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. 2024 Sep 5;46:102882. doi: 10.1016/j.pmedr.2024.102882

The global burden of overweight-obesity and its association with economic status, benefiting from STEPs survey of WHO member states: A meta-analysis

ANM Shamsul Islam a, Hafiza Sultana b, Md Nazmul Hassan Refat a, Zaki Farhana c, Anton Abdulbasah Kamil d, Mohammad Meshbahur Rahman e,⁎
PMCID: PMC11406007  PMID: 39290257

Highlights

  • •

    Globally, overweight/obesity is one of the key risk factors of non-communicable diseases.

  • •

    Using the latest STEPs survey of WHO member states, we estimate the global prevalence of overweight/obesity and its country-economic variations.

  • •

    The prevalence of global overweight/obesity irrespective of country economic status is 37.0 % [95 % CI: 33 % − 42 %]

  • •

    The prevalence has found higher in American Samoa [93.5%] and lower in Democratic People’s Republic of Korea [4.4%].

  • •

    High-income countries are more prevalent and the increase of economic status may lead to 14% increase chance of having overweight/obesity.

Keywords: Prevalence, Overweight and Obesity, NCDs risk, STEPs survey, Meta-analysis

Abstract

Introduction

The World Health Organization (WHO) 2030 agenda for Sustainable Development Goals (SDGs target 3.4) identifies non-communicable diseases (NCDs) as a key challenge for sustainable development. As one of the major NCD risks, here, we estimated the prevalence of overweight/obesity in adults and assessed country-economic variations using meta-analysis.

Methods

The latest STEPwise approach to NCD risk factor surveillance (STEPS) report of WHO member states studied on overweight/obesity from 2000 to 2020 were reviewed and related data were assessed further. The prevalence of overweight/obesity was pooled using the random effects model. The subgroup analysis and meta-regression were performed based on countries’ economic status obtained from the World Bank’s country development index 2019. Study heterogeneity and publication bias were also observed.

Results

Out of 73 studies with 469,766 participants analyzed, the highest overweight/obesity prevalence was found in American Samoa (93.5 %), while Democratic People’s Republic of Korea had the lowest prevalence (4.4 %). The overall weighted pooled prevalence of overweight/obesity regardless of countries economic status was 37.0 % [95 % CI: 33 %-42 %]. There was significant heterogeneity in the prevalence of overweight/obesity (I2 = 99.91 %; p < 0.001). Subgroup analysis revealed a higher prevalence in high-income countries [60 %; 95 % CI: 47 %-72 %]. Meta-regression revealed a significant (p = 0.001) association and a 14 % increase chance of having overweight/obesity with increasing economic status.

Conclusion

The prevalence of overweight/obesity is high worldwide, especially in high-income countries that demands a large-scale awareness campaigns and effective initiatives to control overweight/obesity and the associated risk factors of adults of these countries.

1. Introduction

Overweight/obesity has become one of the major non-communicable diseases (NCDs) risk factors, and this burden as a global pandemic continues to unacceptably rise (Djalalinia et al., 2020). The risk of overweight/obesity was higher in developing and developed countries compared to lower-income countries (YC et al., 2019). According to the World Health Organization (WHO) in 2022, worldwide 2.5 billion (43 %) adults aged 18 years or older were overweight, and 890 million (16 %) of these adults were living with obesity (World Health Organization, 2024a). This burden becomes more than double since 1990 (World Health Organization, 2024a). The relationship between poor health outcomes and obesity is well-established. Worldwide, more than 2.8 million people die each year as a result of being overweight or obese, and 35.8 million (2.3 %) of global disability-adjusted life years (DALYs) are caused by overweight or obesity (World Health Organization, 2024b).

Globally, overweight/obesity is one of the major public health concerns and it decrease persons’ health-related quality of life (HRQL). The existing literature supports that overweight-obesity is negatively correlated with person’s HRQL (Chung et al., 2016, Wu et al., 2014). Overweight/obesity is also a significant risk factors of various non-communicable disease like hypertension (Chowdhury et al., 2020, Chowdhury et al., 2018b, Paul et al., 2021), diabetes (Slagter et al., 2015), strokes and other chronic disorders (Chowdhury et al., 2018a, Matalqah et al., 2021, Mondal et al., 2023, Sarkar et al., 2023).

Overweight and obesity carry an economic burden as individuals with these conditions incur higher medical costs than those who are not obese (Withrow and Alter, 2011). In a study of Withrow and Alter, the authors reported that obese individuals were found to have medical costs that were approximately 30 % greater than their normal weight peers (Withrow and Alter, 2011). Overweight/obesity is also associated with person’s psychological disorder and studies suggested that obesity may lead to higher vulnerability to mental or physical stressors (Petry et al., 2008, Scott et al., 2012, van der Valk et al., 2018).

The WHO 2030 agenda for SDGs target 3.4 identifies NCDs as a key challenge for sustainable development. As part of the action plan, WHO requests his member states to control and prevention of unconditional probability of deaths due to NCDs for people aged 30–70 years by modifying lifestyle and metabolic risk factors, including overweight-obesity (Djalalinia et al., 2020). The WHO is also designed a STEPwise approach to non-communicable disease risk factor surveillance to help member countries to build and strengthen their surveillance capacity (World Health Organization, 2021). According to the global risk reduction agenda of WHO, having reliable information on NCD risk factors, including overweight/obesity, is critical for establishing, executing, and evaluating National Action Plans at the country level. Despite the wealth of research on this issue, there is a lack of comprehensive, up-to-date global analysis that utilizes standardized, comparable data across countries. This study aims to fill the existing gap by leveraging latest STEPs survey data to provide robust global prevalence estimates of overweight and obesity, while also examining the economic variations across different countries. Understanding these variations is crucial, as the relationship between economic status and obesity is complex and context-dependent. By elucidating these patterns, this study seeks to inform more effective, context-specific public health policies and interventions, ultimately aiming to reduce the global burden of obesity-related diseases and promote health equity.

2. Methods

In this meta-analysis, the Preferred Reporting Items for Systematic reviews, and Meta-Analyses (PRISMA)-2020 guideline was followed (Page et al., 2021).

2.1. Ethical consent and permission for data collection

The ethical consent and permission of data collection is not applicable due to the nature of the study.

2.2. STEPS survey and non-communicable disease risk factors

The World Health Organization (WHO) STEPwise approach to surveillance (STEPS) focuses on obtaining core data on the established risk factors of non-communicable diseases (NCDs) that determine the major disease burden (World Health Organization, 2021). It is sufficiently flexible to allow each country to expand on the core variables and risk factors, and to incorporate optional modules related to local or regional interests. The STEPS instrument covers three different levels of “steps” for the assessment of risk factors. These steps are: Step 1 (questionnaire), Step 2 (physical measurements) and Step 3 (biochemical measurements) (World Health Organization, 2021). Following these three steps, the WHO member states consider six major variables for investigation: Current daily smokers, less than 5 servings of fruits & vegetables per day, insufficient physical activity, overweight/obese (Body Mass Index (BMI) ≥ 25 kg/m2), raised blood pressure (Systolic Blood Pressure ≥ 140 and/or Diastolic Blood Pressure ≥ 90 mmHg or currently on medication for raised blood pressure) and raised total cholesterol (≥5.0 mmol/L) and/or currently on medication. In this study, we meta-analyzed overweight/obesity variable which is one of the major NCDs risk factors.

2.3. Data sources and search strategy

The study search period spanned from January to February 2021. During this time, we systematically reviewed the six regional WHO websites for the latest STEPs reports conducted in member states, including all available reports up to January 2021. Initially, 100 countries were found for the STEP surveyed countries. We screened the prevalence of overweight/obesity (BMI≥25 kg/m2) obtained from the latest STEPS survey reports, data books and fact sheets. For better clarification as well as to assess the impact of national economy on overweight/obesity, selected countries were classified into four groups according to the World Bank’s country development index 2019 (gross national income (GNI) per capita in): low-income [GNI: $1,025 or less], lower middle income [GNI: $1,026–––3,995], upper middle income [GNI: $3,996–––12,375] and high income [GNI: $12,375 or more] (The World Bank, 2019). All the records were managed by Mendeley version 1.19.4 software to exclude duplicates.

2.4. Study selection criteria

There were 120 WHO member countries and 100 conducted STEPS survey for assessing NCDs risks. Initially, reports and fact sheets were gathered to obtain the prevalence of overweight/obese of these countries. Out of 100 countries, 73 were selected according to the selection criteria where 15 were from low-income, 26 were from lower middle-income, 20 were from upper middle-income and 12 were from high income countries. A sum of 27 countries’ reports were excluded from the study due to not reporting prevalence of overweight/obese, reported missing and multiple sample size, scientifically weak reporting, and not written in English. The details study screening process are presented in the flowchart (Fig. 1).

Fig. 1.

Fig. 1

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PRISMA 2020 flow diagram for including STEPS studies in the meta-analysis. STEPS: STEPwise approach to NCD risk factors surveillance; LI: Low-income; LMI: Lower middle income; UMI: Upper middle income; HI: High income.

2.5. Data screening and extraction

Two independent investigators (MMR and ANMSI) screened the selected 73 STEPS survey reports and extracted information on the prevalence of overweight/obesity. Disagreements were resolved by consulting with the co-authors. The data were extracted from all eligible studies using a standardize form. For each selected studies, STEPS report’s publication details [title, year of STEPS survey]; design and population [region, country, age group, sample size]; participants' overweight/obesity (BMI≥25 kg/m2) prevalence were extracted.

2.6. Statistical analysis

We defined prevalence as the number of cases observed divided by the total sample. Background statistics of the STEPS survey reports were recorded as a tabular form. A random effect model was, therefore, used to perform the meta-analysis of the prevalence of overweight/obese (Hossain et al., 2022, Rahman et al., 2022, Rahman et al., 2021). Subgroup analysis was examined according to country’s gross national income (GNI) per capita presented by World Bank. Heterogeneity was assessed using Higgin's and Thompson's I2 statistic and Cochran's Q test. To assess the asymmetry and publication bias, we used funnel plot and Egger test [p < 0.001] to test the presence of small-study effects. Meta-regression was also performed to identify the impact of countries economic status (GNI) on the prevalence of overweight/obese. Stata SE version 17.0 (Stata Corporation, College Station, TX, USA 5) was used for all statistical analyses.

3. Results

Out of retrieved 100 WHO surveyed countries, we studied 73 countries STEPs survey reports and excluded 27 countries according to our inclusion and exclusion criteria. The studies selected in this meta-analysis comprised 469,766 respondents where 74,871 (15.9 %) were from the low-income countries, 140,599 (29.9 %) were from lower-middle income, 206,229 (43.9 %) were from upper-middle income and 47,997 (10.2 %) were from the high-income countries. The minimum age of the participants was 15 years and above in all the selected studies. Overall, the maximum prevalence of overweight/obesity was 93.5 %%, found in American Samoa where the minimum prevalence observed in Democratic People's Republic (DPR) of Korea (4.4 %). The male prevalence of overweight/obesity were ranged between 4.1 % (DPR Korea) to 92.7 % (American Samoa) while for female, these values were observed 4.7 (DPR Korea and Vietnam) to 94.4 % (American Samoa). The details summary statistics are presented in supplementary Figure S1 and Table 1.

Table 1.

Characteristics of studies that evaluated the global prevalence of overweight/obesity.

Economic Status Country Year of STEPS Survey Age Group Sample Size Overweight (BMI≥25 kg/m2) (%)
Both M F
Low-income Democratic Republic of the Congo 2005 15–64+ 1948 19.1 11.6 24.1
Low-income Ethiopia 2015 15–69 10,260 6.3 4.4 8.8
Low-income Gambia 2010 25–64 5280 39.5 33.7 45.3
Low-income Liberia 2011 25–64 4320 49.9 43 57
Low-income Madagascar 2005 25–64 5743 12.3 9.4 15.4
Low-income Malawi 2009 25–64 5760 21.9 6.1 28.1
Low-income Mali 2007 15–64 2810 34.6 20.7 44.4
Low-income Mozambique 2005 25–64 3310 21.2 13.5 27.1
Low-income Niger 2007 15–64 2760 12.7 9.1 16.9
Low-income Rwanda 2012–13 15–64 7240 14.32 9.1 19
Low-income Sierra Leone 2009 25–64 5483 22.4 16.2 28.7
Low-income Uganda 2014 18–69 3987 19.1 11.3 27.1
Low-income United Republic of Tanzania 2012 25–64 5770 26 15.1 37.1
Low-income Democratic People's Republic of Korea 2008 25–64 6000 4.4 4.1 4.7
Low-income Nepal 2013 15–69 4200 21.6 21.2 22.1
Lower middle income Andhra Pradesh 2007–08 15–64 6270 15 13.4 16.8
Lower middle income Madhya Pradesh 2007–08 15–65 5922 10.9 9.4 12.6
Lower middle income Maharashtra 2007–08 15–66 6124 6.6 5.7 7.5
Lower middle income Mizoram 2007–08 15–67 4495 8.5 6.9 10.5
Lower middle income Kerala 2007–08 15–68 5067 22.4 20.8 24
Lower middle income Tamil Nadu 2007–08 15–69 5177 17.7 16.3 19.3
Lower middle income Uttarakhand 2007–08 15–70 5444 11.3 10.1 12.6
Lower middle income Indonesia 2006 25–64 2200 13.8 10.4 17.6
Lower middle income Swaziland 2014 15–69 4320 43.8 26 59.9
Lower middle income Mongolia 2013 15–64 6150 54.4 49 59.9
Lower middle income Morocco 2017 18–69 6100 53 42.6 63.4
Lower middle income Bhutan 2014 18–69 2912 33 23.3 40.4
Lower middle income Egypt 2017 15–69 7200 63 53.8 74.1
Lower middle income Papua New Guinea 2007–8 15–64 3000 32.1 30.3 33.9
Lower middle income Viet Nam 2015 18–69 4320 13.9 13.2 4.7
Lower middle income Uzbekistan 2014 18–64 4350 50.1 47.7 52.7
Lower middle income Timor-Leste 2014 18–69 2710 11.2 8.2 16.7
Lower middle income Bangladesh 2018 18–69 9900 20.3 15.8 25
Lower middle income Congo 2004 24–64 2030 27.3 18 37.1
Lower middle income Pakistan 2014 18–69 7669 41.3 38.6 43.2
Lower middle income Sudan 2016 18–69 8145 28.2 22.6 35.6
Lower middle income Cameroon 2003 15–64+ 10,824 42.8 31.4 50.5
Lower middle income Kenya 2015 18–69 6000 19 13.2 24.9
Lower middle income Lesotho 2012 25–64 2310 41.5 24.8 58.2
Lower middle income Myanmar 2014 25–64 9360 22.4 14.1 30.8
Lower middle income Mauritania 2006 15–64 2600 46.6 31.8 59.4
Upper middle income Thailand 2010 15–74 130,849 21.3 18.6 24
Upper middle income Mauritius 2004 20–74 4500 25.4 25.1 25.7
Upper middle income Nauru 2004 15–64 2500 82.2 82.1 82.2
Upper middle income Malaysia 2005 25–64 3040 31.6 30.9 32.4
Upper middle income Turkey 2017 15–69 8644 64.4 62.8 66
Upper middle income Grenada 2010–11 25–64 1736 58.7 48.3 69.2
Upper middle income Saint Lucia 2012 25–64 1834 65.6 53.9 77.1
Upper middle income Maldives 2011 15–64 2000 37.1 32 42.3
Upper middle income Saint Vincent and the Grenadines 2012 18–69 5180 54.2 41.3 67.2
Upper middle income Botswana 2014 15–69 6400 18.8 14.7 23.2
Upper middle income Lebanon 2016–17 18–69 5760 37.9 44.7 31.4
Upper middle income Dominica 2007–8 15–64 1059 45.1 29.5 61.9
Upper middle income Libya 2009 25–64 3590 63.5 57.5 69.8
Upper middle income Belarus 2016–17 18–69 5760 60.6 61.5 60
Upper middle income Iraq 2015 18–60+ 4120 65.4 58.7 73.1
Upper middle income Guatemala 2015 >18 2036 67.2 64 70.2
Upper middle income Armenia 2016 18–69 2349 47.7 45.4 50.1
Upper middle income Georgia 2016 18–69 5554 64.6 65.5 63.8
Upper middle income American Samoa 2004 25–64 2188 93.5 92.7 94.4
Upper middle income Sri Lanka 2015 18–69 7200 23.4 21 26
High income Bermuda 2014 >18 2656 74.6 79.1 69.6
High income Qatar 2012 18–64 2880 70.1 71.8 68.3
High income Cayman Islands 2012 25–64 2105 70.6 67.6 73.7
High income Kuwait 2014 18–69 4391 77.2 78.4 76.1
High income Brunei Darussalam 2015–16 18–69 3808 62.8 63.2 62.5
High income British Virgin Islands 2009 25–64 1802 39.2 45 32.2
High income Aruba 2006 25–64 1565 77.1 82.8 72.1
High income Bahrain 2007 20–64 20,037 33.4 34.8 32
High income Saint Kitts and Nevis 2008 25–64 2750 78.5 74.1 83
High income Trinidad and Tobago 2011 15–64 3020 30 32.9 27.3
High income Barbados 2007 25–64 1283 65.2 54.6 74.3
High income Oman 2006 >20 1700 33.3 39.2 28.7
STEPS: STEPwise approach to NCD risk factor surveillance; BMI: Body Mass Index
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3.1. Global status of overweight/obesity

The weighted pooled prevalence of overweight/obesity regardless of countries economic status was 37.0 % [95 % CI: 33 % − 42 %]. A large amount of heterogeneity was observed in the overall prevalence of overweight/obesity (I2 = 99.91 %; Cochran Q-statistic p < 0.001) (Fig. 2).

Fig. 2.

Fig. 2

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Forest plot of the global prevalence of overweight/obese with 95% confidence intervals (CIs); ES: Effect Size (Prevalence of Overweight/Obesity).

3.2. Subgroup analysis based on the economic status of selected countries

Overweight/obesity is correlated with person’s economic status and we performed a subgroup meta-analysis based on the selected 73 countries economic status, classified based on the World Bank’s country development index. In subgroup analysis, the weighted pooled prevalence of overweight/obesity was higher among the participants from high-income countries, 60 % [95 % CI: 47 % − 72 %]. The other weighted pooled prevalence was 21 % [95 % CI: 14 % − 27 %], 27 % [95 % CI: 21 %-34 %] and 52 % [95 % CI: 41 % − 63 %] in low-income, lower middle-income and upper middle-income countries respectively. A significant (p = 0.001) high heterogeneity was observed among the participants from all group of economic status (Fig. 3).

Fig. 3.

Fig. 3

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Forest plot of the global prevalence of overweight/obese with 95% confidence intervals (CIs), stratified according to the country’s economic status; ES: Effect Size (Prevalence of Overweight/Obesity).

3.3. Meta-regression of the prevalence according to countries’ economic status

It is evident that the prevalence of overweight/obese can vary according to countries economic status and therefore, an economic effect on the prevalence overweight/obese was also explored by meta-regression. Meta-regression showed a significant (p = 0.001) increase of weighted pooled prevalence of overweight/obese among participants with the growth of economic status. More specifically, an increase of economic status may lead to 14 % (p = 0.001; Fig. 5) rise chance of having overweight/obesity among the participants (Fig. 4 and supplementary result S2).

Fig. 5.

Fig. 5

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Funnel plot of global overweight/obesity prevalence on countries economic status; X-axis: ES- Effect Size (Prevalence of Overweight/Obesity); Y-axis: se (ES)- standard error of effect size (Prevalence of Overweight/Obesity). In the graph, the black dots are the standard error of prevalence of overweight/obesity. The straight line is the average standard error and the dash lines are the confidence interval that indicates the presence of asymmetry and publication bias among the prevalence.

Fig. 4.

Fig. 4

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Meta-regression of global overweight/obesity prevalence on countries economic status of the study participants; X-axis: ES- Effect Size (Prevalence of Overweight/Obesity); Y-axis: Economic Status of 73 countries involved in this study. The economic status 1 represent low-income countries; 2 means lower middle income c; 3 means upper middle income; and 4 means high income countries. The straight line represents the average line showing increasing rate of overweight/obesity prevalence with respect to the economic status of WHO member states.

3.4. Publication bias

We also examined the publication bias among the selected studies obtained by funnel plot. The funnel plot indicates the presence of asymmetry and publication bias (Fig. 5), and Egger test significantly (p = 0.001) suggested the existence of small study effects (Supplementary result S3).

4. Discussion

This meta-analysis aimed to determine the global prevalence of overweight/obese and its association with countries’ economic status, utilizing data from the latest WHO STEPs surveys conducted in member states. The drawback of the study was that STEPS studies conducted only in cross-sectional nature among WHO member states were included in the analysis. We also excluded some STEPS studies that were not reported in English language.

Analysis found that globally, 3.7 out of 10 people were suffering from overweight/obesity disorder. In relation to existing reports, more than one in eight people in the world were living with obesity (World Health Organization, 2024a). According to the recent report, the global overweight/obesity can range from 31 %-67 % among this group of people (World Health Organization, 2024a), and the age-standardized prevalence of obesity was increased globally (NCD Risk Factor Collaboration (NCD-RisC), 2021).

Literature also suggests that females are comparatively more prevalent in overweight/obese than male respondents which align with the current study (Phelps et al., 2024). A high heterogeneity in the global prevalence of overweight/obesity was also observed. This variability may be attributed to socio-demographic factors such as gender, age at first pregnancy, marital status, residence, (AlTamimi et al., 2020, Asif et al., 2020, Rahman et al., 2017). Lifestyle factors including sleep disorders, imbalanced diet, smoking and drag habits, sedentary behaviors and screen time, also contributed to this heterogeneity (AlTamimi et al., 2020, Jezewska-Zychowicz et al., 2018, Rahman et al., 2017, Shin, 2018, Biddle et al., 2017). Furthermore, various environmental factors play a role in this variation (Di Cesare et al., 2016, Myers et al., 2015, Slack et al., 2014).

The subgroup meta-analysis revealed that countries with high economic status have a higher prevalence of overweight/obesity, with six out of ten people are suffering from overweight/obesity disorder. Overweight/obese prevalence was also higher in upper middle-income countries, where more than half of the population was found to be overweight and obese. The finding supports the existing literature (Fox et al., 2019, Kim and von dem Knesebeck, 2018, Templin et al., 2019).

This study also investigated the economic impacts of overweight/obesity using meta-regression analysis. The findings indicated that the global prevalence of overweight/obesity increased 14 % with one unit change in economic status which aligns with the existing literature (Specchia et al., 2015). A study of Bu and colleagues (2021) demonstrated a correlation between national economic development and body mass index (Bu et al., 2021). They observed a linear increase in BMI revealing a steady rise in BMI as well as overweight/obesity with economic development. Another study suggested that economic growth leads to rise of overweight/obesity (Seyda Seydel et al., 2016) and 1 % increase in income leads to around a 0.2–0.3 % higher in the prevalence of overweight/obesity (Ferretti and Mariani, 2017).

The health consequences of overweight/obese are enormous. Carrying extra fat in body leads to the serious health complexities including cardiovascular illness, diabetes, and various cancers which ultimately cause premature death and substantial disability (Kiwanuka, 2020, World Health Organization, 2013). The Centers for Disease Control and Prevention (CDC) reported a list of serious diseases and health conditions including hypertension, high cholesterol, diabetes, coronary heart disease, stroke, gallbladder disease, osteoarthritis, sleep apnea, difficulty with physical functioning, mental illness and many types of cancer caused by overweight/obesity (Centers for Disease Control and Prevention-CDC, 2022).

In this study, the evidence of small study effect was detected, which reported higher prevalence of overweight/obesity. The asymmetry of the funnel plot suggested the presence of publication bias among the selected studies. Controlling the high burden of overweight and obesity requires a multifaceted approach. Firstly, educating individuals about the importance of balanced diet and the dangers of consuming unhealthy foods is essential. Governments can also regulate the availability and marketing of unhealthy foods (Vardanjani et al., 2015). Secondly, regular physical activity is crucial for maintaining a healthy weight. Encouraging individuals to engage in exercise and other physical activities can help reduce the prevalence of overweight and obesity (Cox, 2017). Thirdly, healthcare services can play a critical role in controlling overweight and obesity. Health professionals can provide advice, support, and treatment to individuals struggling with these conditions (Pearce et al., 2019). Fourthly, creating supportive environments that make it easy for individuals to make healthy choices can be effective (Allender et al., 2012). This can include implementing policies that promote healthy eating and physical activity in schools, workplaces, and communities. Finally, Public awareness campaigns can help educate individuals about the risks of overweight and obesity and the importance of healthy lifestyles (Gill and Boylan, 2012, Walls et al., 2011). These campaigns can be implemented through various mass media channels, including social media, television, and Radio.

5. Conclusion

This is the first comprehensive study that systematically evaluates the representative survey reports on the prevalence of overweight/obesity, extracting data from the STEPs surveys of WHO listed 73 member states. Considering the high observed heterogeneity among the included studies, our meta-analysis reports a globally high prevalence of overweight/obesity. The prevalence rate found comparatively high among the resident with upper economic status that may lead to 14 % increase chance of having overweight/obesity compared to low-income countries. NCDs is a key challenge for sustainable development where overweight/obesity the major NCD risk factor. As part of the action plan, WHO member states should take effective initiatives to control and prevent overweight/obesity as well as the risk factors of NCDs.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

CRediT authorship contribution statement

A.N.M. Shamsul Islam: . Hafiza Sultana: Writing – review & editing, Resources, Project administration, Investigation, Funding acquisition. Md. Nazmul Hassan Refat: Writing – review & editing, Resources, Investigation, Funding acquisition. Zaki Farhana: Writing – review & editing, Writing – original draft, Visualization, Software, Formal analysis, Data curation. Anton Abdulbasah Kamil: Writing – review & editing, Writing – original draft, Resources, Funding acquisition. Mohammad Meshbahur Rahman: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

We are very thankful to the World Health Organization (WHO) and their member states for conducting STEPs surveys. The study was not possible without the survey information.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

All data are provided in the tables presented in the full-text.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.pmedr.2024.102882.

Appendix A. Supplementary data

The following are the Supplementary data to this article:

Supplementary Fig. 1.

Supplementary Fig. 1

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Spatial distribution of global overweight/obesity obtained from the recent STEPs survey of WHO member states.

Supplementary Data 1
mmc1.docx (12.9KB, docx)
Supplementary Data 2
mmc2.docx (12.8KB, docx)

Data availability

All data involved in this study are provided in the full-text manuscript.

References

  1. Allender S., Gleeson E., Crammond B., Sacks G., Lawrence M., Peeters A., et al. Policy change to create supportive environments for physical activity and healthy eating: which options are the most realistic for local government? Health Promot. Int. 2012;27:261–274. doi: 10.1093/heapro/dar018. [DOI] [PubMed] [Google Scholar]
  2. AlTamimi A.A., Albawardi N.M., AlMarzooqi M.A., Aljubairi M., Al-Hazzaa H.M. Lifestyle behaviors and socio-demographic factors associated with overweight or obesity among Saudi Females attending fitness centers. Diab. Metab. Syndr. Obes. 2020;13:2613–2622. doi: 10.2147/DMSO.S255628. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Asif M., Aslam M., Altaf S., Atif S., Majid A. Prevalence and sociodemographic factors of overweight and obesity among Pakistani adults. J. Obes. Metab. Syndr. 2020;29:58–66. doi: 10.7570/JOMES19039. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Biddle S.J.H., García Bengoechea E., Pedisic Z., Bennie J., Vergeer I., Wiesner G. Screen time, other sedentary behaviours, and obesity risk in adults: A review of reviews. Curr. Obes. Rep. 2017;6:134–147. doi: 10.1007/S13679-017-0256-9. [DOI] [PubMed] [Google Scholar]
  5. Bu T., Popovic S., Huang H., Fu T., Gardasevic J. Relationship between national economic development and body mass index in chinese children and adolescents Aged 5–19 from 1986 to 2019. Front. Pediatr. 2021:337. doi: 10.3389/FPED.2021.671504. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. C. YC, D. C, M. F The epidemiology of obesity. Metabolism. 2019;92:6–10. doi: 10.1016/J.METABOL.2018.09.005. [DOI] [PubMed] [Google Scholar]
  7. Centers for Disease Control and Prevention-CDC, 2022. Defining Adult Overweight & Obesity [WWW Document]. URL https://www.cdc.gov/healthyweight/effects/index.html (accessed 10.16.21).
  8. Chowdhury M.Z.I., Anik A.M., Farhana Z., Bristi P.D., Abu Al Mamun B.M., Uddin M.J., et al. Prevalence of metabolic syndrome in Bangladesh: A systematic review and meta-analysis of the studies. BMC Public Health. 2018 doi: 10.1186/s12889-018-5209-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Chowdhury M.Z.I., Haque M.A., Farhana Z., Anik A.M., Chowdhury A.H., Haque S.M., et al. Prevalence of cardiovascular disease among Bangladeshi adult population: A systematic review and meta-analysis of the studies. Vasc. Health Risk Manag. 2018 doi: 10.2147/VHRM.S166111. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Chowdhury M.Z.I., Rahman M., Akter T., Akhter T., Ahmed A., Shovon M.A., et al. Hypertension prevalence and its trend in Bangladesh: evidence from a systematic review and meta-analysis. Clin Hypertens. 2020;26:10. doi: 10.1186/s40885-020-00143-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Chung W., Park C.G., Ryu O.-H. Association of a new measure of obesity with hypertension and health-related quality of life. PLoS One. 2016;11 doi: 10.1371/JOURNAL.PONE.0155399. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Cox C.E. Role of physical activity for weight loss and weight maintenance. Diabetes Spectrum. 2017;30:157–160. doi: 10.2337/ds17-0013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Di Cesare M., Bentham J., Stevens G.A., Zhou B., Danaei G., Lu Y., et al. Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19·2 million participants. Lancet. 2016;387:1377–1396. doi: 10.1016/S0140-6736(16)30054-X. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Djalalinia S., Saeedi Moghaddam S., Sheidaei A., Rezaei N., Naghibi Iravani S.S., Modirian M., et al. Patterns of obesity and overweight in the Iranian population: findings of STEPs 2016. Front Endocrinol (lausanne) 2020:42. doi: 10.3389/FENDO.2020.00042. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Ferretti F., Mariani M. Simple vs. complex carbohydrate dietary patterns and the global overweight and obesity pandemic. Int. J. Environ. Res. Public Health. 2017;14:1174. doi: 10.3390/IJERPH14101174. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Fox A., Feng W., Asal V. What is driving global obesity trends? Globalization or “modernization”? Globalizat. Health. 2019;15(1):1–16. doi: 10.1186/S12992-019-0457-Y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Gill T.P., Boylan S. Public health messages: why are they ineffective and what can be done? Curr. Obes. Rep. 2012;1:50–58. doi: 10.1007/s13679-011-0003-6. [DOI] [Google Scholar]
  18. Hossain M.S., Tasnim S., Chowdhury M.A., Chowdhury F.I.F., Hossain D., Rahman M.M. Under-five children’s acute respiratory infection dropped significantly in Bangladesh: An evidence from Bangladesh demographic and health survey, 1996–2018. Acta Paediat., Internat. J. Paediatrics. 2022;111:1981–1994. doi: 10.1111/APA.16447. [DOI] [PubMed] [Google Scholar]
  19. Jezewska-Zychowicz M., Gębski J., Guzek D., Świątkowska M., Stangierska D., Plichta M., et al. The associations between dietary patterns and sedentary behaviors in polish adults (Lifestyle study) Nutrients. 2018;10 doi: 10.3390/NU10081004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Kim T.J., von dem Knesebeck O. Income and obesity: what is the direction of the relationship? A systematic review and meta-analysis. BMJ Open. 2018;8:e019862. doi: 10.1136/bmjopen-2017-019862. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Kiwanuka E. The health impact of obesity. Obesity. 2020;73–83 doi: 10.1016/B978-0-12-818839-2.00006-5. [DOI] [Google Scholar]
  22. Matalqah L.M., Radaideh K.M., Thabet R.H. Impact of chronic disorders and obesity on quality of life in Northern Jordan. J. Public Health (Germany) 2021;29:61–67. doi: 10.1007/S10389-019-01103-2. [DOI] [Google Scholar]
  23. Mondal M., Islam M.N., Ullah A., Haque M.R., Rahman M., Bosak L., et al. Clinical manifestations and outcomes of COVID-19 in maintenance hemodialysis patients of a high infectious epidemic country: a prospective cross-sectional study. Ann. Med. Surg. 2023;85 doi: 10.1097/MS9.0000000000001046. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Myers C.A., Slack T., Martin C.K., Broyles S.T., Heymsfield S.B. Regional disparities in obesity prevalence in the United States: A spatial regime analysis. Obesity. 2015;23:481–487. doi: 10.1002/OBY.20963. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. NCD Risk Factor Collaboration (NCD-RisC) Heterogeneous contributions of change in population distribution of body mass index to change in obesity and underweight. Elife. 2021;10:e60060. doi: 10.7554/eLife.60060. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Page M.J., McKenzie J.E., Bossuyt P.M., Boutron I., Hoffmann T.C., Mulrow C.D., et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372 doi: 10.1136/bmj.n71. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Paul G.K., Rahman M.M., Hamiduzzaman M., Farhana Z., Mondal S.K., Akter S., et al. Hypertension and its physio-psychosocial risks factors in elderly people: a cross-sectional study in north-eastern region of Bangladesh. J. Geriatr. Cardiol. 2021;18:75–82. doi: 10.11909/j.issn.1671-5411.2021.01.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Pearce C., Rychetnik L., Wutzke S., Wilson A. Obesity prevention and the role of hospital and community-based health services: a scoping review. BMC Health Serv. Res. 2019;19:453. doi: 10.1186/s12913-019-4262-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Petry N.M., Barry D., Pietrzak R.H., Wagner J.A. Overweight and obesity are associated with psychiatric disorders: Results from the national epidemiologic survey on alcohol and related conditions. Psychosom. Med. 2008;70:288–297. doi: 10.1097/PSY.0B013E3181651651. [DOI] [PubMed] [Google Scholar]
  30. Phelps N.H., Singleton R.K., Zhou B., Heap R.A., Mishra A., Bennett J.E., et al. Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults. Lancet. 2024;403:1027–1050. doi: 10.1016/S0140-6736(23)02750-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Rahman M.M., Bhattacharjee B., Farhana Z., Hamiduzzaman M., Chowdhury M.A.B., Hossain M.S., et al. Age-related Risk Factors and Severity of SARS-CoV-2 Infection: a systematic review and meta-analysis. J. Prev. Med. Hyg. 2021;62:E329–E. doi: 10.15167/2421-4248/JPMH2021.62.2.1946. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Rahman M., Farhana Z., Tani T., Ullah M. Integrating Overweight-obesity and Reproductive Factors of Married Women in Bangladesh. Society & Change. 2017;3:55–72. [Google Scholar]
  33. Rahman M.M., Marzo R.R., Chowdhury S., Qalati S.A., Hasan M.N., Paul G.K., et al. Knowledge, attitude and practices toward coronavirus disease (COVID- 19) in Southeast and South Asia: A mixed study design approach. Front. Public Health. 2022;10 doi: 10.3389/fpubh.2022.875727. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Sarkar S.M., Dhar B.K., Fahlevi M., Ahmed S., Hossain M.J., Rahman M.M., et al. Climate Change and Aging Health in Developing Countries. Global Chall. 2023;7:2200246. doi: 10.1002/gch2.202200246. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Scott K.A., Melhorn S.J., Sakai R.R. Effects of chronic social stress on obesity. Curr. Obes. Rep. 2012;1:16. doi: 10.1007/S13679-011-0006-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Seyda Seydel G., Kucukoglu O., Altinbasv A., Demir O., Yilmaz S., Akkiz H., et al. Economic growth leads to increase of obesity and associated hepatocellular carcinoma in developing countries. Ann. Hepatol. 2016;15:662–672. doi: 10.5604/16652681.1212316. [DOI] [PubMed] [Google Scholar]
  37. Shin J. Joint association of screen time and physical activity with obesity: findings from the Korea media panel study. Osong. Pub. Health Res. Perspect. 2018;9:207. doi: 10.24171/J.PHRP.2018.9.4.10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Slack T., Myers C.A., Martin C.K., Heymsfield S.B. The geographic concentration of us adult obesity prevalence and associated social, economic, and environmental factors. Obesity. 2014;22:868–874. doi: 10.1002/OBY.20502. [DOI] [PubMed] [Google Scholar]
  39. Slagter S.N., Van Vliet-Ostaptchouk J.V., Van Beek A.P., Keers J.C., Lutgers H.L., Van Der Klauw M.M., et al. Health-Related quality of life in relation to obesity grade, type 2 diabetes, metabolic syndrome and inflammation. PLoS One. 2015;10 doi: 10.1371/JOURNAL.PONE.0140599. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Specchia M.L., Veneziano M.A., Cadeddu C., Ferriero A.M., Mancuso A., Ianuale C., et al. Economic impact of adult obesity on health systems: a systematic review. Eur. J. Pub. Health. 2015;25:255–262. doi: 10.1093/EURPUB/CKU170. [DOI] [PubMed] [Google Scholar]
  41. Templin T., Hashiguchi T.C.O., Thomson B., Dieleman J., Bendavid E. The overweight and obesity transition from the wealthy to the poor in low- and middle-income countries: A survey of household data from 103 countries. PLoS Med. 2019;16:e1002968. doi: 10.1371/journal.pmed.1002968. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. The World Bank, 2019. GNI per capita, Atlas method (current US$) | Data [WWW Document]. URL https://data.worldbank.org/indicator/NY.GNP.PCAP.CD (accessed 6.15.19).
  43. van der Valk E.S., Savas M., van Rossum E.F.C. Stress and obesity: are there more susceptible individuals? Curr. Obes. Rep. 2018;7:193. doi: 10.1007/S13679-018-0306-Y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Vardanjani A., Reisi M., Javadzade H., Pour Z., Tavassoli E. The effect of nutrition education on knowledge, attitude, and performance about junk food consumption among students of female primary schools. J. Educ. Health Promot. 2015;4:53. doi: 10.4103/2277-9531.162349. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Walls H.L., Peeters A., Proietto J., McNeil J.J. Public health campaigns and obesity - a critique. BMC Public Health. 2011;11:136. doi: 10.1186/1471-2458-11-136. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Withrow D., Alter D.A. The economic burden of obesity worldwide: a systematic review of the direct costs of obesity. Obes. Rev. 2011;12:131–141. doi: 10.1111/J.1467-789X.2009.00712.X. [DOI] [PubMed] [Google Scholar]
  47. World Health Organization, 2013. Obesity: Health consequences of being overweight [WWW Document]. URL https://www.who.int/news-room/q-a-detail/obesity-health-consequences-of-being-overweight (accessed 10.16.21).
  48. World Health Organization, 2021. STEPwise Approach to NCD Risk Factor Surveillance (STEPS) [WWW Document]. URL https://www.who.int/teams/noncommunicable-diseases/surveillance/systems-tools/steps (accessed 10.13.21).
  49. World Health Organization, 2024a. Obesity and overweight [WWW Document]. World Health Organization. URL https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight# (accessed 10.14.21).
  50. World Health Organization, 2024b. Obesity [WWW Document]. World Health Organization. URL https://www.who.int/data/gho/indicator-metadata-registry/imr-details/3420# (accessed 6.29.24).
  51. Wu S., Wang R., Jiang A., Ding Y., Wu M., Ma X., et al. Abdominal obesity and its association with health-related quality of life in adults: A population-based study in five Chinese cities. Health Qual. Life Outcomes. 2014;12 doi: 10.1186/1477-7525-12-100. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Data 1
mmc1.docx (12.9KB, docx)
Supplementary Data 2
mmc2.docx (12.8KB, docx)

Data Availability Statement

All data involved in this study are provided in the full-text manuscript.

All data are provided in the tables presented in the full-text.

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