A systematic review of the burden of, access to services for and perceptions of patients with overweight and obesity, in humanitarian crisis settings

Abstract

Introduction

Excess body weight causes 4 million deaths annually across the world. The number of people affected by humanitarian crises stands at a record high level with 1 in 95 people being forcibly displaced. These epidemics overlap. Addressing obesity is a post-acute phase activity in non-communicable disease management in humanitarian settings. Information is needed to inform guidelines and timing of interventions. The objective of this review was to explore the prevalence of overweight and obesity in populations directly affected by humanitarian crises; the cascade of care in these populations and perceptions of patients with overweight and obesity.

Methods

Literature searches were carried out in five databases. Grey literature was identified. The population of interest was non-pregnant, civilian adults who had experience of humanitarian crises (armed conflict, complex emergencies and natural disasters). All study types published from January 1^st, 2011, were included. Screening, data extraction and quality appraisal were carried out in duplicate. A narrative synthesis is presented.

Results

Fifty-six reports from forty-five studies were included. Prevalence estimates varied widely across the studies and by subgroups. Estimates of overweight and obesity combined ranged from 6.4% to 82.8%. Studies were heterogenous. Global distribution was skewed. Increasing adiposity was seen over time, in older adults and in women. Only six studies were at low risk of bias. Body mass index was the predominant measure used. There were no studies reporting cascade of care. No qualitative studies were identified.

Conclusion

Overweight and obesity varied in crisis affected populations but were rarely absent. Improved reporting of existing data could provide more accurate estimates. Worsening obesity may be prevented by acting earlier in long-term crises and targeting risk groups. The use of waist circumference would provide useful additional information. Gaps remain in understanding the existing cascade of care. Cultural norms around diet and ideal body size vary.

Introduction

Costing 2.8% of the world’s gross domestic product, affecting over 2 billion people worldwide and causing 4 million deaths annually, excess body weight (including overweight and obesity) is a global health emergency [1]. The World Health Organisation (WHO) identifies excess body weight as a key risk factor for noncommunicable diseases (NCDs) [2]. Globally, more than 15 million die prematurely due to NCDs [2]. Reducing obesity can decrease premature mortality [3], thereby directly contributing to Sustainable Development Goal (SDG) 3.4 [4].

The potential negative impacts of obesity and overweight are not restricted to NCDs. As the global community has learnt over the course of the Covid-19 pandemic, increased adiposity is also a risk factor for morbidity and mortality caused by some infectious diseases [5].

The number of people affected by humanitarian crises including violence, persecution, natural disasters and human rights violations has increased steadily since 2010 and now stands at record high level with 82.4 million people being forcibly displaced at the end of 2020 [6]. The majority of those displaced have remained in their own countries (internally displaced people [IDPs]) and following Colombia, the most affected countries are in Africa and the Middle East. As for those displaced across borders, approximately two thirds come from Syria, Venezuela, Afghanistan, South Sudan and Myanmar [6].

In many of these countries NCDs are now more significant causes of death and disability than communicable diseases and levels of obesity and overweight are increasing [7–9].

Whilst in the past the issue of NCDs in humanitarian crises was largely forgotten, the increasingly overlapping nature of these epidemics is now recognised. There have been calls from practitioners and patients to increase research [10] and to improve prioritisation, recognition, prevention and management of NCDs in these settings [11–14]. An informal working group chaired by the United Nations High Commissioner for Refugees (UNHCR) and with membership of academics, policy makers, WHO and key non-governmental organisations (NGOs) is leading the way on delineating operational considerations for NCD management in humanitarian settings [15]. Obesity features in these discussions as a risk factor for chronic diseases to be addressed after the acute phase of the crisis.

To inform these developments, there are a suite of systematic reviews which bring together the evidence on diabetes [16, 17], substance misuse [18, 19], smoking [20], alcohol [21, 22], cardiovascular disease [23, 24], hypertension [25], mixed NCDs [26–29] and models of care [30, 31] in specific settings. However, to our knowledge, there has not been an attempt to collate information focussing on obesity in the same way.

The objective of this review is to explore the prevalence and incidence of overweight and obesity, and the changes in adiposity over time in populations directly affected by humanitarian crises; the cascade of care in these populations and perceptions of patients with overweight and obesity.

Methods

A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines [32] and applying the Synthesis Without Meta-analysis (SWiM) extension [33]. A scoping exercise was carried out in August 2019. This informed decisions about eligibility, inclusion dates and synthesis.

Eligibility criteria

The PECO criteria described below form part of the eligibility criteria. Further, all study types, published in any language and carried out in any geographical location were considered eligible. For the scoping exercise, studies published from January 1st, 1999, were included. Reviewing the returns showed that the data being presented in the earlier papers were out of date given the context of changing levels of obesity globally. Since we were interested in providing a description which could be used by service providers in the current time, we restricted this review to papers published from January 1st, 2011, onwards.

Conference proceedings, letters, theses, clinical guidelines, opinion pieces and study protocols were excluded. Reports from NGOs are important in this field and were included as long as there was a description of the methods used to gather data.

PECO criteria

The population, exposure, comparator and outcome (PECO) criteria for the study are described below.

Population.

The population of interest was non-pregnant, civilian adults (aged 18 years or older) who had direct experience of humanitarian crises whether they were displaced or not. Economic migrants, Special Immigrant Visa entrants (those granted permanent residence in the USA for reasons including aiding US efforts in Afghanistan and Iraq [34]) and migrants unaffected by crises were not considered eligible. Service and military personnel, local combatants and prisoners of war were excluded. Service users attending general clinics were considered eligible, unless selected on the basis of a specific disease, when they were excluded. Studies with a mixed population were included if the population of interest could be clearly differentiated. For qualitative studies, this meant that the views of participants with overweight or obesity had to be identifiable. The study authors’ definition of the type of migrant was applied.

Exposure.

The crises of interest were armed conflict, complex emergencies and natural disasters (including earthquakes, landslides, tidal waves, tsunamis, floods, cyclones, hurricane and drought). Study authors’ definitions of crises were applied. Exposures that began after or were ongoing in January 1999 were considered eligible. Exposures needed to be ongoing or previous to the time of data collection to be eligible. We did not impose other temporal restrictions on the exposure- outcome relationship.

We did not specifically search for COVID-19 related publications. We felt that the global nature of the pandemic meant that doing so would effectively result in a global prevalence estimate for overweight and obesity.

Comparator.

Comparators were not considered as an eligibility criterium.

Outcome.

Study authors’ definitions of overweight and obesity were applied regardless of the measure and cut-offs used. During risk of bias (ROB) assessment the decisions made by authors in this regard were evaluated.

The primary outcomes of interest were:

1. The prevalence and incidence of overweight and / or obesity as defined by body mass index (BMI).

2. Change in adiposity over time in those diagnosed with overweight or obesity.

3. Cascade of care for overweight and / or obesity including recognition, seeking treatment or support and receiving treatment or support.

4. Patient knowledge and attitude to overweight and / or obesity.

Secondary outcomes were:

1. Understanding of whether or not weight management is included as part of a wider programme of prevention or health promotion.

2. Barriers and facilitators to accessing treatment.

3. Evidence of use of other measures of adiposity.

Information sources

Medline, Embase, PsycINFO, Cumulative Index of Nursing and Allied Health Literature (CINAHL) and Web of Science were searched. Grey literature and newly published peer reviewed material was identified by searching Google, ReliefWEB, UN High Commissioner for Refugees, WHO Institutional Repository for Information Sharing, UNICEF, Médecins Sans Frontières, International Rescue Committee, International Committee of the Red Cross, Centre for Disease Control and Prevention and Active Learning Network for Accountability and Performance (ALNAP). Search terms were adapted from our previous work [25] and can be seen in full in S1 Appendix. Searches were updated in January 2021 (databases) and May 2021 (Google searches). Rayyan was used to manage search returns [35].

Selection processes

Two reviewers independently screened the titles and abstracts against the criteria described above. Conflicts were resolved by discussion. Papers included in the full text screening were also independently screened by two reviewers. Conflicts were again resolved by discussion. Reasons for exclusion were documented.

Data collection

Data collection was carried out by one reviewer and independently checked by a second. Data were extracted into a shared spreadsheet. For each report, details of the publication (authors, year, title), study type, geographical context, a description of the population and a description of the exposure were extracted. For quantitative studies, method(s) of measurement, number with overweight and / or obesity, prevalence, sample size, measure of spread, details of subgroups and secondary outcomes were collected. For case control studies, we collected data from both cases and controls, but have presented data from controls only since cases may be systematically different from the general population due to the disease under study. For longitudinal studies data from each time point were extracted. In any study type, where subgroup data were available, these were extracted but only whole study level data are presented. For studies including adults and children, only data for those aged over 18 years was extracted. Where data for the whole study population were not presented, we used subgroup data to calculate these. In most cases this was done either by summing the numbers in mutually exclusive subgroups, or by applying rates reported in subgroups to the population of the subgroup to give the number in each subgroup.

For qualitative studies (had any been identified) we planned to extract concepts, themes, barriers and facilitators as described by participants.

Risk of bias assessment

A tool for risk of bias (ROB) in prevalence studies proposed by Hoy et al [36] was adapted for our study. The original tool included a question about the study population in relation to the national population. This was not appropriate for our study since we were not seeking nationally representative prevalence estimates. External ROB was judged on choice of sampling frame, method of sample selection and extent of non-responsiveness. Internal ROB was judged on method of data collection, case definition, choice of measure of adiposity, use of standardised procedures and accuracy of reporting. To add further granularity to the discussion about ROB, a score of low, medium or high risk was given in the external, internal and overall domains.

Had we identified any qualitative studies, we planned to use the Critical Appraisal Skills Programme (CASP) checklist [37].

ROB assessment was independently carried out by one reviewer and checked by a second. Discrepancies were resolved by discussion.

Methods of synthesis

Our scoping exercise, initial search results and previous work in this field demonstrated that included studies were heterogeneous [25]. As a result, a narrative synthesis was carried out.

Definitions of overweight and obesity vary according to the anthropometric measure used and even with a single measure such as BMI, different cut-offs are proposed for different populations [38]. For the purposes of this review, we focussed on BMI as our scoping exercise showed that this was the most commonly used measure of adiposity amongst the included studies. Findings of overweight and obesity are reported as defined by individual study authors, but details of the cut-offs used were extracted for the ROB assessment.

Subgroups of interest for the synthesis included geographical setting, type of exposure, displacement status and ROB. Age and sex were considered important factors for the distribution of obesity and overweight [39]. Data are reported from all the included studies, but priority in interpretation is given to those with lower internal ROB since these studies are measuring the same phenomenon across the dataset. Heterogeneity was explored by describing the study type, population, exposure and setting of each study.

Data are presented in separate tables for high income countries (HICs) and low and middle income countries (LMICs), grouped by exposure type and location of study and shaded to indicate ROB. Categorisation as HIC or LMIC was selected to allow comparison to other publications in this field and to allow a rough assessment of resources available at a country level. The World Bank income-based classification system was used [40].

Results

Overall, 20,376 non-duplicate search returns were identified and screened. Four hundred and eighty-one full-text reports were assessed for eligibility. Fifty-six reports from 45 studies were included in the review. Fifty reports were excluded because anthropometric measurements were presented in a way which did not allow categorisation and a further 13 were excluded as details were only given for underweight. The PRISMA flow diagram can be seen in Fig 1 and the S1 Checklist is included in the supplementary material. Reports excluded after full-text screening and the reasons for exclusion are detailed in S2 Appendix.

[Figure omitted. See PDF.]

From: Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. doi: 10.1136/bmj.n71.

Description of the included studies

Characteristics of the included studies are shown in Table 1.

[Figure omitted. See PDF.]

In terms of the exposure, seventeen reports related to conflict situations [41–57], sixteen to long-standing refugee situations [58–73], thirteen to natural disasters [74–86] and ten included mixed exposures [87–96]. Several crises were the subject of multiple studies. The Great East Japan Earthquake was the exclusive exposure of nine reports [77–84, 86], the internal conflict in Syria of seven reports [41, 50–53, 55, 57] and the Palestinian situation of six reports [58, 61, 62, 70, 71, 73]. The map in Fig 2A shows the countries where exposures occurred, based on the number of reports mentioning those countries. The most frequently examined exposures were in Japan [77–86] (10 reports), followed by Syria [41, 50–53, 55, 57, 94, 96] (9 reports), then Palestine [58, 61, 62, 70, 71, 73] (6 reports) and Iraq [41, 42, 45, 47, 48, 55] (6 reports).

[Figure omitted. See PDF.]

A. Showing the frequency of reports by country of exposure. B Showing frequency of reports by study location. Maps were produced in ArcGIS from ESRI using base map data from the World Food Programme accessed via The National Archives (UK). Contains public sector information licensed under the Open Government Licence v3.0.

With regards to setting, thirty five reports were from studies carried out in HICs, twenty were conducted in LMICs and one was conducted in both settings [91]. The map in Fig 2B shows the frequency of reports from different countries. The most common study countries were the United States [45–48, 59, 60, 68, 87–90, 93] (12 reports), then Japan [77–86] (10 reports) followed by Palestine [58, 61, 62, 70, 71, 73] (6 reports).

Data reported in this paper were all collected after the exposure had begun. For those in long-standing refugee situations or going through asylum seeking or refugee resettlement processes, the exposure was considered to be ongoing. For those exposed to natural disasters, data collection took place between 4 months [75] and 4 years after the disaster [80, 82].

Forty reports considered displaced populations, while four considered non-displaced populations [54, 58, 75, 84] and ten included both displaced and non-displaced participants [49, 61, 62, 74, 78–83]. Displacement status was unclear for two reports [71, 85]. Of the HIC reports, twenty-six were related to populations who had been displaced from LMICs.

The sample size also varied across the studies with some reports including as many as 444356 participants [91] whilst the smallest study only included 28 participants [75]. Only 8 studies reported measures of spread for either population level or subgroup estimates [44, 50, 51, 57, 62, 81, 82, 84].

Although studies may have included children, we have restricted data extraction to adults only and the age range of adults included is seen in Table 1. Most studies sampled a wide range of ages, but some were restricted to young adults [54, 58], middle aged adults [41] or older adults [86]. Most studies included men and women. However, Al-Duais and Al-Awthan [54], Modesti et al [95] and Singh et al [56] included men only. Bhatta et al [59, 60], Bayyari et al [58], El Kishawi et al [61], Herrera-Fontana et al [75], Drummond et al [43] and Dhair and Abed [62] included females only.

Prevalence of overweight and obesity

Tables 2 and 3 show the prevalence of overweight, obesity and overweight or obesity combined for LICs and HICs, respectively. Forty-seven reports used WHO recommended BMI cut-offs of 25 kg/m² and 30 kg/m² to define overweight and obesity, respectively [97] or regional variations. Nine reports used non-standard definitions where a single cut-off was applied for both overweight and obesity, or no justification was given for the choice of cut-off used [43, 45, 65, 76, 77, 80, 81, 83, 85].

[Figure omitted. See PDF.]

[Figure omitted. See PDF.]

Prevalence was reported for the whole populations or by subgroups according to the aim of the study. As can be seen by entries in bold in Tables 2 and 3, where information was not available for the whole population, it could be calculated.

In whole populations, prevalence rates for overweight and obesity combined ranged from 6.4% [56] to 82.8% [51]. For overweight alone they ranged from 6.0% [56] to 53% [43] and for obesity alone from 0% [54] to 52.7% [51].

Prevalence of overweight and obesity in subgroups

The prevalence ranges for our subgroups of interest are presented in Fig 3, derived from data presented in Appendix Three.

[Figure omitted. See PDF.]

The range of prevalence estimates remains wide across overweight, obesity and the two combined in most of the subgroups, however, it appears the widest for the estimates of overweight and obesity combined. The upper and lower bounds of the obesity estimates are generally lower than those of the overweight estimates.

In terms of exposures, conflict generates the widest range of estimates. The mixed exposures group generates the narrowest range of estimates across all three metrics. This group includes studies which were carried out as part of routine pre-immigration or post-immigration health checks.

The lowest internal ROB and being displaced are associated with the widest range of prevalence estimates for their subgroups.

It is interesting to note that prevalence estimates are similar for studies carried out in both HICs and LMICs. In studies with low internal ROB, estimates for overweight and obesity combined were between 6.4% [56] and 82.8% [51] for LMICs and between 15.9% [95] and 65% [41] for HICs. Taking a closer look at geography, reports from the NORNS study [63–66] report relatively homogeneous results (overweight 22% [63] to 23.4% [64], obesity 18.9% [64] to 20.34% [65] and the two combined 42.1% [63] to 43% [66]), whereas those from earthquake afflicted populations in Japan are varied (overweight 23.2% [86] to 29.4% [78], obesity 3.4% [78] to 32.7% [83] and the two combined 26.4% [85] to 81.6% [81]). Several studies examined populations from the Middle East region [42, 45, 47, 48, 50–55, 57, 58, 61, 62, 67, 69–71, 73]. The range of estimates for overweight (12.4% [58] to 38.3% [47]), obesity (0% [54] to 52.7% [51] and the two combined (14.1% [58] to 82.8% [51]) remain wide. Nguyen et al compared refugees who had relocated to California from Iraq, Vietnam and Ukraine. They report that those from Ukraine were more likely to be obese or severely obese than the other nationalities, (Adjusted Odds Ratio (AOR) 2.1; CI 1.9–2.3) and (AOR 2.5; CI 2.1–2.8) respectively [93].

Five reports had strikingly low estimates [54, 56, 58, 95, 96] with a combined prevalence of between 6.4% [56] and 23.7% [96].Three reports included male participants only [54, 56, 95], and one was majority (75.4%) male [96]. They also included younger participants either as part of their sampling strategy or due to attendees at the services. The report with the youngest mean age was Bayyari et al, at 20.1 (standard deviation (SD) 1.2) years [58] and the oldest was Singh et al at 37 (SD 16) years [56].

Three reports had strikingly high prevalence estimates [43, 51, 81]. They all sampled populations originating from and living in different geographical settings and with different exposures. Drummond et al [43] examined West African women only with a mean age of 35 (SD 10.6) years and found a combined prevalence of 80%. Ratnayake et al [51] and Takahashi et al [81] sampled both sexes with an older mean age of 56 (SD 13,2) and 60 years for men, 64 years for women, respectively and found combined prevalences of 82.8% and 81.6% respectively.

Some reports did formally compare overweight and / or obesity prevalence estimates between men and women. Several found that these measures were higher in women compared to men [51, 52, 69, 71, 72, 93, 94]. However, Mansour et al found higher rates of obesity in women, but no difference in rates of overweight between the sexes [50]. Damiri et al and Balcilar also report that whilst obesity is more prevalent in women, overweight is more prevalent in men [57, 70].

All the studies that explored the relationship between age and adiposity found that prevalence estimates increased with age [45, 51, 52, 57, 59, 61, 69, 72, 93, 94].

Changes over time and with displacement

Findings related to displacement and longitudinal changes are difficult to tease out, so are reported here together.

Whilst studies did not report on incidence specifically, ten reports mention change in prevalence of obesity and overweight over time. Nine [47, 48, 66, 78, 79, 84, 86, 87, 90] of these reports considered populations in HICs and only one was in a LMIC [73]. All dealt with displaced populations. Of the ten reports, seven noted an increase [47, 48, 78, 79, 86, 87, 90] and one [73] reported no change over time. Two reports suggested an initial increase followed by a decrease, stabilisation or loss of adiposity [66, 84].

Of those that reported increases in overweight or obesity, Jen et al found that in the first year following relocation to the United States there was a significant increase in BMI and an upward shift in the prevalence of overweight and obesity amongst refugee populations [47]. Mulugeta et al found that for every additional year refugees lived in the USA, the risk of overweight or obesity increased by 23% among men (Odds Ratio (OR) = 1.23; 95% CI = 1.09–1.39) and 18% among women (OR = 1.18; 95% CI = 1.04–1.35) when adjusted for confounders [87].

Takahashi et al contribute further to the importance of place of displacement. They report significant increases in body weight in people relocated to temporary housing compared to those not relocated over a five year observation period [83].

Considering changes in BMI over time without categorising into overweight and obesity, three reports noted increases in BMI [65, 66, 94]. However, Modesti et al found no strong evidence for an association between time in an Italian immigration centre and increase in BMI over a 30 month period [95].

Four reports formally compared changes in adiposity before and after exposure to the Great East Japan Earthquake [78, 79, 84, 86]. Hikichi et al report that approximately 2.5 years after the disaster, the prevalence of obesity had increased amongst those displaced (25.0% to 35.1%) but decreased amongst those not displaced (26.9% to 26.6%) compared to 7 months before the disaster. [86] Ebner et al report that the OR of obesity was higher in the year after the disaster, but that this risk was no longer significant in the second year after the disaster (OR 1.31 (95% CI 1.06 to 1.38) and 1.07 (95% CI 0.93 to 1.24) respectively) [84].

Ohira et al report that BMI and obesity increased in earthquake affected populations. This increase was greater in those evacuated compared to those not evacuated and greater in males compared to females [78, 79]. The multivariable adjusted hazard ratio for overweight after the disaster was 1.61 (95% CI 1.47 to 1.77) [78].

Only one non-earthquake study compared BMI before and after exposure. No change was found [73].

Other outcomes

The other outcomes of interest were considered less frequently. There were no papers reporting on the cascade of care for obesity. However, attempts have been made to gather information about risk factors for higher BMI and targets for primary prevention. Balcilar, 2016 reports that 14.1% of Syrian refugees in Turkey were advised to reduce their fat intake [57]. Several reports from countries in the Middle East show that there are poor levels of fruit and vegetable intake and low levels of physical activity in refugee populations in general [50, 52, 57, 69].

We did not identify any qualitative studies which met our selection criteria. However, in a cross-sectional study measuring both self-perceived body size and BMI in Saharawi refugees, Naigaga et al found that there was a preference for overweight applied to individuals of the opposite sex [72]. Comparing perceived body size to BMI indicated that obese men and women did not wish to gain weight and most obese or overweight women wanted to lose weight.

In a study which included service providers and refugees living in Geneva and not selected by BMI, Amstutz et al found that fruit and vegetables were considered healthy and that language and financial hardship were the main barriers to a healthy diet [94].

Alternative measures of adiposity were infrequently used with only thirteen studies recording waist circumference (WC) [59, 60, 63–66, 69, 70, 76, 79, 82, 84, 94].

Risk of bias

S4 Appendix gives details of ROB for each study and Fig 4 summarises this across all studies. There was evidence of good practice in this challenging field, but only six studies were at low ROB overall and nine studies at moderate ROB. The challenge of achieving low ROB in the external domain was largely around choice of sampling frame and methods of participant selection. With internal ROB, the use of self-reported measures, definition of overweight and obesity and some unclear reporting were the main problems noted.

[Figure omitted. See PDF.]

Discussion

This review aimed to explore the prevalence and incidence of overweight and obesity, and the changes in adiposity over time in populations directly affected by humanitarian crises; the cascade of care in these populations and perceptions of patients with overweight and obesity. We included 56 reports derived from 45 studies. We found that prevalence estimates varied widely across the included studies and within subgroups based on study setting, internal ROB, exposure type and displacement status. Most studies report an increase in adiposity over time and compared to pre-exposure measures [47,48,63,78,79,87,90]. However, this relationship appears to be affected by displacement status. There were no reports detailing the cascade of care, but there is some evidence of limited physical exercise alongside a high calorie, low fruit and vegetable diet in refugee settings [50, 52, 57, 69]. We did not identify any studies in which the views of patients with obesity were sought qualitatively. However, a cross-sectional study did demonstrate cultural norms may differ in different settings [72].

Burden of disease

Estimates of overweight range from 6.0% [56] to 53% [43]; for obesity from 0%[54] to 52.7% [51]; and for the two combined from 6.4% [56] to 82.8% [51]. These wide ranges persist in studies at low internal ROB. We did not identify any studies with no overweight and only one study with no obesity [54]. Whilst we were expecting to find overweight and obesity, we were surprised by the extent and ubiquitousness of the issue.

Generating a global prevalence estimate for obesity is complex. WHO estimates suggest that in 2016, 39% of adults were overweight and 13% were obese [97]. In the Global Burden of Disease (GBD) Study [39], estimates of overweight and obesity were higher in developed countries than developing countries in 2013. Our review would suggest that this pattern is not consistently seen in crisis-affected populations. Japanese and South Korean populations are the subject of nearly half of the HIC papers and these countries have some of the lowest levels of obesity and overweight for high income countries in the world [98]. For other HIC studies, populations came from LICs and were likely to be faced with poverty and other challenges in their new settings.

The GBD study points out that BMI tends to reach a peak at around 55 years in men and 60 years in women and that more women than men have a BMI greater than 25 kg/m² [39]. Several studies in this review formally tested the change in obesity and overweight estimates with increasing age and by sex. With regards to age, there was a consistent relationship between increasing age and increasing adiposity [45, 51, 52, 57, 59, 61, 69, 72, 93, 94]. With regards to sex, women were commonly found to have a higher rates of overweight and / or obesity than men in most reports [51, 52, 69, 71, 72, 93, 94]. However, it would appear that in some populations sexes are differentially affected by overweight and obesity [57, 70]. All except two [70, 71] of these reports had low internal ROB. These findings suggest that service providers can expect to find more overweight and obesity in older adults and females within a crisis affected population. The heterogeneity of our studies and the moderate to high external ROB means that it is difficult to generalise the extent of these differences.

Longitudinal changes in BMI are a function of age (as described above) and are part of the migration experience [99]. In migrant populations more generally, an initial health advantage is superseded by increased risk of overweight and obesity compared to the native population. These changes are dependent on where the migrant comes from and how long they remain in the host country [100–102] All the studies reporting on these changes involved displaced populations making it difficult to comment on the differential effects of exposure to crises, acculturation and secular trends. Only five reports commenting on longitudinal changes were at low internal ROB [63, 66, 84, 94, 95]. Two reports from the NORNS study show that increasing duration in South Korea was associated with increase in weight, but that in some individuals weight loss is seen after the initial settling period [63, 66]. The Modesti report, which included only males, had a relatively short follow up period of 30 months which may explain why there was no significant change in adiposity seen [95]. And Ebner et al reported on individuals who had returned home after a relatively short displacement which may explain the change in trajectory of weight gain over time [84].

The Ohira et al papers have generated hazard ratios which show an increased risk of overweight and / or obesity with exposure to earthquakes [78, 79]. It is tempting to interpret this as evidence of a causal link between exposure to earthquakes and weight gain. However, the studies used observational data and a causal framework was not specified.

These findings would suggest that service providers, particularly in protracted situations, need to be prepared for increasing levels of overweight and obesity and the cardiometabolic complications that come with this. Whilst we do not propose that overweight and obesity are addressed in the immediate aftermath of a crisis, this pattern of weight gain points to an opportunity to take preventative action early in the time frame of a crisis.

As seen in other reviews of crisis affected populations [16, 24, 25], the geographical distribution of the studies and the people being examined is skewed. There is a long history of measuring adiposity as part of monitoring the impact of food aid. At the full text screening stage, we found that 63 reports did carry out anthropometric measurements, but that results were either presented in a way which did not allow categorisation or only those who were underweight or malnourished were reported (See S2 Appendix). This does mean that our range of prevalence estimates may have lower minimum bounds than we have identified. It also suggests that changing monitoring and reporting requirements would provide more information about the true prevalence of overweight and obesity and would clarify targets where more research would be most beneficial.

Cascade of care

We could not identify evidence of information or interventions being directed specifically at those who were overweight or obese. It is likely that this reflects a genuine lack of interventions aimed at weight loss rather than NCD management more generally. However, there is evidence that, particularly those studies following WHO STEPS processes [103], were able to identify NCD risk factors. This provides a starting point for the discussion about targets for primary and secondary prevention. Namely, access to low calorie, high nutritional value food and promotion of active lifestyles [50, 52, 57, 69].

Looking at the cascade of care in NCD management more broadly, several recurring research and information gaps are noted. There is generally poor collection of standard data regarding disease states and recognised risk factors, there is a paucity of evidence to guide interventions, and there are infrastructure and supply problems even for those conditions in which treatments are available [16, 17, 31, 104]. Many of these factors are applicable to overweight and obesity. With the additional challenge that overweight and obesity are considered much later in the crisis response [15], by which time resources are arguably too stretched to extend to further activities.

Patient perceptions

We did not identify qualitative studies in which we could differentiate the voices of those with overweight and obesity from other participants. However, cultural ideals and norms in relations to body size and shape were noted [72]. This is echoed particularly in work examining the understanding of African refugees who described the pursuit of thinness as perplexing [105]. Language and financial barriers to seeking care for overweight and obesity and also part of the refugee and migrant experience of seeking health care in general [106, 107].

Crisis affected populations are largely city dwellers [6] and as such multi-pronged and multi-level interventions are needed for both prevention and treatment [108]. However, it is acknowledged that population level weight loss interventions are challenging to implement and sustain even in well-resourced settings [109]. Causal pathways in obesity are complex [109]. Qualitative work is key to understanding the causal relationships between perceptions, understanding and behaviour. We cannot expect to successfully influence disease trajectories without this information.

Use of waist circumference

We were surprised that only13 studies recorded WC [59, 60, 63–66, 69, 70, 76, 79, 82, 84, 94]. There is ongoing discussion about the most appropriate measure to determine increased adiposity [110]. Waist circumference provides important additional information in assessing the risk of death and disease due to increased adiposity [111]. However, WC is no longer explicitly mentioned WHO’s Package of essential non-communicable disease interventions [112].

Risk of bias

ROB poses a challenge in the interpretation of reported results. Identifying representative samples in crisis settings is a challenge, particularly with the chaos associated with displacement. The majority of studies weighed and measured their participants directly or used health records where these measurements were recorded. Several of the papers, however, used self reported heights and weights. These were coded as having a high internal ROB given the potentially inaccurate measurements. In Bhatta et al’s 2015 report, 7 people reported themselves to be overweight but the BMI data showed 70 people out of 120 to be overweight; the difference being a factor of 10 [59].

Strengths and Limitations

One of the main strengths of this review is the number of reports included in the final analysis. These were identified in a systematic manner across databases and online repositories. The reports cover different crises and different regions of the world. Though this gives our analysis breadth, this heterogeneity means that we were unable to perform a meta-analysis and that the findings are not generalisable across all settings.

We used simple mathematics to derive missing prevalence estimates (marked in bold in Tables 2 and 3). In some cases, this involved calculations across multiple subgroups. This approach was taken as a pragmatic alternative to requesting access to individual patient data.

We only included publications from Jan 2011 onwards. On one hand this is a strength as it allows for a contemporaneous picture to emerge. On the other hand, it could be viewed as a weakness, since we will be missing patterns in change over time.

We did not identify reports discussing the cascade of care or the perception of patients with overweight or obesity. We believe that this genuinely reflects a paucity of data of this type. However, an alternative search strategy may have yielded different results. For example, searching for the study type in the settings of interest and then screening for the disease could unearth different information.

Conclusion

This study has shown that the prevalence of overweight and obesity vary in crisis affected populations but are rarely absent. Increases in adiposity over time, in older adults and in women are likely to be seen in most populations. Better quality descriptive information would help to identify precisely to who and when interventions should be offered in different settings. The lack of information about the cascade of care likely reflects limited efforts to address overweight and obesity in these settings. The lack of qualitative research hampers our understanding of which interventions would be most likely to succeed. WC measures should be included as part of standard care.

Supporting information

S1 Checklist. PRISMA 2020 main checklist.

https://doi.org/10.1371/journal.pone.0282823.s001

(DOCX)

S1 Appendix. Full search strategies.

https://doi.org/10.1371/journal.pone.0282823.s002

(DOCX)

S2 Appendix. Studies excluded at full text screening.

https://doi.org/10.1371/journal.pone.0282823.s003

(DOCX)

S3 Appendix. Prevalence ranges in subgroups.

https://doi.org/10.1371/journal.pone.0282823.s004

(DOCX)

S4 Appendix. Details of risk of bias assessment.

https://doi.org/10.1371/journal.pone.0282823.s005

(DOCX)

Citation: Shortland T, McGranahan M, Stewart D, Oyebode O, Shantikumar S, Proto W, et al. (2023) A systematic review of the burden of, access to services for and perceptions of patients with overweight and obesity, in humanitarian crisis settings. PLoS ONE 18(4): e0282823. https://doi.org/10.1371/journal.pone.0282823

About the Authors:

Thomas Shortland

Roles: Methodology, Writing – original draft

Affiliation: Warwick Medical School, University of Warwick, Coventry, United Kingdom

ORICD: https://orcid.org/0000-0001-8434-4019

Majel McGranahan

Roles: Data curation, Methodology, Writing – review & editing

Affiliation: Warwick Medical School, University of Warwick, Coventry, United Kingdom

ORICD: https://orcid.org/0000-0002-5892-0729

Daniel Stewart

Roles: Methodology, Writing – review & editing

Affiliation: National Public Health Specialty Training Programme, South West Training Scheme, Bristol, United Kingdom

ORICD: https://orcid.org/0000-0003-4368-2788

Oyinlola Oyebode

Roles: Methodology, Supervision, Writing – review & editing

Affiliation: Warwick Medical School, University of Warwick, Coventry, United Kingdom

Saran Shantikumar

Roles: Conceptualization, Supervision, Writing – review & editing

Affiliation: Warwick Medical School, University of Warwick, Coventry, United Kingdom

William Proto

Roles: Methodology, Writing – review & editing

Affiliation: Warwick Medical School, University of Warwick, Coventry, United Kingdom

Bassit Malik

Roles: Writing – review & editing

Affiliation: Warwick Medical School, University of Warwick, Coventry, United Kingdom

Roger Yau

Roles: Writing – review & editing

Affiliation: Warwick Medical School, University of Warwick, Coventry, United Kingdom

Maddie Cobbin

Roles: Writing – review & editing

Affiliation: Warwick Medical School, University of Warwick, Coventry, United Kingdom

ORICD: https://orcid.org/0000-0001-6946-4784

Ammar Sabouni

Roles: Conceptualization, Methodology, Writing – review & editing

Affiliation: Syria Development Centre, London, United Kingdom

Gavin Rudge

Roles: Visualization

Affiliation: Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom

Farah Kidy

Roles: Conceptualization, Data curation, Methodology, Project administration, Supervision, Writing – review & editing

E-mail: [email protected]

Affiliation: Warwick Medical School, University of Warwick, Coventry, United Kingdom

ORICD: https://orcid.org/0000-0003-0771-5052

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