Introduction

The World Health Organization (WHO) classifies obesity as a chronic, multifactorial disease characterized by excessive fat accumulation that presents a risk to health, operationalized as a body mass index (BMI) of 30 kg/m² or higher [1]. This metric is calculated by dividing an individual’s weight in kilograms by the square of their height in meters [2]. Obesity ranks as the fifth leading cause of mortality globally and is a major contributor to the rise of non-communicable diseases (NCDs) such as cardiovascular disease, type 2 diabetes, and certain forms of cancer [3]. Although obesity is often associated with high-income nations, it is increasingly prevalent across low- and middle-income countries (LMICs), where the shift from traditional to modern dietary patterns, combined with urbanization and sedentary lifestyles, is accelerating the public health crisis [4].

In 2014, the WHO estimated that over 600 million out of approximately 1.9 billion were people with obesity, with eight of the ten countries most affected being LMICs, including Brazil, China, Egypt, India, Indonesia, Mexico, Pakistan, and Russia [5, 6]. LMICs are nations classified by the World Bank based on gross national income (GNI) per capita, currently defined as those with a GNI per capita of $13,845 or less in 2024 (World Bank, 2024). Notably, 62% of the world’s population with obesity resides in LMICs [6]. Over the past three decades, age-standardised obesity rates have increased significantly, increasing from 3.2% to 10.8% in men and from 6.4% to 14.9% in women [7]. Specifically, in LMICs such as India, Bangladesh, and Nepal, the prevalence of obesity among women increased from 10.6% to 14.8%, from 2.7% to 8.9%, and from 1.6% to 10.1%, respectively, between 1996 and 2006 [8]. Interestingly, recent studies suggest that the prevalence of obesity in LMICs is lower than that in high-income countries [9].

Historically, undernutrition has dominated the public health discourse in LMICs. However, many of these countries are now experiencing a rapid epidemiological transition, where underweight and micronutrient deficiencies coexist with overnutrition and diet-related NCDs [10]. Urbanization, aggressive marketing of ultra-processed foods, declining physical activity, and increasing substance use such as tobacco and alcohol are driving this shift [11]. As a result, conditions like obesity, hypertension, and diabetes are emerging more rapidly in LMICs than in high-income settings. For example, cardiovascular diseases now account for a significant share of mortality in Ethiopia and Kenya, often linked to overweight and obesity [12].

Although obesity rates have started to stabilize in some high-income countries, this trend is not evident in the majority of LMICs, where rates continue to rise. Notably, urban areas in Rwanda, Zambia, and Brazil have shown renewed surges in obesity prevalence after initial periods of stabilization [13]. Among adolescents in LMICs including those in Egypt, Mexico, Vietnam, and Nigeria obesity rates are increasing at alarming rates [13]. A 2021 study spanning seven LMICs reported a consistent rise in overweight among adolescents, highlighting a troubling trajectory that portends a future wave of adult obesity and associated health complications [14]. Without comprehensive prevention programs, this trend is expected to continue, albeit at a potentially slower pace. However, even modest increases in obesity among youth populations can translate into significant public health costs in the long term, given the chronic nature of obesity and its complications [15].

Obesity is associated with several chronic lifestyle conditions, including hypertension, diabetes, cancer, and heart disease [16, 17]. These conditions contribute to disability, significant healthcare expenditures, and increased mortality rates. Obesity is reported to reduce life expectancy by an average of 5–8 years for women and 13–20 years for men [16]. Additionally, it is linked to various health issues, such as pain, fatigue, sleep disorders, and depressive moods [18]. Studies estimate that obesity can reduce life expectancy by 5–8 years in women and 13–20 years in men [19]. Moreover, obesity is associated with physical limitations such as chronic fatigue, sleep disturbances, joint pain, and depressive symptoms. For example, in Nigeria, patients with obesity undergoing cardiac surgery had significantly longer hospital stays compared to their non-obese counterparts, placing strain on already limited healthcare infrastructure [20]. Across LMICs, obesity-related disability-adjusted life years (DALYs) have been on a steady rise since 1990, and projections suggest they could double over the next four decades without effective intervention [21].

Despite the growing burden of obesity in LMICs, there is a lack of comprehensive synthesis evaluating its clinical and economic impacts in these settings. Although multiple studies have estimated the burden of obesity globally, significant variability in methods such as diagnostic criteria, cost components, population selection, and analytical approaches has led to inconsistent findings [22]. Most existing systematic reviews are either outdated, limited to high-income countries, or lack rigorous methodological assessments, such as quality appraisal, standardized cost conversions, or inclusion of both direct and indirect costs [22,23,24,25]. Furthermore, few reviews have focused specifically on LMICs, despite the rapidly increasing prevalence and unique contextual factors in these countries. Given the double burden of malnutrition and non-communicable diseases now faced by LMICs, a systematic review focused on these countries is urgently needed.

Methods

A systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [26]. The review has been registered with PROSPERO, the International Prospective Register of Systematic Reviews, under the registration number CRD42022331948. This registration ensures transparency and helps prevent duplication of research efforts.

Search strategy

The literature search in this systematic review used the CINAHL, MEDLINE, PubMed, Web of Science, and Scopus databases and included articles published from inception to the 28th of March 2025. The search terms used were obesity, overweight, obesity, unhealthy weight, high BMI, cost, expense, affordability, financial burden, health care costs, healthcare resource utilisation, emergency department visit, physician visits, hospitalisation, LMICs, low-income countries, middle-income countries, developing countries, Africa, Asia, Latin America, South America, and Central America. In addition to these databases, hand searches of the references of the included studies were also performed. The terms were matched with terms in the Medical Subject Heading (MeSH) database. All references were downloaded to EndNote, and duplicates were removed. The search was performed independently by two reviewers (TG & CM) to avoid the presence of bias in the selection and exclusion of studies. Disagreements were resolved by discussion with a third reviewer (FF). The details of the searching strategy with key words and initial hits are provided in Appendix A to ensure reproducibility and transparency of the work.

Inclusion and exclusion criteria

On the basis of the predefined eligibility criteria, the reviewers (TG & CM) identified publications independently to be included in this review. Discrepancies were solved with the agreement of the reviewers (FF). The inclusion criteria for the search were studies on economic consequences or burdens of obesity (e.g., healthcare costs, societal costs, and treatment costs), the WHO’s recommendations for the definition of obesity (BMI ≥ 30 kg/m2), studies on humans related to obesity, studies published in the English language, and studies conducted in LMICs. The exclusion criteria included studies not related to the economic consequences of obesity; studies that did not calculate the burden of obesity; studies that characterised full economic evaluations, reviews, meta-analyses, cost-effectiveness studies, letters, notes, editorials and conferences; reports; letters to the editors; comments; opinions; protocols; studies with insufficient methodological details; and studies not published in the English language. These criteria ensured a focused selection of studies relevant to the clinical and economic impact of obesity in LMICs.

Data extraction, quality assessment and reporting

The study selection process is illustrated in the PRISMA flow diagram (Fig. 1). Data extraction was performed by one reviewer (TG) and entered into a standardised data extraction table in Microsoft Excel. To ensure accuracy, the extracted data were independently verified by two additional reviewers (TG & CM). The following information was extracted from the selected studies: general details such as the first author and publication year, study country, type of cost analysis, sample size, socioeconomic status, BMI, study period, and study perspective. For cost-related data, we gathered information on the currency and cost year, along with mean or median total costs and costs attributable to obesity. The quality of the included studies was assessed via the Newcastle‒Ottawa Scale (NOS) by two reviewers (TG & CM). The NOS consists of nine items categorised into three dimensions: selection of the population, comparability of groups, and outcomes or exposures of interest [27]. Each study was scored on a scale with a maximum of nine points, where a score of ≥ 6 indicated high quality, 3 to 6 indicated moderate quality, and ≤ 3 indicated low quality. Any disagreements in scoring were resolved through consultation with a third reviewer (FF).

Fig. 1
figure 1

PRISMA flow diagram of publications included and excluded from the review.

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Data analysis

In this study, summary descriptive statistics were utilised to outline the background and types of costs associated with obesity. Both healthcare and nonhealthcare costs were further analysed through quantitative methods. A comparison of costs across countries was conducted on the basis of the methodologies used in each included study. To facilitate these comparisons, costs were converted to US dollars (US$) via country-specific gross domestic product (GDP) deflators [28] and purchasing power parities (PPP) [29]. These conversions were performed as of August 2024. Estimated cost values were adjusted by multiplying them by the 2024 GDP coefficient, then dividing by the GDP of the reference year for each study, and finally adjusted by the PPP conversion factor for 2024. This approach ensured a standardised framework for comparing costs across different countries, taking into account economic variations.

Results

The literature search retrieved 676 potentially relevant studies from PubMed (n = 167), Medline & CINHAL (n = 167), Scopus (n = 98) and Web of Science (n = 244) (see the PRISMA flowchart in Fig. 1). Of these, 182 were duplicates. After screening the titles and abstracts, 452 publications were excluded, leaving 42 articles for further full-text review. Thirteen studies met the inclusion criteria and were included in the review. The studies included in this systematic review were deemed to be of moderate quality (Appendix B).

Characteristics of the included studies

The characteristics of each included study are detailed in Table 1. The studies reported data from various countries: Brazil (n = 6), Ghana (n = 1), China (n = 2), South Africa (n = 1), Mexico (n = 1), Iran (n = 1), and Thailand (n = 1). Among the 13 studies, three [30,31,32] were conducted from a societal perspective, whereas the remaining studies [33,34,35,36,37,38,39,40] focused on the health system perspective. Additionally, two studies [41, 42] specifically reported the clinical impact of obesity. Economic impact was assessed via different methodologies: prevalence-based [32, 33, 35, 37, 41], survey-based [30, 31, 36, 39], and modelling [34, 38, 40]. These studies were published between 2012 and 2022 and adopted varying time horizons, ranging from 6 months to 50 years.

Table 1 Characteristics of the studies included in the review.
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Summary of findings

The key findings from the included studies are summarised in Table 2. The annual costs related to obesity in low- and middle-income countries (LMICs) are estimated to be between US$ 544 million and USD 12.56 billion for direct expenses and between USD 203 million and USD 227.5 million for indirect expenses. In Brazil, hospitalisation costs for individuals with class III obesity were found to be twice as high, with indirect costs nearly double those of individuals with a normal BMI [18]. Specifically, the annual physician and hospitalisation costs for class III obesity patients were reported at USD 188.14 and USD 1,839.70, respectively. In Ghana, a cost of illness study estimated that the annual direct healthcare costs for female and male patients were USD5,530 and USD4923, respectively [22]. Moreover, in rural Yunnan Province, China, the direct cost for individuals aged 35 years and older was estimated at USD 3.774 billion, whereas the indirect cost was estimated at USD 203 million [19]. These findings highlight the substantial economic burden of obesity across different LMICs. Three studies from Brazil, China, and Thailand estimated the indirect costs associated with obesity [18,19,20]. In Brazil, the average annual indirect cost estimate for individuals with class III obesity is USD 970.00 [18]. This study utilised the Work Productivity and Activity Impairment-General Health Questionnaire, a validated instrument that measures absenteeism, presenteeism, overall work productivity loss, and activity impairment. In China, the indirect cost related to obesity is estimated at USD 203 million [19], whereas in Thailand, it is estimated at USD 227 million [20]. Table 3 summarizes the cost components included across the studies. Hospitalisation emerged as the primary cost driver in six of the included studies [18,19,20,21, 23, 24]. In Brazil, the average length of hospital stay for obesity-related diseases was reported to be 7.9 days for men and 6.8 days for women [29]. The burden of hospitalisation associated with obesity was notably substantial in both Brazil [29] and Iran [30], underscoring the need for effective management strategies to address this issue.

Table 2 Summary of findings—direct costs, hospitalisation rates and indirect costs.
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Table 3 Cost components included.
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Discussion

This systematic review provides valuable insights into the clinical and economic impact of obesity in LMICs, highlighting significant variations in costs across different nations. The review identified only 13 studies that met the inclusion criteria, indicating a limited but crucial body of literature on this topic. The estimated annual costs associated with obesity, including both direct and indirect expenses, varied widely from US$544 million to US$12.56 billion for direct costs and US$ 223 million to US$227.5 million for indirect costs. These figures emphasise the substantial economic burden of obesity, with hospitalisation identified as the primary cost driver. One critical point is the heterogeneity among the studies, stemming from differences in methodological approaches, perspectives, and target populations. Beyond these high-level factors, heterogeneity also appeared to arise from variations in how obesity was defined and measured across studies, the types of cost components included, and the timeframes over which costs were assessed. Additionally, some studies employed national datasets while others relied on sub-national or hospital-level data, leading to differences in representativeness and scale. Economic modelling techniques and assumptions such as discount rates, cost inflation adjustments, and currency conversions also varied, further complicating cross-study comparisons [43]. These methodological disparities underscore the challenge of synthesising findings across settings and reinforce the need for unified costing frameworks to better inform policy responses in LMICs.

This systematic review aligns with the literature indicating that individuals with obesity experience significantly greater healthcare resource utilisation than those with a normal BMI [44, 45]. The current review highlights the multifaceted costs of obesity, including humanistic impacts such as binge eating, anxiety, and depression, as well as societal costs linked to lost productivity, which encompasses both directly missed workdays and the reduction of future earnings due to morbidity and mortality [30,31,32]. Despite limited discussion across studies, productivity costs particularly in LMICs represent a substantial but often underreported component of the economic burden. The studies included in this review employed the human capital approach, which estimates productivity loss by assigning a monetary value to time lost from work due to illness or premature death. This method typically involved calculating missed workdays multiplied by the average daily wage for morbidity-related losses and projecting lost future income for mortality-related losses, adjusted with discount rates. However, assumptions varied across studies regarding the inclusion of caregiver absenteeism, time per outpatient visit, and wage metrics, contributing to variability in estimates. The underrepresentation of broader productivity impacts, such as reduced job performance and long-term employment limitations, suggests a need for more comprehensive methodologies. Given these findings, it is crucial for public health authorities to prioritise preventive interventions aimed at reducing obesity. Strategies should focus on promoting physical activity and encouraging healthy lifestyle choices [46]. These interventions could help reduce direct costs related to physician services, hospitalisation, and outpatient care, ultimately benefiting both individuals and the wider economy [22]. This comprehensive approach can help address the growing obesity epidemic and its associated burdens in LMICs.

Dee et al. [47] found that, in high-income countries, the indirect costs of overweight and obesity such as lost productivity tended to surpass direct medical expenses. However, in contrast, two studies included in the current review [30, 31] found that direct healthcare costs related to obesity were greater than the indirect costs. Notably, only a few studies in this review [30,31,32] considered indirect costs, suggesting that the full economic burden from productivity losses may be underestimated. Additionally, while Dee et al.’s findings were based on high-income settings, the present review focuses on LMICs, where estimating indirect costs is particularly challenging. Many individuals in LMICs are employed in the informal sector, where income is often unstable and difficult to measure [48, 49]. Furthermore, cultural perceptions of obesity may influence healthcare-seeking behaviors and the distribution of health resources, potentially leading to higher direct spending [50]. These contextual factors may help explain why, in LMICs, direct costs appear to outweigh indirect ones.

In this systematic review, five studies employed a prevalence-based approach to estimate the costs associated with obesity [32, 33, 35, 37, 41]. This method allows for the estimation of costs incurred over a specified period, measuring the economic burden of obesity without considering when the condition first developed [51]. While prevalence-based cost-of-illness analyses provide valuable insights, it is essential to acknowledge potential limitations. The reported cost estimates may not fully reflect the current economic impact or be entirely applicable to the specific population under study. This variability highlights the need for caution when interpreting the results, as the estimates may lack accuracy and timeliness. Future research should consider incorporating more dynamic modelling approaches that account for the temporal aspects of obesity-related costs to improve the precision and relevance of findings.

The variation in healthcare systems across countries poses a critical challenge to generalising obesity cost estimates, as most studies in this review offer country-specific data that reflect distinct healthcare infrastructures and economic conditions within LMICs. This limits the transferability of findings across settings and underscores the need for robust multinational studies. The World Obesity Atlas exemplifies such efforts, providing a globally harmonised model that estimated the economic impact of overweight and obesity at US$1.96 trillion in 2020, or 2.4% of global GDP, with projections rising to nearly 3% by 2035 [52]. These figures capture both direct and indirect costs, including productivity losses and premature mortality, and highlight the disproportionate burden on LMICs, which are expected to house two-thirds of adults with severe obesity by 2030 [43]. This disparity reveals the limitations of isolated national studies and demonstrates the value of cross-country analyses like those used in the World Obesity Atlas and Global Burden of Disease project. Integrating such global models with local data enables more accurate forecasting, promotes international benchmarking, and supports the development of context-specific, scalable interventions ultimately enhancing the global response to the growing obesity epidemic.

This systematic review is the first to comprehensively assess both the clinical and economic burden of obesity in LMICs, offering valuable and policy-relevant insights into an underexplored area of global health. The study followed a structured and transparent methodology, drawing from five major databases; however, this may have limited the inclusion of relevant studies indexed elsewhere [53]. The exclusion of non-English publications could also have led to the omission of important research from non-English-speaking LMICs. Significant heterogeneity in study designs, outcome measures, and costing approaches prevented meta-analysis and highlighted the urgent need for standardized reporting guidelines in obesity research. Only two [41, 42] of the thirteen included studies focused on clinical outcomes, restricting insights into the broader health impacts of obesity. Moreover, the reliability of cost estimates is constrained by the generally moderate to low quality of included studies, as assessed using the Newcastle-Ottawa Scale. In spite of these limitations, we believe that this review was systematic in nature and summarizes all available and relevant clinical and economic burden results from the literature.

Conclusion

This is the first systematic review to summarise the clinical and economic burdens associated with obesity in LMICs. The clinical and economic burden of obesity on individuals, families, healthcare systems and society is significant. In addition, obesity in LMICs is associated with significant direct healthcare costs. These findings underscore the need for effective prevention and management strategies to reduce the associated burden. However, the studies included in this review utilised diverse approaches, and many presented methodological shortcomings related to resource use measurement and cost allocation. To increase the validity and comparability of findings, future research should adopt a standardised cost-of-illness methodology. This would help ensure more accurate assessments of the economic impact of obesity, facilitating evidence-informed decision-making for public health interventions. By addressing these methodological challenges, we can better understand the true burden of obesity and develop more effective strategies to mitigate its impact.