Background
Over the last 40 years, there has been a significant increase in obesity, reflected by a high body mass index (BMI); between 1975 and 2014, global prevalence increased by 7.6 and 8.5% in men and women, respectively [
1]. This pattern of increase however, is not uniform across countries or within countries, with different groups at greater or lesser risk both of becoming obese and also succumbing to associated health problems. A recent paper from Peru, using panel survey data on rural-to-urban migrants collected between 2007-08 and 2012–13 found that, compared to the rural group, rural-to-urban migrants and urban dwellers had an almost ten-fold greater risk of developing obesity [
2]. Such discrepancies could be due to socio-economic differences, which determine exposure to known obesogenic risk factors such as sedentarism (low level of physical activity), limited access to healthy food choices among the worse-off [
3], and stress [
4,
5].
Although the risk factors for, and health consequences of, obesity are well known, how the probabilities of being obese vary according to different within-country internal migration profiles, and how these relationships behave over time, remains unknown in most low and middle-income countries (LMICs). Rapid urbanization is placing an undeniable strain on public resources in LMICs, and urban environments provide a range of challenges to newcomers from rural settings, particularly with regards to health and nutrition [
6].
Collecting reliable longitudinal data in LMICs is expensive and difficult, and remains a challenge, particularly in settings where rural-to-urban migration is widespread, rapid and regular. Using repeated cross-sectional population-based surveys could be a pragmatic option to gain information both on migration profiles and health indicators. The aim of this paper is to assess the probabilities of being obese for different profiles of internal migration in Peru using the Peruvian Demographic and Health Surveys (DHS) from 2005 to 2012 [
7]. In doing so we show how the use of cross-sectional household survey data can reflect patterns of obesity, and its relationship with migration, and could be used in many more LMICs.
Discussion
Most studies have characterized BMI levels and obesity rates in national aggregates, stratifying results by rural and urban settings. Yet, the nature of demographic change in most LMICs today means population distributions are changing rapidly as a result of ongoing internal migration processes. In this study we aimed to characterize obesity rates by expanding into a more detailed profiling of populations and benefiting from existing extensive data of internal migration in Peru. Over the 7-year study period we observed an increase in the prevalence of obesity in all study groups, with the intra-urban migrants and rural-to-urban migrants having the highest rates of obesity (around 21% in 2012); those remaining in rural had the lowest rates (10% in 2012). The largest increase in obesity rates over the period was observed among the intra-rural migrants, from 6.6% in 2005 to 13% in 2012. We found that, relative to rural stayers, all those who exposed to urban environments, including urban-to-rural migrants, had from two- to three-fold greater odds of obesity. These raised probabilities are also observed in the intra-rural migrant group, which had 42% higher obesity odds relative to rural non-migrants. Taken together, these findings indicate that both exposure to urban environments and migration are associated with higher odds of obesity, and our study advances the characterization of the association of migration-obesity profiles.
Given the rapid pace of urbanization in most LMICs, driven largely by rural to urban migration, these findings help illustrate the impact of urbanization, and the exposure to urban settings more precisely, is not uniform across groups; important changes are occurring across the spectrum of migration profiles: while migrants to an urban setting have much higher rates of obesity, the increase in the magnitude of obesity prevalence is considerably greater among intra-rural migrants in Peru. In general, any pattern of migration, i.e. migration into an urban or into a rural area, is associated with obesity.
We have previously shown that exposure to urban environments is linked to higher odds of obesity, with a clear relationship with duration of residence in urban settings. This supports comparable findings with other studies focused on rural-to-urban only migrants in Peru [
2,
11], and in other LMICs undergoing internal migration [
12]. In this study, we were able to also observe a relationship, albeit smaller, in the same direction of increased obesity with rural migration. This means that any migration, irrespective of the host environment, is associated with increased odds of obesity. Although this appears counter-intuitive, particularly for intra-rural migrants, as most of the literature signals that exposure to rural areas exert a protective factor for obesity. Our study adds further granularity into this human-environment exposure in that migration per se introduces changes, some of them behavioral ones, that override the protective factor of being exposed to a rural area. Hence, internal migration per se, and not only rural-to-urban migration, becomes a risk factor for obesity.
A recent population-based study in India, also using cross-sectional data, found higher obesity odds among intra-urban and rural-to-urban women migrants, though the results for men were not conclusive [
13]. That said, in contrast to our results, Varadharajan et al. found that urban-to-rural migration in women was a protective factor against obesity [
13]. These differences could be due to the fact that India is less urbanized than Peru [
14] and significantly poorer [
15], and thus migrants in Peru are more exposed to the unhealthy lifestyles associated with prolonged exposure to urban living. Our results, including prospective studies [
2], could signal an alert for a developing obesity epidemiological scenario in India and other LMICs which while still less urbanized than Peru [
14], are indeed undergoing urbanization.
Our results could be explained by differences in socioeconomic status amongst migration groups. It is often argued that migration towards urban sites is motivated and facilitated by better socio-economic status i.e. people migrate to look for work or better living conditions in urban areas, but also that those able to migrate are relatively better off [
16,
17]. In fact, a previous cross-sectional study with Peruvian rural-to-urban migrants found that lower socio-economic status and the conditions of poverty were associated with lower odds of obesity [
18]. In a way, this reflects the fact that obesogenic drivers closely follow lifestyles associated with, or made possible by, a higher socio-economic status. From a pragmatic point of view, our findings support calls to promote obesity-prevention strategies in rural areas and in all those sites undergoing economic improvement, to stave off potential obesity epidemics and the health problems and costs this entails [
19].
Over the last decades, Peruvians have moved from rural to urban areas seeking of better opportunities: improved education, access to services and housing [
20,
21]. However, this is not always possible and rural-to-urban migrants usually move to the outskirts of Lima (capital city) and build their own houses, lacking access to basic services (e.g., potable water). Overall, rural-to-urban migrants have been characterized as: young rural women, mostly indigenous, illiterate, with dearth of knowledge about living in the city, with poor household background, and mostly working in household and agriculture [
22]. Therefore, what could have driven our results is probably the exposure to urbanization, either because of rural-to-urban migration or due to the increasing urbanization of rural areas, rather than the migrants having different characteristics than their predecessors.
Our study has found that different migration profiles are associated with greater or lesser odds of being and becoming obese. Increases in obesity rates at the population level reflect shifting BMI levels, where increases of up to 5 Kg/m
2 units have been linked to a three-time greater risk of developing diabetes [
23]. Hence, if we were to apply these assumptions to a LMIC like Peru, our analysis would support both the understanding
and forecasting of the epidemic of diabetes, a costly cardio-metabolic risk factor associated to loss of productivity and early mortality [
24]. Understanding both urban and rural settings merit attention and is important. Whilst greater focus has been placed on public health and nutrition in urban settings, no doubt due to the greater perceived burden, a significant proportion of the population are still based in rural areas; our findings could well inform preventive strategies and health messages which could be applied to tackle growing obesity in both rural and urban settings. Also important is developing public health messages to parents and grandparents to stave off obesity in young children, given the rising prevalence of obesity across the world [
19].
Our study benefitted from a large sample size from a population-based survey of Peruvian women, with almost annual data from 2005 to 2012 and with a dynamic within-country migration profile. The use of the Peru DHS is an asset because it allows for an examination of the geographic and sociodemographic differences in health outcomes according to internal migration profiles. We acknowledge the limitations of this study. We were unable to control the regression models for the initial conditions of the population, particularly participants’ BMI
prior to migration. However, our aim was not to assess the longitudinal trajectory of individual BMI or to assess changes over time for a given group. We aimed, instead, to assess the effect of internal migration and to see if such an effect varied over a period of time. Also, our regression models did not account for other overweight determinants, such as diet or physical activity, because these variables were not available. Future studies on trajectories of weight should include these variables, particularly when assessing people changing from rural to urban sites, or vice versa. For some migration categories, i.e., urban-to-rural or intra-rural migrants, there were small numbers in the sample (Table
1), and this could have compromised the statistical power of the regression analysis. In addition, other co-variables also had small sample size in some categories (e.g., ethnicity). Thus, results for these small groups deserve a cautious interpretation. Nonetheless, this should not be regarded as a limitation, because our results are robust for rural-to-urban migration, which happens to be the most frequent migration. Finally, the large sample size available only included adult women aged 15–49 years in the study, given there were no similar data on men for the same time period. Therefore, our results partially inform of the overall scenario in Peru, without including men or older women.