner, manufacturer.

^bSelf‐employed (2): electrician, mason, painter, turner, automotive painter.

3.2. Social and Economic Factors

Several social and economic factors were significantly associated with overweight and obesity (Table 2). Place of residence emerged as an important determinant, with notable variation across districts. Participants residing in District 10 had the highest prevalence of overweight and obesity, while significantly lower odds were observed in several other districts after adjustment.

Employment status was also associated with excess body weight. Retired individuals and housewives exhibited the highest prevalence of overweight and obesity, whereas students demonstrated substantially lower odds compared with employed participants. In addition, limited engagement in active transportation was associated with a higher prevalence of overweight and obesity.

Economic status showed a positive association with excess body weight. Compared with individuals in the low economic status group, those with moderate and high economic status had higher odds of overweight and obesity, with the strongest association observed among participants with high economic status.

Greater access to public transportation was associated with increased odds of overweight and obesity, particularly among participants reporting high levels of access. In contrast, after adjustment, no statistically significant associations were observed for private vehicle ownership, access to parks or green spaces, perceived availability or quality of physician and nutritionist services, social support, or housing status (Table 3).

Table 3.

Association between overweight/obesity and selected social and environmental factors.

Variable	Category	Crude odds ratio (95% CI)a	Adjusted odds ratio (95% CI)b
Inactive transportation (ref: high)	Low	0.70 (0.48–1.01)	0.68 (0.45–1.03)
	Moderate	0.73 (0.52–1.02)	0.78 (0.55–1.12)
Access to parks, green spaces (ref: high)	Low	0.96 (0.59–1.55)	1.08 (0.64–1.81)
	Moderate	0.80 (0.58–1.10)	0.88 (0.63–1.25)
Access to physician and nutritionist services (ref: high)	Low	0.90 (0.33–2.44)	0.96 (0.33–2.78)
	Moderate	1.12 (0.67–1.88)	1.20 (0.69–2.09)
Quality of physician and nutritionist services (ref: good)	Low	0.61 (0.42–0.91)	0.75 (0.49–1.15)
	Moderate	0.87 (0.47–1.58)	0.94 (0.49–1.81)
Social support (ref: high)	Low	1.03 (0.65–1.64)	0.94 (0.56–1.55)
	Moderate	0.89 (0.63–1.27)	0.77 (0.52–1.12)
Housing (ref: not owned)	Owned	1.81 (0.90–1.57)	0.97 (0.72–1.32)

^aThe reference group for each of them and the crude odds ratio with a 95% confidence interval are reported for the other groups in comparison with the reference group.

^bThe reference group for each of them and the adjusted odds ratio with a 95% confidence interval are reported for the other groups in comparison with the reference group.

3.3. Behavioral Factors

Behavioral factors demonstrated meaningful associations with overweight and obesity. Physical activity exhibited a strong inverse relationship, with the highest prevalence of overweight and obesity observed among individuals reporting low levels of physical activity.

Regarding tobacco use, obesity prevalence was lower among participants who reported smoking during the past year; however, this association was borderline significant and limited to obesity. Dietary behavior was significantly associated with overweight and obesity, with a higher prevalence observed among participants reporting healthier eating patterns (Table 2).

4. Discussion

This study examined socioeconomic, behavioral, and environmental determinants of overweight and obesity among adults living in southern Tehran. Several key factors—including age, gender, marital status, educational attainment, economic status, physical activity, residential location, and active transportation—were meaningfully associated with overweight and obesity. In contrast, social support, perceived access to health services, and the availability of community facilities were not significantly associated with excess body weight

Consistent with prior national and international studies, overweight and obesity were more prevalent among older adults and women [21, 26]. Biological differences, hormonal factors, and pregnancy‐related weight changes may partially explain the higher burden observed among women. Marital status was also associated with overweight and obesity, with married individuals exhibiting higher odds, a finding that aligns with previous research suggesting lifestyle changes after marriage, including altered dietary habits and reduced physical activity [27, 28].

Education attainment emerged as a strong protective factor against overweight and obesity. Adults with lower levels of education had significantly higher odds of excess weight compared with those with higher educational attainment, consistent with existing evidence [29]. Education likely influences health literacy, nutritional awareness, and lifestyle‐related decision‐making, reinforcing the role of educational policies as long‐term investments in population health.

Economic status showed a positive association with overweight and obesity, with individuals in higher‐income groups demonstrating greater odds of excess body weight. Unlike high‐income countries, where obesity is often concentrated among socioeconomically disadvantaged populations, middle‐income settings may exhibit an opposite pattern [30, 31]. In such contexts, higher economic status may facilitate increased consumption of energy‐dense foods, greater reliance on motorized transportation, and more sedentary lifestyles. These findings highlight the importance of interpreting socioeconomic patterns of obesity within their specific cultural and economic contexts.

The observed variation in overweight and obesity across residential districts underscores the influence of environmental determinants. Although green spaces were present in areas such as District 10, participant‐reported concerns related to environmental quality and neighborhood safety appeared to limit their utilization. This finding is consistent with previous research demonstrating that the mere presence of parks is insufficient to promote physical activity; accessibility, maintenance, and perceived safety are equally critical [32, 33, 34, 35].

Active transportation showed a clear protective association with overweight and obesity, consistent with prior evidence [36, 37, 38]. Structural barriers such as unsafe sidewalks, inadequate cycling infrastructure, and heavy traffic may discourage walking and cycling in Tehran. Urban planning interventions that enhance walkability, improve cycling networks, and address safety concerns may therefore yield substantial public health benefits.

The absence of significant associations between overweight and obesity and social support or access to health services should be interpreted with caution. Cultural norms, limited awareness, prior negative experiences, and variability in the quality of nutritional counseling may influence both service utilization and perceived effectiveness. Similarly, the non‐significant findings related to access to parks and housing conditions reflect the complex interplay between environmental availability and actual behavioral engagement.

This study has several strengths, including a representative sample drawn from all affiliated health centers and the use of a rigorously validated questionnaire. Nevertheless, several limitations should be acknowledged. The cross‐sectional design precludes causal inference, allowing only the identification of associations. Reliance on BMI alone limits assessment of central adiposity and body composition. Telephone‐based data collection may introduce recall and social desirability biases, and dietary behavior was assessed using broad indicators rather than detailed intake measures. Although interactions between independent variables were not examined, the random sampling strategy supports the generalizability of the findings to the target population. Despite these limitations, the study provides valuable insights into the socioeconomic and behavioral drivers of overweight and obesity in an urban, middle‐income setting.

5. Conclusion

This study demonstrates that socioeconomic, demographic, and behavioral determinants substantially influence the prevalence of overweight and obesity among adults in southern Tehran. Older age, female sex, lower educational attainment, higher economic status, reduced physical activity, and limited engagement in active transportation emerged as key predictors, with neighborhood characteristics also playing a meaningful role.

Given the modifiable nature of many identified determinants, particularly physical activity and transportation behaviors, targeted interventions should prioritize enhancing walkability, developing safe active transportation infrastructure, and strengthening public health education. Improving neighborhood safety, upgrading community facilities, and expanding access to effective health counseling may further support healthier weight outcomes. Ultimately, sustained intersectoral collaboration across health, transportation, urban planning, and community development sectors is essential for effective and sustainable obesity prevention in this population.

Author Contributions

Shahla Arameshiyan: data curation, investigation, formal analysis, writing – original draft. Masud Yunesian: methodology, formal analysis, writing – review and editing, software. Alireza Oliyaimanesh: conceptualization, writing – review and editing, resources. Najmeh Almolouk Amini: data curation, investigation, writing – review and editing. Mahnaz Ashoorkhani: conceptualization, methodology, investigation, supervision, writing – review and editing.

Conflicts of Interest

The authors declare no conflicts of interest.

Transparency Statement

The corresponding author (Dr. Mahnaz Ashoorkhani) affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned have been explained.

Supporting information

Supporting File 1

Supporting File 2

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request. Due to ethical considerations and the inclusion of sensitive participant information, the data are not publicly available.