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Aging and Health Status of Elderly in Latin America and the Caribbean: Preliminary Findings

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Abstract

Aging in Latin America and the Caribbean will not proceed along known paths already followed by more developed countries. In particular, the health profile of the future elderly population is less predictable due to factors associated with their demographic past that may haunt them for a long time and make them more vulnerable, even if economic and institutional conditions turn out to be better than what they are likely to be. This paper answers a set of questions regarding the nature and determinants of health status among the elderly in Latin America and the Caribbean using SABE (Survey on Health and Well-Being of Elders), a cross-sectional representative sample of over 10,000 elderly aged 60 and above in private homes in seven major cities in Latin America and the Caribbean. We examine health outcomes such as self-reported health, functional limitations–Activities of Daily Living (ADL’s) and Instrumental Activities of Daily Living (IADL’s), obesity (ratio of weight in kilograms to the square of height in centimeters), and self-reported chronic conditions (including diabetes). The findings include: (a) Countries differ in self-reported health but exhibit much less differences in terms of functional limitations. The number of chronic conditions increase with age and is higher among females than among males; (b) On average SABE countries display levels of self-reported diabetes (and obesity) that are as high if not higher than those found in the US; (c) There is evidence, albeit weaker than expected, suggesting deteriorated health and functional status in the region; (d) There is important evidence pointing toward rather strong inequalities (by education and income) in selected health outcomes. Preliminary findings from SABE confirm that Latin America and the Caribbean display peculiarities in the health profile of elderly, particularly with regard to diabetes and obesity. It is important that new policy initiatives begin to seriously target the region’s elderly, especially with an emphasis on the prevention and treatment of diabetes and obesity.

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Notes

  1. A more thorough examination of the aforementioned features can be found in Palloni et al., 2002.

  2. The argument holds, of course, if we assume that the effects of mortality selection are lonely mild and if the effects of changes in behavioral profiles and medical technology (exogenous or not) are only weak.

  3. For more information on the nature of the samples see Palloni and Pelaez, 2002 and Albala et al., 2005.

  4. Because all samples are urban samples, our ability to generalize to the total population is impaired. However, readers should bear in mind that the proportion of the total population living in urban areas in these countries is substantial, varying from close to 100% in Barbados to about 74 or 75% in Mexico and Cuba, respectively (United Nations, 2000). This suggests that our results should not be too different from what we would have obtained had SABE been based on national samples. And, indeed, it has been shown that the demographic profile at least of the samples is quite close to national averages (Palloni & Pelaez, 2002).

  5. In the rest of the paper we refer use the words “country” or “city” to refer to the city samples. By using the word country we are in no way assuming that the SABE data are exactly representative of elderly populations in each of the countries who participated in the project.

  6. The definition of ADL, IADL, and self-reported conditions selected for study in this paper appears in the Appendix. They are strictly comparable to those used in other surveys of elderly, particularly the Health and Retirement Survey (HRS) (2002).

  7. For more information on the HRS study and sample see the electronic version of papers by Servais (2004) at http://hrsonline.isr.umich.edu/docs/dmgt/OverviewofHRSPublicData.pdf or Hauser and Willis (2005) at http://hrsonline.isr.umich.edu/papers/background/PDR30suppHAUSER.pdf).

  8. To simplify analyses we focus on a single indicator for the presence of ADL and IADL, namely, whether or not individuals declare at least one of them. We could have used the entire frequency distribution and worked instead with the “number of ADL” or the “number of IADL.” But this complicates the analyses unnecessarily since these are discrete, bounded variables and their distribution can only be mimicked by a handful of discrete distributions. Treating them as categories leads to unwieldly results. Finally, because the number of possible ADL (6) and IADL(6) is relatively few, the proportion of individuals declaring 0 turns out to be an excellent predictor of the shape of the entire distribution. Inferences drawn with the simplified indicator chosen here do not change if the dependent variables are fine-tuned (Palloni & McEniry, 2004). The same applies for self-reported health status.

  9. This statement is established by estimating a model where the age effects are constrained to be the same.

  10. Simple analyses of variance (Palloni & McEniry, 2004) reveal that the residual variance explained by country heterogeneity is significant whereas the residual variance explained by age and sex is not.

  11. See Footnote 5.

  12. Analyses of variance (Palloni & McEniry, 2004) suggest that the fraction of total variance explained by country variability is statistically insignificant.

  13. Montevideo is also the only city in the SABE sample where institutionalization of the elderly is more than trivial. The peculiar relation between self-reported health and ADL and IADL in Montevideo might be a result of heavy selection among elderly who remain independent instead of becoming institutionalized.

  14. See Appendix for definition of chronic conditions.

  15. In this paper we reserve the term diabetes to refer to a mixture of diabetes 1 and diabetes mellitus or type 2. However, for the most part those individuals self-reporting diabetes are afflicted by diabetes type 2.

  16. Self-reports of diabetes are not perfect. We know that it underestimates true prevalence. But it is quite accurate as it has very high specificity but lower sensitivity in very different cultural contexts (Palloni, Soldo, & Wong, 2003; Goldman, I-Fen, Weinstein, & Yu-Hsung, 2002).

  17. The declining pattern with age is probably a result of the heavier attrition of diabetics as age increases.

  18. See Palloni and McEniry, 2004. Since most of the Barbados population if of African descent, a similar test cannot be applied there.

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Acknowledgments

This paper is only possible thanks to the collaboration of the principal investigators of the SABE study, Cecilia Albala, Anselm Hennis, Roberto Ham, Maria Lucia Lebrao, Esther de Leon, Edith Pantelides, and Omar Pratts. We are thankful to Dr. Guido Pinto for many discussions and for extensive work on the data set and to Dr. Martha Pelaez, from the Pan American Health Organization, without whose initiative the project would not have been possible. The research for this paper was supported by NIA grants R01 AG16209 and R03 AG15673 to Palloni. Both authors work in the Center for Demography and Ecology supported by core grant P30 HD05876, and in the Center Core supported by core grant P30 AG17266.

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Authors and Affiliations

  1. Center for Demography and Ecology, University of Wisconsin-Madison, Madison, WI, USA

    Alberto Palloni

  2. Center for Demography of Health and Aging (CDHA), University of Wisconsin-Madison, Madison, WI, USA

    Mary McEniry

  3. Room 4434 Social Science Building, Madison, WI, 53706, USA

    Alberto Palloni

Authors
  1. Alberto Palloni
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  2. Mary McEniry
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Correspondence to Alberto Palloni.

Appendices

Appendix

ADL and IADL

1.1. ADL’s:

  • Walking across the room

  • Dressing

  • Bathing

  • Eating

  • Getting in and out of bed

  • Using bathroom

1.2 . IADL’s

  • Preparing meals

  • Managing money

  • Difficulty with getting to places (only in SABE)

  • Buying food or clothing

  • Using the phone (in SABE asked of those who had a phone)

  • Doing heavy housework (only in SABE)

  • Doing light housework (only in SABE)

  • Taking medicines

For multivariate analyses, we used only those SABE IADLs that were strictly comparable with HRS: preparing meals, managing money, buying food or clothing, using the phone, and taking medicine.

Chronic Conditions

  • Arthritis

  • Cancer

  • Diabetes

  • Respiratory Illness

  • Heart Disease

  • Stroke

Targets, Spouses and Proxies

In three countries (Argentina, Chile, and Uruguay) only one individual per household was interviewed. In two countries, Brazil and Mexico, interviewers proceeded to interview all individuals 60 and older found in selected household. In virtually all these cases, the additional interviews corresponded to spouses (one per household). In Cuba interviewers selected a target individual and a spouse.

In our analyses we include all individuals interviewed. This has the advantage of maximizing observation at the expenses of introducing dependence of observations in the countries where more than one individual per household was interviewed. In order to protect our inferences we repeated some of the analyses using clustering procedures to adjust for lack of independence but since the inferences remain unchanged we have chosen to present results based on the larger samples.

Sampling Weights

Only the sample from Santiago is self-weighted. All others require weights to expand the sample population to the city population. Since in two countries no sample weights have been calculated we chose to ignore them in all the others. However, to ensure that none of our conclusions was sensitive to this choice, we proceeded to re-estimate models using sampling weights for those countries that had them available. None of the inferences changed, and it is highly unlikely that they will even in the countries where there were no weights available yet.

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Palloni, A., McEniry, M. Aging and Health Status of Elderly in Latin America and the Caribbean: Preliminary Findings. J Cross Cult Gerontol 22, 263–285 (2007). https://doi.org/10.1007/s10823-006-9001-7

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