Skip to main content
Download PDF
Download ePub

Aging, Gender and Quality of Life (AGEQOL) study: factors associated with good quality of life in older Brazilian community-dwelling adults

Health and Quality of Life Outcomes volume 12, Article number: 166 (2014) Cite this article

Abstract

Background

In Brazil, a rapidly aging country suffering from large inequalities, the study of the quality of life (QOL) of aged people is important for the future health. The aim of this study was to examine the associations among QOL, gender, and physical and psychosocial health in older Brazilian community-dwelling adults to identify factors that are associated with better QOL.

Methods

The “Aging, Gender and Quality of Life (AGEQOL)” study, which included 2,052 respondents aged 60 or older, was conducted in Sete Lagoas, Brazil between January and July 2012. The respondents answered questions regarding their socioeconomic and demographic information, health and social situations, cognitive impairment, depressive symptoms and family satisfaction. The authors also applied the Brazilian version the World Health Organization Quality of Life QOL Assessment-Brief Instrument (WHOQOL-BREF) and the World Health Organization Quality of Life Instrument-Older Adults Module (WHOQOL-Old). Ordinal logistic regression with the Proportional-Odds and Logit function was used to test the association between QOL and physical and psychosocial health according to age and socioeconomic status.

Results

Older adults of both genders with five or more years of education, good self-rated health, an absence of depressive symptoms, and no family dysfunction reported better QOL. Retired men had a better QOL compared to non-retired men (OR = 2.2; 95% CI = 1.4–3.2), but this association was not observed in females. Men living in mixed arrangements (OR = 0.5; p = 0.033) and women who did not practice physical activity (OR = 0.7; p = 0.022) tended to have poorer QOL.

Conclusions

We conclude that there are gender differences related to better QOL in this sample. Women with good physical and psychosocial health are more likely to have a better QOL. For men, the best QOL was associated with high socioeconomic conditions and good physical and psychosocial health.

Background

In Brazil, an increase in life expectancy and a decrease in the fertility rate have led to a significant aging population. In South America, the aging population/proportion of older people is increasing at a more rapid rate than in most developed countries [1],[2].

Aging is a complex phenomenon that requires increasing numbers of multidisciplinary studies. The term “active aging”, which was adopted by the World Health Organization (WHO), involves optimizing the opportunities for health, participation and security to improve the quality of life (QOL) as individuals age [3]. The challenge for aging studies is to understand the conditions associated with aging as a positive process and old age as a stage of life in which health, well-being, pleasure and QOL can be increased [4]-[6].

The QOL of older adults could be good, or at least preserved, provided they have autonomy, independence and good physical health and provided they fulfill social roles, remain active and enjoy a sense of personal meaning [7]. Epidemiological population-based studies are important for identifying the determinants and etiological factors associated with aging. To investigate the determinants of aging, questions must be answered using longitudinal surveys [8]. Longitudinal studies specifically designed to assess health, QOL and associated risk factors are not abundant in the literature, particularly those performed in underdeveloped countries in which poverty and a low educational level might lead to a different set of variables that affect the aging process [9].

In Brazil, a country that is rapidly aging and that suffers from large inequalities, the study of the QOL among aged people is important for the future health. This study sought to examine the association between QOL, gender and physical and psychosocial health among older Brazilian community-dwelling adults, with the aim to identify potential factors associated with better QOL.

Methods

The Aging, Gender and Quality of Life (AGEQOL) study is an observational, cohort study of a community-dwelling population aged 60 years and older. The sample is representative of the city of Sete Lagoas in the state of Minas Gerais, Brazil, which has a population of approximately 21,000 older adults (10.2% of the population) [10]. This city is divided into 17 administrative regions, one district and four rural areas [11].

Sample

A complex sampling design was adopted for this study and consisted of a combination of probabilistic sampling methods for selecting a representative sample of the population [12]. For this sampling, the following two calculations were performed: an estimation of the number of older adults and an estimation of the number of households to be visited.

The sample size calculation was performed to compare genders by considering the prevalence of functional impairment in instrumental activities for males (86.6%) and females (72.9%) [13]. The estimated error was up to 5%, with a power of 80% at 95% confidence intervals (95% CI) when considering a design effect of two. An estimated additional 20% of the sample size was added to compensate for refusals. The samples from each group (men and women) were stratified by age in relation to the population and were corrected based on the probability of dying.

Of the total potential participants living in the selected dwellings, 25 (1.2%) were excluded because they could not answer the questionnaire or because of cognitive impairment/dementia or difficulty speaking. One hundred and twenty-five subjects (5.8%) refused to participate in the study, and 100 (4.8%) could not be located or had died. The final sample consisted of 2,052 individuals, of whom 59.7% were female.

The sampling process was conducted in two stages. The census tracts were first selected, and the households within each sector were then selected [10]. In each household, all residents aged 60 years or older were interviewed, regardless of marital status or kinship.

Data collection

A pilot study including 107 older adults (approximately 10% of the sample) was conducted prior to data collection. All of the instruments were validated for Portuguese in Brazil, and the test/retest method was used to assess reliability and concordance. Coefficients greater than 0.80 were obtained (p < 0.001) and included a weighted Kappa (95%) value of 0.81 (0.71 to 0.91) and an adjusted Kappa value of 0.86.

The data collection was conducted in the homes of the older adults between January and July 2012 and involved household interviews and examinations conducted by three examiners and three annotators.

All persons 60+ years in the selected households were informed of the study and were asked to sign an informed consent form that had been previously approved by the Ethical Committee of the Federal University of Minas Gerais. The interviews lasted 40 to 60 minutes. At the end of the interviews, each subject in the city received guidance regarding health care and activity options as well as the personal contact information of the researcher responsible for the questionnaire.

Measures

The socioeconomic and demographic data included age, gender, marital status, income categorized by the median value, years of education, residence and occupation. Most independent variables were dichotomized to enhance the interpretability of the logistic regression coefficients.

Physical activity and social participation were measured using a single question with a dichotomous answer (yes or no). The health-related component included self-reported health conditions, which were assessed using a Likert scale, and access to and utilization of health services. For this study, the categories were grouped into poor (very poor and poor), regular and good (good and very good). With regard to the chronic diseases previously reported to be most relevant to the loss of functionality in aging subjects (hypertension, diabetes, cardiovascular disease, musculoskeletal disorders and respiratory diseases), the number of diseases was recorded as 0, 1 or ≄2.

Functional limitations were evaluated by combining the participants’ responses to questions about six basic activities of daily living (eating, dressing and undressing, grooming, walking, getting in and out of bed, bathing and continence) [14] and seven instrumental activities (using the telephone, travel, shopping, meal preparation, housework, taking medicine and management of finances) [15]. To evaluate the cognitive status of the older people, we used the Mini Mental State Examination, which has been validated in Brazil [16] and has a cut-off of 21/22 points [17]. A score ≀21 indicated cognitive impairment.

The presence or absence of a functional limitation was determined depending on the type of daily living activity and cognitive status, as adapted from Albala [18]. The subjects were classified as restricted if they had one or more limitation in basic or instrumental activities or if they had cognitive impairment.

The presence of depressive symptoms was assessed using the short version of the Geriatric Depression Scale (GDS-15) [19], with a cutoff of 5/6; a score ≄6 indicated suspected depression. Family functioning was assessed using the five-item Family Adaptability, Partnership, Growth, Affection, and Resolve (APGAR) scale, which measures the satisfaction of older adults in relation to various aspects of family life [20]. The responses consist of values between 1 (hardly) and 3 (but not always), and the total score ranges from 5 to 15. A score ≄10 indicates family satisfaction [21].

QOL

We used the World Health Organization Quality of Life Assessment-Brief Instrument (WHOQOL-BREF) [22] and the World Health Organization Quality of Life Instrument-Older Adults Module (WHOQOL-Old) to evaluate QOL [23]. The first instrument is composed of 24 facets that are grouped into four domains that focus on physical, psychological, social and environmental aspects. There is no total score for this instrument, and each item contains five Likert response options that are recorded as scores of 1–5. The WHOQOL-Old module consists of 24 items that are divided into the following six domains: sensory abilities (SAB); autonomy (AUT); past, present and future activities (PPF); social participation (SOP); death and dying (DAD); and intimacy (INT). The scores of all domains are combined to produce an overall score for QOL in older adults, with higher scores indicating good QOL. The instruments were previously validated by Fleck et al.[24],[25] and showed good reliability and validity in the assessment of QOL of Brazilian older adults (the Cronbach’s alpha score ranged from 0.7 to 0.8 for the WHOQOL-Bref and from 0.7 to 0.9 for the WHOQOL-Old).

Statistical analysis

SPSS software (SPSS Institute, Chicago, IL, USA) version 19.0 was used for the analysis and included χ2 tests and ordinal logistic regression.

K-means clustering analysis was used to obtain three groups by considering the better distance between the mean scores of the four dimensions in WHOQOL-BREF and the mean of the total WHOQOL-Old score (Figure 1). The F test was used to analyze the differences and characterize the groups with a significance level of 5%. This type of analysis is an analytical statistical tool that is used to define the development of mutually exclusive, significant subgroups based on the similarities among individuals, without prior knowledge of the allocation within the groups. In cases in which the grouping of the data is successful, the groups are internally homogeneous but have high external heterogeneity [26].

Figure 1
figure 1

Canonical discriminant functions based on the QOL level of older people.

Full size image

Canonical discriminant analysis was used posteriorly to validate the cluster analysis described by two functions. The objective of discrimination is to maximize the variance between and within groups and to verify the efficiency of the overall correct classification of the model [26].

The QOL level among the clusters was adapted from Oliveira et al.[27]; for all the WHOQOL domains, there was a group with good QOL scores, a group with intermediate QOL scores and a group with worse QOL scores.

Ordinal logistic regression was used to test the association between QOL and physical and psychosocial health after controlling for age and socioeconomic status. All analyses were performed separately for each gender. In this study, we applied the Polytomous Universal Model (PLUM), which incorporates the ordinal nature of the dependent variable in the analysis; thus, a logistic regression model with proportional-odds and Logit function [28] was performed. The odds between the categories of the dependent variable were compared by calculating the crude and adjusted odds ratio (OR), and tests evaluating the homogeneity of slopes and multicollinearity were conducted using Pearson's adjustment to analyze the validity of the model. To ascertain the possible interference of a small number of observations, we used residual analysis for ordinal data, as proposed by McCullagh [29]. All of these tests showed that the model satisfied all of the assumptions, and the effect of the complex sample design was considered in all of the analyses.

Results

The age of the total sample at baseline ranged from 60 to 106 years old, and the mean age of all participants at baseline was 70.89 ± 8.14 years (71.03 ± 8.35 for women and 70.69 ± 7.83 for men). Table 1 shows the descriptive statistics of the socioeconomic and health conditions of the participants according to gender. Thirty percent (625) of the participants were more than 74 years old, and 317 (15.4%) older adults were octogenarians. Most men (70.8%) and women (68.7%) were between 60 and 74 years old, and there was no difference in age distribution between genders.

Table 1 Characteristics of the sample by gender (N =2052)
Full size table

Forty-eight types of living arrangements were identified among older adults in the city under study. When taking the three groups of living arrangements that were established in this study into account, it was observed that the majority of older adults who lived alone were women (71.5%), whereas 75.5% of men lived with their partners (p < 0.001). There were no differences in the years of education between the different genders; however, 10.4% of men and 8.6% of women had completed over 4 years of study (Table 1).

Additionally, there were significant differences related to marital status, income, retirement, and living arrangement between genders. The majority of men in the sample were married (74.5%), while 61.7% of women were single, separated or widowed. Most older adults had low monthly income (66.1%), and this percentage was higher for females (71.5%) compared to males (58.1%) (Table 1).

The self-perceived health status was different between men and women (p <0.001). While 50.8% of men rated their health as good, most women rated their health as fair (37.8%) or good (41.8%). Only 15.9% of the older adults did not have chronic diseases; however, the percentage of women (59.6%) with more than two diseases was statistically higher (p < 0.001) than that of men (44.6%). The prevalence of cognitive impairment was 35.3%, with a slightly larger proportion of women (36.0%) than men (34.3%) reporting this condition. In relation to depression, there was a 30.2% prevalence of depressive symptoms and a statistically significant (p < 0.001) difference between genders (23.8% for men and 34.4% for women). There was a high prevalence of functional limitations (36.7%) and a significant difference (p = 0.001) in functional limitations between men (32.6%) and women (39.6%) (Table 1).

Cluster analysis (k-means) resulted in the formation of the following three groups of older adults in relation to QOL (Table 2): subjects with poor, fair and good QOL. The majority of the older adults were included in the fair QOL group (51.4%), which corresponded to the average level of scores in the WHOQOL. The group with worse QOL included 371 people (18.1%), whereas the good QOL group included 627 subjects (30.6%). The results of the test for equality of the group means between the groups were significant, indicating that the groups differed in all QOL domains. The overall correct classification of the canonical discriminant functions was 97.9%, with a correlation coefficient of 0.89.

Table 2 Means and standard deviations of QOL clusters of older adults
Full size table

Differences were observed in all of the QOL variables, except for those of retired individuals. In this case, the socioeconomic distribution between the genders was reversed, with 47.7% of the older adults in the higher QOL group being male and 67.7% of those in the lower QOL group being female. Additionally, there was a gradient association between low QOL and worse health perception, cognitive impairment, depressive symptoms, family dysfunction and functional limitation. Most of the older adults who reported two or more chronic diseases (70.4%) were allocated to the low QOL group (Table 3).

Table 3 Characteristics of the sample by QOL
Full size table

The results of the ordinal regression model, which estimates the OR of good QOL by gender, are shown in Table 4. Age, marital status, income, and cognitive impairment did not remain associated with QOL in the final model.

Table 4 Results of ordinal logistic regression that explain better QOL in older adults, separated by gender
Full size table

There was an education gradient for the QOL of men. Men with 1–4 and >5 years of education were 2.2 and 4.2 times more likely to have a better QOL than illiterate men. Similarly, women with five or more years of education were associated with good QOL (OR = 2.2; p < 0.001) (Table 4).

Retired men had better QOL when compared to non-retired men (OR = 2.2; 95% CI = 1.4–3.2), but this association was not observed in females. Men living in mixed arrangements (OR = 0.5; p = 0.033) and women who did not practice physical activity (OR = 0.7; p = 0.022) tended to have a poorer QOL (Table 4).

As shown in Table 4, there was an increase in the OR for the association between QOL and self-rated health for both genders once the model was adjusted for demographic variables and psychosocial health. Men with fair health (OR = 3.0; 95% CI = 2.2–4.3) and, in particular, good health (OR = 5.0; 95% CI = 3.5–9.4) were associated with good QOL. Women with good and fair health were 4.2 (OR = 4.2; 95% CI = 2.8–6.2) and 3.0 (OR = 3.0; 95% CI = 2.3–4.0) times more likely to have a good QOL, respectively.

For both genders, there was a robust association between QOL and all psychosocial variables, except cognitive impairment. Men without depressive symptoms and women without family dysfunction were 3.6 (OR = 3.6; 95% CI = 2.5–5.2) and 3.0 (OR = 3.0; 95% CI = 2.3–4.0) times more likely to have good QOL, respectively (Table 4).

Discussion

The physical and psychosocial health and socio-demographic variables examined in this study were evaluated using ordinal logistic regression, which resulted in the following five variables being associated with good QOL for both genders: self-rated health, depressive symptoms, years of education, chronic diseases, and family dysfunction. Additionally, good QOL for men was associated with retirement, mixed living arrangements, and physical activity, whereas good QOL for women was associated with physical activity; these results are similar to those of other studies [27],[30],[31]. These factors represent targets for policy action because they have the potential to affect the health of older individuals in the general population.

A number of studies have been performed on QOL in older adults. This study is original and innovative because it used a representative sample to provide information regarding an ordinal positive relationship between QOL and self-rated health. Furthermore, our results indicate that the most important factors for a good QOL for both genders is a good health perception and a lack of depression, even when the model was adjusted for socioeconomic conditions.

We observed a significant difference of 4.4% when comparing good self-rated health between the low and high QOL groups. In the ordinal regression, the men and women who reported having good health were 5.7 and 4.2 times more likely to have good QOL, respectively.

Previous studies on QOL in older adults have also shown a direct relationship with self-rated health [27],[32],[33]. In particular, older adults who evaluated themselves as having good health tended to have good QOL [27].

The perception of health in older adults was generally positive because most of the older adults in this sample rated their health as good (52.1%), including 58.0% of the men and 48.2% of the women (48.2%). However, the percentage of poorer self-rated health was higher in women compared to men. This study provides further evidence that QOL can be explained by self-rated health and its associated factors among older men and women.

In the SABE study (Salud, Bienestar y Envejecimiento) in SĂŁo Paulo, Brazil, 8.9% of women and 7.2% of men demonstrated poor health. In other SABE study countries, the participants reporting good/very good health ranged from 27.9% of women (Mexico) to 69.0% of men (Uruguay) [2].

A previous study on the components of self-rated health among adults suggested that physical health (chronic diseases and functional limitations) most likely comprises the majority of an individual’s perception of health status [34], and this result was observed in this study.

Health perception involves an individual’s evaluation of his/her body in relation to his/her feelings, including feelings regarding health and well-being, and this perception can be altered by environmental stressors and the social context [35]. For older adults, the concept of self-rated health remains stable despite significant health problems, although over time, there might be a reduction in the standard of good self-rated health [36].

Self-rated health has been shown to be a reliable method for measuring health status [37] and to be a consistent predictor of mortality in older adults [38]. It is essential to use the association between perceived health and QOL in patients, especially in regards to the dual direction of this association.

We observed a very strong association between QOL and depressive symptoms, which corroborates the findings of other studies [32],[33],[39],[40]. Thus, the choice of good QOL was 3.6 and 2.2 times higher for men and women without depressive symptoms than for those experiencing depressive symptoms, respectively. This finding could be explained by the high prevalence of depression (30.2%) in this sample; this figure reached 34.4% in women and 70.4% in women with poor QOL. These disorders are more prevalent in females, but this gender vulnerability varies with age [41].

In a study conducted in ƁódĆș, Poland, 30.9% of older adults (56.5% females) were found to suffer from depression. According to the authors, the chances of good self-rated QOL were 9.9 (95% CI = 5.0–19.6) times higher in older adults without depression [42].

Considering the importance of the physical and psychosocial aspects of active aging and of QOL in older adults, other results of this study should be briefly discussed. In this study, an increase in the number of chronic diseases was associated with a decrease in QOL, and statistically significant gender differences were observed between chronic diseases and QOL. Most women who reported two or more diseases were classified as having poor (73.3%) or fair (62.3%) QOL (data not shown). In general, the prevalence of chronic disease among older people in Brazil is high and differs between genders [11], resulting in negative repercussions on QOL [43]. Preventive actions and the promotion of policies for controlling the effect on health conditions could result in good QOL in this population [30].

Physical activity is a protective factor for QOL and has been previously discussed in the literature [44],[45]. For women, we observed a significant association between QOL and physical activity (OR = 0.7; 95% CI = 0.5–1.0), i.e., the choice of good QOL was 1.4 times higher for women who practiced physical activity. However, this association was not accurate once the confidence intervals included a value of 1.0. Physical activity was measured using a single yes/no question, which is an important limitation of this study because these results assume that any level of physical activity will be associated with health.

We did not observe an association between marital status and QOL, although we observed an inverse association between QOL and family dysfunction. Men and women who were satisfied with their family relationship had 1.8 and 3.0 times higher odds of good QOL, respectively. Frequent contacts and visits with friends or family have been shown to motivate activity and increase self-rated QOL [46].

Additionally, we found a high percentage of individuals with poor QOL living in mixed arrangements, i.e., sharing the household with their sons and frequently with sons and grandchildren (44.6%). This situation, which is common in other Brazilian regions, is in contrast to the living arrangements in developed countries [11].

As shown in Table 4, men living in mixed arrangements had worse QOL than those living alone. In our dataset, most men who lived in mixed arrangements had functional limitations and reported more than two chronic diseases (63.3%). It is possible that men in our sample could have been living in mixed arrangements because they had poorer health and therefore needed daily assistance; however, these results should be interpreted with caution, as there was a low percentage of men living alone (9.7%). Size, sample stratification and corrections minimized these effects, thus permitting comparisons in this study.

A mixed living arrangement could have a negative effect on the older population [20],[47]. However, living alone presents a greater risk of loneliness and isolation because loneliness increases as the social contacts of older individuals decrease [46].

Similar to the results of other studies [33],[48],[49], we found an association between QOL and education. Sete Lagoas is a Brazilian city with high life expectancy (73.9 years) and good social indicators [50]. In addition, illiteracy is high in this sample (28.2%) compared to the current national data (24%) [51]. These results are often found in most Latin American countries [2] and in some regions in Brazil, where very different educational opportunities are available for the rich and poor.

A low level of education is an important aspect to be considered when developing public policies for older adults and a proposed collective action. In our study, the illiteracy rate was similar between genders (29.1% for men and 27.7% for women). A previous study investigated trends in educational inequalities in terms of old-age mortality in Norway from 1961 to 2009, and the authors observed that relative educational inequalities in old-age mortality were increased for both genders [52].

The association of years of education with QOL was different between the genders. We observed an ordinal crescent impact of years of education on QOL for men, indicating that education can be a protective factor for good QOL among men. The QOL among women with 1–4 years of education was no different than that of illiterate women.

Our results correspond to the baseline data reported for the AGEQOL study. However, the lack of understanding of the ways in which specific levels of education interfere in the association between SES and QOL is the first limitation of this study. A longitudinal follow-up study of older adults would permit better comparisons of this study with others, although such comparisons might be hampered by differences in the QOL models and measures that are employed across studies. It is not yet possible to determine whether there is a temporal relationship between the studied variables.

The response rate in this study could be considered high (98.8%); therefore, this study is one of the few studies that have been performed using a probabilistic sample of older adult community residents with an adequate number of participants to perform an ordinal logistic regression. Our results are valid and representative of the population living in the community that lacks significant cognitive and/or physical deficits.

In addition to the limitations of this being a cross-sectional study, it should be emphasized that the evaluation of QOL presupposes the quantification of a construct that is sensibly marked by the subjectivity of individual experiences, beliefs, expectations and perceptions [24].

In this sense, it is necessary to discuss the instruments used to measure QOL in older adults. We used the WHOQOL-BREF and WHOQOL-Old, which were developed by the WHO, are widely reported in the scientific literature and have been validated in Brazil [24],[25]. The results of this study corroborate those reported by the Brazilian WHOQOL group. For older Brazilian adults, a positive QOL includes several aspects such as activity, income, social life and family relationships, whereas a negative QOL is related to poor health, which differs between individuals [53].

WHOQOL-Old is a supplementary module for older adults and can be added to the existing WHOQOL instruments [22]. Bowling [7] compared generic QOL scales used for older adults and showed that the WHOQOL-Old was the most comprehensive instrument; the questions in this instrument are based on measuring suffering, but the questionnaire is relatively long, and the Likert scale format might be boring to the subjects (although there is no evidence that this characteristic has adversely affected responses to date).

Additionally, Bowling emphasized the need for a generic, truly multidimensional QOL measure with minimal respondent burden for evaluating the outcomes of health and social care in older populations [7]. The reason for the existing difficulties in the assessment of QOL that limits its inclusion in clinical practice and public health services is relevant [54].

To minimize these limitations, one specific method of analysis was conducted in this study. Based on a Brazilian study [27], we used cluster analyses and canonical discriminant analyses to compile both WHOQOL instruments into a unique measure for QOL. This analysis was performed to provide an ordinal variable with three internally more homogeneous groups that were distinct from each other. Additionally, we minimized the variations between the mean scores of the five dimensions of QOL that were considered. We found a high percentage of correct classification (97.9%) and a high correlation coefficient (0.89), which indicated the likelihood that we had constructed a good measure of QOL for older adults in this sample.

In future studies, we suggest replicating this statistical model, considering gender and age stratification variations and including other independent variables concerning nutrition and lifestyle. Adaptation and resilience might also play a role in maintaining good QOL [55].

Despite these limitations, this study confirmed that the QOL of older adults differed between the three clusters that were formed, with a good QOL being strongly associated with good self-rated health, the absence of depressive symptoms, and family satisfaction.

Overall, the results demonstrate that active aging in Sete Lagoas, Brazil, does not occur evenly across genders. Better healthcare requires the inclusion of such differences as part of the comprehensive evaluation of older adults [56].

The discussions of aging in the different genders in relation to living conditions and perceived health that are presented in this study need to be further explored, as there are particularities of each group that may have been missed during routine analysis. We believe that this study may contribute to the formulation of new public health and social care policies for older adults in the medium and long term. Older adults will benefit from interdisciplinary monitoring that focuses on promoting health, improving QOL and active aging.

Conclusions

We conclude that there are gender differences related to better QOL in this sample cohort. Women with good physical and psychosocial health are more likely to have a better QOL. For men, the best QOL was associated with high socioeconomic conditions and good physical and psychosocial health. We hope that our study contributes to future discussions on the most important predictors for assessing QOL in older adults and on long-term changes in the perception of QOL in this population.

Abbreviations

ADL:

Activity of daily living

APGAR:

Adaptability, partnership, growth, affection, and resolve

AUT:

Autonomy

DAD:

Death and dying

GDS:

Geriatric depression scale

IADL:

Instrumental activities of daily living

95% CI:

95% confidence interval

INT:

Intimacy

MMSE:

Mini-mental state examination

OR:

Odds ratio

PPF:

Past, present and future activities

PLUM:

Polytomous universal model

QOL:

Quality of life

SAB:

Sensory abilities

SOP:

Social participation

WHO:

World Health Organization

WHOQOL-BREF:

World Health Organization Quality of Life Assessment-Brief Instrument

WHOQOL-Old:

World Health Organization Quality of Life Instrument-Old

References

  1. Paskulin LMG, Aires M, Gonçales AV, Kottwitz CCB, Morais EP, Brondani MA: Aging, diversity, and health: the Brazilian and the Canadian context. Acta Paul Enferm 2011, 24: 851–856. 10.1590/S0103-21002011000600021

    Article  Google Scholar 

  2. Albala C, Lebrão ML, León Díaz EM, Ham-Chande R, Hennis AJ, Palloni A, Peláez M, Pratts O: Encuesta Salud, Bienestar y Envejecimiento (SABE): metodología de la encuesta y perfil de la población estudiada. Rev Panam Salud Publica 2005, 17: 307–322. 10.1590/S1020-49892005000500003

    Article  PubMed  Google Scholar 

  3. World Health Organization: Active Ageing: A Policy Framework. Geneva: 2002 , [http://whqlibdoc.who.int/hq/2002/who_nmh_nph_02.8.pdf]

  4. Lowsky DJ, Olshansky SJ, Bhattacharya J, Goldman DP: Heterogeneity in healthy aging. J Gerontol A Biol Sci Med Sci 2014, 69: 640–649. 10.1093/gerona/glt162

    Article  PubMed Central  PubMed  Google Scholar 

  5. Hekmatpou D, Shamsi M, Zamani M: The effect of a healthy lifestyle program on the elderly's health in Arak. Indian J Med Sci 2013, 67: 70–77. 10.4103/0019-5359.121119

    Article  CAS  PubMed  Google Scholar 

  6. De Luca d’Alessandro E, Bonacci S, Giraldi G: Aging populations: the health and quality of life of the elderly. Clin Ter 2011, 162: e13-e18.

    PubMed  Google Scholar 

  7. Bowling A: Quality of life in older age: what older people say. In Quality of Life in Old Age. International and Multi-Disciplinary Perspectives. Edited by: Mollenkopf H, Walker A. Springer, Dordrecht; 2007.

    Google Scholar 

  8. Ramos LR: Fatores determinantes do envelhecimento saudável em idosos residentes em centro urbano: Projeto Epidoso, São Paulo. Cad Saude Publica 2003, 19: 793–798. 10.1590/S0102-311X2003000300011

    Article  PubMed  Google Scholar 

  9. Ramos LR, Toniolo JN, Cendoroglo MS, Garcia JT, Najas MS, Perracini M, Paola CR, Santos FC, Bilton T, Ebel SJ, Macedo MBM, Almada CM, Nasri F, Miranda RD, Gonçalaves M, Santos ALP, Fraietta R, Vivacqua IN, Alves MLM, Tudisco ES: Two-year follow-up study of elderly residents in S. Paulo, Brazil: methodology and preliminary results. Rev Saude Publica 1998, 32: 397–407. 10.1590/S0034-89101998000500001

    Article  CAS  PubMed  Google Scholar 

  10. Instituto Brasileiro de Geografia e EstatĂ­stica: Censo 2010. , [http://www.ibge.gov.br]

  11. Lei n° 7804 de 14 de Outubro de 2009 de Sete Lagoas, Define as RegiĂ”es Administrativas - RA no PerĂ­metro Urbano do Distrito Sede do MunicĂ­pio de Sete Lagoas. , [http://www.jusbrasil.com.br/legislacao/834407/lei-7804–09-sete-lagoas]

  12. Kish L: Survey Sampling. John Wiley, New York; 1965.

    Google Scholar 

  13. Santos JLF, LebrĂŁo ML, Duarte YAO, Lima FD: Desempenho funcional de idosos nas atividades instrumentais da vida diĂĄria: uma anĂĄlise no MunicĂ­pio de SĂŁo Paulo, Brasil. Cad SaĂșde PĂșblica 2008, 24: 879–886.

    PubMed  Google Scholar 

  14. Lino VTS, Pereira SRM, Camacho LAB, Ribeiro Filho ST, Buksman S: Adaptação transcultural da Escala de IndependĂȘncia em Atividades da Vida DiĂĄria (Escala de Katz). Cad SaĂșde PĂșblica 2008, 24: 103–112. 10.1590/S0102-311X2008000100010

    Article  PubMed  Google Scholar 

  15. Santos RL, Virtuoso JĂșnior JS: Confiabilidade da versĂŁo brasileira da Escala de atividades Instrumentais da vida DiĂĄria. RBPS 2008, 21: 290–296. 10.5020/18061230.2008.p290

    Article  Google Scholar 

  16. Brucki SMD, Nitrini R, Caramelli P, Bertolucci PHF, Okamoto IH: SugestĂ”es para o uso do mini-exame do estado mental no Brasil. Arq Neuropsiquiatr 2003, 61: 77–81. 10.1590/S0004-282X2003000500014

    Google Scholar 

  17. Quiroga P, Albala C, Klaasen G: Validación de un test de tamizaje para el diagnóstico de demencia asociada a edad, en Chile. Rev Med Chil 2004, 132: 467–478. 10.4067/S0034-98872004000400009

    Article  PubMed  Google Scholar 

  18. Albala C, Lera L, GarcĂ­a C, Arroyo P, Marin PP, Bunout D: Searching a common definition for functional limitation in Latin America. Gerontologist 2004, 44: 550.

    Google Scholar 

  19. Almeida OP, Almeida AS: Confiabilidade da versão brasileira da Escala de Depressão em Geriatria (GDS) versão reduzida. Arq Neuropsiquiatr 1999, 57: 421–426. 10.1590/S0004-282X1999000300013

    Article  CAS  PubMed  Google Scholar 

  20. Duarte YAO, Lebrão ML, Lima FD: Contribuição dos arranjos domiciliares para o suprimento de demandas assistenciais dos idosos com comprometimento funcional em São Paulo, Brasil. Pan Am J Public Health 2005, 17: 370–378. 10.1590/S1020-49892005000500009

    Article  Google Scholar 

  21. Mengel M: The use of the family APGAR in screening for family dysfunction in a family practice center. J Fam Practice 1987, 24: 394–398.

    CAS  Google Scholar 

  22. Harper A, Power M: Development of the World Health Organization WHOQOL-BREF quality of life assessment. The WHOQOL Group. Psychol Med 1998, 28: 551–558. 10.1017/S0033291798006667

    Article  Google Scholar 

  23. Power M, Quinn K, Schmidt S: Development of the WHOQOL-old module. Qual Life Res 2005, 14: 2197–2214. 10.1007/s11136-005-7380-9

    Article  PubMed  Google Scholar 

  24. Fleck MPA, Louzada S, Xavier M, Chachamovich E, Vieira G, Santos L, Pinzon V: Application of the Portuguese version of the abbreviated instrument of quality life WHOQoL-bref. Rev Saude Publica 2000, 34: 178–183. 10.1590/S0034-89102000000200012

    Article  CAS  PubMed  Google Scholar 

  25. Fleck MPA, Chachamovich E, Trentini CM: Development of the WHOQoL-old module in Brazil. Rev Saude Publica 2006, 40: 785–791. 10.1590/S0034-89102006000600007

    Article  PubMed  Google Scholar 

  26. Hair JF, Black WC, Babin JB, Anderson RE, Tatham RL: Multivariate Data Analysis. Prentice-Hall, Upper Saddle River; 2009.

    Google Scholar 

  27. Oliveira SES, Hohendorff JV, MĂŒller JL, Bandeira DR, Koller SH, Fleck MPA, Trentini CM: Associations between self-perceived quality of life and socio-demographic, psychosocial, and health variables in a group of elderly. Cad SaĂșde PĂșblica 2013, 29: 1437–1448. 10.1590/S0102-311X2013001100017

    Article  PubMed  Google Scholar 

  28. De Carlo LT: Using the PLUM procedure of SPSS to fit unequal variance and generized signal detection models. Behav Res Methods Instrum Comput 2003, 35: 49–56. 10.3758/BF03195496

    Article  Google Scholar 

  29. McCullagh P: Regression models for ordinal data. J R Stat Soc Series B Stat Methodol 1980, 42: 109–142.

    Google Scholar 

  30. Lera L, Fuentes-Garcia A, Sanchez H, Albala C: Validity and reliability of the SF-36 in Chilean older adults: the ALEXANDROS study. Eur J Ageing 2013, 10: 127–134. 10.1007/s10433-012-0256-2

    Article  Google Scholar 

  31. Estrada A, Cardona D, Segura AM, Chavarriaga LM, Ordóñez J, Osorio JJ: Calidad de vida de los adultos mayores de MedellĂ­n. Biomedica 2011, 31: 492–502. 10.7705/biomedica.v31i4.399

    Article  PubMed  Google Scholar 

  32. Trentini CM, Chachamovich E, Wagner GP, MĂŒller DH, Hirakata VN, Fleck MPA: Quality of life (QoL) in a Brazilian sample of older adults: the role of sociodemographic variables and depression symptoms. Appl Res Qual Life 2011, 6: 291–309. 10.1007/s11482-010-9128-0

    Article  Google Scholar 

  33. Burström K, Johannesson M, Diderichsen F: Health-related quality of life by disease and socio-economic group in the general population in Sweden. Health Policy 2001, 55: 51–69. 10.1016/S0168-8510(00)00111-1

    Article  PubMed  Google Scholar 

  34. Onadja Y, Bignami S, Rossier C, Zunzunegui MV: The components of self-rated health among adults in Ouagadougou, Burkina Faso. Popul Health Metr 2013, 11: 15. 10.1186/1478-7954-11-15

    Article  PubMed Central  PubMed  Google Scholar 

  35. Blazer DG: How do you feel about
? Health outcomes in late life and self-perceptions of health and well-being. Gerontologist 2008, 48: 415–422. 10.1093/geront/48.4.415

    Article  PubMed  Google Scholar 

  36. Galenkamp H, Huisman M, Braam AW, Deeg DJ: Estimates of prospective change in self-rated health in older people were biased owing to potential recalibration response shift. J Clin Epidemiol 2012, 65: 978–988. 10.1016/j.jclinepi.2012.03.010

    Article  PubMed  Google Scholar 

  37. Bailis DS, Segall A, Chipperfield JG: Two views of self-rated general health status. Soc Sci Med 2003, 56: 203–217. 10.1016/S0277-9536(02)00020-5

    Article  PubMed  Google Scholar 

  38. Ferraro KF, Wilkinson LR: Alternative measures of self-rated health for predicting mortality among older people: is past or future orientation more important? Gerontologist 2013, [doi:10.1093/geront/gnt098].

  39. Renaud J, BĂ©dard E: Depression in the elderly with visual impairment and its association with quality of life. Clin Interv Aging 2013, 8: 931–943. 10.2147/CIA.S27717

    Article  PubMed Central  PubMed  Google Scholar 

  40. Campbell KH, Huang ES, Dale W, Parker MM, John PM, Young BA, Moffet HH, Laiteerapong N, Karter AJ: Association between estimated GFR, health-related quality of life, and depression among older adults with diabetes: the Diabetes and Aging Study. Am J Kidney Dis 2013, 62: 541–548. 10.1053/j.ajkd.2013.03.039

    Article  PubMed Central  PubMed  Google Scholar 

  41. Faravelli C, Alessandra Scarpato M, Castellini G, Lo Sauro C: Gender differences in depression and anxiety: the role of age. Psychiatry Res 2013, 210: 1301–1303. 10.1016/j.psychres.2013.09.027

    Article  PubMed  Google Scholar 

  42. BryƂa M, BurzyƄska M, Maniecka-BryƂa I: Self-rated quality of life of city-dwelling elderly people benefitting from social help: results of a cross-sectional study. Health Qual Life Outcomes 2013, 11: 181. 10.1186/1477-7525-11-181

    Article  PubMed Central  PubMed  Google Scholar 

  43. Lima MG, Barros MB, CĂ©sar CL, Goldbaum M, Carandina L, Ciconelli RM: Impact of chronic disease on quality of life among the elderly in the state of SĂŁo Paulo, Brazil: a population-based study. Rev Panam Salud Publica 2009, 25: 314–321. 10.1590/S1020-49892009000400005

    Article  PubMed  Google Scholar 

  44. Sampaio PY, Ito E: Activities with higher influence on quality of life in older adults in Japan. Occup Ther Int 2013, 20: 1–10. 10.1002/oti.1333

    Article  PubMed  Google Scholar 

  45. Bowling A, Banister D, Sutton S, Evans O, Windsor J: A multidimensional model of the quality of life in older age. Aging Ment Health 2002, 6: 355–371. 10.1080/1360786021000006983

    Article  CAS  PubMed  Google Scholar 

  46. Noro A, Aro S: Health-related quality of life among the least dependent institutional elderly compared with the non-institutional elderly population. Qual Life Res 1996, 5: 355–366. 10.1007/BF00433920

    Article  CAS  PubMed  Google Scholar 

  47. Wang H, Chen K, Pan Y, Jing F, Liu H: Associations and impact factors between living arrangements and functional disability among older Chinese adults. PLoS One 2013, 8: e53879. 10.1371/journal.pone.0053879

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  48. Molzahn AE, Kalfoss M, Schick Makaroff K, Skevington SM: Comparing the importance of different aspects of quality of life to older adults across diverse cultures. Age Ageing 2011, 40: 192–199. 10.1093/ageing/afq156

    Article  PubMed  Google Scholar 

  49. Pereira RJ, Cotta RM, Franceschini Sdo C, Ribeiro Rde C, Sampaio RF, Priore SE, Cecon PR: InfluĂȘncia de fatores sociossanitĂĄrios na qualidade de vida dos idosos de um municĂ­pio do Sudeste do Brasil. CiĂȘncia & SaĂșde Coletiva 2011, 16: 2907–2917. 10.1590/S1413-81232011000600028

    Article  Google Scholar 

  50. Pesquisa Nacional por Amostra de DomicĂ­lios: Perfil do MunicĂ­pio de Sete Lagoas, MG. , [http://atlasbrasil.org.br/2013/pt/perfil_m/sete-lagoas_mg]

  51. Instituto Brasileiro de Geografia e Estatística: Síntese de Indicadores Sociais: uma Anålise das CondiçÔes de Vida da População Brasileira, 2012. Rio de Janeiro: 2012 , [ftp://ftp.ibge.gov.br/Indicadores_Sociais/Sintese_de_Indicadores_Sociais_2012/SIS_2012.pdf]

  52. Moe JO, Steingrímsdóttir ÓA, Strand BH, Grþholt EK, Néss Ø: Trends in educational inequalities in old age mortality in Norway 1961–2009: a prospective register based population study. BMC Public Health 2012, 27: 911. 10.1186/1471-2458-12-911

    Article  Google Scholar 

  53. Xavier FMF, Ferraz MPT, Norton M, Escosteguy NU, Moriguchi EH: Elderly people’s definition of quality of life. Rev Bras Psiquiatr 2003, 25: 31–39. 10.1590/S1516-44462003000100007

    Article  PubMed  Google Scholar 

  54. Seidl EMF, Zannon CMLC: Qualidade de vida e saĂșde: aspectos conceituais e metodolĂłgicos. Cad SaĂșde PĂșblica 2004, 20: 580–588. 10.1590/S0102-311X2004000200027

    Article  PubMed  Google Scholar 

  55. Netuveli G, Blane D: Quality of life in older ages. Brit Med Bull 2008, 85: 113–126. 10.1093/bmb/ldn003

    Article  PubMed  Google Scholar 

  56. Samper-Ternent R, Kuo YF, Ray LA, Ottenbacher KJ, Markides KS, Al Snih S: Prevalence of health conditions and predictors of mortality in oldest old Mexican Americans and non-Hispanic whites. J Am Med Dir Assoc 2012, 13: 254–259. 10.1016/j.jamda.2010.07.010

    Article  PubMed Central  PubMed  Google Scholar 

Download references

Acknowledgments

The study was conducted with financial help from the National Council for Scientific and Technological Development – CNPq (481672/2011-7, 305032/2012-7 and 141307/2011-0); the Coordination of Improvement of Higher Education – CAPES (process 8314/13-6); Pró-Reitoria de Pesquisa da Universidade Federal de Minas Gerais – PRPq/UFMG; and Fundação de Amparo à Pesquisa de Minas Gerais – FAPEMIG.

Author information

Authors and Affiliations

  1. School of Dentistry, Universidade Federal de Minas Gerais, Presidente AntĂŽnio Carlos 6627, Belo Horizonte, 31270-901, Minas Gerais, Brazil

    Ana Cristina Viana Campos

  2. Department of Community and Preventive Dentistry, School of Dentistry, Universidade Federal de Minas Gerais, Presidente AntĂŽnio Carlos 6627, Belo Horizonte, 31270-901, Minas Gerais, Brazil

    EfigĂȘnia Ferreira e Ferreira & AndrĂ©a Maria Duarte Vargas

  3. Unidad NutriciĂłn, Salud PĂșblica y Envejecimiento Saludable, INTA, Universidad de Chile, El LĂ­bano 5524 Macul, Santiago, 138-11, Chile

    Cecilia Albala

Authors
  1. Ana Cristina Viana Campos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. EfigĂȘnia Ferreira e Ferreira
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andréa Maria Duarte Vargas
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Cecilia Albala
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ana Cristina Viana Campos.

Additional information

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

ACVC conducted all data analyses and drafted the manuscript. AMDV and EFF contributed to the conception and design of the study. CA participated in the interpretation and discussion of the data and made critical revisions to the manuscript. All authors read and approved the final manuscript.

Authors’ original submitted files for images

Below are the links to the authors’ original submitted files for images.

Authors’ original file for figure 1

Rights and permissions

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Campos, A.C.V., e Ferreira, E.F., Vargas, A.M.D. et al. Aging, Gender and Quality of Life (AGEQOL) study: factors associated with good quality of life in older Brazilian community-dwelling adults. Health Qual Life Outcomes 12, 166 (2014). https://doi.org/10.1186/s12955-014-0166-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12955-014-0166-4

Keywords

Health and Quality of Life Outcomes

ISSN: 1477-7525

Contact us