On-line version ISSN 1680-5348
Print version ISSN 1020-4989
Rev Panam Salud Publica vol.26 n.1 Washington Jul. 2009
INVESTIGACIÓN ORIGINAL ORIGINAL RESEARCH
Estimación de la esperanza de vida con y sin diabetes en México y siete ciudades importantes de América Latina y el Caribe
Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Illinois, United States of America
OBJECTIVES: To estimate diabetes and diabetes-free life expectancy in seven major cities in Latin America and the Caribbean, plus Mexico as a whole.
METHODS: Data from the Survey on Health, Well-being, and Aging in Latin America and the Caribbean (n = 10 602) and the Mexican Health and Aging Study (n = 6 953) on individuals 60 or more years of age were used in this study. Estimates of diabetes and diabetes-free life expectancy were obtained by applying the Sullivan method.
RESULTS: Diabetes life expectancy for men 60 years of age was highest in Mexico City (4.5 years) and Bridgetown (3.4 years), and lowest in Havana (1.3 years). Diabetes-free life expectancy for men 60 years of age was highest in Santiago (17.6 years) and lowest in Bridgetown (14.2 years) and São Paulo (14.3 years). For women, diabetes life expectancy was highest in Bridgetown (5.4 years), followed by Mexico City and Havana; but these three cities also had the lowest diabetes-free life expectancy. Women 60 years of age in Buenos Aires had the lowest diabetes life expectancy (2.5 years), and in Santiago, the highest, with a diabetes-free life expectancy of 20.7 years.
CONCLUSIONS: Older individuals in Latin America and the Caribbean can expect to live a large proportion of their remaining lives with diabetes. There were also important differences across settings; in particular, the pronounced diabetes burden in Barbados and Mexico and among women. Given the fast growth of the elderly population in these societies, it is crucial to promote healthy eating and exercise as a way of reducing the burden of diabetes.
Key words: Diabetes mellitus, data analysis, life expectancy, Latin America, Caribbean Region, Mexico.
OBJETIVO: Estimar la esperanza de vida en personas con y sin diabetes en siete ciudades importantes de América Latina y el Caribe y en México.
MÉTODOS: Para América Latina y el Caribe se tomaron los datos del estudio Salud, Bienestar y Envejecimiento (SABE) (n = 10602) y para México se utilizaron los datos del Estudio Nacional de Salud y Envejecimiento (n = 6953) en personas de 60 años o más. Se utilizó el método de Sullivan para estimar la esperanza de vida con o sin diabetes.
RESULTADOS: La esperanza de vida con diabetes en hombres de 60 años fue mayor en la Ciudad de México (4,5 años) y Bridgetown (3,4 años) y menor en La Habana (1,3 años). La esperanza de vida sin diabetes en hombres de 60 años fue mayor en Santiago (17,6 años) y menor en Bridgetown (14,2 años) y Sao Paulo (14,3 años). En mujeres, la esperanza de vida con diabetes fue mayor en Bridgetown (5,4 años), seguida por Ciudad de México y La Habana; pero estas tres ciudades presentaron la menor esperanza de vida sin diabetes. Las mujeres de 60 años de Buenos Aires presentaron la menor esperanza de vida con diabetes (2,5 años) y el mayor valor se observó en Santiago, con una esperanza de vida sin diabetes de 20,7 años.
CONCLUSIONES: Las personas de mayor edad de América Latina y el Caribe pueden esperar vivir una gran parte de su vida restante con diabetes. Se observaron grandes diferencias entre las ciudades, en particular, la considerable carga de diabetes en Barbados y México y en mujeres. Dado el rápido crecimiento de la población de adultos mayores en estas sociedades, es vital promover la alimentación sana y la práctica de ejercicios físicos como una forma de reducir la carga de diabetes.
Palabras clave: Diabetes mellitus, análisis de datos, esperanza de vida, América Latina, región del Caribe, México.
During the last 50 years, life expectancy at birth in Latin America has increased from 52 years to almost 72, with further increases expected in the next decades (1). Moreover, since the elderly population is growing faster than the younger, the percentage of those 65 or more years of age is expected to rise from its current 5.5% to 10% by 2025 (1). Therefore, a pressing concern is whether or not increases in life expectancy will imply better health for a larger, aging population.
Along with the demographic transition, epidemiologic and nutritional transitions are underway (2-8). Noncommunicable diseases are becoming increasingly more important and obesity is on the rise in Latin America and the Caribbean. One of the fastest growing diseases is diabetes, which in 1995 had a prevalence rate of 5.7% among the general population; by 2025, it is expected to reach 8.1% (9). That is, the number of cases in Latin America will rise from 15 million in 1995 to 39 million in 2025 (9), with 50% in Brazil and Mexico. Diabetes prevalence among the elderly is even higher. Current estimates indicate that in Latin America and the Caribbean there are more than 5 million adults 60 years of age and over with diabetes (9).
The relatively high prevalence of diabetes in Latin America and the Caribbean imposes high costs for its populations. Barceló and colleagues (10) estimate a total annual diabetes-associated cost of more than US$ 65 billion. The indirect costs contributed 82% of the overall cost. Indirect costs include over 330000 deaths occurring in the year 2000 (over 757000 years of productive life lost) and approximately 178000 individuals with permanent disability (over 136000 years of productive life lost). However, in geographic areas as diverse as Latin America and the Caribbean, prevalence levels are hardly homogeneous (11, 12). Diabetes prevalence rates are generally higher in urban settings and among women and older people (13, 14), and increase parallel to the rise in obesity (4, 15-17). The area's obesity prevalence is more marked in the lower socioeconomic strata, urban settings, and among women (14, 15, 18, 19).
Previous studies (12, 20-22) have estimated the prevalence of diabetes in seven cities in Latin America and the Caribbean, and in Mexico as a whole, based on data from two surveys: the Survey on Health, Well-Being, and Aging in Latin America and the Caribbean, 2000 (SABE); and the Mexican Health and Aging Study, 2001 (MHAS). The goal herein is to provide estimates of diabetes and diabetes-free life expectancy in seven major cities in Latin America, the Caribbean, and Mexico, by applying the Sullivan method to SABE and MHAS data for the first time. These estimates allow for a better understanding of the disease burden from diabetes-in terms of years lived with and without the disease-in this area of the world.
MATERIALS AND METHODS
Data on self-reported diabetes prevalence in seven major cities in Latin America, the Caribbean, and Mexico come from SABE and MHAS. Both samples include detailed information on demographics and diabetic health status. In both surveys, individuals were asked if they had ever been told by a doctor that they have diabetes. Those responding affirmatively received additional questions on the use of oral medication, insulin injections, and diet.
SABE was a multicenter project that surveyed the health and well-being of older individuals (60 or more years of age), and in some cases, of the surviving spouse, in seven capital/major cities: Buenos Aires, Argentina; Bridgetown, Barbados; São Paulo, Brazil; Santiago, Chile; Havana, Cuba; Mexico City, Mexico; and Montevideo, Uruguay (23, 24). The general survey was funded and supported by the Pan American Health Organization (PAHO; Washington, D.C., United States); the Center for Demography and Ecology, University of Wisconsin-Madison (Madison, Wisconsin, United States); and the National Institute on Aging (Bethesda, Maryland, United States). In each country, international and national institutions contributed to the project.
The questionnaire design was intentionally geared toward producing information that could be compared across countries. In particular, the aim was to include modules and sections modeled after the Health and Retirement Study (HRS) conducted in the United States (25). A standardized questionnaire was used to collect detailed information through face-to-face interviews. Samples were drawn using multistage clustered sampling with stratification of the units at the highest levels of aggregation. Detailed information on sample selection is presented elsewhere (26).
The initial sample was composed of 10 602 individuals who were 60 or more years of age. Fifty-seven (0.54% of the sample) were excluded because they did not answer the question regarding a prior diabetes diagnosis. There were no age or sex differences between those who answered the question and those who did not. Of those who answered the question, 8 782 did not have a previous diabetes diagnosis and 1763 did. Five individuals had missing values on the sample weight variable and were excluded. The final sample was composed of 10540 individuals; the mean age was 70.2 years of age (weighted estimates) and females accounted for 59.7% of the sample. Among the diabetics, the mean age was 70 years, and females, 59.3%. For non-diabetics, the mean age was 70.2 years, and females, 59.8%.
MHAS was a prospective two-wave panel study of a nationally representative cohort of Mexicans born prior to 1951 (50 years or more). The survey has national and urban/rural representation. The baseline interview was conducted in 2001, and the second wave in 2003. In 2001, individuals 50 or more years of age and their surviving spouses/partners, regardless of their age if residing in the same household, were interviewed. Data collection was done in collaboration with the Instituto Nacional de Estadística y Geografía (National Institute of Statistics and Geography in Mexico, INEGI). The study was designed with field protocol and content similar to that of HRS. Data were collected based on face-to-face interviews with the target individuals or proxy respondents. Detailed information is presented elsewhere (23, 27).
Only data obtained during the first wave of the MHAS were used in this study. A total of 15144 complete interviews were obtained (94.2% response rate at the household level). From the initial 15 144, there were 7988 excluded due to being less than 60 years of age. Another 203 individuals who did not report their diabetic status at baseline were also excluded. The final sample was composed of 6 953 individuals 60 or more years of age with complete information on age, sex, and diabetic status. There were no age differences between those with complete and those with incomplete information on diabetic status, but more males than females lacked this information. Of the final sample (6 953), the mean age was 69.8 years (weighted estimate) and females accounted for 53%. Among the diabetics, the mean age was 69.4 years and females, 58.9%. Among non-diabetics, the mean age was 69.9 years and the females, 51.4%.
Prevalence estimates were obtained taking into account the complex survey design and the need for results that could be generalized to the SABE and MHAS populations. This study used STATA SE 9.0 software (StataCorp LP, College Station, Texas, United States) and applied SABE and MHAS weights in all analyses. All reported P values are 2-sided.
Mortality data were obtained from official sources, except for in the case of Havana. Data for Buenos Aires came from the "Anuario Estadistico" for the years 2000 and 2001, and deaths from both years were averaged. Population estimates for Buenos Aires were obtained from the Instituto Nacional de Estadísticas y Censos (National Institute for Statistics and Census, Argentina) based on census data (28, 29). Data were not available for Bridgetown, so the 2001 life table for Barbados, produced by World Health Organization, was used for Bridgetown (30). Using the Barbados life table was justifiable since, according to PAHO, 37% of the nation's population lives in Bridgetown. For the São Paulo metropolitan area, population data were obtained from Brazil's national census bureau, and mortality data, from the foundation that analyzes social, demographic, and economic data for the state (31). Data for Santiago came from life tables for the period 2001-2002 published by Chile's national population bureau, disaggregated by sex and regions (32). Life tables for Havana were created by the Center of Population and Development Studies (CEPDE) of the National Statistics Office in Cuba.1 The life table for Mexico was obtained from the WHO website, while the life table for Mexico City uses data from the country's national population bureau, the Consejo Nacional de Población (33). The life table for Montevideo refers to the year 2000 and was published by the Uruguayan population bureau on its website (34).
The Sullivan method was used to estimate the diabetes and diabetes-free life expectancy based on prevalence data from SABE and MHAS 2001. The Sullivan method is the most widely used method to estimate population health indicators. It is based on a standard life table with two states (alive and dead). The "alive" state is subdivided into healthy and diseased/disabled using observed prevalence of disease (35-38). The main inputs are: age-specific prevalence of the population in the healthy and diseased states, and age-specific mortality rates. Data were also disaggregated to include the covariate sex.
The Sullivan method provides estimates of diabetes-free life expectancy (DFLE), and diabetes life expectancy (DLE). Total life expectancy is therefore the sum of healthy (diabetes-free) and unhealthy (diabetic) years of life. Both DFLE and DLE are independent of the age structure of the population. DFLE and DLE can be obtained using the following equations (36):
Where Li(DF) and Li(D) are the number of person years lived from age x onwards in the diabetes-free (DF) and diabetic (D) states, respectively. Given the hypothesis that the number of person years lived from age x onwards in the diseased state is proportional to the prevalence of diabetes at age I (pi), we have:
Therefore, DFLE and DLE formulas can be rewritten as:
DFLE incorporates a dichotomous weighting in which '1' is ascribed to perfect health and '0' to the diseased and death statuses (36).
Bridgetown and Mexico City had the highest self-reported prevalence rates of diabetes mellitus in 2000. In Bridgetown, self-reported prevalence of diabetes among individuals 60 or more years of age was 21.7% (95% Confidence Interval (95%CI): 19.6-23.8); in Mexico City, it was 21.6% (95%CI: 19.3-23.9). There was no statistical difference between these two cities. São Paulo had an intermediate rate, 18% (95%CI: 16.1-19.9). Buenos Aires, Havana, Montevideo, and Santiago had the lowest rates of self-reported diabetes: Buenos Aires, 12.4% (95%CI: 10.2-14.5); Havana, 14.8 (95%CI: 13.1-16.4); Montevideo, 13.3% (95%CI: 11.7-15.7); and Santiago, 13.7% (95%CI: 10.8-15.8%).
In Mexico, self-reported prevalence rates in 2001 were higher in urban settings than in rural ones: 21% (95%CI: 18.6-23.5%) versus 13.5% (95%CI: 11.2- 15.9). Overall rates for Mexico were lower than Mexico City, reinforcing the evidence that large urban areas may have higher prevalence rates than smaller or rural areas. Prevalence rates in some of these areas were as high or higher than that of the United States and the rate of increase was twice as high (9, 22). Using SEGI and WHO standard populations to obtain age standardized rates does not change these analyses (results available from the authors upon request).
Data from SABE indicate that there are no statistical differences in self-reported diabetes prevalence rates between males and females in São Paulo and Santiago. In Bridgetown and Montevideo, women were more likely to report diabetes than men (23.6% vs. 18.7% and 14.5% vs. 12.4%, respectively). In Havana, there was a significant difference in self-reported diabetes between women and men, 20% vs. 7.3%, respectively. On the other hand, in Buenos Aires and Mexico City, women were less likely to report having diabetes than men. In Buenos Aires, 11.4% of women and 14% of men 60 or more years of age reported having diabetes. In Mexico City, 21% of women and 22.4% of men 60 or more years of age reported the condition. The finding that women in Mexico City were less likely to report being diabetic contrasts with a higher prevalence of diabetes among women in Mexico (Table 1). Age differentials between males and females do not explain the differences in prevalence rates (results available upon request).
In 2000-2001, men 60 years of age had a total life expectancy (Table 2) of 17.2 additional years in São Paulo to 20.4 years in Mexico. Life expectancy at age 60 for women was also lowest in São Paulo at 21.9 years, but highest in Santiago at 24.0 years.
As a result of high prevalence rates, at 60 years of age, men in Mexico City can expect to live 20.3 years, 22.2% of those years with diabetes. Their female counterparts are expected to live longer (22.1 years), but a similar number of years with diabetes (4.6 years). The prevalence of diabetes in Mexico was lower than in Mexico City and, as a consequence, the expected number of years with diabetes was lower for both men and women. At 69 years of age, Mexican men are expected to live an average of 2.9 years with diabetes, while their female counterparts are expected to live 4.1 years.
Given the high prevalence of diabetes in Bridgetown, at 60 years of age men are expected to live 3.4 years (19.3% of their remaining lives) and women, 5.4 years (23.7% of their remaining lives) with diabetes.
In São Paulo, at 60 years of age, males are expected to live about 3 years or 16.9% of their remaining lives with diabetes, while their female counterparts will live about 4 years or 18.3% of their life expectancy with diabetes.
In Havana, there was a statistically significant difference between diabetes prevalence rates among men 60 or more years of age and that of women of the same age. As a result, at 60 years of age, men are expected to live 1.3 years with diabetes, but women are expected to live 4.5 years with the disease.
The lower prevalence of diabetes in Buenos Aires translated into a smaller percentage of remaining years of life expected with diabetes. For men residing in Buenos Aires, 13.8% of the years lived beyond 60 years of age were expected to be lived with diabetes. Their female counterparts were expected to live about 11% of their remaining years with diabetes.
Tables 3 and 4 present the total, diabetes and diabetes-free life expectancies by age, for men and women, respectively. In general, results indicate that the impact of diabetes on life expectancy declines with age given the lower prevalence rates at older ages. At age 75, diabetes-life expectancy among men ranges from 0.5 years in Havana to 2.5 years in Mexico City. This means that between 5.4% of the remaining lives of men in Havana to over 22% of the remaining lives of men in Mexico City will be spent with diabetes. Among their female counterparts, diabetes life expectancy ranges from 1.2 years in Buenos Aires to 2.8 years in Bridgetown, which represents 11.4% and 22.5% of their remaining lives, respectively, with diabetes.
Data from the two surveys, SABE and MHAS, show that diabetes imposes a serious burden on the health status of the populations in Latin America and the Caribbean. Results indicate that in these geographic areas, a large proportion of the life-years remaining for those age 60 or more is lived with diabetes. However, there are important differences among the various cities. For instance, men who are 60 years of age in Havana are expected to live 7.1% of their remaining years with diabetes, while in Mexico City that percentage is 22.2%. Among women, the diabetes burden is even more pronounced. In Buenos Aires, women who are 60 or older are expected to live 11.3% of their remaining years with diabetes, but for their counterparts in Bridgetown, the rate is 23.7%.
Results show that, of the areas included in the study, diabetes prevalence is highest in Bridgetown and Mexico City. Buenos Aires, Montevideo, and Santiago have the lowest rates, while São Paulo has intermediate levels (12, 20, 21, 39). Differences in prevalence rates across settings may be due to several factors. Data from SABE and MHAS used similar questionnaire content, but differences in culture and economic conditions, including education and access to health care, can influence the proportion of individuals aware of their diabetic status, the way their health status is reported (40, 41), and survivorship of those with the condition. Differences in translation may also influence the results. In addition, environmental differences, in particular, diet and physical activity and its interaction with genetic markers, may result in true differences across settings (20, 21).
The aging of the populations in Latin America and the Caribbean is happening at a fast rate and has impacted the prevalence of diabetes in these areas. Urbanization and economic growth have changed diets and lifestyles, which in turn, have contributed to the prevalence of diabetes (42-46). Economic development has increased the availability of foods rich in saturated fat and refined carbohydrates, but low in complex carbohydrates and fiber, and reduced the consumption of beans, fruits, and legumes (46). Modernization and urbanization have given rise to sedentary lifestyles and its associated Type 2 diabetes prevalence (44, 45). Some risk factors, such as gestational diabetes and impaired glucose tolerance, are more common among Latinos. Other early life experiences, such as low birth weight and early alnutrition, which have been linked to obesity and diabetes later in life, are also common in Latin America and the Caribbean. As a result of these changes, prevalence rates are expected to continue rising in Latin America and the Caribbean, with consequences in morbidity, disability, and mortality.
This study had many limitations. The first is that data on diabetes prevalence is self-reported, and therefore, its prevalence may be underestimated. There is some evidence that undiagnosed rates are quite high in Latin America and the Caribbean. In a national survey conducted in Mexico's urban areas in the early 1990s, 42% of diabetics less than 40 years of age were unaware of their condition, while 74% of diabetics 40 years or more were aware of their diabetic status (47). In Argentina, undiagnosed cases account for about half of the cases (48). In Brazil, levels of undiagnosed diabetes are estimated to be around 40-50% (49, 50), but there is some evidence that awareness is higher among older individuals (41). In Chile, 45% of diabetes cases among the population 20 years of age and younger were undiagnosed (51). However, undiagnosed rates can be even higher among certain social groups. For instance, Goldenberg et al. (52) report that almost 60% of men in São Paulo had undiagnosed cases, while among women the percentage was about 41%. Therefore, the values presented in this paper are conservative estimates of the real burden because undiagnosed diabetes and pre-diabetes-those with impaired glucose tolerance and impaired fasting glucose-have not been taken into account. It is important to note, though, that awareness of diabetic status increases with age (51). This is consistent with the fact that older -people have more time to develop the disease and to present complications that may trigger medical diagnosis and treatment. Thus, the self-reporting bias may be smaller than among younger age groups.
Other limitations of the paper originate from the empirical application of the Sullivan method. First, the data from diabetes prevalence in Latin America and the Caribbean refers to the non-institutionalized population, while mortality data refers to the total population. Data from SABE and MHAS focused on the general population not residing in institutions. As a result, estimates may be biased downwards if one expects that institutionalized populations, particularly those residing in nursing homes, are more likely to have poor health, in particular, higher prevalence of diabetes, than the non-institutionalized population. However, the institutionalized population in Latin America and the Caribbean is relatively small. Thus, this bias is likely to be small. Second, estimates based on the Sullivan method are interpreted following the stationary population approach since the obtained health indicator, in this case diabetes prevalence, is meant to reflect the current population health status. As a consequence, results should be interpreted with caution since data are dependent on past conditions of the population (38).
Finally, results reported in this study are based on self-reports obtained in the early 2000s. Given the undiagnosed rates in Latin America and the Caribbean, the estimates provided in this study underestimate the burden of diabetes in this geographic area.
It is well known that diabetes reduces quality of life. This study showed that many older individuals are expected to spend a significant amount of their remaining lives with diabetes. For the next decades, the impact of diabetes on healthy life expectancy is expected to rise unless preventive measures are taken. Social and economic costs associated with the disease, including comorbid macrovascular/microvascular complications and disability-related/premature mortality are also expected to increase. Recent studies have indicated that changes in lifestyle, particularly in diet and exercise, and some medications can delay the onset of the condition. Therefore, health promotion campaigns that emphasize healthy eating and exercise are needed to encourage healthier lives. Changes in urban planning that promote physical activity should also be implemented. Finally, better access to health care can also improve diabetes management and reduce adverse consequences associated with poor glycemic control.
Acknowlegments. An earlier version of this paper was presented at the 2004 and 2006 annual meetings of the Population Association of America. The author gratefully acknowledges the support of CAPES/Brazil and Fogarty/National Institutes of Health for doctoral studies. The author also acknowledges use of the Center for Demography and Ecology at the University of Wisconsin Madison, funded in part by NICHD Center Grant HD05876, and use of the Center for the Demography of Health and Aging, funded by NIA Center Grant P30 AG 17266. Finally, the author would like to thank the editor and the anonymous reviewers for their insightful comments.
1. Centro Latinoamericano y Caribeño de Demografía. América Latina: Tablas de mortalidad, 1950-2025. Bol Demográfico. 2004;74:1-344. [ Links ]
2. Albala C, Vio F, Kain J, Uauy R. Nutrition transition in Chile: determinants and consequences. Public Health Nutr. 2002;5(1):123-8. [ Links ]
3. Uauy R, Albala C, Kain J. Obesity trends in Latin America: transiting from under- to overweight. J Nutr. 2001;131(suppl 3):893-9. [ Links ]
4. Vio F, Albala C. Nutrition policy in the Chilean transition. Public Health Nutr. 2000; 3(1): 49-55. [ Links ]
5. Monteiro CA, Conde WL, Lu B, Popkin BM. Obesity and inequities in health in the developing world. Int J Obes Relat Metab Disord. 2004;28(9):1181-6. [ Links ]
6. Monteiro CA, Conde WL, Popkin BM. The burden of disease from undernutrition and overnutrition in countries undergoing rapid nutrition transition: a view from Brazil. Am J Public Health. 2004;94(3):433-4. [ Links ]
7. Monteiro CA, D'A Benicio MH, Conde WL, Popkin BM. Shifting obesity trends in Brazil. Eur J Clin Nutr. 2000;54(4):342-6. [ Links ]
8. Monteiro CA, Mondini L, de Souza AL, Popkin BM. The nutrition transition in Brazil. Eur J Clin Nutr. 1995;49(2):105-13. [ Links ]
9. King H, Aubert R, Herman W. Global burden of diabetes, 1995-2025: prevalence, numerical estimates, and projections. Diabetes Care. 1998; 21(9):1414-31. [ Links ]
10. Barceló A, Aedo C, Rajpathak S, Robles S. The cost of diabetes in Latin America and the Caribbean. Bull World Health Organ. 2003;81(1):19-27. [ Links ]
11. Palloni A, Pelaez M, Wong R. Introduction: aging among Latin American and Caribbean populations. J Aging Health. 2006;18(2):149-56. [ Links ]
12. Palloni A, McEniry M, Wong R, Pelaez M. The tide to come: elderly health in Latin America and the Caribbean. J Aging Health. 2006;18(2):180-206. [ Links ]
13. Aschner P. Diabetes trends in Latin America. Diabetes Metab Res. 2002;18 Suppl 3:S27-31. [ Links ]
14. Schargrodsky H, Hernández-Hernández R, Champagne BM, Silva H, Vinueza R, Silva Ayçaguer LC, et al. CARMELA: Assessment of Cardiovascular Risk in Seven Latin American Cities. Amer J Med. 2008;121(1):58-65. [ Links ]
15. Braguinsky J. Obesity prevalence in Latin America. An Sist Sanit Navar. 2002;25(suppl 1):109-15. [ Links ]
16. Rivera JA, Barquera S, Campirano F, Campos I, Safdie M, Tovar V. Epidemiological and nutritional transition in Mexico: rapid increase of non-communicable chronic diseases and obesity. Public Health Nutr. 2002;5(1A):113-22. [ Links ]
17. Rivera JA, Barquera S, Gonzalez-Cossio T, Olaiz G, Sepulveda J. Nutrition transition in Mexico and in other Latin American countries. Nutr Rev. 2004;62(suppl 7):149-57. [ Links ]
18. Filozof C, Gonzalez C, Sereday M, Mazza C, Braguinsky J. Obesity prevalence and trends in Latin-American countries. Obes Rev. 2001;2(2):99-106. [ Links ]
19. Kain J, Vio F, Albala C. Obesity trends and determinant factors in Latin America. Cad Saude Publica. 2003;19(suppl 1):77-86. [ Links ]
20. Barceló A, Pelaez M, Rodriguez-Wong L, Pastor-Valero M. The prevalence of diagnosed diabetes among the elderly of seven cities in Latin America and the Caribbean: The Health, Wellbeing, and Aging (SABE) Project. J Aging Health. 2006;18(2):224-39. [ Links ]
21. Barceló A, Gregg EW, Pastor-Valero M, Robles SC. Waist circumference, BMI and the prevalence of self-reported diabetes among the elderly of the United States and six cities of Latin America and the Caribbean. Diabetes Res Clin Pract. 2007;78(3):418-27. [ Links ]
22. Palloni A, McEniry M. Aging and health status of elderly in Latin America and the Caribbean: preliminary findings. J Cross Cult Gerontol. 2007;22(3):263-85. [ Links ]
23. Wong R, Pelaez M, Palloni A, Markides K. Survey Data for the Study of Aging in Latin America and the Caribbean: Selected Studies. J Aging Health. 2006;18(2):157-79. [ Links ]
24. Pelaez M, Palloni A, Albala C, Alfonso JC, Ham-Chande R, Hennis A, et al. SABE-Survey on Health, Well-Being, and Aging in Latin America and the Caribbean, 2000. Available from: http://www.icpsr.umich.edu/ICPSR/. Accessed on 15 August 2005. [ Links ]
25. Juster FT, Suzman R. An overview of the health and retirement study. J. Human Res. 1995;30(5):S7-S56. [ Links ]
26. Palloni A, Pelaez M. Survey on Health and Well-Being of Elders: preliminary report. Pan American Health Organization; Washington DC: 2002. [ Links ]
28. Instituto Nacional de Estadísticas y Censos. Anuario estadístico de la República Argentina. Buenos Aires: INDEC; 2001. [ Links ]
29. Instituto Nacional de Estadísticas y Censos. Anuario estadístico de la República Argentina. Buenos Aires: INDEC; 2002. [ Links ]
30. World Health Organization. Mortality Database. Available from: http://www3.who.int/whosis/life/life_tables/life_tables.cfm?path=life_tables. Accessed on 5 June 2006. [ Links ]
31. Instituto Brasileiro de Geografia e Estatística. Censo Demográfico de 2000. Rio de Janeiro: IBGE; 2000. [ Links ]
32. Instituto Nacional de Estadísticas. Chile: Tablas abreviadas de mortalidad, por sexo. país y regiones, 2001-2002. Santiago: INE; 2004. [ Links ]
33. Consejo Nacional de Población. Censo General de Población y Vivienda, 2000. CONAPO, Mexico, D.F.: CONAPO; 2000. [ Links ]
35. Barendregt JJ. Incidence- and prevalence-based SMPHs: Making the twain meet. In: Murray C, Salomon JA, Mathers CD, Lopez AD, eds. Summary measures of population health: concepts, ethics, measurement and applications. Geneva: World Health Organization; 1999. Pp. 221-31. [ Links ]
36. Jagger C, Hauet E, Brouard N. Health expectancy calculation by the Sullivan Method: A practical guide. Leicester: Euro-REVES; 2001. Pp.1-29. [ Links ]
37. Nusselder WJ. Compression of morbidity. In: Robine J, Jagger C, Mathers CD, Crimmins EM, Suzman RM, eds. Determining health expectancies. Chichester, West Sussex, England: John Wiley & Sons Ltd; 2003. Pp. 35-58. [ Links ]
38. Mathers C. Health expectancies: an overview and critical appraisal. In: Murray C, Salomon JA, Mathers CD, Lopez AD, eds. Summary measures of population health: concepts, ethics, measurement and applications. Geneva: World Health Organization; 1999. Pp. 177-204. [ Links ]
39. Barceló A, Gregg EW, Pastor-Valero M, Robles SC. Waist circumference, BMI and the prevalence of self-reported diabetes among the elderly of the United States and six cities of Latin America and the Caribbean. Diabetes Res Clin Pract. 2007;78(3):418-27. [ Links ]
40. Angel RJ, Angel JL, Hill TD. A Comparison of the health of older Hispanics in the United States and Mexico: Methodological challenges. J Aging Health. 2008;20(1):3-31. [ Links ]
41. Lima-Costa MF, Peixoto SV, Firmo JOA, Uchoa E. Validity of self-reported diabetes and its determinants: Evidences from the Bambui? study. Rev. Saúde Pública. 2007;41(6):947-53. [ Links ]
42. Barceló A, Rajpathak S. Incidence and prevalence of diabetes mellitus in the Americas. Rev Panam.Salud Publica. 2001;10(5):300-8. [ Links ]
43. Fall CH. Non-industrialised countries and affluence. Br Med Bull. 2001;60:33-50. [ Links ]
44. James WP. The fundamental drivers of the obesity epidemic. Obes Rev. 2008;9(suppl 1):6-13. [ Links ]
45. James WP. The epidemiology of obesity: the size of the problem. J Intern Med. 2008; 263(4):336-52. [ Links ]
46. Kabagambe EK, Baylin A, Ruiz-Narvarez E, Siles X, Campos H. Decreased consumption of dried mature beans is positively associated with urbanization and nonfatal acute myocardial infarction. J Nutr. 2005;135(7):1770-5. [ Links ]
47. Aguilar-Salinas CA, Rojas R, Gomez-Perez FJ, Garcia E, Valles V, Rios-Torres JM, et al. Prevalence and characteristics of early-onset type 2 diabetes in Mexico. Am J Med. 2002; 113(7):569-74. [ Links ]
48. Hernández RE, Cardonnet LJ, Libman C, Gagliardino JJ Prevalence of diabetes and obesity in an urban population of Argentina. Diabetes Res Clin Pract. 1987;3(5):277-83. [ Links ]
49. Malerbi DA, Franco LJ. Multicenter study of the prevalence of diabetes mellitus and impaired glucose tolerance in the urban Brazilian population aged 30-69 yr. Diabetes Care. 1992;15(11):1509-16. [ Links ]
50. Sakata K, Bigolin S, Bryk Junior A, Komatsu MCG, Sakata L, Vanzo LRC, et al. Estudo dos conhecimentos de pacientes com hipertensão, diabetes ou glaucoma sobre suas doenças. Arq. Bras. Oftalmol. 2002;65:467-9. [ Links ]
51. Baechler R, Mujica V, Aqueveque X, Ramos L, Soto A. Prevalencia de diabetes mellitus en la VII Región de Chile [Prevalence of diabetes mellitus in the Seventh Region of Chile]. Rev Med Chil. 2002;130(11):1257-64. [ Links ]
52. Goldenberg P, Franco LJ, Pagliaro H, Silva Rd, Santos CA. Diabetes mellitus auto-referido no Município de São Paulo: prevalëncia e desigualdade [Self-reported diabetes mellitus in the city of São Paulo: prevalence and inequality]. Cad Saúde Pública. 1996;12 (1):37-45. [ Links ]
Manuscript received on 8 August 2008.
Revised version accepted for publication on 10 January 2009.
* Send correspondence to: Flavia Andrade, University of Illinois at Urbana-Champaign, Department of Kinesiology and Community Health, 123 Huff Hall, 1206 S. Fourth, Champaign, IL 61820, USA; telephone: +01-217-333-3675; fax: +01-217-333-2766; email: firstname.lastname@example.org
1 Life tables for Havana were provided by Dr. Esther María León Diaz, SABE co-investigator in Cuba (Personal communication, May, 2006).