Explaining health differences between men and women in later life: A cross-city comparison in Latin America and the Caribbean

Social Science & Medicine 68 (2009) 235–242

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Explaining health differences between men and women in later life: A cross-city comparison in Latin America and the Caribbean Maria-Victoria Zunzunegui a, *, Beatriz-Eugenia Alvarado b, François Be´land a, Bilkis Vissandjee a a b

Me´decine sociale et preventive, Universite de Montreal, Quebec, Canada McGill University, Quebec, Canada

a r t i c l e i n f o

a b s t r a c t

Article history: Available online 25 November 2008

This paper describes differences in health and functional status among older men and women and attempts to anchor the explanations for these differences within a lifecourse perspective. Seven health outcomes for men and women 60 years and older from seven Latin American and Caribbean cities are examined, using data from the 2000 SABE survey (Salud, Bienestar y Envejecimientodn ¼ 10,587). Ageadjusted as well as city- and sex-specific prevalence was estimated for poor self-rated health, comorbidity, mobility limitations, cognitive impairment, depressive symptoms and disability in basic and instrumental activities of daily living. Logistic regressions were fitted to determine if the differences between men and women in each outcome could be explained by differential exposures in childhood (hunger, poverty), adulthood (education, occupation) and old age (income) and/or by differential vulnerability of men and women to these exposures. Sao Paulo, Santiago and Mexico, cities in countries with a high level of income inequalities, presented the highest prevalence of disability, functional limitations and poor physical health for both women and men. Women showed poorer health outcomes as compared with men for all health indicators and in all cities. Controlling for lifecourse exposures in childhood, adulthood and old age did not attenuate these differences. Women’s unadjusted and adjusted odds of reporting poor self-rated health, cognitive impairment and basic activities of daily living disability were approximately 50% higher than for men, twice as high for number of comorbidities, depressive symptoms and instrumental activities of daily living disability, and almost three times as high for mobility limitations. Higher vulnerability to lifecourse exposures in women as compared with men was not found, meaning that lifecourse exposures have similar odds of poor health outcomes for men and women. A more integrated understanding of how sex and gender act together to influence health and function in old age needs consideration of additional biological and social factors. Ó 2008 Elsevier Ltd. All rights reserved.

Keywords: Latin America and Caribbean Health status Aging Gender Lifecourse Inequalities Men Women Lifecourse

Health differences in older men and women can be traced to biological and social factors (Doyal, 2001; Rieker & Bird, 2005). A number of scholars have tried to clarify the differences between sex and gender in order to use these terms more precisely for research and policy purposes (Health-Canada, 2003; Spitzer, 2005). Sex generally refers to the biological characteristics that distinguish males and females such as anatomy (e.g., body size and shape) and physiology (e.g., hormonal activity or functioning of organs). Gender refers to the array of socially constructed roles and relationships, personality traits, attitudes, behaviors, values, relative power and influence that society differentially ascribes to each sex.

* Corresponding author. Me´decine sociale et preventive, Universite de Montreal, 1420 Boulevard Mont Royal, local 3134-2, Montreal, Quebec, H2V 4P3 Canada. Tel.: þ1 514 3436086; fax: þ1 514 3435645. E-mail addresses: [email protected] (M.-V. Zunzunegui), [email protected] (B.-E. Alvarado), [email protected] (F. Be´land), [email protected] (B. Vissandjee). 0277-9536/$ – see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.socscimed.2008.10.031

Gender determines the nature of health exposures during infancy, childhood, adolescence and adult life (Moen & Chermack, 2005; Spitzer, 2005). In this paper, health status differences between older men and women living in seven main cities of Latin America and the Caribbean are explored: Buenos Aires (Argentina), Bridgetown (Barbados), Sao Paulo (Brazil), Santiago de Chile (Chile), La Havana (Cuba), Ciudad de Mexico (Mexico), Montevideo (Uruguay). The extent of health differences between men and women in later life are examined, and differences or similarities in the distribution of these inequalities in the seven cities are assessed. The results obtained are used to test two non-exclusive hypotheses on the generation of health status differences among men and women (McDonough & Walters, 2001): differential exposure and differential vulnerability. The first hypothesis, differential exposure, proposes that exposure to social factors during the lifecourse is different for men and women, and that these differences result in different health outcomes. More specifically, we argue that health

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differences between older men and women should be smaller where lifecourse exposures are similar and larger where exposures differ in magnitude; and that health differences between men and women should decrease (or disappear) after controlling for these differences in lifecourse social factors. The second hypothesis, differential vulnerability, states that men and women have different vulnerability to an exposure, that is, the probability of a health outcome associated with a given exposure is different for men and women. For instance, Visser et al. (2005) have shown that obesity leads to a higher risk of disability in women than in men. Widowhood together with economic difficulties is associated with a higher risk of depression in men as compared with women (van Grootheest, Beekman, Broese van Groenou, & Deeg, 1999; Sonnenberg, Beekman, Deeg, & van Tilburg, 2000). Because differential vulnerability may be biological in nature in some cases or socially rooted in some others, or both, we can assume that for the biological root causes, the differential risk associated with a given exposure for a given health outcome between women and men will be constant across societies. If vulnerability is socially rooted, the differential risk associated with an exposure would change with context. In this paper we attempt to answer the following questions: What are the health differences between urban older women and men in Latin America? Are these differences explained by differential lifecourse exposures in women and men? Are these differences explained by differential vulnerability to these exposures in women and men? Context Population aging in Latin America and the Caribbean (LAC) is accelerated as compared to aging in North America and Europe (Palloni, Pinto-Aguirre, & Pelaez, 2002). In LAC population aging coincides with social inequalities created by sustained poverty, unemployment, violence and malnutrition; these inequalities have increased after a period of structural adjustment and the dismantling of the incipient welfare state in the 1990s (Engler, 2002; Paddison, 2006; Pinzon et al., 2002). Palloni and McEniry (2006) (Palloni & McEniry, 2006) have noted that social structural factors, such as dislocation of the social security system and institutional volatility, directly affect the lifecourse of elderly persons in Latin American countries. The structural arrangements demanded by the World Bank and the International Monetary Fund have led to a profound reorganization of pensions and welfare systems (Bruton & Masci, 2005; Pinzon & Solas, 2002). More than two thirds of LAC elderly persons live under the poverty level, with a sizeable proportion of them needing to work in order to survive. Old age pensions, well below the average cost of living, cover only a small proportion of aging women and men in LAC. Indeed, reforms of health systems in the 1990s have resulted in large portions of the population being excluded from health insurance coverage (CEPAL, 2006). Lacking economic security, and with poor and limited health services and almost non-existent social services, older people in LAC must rely almost exclusively on their families for economic and social support. Not having a pension is more frequent among older women as compared with older men in LAC, while lacking family support is more common in men than in women, at least in the Caribbean (Zunzunegui, Alvarado, Cloos, Simeon, & EldemireShearer, 2008). The prices of food, utilities, health services and medication have increased, whereas pensions have not been upgraded. The middle class has shrunk and families give priority to the young – children and adolescents – while older people are increasingly facing social exclusion (Zunzunegui et al., 2002). LAC countries differ in size, language, ethnic affiliation, population aging and socio-economic characteristics. The smallest of the countries included in the present work is Barbados, with

a population of 267,000 inhabitants, and the largest is Brazil, with a population of 170,690,000. Gross national income (GNI) of Cuba and Brazil is under US$3,000. Barbados has the highest GNI (US$17,000), while Uruguay, Chile, Mexico and Argentina are located midway with respect to the GNI distribution. Brazil and Mexico have the highest illiteracy levels. The participation of elderly women in the labour force varies widely among countries, but is lower than that of men in all of them. Economic inequality is often measured by Gini coefficients (an index of income inequality ranging from 0 to 100, where a value of 0 represents absolute equality, and a value of 100 absolute inequality). Gini coefficients are high in all seven countries whose main cities are included in this study, ranging from 38 in Barbados to 57 in Brazil (UNDP, 2007) while the corresponding values for Canada and Europe are between 32 and 30. Methods Data The aim of the SABE survey was to study the health and the wellbeing of older people in seven cities of Latin America and the Caribbean. SABE was coordinated by researchers from the Pan American Health Organization (PAHO), the Center for Demography and Ecology at the University of Wisconsin–Madison, and local principal investigators in each country (Wong, Pelaez, Palloni, & Markides, 2006). With the exception of Bridgetown and Santiago, where simple random samples were used, the samples were all multistage, stratified, clustered samples (see details in Wong et al., 2006). The SABE questionnaire was modeled after various instruments used in previous studies carried out in the United States (Wong et al., 2006). In cases where the person could not respond directly to a brief cognitive assessment, a proxy was selected, and a special instrument was applied. A total of 10,587 persons 60 years old and over were interviewed at home using a structured questionnaire on their living conditions, health status and health services use during the year 2000 (Pelaez et al., 2004). Response rates were 60% in Buenos Aires (n ¼ 1043), 85% in Bridgetown (n ¼ 1812), 85% in Sao Paulo (n ¼ 2143), 84% in Santiago (n ¼ 1306), 95% in Havana (n ¼ 1905), 85% in Mexico (n ¼ 1311), and 66% in Montevideo (n ¼ 1450). Assisted interviews were carried out in 1.1% of cases in Montevideo, 3.8% in Buenos Aires, 4.3% in Bridgetown, 5.9% in Mexico D.C, 9.0% in Havana, 9.2% in Santiago and 12.9% in Sao Paulo. Measures Outcome variables Health outcomes included self-rated health, cognitive function and depressive symptoms, selected chronic conditions, mobility limitations, disabilities in Activities of Daily Living (ADL) and disabilities in Instrumental Activities of Daily Living (IADL). Self-rated health (SRH) has been shown to be a valid indicator of health status in both the general and the elderly population (Idler & Benyamini, 1997; Mossey & Shapiro, 1982; Wong, Pelaez, & Palloni, 2005). Participants were asked to respond to a single question (‘‘How would you rate your health?’’) by selecting one of five possible responses, ‘‘very good,’’ ‘‘good,’’ ‘‘fair,’’ ‘‘poor’’ and ‘‘very poor.’’ Respondents’ distributions over response categories were skewed. SRH was dichotomized, merging on the one hand ‘‘good’’ and ‘‘very good’’ [0] and, on the other, ‘‘fair’’ ‘‘poor’’ and ‘‘very poor’’ [1]. Chronic conditions (hypertension, diabetes, cancer, hip fracture, stroke, cardiovascular diseases, and arthritis) were assessed by answers to questions formulated as: ‘‘Has a doctor or nurse ever told you that you had.’’ (for diabetes: that is to say, high blood

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sugar levels)? Comorbidity was dichotomized as none or one chronic condition versus two or more (Fried, Ferrucci, Darer, Williamson, & Anderson, 2004). The modified Mini-Mental State Examination (MMSE) used in SABE included six tests, as in the original MMSE (Nguyen, Couture, Alvarado, & Zunzunegui, 2008): 1) naming the current day, month and year (3 points); 2) immediate recall of three objects (3 points); 3) repetition of five numbers in reverse order (5 points); 4) folding a paper according to instructions (3 points); 5) delayed recall memory of the same three objects named previously (3 points); 6) drawing intersecting circles (2 points). The maximum score was 19 and a cut-off point of 12/13 identified persons with cognitive deterioration. Respondents with scores under 13 in the modified MMSE were asked to respond to the Pfeffer Scale. Respondents with scores under 13 on the modified MMSE and a score of 6 or more on the Pfeffer scale were considered cognitively impaired (Nguyen et al., 2008). The Geriatric Depression Scale (GDS) assessed the presence of depressive symptoms (Yesavage et al., 1982). This scale is composed of 15 items with dichotomous ‘‘Yes/No’’ responses. A yes response (value of one on the scale) is considered as positive for depressive symptoms and it has been validated in Spanish – (sensitivity ¼ 81%; specificity ¼ 76%) (Martinez de la Iglesia et al., 2005) and Portuguese-speaking populations (Almeida & Almeida, 1999b). Reliability of the scale has been reported as ranging from 0.80 to 0.86 (Almeida & Almeida, 1999a). GDS scores were dichotomized using, acknowledged cut-off points: 0–5 was considered as no depression, and 6 or more suggestive of depression. Mobility limitations were defined as the number of limitations reported in five physical tests: lifting and carrying 10 pounds, walking several blocks, climbing a flight of stairs, kneeling/stooping/crouching, and getting up from a chair (Nagi, 1991). People who reported that they did not perform the activity were recorded as missing (n ¼ 844; 9.3%). This variable was categorized in two levels: no difficulties [0]; and with at least one difficulty [1]. Disability in eight IADL was evaluated: managing money, shopping, using the telephone, taking medications, using transportation, preparing meals, doing light housework and doing heavy housework (Lawton & Brody, 1969). Respondents selfclassified themselves as experiencing difficulty, unable to perform the activity, or not performing the activity. In this paper, three activities were excluded since more men than women stated that they never prepared meals or performed either light or heavy housework. For respondents stating that they did not carry out the activity, imputation was carried out using information on cognitive function (n ¼ 2822). Those with cognitive impairment according to the SABE protocole (Pelaez et al., 2004) were classified as unable to perform the task. IADL disability was dichotomized as having difficulty with at least one task versus no difficulty. ADL disabilities were assessed for bathing, toileting, dressing, walking across the room, eating and getting out of bed (Pearson, 2000). Response categories were whether respondents performed the activities with or without difficulty. Activities performed with difficulty were added (range 0–6) and the final ADL scores was dichotomized as: no difficulties at all [0], presence of one or more difficulties [1].

237

eat and were hungry? (yes/no). Adult socio-economic status was defined by: 1) level of education, measured by asking the respondent the highest level of school achievement; and 2) lifelong occupation, recorded according to the International Standard Classification of Occupations (ISCO-88) and sorted into five categories: a) white-collars (members of executive branch, business management, scientific and intellectual professionals and mid-level technical personnel and professionals); b) blue collars (office employees, service workers and salespersons involved in trade and commerce); c) semi- and unskilled workers (office workers, artisans in the mechanical arts and other types of arts; machine and equipment operators, unskilled workers; armed forces); d) housewives; and e) farm workers. Current socioecononomic status: perceived sufficiency of income (sufficient vs insufficient) was used as indicator of current material resources. Marital status was categorized in two groups: presence or absence of a partner. Statistical analysis To answer the first research question, health outcomes were compared in each city for men and women, controlling for age. Direct standardization of prevalence of health outcomes was performed for six age groups (60–64; 65–69; 70–74; 75–79; 80–84 and 85 and more) and for men and women. We used the weighted population of the total number of persons aged 60 and over (women/men) as the reference population. Odds ratios for women compared with men for each outcome were estimated using logistic regression including sex and controlling for age only. Homogeneity of the odds ratios of women compared with men across cities was tested using RevMan software (RevMan, 2003), with fixed-effects for cities. For the second research question, we examined the hypothesis that lifecourse exposures account for the health differences between men and women by adding childhood social and health circumstances, adulthood socio-economic position, and current social and material circumstances to the age and sex logistic regressions. Here again, we tested for homogeneity of the odds ratios of women compared with men across cities using RevMan software (RevMan, 2003), with fixed-effects for cities. The number of cases with depression in Bridgetown, and with cognitive impairment in Buenos Aires, Montevideo and Bridgetown was too low to run logistic regressions to test homogeneity among cities and multiplicative interaction terms for sex and lifecourse exposures (see below). They were excluded from these particular analyses. To answer the third question on differential vulnerability of men and women to lifecourse exposures, the multiplicative interactions of sex (1 ¼ women, 0 ¼ men) with lifecourse exposures were included in the logistic models. We ran 7 regressions (one for each health outcome) for each of the seven cities, except for depression in Bridgetown, and cognitive impairment in Buenos Aires, Montevideo and Bridgetown. In each equation, the 8 interactions of lifecourse exposures and sex were tested simultaneously, adding to 45 tests. The p-level was thus set at 0.05/45 ¼ 0.001 to take into account the multiple tests used in this study. Results

Lifecourse exposures Socio-economic conditions during childhood were assessed by the following questions: During the first 15 years of your life 1) did you live in a rural area for 5 years or more? (yes/no); 2) what was your family’s economic situation? (good/average or poor); 3) would you say that your health during the first 15 years of your life was excellent, good or poor? Dichotomized to excellent/good or poor; 4) was there a time when you did not have enough to

Table 1 shows the distribution of eight lifecourse exposures considered in this study for men and women in each city. The material, social and health circumstances of men and women in the seven cities partially reflect their respective countries’ demographic and socio-economic characteristics (Table 1). Forty to sixty per cent of SABE respondents lived in rural areas in their childhood, and over 50% reported coming from families with low SES. Also, 10–35%

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Table 1 Distribution of lifecourse exposures in women and men from seven Latin American and Caribbean cities: SABE study.

Childhood circumstances Rural life (yes) Perception of SES (regular/bad) Perception of health (regular/bad) Hunger (yes)

B/Aires (Argentina)

Bridgetown (Barbados)

Sao Paulo (Brasil)

Santiago de Chile (Chile)

Havana (Cuba)

Mexico (Mexico)

Montevideo (Uruguay)

Women

Men

Women

Men

Women

Men

Women

Men

Women

Men

Women

Men

Women

Men

n ¼ 660

n ¼ 383

n ¼ 921

n ¼ 583

n ¼ 1262

n ¼ 881

n ¼ 855

n ¼ 446

n-1197

n ¼ 708

n ¼ 740

n ¼ 507

n ¼ 920

n ¼ 530

37.5 52.9

39.8 49.0

49.3 81.6

52.2 83.3

58.1* 67.5*

69.2 73.1

48.3 53.0*

51.6 63.4

49.7 70.1*

53.4 79.5

53.7 74.9*

56.5 81.3

42.2 62.0*

45.1 68.4

47.1

51.0

51.5*

43.5

51.9

48.8

65.9

61.0

64.2

64.1

53.8

50.5

57.8

58.2

10.1

12.2

14.9*

22.4

18.5

21.8

19.0

22.6

20.8*

28.2

25.5*

34.8

10.6*

13.8

3.0

2.3

3.8

29.3*

21.1

17.7*

11.6

5.2

4.3

27.9*

19.4

6.6

5.0

44.5 55.5

55.5 37.2 7.3

51.4 48.6

23.0* 67.0 10.0

35.4 64.6

20.7* 64.7 14.7

37.6 62.4

41.9 33.8 24.2

40.7 59.3

29.5* 45.8 24.8

37.4 62.6

24.0* 64.7 11.3

37.0 63.0

63.2

66.5*

57.8

69.0

67.9

70.4*

63.6

80.6

76.5

49.2

47.1

59.4

54.6

25.4

76.6*

47.3

58.6*

20.9

57.5*

21.7

76.9*

35.5

61.1*

23.1

64.7*

27.4

Adult socio-economic status Level of education 6.6* (illiterate/no schooling) Occupation No-manual 33.6* Manual 53.1 Housewives 13.3 Current socio-economic status Perception of income 68.4** (insufficient) Marital status 56.7* (no partner) *p < 0.01; **0.05

0.01.

experienced hunger, and 44–66% perceived that their health status was poor. A larger proportion of elderly respondents living in Santiago, Sao Paulo and Mexico reported having lived in rural areas when they were young and having experienced hunger as compared to their counterparts from Buenos Aires, Bridgetown and Montevideo. In adulthood, most men were blue collar workers. In Buenos Aires, Montevideo, Santiago and Sao Paulo, a majority of women worked in manual occupations. More than half of SABE respondents perceived their income as insufficient. In Havana, approximately 80% of both women and men thought their income was inadequate to meet their needs. As expected, more women than men did not have a life partner. Women perceived their income as insufficient more often than men in Buenos Aires, Bridgetown and Santiago. The rate of illiteracy was higher in women than men in four cities (Buenos Aires, Santiago, Sao Paulo, and Mexico); accordingly, more men than women had worked in non-manual occupations. Finally, women’s living conditions in childhood were generally better than men’s; the differences were statistically significant mainly for perceived SES and hunger in four out of seven cities.

health (OR: 1.56; 95% CI: 1.42–1.72) and comorbidity (OR: 2.04; 95% CI: 1.83–2.28). Odds ratios comparing the functional indicators (mobility limitations and IADL and ADL disability) in women versus men are shown in Table 4 for each city and for the pooled data, when homogeneity across cities was not rejected at p ¼ 0.05 (which was the case for IADL and ADL disability). The odds of disability were higher in women and men in all cities. When Sao Paulo was excluded from the analysis, differences between men and women across cities also became homogeneous for mobility limitations (OR: 2.96; 95% CI: 2.53–3.47).

Age-adjusted gender differences in the seven health outcomes by city The age-adjusted prevalence of the seven health outcomes for men and women is presented in Table 2. Men and women living in Havana, Santiago, Sao Paulo and Mexico had lower health status on self-rated health, mobility limitations and IADL and ADL disability than men and women living in Buenos Aires, Bridgetown and Montevideo. Cognitive impairment was also high in Havana, Santiago, Sao Paulo and Mexico. Depressive symptoms were higher in Santiago than in other cities. Odds ratios for women compared with men for SRH, comorbidity, depressive symptoms and cognitive impairment are shown in Table 3 for each city and for the pooled data when homogeneity across cities was not rejected at p ¼ 0.05 (this was the case for cognitive impairment and depression). The odds for women were higher for all conditions. When Sao Paulo was excluded from the analysis, differences between men and women across cities also became homogeneous for self-rated

Table 2 Age-adjusted prevalence of health and functional outcomes for elderly men and women from seven cities. Buenos Aires

Bridge town

Sao Paulo

Santiago

Havana

Mexico

Monte video

Poor self-rated health Men 25.7 41.4 Women 39.1 53.6

52.8 56.0

57.8 67.9

54.9 67.7

66.3 72.3

31.8 39.8

Comorbidity Men 33.7 Women 49.1

31.9 50.8

37.5 46.7

30.4 48.3

32.8 54.8

24.8 37.9

32.5 48.1

Cognitive impairment Men 4.1 3.1 Women 5.2 4.4

9.9 12.1

7.2 10.1

6.0 9.8

7.3 12.7

0.6 1.8

Depressive symptoms Men 11.7 5.2 Women 17.4 4.5

13.1 21.8

21.8 29.1

12.8 27.2

16.8 22.6

11.3 21.5

Mobility limitations Men 16.0 9.4 Women 41.7 23.9

22.9 40.7

21.6 46.6

16.8 42.6

26.5 45.5

19.2 36.0

IADL disability Men 11.4 Women 25.2

13.9 23.3

22.8 38.0

17.4 31.5

15.1 25.8

17.7 32.3

9.9 17.3

ADL disability Men 14.0 Women 23.6

10.3 16.3

17.7 25.0

18.2 29.1

15.1 22.8

19.6 20.8

12.8 20.8

*Prevalence are adjusted by the direct method using as standard population the weighted population older than 60.

1.71 1.05 1.34 1.76 1.19 1.87 1.42 1.67 1.90

2.15

1.63

1.45

4.03 2.09 5.88 8.50 1.60 1.19 0.64 1.56 1.67 0.42 2.19 1.15 3.03 3.77 0.82 1.39 1.20 1.89 1.15 1.62 1.81 1.64 2.47 1.58 2.25

2.35 2.24 3.24 2.18 3.12

1.07 1.58

NA

2.33 2.78 2.10 3.07 3.36 2.22 2.56 1.30 1.70 1.40 1.69 2.16 1.26 1.58 1.74 2.17 1.72 2.28 2.69 1.68 2.01

NA 1.68 1.39 1.53

1.20 1.27 1.00 1.10 1.53 1.05 1.16 1.65 1.62 1.23 1.48 1.92 1.40 1.51

NA Pooled

1.33 1.38 0.96 1.22 1.46 1.05 1.16 1.77 1.73 1.15 1.59 1.79 1.35 1.47 Buenos Aires Bridgetown Sao Paulo Santiago Havana Mexico Montevideo

CI 95%

2.36 2.17 1.37 2.06 2.20 1.74 1.85

CI 95%

2.26 2.07 1.51 1.99 2.41 1.87 1.96

1.77 2.21 1.52 2.23 2.47 1.72 1.85

1.35 1.76 1.27 1.70 2.02 1.33 1.48

2.32 2.79 1.82 2.92 3.01 2.23 2.31

All-adjusted*

OR CI 95% OR OR

OR

Age-adjusted All-adjusted* Age-adjusted

*Controlling for age, childhood conditions (hunger, rural life, low socio-economic status, poor health), adulthood socio-economic status (education and occupation), current social conditions (widowhood and insufficient income). NA ¼ heterogeneity of effects were significant at p ¼ 0.05.

0.93 1.58 1.97 2.24

0.64 0.80 1.15 1.16

CI 95% OR

2.48 2.71 1.70 2.85 2.69 2.90 11.70 0.44 0.70 0.92 0.94 1.12 0.98 0.55 1.05 1.38 1.25 1.64 1.73 1.69 2.54 2.41 2.34

1.03

0.44

CI 95% OR

Age-adjusted

CI 95%

All-adjusted*

OR CI 95% OR

Age-adjusted

CI 95%

Depressive symptoms Comorbidity Poor self-rated health

Table 3 Age and lifecourse adjusted odds of women compared with men for poor self-rated health, comorbidity, depressive symptoms and cognitive impairment.

Cognitive impairment

All-adjusted*

1.37 3.12 3.38 4.33

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Differential exposure hypothesis Odds ratios for women compared with men in the seven health outcomes, controlling for all lifecourse exposures (‘‘all-adjusted’’) are also shown in Table 3. Their values are similar to the ageadjusted odds ratios. Using pooled estimates, women’s odds of reporting poor SRH, higher cognitive impairment, depression and ADL disability, were 34–63% higher than for men. Odds ratios for comorbidity, and IADL disability were twice as high in women as in men. Finally, the odds for mobility limitations were 2.9 times higher in women than in men. No heterogeneity among cities was found in the differences between women and men, except for comorbidity (p ¼ 0.04) where the odds were 67–73% higher for women than for men in Buenos Aires, Sao Paulo and Mexico, while they were at least twice as high in women as in men in Montevideo, Bridgetown, Havana and Santiago. Briefly, taking lifecourse exposures into account did not explain women’s excess poor health in comparison with men, but heterogeneity across cities was even smaller after adjusting for lifecourse exposures. Differential vulnerability hypothesis The only two interaction terms significant at the set p-level of 0.001 were found for Santiago men without a spouse, who had higher odds than Santiago women without a spouse for depression and presence of comorbidity. Thus, the differential vulnerability of men and women to lifecourse exposures hypothesis was not supported by these data: Women and men have the same odds for exposure to each of the lifecourse conditions examined: socioeconomic position, rural background, health status in childhood and hunger experience in childhood, level of education and occupation in adulthood and marital status and sufficiency of income in old age, with the possible exception of widowed men in Santiago. Discussion Excess female morbidity and poorer function were observed in this study for the elderly population of seven LAC cities in countries that vary in size, language, ethnicity, socio-economic indicators, health services and social security schemes. Differences between men’s and women’s prevalence of health indicators are homogeneous in spite of the wide socio-economic diversity of the countries where the cities in this study are located. These results are in agreement with previous studies done in wealthier countries. Differences among older men and women in prevalence of poor self-rated health are found in most published studies with the exceptions of Finland (Bardage et al., 2005), Britain (Arber & Cooper, 1999) and Canada (Zunzunegui et al., 2004). In our study, the odds of women reporting poor health were homogeneous at 53% higher than in men. The mean number of chronic diseases was higher in elderly women than in men of similar age as reported in United States populations (Case & Paxson, 2005) although there are differences in the prevalence of specific conditions, particularly diabetes, that may explain the heterogeneity across cities. Diabetes was more frequent among women than men in Bridgetown and Havana (Barcelo, Pelaez, Rodriguez-Wong, & Pastor-Valero, 2006), and in Santiago, but diabetes was more frequent in men in Mexico and Buenos Aires (Palloni & McEniry, 2006). While most studies have not found differences in cognitive decline or incidence of dementia between men and women after adjusting for age and comorbidity (Brayne, Gill, Paykel, Huppert, & O’Connor, 1995; Kukull et al., 2002), differences have been reported in countries where women have been denied education in early life, mental stimulation through highly skilled occupations,, and a large social networkdall of which are associated with cognitive maintenance (Alvarado, Zunzunegui, Del Ser, & Beland, 2002; Zhang,

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Table 4 Age and lifecourse adjusted odds of women compared with men for functional outcomes. City and pooled data. Mobility limitations Age-adjusted OR

CI 95%

Buenos Aires Bridgetown Sao Paulo Santiago Havana Mexico Montevideo

3.24 3.32 2.39 3.45 3.74 2.42 2.35

2.27 2.18 1.93 2.52 2.91 1.84 1.78

Pooled

NA

IADL disability All-adjusted*

4.63 5.05 2.96 4.73 4.83 3.17 3.08

ADL disability

Age-adjusted

All-adjusted*

Age-adjusted

All-adjusted*

OR

CI 95%

OR

CI 95%

OR

CI 95%

OR

CI 95%

OR

CI 95%

2.65 3.43 2.54 2.97 3.77 2.71 2.39

1.81 2.18 2.00 2.10 2.85 1.99 1.75

3.88 5.38 3.23 4.20 5.01 3.68 3.26

2.39 1.83 2.26 2.20 2.01 2.33 1.97

1.58 1.33 1.83 1.57 1.52 1.68 1.35

3.62 3.52 2.80 3.09 2.68 3.22 2.87

1.78 1.74 2.04 1.91 2.10 2.59 1.68

1.14 1.24 1.60 1.32 1.52 1.81 1.11

2.77 2.45 2.60 2.78 2.88 3.71 2.55

1.59 1.62 1.49 1.94 1.66 1.23 1.82

1.09 1.14 1.20 1.40 1.27 0.90 1.32

2.33 2.29 1.86 2.69 2.17 1.69 2.50

1.33 1.70 1.53 1.81 1.83 1.30 1.79

0.89 1.17 1.19 1.26 1.36 0.92 1.26

1.99 2.47 1.96 2.59 2.45 1.85 2.54

2.89

2.56

3.25

2.14

1.91

2.4

2.00

1.76

2.27

1.59

1.42

1.78

1.60

1.41

1.82

*Controlling for age, childhood conditions (hunger, rural life, low socio-economic status, poor health), adulthood socio-economic status (education and occupation), current social conditions (widowhood and insufficient income). NA ¼ heterogeneity of effects were significant at p < 0.05.

2006). In the SABE population, elderly women had 34% higher odds of being cognitively impaired, and this gender difference increased among those over 80 (Nguyen et al., 2008). Most studies report higher depression in elderly women as compared with men (Djernes, 2006). Overall, elderly women had 63% higher odds for depressive symptoms compared with elderly men in LAC cities. Mobility limitations are more frequent in women than in men (Wray & Blaum, 2001). In LAC cities women have nearly threefold odds of being limited in their mobility as compared with men. Focusing on disability in older men and women, a European study reported that men and women performed differently in selected IADL and that the direction of these differences was similar across countries. Though the IADL considered were sensitive to cultural factors, results were remarkably similar across countries (Nikula et al., 2003). Similar results were obtained in this study of the elderly population in seven LAC cities. The CLESA study found that women reported more frequent ADL disability than men in Spain, The Netherlands, Finland, Israel and Sweden; only among Italian elders was this difference not observed (Pluijm et al., 2005). Individual studies in North America have consistently shown that women are more disabled than men (Murtagh & Hubert, 2004). The main contribution of our study is that it tests the hypotheses of differential exposure and differential vulnerability. The results show that independently of lifecourse exposures considered in this analysis, women have an excess morbidity and poorer function compared with men, and that this poorer health is not the result of higher exposure or higher vulnerability to these lifecourse conditions. In addition, these differences do not vary in magnitude in spite of the different contexts across cities. In fact, Latin American women have less education than men; they are not encouraged to be socially or economically independent during their lives, and they more often report insufficient income in later life. Most Latin American women have not worked in the formal sector; they mostly engage in informal labour with no social security benefits and in housework. For the minority of women who have worked in the formal sector and are entitled to pensions, there are two additional factors that lower the amount of pension they receive (Pinzon & Solas, 2002). First, they have on average worked fewer years than men since they are responsible for childbirth, raising children and taking care of sick and dependant family members. Second, women are paid less money for the same work. In old age, most women depend on their husband’s pension or on economic help from the family. In addition, gender roles are still enforced by most Latin American societies, and women have less decision making power than men. Therefore, after widowhood many women find themselves facing insecurity as they are unable to make decisions about or to control their future.

Differences between women and men in health and function may be a function of biologic factors and other social lifecourse exposures. Among the biological variables, models should include at least the type and severity of chronic conditions, and possibly neuroendocrine and immune markers (Seeman, Singer, & Charpentier, 1995; Varadhan et al., 2008). Among the psychosocial variables, the lifecourse experience of domestic violence, the lack of autonomy in decision making, family and employment history, and the use of everyday time in productive and leisure activity should be included (Artazcoz et al., 2004; Doyal, 2004). Health services access, utilization and gender preferences of health providers may be additional factors (Sen & Ostlin, 2008). Health differences between older men and women could also be due to differential vulnerability to social conditions such as poverty in childhood, stressful events in the lifecourse, unemployment and lack of social support. Supporting this hypothesis, some studies have found that women are more likely to report and react to stressors experienced in childhood (Veijola et al., 1998), and that men are more likely to react to economic stressors (Kessler & McLeod, 1984). In The Netherlands, older men appear to be more susceptible to widowhood, retirement and health conditions and consequently to develop higher depressive symptomatology (Beekman et al., 1995; van Grootheest et al., 1999; Sonnenberg et al., 2000). Among Canadian women, socio-structural factors (living arrangements, education, occupation) play an important role in predicting SRH and functional status; while among Canadian men smoking and alcohol are more salient (Denton, Prus, & Walters, 2004; Denton & Walters, 1999). Our finding of lack of differential vulnerability of women and men in LAC populations is contrary to our hypothesis and current evidence (Matthews & Power, 2002) Women’s and men’s social support, ways of socialization and biological responses to stress may be responsible for the previously-mentioned differential vulnerabilities in European and Canadian populations. Though we have not assessed the role of these factors in shaping vulnerability, we have reported that, although women and men have different social support networks, positive support has a similar effect on older women and men facing health and economic distress in Havana (Sicotte, Alvarado, Leon, & Zunzunegui, 2008). Most data are self-reported in SABE. However, self-reported data have been shown to have value in predicting mortality (Idler & Benyamini, 1997), and strong correlations have been reported between self-reported chronic health conditions and medical diagnosis (Kriegsman, Penninx, Van Eijk, Boeke, & Deeg, 1996). Selfreport of life-threatening conditions (such as heart disease, diabetes, hypertension, cerebrovascular accidents and Parkinson) has been shown to be concordant with medical history reviews (Martin, Leff, Calonge, Garrett, & Nelson, 2000). Answers to

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functional scales seem to be reasonably valid, at least for population-based research purposes (Ferraro & Su, 2000; Fried, Young, Rubin, & Bandeen-Roche, 2001). However, women are said to be more aware and concerned about health-related problems due to childhood socialization and adult role expectations (Verbrugge, 1985). It has been suggested that women’s lower threshold to report poor health leads them to report less severe problems compared with men. Alternatively, at least in Caribbean and probably in some Latin American populations, it seems to be more acceptable for women to discuss chronic illness than for men which may also affect their reporting of ill health (Curtis & Lawson, 2000). For instance, taking an uncomplaining stance towards illness and trying not to allow illness to interfere with the family provider’s role may be more important for men than for women in societies that value traditional gender roles. However, validation studies of self-reports of disability show little difference in men and women (Melzer, Lan, Tom, Deeg, & Guralnik, 2004). The SABE data provide a benchmark to describe the evolution of the health status and health needs of the older population of Latin America and to examine the gender, ethnic and socio-economic inequalities in health and access to health care. Conclusions Health status differed widely among all seven LAC cities, but older women had poorer health than older men for all health outcomes in all seven cities. The seven cities included in the SABE study varied in demographic, social, economic and cultural dimensions. The gap in health status between older men and women did not vary across cities with different levels of inequality. Taking into account eight lifecourse exposures that are wellaccepted risk factors for poor health (Alvarado, Guerra, & Zunzunegui, 2007; Alvarado et al., 2002; Palloni & McEniry, 2006) did not explain the health gap between older men and women. This argues the need to develop a wider conceptual framework including additional biological and social factors. Further work needs to be done to evaluate differences in the health status of older men and women and to explain women’s resilience and longer life expectancy compared with men. Acknowledgements We are indebted to the several thousands of people from Latin America and the Caribbean who voluntarily and generously participated in this project. This research was funded by the Institute of Gender and Health of the Canadian Institutes for Health Research. We thank our anonymous reviewers for their helpful comments. References Almeida, O. P., & Almeida, S. A. (1999a). [Reliability of the Brazilian version of the þþabbreviated form of Geriatric Depression Scale (GDS) short form]. Arquivos de Neuropsiquiatr, 57(2B), 421–426. Almeida, O. P., & Almeida, S. A. (1999b). Short versions of the geriatric depression scale: a study of their validity for the diagnosis of a major depressive episode according to ICD-10 and DSM-IV. International Journal of Geriatric Psychiatry, 14(10), 858–865. Alvarado, B. E., Guerra, R. O., & Zunzunegui, M. V. (2007). Gender differences in lower extremity function in Latin American elders: seeking explanations from a life-course perspective. Journal of Aging and Health, 19(6), 1004–1024. Alvarado, B. E., Zunzunegui, M. V., Del Ser, T., & Beland, F. (2002). Cognitive decline is related to education and occupation in a Spanish elderly cohort. Aging Clinical and Experimental Research, 14(2), 132–142. Arber, S., & Cooper, H. (1999). Gender differences in health in later life: the new paradox? Social Science & Medicine, 48(1), 61–76. Artazcoz, L., Artieda, L., Borrell, C., Cortes, I., Benach, J., & Garcia, V. (2004). Combining job and family demands and being healthy: what are the differences between men and women? European Journal of Public Health, 14(1), 43–48. Barcelo, A., Pelaez, M., Rodriguez-Wong, L., & Pastor-Valero, M. (2006). The prevalence of diagnosed diabetes among the elderly of seven cities in Latin America

241

and the Caribbean: the health wellbeing and aging (SABE) project. Journal of Aging and Health, 18(2), 224–239. Bardage, C., Pluijm, S., Pedersen, N. L., Deeg, D. J., Jylha, M., Noale, M., et al. (2005). Self-rated health among older adults:a cross-national comparison. European Journal of Aging, 2, 149–158. Beekman, A. T., Deeg, D. J., van Tilburg, T., Smit, J. H., Hooijer, C., & van Tilburg, W. (1995). Major and minor depression in later life: a study of prevalence and risk factors. Journal of Affective Disorders, 36(1–2), 65–75. Brayne, C., Gill, C., Paykel, E. S., Huppert, F., & O’Connor, D. W. (1995). Cognitive decline in an elderly population–a two wave study of change. Psychological Medicine, 25(4), 673–683. Bruton, P. D., & Masci, P. (2005). Workable pension systems: Reforms in the Caribbean. Washington, DC: IADB. Case, A., & Paxson, C. (2005). Sex differences in morbidity and mortality. Demography, 42(2), 189–214. CEPAL. (2006). La proteccion social de cara al futuro: accesso, finaciamiento y solidaridad. In CEPAL. (Ed.). Santiago de Chile: Comision economica para America Latina y el Caribe. Curtis, S., & Lawson, K. (2000). Gender, ethnicity and self-reported health: the case of African-Caribbean populations in London. Social Science & Medicine, 50(3), 365–385. Denton, M., Prus, S., & Walters, V. (2004). Gender differences in health: a Canadian study of the psychosocial, structural and behavioural determinants of health. Social Science & Medicine, 58(12), 2585–2600. Denton, M., & Walters, V. (1999). Gender differences in structural and behavioral determinants of health: an analysis of the social production of health. Social Science & Medicine, 48(9), 1221–1235. Djernes, J. K. (2006). Prevalence and predictors of depression in populations of elderly: a review. Acta Psychiatrica Scandinavica, 113(5), 372–387. Doyal, L. (2001). Sex, gender, and health: the need for a new approach. BMJ, 323(7320), 1061–1063. Doyal, L. (2004). Gender and the 10/90 gap in health research. Bull World Health Organ, 82(3), 162. Engler, T. (2002). Una ventana para la vejez: poblacion pobreza y posibilidades. In T. Engler, & M. Pelaez (Eds.), Mas vale por viejo. Washington, DC: Banco Interamericano de Desarrollo. Ferraro, K. F., & Su, Y. P. (2000). Physician-evaluated and self-reported morbidity for predicting disability. American Journal of Public Health, 90(1), 103–108. Fried, L. P., Ferrucci, L., Darer, J., Williamson, J. D., & Anderson, G. (2004). Untangling the concepts of disability, frailty, and comorbidity: implications for improved targeting and care. Journal of Gerontology. Series A, Biological Sciences and Medical Sciences, 59(3), M255–M263. Fried, L. P., Young, Y., Rubin, G., & Bandeen-Roche, K. (2001). Self-reported preclinical disability identifies older women with early declines in performance and early disease. Journal of Clinical Epidemiology, 54(9), 889–901. van Grootheest, D. S., Beekman, A. T., Broese van Groenou, M. I., & Deeg, D. J. (1999). Sex differences in depression after widowhood. Do men suffer more? Social Psychiatry and Psychiatric Epidemiology, 34(7), 391–398. Health-Canada. (2003). Exploring concepts of gender and health. In Health-Canada. (Ed.). Ottawa, Canada: Health Canada. Idler, E. L., & Benyamini, Y. (1997). Self-rated health and mortality: a review of twenty-seven community studies. Journal of Health and Social Behavior, 38(1), 21–37. Kessler, R. C., & McLeod, L. D. (1984). Sex differences in vulnerability to life events. American Sociological Review, 19, 620–631. Kriegsman, D. M. W., Penninx, B. W. J. H., Van Eijk, J. T. M., Boeke, A. J. P., & Deeg, D. J. H. (1996). Self-reports and general practitioner information on the presence of chronic diseases in community dwelling elderly: a study on the accuracy of patients’ self-reports and on determinants of inaccuracy. Journal of Clinical Epidemiology, 49(12), 1407–1417. Kukull, W. A., Higdon, R., Bowen, J. D., McCormick, W. C., Teri, L., Schellenberg, G. D., et al. (2002). Dementia and Alzheimer disease incidence: a prospective cohort study. Archives of Neurology, 59(11), 1737–1746. Lawton, M. P., & Brody, E. M. (1969). Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist, 9(3), 179–186. Martin, L. M., Leff, M., Calonge, N., Garrett, C., & Nelson, D. E. (2000). Validation of self-reported chronic conditions and health services in a managed care population. American Journal of Preventive Medicine, 18(3), 215–218. Martinez de la Iglesia, J., Onis Vilches, M. C., Duenas Herrero, R., Aguado Taberne, C., Albert Colomer, C., & Arias Blanco, M. C. (2005). [Abbreviating the brief. Approach to ultra-short versions of the Yesavage questionnaire for the diagnosis of depression]. Atencion Primaria, 35(1), 14–21. Matthews, S., & Power, C. (2002). Socio-economic gradients in psychological distress: a focus on women, social roles and work-home characteristics. Social Science & Medicine, 54(5), 799–810. McDonough, P., & Walters, V. (2001). Gender and health: reassessing patterns and explanations. Social Science & Medicine, 52(4), 547–559. Melzer, D., Lan, T.-Y., Tom, B. D. M., Deeg, D. J. H., & Guralnik, J. M. (2004). Variation in thresholds for reporting mobility disability between national population subgroups and studies. Journal of Gerontology. Series A, Biological Sciences and Medical Sciences, 59(12), 1295–1303. Moen, P., & Chermack, K. (2005). Gender disparities in health: strategic selection, careers, and cycles of control. Journal of Gerontology Series B: Psychological Sciences and Social Sciences, 60(Spec No 2), 99–108. Mossey, J. M., & Shapiro, E. (1982). Self-rated health: a predictor of mortality among the elderly. American Journal of Public Health, 72(8), 800–808.

242

M.-V. Zunzunegui et al. / Social Science & Medicine 68 (2009) 235–242

Murtagh, K. N., & Hubert, H. B. (2004). Gender differences in physical disability among an elderly cohort. American Journal of Public Health, 94(8), 1406–1411. Nagi, S. Z. (1991). Disability concepts revisited: Implications for prevention. Institute of Medicine. Washington, DC: National Academy Press. Nguyen, C., Couture, M. C., Alvarado, B. E., & Zunzunegui, M. V. (2008). Life-course socio-economic disadvantages and prevalence of cognitive impairment in Latin American elders. Journal of Aging and Health, 20(3), 347–362. Nikula, S., Jylha, M., Bardage, C., Deeg, D. J., Gindin, J., Minicuci, N., et al. (2003). Are IADLs comparable across countries? Sociodemographic associates of harmonized IADL measures. Aging Clinical and Experimental Research, 15(6), 451–459. Paddison, O. (2006). Social security in the English-speaking Caribbean. In ECLAC. (Ed.), Project document collection. Santiago de Chile: ECLAC. Palloni, A., & McEniry, M. (2006). Aging and health status of elderly in Latin America and the Caribbean: preliminary findings. Journal of Cross-Cultural Gerontology. Palloni, A., Pinto-Aguirre, G., & Pelaez, M. (2002). Demographic and health conditions of ageing in Latin America and the Caribbean. International Journal of Epidemiology, 31(4), 762. Pearson, V. (2000). Assessment of function. In R. L. Kane, & R. A. Kane (Eds.), Assessing older persons: Measures, meaning, and practical applications (pp. 17– 48). New York: Oxford University Press. Pelaez, M., Palloni, A., Albala, C., Alfonso, J. C., Ham-Chande, R., Hennis, A. J., et al. (2004). SABE survey on health, well-being, and aging in the Latin America and the Caribbean, 2000. In P. A. H. O. W. H. Organization (Ed.). Ann Arbor: Interuniversity Consortium for Political and Social Research. Pinzon, S. A., Morales, C., Zunzunegui, M. V., Engler, T., Pantelides, E. A., Albala, C., et al. (2002). Prevision, promesas y economia social. In T. Engler, & M. Pelaez (Eds.), Mas vale por viejo. Washington, DC: Banco Interamericano de Desarrollo. Pinzon, S. A., & Solas, O. (2002). Politicas y marco juridico. In T. Engler, & M. Pelaez (Eds.), Mas vale por viejo. Washington, DC: Banco Interamericano de Desarrollo. Pluijm, S. M., Bardage, C., Nikula, S., Blumstein, T., Jylha, M., Minicuci, N., et al. (2005). A harmonized measure of activities of daily living was a reliable and valid instrument for comparing disability in older people across countries. Journal of Clinical Epidemiology, 58(10), 1015–1023. RevMan, A. (2003). Review manager (RevMan) 4.2. Version 1.0 for Windows. Copenhagen: The Nordic Cochrane Centre, The Cochrane Collaboration,. Rieker, P. P., & Bird, C. E. (2005). Rethinking gender differences in health: why we need to integrate social and biological perspectives. Journal of Gerontology Series B: Psychological Sciences and Social Sciences, 60(Spec No 2), 40–47. Seeman, T. E., Singer, B., & Charpentier, P. (1995). Gender differences in patterns of HPA axis response to challenge: Macarthur studies of successful aging. Psychoneuroendocrinology, 20(7), 711–725. Sen, G., & Ostlin, P. (2008). Gender inequity in health: why it exists and how we can change it. Global Public Health, 3, 1–12. Sicotte, M., Alvarado, B. E., Leon, E. M., & Zunzunegui, M. V. (2008). Social networks and depressive symptoms among elderly women and men in Havana, Cuba. Aging & Mental Health, 12(2), 193–201.

Sonnenberg, C. M., Beekman, A. T., Deeg, D. J., & van Tilburg, W. (2000). Sex differences in late-life depression. Acta Psychiatrica Scandinavica, 101(4), 286–292. Spitzer, D. L. (2005). Engendering health disparities. Canadian Journal of Public Health, 96(Suppl. 2), S78–S96. Varadhan, R., Walston, J., Cappola, A. R., Carlson, M. C., Wand, G. S., & Fried, L. P. (2008). Higher levels and blunted diurnal variation of cortisol in frail older women. Journal of Gerontology. Series A, Biological Sciences and Medical Sciences, 63(2), 190–195. UNDP. (2007). Human development reports. In UNDP. (Ed.), United Nations Development Program. Veijola, J., Puukka, P., Lehtinen, V., Moring, J., Lindholm, T., & Vaisanen, E. (1998). Sex differences in the association between childhood experiences and adult depression. Psychological Medicine, 28(1), 21–27. Verbrugge, L. M. (1985). Gender and health: an update on hypotheses and evidence. Journal of Health and Social Behavior, 26(3), 156–182. Visser, M., Goodpaster, B. H., Kritchevsky, S. B., Newman, A. B., Nevitt, M., Rubin, S. M., et al. (2005). Muscle mass, muscle strength, and muscle fat infiltration as predictors of incident mobility limitations in well-functioning older persons. Journal of Gerontology. Series A, Biological Sciences and Medical Sciences, 60(3), 324–333. Wong, R., Pelaez, M., & Palloni, A. (2005). [Self-reported general health in older adults in Latin America and the Caribbean: usefulness of the indicator]. Revista Panamericana de Salud Publica, 17(5–6), 323–332. Wong, R., Pelaez, M., Palloni, A., & Markides, K. (2006). Survey data for the study of aging in Latin America and the Caribbean: selected studies. Journal of Aging and Health, 18(2), 157–179. Wray, L. A., & Blaum, C. S. (2001). Explaining the role of sex on disability: a population-based study. Gerontologist, 41(4), 499–510. Yesavage, J. A., Brink, T. L., Rose, T. L., Lum, O., Huang, V., Adey, M., et al. (1982). Development and validation of a geriatric depression screening scale: a preliminary report. Journal of Psychiatric Research, 17(1), 37–49. Zhang, Z. (2006). Gender differentials in cognitive impairment and decline of the oldest old in China. Journal of Gerontology Series B: Psychological Sciences and Social Sciences, 61(2), S107–S115. Zunzunegui, M. V., Alvarado, B. E., Cloos, P., Simeon, D., & Eldemire-Shearer, D. (2008). Caribbean aging project: Final report for the InterAmerican development bank Montreal. Canada: Universite de Montreal-University of West Indies. Zunzunegui, M. V., Kone, A., Johri, M., Beland, F., Wolfson, C., & Bergman, H. (2004). Social networks and self-rated health in two French-speaking Canadian community dwelling populations over 65. Social Science & Medicine, 58(10), 2069–2081. Zunzunegui, M. V., Pinzon, S. A., Beland, F., Pantelides, E. A., Albala, C., & Pratts, O. (2002). Estado de salud, capacidad funcional y necesidades. In T. Engler, & M. Pelaez (Eds.), Mas vale por viejo. Washington, DC: Banco Interamericano de Desarrollo.