Although migration and urbanization have been linked with higher obesity rates, especially in low-resource settings, prospective information about the magnitude of these effects is lacking. We estimated the risk of obesity and central obesity among rural subjects, rural-to-urban migrants and urban subjects. Prospective data from the PERU MIGRANT Study were analyzed. Baseline data were collected in 2007–2008 and participants re-contacted in 2012–2013. At follow-up, outcomes were obesity and central obesity measured by body mass index and waist circumference. At baseline, the primary exposure was demographic group: rural, rural-to-urban migrant and urban. Other exposures included an assets index and educational attainment. Cumulative incidence, incidence ratio (IR) and 95% confidence intervals (95% CI) for obesity and central obesity were estimated with Poisson regression models. At baseline, mean age (±s.d.) was 47.9 (±12.0) years, and 53.0% were females. Rural subjects comprised 20.2% of the total sample, whereas 59.7% were rural-to-urban migrants and 20.1% were urban dwellers. A total of 3598 and 2174 person-years were analyzed for obesity and central obesity outcomes, respectively. At baseline, the prevalence of obesity and central obesity was 20.0 and 52.5%. In multivariable models, migrant and urban groups had an 8- to 9.5-fold higher IR of obesity compared with the rural group (IR migrants=8.19, 95% CI=2.72–24.67; IR urban=9.51, 95% CI=2.74–33.01). For central obesity, there was a higher IR only among the migrant group (IR=1.95; 95% CI=1.22–3.13). Assets index was associated with a higher IR of central obesity (IR top versus bottom tertile 1.45, 95% CI=1.03–2.06). Peruvian urban individuals and rural-to-urban migrants show a higher incidence of obesity compared with their rural counterparts. Given the ongoing urbanization occurring in middle-income countries, the rapid development of increased obesity risk by rural-to-urban migrants suggests that measures to reduce obesity should be a priority for this group.

Rapid urbanization, increase in food availability, and changes in diet and lifestyle patterns have been changing nutritional profiles in developing nations. We aimed to describe nutritional changes in children under 5 years and women of reproductive age in Peru, during a 15-year period of rapid economic development and social policy enhancement. Trend analyses of anthropometric measures in children of preschool age and women between 15–49 years, using the Peruvian National Demographic and Family Health Surveys (DHS) from 1996 to 2011. WHO growth curves were used to define stunting, underweight, wasting and overweight in children 19 years, body mass index (BMI) was analyzed both categorically and as a continuous variable. To statistically analyze the trends, we used regression models: Linear and Poisson for continuous and binary outcomes, respectively. We analyzed data from 123 642 women and 64 135 children, from 1996 to 2011. Decreases over time were evidenced for underweight (p<0.001), wasting (p<0.001), and stunting (p<0.001) in children under 5y. This effect was particularly noted in urban settings. Overweight levels in children reduced (p<0.001), however this reduction stopped, in urban settings, since 2005 (∼12%). Anemia decreased in children and women (p<0.001); with higher reduction in urban (↓43%) than in rural children (↓24%). BMI in women aged 15–19 years increased (p<0.001) across time, with noticeable BMI-curve shift in women older than 30 years. Moreover, obesity doubled during this period in women more than 19y. Nutrition transition in Peru shows different patterns for urban and rural populations. Public policies should emphasize targeting both malnutrition conditions—undernutrition/stunting, overweight/obesity and anemia—considering age and place of residence in rapid developing societies like Peru.

Urbanization can be detrimental to health in populations due to changes in dietary and physical activity patterns. The aim of this study was to determine the effect of migration on the incidence of hypertension. Participants of the PERU MIGRANT study, that is, rural, urban and rural-to-urban migrants, were re-evaluated after 5 years after baseline assessment. The outcome was incidence of hypertension; and the exposures were study group and other well-known risk factors. Incidence rates, relative risks (RRs) and population attributable fractions (PAFs) were calculated. At baseline, 201 (20.4%), 589 (59.5%) and 199 (20.1%) participants were rural, rural-to-urban migrant and urban subjects, respectively. Overall mean age was 47.9 (s.d.±12.0) years, and 522 (52.9%) were female. Hypertension prevalence at baseline was 16.0% (95% confidence interval (CI) 13.7–18.3), being more common in urban group; whereas pre-hypertension was more prevalent in rural participants (P<0.001). Follow-up rate at 5 years was 94%, 895 participants were re-assessed and 33 (3.3%) deaths were recorded. Overall incidence of hypertension was 1.73 (95%CI 1.36–2.20) per 100 person-years. In multivariable model and compared with the urban group, rural group had a greater risk of developing hypertension (RR 3.58; 95%CI 1.42–9.06). PAFs showed high waist circumference as the leading risk factor for the hypertension development in rural (19.1%), migrant (27.9%) and urban (45.8%) participants. Subjects from rural areas are at higher risk of developing hypertension relative to rural–urban migrant or urban groups. Central obesity was the leading risk factor for hypertension incidence in the three population groups.

Historically in developing countries, the prevalence of obesity has been greater in more advantaged socioeconomic groups. However, in recent years the association between socioeconomic status (SES) and obesity has changed and varies depending on the country's development stage. This study examines the relationship between SES and obesity using two indicators of SES: education or possession assets. Using the cross‐sectional 2008 National Demographic and Family Health Survey of Peru (ENDES 2008), we investigated this relationship in women aged 15–49 years living in rural and urban settings. Descriptive, linear and logistic regressions analyses were conducted accounting for the multistage nature of the sampling design. The overall prevalence of obesity in this study was 14.1% (95% confidence interval (CI): 13.3–14.8); 8.4% (95% CI: 7.5–9.3) in rural areas and 16.2% (95% CI: 15.2–17.2) in urban areas. Wealthier women were more likely to be obese, and this association was stronger in rural areas. Conversely, more educated women were less likely to be obese, especially in urban areas. The distribution of obesity in Peruvian women is strongly related to socioeconomic position, and differs whether measured as possession assets or by level of education. These findings could have important implications for policy development in Peru.

It is unclear how well currently available risk scores predict cardiovascular disease (CVD) risk in low-income and middle-income countries. We aim to compare the American College of Cardiology/American Heart Association (ACC/AHA) Pooled Cohort risk equations (ACC/AHA model) with 6 other CVD risk tools to assess the concordance of predicted CVD risk in a random sample from 5 geographically diverse Peruvian populations. We used data from 2 Peruvian, age and sex-matched, population-based studies across 5 geographical sites. The ACC/AHA model were compared with 6 other CVD risk prediction tools: laboratory Framingham risk score for CVD, non-laboratory Framingham risk score for CVD, Reynolds risk score, systematic coronary risk evaluation, World Health Organization risk charts, and the Lancet chronic diseases risk charts. Main outcome was in agreement with predicted CVD risk using Lin’s concordance correlation coefficient. Two thousand one hundred and eighty-three subjects, mean age 54.3 (SD ± 5.6) years, were included in the analysis. Overall, we found poor agreement between different scores when compared with ACC/AHA model. When each of the risk scores was used with cut-offs specified in guidelines, ACC/AHA model depicted the highest proportion of people at high CVD risk predicted at 10 years, with a prevalence of 29.0% (95% confidence interval, 26.9–31.0%), whereas prevalence with World Health Organization risk charts was 0.6% (95% confidence interval, 0.2–8.6%). In conclusion, poor concordance between current CVD risk scores demonstrates the uncertainty of choosing any of them for public health and clinical interventions in Latin American populations. There is a need to improve the evidence base of risk scores for CVD in low-income and middle-income countries.