Obesity, Employment, and Economic Productivity in Europe: A Systematic Literature Review

Lise Meinicke Hagelund; Amy Gamble; Alexandra Furber; Patrice Carter

doi:10.1111/obr.70174

. 2026 Jun 9;27(9):e70174. doi: 10.1111/obr.70174

Obesity, Employment, and Economic Productivity in Europe: A Systematic Literature Review

Lise Meinicke Hagelund ¹, Amy Gamble ², Alexandra Furber ², Patrice Carter ^2,^✉

PMCID: PMC13460541 PMID: 42261746

ABSTRACT

Introduction

The economic burden that obesity places on society, both directly and indirectly, is considerable. It is acknowledged that obesity is associated with decreased economic productivity; however, the evidence examining the exact relationship between obesity and key metrics of employment in the European context remains to be consolidated.

Methods

A systematic literature review was performed to determine the association between body mass index (BMI) and employment outcomes. Searches were conducted across three electronic databases (MEDLINE, Embase, and Epistemonikos). Studies were eligible if they were published in or after 2014 and conducted in a European setting.

Results

In total, 34 studies were identified; of these, 20 studies provided evidence to show fewer people with obesity were employed as compared to people with overweight or who had a healthy BMI. Obesity was associated with increased sickness absence (n = 7) and impaired work performance (n = 7).

Conclusion

Broad evidence collected from across Europe demonstrates that obesity is associated with unemployment, sickness leave, absenteeism, and presenteeism. Preventing and treating obesity would be expected to have a relatively rapid benefit in terms of improved employment outcomes, including productivity, as well as long‐term benefits to population health.

Keywords: absenteeism, body mass index, employment, obesity, overweight, presenteeism, unemployment

1. Introduction

Overweight and obesity are global public health concerns. In 2013, United Nations member states agreed to targets to prevent and control non‐communicable diseases (NCDs), including obesity [1]. A key target was to halt the rise in the prevalence of obesity, with no increase between 2010 and 2025 [2]. In early 2025, the World Obesity Federation announced that the world is unlikely to achieve this target [3]. The global prevalence of overweight and obesity has increased from approximately 37% of adult males and 38% of adult females in 2010 [4], to approximately 43% of males and 47% of females in 2021 (more than two billion adults in total) [5]. In Western Europe, 42% of males and 29% of females are living with overweight, and 22% of males and 26% of females are living with obesity [5]. As the majority of European countries operate universal health coverage, funded either directly through taxation or semi‐directly through mandated insurance [6], increasing rates of overweight and obesity, and resultant use of healthcare resources, will translate into an increasing cost burden on all people of working age across Europe.

However, the burden of obesity extends beyond direct costs; it is generally acknowledged that overweight or obesity impacts negatively on economic productivity. At a per‐person level, living with obesity increases risk of unemployment and the number of workdays missed (absenteeism), while reducing productivity at work (presenteeism) [7, 8, 9]. At a societal level, it is critical to understand the impact of excess body mass index (BMI) on working‐age adults. The prevalence of overweight/obesity in adults is predicted to increase further [5] and as European populations continue to age, the total labor supply will decrease [10]. In response to this, managing weight and weight‐related conditions in working‐age adults as a means of boosting productivity (previously evidenced at an employer level [11, 12]) may become a strategic necessity for governments as well as employers [13]. Several previously published reviews concluded that the impacts of overweight/obesity on various direct and indirect economic costs are substantial, and are likely to increase over time [7, 8, 9, 14, 15, 16, 17, 18, 19]. However, as of 2025, the evidence on the broader impacts of excess BMI on attachment to the labor market, employment outcomes, and economic productivity in the European context remains to be fully synthesized and consolidated.

To address this evidence gap, we performed a systematic literature review (SLR) to characterize the relationships between excess BMI and employment outcomes in European countries.

2. Methods

This review was registered with the International Prospective Register of Systematic Reviews (PROSPERO) (CRD42024579034) and is reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) [20, 21].

2.1. Search Strategy and Selection Criteria

To identify relevant literature, comprehensive multistring search strategies were uniquely developed for the following electronic databases: MEDLINE and Embase via the OVID platform, and the Epistemonikos database. To balance specificity with sensitivity and to ensure that results remained manageable while still identifying the most relevant evidence, our search strategy was developed in consultation with an experienced information specialist. Search strategies were composed of a combination of free text words and thesaurus headings (e.g., MeSH in MEDLINE and Emtree in Embase). All searches were conducted on August 7, 2024, and re‐run on February 25, 2026 to ensure up to date evidence was sourced. Searches were limited to those published after 2014 and are available in Table S1. To ensure completeness, the reference lists of included studies and identified SLRs were checked for potentially relevant publications not previously identified.

2.2. Identification of Relevant Studies

Eligibility for inclusion (Table S2) was based on the Population, Exposure, Comparator and Outcomes (PECO) framework [22]. To be eligible for the review, studies were required to include an adult population (≥ 18 years old) considered generalizable (defined as populations that do not focus only on specific co‐existing health conditions, e.g., type 2 diabetes and mental health conditions). Outcomes of interest included percentage of employment (e.g., full‐time, part‐time, and unemployed), productivity losses, number of sick days, and measures of absenteeism and presenteeism. Outcomes of interest were considered relevant if reported in relation to any BMI category (e.g., number of sick days reported for those with a healthy BMI, or with overweight, or with obesity). While definitions vary in the literature, in this review we define “presenteeism” as a reduction in work productivity or work ability, working while sick, or physically present yet functionally absent; we define “absenteeism” as any missed days of work, for reasons not related to illness, and “sick leave” as absent from work due to illness [23]. Only full‐text peer‐reviewed publications of observational studies conducted in Europe and reported in the English language were included.

2.3. Study Selection and Data Extraction

Bibliographic details and abstracts of all records retrieved by each of the electronic searches were downloaded into a single EndNote library [24]. Records were deduplicated before being transferred to the systematic review software, Covidence [25]. Ten percent of the titles and abstracts of the records were dual‐screened against the eligibility criteria to ensure ≥ 90% interrater reliability was achieved; the remaining records were screened by a single reviewer. The full text of records deemed potentially eligible were retrieved and rescreened using the same eligibility criteria. In cases of uncertainty, an arbiter was consulted and eligibility decisions resolved via consensus. Data extraction of included studies was conducted in a piloted data extraction workbook in Microsoft Excel. Data were extracted by a single reviewer, then quality assessed by a second reviewer to ensure comprehensiveness and accuracy. Any discrepancies between the two reviewers were resolved by consensus.

2.4. Quality Assessment

For each included study, a risk assessment of bias was carried out by two independent reviewers using the Joanna Briggs Institute (JBI) critical appraisal tool for cohort studies, cross‐sectional studies, or case control studies, as appropriate [26]. Each study was assessed against key domains of bias, including selection bias, performance bias, detection bias, attrition bias, and reporting bias. Quality assessment did not determine study inclusion; the assessment was performed to support interpretation of the SLR results with appropriate consideration of study quality.

3. Results

3.1. Study Selection

The electronic database searches identified a total of 6611 records. Following deduplication, the titles and abstracts of the remaining 5144 records were screened for eligibility; 5007 were excluded. The eligibility criteria were reapplied to the remaining 137 full‐text records, resulting in the inclusion of 34 studies. No further studies were identified through supplementary searches. In total, 34 publications corresponding to 34 unique studies were identified as relevant for this review. An overview of the study flow is provided in Figure 1.

PRISMA flow diagram presenting the study flow throughout the SLR. Abbreviation: SLR, systematic literature review.

3.2. Study and Population Characteristics

Studies were conducted across a range of European countries, including Germany (n = 5) [27, 28, 29, 30, 31], the United Kingdom (n = 6) [32, 33, 34, 35, 36, 37], the Netherlands (n = 4) [38, 39, 40, 41], Denmark (n = 3) [42, 43, 44], Norway (n = 3) [45, 46, 47], Finland (n = 3) [48, 49, 50], Sweden (n = 1) [51], Spain (n = 1) [52], Austria (n = 1) [53], Belgium (n = 1) [54], Ireland (n = 1) [55], Italy (n = 1) [56], Poland (n = 1) [57], and Portugal (n = 1) [58]. Two studies combined data from multiple European locations [59, 60].

Most studies were cross‐sectional by design (n = 24) [27, 28, 29, 31, 34, 35, 36, 37, 38, 40, 41, 42, 46, 47, 50, 52, 53, 54, 55, 56, 57, 58, 59, 60] and examined obesity and employment‐related outcomes in either general or working age populations (n = 31) [28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46, 48, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61], or in those with obesity seeking bariatric surgery (n = 2) [27, 47]. The number of patients enrolled in the included studies varied, ranging from 97 [38] to 230,791 [36], with the mean age ranging from 39.6 [34] to 60.0 years [60] and the average proportion of males ranging from 0% [48] to 100% [60] (Tables 1 and S3).

TABLE 1.

An overview of patient characteristics reported by included studies.

Population characteristic	Range, min‐max	Number of studies reporting characteristic (out of 34), n
Sex, % male	0–100	29 [27, 28, 29, 30, 31, 32, 34, 36, 37, 38, 39, 41, 42, 43, 44, 46, 47, 48, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 61]
Mean age, years	39.6–60.0	19 [27, 28, 30, 31, 34, 35, 36, 37, 38, 39, 41, 42, 43, 45, 47, 50, 51, 52, 59]
Current smoker, %	18.1–31.6	14 [31, 34, 40, 41, 42, 44, 45, 46, 52, 55, 56, 58, 59, 61]
Co‐existing health conditions
HTN, %	7.6–65.6	6 [31, 47, 52, 55, 56, 59]
Diabetes, %	3.1–28.5	6 [29, 31, 47, 52, 58, 59]
Education level ^a
Below high school level/primary, %	1.1–53.9	10 [43, 44, 47, 48, 50, 52, 53, 54, 55, 57]
High school/secondary, %	20.7–64.1	8 [43, 44, 47, 48, 52, 53, 55, 57]
Higher education, %	12.3–46.3	10 [43, 44, 46, 47, 48, 52, 53, 54, 55, 57]
BMI
Underweight, %	0–3.9	18 [28, 30, 31, 32, 38, 42, 43, 44, 46, 48, 50, 53, 54, 55, 57, 58, 59, 61]
Healthy, %	0–65.3	20 [28, 29, 30, 31, 32, 38, 40, 42, 43, 44, 46, 48, 53, 54, 55, 56, 57, 58, 59, 61]
Overweight, %	0–49.2	20 [28, 29, 30, 31, 32, 38, 40, 42, 43, 44, 46, 48, 53, 54, 55, 56, 57, 58, 59, 61]
Obesity, %	9.3–100	26 [28, 29, 30, 31, 32, 34, 35, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 51, 52, 53, 54, 55, 56, 58, 59, 61]
Mean BMI	24.5–47.7	8 [29, 34, 35, 36, 42, 45, 47, 57]

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Abbreviations: BMI, body mass index; HTN, hypertension.

^{^a}

Moe et al. (2025) [45] describe “low education” as a co‐variate but provides no further detail in terms of patient characteristics.

BMI was most commonly stratified into four categories, comprising underweight (BMI < 18.5 kg/m²), healthy (BMI 18.5–24.9 kg/m²), overweight (BMI 25.0–29.9 kg/m²) and obesity (BMI ≥ 30.0 kg/m²), with the proportion of the study populations in each category ranging between 0% [28, 31, 33, 43, 48, 50, 55, 57, 59, 61] and 3.9% [32], 0% [38, 43] and 65.3% [51], 0% [43] and 49.2% [60], and 9.3% [56] and 100% [43], respectively. In some studies, obesity was further stratified into Class I (BMI 30–34.9 kg/m²), Class II (BMI 35–39.9 kg/m²), and Class III (BMI ≥ 40 kg/m²) [27, 29, 37, 43, 44, 50, 59]. Three studies included participants with obesity only [27, 43, 47]. Two other studies examined a combined population of people living with either overweight or obesity (i.e., BMI > 25 kg/m²) [37, 38].

The proportion of the overall population with different health conditions alongside obesity was infrequently reported across studies; however, the proportion of patients in different BMI categories with diabetes (N = 5 [29, 31, 33, 47, 59]; range: 3.1% [31] to 28.5% [47]) or with hypertension (N = 5 [31, 33, 56, 59]; range: 7.6% [56] to 65.6% [47]) was reported across a small number of studies. Across the studies that reported the proportion of individuals by BMI category, the prevalence of other co‐existing health conditions generally increased with higher BMI. For example, the prevalence of hypertension and diabetes mellitus was consistently greater in populations with overweight or obesity compared with populations with a healthy BMI (Table S4) [29, 31, 56, 59].

3.3. Employment Status

A total of 20 studies reported outcomes relating to BMI and employment status, including employment (full or part time) and unemployment [29, 31, 32, 33, 34, 35, 36, 38, 41, 44, 47, 50, 52, 53, 54, 56, 57, 59, 60, 61].

3.3.1. Percentage of People in Employment

Eleven studies reported the proportion of people who were either unemployed or employed by BMI category [29, 34, 38, 44, 47, 50, 52, 56, 57, 59, 60], with an additional four studies reporting in overall populations only [36, 53, 54, 61]. On average, studies demonstrated that fewer people with obesity were employed (52.8%; range: 47.1% [50] to 85.6% [44]; N = 7) [44, 47, 50, 52, 56, 59, 60] compared with individuals with healthy BMI (65.6%; range: 52.9% [50] to 90.2% [44]; N = 3) [44, 50, 59] or with overweight (60.7%; range: 48.0% [60] to 88.7% [44]; N = 4) [44, 50, 59, 60] (Figure 2). Additionally, when people with obesity are further subdivided by obesity class, those with Class II (BMI 35–39.9 kg/m²) and Class III (BMI ≥ 40 kg/m²) obesity have on average lower employment rates and higher unemployment rates compared with those with Class I obesity. Furthermore, three of these studies found that a lower percentage of women with obesity were employed compared with men with obesity [47, 52, 53].

The average proportion of patients who were either (A) employed or (B) unemployed by their BMI status. † Data from three studies [44, 50, 59]; ‡ data from four studies [44, 50, 59, 60]; § data from seven studies [44, 47, 50, 52, 56, 59, 60]; ¶ data from two studies [50, 59];. ¥ data from four studies [29, 44, 50, 57]; Ψ data from three studies [29, 44, 50];†† data from five studies [29, 34, 44, 47, 50]; ‡‡ Data from four studies [29, 44, 47, 50]. Abbreviations: BMI, body mass index.

Only one study compared the rates of full‐time and part‐time employment by BMI status. Lehnert et al. found that the highest levels of part‐time employment were recorded in those with a healthy BMI, compared with those within the overweight or obesity BMI categories; however, the significance of this finding was not reported [31].

3.3.2. Employment and the Risk of Obesity

Four studies assessed the association between employment status and the risk of obesity (Table S5) [41, 52, 53, 56]. The studies demonstrated that either being unemployed [41, 52, 53] or being temporarily employed [56] was significantly associated with a higher risk of developing obesity. However, Palomo et al. reported that the association between unemployment and increased risk of obesity was only significant in women (odds ratio [OR]: 1.4; 95% CI: 1.1, 1.9) [52].

3.3.3. Association of Risk Between BMI and Employment Status

Seven studies utilized OR, hazards ratios (HRs) or relative risk ratios (RRRs) to examine the association between obesity and its effects on employment status (Table S5) [29, 31, 32, 33, 36, 44, 60]. All seven studies reported that a higher BMI was associated with a higher risk of unemployment [29, 31, 32, 33, 36, 44, 60], although this association was only statistically significant in three studies (p < 0.05) [33, 36, 44].

3.3.4. Correlational Evidence Linking BMI and Employment Status

Two studies reported correlation data between BMI and employment status [34, 35]. Both studies report that BMI is negatively correlated with employment, indicating that those with obesity are less likely to be in employment [34, 35].

3.3.5. Gender Based Differences in Association Between BMI and Employment Status

Linaker et al. found the association of higher BMI with higher risk of unemployment was only significant among women with Class III obesity (BMI ≥ 40 kg/m²) (p < 0.05); the association did not reach statistical significance for women with overweight, Class I or Class II obesity, nor for men in any category [33]. Lehnert et al. [31] reported a similar but statistically insignificant association between women with obesity and unemployment. However, they reported statistically significant associations in men; men with overweight were less likely to be unemployed compared to men with healthy BMI (OR: 0.64; 95% CI: 0.54–0.72).

3.4. Sickness Absence From Work

A total of 10 studies reported outcomes relating to BMI and sickness absence [28, 30, 31, 33, 38, 44, 45, 47, 50, 61].

3.4.1. Association Between BMI and Sickness Absence

Eight studies (Table S6) reported data on the association between BMI and its effects on sickness absence (e.g., studies reported OR, HRs, and incidence rate ratio [IRR]) [28, 30, 31, 33, 44, 45, 47, 61], four studies reported the proportion of those who have experienced sickness absence [38, 44, 47, 50], two studies reported on the number of sick days experienced per person [31, 50], and one study reported on the number of excess sick leave days and their associated costs [31].

Of the eight studies reporting on the association between obesity and its effect on sickness, seven reported that obesity was significantly associated with increased sickness absence [28, 30, 33, 44, 45, 47, 61]. In two of these studies, this association was only identified in women [30, 33]. The remaining study, Lehnert et al., reported that there was no association between BMI and sick leave in both men and women with overweight or obesity when compared to those with healthy BMI [31].

3.4.2. Rates of Sickness Absence Across BMI Categories

Five studies reported the number of sick days or the proportion of the population reporting sick days (Table S7) [31, 38, 44, 47, 50]. Only two compared rates of sick leave across distinct BMI categories [44, 50]. Both studies demonstrated that a greater proportion of people with obesity had experienced sick days when compared with people with healthy BMI [44, 50]. Vesikansa et al. reported that the proportion of individuals with more than 14 sick days over a 12‐month period was significantly higher in those with Class I obesity (BMI 30–34.9 kg/m²) (19.2%; p < 0.001), Class II (BMI 35–39.9 kg/m²) (22.4%; p = 0.001), and Class III (BMI ≥ 40 kg/m²) (21.1%; p = 0.0066) compared with those with healthy BMI (10.3%) [50]. The remaining two studies reported the proportion of individuals with any class of obesity who had experienced at least 1 day of sickness absence (66.3%) [47], and the proportion of individuals with overweight or obesity who had experienced sickness absence within the past 6 months (34%) [38].

3.4.3. Mean Number of Sick Days

Only two studies reported the mean number of sick days per person according to BMI category (Table S7) [31, 50]. In Vesikansa et al., the mean number of sick days accrued in 12 months was significantly higher in individuals with Class I and Class II obesity compared with those with a healthy BMI (11.3 and 12.9 days vs. 7.7 days; p < 0.05) [50]. Similarly, Lehnert et al. [31] reported on average a significantly greater rate of sickness leave days in those with obesity compared to those with healthy BMI (p < 0.001).

3.4.4. Excess Sick Leave

One study calculated the mean excess sick leave days attributable to overweight and obesity and their associated costs with reference to the healthy BMI population (Table S7) [31]. In both men and women, overweight and obesity were associated with significant excess sickness leave days when compared to those with healthy BMI (p < 0.05) [31]. The reported total population costs associated with overweight and obesity related to excess sick leave days in Germany in 2009 was €2.18 billion, with costs appearing to be higher in women with obesity and overweight compared with men with overweight and obesity (€1.37 billion and €814 million, respectively) [31].

3.5. Absenteeism

In total, eight studies reported outcomes related to the relationship between BMI and absenteeism [28, 30, 37, 38, 54, 55, 58, 59]. Of these, four reported the number of days absent from work by BMI category [38, 55, 58, 59], two reported on the association between BMI and the effects on absenteeism [30, 37, 55], and five studies reported on the cost of absenteeism [28, 38, 54, 58, 59]. None of the included studies compared absenteeism among other sub‐groups of interest (e.g., gender and employment type).

3.5.1. Absenteeism by BMI Category

Of the four studies which reported the number of days absent from work, three reported the number of absent workdays across distinct BMI categories [55, 58, 59]. All three studies concluded that people with obesity experienced more absent workdays (e.g., number of workdays missed) compared with those with healthy BMI [55, 58, 59]. However, only one reported statistical significance (p < 0.001) [59]. Moreover, a study by Hecker et al. reported the average days absent per person with either overweight or obesity across a 6‐month period (6.97 days), but did not compare this to other BMI categories [38].

3.5.2. Association of BMI and Absenteeism

The association between BMI and absenteeism was examined by Reber et al. [30], Fitzgerald et al. [55], and Leith et al. [37] (Table S9). The study by Reber et al. reported that transitions from the healthy BMI category to the overweight BMI category were associated with an increase in the probability of long‐term absenteeism in women (overweight, [OR: 1.41, 95% CI: 1.08, 1.85]; p < 0.05), but not in men (overweight, [OR: 0.84, 95% CI: 0.65, 1.09]; p > 0.05) [30]. Fitzgerald et al. measured obesity using two different metrics, BMI (25.0 to < 30 kg/m²) and central obesity (waist circumference of ≥ 94 cm for men and ≥ 80 cm for women) [55]. A significant, positive association between central obesity and absenteeism was observed; those with obesity had a higher mean number of predicted days absent compared to those with a healthy weight (3.5 vs. 2.3 days) and central obesity was associated with an expected increase in the rate of absenteeism by 72%. However, there was no observable difference between those with overweight to those with a healthy weight (mean number of predicted days absent was 2.3 days for both groups) [55]. The study by Leith et al. found no difference in reported absenteeism per BMI category between those with overweight as compared to obesity (p = 0.32) [37]; absenteeism was calculated as a score measured using the Work Productivity and Activity Impairment: Specific Health Problem questionnaire [62] and authors noted the small sample size (range 8–45) [37] (Table S8).

3.5.3. Costs of Absenteeism

Of the five studies that reported on the cost of absenteeism [28, 38, 54, 58, 59], three studies demonstrated that the annual costs of absenteeism per employee were consistently higher in populations with obesity compared with those with healthy BMI [54, 58, 59], although only two reported a statistically significant difference (p < 0.05) (Table S10) [54, 59]. Furthermore, Hecker et al. reported the mean cost of absenteeism over a 6‐month period in people with obesity in the Netherlands (€1511.93); however, the data was not compared with other BMI categories [38]. Yates et al. conducted a regression analysis, reporting that annual sick day costs were significantly higher in populations with overweight (p < 0.001), Class I obesity (p < 0.001), and Class III obesity (p < 0.01) when compared with those with healthy BMI [28]. Similarly, annual indirect costs, defined as costs incurred due to productivity losses, were also significantly higher in populations with pre‐obesity and across all obesity classes (I–III) compared with populations with healthy BMI (p < 0.05) [28].

3.6. Presenteeism and Work Functioning

Presenteeism and/or work functioning data were reported by 10 studies (Table S11) [27, 33, 34, 37, 39, 40, 42, 46, 51, 59].

3.6.1. Productivity Losses by BMI Category

Three studies compared employees with healthy BMI to employees with obesity, with all studies reporting higher rates of work productivity loss in employees with obesity [39, 40, 59]. Nigatu et al. (2015) reported that employees with obesity were significantly more likely to report higher work performance impairment compared with healthy employees (OR: 1.39, 95% CI: 1.07, 1.8) [39]. Similarly, DiBonaventura et al. reported that the rates of work productivity loss (assessed using the Work Productivity and Activity Impairment‐General Health questionnaire) in individuals with class III obesity (BMI ≥ 40) were greater when compared with those with a healthy BMI in both German and Italian populations [59]. Work productivity loss in individuals with class III obesity was significantly higher compared with those with healthy BMI in the German population (4.98 vs. 13.52, p < 0.001), but not in the Italian population (3.59 vs. 5.2, p = 0.505) [59]. Nigatu et al. (2016) reported that the mean score on the Dutch Work Role Functioning Questionnaire decreased from 87.7 in the healthy BMI population to 84.1 in the population with obesity, but statistical significance was not reported [40].

3.6.2. The Association of BMI and Work Ability

Seven studies explored the association between BMI and work ability [27, 33, 34, 37, 42, 46, 51]; however, the outcomes reported varied. Oellingrath et al. explored the association of low work ability with distinct BMI categories [46]. When adjusting for lifestyle factors, gender, age, educational level, and occupational group, the population with obesity was statistically more likely to have lower work ability (OR: 1.5, 95% CI: 1.3, 1.7, p < 0.05) than the population with healthy BMI [46]. Andersen et al. reported that BMIs above the healthy range were significantly and progressively associated with lower work ability in relation to the physical demands of the job, but not the mental demands [42]. Linaker et al. found that only women with severe obesity were at a significantly increased risk of having to cut down, avoid, or change what they did at work because of a health problem when compared with women with healthy BMI (p ≤ 0.05) [33]. Kohler et al. reported a non‐statistically significant association between BMI and work ability (p = 0.172) [27]. One study by Kinge (2016) modeled data on BMI and employment status over time and reported a positive association between BMI and employment disability in both men and women [34]. A further study by Leith et al. reported a non‐significant trend for greater presenteeism (p = 0.63) and overall work impairment (p = 0.50) in those with overweight as compared to obesity [37]; however, authors noted the small sample size. Furthermore, this study stated that 7% of people living with overweight or obesity reported being unemployed, on long‐term sick leave or retired owing to their weight, but did not provide a comparison with healthy weight counterparts [37]. Finally, one study used “Disability Pension” as a proxy for work disability and reported the risk of work disability was increased for those with overweight (HR: 1.30, 95% CI: 1.13–1.51) and obesity (HR: 1.74, 95% CI: 1.44–2.10) when compared to those with a healthy BMI [51].

3.7. Quality Assessment

All studies underwent a quality assessment (Figures S1–S3). Overall, none of the included studies were considered at high risk of bias. Across all studies, outcomes were measured in “a standard, valid and reliable” way, and appropriate statistical analyses were performed. However, several cross‐sectional studies were flagged as having unclear reporting of the identification of confounding variables and methods to deal with confounding factors (n = 9) [27, 29, 37, 38, 50, 53, 54, 57, 58]. In a small number of cohort studies, the identification of confounding factors was either unclear (n = 1) [30] or not reported (n = 1) [39]. Uncertainty was noted with respect to the completeness of follow‐up data in some cohort studies. This included unclear strategies to deal with incomplete follow‐up (n = 2) [30, 33], missing reasons for incomplete follow‐up (n = 1) [48], and lack of clarity on whether follow‐up was complete (n = 1) [30].

4. Discussion

Previous reviews conducted on the topic of obesity and work productivity have concluded that overweight and obesity have a substantial cost burden outside of the healthcare sector [8], with decreasing work productivity and worse employment outcomes [63] contributing to escalating indirect costs [17]. For example, it has been reported that workers with obesity miss more workdays due to illness, injury and disability [9] and that people with obesity experience longer periods of unemployment than those without obesity, as well as increased rates of absenteeism with increased BMI [64]. However, much of the information within these previous reviews focused on the US population [8, 9, 17, 63, 64]. Prior to this review, evidence on the impact of excess BMI on employment and productivity outcomes, specifically from a contemporary European perspective, was disjointed. Our review aimed to address this gap, with the intent to consolidate published evidence, characterize the relationship between excess BMI and employment outcomes, and to better understand the burden imposed by overweight/obesity on society in Europe. The review demonstrated that overweight and obesity are consistently associated with worse employment‐related outcomes, regardless of which metric is considered (e.g., employment status, sickness absence, absenteeism, or presenteeism). The results of this review indicate that at a population‐level, there may be a substantial economic benefit to be realized through reducing unemployment and increasing work ability by either reducing the prevalence of obesity or otherwise counteracting current trends. However, a multipronged, comprehensive approach at the society level is required to achieve this, as the causes of the overweight/obesity epidemic are multifaceted and complex. As emphasized by the World Health Organization in their action plan to stop obesity [65] and in recent treatment guidelines [66], strategies for disease prevention and access to effective treatments will play complementary roles. The cost of taking action beyond healthcare (e.g., through the creation of supportive environments that encourage healthy behaviors and which prevent obesity), and, in European countries with universal health coverage, the budget impact of greater use of anti‐obesity medications may be partially offset by greater productivity, increased worker contributions (either through tax or mandated health insurance payments), and by reduced demand for healthcare resource due to excess BMI.

Several of the studies identified indicate that obesity is associated with a higher risk of unemployment. Our findings are supported by the wider literature, where it has previously been shown that individuals with obesity often face discrimination and inequality in the workplace with respect to hiring, wages, promotions, and job termination [67]. Indeed, experimental studies have demonstrated that job applicants with obesity are often rated more negatively and are less likely to be hired compared with applicants with healthy BMI [68]. Given that equality, diversity, and inclusion are described as “a top priority” for the European Commission [69], further efforts and initiatives may be needed to address the social barriers faced by people living with obesity.

Our review highlighted that employment status could potentially have an impact on an individual's BMI, with evidence to suggest that unemployment could also increase the risk of obesity [41, 52, 53, 56]. Potential adverse effects of unemployment on BMI may be attributable to a reduced standard of living and well‐being [70, 71, 72], as well as increased psychological distress in the form of depression, anxiety, or low self‐esteem [73].

In addition to the direct costs imposed on European healthcare systems by obesity, substantial indirect costs arise because of lost or reduced workforce productivity (i.e., absenteeism and presenteeism). This SLR demonstrates that individuals with obesity experience more absent workdays (e.g., time away from work for any reason, not limited to sickness), sick days, and increased levels of presenteeism compared with their colleagues with a healthy BMI.

There was some evidence to suggest that absenteeism was more pronounced in women with obesity compared with men with obesity. While rates of sick leave in the general population are generally higher in women compared with men [74], the effect of sex and BMI on both sick leave and absenteeism may be explained by psychological factors. Obesity is thought to have a negative impact on body image, the impact of which tends to be more pronounced in women compared with men [75, 76]. We hypothesize that this may contribute to psychological distress, with the eventual result of withdrawal behaviors and absenteeism [77, 78]. Although drawing such inferences is beyond the scope of this review, this could be the subject of future investigation.

This review found that indirect costs were consistently higher in populations with obesity compared with populations with healthy BMI, owing to increased absenteeism and presenteeism. In 2019, it was estimated that costs related to overweight/obesity in Europe amounted to approximately €464 billion, of which approximately €323 billion were attributable to indirect costs [79, 80]. Predictions indicate that these costs will more than double between 2020 and 2060 [80].

Despite our review following a robust methodology, there are limitations worth noting. Firstly, our search strategies were developed to capture the most recent and relevant evidence. However, as with all literature searches, it is possible that some relevant publications may not have been identified. We limited our searches to include only publications since 2014, with the rationale that this would exclude “outdated” evidence and would capture evidence reflective of more recent developments in the field of obesity management and of changes in employment patterns post‐COVID‐19 pandemic. Furthermore, our review was restricted to observational studies, and interventional studies were excluded. Therefore, data on the uptake of treatments for BMI reduction and any subsequent effect on work productivity could not be discussed. There is the potential for the trajectory of the obesity epidemic to change in response to further changes in the treatment paradigm; our findings may require revisiting in future in light of emergent evidence. Secondly, the review was limited to full‐text peer‐reviewed publications, meaning that emerging data in gray literature, such as recent conference proceedings, will not have been captured. Thirdly, our review only considered overweight/obesity and employment in a European setting. Similarities between European countries, in terms of World Bank Income Group status, population characteristics, employment and industry patterns, working culture, and healthcare model, support the generalizability across countries of evidence presented at the individual country‐level. Conversely, observations in European countries may not be readily generalized to other high‐income settings; for example, people with obesity may be less likely to demonstrate absenteeism in the United States, due to differences in the provision of sick pay compared with European countries, or Japan, due to different societal expectations around work. Our findings may therefore not be generalizable to regions beyond Europe. Additionally, we excluded studies that were not reported in English.

Although not used to inform study exclusion or inclusion, the quality assessment deemed the overall risk of bias of the included studies as low risk; however, several studies flagged unclear reporting of the identification of confounding variables and the strategies to deal with them. Regardless, most studies reporting association data adjusted for confounding variables. Moreover, the confounders adjusted for within models varied, with most studies adjusting for age, sex, co‐existing health conditions, and various lifestyle factors, such as physical activity and smoking status. However, obesity is influenced by many wider determinants of disease, encompassing social, economic, and environmental factors [81], with many of these factors also interacting with employment. Despite several studies adjusting for socioeconomic factors such as household income, education level and social status/class, very few studies controlled for environmental confounders such as weight‐based stigma and prejudice. Weight‐based stigma can contribute toward lower work ability and lower employment rates in people with obesity [67, 82], as well as an increase in BMI and incident obesity [83]. The multifactorial nature of obesity, coupled with its shared determinants with employment, highlights the importance of considering confounding variables when studying the associations between obesity and employment outcomes.

5. Conclusion

Our study provides strong evidence of the negative impact of overweight/obesity on employment and productivity outcomes from a European perspective. Although national and international stakeholders recognize the increasing burden of obesity in terms of long‐term health outcomes, successfully reducing the prevalence of overweight and obesity may also have economic benefits in the short‐to‐medium term by increasing work‐based economic productivity. In the context of European countries, policymakers directing healthcare resource use could consider wider treatment of obesity as part of strategies to future‐proof budgets, while governments might consider additional initiatives at a societal level and legislation aimed at preventing incident obesity. Future studies should attempt to determine the causal effects of gaining or losing weight on productivity outcomes.

Author Contributors

L.M.H. and P.C. conceptualized and designed the study. A.G. and A.F. were responsible for data collection and analysis. All authors contributed to interpretation of the results, preparation and review of the manuscript, and approval of the final manuscript for publication.

Funding

This work was supported by Novo Nordisk who provided support for literature review, analysis, and medical writing for this study.

Conflicts of Interest

L.M.H. was an employee of Novo Nordisk and owns shares in Novo Nordisk. A.F. and P.C. are, and A.G. was, employees of Health Economics and Outcomes Research Ltd. Health Economics and Outcomes Research Ltd. received fees from Novo Nordisk A/S in relation to this study.

Supporting information

Table S1: Ovid MEDLINE, Ovid Embase and Epistemonikos search strategies. Original search, 07/08/2024; Updated search, 25/02/2026.

Table S2: PECO and eligibility criteria for the identification of evidence association between obesity and employment.

Table S3: Population characteristics of included studies.

Table S4: Studies reporting co‐existing health condition data stratified by BMI category.

Table S5: Studies that examined the association between BMI and employment status.

Table S6: Studies that examined the association between BMI and sick days/sickness absence.

Table S7: Studies that reported data on the number of sick days or proportion of the population reporting sick days.

Table S8: Studies that examined absenteeism according to BMI category.

Table S9: Studies that examined the association between BMI and absenteeism.

Table S10: Studies that reported cost of absence data stratified by BMI category.

Table S11: Studies that examined the association between BMI and presenteeism or work functioning/ability.

Figure S1: Quality assessment of included case‐control studies.

Figure S2: Quality assessment of included cohort studies using the Joanna Briggs Institute quality assessment tool.

Figure S3: Quality assessment of included cross‐sectional studies using the Joanna Briggs Institute tool.

OBR-27-e70174-s001.pdf^{(991.5KB, pdf)}

Acknowledgements

The authors thank Shelley DeKock, Information Scientist, Emily Matthews, Scientific Researcher, and Geraint Roberts, Senior Medical Writer, all of Health Economics and Outcomes Research Ltd., for assisting in the development of search strategies; for supporting evidence review and synthesis, and for providing medical writing support respectively, which was funded by Novo Nordisk in accordance with Good Publication Practice (GPP3) guidelines (http://www.ismpp.org/gpp3).

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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