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Social Science & Medicine 68 (2009) 235–242

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Social Science & Medicine

journal homepage: www.elsevier .com/locate/socscimed

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

Maria-Victoria Zunzunegui a,*, Beatriz-Eugenia Alvarado b, François Beland a, Bilkis Vissandjee a

a Medecine sociale et preventive, Universite de Montreal, Quebec, Canadab McGill University, Quebec, Canada

a r t i c l e i n f o

Article history:Available online 25 November 2008

Keywords:Latin America and CaribbeanHealth statusAgingGenderLifecourseInequalitiesMenWomenLifecourse

* Corresponding author. Medecine sociale et preven1420 Boulevard Mont Royal, local 3134-2, Montreal, Qþ1 514 3436086; fax: þ1 514 3435645.

E-mail addresses: maria.victoria.zunzunegui@umobeatriz.alvaradollano@mail.mcgill.ca (B.-E. Alvarado), f(F. Beland), bilkis.vissandjee@umontreal.ca (B. Vissand

0277-9536/$ – see front matter � 2008 Elsevier Ltd.doi:10.1016/j.socscimed.2008.10.031

a b s t r a c t

This paper describes differences in health and functional status among older men and women andattempts to anchor the explanations for these differences within a lifecourse perspective. Seven healthoutcomes for men and women 60 years and older from seven Latin American and Caribbean cities areexamined, using data from the 2000 SABE survey (Salud, Bienestar y Envejecimientodn¼ 10,587). Age-adjusted as well as city- and sex-specific prevalence was estimated for poor self-rated health, comor-bidity, mobility limitations, cognitive impairment, depressive symptoms and disability in basic andinstrumental activities of daily living. Logistic regressions were fitted to determine if the differencesbetween 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 differentialvulnerability of men and women to these exposures. Sao Paulo, Santiago and Mexico, cities in countrieswith a high level of income inequalities, presented the highest prevalence of disability, functionallimitations and poor physical health for both women and men. Women showed poorer health outcomesas compared with men for all health indicators and in all cities. Controlling for lifecourse exposures inchildhood, adulthood and old age did not attenuate these differences. Women’s unadjusted and adjustedodds of reporting poor self-rated health, cognitive impairment and basic activities of daily livingdisability 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 highfor mobility limitations. Higher vulnerability to lifecourse exposures in women as compared with menwas not found, meaning that lifecourse exposures have similar odds of poor health outcomes for menand women. A more integrated understanding of how sex and gender act together to influence healthand function in old age needs consideration of additional biological and social factors.

� 2008 Elsevier Ltd. All rights reserved.

Health differences in older men and women can be traced tobiological and social factors (Doyal, 2001; Rieker & Bird, 2005). Anumber of scholars have tried to clarify the differences between sexand gender in order to use these terms more precisely for researchand policy purposes (Health-Canada, 2003; Spitzer, 2005). Sexgenerally refers to the biological characteristics that distinguishmales and females such as anatomy (e.g., body size and shape) andphysiology (e.g., hormonal activity or functioning of organs).Gender refers to the array of socially constructed roles and rela-tionships, personality traits, attitudes, behaviors, values, relativepower and influence that society differentially ascribes to each sex.

tive, Universite de Montreal,uebec, H2V 4P3 Canada. Tel.:

ntreal.ca (M.-V. Zunzunegui),rancois.beland@umontreal.cajee).

All rights reserved.

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 andwomen living in seven main cities of Latin America and theCaribbean are explored: Buenos Aires (Argentina), Bridgetown(Barbados), Sao Paulo (Brazil), Santiago de Chile (Chile), La Havana(Cuba), Ciudad de Mexico (Mexico), Montevideo (Uruguay). Theextent of health differences between men and women in later lifeare examined, and differences or similarities in the distribution ofthese inequalities in the seven cities are assessed. The resultsobtained are used to test two non-exclusive hypotheses on thegeneration of health status differences among men and women(McDonough & Walters, 2001): differential exposure and differ-ential vulnerability. The first hypothesis, differential exposure,proposes that exposure to social factors during the lifecourse isdifferent for men and women, and that these differences result indifferent health outcomes. More specifically, we argue that health

M.-V. Zunzunegui et al. / Social Science & Medicine 68 (2009) 235–242236

differences between older men and women should be smallerwhere lifecourse exposures are similar and larger where exposuresdiffer in magnitude; and that health differences between men andwomen should decrease (or disappear) after controlling for thesedifferences in lifecourse social factors. The second hypothesis,differential vulnerability, states that men and women havedifferent vulnerability to an exposure, that is, the probability ofa health outcome associated with a given exposure is different formen and women. For instance, Visser et al. (2005) have shown thatobesity leads to a higher risk of disability in women than in men.Widowhood together with economic difficulties is associated witha higher risk of depression in men as compared with women (vanGrootheest, Beekman, Broese van Groenou, & Deeg, 1999; Son-nenberg, Beekman, Deeg, & van Tilburg, 2000). Because differentialvulnerability may be biological in nature in some cases or sociallyrooted in some others, or both, we can assume that for the bio-logical root causes, the differential risk associated with a givenexposure for a given health outcome between women and men willbe constant across societies. If vulnerability is socially rooted, thedifferential risk associated with an exposure would change withcontext.

In this paper we attempt to answer the following questions:What are the health differences between urban older women andmen in Latin America? Are these differences explained by differ-ential lifecourse exposures in women and men? Are these differ-ences explained by differential vulnerability to these exposures inwomen and men?

Context

Population aging in Latin America and the Caribbean (LAC) isaccelerated as compared to aging in North America and Europe(Palloni, Pinto-Aguirre, & Pelaez, 2002). In LAC population agingcoincides with social inequalities created by sustained poverty,unemployment, violence and malnutrition; these inequalities haveincreased after a period of structural adjustment and the disman-tling 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 institutionalvolatility, directly affect the lifecourse of elderly persons in LatinAmerican countries. The structural arrangements demanded by theWorld Bank and the International Monetary Fund have led toa profound reorganization of pensions and welfare systems (Bruton& Masci, 2005; Pinzon & Solas, 2002). More than two thirds of LACelderly persons live under the poverty level, with a sizeableproportion of them needing to work in order to survive. Old agepensions, well below the average cost of living, cover only a smallproportion of aging women and men in LAC. Indeed, reforms ofhealth systems in the 1990s have resulted in large portions of thepopulation being excluded from health insurance coverage (CEPAL,2006). Lacking economic security, and with poor and limited healthservices and almost non-existent social services, older people inLAC must rely almost exclusively on their families for economic andsocial support. Not having a pension is more frequent among olderwomen as compared with older men in LAC, while lacking familysupport is more common in men than in women, at least in theCaribbean (Zunzunegui, Alvarado, Cloos, Simeon, & Eldemire-Shearer, 2008). The prices of food, utilities, health services andmedication have increased, whereas pensions have not beenupgraded. The middle class has shrunk and families give priority tothe young – children and adolescents – while older people areincreasingly facing social exclusion (Zunzunegui et al., 2002).

LAC countries differ in size, language, ethnic affiliation, pop-ulation aging and socio-economic characteristics. The smallest ofthe countries included in the present work is Barbados, with

a population of 267,000 inhabitants, and the largest is Brazil, witha population of 170,690,000. Gross national income (GNI) of Cubaand Brazil is under US$3,000. Barbados has the highest GNI(US$17,000), while Uruguay, Chile, Mexico and Argentina arelocated midway with respect to the GNI distribution. Brazil andMexico have the highest illiteracy levels. The participation ofelderly women in the labour force varies widely among countries,but is lower than that of men in all of them. Economic inequality isoften measured by Gini coefficients (an index of income inequalityranging from 0 to 100, where a value of 0 represents absoluteequality, and a value of 100 absolute inequality). Gini coefficientsare high in all seven countries whose main cities are included inthis study, ranging from 38 in Barbados to 57 in Brazil (UNDP, 2007)while the corresponding values for Canada and Europe are between32 and 30.

Methods

Data

The aim of the SABE survey was to study the health and the well-being of older people in seven cities of Latin America and theCaribbean. SABE was coordinated by researchers from the PanAmerican Health Organization (PAHO), the Center for Demographyand Ecology at the University of Wisconsin–Madison, and localprincipal 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 allmultistage, stratified, clustered samples (see details in Wong et al.,2006). The SABE questionnaire was modeled after various instru-ments used in previous studies carried out in the United States(Wong et al., 2006). In cases where the person could not responddirectly to a brief cognitive assessment, a proxy was selected, anda special instrument was applied. A total of 10,587 persons 60 yearsold and over were interviewed at home using a structured ques-tionnaire on their living conditions, health status and healthservices use during the year 2000 (Pelaez et al., 2004). Responserates were 60% in Buenos Aires (n¼ 1043), 85% in Bridgetown(n¼ 1812), 85% in Sao Paulo (n¼ 2143), 84% in Santiago (n¼ 1306),95% in Havana (n¼ 1905), 85% in Mexico (n¼ 1311), and 66% inMontevideo (n¼ 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% inSao Paulo.

Measures

Outcome variablesHealth outcomes included self-rated health, cognitive function

and depressive symptoms, selected chronic conditions, mobilitylimitations, disabilities in Activities of Daily Living (ADL) anddisabilities in Instrumental Activities of Daily Living (IADL).

Self-rated health (SRH) has been shown to be a valid indicator ofhealth 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 fivepossible responses, ‘‘very good,’’ ‘‘good,’’ ‘‘fair,’’ ‘‘poor’’ and ‘‘verypoor.’’ Respondents’ distributions over response categories wereskewed. 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 byanswers to questions formulated as: ‘‘Has a doctor or nurse evertold you that you had.’’ (for diabetes: that is to say, high blood

M.-V. Zunzunegui et al. / Social Science & Medicine 68 (2009) 235–242 237

sugar levels)? Comorbidity was dichotomized as none or onechronic condition versus two or more (Fried, Ferrucci, Darer,Williamson, & Anderson, 2004).

The modified Mini-Mental State Examination (MMSE) used inSABE included six tests, as in the original MMSE (Nguyen, Couture,Alvarado, & Zunzunegui, 2008): 1) naming the current day, monthand year (3 points); 2) immediate recall of three objects (3 points);3) repetition of five numbers in reverse order (5 points); 4) foldinga paper according to instructions (3 points); 5) delayed recallmemory of the same three objects named previously (3 points); 6)drawing intersecting circles (2 points). The maximum score was 19and a cut-off point of 12/13 identified persons with cognitivedeterioration. Respondents with scores under 13 in the modifiedMMSE were asked to respond to the Pfeffer Scale. Respondents withscores under 13 on the modified MMSE and a score of 6 or more onthe Pfeffer scale were considered cognitively impaired (Nguyenet al., 2008).

The Geriatric Depression Scale (GDS) assessed the presence ofdepressive symptoms (Yesavage et al., 1982). This scale is composedof 15 items with dichotomous ‘‘Yes/No’’ responses. A yes response(value of one on the scale) is considered as positive for depressivesymptoms and it has been validated in Spanish – (sensitivity¼ 81%;specificity¼ 76%) (Martinez de la Iglesia et al., 2005) andPortuguese-speaking populations (Almeida & Almeida, 1999b).Reliability of the scale has been reported as ranging from 0.80 to0.86 (Almeida & Almeida, 1999a). GDS scores were dichotomizedusing, acknowledged cut-off points: 0–5 was considered as nodepression, and 6 or more suggestive of depression.

Mobility limitations were defined as the number of limitationsreported in five physical tests: lifting and carrying 10 pounds,walking several blocks, climbing a flight of stairs, kneeling/stoop-ing/crouching, and getting up from a chair (Nagi, 1991). People whoreported that they did not perform the activity were recorded asmissing (n¼ 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 trans-portation, preparing meals, doing light housework and doingheavy housework (Lawton & Brody, 1969). Respondents self-classified themselves as experiencing difficulty, unable to performthe activity, or not performing the activity. In this paper, threeactivities were excluded since more men than women stated thatthey never prepared meals or performed either light or heavyhousework. For respondents stating that they did not carry outthe activity, imputation was carried out using information oncognitive function (n¼ 2822). Those with cognitive impairmentaccording to the SABE protocole (Pelaez et al., 2004) wereclassified as unable to perform the task. IADL disability wasdichotomized as having difficulty with at least one task versus nodifficulty.

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 performedthe activities with or without difficulty. Activities performed withdifficulty were added (range 0–6) and the final ADL scores wasdichotomized as: no difficulties at all [0], presence of one or moredifficulties [1].

Lifecourse exposuresSocio-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) whatwas your family’s economic situation? (good/average or poor); 3)would you say that your health during the first 15 years of yourlife was excellent, good or poor? Dichotomized to excellent/goodor poor; 4) was there a time when you did not have enough to

eat and were hungry? (yes/no). Adult socio-economic status wasdefined by: 1) level of education, measured by asking therespondent the highest level of school achievement; and 2) life-long occupation, recorded according to the International StandardClassification of Occupations (ISCO-88) and sorted into fivecategories: a) white-collars (members of executive branch, busi-ness management, scientific and intellectual professionals andmid-level technical personnel and professionals); b) blue collars(office employees, service workers and salespersons involved intrade and commerce); c) semi- and unskilled workers (officeworkers, artisans in the mechanical arts and other types of arts;machine and equipment operators, unskilled workers; armedforces); d) housewives; and e) farm workers. Current socio-econonomic status: perceived sufficiency of income (sufficient vsinsufficient) was used as indicator of current material resources.Marital status was categorized in two groups: presence orabsence of a partner.

Statistical analysis

To answer the first research question, health outcomes werecompared in each city for men and women, controlling for age.Direct standardization of prevalence of health outcomes was per-formed for six age groups (60–64; 65–69; 70–74; 75–79; 80–84 and85 and more) and for men and women. We used the weightedpopulation of the total number of persons aged 60 and over(women/men) as the reference population. Odds ratios for womencompared with men for each outcome were estimated usinglogistic regression including sex and controlling for age only.Homogeneity of the odds ratios of women compared with menacross cities was tested using RevMan software (RevMan, 2003),with fixed-effects for cities.

For the second research question, we examined the hypothesisthat lifecourse exposures account for the health differencesbetween men and women by adding childhood social and healthcircumstances, adulthood socio-economic position, and currentsocial and material circumstances to the age and sex logisticregressions. Here again, we tested for homogeneity of the oddsratios of women compared with men across cities using RevMansoftware (RevMan, 2003), with fixed-effects for cities. The numberof cases with depression in Bridgetown, and with cognitiveimpairment in Buenos Aires, Montevideo and Bridgetown was toolow to run logistic regressions to test homogeneity among citiesand multiplicative interaction terms for sex and lifecourse expo-sures (see below). They were excluded from these particularanalyses.

To answer the third question on differential vulnerability of menand women to lifecourse exposures, the multiplicative interactionsof sex (1¼women, 0¼men) with lifecourse exposures wereincluded in the logistic models. We ran 7 regressions (one for eachhealth outcome) for each of the seven cities, except for depressionin Bridgetown, and cognitive impairment in Buenos Aires,Montevideo and Bridgetown. In each equation, the 8 interactions oflifecourse exposures and sex were tested simultaneously, adding to45 tests. The p-level was thus set at 0.05/45¼ 0.001 to take intoaccount the multiple tests used in this study.

Results

Table 1 shows the distribution of eight lifecourse exposuresconsidered in this study for men and women in each city. Thematerial, social and health circumstances of men and women in theseven cities partially reflect their respective countries’ demographicand socio-economic characteristics (Table 1). Forty to sixty per centof SABE respondents lived in rural areas in their childhood, and over50% reported coming from families with low SES. Also, 10–35%

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

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

n¼ 660 n¼ 383 n¼ 921 n¼ 583 n¼ 1262 n¼ 881 n¼ 855 n¼ 446 n-1197 n¼ 708 n¼ 740 n¼ 507 n¼ 920 n¼ 530

Childhood circumstancesRural life (yes) 37.5 39.8 49.3 52.2 58.1* 69.2 48.3 51.6 49.7 53.4 53.7 56.5 42.2 45.1Perception of SES

(regular/bad)52.9 49.0 81.6 83.3 67.5* 73.1 53.0* 63.4 70.1* 79.5 74.9* 81.3 62.0* 68.4

Perception of health(regular/bad)

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

Hunger (yes) 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

Adult socio-economic statusLevel of education

(illiterate/no schooling)6.6* 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

OccupationNo-manual 33.6* 44.5 55.5 51.4 23.0* 35.4 20.7* 37.6 41.9 40.7 29.5* 37.4 24.0* 37.0Manual 53.1 55.5 37.2 48.6 67.0 64.6 64.7 62.4 33.8 59.3 45.8 62.6 64.7 63.0Housewives 13.3 7.3 10.0 14.7 24.2 24.8 11.3

Current socio-economic statusPerception of income

(insufficient)68.4** 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

Marital status(no partner)

56.7* 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

*p< 0.01; **0.05<p >0.01.

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

BuenosAires

Bridgetown

SaoPaulo

Santiago Havana Mexico Montevideo

Poor self-rated healthMen 25.7 41.4 52.8 57.8 54.9 66.3 31.8

M.-V. Zunzunegui et al. / Social Science & Medicine 68 (2009) 235–242238

experienced hunger, and 44–66% perceived that their health statuswas poor. A larger proportion of elderly respondents living in San-tiago, Sao Paulo and Mexico reported having lived in rural areaswhen they were young and having experienced hunger ascompared to their counterparts from Buenos Aires, Bridgetown andMontevideo. In adulthood, most men were blue collar workers. InBuenos Aires, Montevideo, Santiago and Sao Paulo, a majority ofwomen worked in manual occupations. More than half of SABErespondents perceived their income as insufficient. In Havana,approximately 80% of both women and men thought their incomewas inadequate to meet their needs. As expected, more women thanmen did not have a life partner. Women perceived their income asinsufficient more often than men in Buenos Aires, Bridgetown andSantiago. The rate of illiteracy was higher in women than men infour cities (Buenos Aires, Santiago, Sao Paulo, and Mexico);accordingly, more men than women had worked in non-manualoccupations. Finally, women’s living conditions in childhood weregenerally better than men’s; the differences were statisticallysignificant mainly for perceived SES and hunger in four out of sevencities.

Women 39.1 53.6 56.0 67.9 67.7 72.3 39.8

ComorbidityMen 33.7 31.9 37.5 30.4 32.8 24.8 32.5Women 49.1 50.8 46.7 48.3 54.8 37.9 48.1

Cognitive impairmentMen 4.1 3.1 9.9 7.2 6.0 7.3 0.6Women 5.2 4.4 12.1 10.1 9.8 12.7 1.8

Depressive symptomsMen 11.7 5.2 13.1 21.8 12.8 16.8 11.3Women 17.4 4.5 21.8 29.1 27.2 22.6 21.5

Mobility limitationsMen 16.0 9.4 22.9 21.6 16.8 26.5 19.2Women 41.7 23.9 40.7 46.6 42.6 45.5 36.0

IADL disabilityMen 11.4 13.9 22.8 17.4 15.1 17.7 9.9Women 25.2 23.3 38.0 31.5 25.8 32.3 17.3

ADL disabilityMen 14.0 10.3 17.7 18.2 15.1 19.6 12.8Women 23.6 16.3 25.0 29.1 22.8 20.8 20.8

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

Age-adjusted gender differences in the seven healthoutcomes by city

The age-adjusted prevalence of the seven health outcomes formen and women is presented in Table 2. Men and women living inHavana, Santiago, Sao Paulo and Mexico had lower health status onself-rated health, mobility limitations and IADL and ADL disabilitythan men and women living in Buenos Aires, Bridgetown andMontevideo. Cognitive impairment was also high in Havana,Santiago, Sao Paulo and Mexico. Depressive symptoms were higherin Santiago than in other cities.

Odds ratios for women compared with men for SRH,comorbidity, depressive symptoms and cognitive impairment areshown in Table 3 for each city and for the pooled data whenhomogeneity across cities was not rejected at p¼ 0.05 (this wasthe case for cognitive impairment and depression). The odds forwomen were higher for all conditions. When Sao Paulo wasexcluded from the analysis, differences between men andwomen across cities also became homogeneous for self-rated

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 indi-cators (mobility limitations and IADL and ADL disability) inwomen versus men are shown in Table 4 for each city and forthe pooled data, when homogeneity across cities was notrejected at p¼ 0.05 (which was the case for IADL and ADLdisability). The odds of disability were higher in women andmen in all cities. When Sao Paulo was excluded from the anal-ysis, differences between men and women across cities alsobecame homogeneous for mobility limitations (OR: 2.96; 95% CI:2.53–3.47).

Tab

le3

Age

and

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cou

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Dep

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95

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95

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Bu

enos

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es1.

771.

33

2.3

61.

65

1.2

02

.26

1.77

1.3

52

.32

1.74

1.3

02

.33

1.5

81.

072

.34

1.0

30

.44

2.4

11.

05

0.4

42

.48

Bri

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n1.

731.

38

2.1

71.

62

1.27

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1.76

2.7

92

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1.7

02

.78

1.3

80

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2.7

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1.15

0.9

61.

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23

1.0

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52

1.27

1.8

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40

2.1

01.

81

1.3

92

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2.1

91.

194

.03

1.2

50

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1.7

00

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0.6

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1.5

91.

22

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61.

48

1.10

1.9

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1.7

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2.2

81.

69

3.0

71.

64

1.2

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.24

1.15

0.6

42

.09

1.6

40

.94

2.8

51.

58

0.8

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Hav

ana

1.7

91.

46

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01.

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1.5

32

.41

2.4

72

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3.0

12

.69

2.1

63

.36

2.4

71.

89

3.2

43

.03

1.5

65

.88

1.73

1.12

2.6

91.

971.

153

.38

Mex

ico

1.35

1.0

51.

741.

40

1.0

51.

87

1.72

1.3

32

.23

1.6

81.

26

2.2

21.

58

1.15

2.1

83

.77

1.6

78

.50

1.6

90

.98

2.9

02

.24

1.16

4.3

3M

onte

vid

eo1.

471.

161.

85

1.51

1.16

1.9

61.

85

1.4

82

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2.0

11.

58

2.5

62

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1.6

23

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0.8

20

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1.6

02

.54

0.5

511

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ole

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53

1.3

91.

68

NA

NA

1.9

01.

672

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31.

42

1.8

71.

45

1.19

1.76

1.3

41.

05

1.71

*Con

trol

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M.-V. Zunzunegui et al. / Social Science & Medicine 68 (2009) 235–242 239

Differential exposure hypothesis

Odds ratios for women compared with men in the seven healthoutcomes, controlling for all lifecourse exposures (‘‘all-adjusted’’)are also shown in Table 3. Their values are similar to the age-adjusted odds ratios. Using pooled estimates, women’s odds ofreporting poor SRH, higher cognitive impairment, depression andADL disability, were 34–63% higher than for men. Odds ratios forcomorbidity, and IADL disability were twice as high in women as inmen. Finally, the odds for mobility limitations were 2.9 timeshigher in women than in men. No heterogeneity among cities wasfound in the differences between women and men, except forcomorbidity (p¼ 0.04) where the odds were 67–73% higher forwomen than for men in Buenos Aires, Sao Paulo and Mexico, whilethey were at least twice as high in women as in men in Montevideo,Bridgetown, Havana and Santiago. Briefly, taking lifecourse expo-sures into account did not explain women’s excess poor health incomparison with men, but heterogeneity across cities was evensmaller after adjusting for lifecourse exposures.

Differential vulnerability hypothesis

The only two interaction terms significant at the set p-level of0.001 were found for Santiago men without a spouse, who hadhigher odds than Santiago women without a spouse for depressionand presence of comorbidity. Thus, the differential vulnerability ofmen and women to lifecourse exposures hypothesis was not sup-ported by these data: Women and men have the same odds forexposure to each of the lifecourse conditions examined: socio-economic position, rural background, health status in childhoodand hunger experience in childhood, level of education and occu-pation in adulthood and marital status and sufficiency of income inold age, with the possible exception of widowed men in Santiago.

Discussion

Excess female morbidity and poorer function were observed inthis study for the elderly population of seven LAC cities in countriesthat vary in size, language, ethnicity, socio-economic indicators,health services and social security schemes. Differences betweenmen’s and women’s prevalence of health indicators are homoge-neous in spite of the wide socio-economic diversity of the countrieswhere the cities in this study are located.

These results are in agreement with previous studies done inwealthier countries. Differences among older men and women inprevalence of poor self-rated health are found in most publishedstudies with the exceptions of Finland (Bardage et al., 2005), Britain(Arber & Cooper, 1999) and Canada (Zunzunegui et al., 2004). In ourstudy, the odds of women reporting poor health were homoge-neous at 53% higher than in men. The mean number of chronicdiseases was higher in elderly women than in men of similar age asreported in United States populations (Case & Paxson, 2005)although there are differences in the prevalence of specific condi-tions, particularly diabetes, that may explain the heterogeneityacross cities. Diabetes was more frequent among women than menin Bridgetown and Havana (Barcelo, Pelaez, Rodriguez-Wong, &Pastor-Valero, 2006), and in Santiago, but diabetes was morefrequent in men in Mexico and Buenos Aires (Palloni & McEniry,2006). While most studies have not found differences in cognitivedecline or incidence of dementia between men and women afteradjusting for age and comorbidity (Brayne, Gill, Paykel, Huppert, &O’Connor, 1995; Kukull et al., 2002), differences have been reportedin countries where women have been denied education in early life,mental stimulation through highly skilled occupations,, and a largesocial networkdall of which are associated with cognitive main-tenance (Alvarado, Zunzunegui, Del Ser, & Beland, 2002; Zhang,

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

Mobility limitations IADL disability ADL disability

Age-adjusted All-adjusted* Age-adjusted All-adjusted* Age-adjusted All-adjusted*

OR CI 95% OR CI 95% OR CI 95% OR CI 95% OR CI 95% OR CI 95%

Buenos Aires 3.24 2.27 4.63 2.65 1.81 3.88 2.39 1.58 3.62 1.78 1.14 2.77 1.59 1.09 2.33 1.33 0.89 1.99Bridgetown 3.32 2.18 5.05 3.43 2.18 5.38 1.83 1.33 3.52 1.74 1.24 2.45 1.62 1.14 2.29 1.70 1.17 2.47Sao Paulo 2.39 1.93 2.96 2.54 2.00 3.23 2.26 1.83 2.80 2.04 1.60 2.60 1.49 1.20 1.86 1.53 1.19 1.96Santiago 3.45 2.52 4.73 2.97 2.10 4.20 2.20 1.57 3.09 1.91 1.32 2.78 1.94 1.40 2.69 1.81 1.26 2.59Havana 3.74 2.91 4.83 3.77 2.85 5.01 2.01 1.52 2.68 2.10 1.52 2.88 1.66 1.27 2.17 1.83 1.36 2.45Mexico 2.42 1.84 3.17 2.71 1.99 3.68 2.33 1.68 3.22 2.59 1.81 3.71 1.23 0.90 1.69 1.30 0.92 1.85Montevideo 2.35 1.78 3.08 2.39 1.75 3.26 1.97 1.35 2.87 1.68 1.11 2.55 1.82 1.32 2.50 1.79 1.26 2.54

Pooled NA 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), currentsocial conditions (widowhood and insufficient income).NA¼ heterogeneity of effects were significant at p< 0.05.

M.-V. Zunzunegui et al. / Social Science & Medicine 68 (2009) 235–242240

2006). In the SABE population, elderly women had 34% higher oddsof being cognitively impaired, and this gender difference increasedamong those over 80 (Nguyen et al., 2008). Most studies reporthigher depression in elderly women as compared with men(Djernes, 2006). Overall, elderly women had 63% higher odds fordepressive 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 threefoldodds of being limited in their mobility as compared with men.Focusing on disability in older men and women, a European studyreported that men and women performed differently in selectedIADL and that the direction of these differences was similar acrosscountries. Though the IADL considered were sensitive to culturalfactors, results were remarkably similar across countries (Nikulaet al., 2003). Similar results were obtained in this study of theelderly population in seven LAC cities. The CLESA study found thatwomen reported more frequent ADL disability than men in Spain,The Netherlands, Finland, Israel and Sweden; only among Italianelders was this difference not observed (Pluijm et al., 2005). Indi-vidual studies in North America have consistently shown thatwomen are more disabled than men (Murtagh & Hubert, 2004).

The main contribution of our study is that it tests the hypothesesof differential exposure and differential vulnerability. The resultsshow that independently of lifecourse exposures considered in thisanalysis, women have an excess morbidity and poorer functioncompared with men, and that this poorer health is not the result ofhigher exposure or higher vulnerability to these lifecourse condi-tions. In addition, these differences do not vary in magnitude inspite 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 indepen-dent during their lives, and they more often report insufficientincome in later life. Most Latin American women have not workedin the formal sector; they mostly engage in informal labour withno social security benefits and in housework. For the minority ofwomen who have worked in the formal sector and are entitled topensions, there are two additional factors that lower the amountof pension they receive (Pinzon & Solas, 2002). First, they have onaverage worked fewer years than men since they are responsiblefor childbirth, raising children and taking care of sick anddependant family members. Second, women are paid less moneyfor the same work. In old age, most women depend on theirhusband’s pension or on economic help from the family. Inaddition, gender roles are still enforced by most Latin Americansocieties, and women have less decision making power than men.Therefore, after widowhood many women find themselves facinginsecurity as they are unable to make decisions about or tocontrol their future.

Differences between women and men in health and functionmay be a function of biologic factors and other social lifecourseexposures. Among the biological variables, models should includeat least the type and severity of chronic conditions, and possiblyneuroendocrine and immune markers (Seeman, Singer, & Char-pentier, 1995; Varadhan et al., 2008). Among the psychosocialvariables, the lifecourse experience of domestic violence, the lack ofautonomy in decision making, family and employment history, andthe use of everyday time in productive and leisure activity shouldbe included (Artazcoz et al., 2004; Doyal, 2004). Health servicesaccess, utilization and gender preferences of health providers maybe additional factors (Sen & Ostlin, 2008).

Health differences between older men and women could also bedue to differential vulnerability to social conditions such as povertyin childhood, stressful events in the lifecourse, unemployment andlack of social support. Supporting this hypothesis, some studieshave found that women are more likely to report and react tostressors experienced in childhood (Veijola et al., 1998), and thatmen are more likely to react to economic stressors (Kessler &McLeod, 1984). In The Netherlands, older men appear to be moresusceptible to widowhood, retirement and health conditions andconsequently to develop higher depressive symptomatology(Beekman et al., 1995; van Grootheest et al.,1999; Sonnenberg et al.,2000). Among Canadian women, socio-structural factors (livingarrangements, education, occupation) play an important role inpredicting SRH and functional status; while among Canadian mensmoking and alcohol are more salient (Denton, Prus, & Walters,2004; Denton & Walters, 1999).

Our finding of lack of differential vulnerability of women andmen in LAC populations is contrary to our hypothesis and currentevidence (Matthews & Power, 2002) Women’s and men’s socialsupport, ways of socialization and biological responses to stressmay be responsible for the previously-mentioned differentialvulnerabilities in European and Canadian populations. Though wehave not assessed the role of these factors in shaping vulnerability,we have reported that, although women and men have differentsocial support networks, positive support has a similar effect onolder women and men facing health and economic distress inHavana (Sicotte, Alvarado, Leon, & Zunzunegui, 2008).

Most data are self-reported in SABE. However, self-reported datahave been shown to have value in predicting mortality (Idler &Benyamini, 1997), and strong correlations have been reportedbetween self-reported chronic health conditions and medicaldiagnosis (Kriegsman, Penninx, Van Eijk, Boeke, & Deeg, 1996). Self-report of life-threatening conditions (such as heart disease, dia-betes, hypertension, cerebrovascular accidents and Parkinson) hasbeen shown to be concordant with medical history reviews(Martin, Leff, Calonge, Garrett, & Nelson, 2000). Answers to

M.-V. Zunzunegui et al. / Social Science & Medicine 68 (2009) 235–242 241

functional scales seem to be reasonably valid, at least for pop-ulation-based research purposes (Ferraro & Su, 2000; Fried, Young,Rubin, & Bandeen-Roche, 2001). However, women are said to bemore aware and concerned about health-related problems due tochildhood socialization and adult role expectations (Verbrugge,1985). It has been suggested that women’s lower threshold toreport poor health leads them to report less severe problemscompared with men. Alternatively, at least in Caribbean andprobably in some Latin American populations, it seems to be moreacceptable for women to discuss chronic illness than for men whichmay also affect their reporting of ill health (Curtis & Lawson, 2000).For instance, taking an uncomplaining stance towards illness andtrying not to allow illness to interfere with the family provider’srole may be more important for men than for women in societiesthat value traditional gender roles. However, validation studies ofself-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 ofthe health status and health needs of the older population of LatinAmerica and to examine the gender, ethnic and socio-economicinequalities in health and access to health care.

Conclusions

Health status differed widely among all seven LAC cities, butolder women had poorer health than older men for all healthoutcomes in all seven cities. The seven cities included in the SABEstudy varied in demographic, social, economic and culturaldimensions. The gap in health status between older men andwomen did not vary across cities with different levels of inequality.Taking into account eight lifecourse exposures that are well-accepted risk factors for poor health (Alvarado, Guerra, & Zunzu-negui, 2007; Alvarado et al., 2002; Palloni & McEniry, 2006) did notexplain the health gap between older men and women. This arguesthe need to develop a wider conceptual framework includingadditional biological and social factors. Further work needs to bedone to evaluate differences in the health status of older men andwomen and to explain women’s resilience and longer life expec-tancy compared with men.

Acknowledgements

We are indebted to the several thousands of people from LatinAmerica and the Caribbean who voluntarily and generouslyparticipated in this project. This research was funded by the Insti-tute of Gender and Health of the Canadian Institutes for HealthResearch. We thank our anonymous reviewers for their helpfulcomments.

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