High Rates of Obesity and Non-Communicable Diseases Predicted across Latin America

Article (PDF Available)inPLoS ONE 7(8):e39589 · August 2012with45 Reads
DOI: 10.1371/journal.pone.0039589 · Source: PubMed
Non-communicable diseases (NCDs) such as cardiovascular disease and stroke are a major public health concern across Latin America. A key modifiable risk factor for NCDs is overweight and obesity highlighting the need for policy to reduce prevalence rates and ameliorate rising levels of NCDs. A cross-sectional regression analysis was used to project BMI and related disease trends to 2050. We tested the extent to which interventions that decrease body mass index (BMI) have an effect upon the number of incidence cases avoided for each disease. Without intervention obesity trends will continue to rise across much of Latin America. Effective interventions are necessary if rates of obesity and related diseases are to be reduced.
High Rates of Obesity and Non-Communicable Diseases
Predicted across Latin America
Laura Webber
*, Fanny Kilpi
, Tim Marsh
, Ketevan Rtveladze
, Martin Brown
, Klim McPherson
1 National Heart Forum, London, England, 2 New College, University of Oxford, Oxford, England
Non-communicable diseases (NCDs) such as cardiovascular disease and stroke are a major public health concern across
Latin America. A key modifiable risk factor for NCDs is overweight and obesity highlighting the need for policy to reduce
prevalence rates and ameliorate rising levels of NCDs. A cross-sectional regression analysis was used to project BMI and
related disease trends to 2050. We tested the extent to which interventions that decrease body mass index (BMI) have an
effect upon the number of incidence cases avoided for each disease. Without intervention obesity trends will continue to
rise across much of Latin America. Effective interventions are necessary if rates of obesity and related diseases are to be
Citation: Webber L, Kilpi F, Marsh T, Rtveladze K, Brown M, et al. (2012) High Rates of Obesity and Non-Communicable Diseases Predicted across Latin
America. PLoS ONE 7(8): e39589. doi:10.1371/journal.pone.0039589
Editor: Noel Christopher Barengo, Fundacio
n para la Prevencio
n y el Control de las Enfermedades Cro
nicas No Transmisibles en Ame
rica Latina (FunPRECAL),
Received January 5, 2012; Accepted May 24, 2012; Published August 13, 2012
Copyright: ß 2012 Webber et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This research was funded by a non-discretionary educational grant from GlaxoSmithKline (number 27875780). The funder had no role in study design,
data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The study was funded by a commercial source - GlaxoSmithKline. However, this does not alter the authors’ adherence to all the PLoS ONE
policies on sharing data and materials.
* E-mail: laura.webber@heartforum.org.uk
Internationally the obesity epidemic is driving up the burden of
several non-communicable diseases (NCDs) such as cancers, heart
disease and diabetes. In Latin America, non-communicable
diseases are amongst the biggest killers and rates of these diseases
are expected to escalate. For example, diabetes is predicted to
increase by more than 50% with 32.9 million suspected sufferers
by 2030 in Latin America [1]. Overweight and obesity are key
modifiable risk factors for NCDs and with rates as high as 60%
amongst adults (Ministry of Health Belize, Ministerio de Salud
Nicaragua, Ministry of Public Health, El Salvador) the epidemic is
a major public health concern both to individual quality of life,
longevity, and costs to health systems.
Across Latin America the obesity epidemic has been driven by
the rapid demographic and nutritional transition as countries go
through a changing economic climate and emerge from poverty
[2,3]. Demographically, populations of Latin America are ageing
as there has been a shift from high to low fertility and mortality.
Nutritionally, an increased intake of energy dense foods high in
sugars and saturated fats coupled with increased inactivity levels
are key factors explaining the rise in obesity. Urbanisation and
economic growth has driven this change. Alongside this, sedentary
lifestyles are commonplace with between 30–60% not meeting the
recommended levels of physical activity each day [4] with a shift to
industrialised cities and the loss of the protective rural aboriginal
environment [5]. The highest levels of obesity are seen in urban
poor women, though they affect both genders. The World Health
Organisation has responded to the increasing burden of NCDs by
putting in place global strategies such as that for the prevention
and control of chronic diseases. Aims have been made to increase
surveillance, improve public awareness and facilitate quality of
care for chronically ill patients [6]. Though there is a long way to
go and greater prevention strategies.
Knowing the direction and speed of change of obesity rates is
necessary if health policies that aim to reduce obesity are well-
placed and effective. This study used microsimulation modelling to
project obesity trends and related burden of disease in Latin
America to 2050 using the data available.
Data sources
BMI data was collected by reviewing the literature using
Pubmed (supplemented by Google scholar). Unpublished data was
collected through personal communication with researchers and
authors of published studies. The WHO BMI database was used as
a further source of BMI data and references. Articles were
included if they contained BMI data presented by age and sex (see
Table S1 for a table of references used for BMI data). Because of
the scarcity of data, sub-national and national, measured and self-
reported data were included.
A second review of the literature was carried out to locate
country-specific incidence, survival and mortality rates of obesity-
related diseases type 2 diabetes, coronary heart disease, stroke
and obesity-related cancers (colorectal, pancreas, breast, kidney,
liver, endometrial and oesophageal).
Extrapolation of missing data
Few countries had more than two data points and three
countries Costa Rica, Cuba, Panama - only had one. For these
countries 2008 estimates were used based on a recent analysis by
Finucane and colleagues [7]. This extrapolates from their
estimated mean: the BMI-distribution is assumed to have the
PLOS ONE | www.plosone.org 1 August 2012 | Volume 7 | Issue 8 | e39589
form {p,(1-p)/2,(1-p/2)} where p is the prevalence of normal
weight; p is then determined from the known mean.
For Bolivia, Nicaragua and Peru only data on females was
Data manipulation
It was necessary to manipulate the BMI data in a number of
ways: We sorted the source data into three mutually exclusive BMI
categories: normal weight (,25 kg/m
), overweight (25–29.9 kg/
), and obese ($30 kg/m
). Where some data were in wide age
groups (e.g. 20 year age groups) they were divided into 10-year
and 5-year age groups, doubling or quadrupling the variance of
the estimates as appropriate. Variance was calculated using the
equation (p(1-p)/n) where: n is the sample size and p the
Proxy country data
Where disease data were not available then data from a proxy
country were used instead. Proxy countries were chosen based on
the proximity and similarity to the target country. For fatal
diseases (coronary heart disease, stroke, cancers) a proxy country’s
incidence data was adjusted using the, known, target country’s
mortality rate: the ratio of the target-to-proxy countries’ mortality
rates was used to scale the proxy country’s incidence rates. To
estimate incidence rates from proxy countries for non-fatal diseases
(e.g. Type II diabetes the proxy country’s BMI, relative incidence
rate statistics and population statistics are used to determine the
incidence rate for people having normal BMI in the proxy
country. In the target country, its own BMI statistics are used to
estimate the incidence stats (the relative risk statistics are assumed
to be universal). For fatal diseases target country death rates were
also included in the calculation.
For coronary heart disease, Mexico was used as a proxy for all
Latin American countries. UK CHD incidence figures were
adjusted for the difference in CHD mortality between the UK and
each target country using the WHO Global Infobase. This was
done by scaling the UK incidence figures by the ratio of the age
standardised mortality rate in 2008 to the same figure for the UK
[8]. The idea is that mortality/incidence is approximately constant
so that, suppose in the Target country mortality is known but
incidence is not known, then incidence
= (incidence
[T = target P = proxy]. The same database was also
used for CHD mortality rates. Survival data from the US was used
as a proxy [9].
For stroke, Chile was used as a proxy for all Latin America
countries adjusted for the difference in CHD mortality for each
target country by the method described. Stroke survival was taken
from US figures [10].
Mexico was the only country with appropriate incidence data
for diabetes [8] and so this was used as a proxy for all other Latin
American countries.
Globocan 2008 [11] was used for incidence and mortality rates
in Latin America. Survival rates for cancers were taken from Costa
Rica for breast cancer [12] Cuba for colorectal and kidney cancer
[13,14], Brazil for endometrial [15], Puerto Rico for Liver cancer
and the US for Oesophagus and Pancreatic cancer [16]. A table of
disease references for each country is presented in Table S2.
Incidence, mortality and survival input data are presented in
Table S3.
Calculation of survival
For survival data, the probability of survival, p, for a number of
years, T, after acquiring a fatal disease was modelled in one of two
ways depending on the disease. Either as a simple exponential
distribution p = e
, or as an exponential distribution allowing
for different probability, p
, of survival in the first year,
. Stroke used the latter model; other fatal diseases
the former. Disease survival statistics consist of the rate R or the
rate R together with the first year survival probability p
. These
statistics are further classified by age group and gender. The rate R
was usually inferred from quoted 5-year survival statistics.
For coronary heart disease, Mexico was used as a proxy for all
Latin American countries. Incidence statistics were adjusted for
the difference in CHD mortality for each target country [8] and
the UK by scaling the UK incidence figures by the ratio of the age
standardised mortality rate in 2008 to the same figure for the UK
[8]. The same database was also used for CHD mortality rates.
Survival data from the US was used as a proxy [9].
Statistical analysis
BMI trends and future obesity-related disease burdens were
estimated to 2050 by age and sex for 10 Latin American countries
using micro-simulation modelling. This method is described in
greater detail in Wang and colleagues [17] and Methods S1 of the
supplementary information. A dual-module modelling process was
developed by the UK Foresight working group [18,19] which was
applied and refined for this study. Module one uses a non-linear
multivariate, categorical regression model fitted to cross-sectional
BMI data from each of the countries. Module two uses a micro-
simulation program to produce longitudinal projections to 2050.
This creates a virtual population cohort based on module one BMI
distributions from 2010 to 2050. A BMI value is probabilistically
assigned as a function of age, sex and calendar year. BMI
trajectories were projected using the simulation model with the
assumption that an individuals’ BMI percentile in the same age
cohort stays the same over time. Population size, births and deaths
were also simulated in a large number of individuals as they age
using data from the World Health Organization and United
Nations. Simulated individuals are at risk of getting a particular
disease each year if he or she did not have the disease at the
beginning of the year; they can continue living with the disease or
die from it (if it is fatal). The software for this program was written
in C++. Outliers were removed. One million Monte Carlo runs
per country were carried out, but due to the scarcity of some if
BMI data, running a whole population might have resulted in
smoother curves. To estimate the disease burden associated with
the trends in overweight and obesity, as well as the effect of
possible interventions, future increases in obesity-related diseases
were projected from 2008 to 2050, using three different trend
scenarios: scenario 0: obesity trends go unchecked; scenario 1:
obesity levels decrease by 1% and scenario 2: obesity levels
decrease by 5%.
The results presented below were simulated from three separate
scenarios which differed in the environment assumed to prevail for
the period 2000 to 2050.
Scenario 0: 2000 to 2050; unrestricted BMI growth as
Scenario 1: 2000 to 2050; 1% BMI reduction relative to
scenario 0
Scenario 2: 2000 to 2050; 5% BMI reduction relative to
scenario 0
Figure 1 and Figure 2 shows projected prevalence of overweight
($25 kg/m
) in males and females respectively aged 20+ years.
Obesity Trends and NCDs in Latin America
PLOS ONE | www.plosone.org 2 August 2012 | Volume 7 | Issue 8 | e39589
Across all countries overweight and obesity are projected to
increase by 2050 in both males and females. In females, much
lower levels were seen in Argentina than other countries.
However, since only two sub-national data points were available,
interpretation of this result should be made with caution. By 2030
more than 50% of males and 60% of females (excluding
Argentina) will be overweight or obese. The highest projected
rates were seen in Cuba and Panama. Table S4 and S5 presents
the projected percentage of overweight and obese males and
females respectively.
Figure 3 presents the cumulative incidence cases avoided of
cancer, CHD & stroke, diabetes per 100,000 population across
Latin America by 2030. Effective interventions that reduce obesity
levels will have a dramatic effect upon the cumulative incidence
cases avoided. The biggest impact would be in Cuba where more
than 1300 cases of CHD and stroke and more than 2000 cases of
type 2 diabetes are avoided per 100,000 of the population with a
5% reduction in BMI. Based on total population figures (UN
population data 2011), the number of cases of CHD & Stroke in
Cuba will rise to over 1340000, cases of diabetes to 1030000, and
cases or cancer to 220000 by 2030 in the total adult population
(aged 20+ years). To use two other examples, in Colombia, 175000
people will have CHD & Stroke, 149147 will have diabetes and
35300 will have cancer in the total population by 2030. In
Uruguay, 340000 will have CHD&Stroke, 207000 will have
diabetes, 76000 will have cancer by 2030. A table of cumulative
incidence cases avoided for 2030 is presented in table S6. It is
important to note that disease projections for Bolivia, Peru and
Nicaragua are based on female only BMI data.
Using sophisticated modelling techniques the results of this
study illustrated how the obesity epidemic will unfold across Latin
America. Over the next twenty years overweight and obesity was
projected to increase. Reflecting these trends the incidence of each
disease is also set to increase. Interventions that are effective in
reducing BMI will be important in reducing rates of cardiovas-
cular disease and diabetes.
In general, overweight and obesity levels were projected to
increase in all countries with the highest rates seen in Cuba. Rates
of change are likely to be a result, at least in some part, to
economic changes. The positive effect of the economic transition
has been to help eradicate undernutrition, but, unregulated, it also
promotes unhealthy lifestyles which favour obesity. Traditional
diets in the region are often meat-based, in particular red meats
are popular staples and food preparation involving frying is also
popular. When food security and incomes increase, it is not
surprising that the per-capita consumption of these traditional
foods increase, together with other Western influences on diets.
Chile had the best available data of any Latin American country
allowing for more accurate predictions than other countries. In the
1990s Chile doubled its per capita income. An important amount
of this increase has been spent on modern living such as television
sets, cars and unhealthy high fat, sugar and salted processed foods
fuelling energy imbalance and subsequent increases in obesity
[20]. Similar transition has occurred across Latin America.
Cuba is an interesting case due to the macroeconomic changes
and their consequences on health behaviour and obesity. Since the
1950s the economy grew at a rate higher than the rest of Latin
America despite the US embargo. The collapse of the Soviet
Union in the 1990s cut Cuban trade by 80% and GDP decreased
by 33% [21] resulting in food shortages, increased physical activity
and reduced BMI [22]. However, the budget for education and
health increased during this period. Since then, availability of high
fat foods has increased, whereas labour-intensive activity has
reduced, which may be one contributing factor of the plethora of
causes explaining the obesity epidemic and the high levels of
obesity and cardiovascular diseases observed in the present study.
Cumulatively, from 2010 to 2030 the incidence rate of CHD &
Stroke in Cuba would rise to a staggering 15022 per 100,000 of
the population, diabetes would rise to 11624, and cancer to 2523
per 100,000 of the population. Cuba has a good quality health
Figure 1. Past and projected prevalence of obesity in Latin American males (BMI
25 kg/m2) based on module 1, scenario 0.
Obesity Trends and NCDs in Latin America
PLOS ONE | www.plosone.org 3 August 2012 | Volume 7 | Issue 8 | e39589
Figure 2. Past and projected prevalence of obesity in Latin American females (BMI
25 kg/m2) based on module 1, scenario 0.
Figure 3. Cumulative incidence cases avoided of cancer, CHD
stroke and diabetes per 100,000 population across Latin America by
Obesity Trends and NCDs in Latin America
PLOS ONE | www.plosone.org 4 August 2012 | Volume 7 | Issue 8 | e39589
system relative to the rest of Latin America. That the current
estimates of Cuba’s CHD rates are higher than elsewhere in Latin
America might be a result of higher survival rates due to better
quality health care.
As well as economic changes, socio-economic and demographic
differences between and within countries are likely to impact the
rate of obesity. Obesity is shifting to be a disease of the poor, as it is
in most high-income countries [23] which underscores obesity as a
social phenomenon highlighting the need to take action on
sociocultural and economic factors. This social shift in obesity
usually happens first in urban women perhaps because of
differences in working patterns between men and women with
men being in more traditionally manual labour roles. A study by
PAHO/WHO [6] on obesity in Latin America found that a higher
prevalence of obesity is correlated with per capita income
especially in urban areas. Despite this, under nutrition is still a
major problem and it is increasingly apparent that Latin America
is experiencing a double burden of disease where both under-
weight and obesity coexist.
Interestingly, more urban countries show a higher rate of disease
than less urban countries. For example, in highly urbanised Chile
the cumulative incidence rate of CHD and stroke by 2030 is around
8100 per 100,000 of the population in 2010 [24]. In Nicaragua,
where urbanisation has reached 58%, the projected cumulative
incidence (for women) is 5400 by 2050 per 100,000 of the
population in 2010. There is also a huge ethnic diversity within
and between countries in Latin America. Interestingly, nations with
the highest white or European ethnicities and that are highly
urbanised (e.g. Uruguay, Chile) have the highest projected
prevalence rate of cardiovascular diseases compared with countries
such as Bolivia, Colombia and Nicaragua where the ethnic makeup
is mostly Amerindian and/or Mestizo. However, in Bolivia and
Nicaragua we were only exploring women who have a lower risk of
CVD than men especially in the younger age groups and these
populations are still quite young. Sampling both men and women
will allow for more accurate comparisons to be made.
The results of this study have important policy implications.
Given the high social and economic cost of NCDs, further work
into the health economics of obesity in Latin America is necessary
so that future health policy can be planned for. In 2000, diabetes
was estimated at US$65.2 billion across Latin America [25] and a
recent review reported that obesity accounted for 0.7–2.8% of a
country’s total health care costs and medical costs were 30%
higher for obese than normal weights [26]. Thus the problem of
obesity poses an enormous challenge and institutionally Latin
America needs to be equipped to deal with increasing numbers of
chronic diseases. Some countries have responded to the obesity
challenge by implementing interventions. Chile introduced nutri-
tion and physical activity initiatives to reduce obesity in preschool
children though this was not enough to shift the rising rate of
obesity. Although, it was argued that the intervention’s lack of
success is perhaps because obesity rates have reached a plateau
[27]. To address the problem of sedentary lifestyles, Colombia has
launched a free bike scheme and bicycle lanes in the capital
Bogota which has since been named the worlds 3
most bike-
friendly city. The Caribbean Public Health Agency (CARPHA)
[28] have recently set non-communicable diseases as a key public
health priority (2011) and in early 2011 more than 40 Latin
American organisations launched The Healthy Latin American
Coalition (HLAC) to develop a declaration recognising the public
health emergency of NCDs and the importance of government
action. Clearly, if trends are set to continue rising more work is
Very little data were available for Latin American countries
making analysis of obesity trends in this area limited and it difficult
to draw affirmative conclusions. For Bolivia, Nicaragua and Peru
data for females only was available. These data were from
Demographic Health Surveys which only measure women who
had had a child in the past five years, thus biasing the data. The
direction of the bias is unclear, and might differ according to age,
but higher rates of overweight might be expected since mothers
often do not return to pre-pregnancy weight. For Costa Rica, Cuba
and Panama only one data point was available and so 2008
estimates were used based on a recent analysis by Finucane and
colleagues [7] which used BMI means. This is disadvantageous
since one cannot then reliably infer the proportion of obese to
overweight. However the estimates are of use when looking at the
proportion of normal weight to overweight and obese combined.
This highlights the need for greater surveillance work across Latin
America which samples both men and women in nationally
representative samples. This is imperative if accurate estimates of
trends are to be made and policies to be built around more precise
The projections can only be as good as the data that is input. Our
extensive searches found very little data were available for Latin
America and no set of complete age and sex-specific BMI and
disease data for one Latin American country. We were also unable
to include data on children due to lack of consistently measured
data. Since projections are mere extrapolations from these data,
inaccuracies in the output are likely. However, there was insufficient
time to undertake time consuming error analysis. Furthermore, we
have insufficient knowledge of BMI growth patterns following
interventions and insufficient knowledge of the future.
Our model incorporates a sophisticated economic module using
Morkov-type simulation estimation of long-term health benefits,
health care costs and the cost-effectiveness of specified interven-
tions. With access to country-specific cost data our model can be
adapted to include cost burden and allow us to simulate costs of
obesity-related diseases for application in public health policy. Our
recent work projected future health and related medical costs
based on available disease data in the UK and US [17] allowing
for more accurate projections to be forecast, however these data
were not available for Latin America and so could not be included
in the present study. Other diseases beyond those studied here
have been related to obesity such as infertility [29], sleep apnoea
[30], osteoarthritis [31], asthma [32]. It was beyond the scope of
this study to include them. The flexibility of our model means that
there is scope to model these diseases given the right input data are
made available.
The programme is limited in that it assumes that people do not
reverse in the BMI categories. Unfortunately this mirrors reality,
where body weight loss is often only temporary. Moreover, the
analysis has not taken into account unforeseeable changes in
circumstances, such as fluctuations in food prices and changes in
medicine. It relies on our best estimate based on previous trends.
The 95% confidence intervals for the microsimulation were
derived from simulation of the BMI distributions corresponding to
the upper and lower limits of each of the obesity growth scenarios.
Unfortunately there was insufficient time to undertake time
consuming error analysis and this has been noted in the limitation
of the paper. The results do not vary significantly when different
simulations are run.
Despite some limitations, this study is timely and an important
first step in quantifying the future burden of obesity-related
diseases in Latin America. It highlights the need for urgent action
to curb obesity levels and reduce the burden of disease. The
challenge is to understand how best to initiate change and to
Obesity Trends and NCDs in Latin America
PLOS ONE | www.plosone.org 5 August 2012 | Volume 7 | Issue 8 | e39589
quantify the cost of health consequences of obesity. If governments
take action by implementing effective policies that reduce
overweight and obesity, then a substantial number of new cases
of cancer and cardiovascular diseases can be avoided in the
coming decades.
Supporting Information
Methods S1 Statistical methods.
Table S1 References used for BMI data in each country.
Table S2 References used for each disease in each
Table S3 Incidence, mortality and survival input data
for each country.
Table S4 Percentage of overweight and obese males in
Latin America projected to 2050.
Table S5 Percentage of overweight and obese females
in Latin America projected to 2050.
Table S6 Cumulative incidence cases avoided per
100,000 of the population in 2010 with a 1% (scenario
1) and 5% (scenario 2) decrease in body mass index by
Author Contributions
Conceived and designed the experiments: LW FK KR MB TM KM.
Performed the experiments: MB TM KM. Analyzed the data: LW FK KR.
Contributed reagents/materials/analysis tools: LW FK KR MB TM KM.
Wrote the paper: LW FK KM. Revised the article critically for intellectual
content and approved of the final version: KR MB TM. Approved the final
version: LW FK KM MB TM KM.
1. Wild S, Roglic G, Green A, Sicree R, King H (2004) Global Prevalence of
Diabetes. Diabetes Care 27: 1047–1053.
2. Frenk J, Bobadilla JL, Stern C, Frejka T, Lozano R (1991) Elements for a theory
of the health transition. Health Transit Rev 1: 21–38.
3. Omran AR. The epidemiologic transition theory (1983) A preliminary update.
J Trop Pediatr 29: 305–316.
4. Pratt M, Jacoby ER, Neiman A (2011) Promoting physical activity in the
Americas. Food Nutr Bull 25: 183–193.
5. Uauy R, Albala C, Kain J (2001) Obesity trends in Latin America: Transiting
from under- to overweight. J Nutr 131: 893S–899S.
6. PAHO website. PAHO Prevention and control of chronic diseases including
diet, physical activity and health. Available: http://www.paho.org/english/ad/
dpc/nc/reg-strat-cncds.htm. Accessed 2011 Aug 10.
7. Finucane MM, Stevens GA, Cowan M J, Danaei G, Lin JK, et al. (2011)
National, regional, and global trends in body-mass index since 1980: systematic
analysis of health examination surveys and epidemiological studies with 960
country-years and 9?1 million participants. Lancet 377: 557–567. doi:10.1016/
8. The WHO Global Infobase. Available: http://www.who.int/ healthinfo/global_
burden _disease/estimates_country/en/index.html. Accessed 2011 Jun 27.
9. American Heart Association website. Available: http://www.americanheart.org.
Accessed 2011 Jul 18.
10. Secretaria de Salud. Heart Disease and Stroke Statistics. Available: http://
portal.salud.gob.mx. Accessed 2011 Jun 14.
11. GLOBOCAN website. Available: http://globocan.iarc.fr/. Accessed 2011 Aug
12. Ortiz-Barboza A, Gomez L, Cubero C, Bonilla G, Mena H (2011) Cancer
survival in Costa Rica, 1995–2000. IARC Sci Publ 162: 85–8.
13. Coleman MP, Quaresma M, Berrino F, Lutz J, Angelis RD, et al. (2008) Cancer
survival in five continents: a worldwide population-based study (CONCORD).
Lancet Oncol 9: 730–56.
14. Arez Vald e´s Y (2009) Correlation between symptoms and survival in patients
with renal cell carcinoma. Arch Esp Urol 62: 201–6.
15. Viani GA, Patia BF, Pellizzon AC, De Melo MD, Novaes PE, et al. (2006) High-
risk surgical stage 1 endometr ial cancer: analysis of treatment outcome.
Radiation Oncol 1:24 doi:10.1186/1748-717X-1-24
16. National Cancer Institute website. SEER Cancer Statistics Review 1975–2008.
Available: http://seer.cancer.gov/csr/1975_2008/results_merged/topic_
survival.pdf. Accessed 2011 July 2.
17. Wang YC, McPherson K, Marsh T, Gortmaker SL, Brown M (2011) Health
and economic burden of the projected obesity trends in the USA and the UK.
Lancet 378: 815–825.
18. Kopelman P, Jebb SA, Butland B (2007) Executive summary: Foresight
‘‘Tackling Obesities: Future Choices’’ project. Obes Rev 8 Suppl 1:vi–ix.
19. McPherson K, Marsh T, Brown M (2007) Tackling Obesities: Future Choices
Modelling Future Trends in Obesity & Their Impact on Health Available:
http://veilleagri.hautetfort.com/media/02/00/2025691480.pdf. Accessed 2011
Oct 15.
20. Vio F, Albala C (2000) Nutrition policy in the Chilean transition. Public Health
Nutr 3: 49–55.
21. Brundenius C (2009) Revolutionary Cuba at 50 Lat Am Perspect 36: 31–48.
22. Porrata C, Rodriguez-Ojea A (2000) La transicio´n epidemiolo´gica en Cuba. In:
La obesidad en la pobreza: un nuevo reto para la salud pu´blica. Washington
D.C.: Organizacio´ n Panamericana de la Salud: 57–72.
23. Albala C, Vio F, Kain J, Uauy R (2002) Nutrition transition in Chile:
determinants and consequences. Public Health Nutr 5: 123–128
24. Population Reference Bureau. Available: http://www.prb.org/DataFinder.
Accessed 2011 Jul 2011.
25. Barcelo´ A, Aedo C, Rajpathak S, Robles S (2003) The cost of diabetes in Latin
America and the Caribbean. Bull World Health Organ 81, 19–27.
26. Withrow D, Alter DA (2011) The economic burden of obesity worldwide: a
systematic review of the direct costs of obesity. Obesity Rev 12 131–141.
27. Vio F, Albala C, Kain J (2008) Nutrition transition in Chile revisited: mid-term
evaluation of obesity goals for the period 2000–2010. Public Health Nutr 11
28. Pena M, Bacallao J (2010) Obesity and Poverty: A new public health challenge.
Available: http://new.paho.org/hq/ index.php?option = com_docman
&task = doc_view&gid = 16097&Itemid = . Accessed 2011 September 3.
29. Guh DP, Zhang W, Bansback N, Amarsi Z, Birmingham C L, et al. (2009) The
incidence of co-morbidities related to obesity and overweight: A systematic
review and meta-analysis. BMC Public Health 9: 88. Available: www.
30. Gami AS, Hodge DO, Herges RM, Olson EJ, Nykodym J, et al. (2007)
Obstructive Sleep Apnea, Obesity, and the Risk of incident atrial fibrillation.
J Am Coll Cardiol 49: 565–571.
31. Yusuf E (2012) Metabolic factors in osteoarthritis: obese people do not walk on
their hands. Arthritis Res Ther 14:123.
32. Fenger RV, Gonzalez-Quintela A, Vidal C, Gude F, Husemoen LL, et al. (2012)
Exploring the obesity-asthma link: Do all types of adiposity increase the risk of
asthma? Clin Exp Allergy 42: 1237–45. doi: 10.1111/j.1365-2222.2012.03972.x
Obesity Trends and NCDs in Latin America
PLOS ONE | www.plosone.org 6 August 2012 | Volume 7 | Issue 8 | e39589
    • "Similarly reported by other authors [26, 32, 35], an increased prevalence of obesity for both sexes was found in this ethnic group, where the high consumption of fatty meals is deeply rooted in the Afro-Caribbean culture and contributes to the development of obesity [14]. Significant nutritional and lifestyle changes have happened in the Panamanian society in the last 3 decades [27,36373839; the per capita gross domestic product of Panama increased almost five fold and the percentage of people living in urban areas grew from 50 to 75 %394041. This rapid increase in economic growth and urbanization has lead to changes in occupation, transportation and technology directed at leisure time activities at home which have also contributed to increased sedentary behavior and reduced physical activity [39, 42, 43]. "
    [Show abstract] [Hide abstract] ABSTRACT: Background To estimate the prevalence of obesity in Panama and determine some risk factors and associated diseases in adults aged 18 years and older. Methods A cross-sectional descriptive study was conducted in the provinces of Panama and Colon where 60.4 % of all Panamanians 18 years or older reside, by administering a survey regarding the consumption of protective and predisposing foods and assessing the development of obesity by measuring the weight, height, and waist circumference of 3590 people. A single-stage, probabilistic, and randomized sampling strategy employing multivariate stratification was used. Individuals with a body mass index ≥ 30 kg/m2 (men and women) were considered obese. Prevalence and descriptive analysis were conducted according to sex using Odds Ratio, with statistical significance set at a p value ≤ 0.05. Results The general prevalence of obesity was 27.1 % (30.9 % women and 18.3 % men). In women, obesity was associated with living in urban areas, being 40–59 years of age, being Afro-Panamanian, consuming beverages / foods rich in sugar, being physically inactive and having a family history of obesity. In men, obesity was associated with living in urban areas, consuming beverages/foods rich in sugar, and having a family history of obesity. Almost the totality of obese women (97.9 %), and 80.0 % of men with obesity had abdominal obesity according to the WHO classification. In both sexes, obesity was a risk factor associated to type 2 Diabetes Mellitus, hypertension, LDL values ≥ 100 mg/dL, and low HDL values (<50 mg/dL for women and < 40 mg/dL for men), Odds Ratio > 1.0; P < 0.05. Conclusions Obesity represents a very serious threat to Panamanian public health. Our study confirms a direct association in Panama between excess weight, hypertension, type 2 Diabetes Mellitus, LDL values ≥ 100 mg/dL and low HDL values for women and men (<50 mg/dL and < 40 mg/dL, respectively). Intervention / treatment programs should be targeted, specially, to Afro-Panamanian women, whom are 40–59 years old, living in urban areas, and those having a family history of obesity.
    Full-text · Article · Dec 2015
    • "Levels of overweight and obesity in Latin America have increased over time [2] and have approached levels found in higher-income countries, with a disproportionate increase in waist circumference compared to BMI over the past 20 years [3, 4]. Overweight and obesity and are projected to continue rising [5]. The World Health Organization (WHO) recommended cut-off points for overweight and obesity, at BMI values of 25 kg/m 2 and 30 kg/m 2 , respectively, are based on a large number of studies in predominantly Caucasian populations. "
    [Show abstract] [Hide abstract] ABSTRACT: Objective: We aimed to characterize metabolic status by body mass index (BMI) status. Methods: The CRONICAS longitudinal study was performed in an age-and-sex stratified random sample of participants aged 35 years or older in four Peruvian settings: Lima (Peru's capital, costal urban, highly urbanized), urban and rural Puno (both high-altitude), and Tumbes (costal semirural). Data from the baseline study, conducted in 2010, was used. Individuals were classified by BMI as normal weight (18.5-24.9 kg/m2), overweight (25.0-29.9 kg/m2), and obese (≥30 kg/m2), and as metabolically healthy (0-1 metabolic abnormality) or metabolically unhealthy (≥2 abnormalities). Abnormalities included individual components of the metabolic syndrome, high-sensitivity C-reactive protein, and insulin resistance. Results: A total of 3088 (age 55.6±12.6 years, 51.3% females) had all measurements. Of these, 890 (28.8%), 1361 (44.1%) and 837 (27.1%) were normal weight, overweight and obese, respectively. Overall, 19.0% of normal weight in contrast to 54.9% of overweight and 77.7% of obese individuals had ≥3 risk factors (p<0.001). Among normal weight individuals, 43.1% were metabolically unhealthy, and age ≥65 years, female, and highest socioeconomic groups were more likely to have this pattern. In contrast, only 16.4% of overweight and 3.9% of obese individuals were metabolically healthy and, compared to Lima, the rural and urban sites in Puno were more likely to have a metabolically healthier profile. Conclusions: Most Peruvians with overweight and obesity have additional risk factors for cardiovascular disease, as well as a majority of those with a healthy weight. Prevention programs aimed at individuals with a normal BMI, and those who are overweight and obese, are urgently needed, such as screening for elevated fasting cholesterol and glucose.
    Full-text · Article · Nov 2015
    • "The publication also reported that effective interventions to reduce prevalence of excess body weight could have a major effect on cumulative incidence of chronic NCDs by the year 2030.[11] The highest impact would likely occur in Cuba, where it was predicted that >2000 cases of type 2 diabetes and >1300 cases of coronary heart disease and stroke per 100,000 population could be avoided by a mere 5% reduction in obesity (BMI>30) rates.[11] In Cuban children, higher rates of excess body weight, coinciding with defi ciency-related NCDs such as anemia,[13] impose a double burden on the health system, requiring design of more targeted health programs and interventions for pregnant women, infants, and children during the fi rst two years of life. "
    [Show abstract] [Hide abstract] ABSTRACT: The Cuban population exhibits high prevalence of overweight and associated chronic non-communicable diseases, trends that begin in childhood. In addition to factors related to the mother’s health, factors contributing to excess weight gain in Cuban children are: reduced prevalence of exclusive breastfeeding of infants up to six months of age, full-term low birth weight infants and nutritional mismanagement of this group, incorrect complementary feeding, obesogenic diet, family history and sedentary lifestyles. Thus, it is important to adopt comprehensive, multisectoral strategies that promote adequate nutrition and weight control. This is particularly important for full-term low birth weight infants, predisposed to body fat storage. ISSN 1527-3172
    Full-text · Article · May 2015
Show more