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Prevalence of Loneliness and Associated Factors among Older Adults in South Africa

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OBJECTIVE: Loneliness can be detrimental to health. The aim of this study is to estimate the prevalence of loneliness as well as its risk factors in older adults in South Africa.MATERIALS & METHODS: This cross-sectional population based study investigated factors associated with loneliness in a nationally representative sample (n=3624) of older South Africans who took part in the “Study of Global Ageing and Adults Health (SAGE)” wave 1 in 2008. The outcome variable was self-reported prevalence of loneliness and the exposure variables were socio-demographic characteristics and health variables.RESULTS: The overall prevalence of self-reported loneliness was 9.9%. Prevalence of loneliness was 10.2% for females and 9.5% for males, lowest among those married (7.5%), and highest among the 70+ years olds (12.5%). Individuals with highest level of education had the lowest prevalence of loneliness (5.9%). Indians or Asians were significantly more likely to experience loneliness than other population groups (Adjusted Odds Ratio=AOR: 3.20; 95% Confidence Interval=CI: 1.31, 7.80). Married or cohabiting individuals were significantly less likely to experience loneliness than unmarried or non-cohabiting ones, respectively (AOR: 0.55; 95% CI: 0.37, 0.81). In multivariable logistic regression, individuals with good subjective health were less likely to experience loneliness than those with poor health (AOR: 0.40, 95% CI: 0.22, 0.73). Similarly, individuals with good cognitive functioning were significantly less likely to experience loneliness than those with poor cognitive functioning (AOR: 0.55, 95% CI: 0.32, 0.97).CONCLUSION: The study found that the prevalence of loneliness among older adults in South Africa is significant. Preventative interventions that address the identified factors, including poor health status and low cognitive functioning, associated with loneliness need to be developed.
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Global Journal of Health Science; Vol. 9, No. 12; 2017
ISSN 1916-9736 E-ISSN 1916-9744
Published by Canadian Center of Science and Education
1
Prevalence of Loneliness and Associated Factors among Older Adults
in South Africa
Nancy Phaswana-Mafuya1,2,3 & Karl Peltzer1,4
1 HIV/AIDS/STIs and TB Research Programme, Human Sciences Research Council, Pretoria, South Africa
2 Department of Social Work, University of Limpopo, Turfloop, South Africa
3 Office of the Deputy Vice Chancellor: Research & Engagement, Nelson Mandela Metropolitan University, Port
Elizabeth, South Africa
4 Department of Research & Innovation, University of Limpopo, Turfloop, South Africa
Correspondence: Karl Peltzer, HIV/AIDS/STIs and TB Research Programme, Human Sciences Research Council;
Private Bag X41, Pretoria 0001, South Africa. Tel: 27-12-302-2000. E-mail: kpeltzer@hsrc.ac.za
Received: June 5, 2017 Accepted: June 21, 2017 Online Published: September 20, 2017
doi:10.5539/gjhs.v9n12p1 URL: https://doi.org/10.5539/gjhs.v9n12p1
Abstract
Objective: Loneliness can be detrimental to health. The aim of this study is to estimate the prevalence of
loneliness as well as its risk factors in older adults in South Africa.
Materials and Methods: This cross-sectional population based study investigated factors associated with
loneliness in a nationally representative sample (n=3624) of older South Africans who took part in the “Study of
Global Ageing and Adults Health (SAGE)” wave 1 in 2008. The outcome variable was self-reported prevalence of
loneliness and the exposure variables were socio-demographic characteristics and health variables.
Results: The overall prevalence of self-reported loneliness was 9.9%. Prevalence of loneliness was 10.2% for
females and 9.5% for males, lowest among those married (7.5%), and highest among the 70+ years olds (12.5%).
Individuals with highest level of education had the lowest prevalence of loneliness (5.9%). Indians or Asians were
significantly more likely to experience loneliness than other population groups (Adjusted Odds Ratio=AOR: 3.20;
95% Confidence Interval=CI: 1.31, 7.80). Married or cohabiting individuals were significantly less likely to
experience loneliness than unmarried or non-cohabiting ones, respectively (AOR: 0.55; 95% CI: 0.37, 0.81). In
multivariable logistic regression, individuals with good subjective health were less likely to experience loneliness
than those with poor health (AOR: 0.40, 95% CI: 0.22, 0.73). Similarly, individuals with good cognitive
functioning were significantly less likely to experience loneliness than those with poor cognitive functioning
(AOR: 0.55, 95% CI: 0.32, 0.97).
Conclusion: The study found that the prevalence of loneliness among older adults in South Africa is significant.
Preventative interventions that address the identified factors, including poor health status and low cognitive
functioning, associated with loneliness need to be developed.
Keywords: Loneliness, health variables, older adults, South Africa
1. Introduction
Loneliness is a common phenomenon associated with old age due to changes in the quality and quantity of social
relationships (Luanaigh & Lawlor, 2008; Qualter et al., 2015). Consequently, elderly people may experience
feelings of distress, loss and unmet social needs (Hawkley & Cacioppo, 2010). A recent study on late-life
loneliness conducted in 11 countries across Europe among older adults, found the prevalence of loneliness to be
common, ranging from 10 to just over 30%, i.e. 10% in France and Norway; 20% in Russia and Czech Republic
and more than 30% in Bulgaria and Georgia (Hansen & Slagsvold, 2016). Loneliness may have adverse mental
and physical health outcomes such as hypertension, sleep problems, lower immunity and poorer cognition
functioning (Luanaigh & Lawlor, 2008; Boss, Kang & Branson, 2015). It is therefore important to determine
factors associated with loneliness among elderly people. Cohen-Mansfield, Hazan, Lerman and Shalom (2016,
p.557) found following factors to be associated with loneliness: “female gender, non-married status, older age,
poor income, lower educational level, living alone, low quality of social relationships, poor self-reported health,
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poor functional status, poor mental health, low self-efficacy beliefs, negative life events, and cognitive deficits.”
Petitte (2015) found common chronic diseases such as metabolic disorders, cardiovascular disorders, hypertension,
lung disease, and obesity to be associated with loneliness. Conflicting associations were found between smoking
and loneliness (Dyal & Valente, 2015). There is a dearth of information on the factors associated with loneliness
among older adult populations in Africa (Van der Geest, 2004). Therefore, the aim of this study is to estimate the
prevalence of loneliness as well as its risk factors in older adults in South Africa.
2. Methods
2.1 Sample and Procedure
The “Global Study on Ageing and Adult Health (SAGE Wave 1)” involved a nationally representative
population-based cross-sectional sample of 3624 (77% response rate) South Africans aged 50 years or older. The
SAGE sampling design involves a probability sample (two-stage) that produces national and provincial estimates
in relation to geo-locality type and population group (Black Africans and others); in more detail (Kowal et al.,
2012). Research Ethics Approval was attained from the “HSRC Research Ethics Committee (Protocol REC
5/13/04/06)”. Informed consent was obtained from all study respondents prior to the interview assessment.
2.2 Measures
Primary Outcome: One binary question was asked to assess the primary outcome, loneliness, “Did you feel lonely
for much of the day yesterday?” (“Yes” or “No”).
Exposure variables: Exposure variables included socio-demographic and health variables and they have been
described below.
Sociodemographic variables: Age (50-59, 60-69, 70+), sex (male, female), marital status (married, cohabitating,
single, widowed), formal education (none, 1-7 years, 8-11 years, 12 or more), residence (urban or rural),
population group and economic or wealth status. With regard to population group, the response categories were
“Black African, Coloured, Indian/Asian, White” and “other” as described by “Statistics South Africa” (2014,
p.12).
Economic or wealth status of a given household was estimated based on a list of household assets, and
subsequently, wealth quintiles were created from these (Ferguson, Murray, Tandon & Gakidou, 2003).
Health variables. Health variables included self-rated health, chronic conditions, lifetime tobacco and alcohol use,
obesity, cognitive capacity, and functional disability.
Overall, “self-rated health status” was measured through a 5-point scale on how participants rated their current
health: “very good, good, moderate, bad, and very bad.” The five categories were collapsed into two categories, 0=
‘very good’, ‘good’, or ‘moderate’ and 1= ‘bad’ or ‘very bad’ (Phaswana-Mafuya et al., 2013).
Chronic diseases such as angina, asthma, arthritis, chronic lung disease, diabetes mellitus and stroke, were
assessed by self-report of ever having diagnosed (coded 0=no condition and 1= having any of the six conditions).
Tobacco use was assessed with two questions in order to establish daily tobacco use (WHO, 1998).
Lifetime alcohol use was assessed through asking participants whether they had “ever consumed a drink that
contains alcohol (such as beer, wine, spirits, etc.)”(Response categories: ‘yes’ or ‘no, never’). Those who
responded with “yes”, were asked: “During the past 7 days, how many drinks of any alcoholic beverage did you
have each day?”. Risky alcohol use was classified as having had 10 drinks in the past week (Peltzer &
Phaswana-Mafuya, 2013).
Obesity was measured using “Body mass index (BMI)” (weight in kg divided by height metre squared) (BMI 30
kg/m2) (WHO, 2016).
Cognitive capacity was assessed using cognitive tests to measure various aspects of cognitive performance,
namely: concentration, attention, immediate memory, verbal fluency (executive function), verbal recall
(immediate and delayed), and digit span (forward and backward) (Peltzer & Phaswana-Mafuya, 2012). An overall
cognitive score was calculated that was converted to a scale from 0= worst cognition to 100= best cognition, and
dichotomised into poor or good by using the median of 48 as a cut-off point (Peltzer & Phaswana-Mafuya, 2012).
The “World Health Organization (WHO) Disability Assessment Schedule (WHODAS-II)” was used to assess
health, functioning, and disability in the past 30 days (Üstün, Kostanjsek, Chatterji & Rehm, 2010). To estimate the
severity of disability participants are asked about the level of difficulty with instrumental activities of daily living
(IADLs) (ability to perform more complex tasks). Scores from the 12 item WHODAS-II are added up to get a total
score, which is then converted to the following disability categories: “No problem (0% – 4%); Mild problem (5%
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– 24%); Moderate problem (25% – 49%); Severe problem (50% – 95%); Extreme problem (95% – 100%)” (Üstün
et al., 2010).
2.3 Data Analysis
STATA software version 13.0 (“Stata Corporation, College Station, Texas, USA”) was used to analyse the data
taking into account for the sampling design. Logistic regression analysis was performed to determine the Odds
Ratio with 95% confidence interval (CI) in order to estimate the associations between independent variables
(sociodemographic characteristics and health variables) and loneliness. All variables, which were statistically
significant (P < .05) in bivariate analyses, were subsequently included in the overall multivariable models. The
association between loneliness and health variables (self-rated health status, cognitive functioning, and IADL) was
also estimated using multivariable logistic regression. Model 1 was adjusted for sociodemographic variables and
model 2 was adjusted for sociodemographic and other health variables.
3. Results
3.1 Sample Characeristics
The total sample included 3624 South Africans aged 50 years and above, 44.1% men and 55.9% women. The
largest population group was Black African (74.0%), followed by Coloured (12.8%), Whites (9.3%) and 3.8%
Indian or Asian groups. The self-reported prevalence of loneliness was 9.9%, 10.2% for females and 9.5% for
males. It was lowest among those married (7.5%). Prevalence was highest among the 70+ years olds (12.5%).
Indians or Asians (22.8%) had highest prevalence of loneliness than other racial groups. Individuals with highest
level of education had lowest prevalence of loneliness (5.9%) (see Table 1).
Table 1. Sample characteristics by population group and gender (N=3624)
Variable Response option Total
Percentage
Loneliness
Frequency Percentage
All 3275
349
90.1
9.9
Gender
Female
Male
55.9
44.1
215
134
10.2
9.5
Age 50-59
60-69
70 or more
49.9
30.6
19.5
144
108
97
9.9
8.4
12.5
Population group
Black African
White
Coloured
Indian or Asian
74.0
9.3
12.8
3.8
183
18
55
48
9.6
8.7
6.0
22.8
Education
None
1-7 years
8-11
12 or more
25.5
27.0
32.7
14.9
53
78
114
30
11.9
9.5
12.1
5.9
Marital status Single
Married
Cohabitating
Widowed
14.3
55.9
5.9
23.9
48
132
32
130
12.7
7.5
15.5
12.6
Wealth
Low
Medium
High
40.6
18.2
41.2
142
66
139
11.3
9.2
8.9
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Residence
Rural
Urban
35.1
64.9
100
249
11.6
9.0
Health state Very good/good
Moderate
Bad/very bad
37.9
44.6
17.5
82
169
98
4.7
10.8
19.3
Chronic condition
No
Ye s
65.5
34.5
142
207
8.8
11.0
Instrumental Activity of Daily Living (IADL) None-mild
Moderate
Severe/extreme
59.8
21.6
18.6
165
88
96
6.9
12.0
16.9
Obese No
Ye s
53.3
46.7
198
142
10.8
9.1
Alcohol use (10 or more/week) No
Ye s
96.3
3.7
326
23
9.9
10.2
Daily tobacco use No
Ye s
79.6
20.4
263
86
10.1
9.4
Cognitive functioning Poor
Good
48.0
52.0
213
113
14.4
6.1
3.2 Associations between Loneliness and Socio-Demographic Variables
In multivariable logistic regression analysis, sociodemographic variables (being Indian or Asian and being single
or widowed) and health variables (poor self-rated health status and low cognitive functioning) were found to be
associated with loneliness (see Table 2).
Adjusting for all variables, Indians or Asians were significantly more likely to experience loneliness than other
population groups (AOR: 3.20; 95%CI: 1.31, 7.80). Married or cohabiting individuals were significantly less
likely to experience loneliness than unmarried or non-cohabiting ones, respectively (AOR: 0.55; 95% CI: 0.37,
0.81). Individuals with moderate and very bad health were more likely to experience loneliness compared to
individuals with good health. Individuals with high cognitive functioning (AOR: 0.49, 95% CI: 0.30, 0.81) were
less likely to experience loneliness than those with low cognitive functioning.
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Table 2. Predictors of loneliness
CrOR (95% CI) AOR (95% CI)
Gender
Female
Male
1 (Reference)
0.92 (0.55, 1.54)
---
Age 50-59
60-69
70 or more
1 (Reference)
0.84 (0.56, 1.24)
1.30 (0.88, 1.94)
---
Population group
Black African
White
Coloured
Indian or Asian
1 (Reference)
0.90 (0.40, 2.00)
0.60 (0.33, 1.08)
2.80 (1.21, 6.48)*
1 (Reference)
1.73 (0.63, 4.75)
0.78 (0.45, 1.36)
3.27 (1.31, 8.13)*
Education
7 years
8-11
12 or more
1 (Reference)
1.02 (0.58, 1.79)
0.46 (0.19, 1.11)
---
Marital status Single, Widowed
Married, Cohabitating
1 (Reference)
0.54 (0.36, 0.82)**
1 (Reference)
0.55 (0.37, 0.82)**
Wealth
Low
Medium
High
1 (Reference
0.80 (0.49, 1.30)
0.76 (0.47, 1.22)
---
Residence
Rural
Urban
1 (Reference)
0.75 (0.42, 1.31)
---
Health state Very good/good
Moderate
Bad/very bad
1 (Reference)
2.46 (1.32, 4.60)**
4.88 (2.82, 8.41)***
1 (Reference)
2.06 (1.08, 3.92)*
2.98 (1.55, 5.75)**
Chronic condition No
Ye s
1 (Reference)
1.20 (0.84. 1.94)
---
IADL None-moderate
Severe
1 (Reference)
2.75 (1.72, 4.41)***
1 (Reference)
1.30 (0.81, 2.08)
Obese No
Ye s
1 (Reference)
0.82 (0.58, 1.16)
---
Alcohol use (10 or
more/week)
No
Ye s
1 (Reference)
1.03 (0.50, 1.71)
---
Daily tobacco use No
Ye s
1 (Reference)
0.93 (0.59, 1.45)
---
Cognitive functioning Low
High
1 (Reference)
0.38 (0.22, 0.66)***
1 (Reference)
0.49 (0.30, 0.81)**
IADL=instrumental activities of daily living ***P<0.001; **P<0.01; *P<0.05.
3.3 Associations between Loneliness and Health Variables
In multivariable logistic regression, individuals with good subjective health were significantly less likely to
experience loneliness than those with poor health in both model 1 (AOR: 0.35; 95% CI:0.19, 0.64) and model 2
(AOR: 0.40, 95% CI: 0.22, 0.73). Similarly, individuals with good cognitive functioning were significantly less
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likely to experience loneliness than those with poor cognitive functioning in both model1 (AOR: 0.43, 95% CI:
0.24, 0.75) and model 2 (AOR: 0.55, 95% CI: 0.32, 0.97). Further individuals with severe IADL were more likely
to experience loneliness than those without IADL in model 1 (AOR: 2.43, 95% CI: 1.18, 5.00) but not in model 2
(see Table 3).
Table 3. Multivariable logistic regression analyses of the association between loneliness and health variables
Variable (Outcome) Lonelines
Model 1 Model 2
Model 1: Adjusted Odds
Ratio (95% CI)
Model 2: Adjusted Odds Ratio
(95% CI)
Health status
Poor subjective health
Good subjective health
Poor
Good
1 (Reference)
0.35 (0.19, 0.64)***
0.40 (0.22, 0.73)**
Cognitive functioning Poor
Good
1 (Reference)
0.43 (0.24, 0.75)***
1 (Reference)
0.55 (0.32, 0.97)*
IADL None-moderate
Severe
1 (Reference)
2.02 (1.22, 3.32)**
1 (Reference)
1.37 (0.83, 2.25)
IADL=instrumental activities of daily living;
Model 1: Adjusted for sociodemographic variables (age, sex, population group, education, socioeconomic status, geolocality
and marital status); Model 2: Adjusted for sociodemographic and health variables (health status, chronic condition, IADL,
obesity, alcohol use, tobacco use and cognitive functioning). CI=Confidence Interval; ***P<0.001; **P<0.01; *P<0.05
4. Discussion
This study investigated the prevalence of loneliness and related factors in a nationally representative sample of
older South Africans who took part in SAGE in 2008.This population based study found loneliness to be a
relatively common phenomenon among elderly South Africans, i.e. prevalence of 9.9% as found in other studies
(Hansen & Slagsvold, 2016). Loneliness is attributed to age-related changes and losses (Luanaigh & Lawlor, 2008).
There were gender, age and racial disparities in loneliness with prevalence being higher among females, older
individuals, unmarried individuals, Indians or Asians and individuals with no education or lower educational level.
Cohen-Mansfield et al. (2016) also found “female gender, non-married status, older age, poor income, lower
educational level, living alone, low quality of social relationships, and cognitive deficits” to be associated with
loneliness. This suggests that loneliness interventions should focus on individuals with these socio-demographic
characteristics.
This cross-sectional study found cognitive functioning to be associated with loneliness as found in
Cohen-Mansfield et al (2016). More research is needed in order to determine causal factors underlying the
association between loneliness and cognitive functioning (Boss et al. 2015). Further, as found in a number of
previous studies (Cacioppo et al., 2002; Cohen-Mansfield et al, 2016; Hawkley & Cacioppo, 2010; Petitte et al.,
2015; Stickley et al., 2013), this study found that loneliness was associated with poor subjective health status.
According to Hawkley and Cacioppo (2003) there might be several possible ways linking loneliness with poor
health. It is possible, for instance, that poor subjective health status co-occurs with poor sleep and short sleep
duration and may reinforce each other over time. It has been proposed that loneliness may generate anxiety-related
thoughts that may hinder relaxation resulting in poorer and shorter sleep (Hawkley & Cacioppo, 2003; Stickley et
al., 2015). In addition, the study found an association between different stressors and loneliness, so stress could be
a mechanism linking loneliness with poor health (Stickley et al., 2015).
4.1 Study Limitations
This investigation had several limitations. The study was cross-sectional in nature, so that causal inferences cannot
be drawn. As the questionnaire relied on self-report, it is possible that some respondents biased their responses.
The questionnaire utilized in this investigation assessed certain concepts such as loneliness with a single item.
However, several authors seem to emphasize a high correlation between single-item and multi-item indices
(Stickley et al., 2015).
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5. Conclusions
The study found that the prevalence of loneliness among older adults in South Africa is significant. Several
predictors for loneliness were identified as well as associations between loneliness and health variables, including
poor health status and low cognitive functioning, were identified which may help in the development of loneliness
prevention and intervention programmes in this older adult population.
Competing Interests Statement
The authors declare that they have no significant competing financial, professional, or personal interests that might
have influenced the performance or presentation of the work described in this manuscript.
Acknowledgements
Funding was provided predominantly from the National Department of Health with additional funding provided by
United States National Institute on Aging through an interagency agreement with the World Health Organization,
and the Human Sciences Research Council, South Africa.
Competing Interests Statement
The authors declare that there are no competing or potential conflicts of interest.
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... In 25 European countries, 16.9% of people aged ≥80 years were reported to often experience loneliness. 3 Other results included 9% of older persons aged ≥65 years in the United Kingdom who experienced loneliness, 4 43% of people (aged ≥60 years) in the United States, 5 37.6% of people (aged ≥60 years) in India, 6 9.9% of people (aged ≥50 years) in South Africa, [6][7][8] and about 10%∼30% of people (aged ≥65 years) in China. [9][10][11] A study in 2001 showed that 60.2% of older community-dwelling Taiwanese adults (aged ≥65 years) suffered a moderate to high level of loneliness, among whom 3.5% experienced a high level of loneliness. ...
... The current study showed that the prevalence of loneliness in older Taiwanese was 10.5%, which despite similarities to 9.0% in the United Kingdom, 9.7% in South Africa and 11.6% in Norway, 4,7,8 and it significantly differed from prevalences in most countries. This may be attributed to differences in subjects, research tools, and research methods, or disparities in cultural and social backgrounds. ...
... 8,39 Different research methods may have been adopted; for example, some studies performed regional surveys using a cross-sectional research design, 8,12,41,43 while others conducted a secondary analysis of data collected from databases. 7,11,40,44 A 2001 study revealed that 60.2% of older Taiwanese experienced a moderate to high degree of loneliness. 12 That prevalence is substantially higher than results from this study, which is possibly because this study collected samples throughout Taiwan, but the study by Wang et al. collected samples only from southern rural communities of Taiwan. ...
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Background Loneliness is a common problem among older populations, and very few studies have examined loneliness among older adults in Taiwan. Aim This study aimed to understand the prevalence of loneliness and factors associated with it among older adults in Taiwan. Methods Data from the Taiwan Longitudinal Study of Aging collected in 2015 were analyzed and involved 4588 participants aged ≥65 years. The outcome variable was a self-reported loneliness question, and independent variables included demographic characteristics, a self-reported health status, physical function, number of comorbidities, cognitive function, and social support. A multivariate logistic regression was used to identify predictors of loneliness. Results The prevalence of loneliness among older adults in Taiwan was 10.5%. The multivariate logistic regression showed that old persons who were male, lived alone, perceived that they had a poor health condition, had no spouse, had no job, and had poor emotional support had higher likelihood of feeling lonely. Conclusions This study investigated loneliness in a nationally representative sample of older adults and revealed that one-tenth of this older population might experience loneliness which requires immediate action. Special attention should be given to the aforesaid factors in older adults to identify problems and provide interventions as early as possible in order to prevent loneliness and thus reduce the resultant negative effects on physical and mental conditions. Appropriate interventions should be developed to prevent or ameliorate feelings of loneliness among older populations using rigorous research designs such as randomized controlled trials.
... Consequently, the lack of social interconnectedness is related to loneliness in South Africa (van Staden & Coetzee, 2010). In this country, loneliness has been estimated to affect between 10% of 50+ South African adults and 12.5% of those over 70 (Phaswana-Mafuya & Peltzer, 2017). Other studies showed that between 14.6% and 31.5% of South African older adults reported feeling lonely quite often (Geffen et al., 2019;Makiwane & Kwizera, 2006). ...
... En consecuencia, en Sudáfrica, la falta de conexiones sociales se relaciona con la soledad (van Staden & Coetzee, 2010). En este país, se estima que la soledad afecta alrededor de un 10% de la población mayor de 50 años y en torno al 12.5% de la población mayor de 70 años (Phaswana-Mafuya & Peltzer, 2017). Otros estudios revelan que entre un 14.6% y un 31.5% de los sudafricanos de edad avanzada indicaron sentirse solos con frecuencia (Geffen et al., 2019;Makiwane & Kwizera, 2006). ...
Article
This study aimed to explore and gain insight from the dialogue and subjective perception of older people about loneliness and social support using a cross-cultural approach. A total of six focus groups with 43 community-dwelling older adults were conducted in two different cultural contexts: the Spanish and the South African. Data were analysed around two key topics — loneliness and social support — using methods of thematic analysis. Findings around the topic of loneliness showed that participants from both countries agreed that loneliness was a subjective feeling and a negative experience. Besides, solitude emerged in the focus groups discussions as a pleasant and desired experience. As for social support, the lack of it was seen as the most frequent risk factor for loneliness. Differences among both countries were found on the key sources of social support. Spanish participants identified the family as the major source of social support, while among South African participants the community was presented as a main source of social support. We concluded that considering these cultural aspects and social support perceptions when developing programmes and strategies aimed at alleviating loneliness may lead to an increase in the effectiveness of these kinds of interventions, and thus to more positive outcomes.
... Several researchers have analyzed the association of loneliness with the old age. The prevalence of feeling of loneliness was 9.9% in South Africa [17]. In Assuit at Egypt, about 72% of elderly people had severe l0neliness, while 26% of them had moderate loneliness [18]. ...
... P: Expected proportion in population-based on previous studies' prevalence of loneliness in elderly =9.9 according to Phaswana, et al.[17]. ...
... Several researchers have analyzed the association of loneliness with the old age. The prevalence of feeling of loneliness was 9.9% in South Africa [17] . In Assuit at Egypt, about 72% of elderly people had severe l0neliness, while 26% of them had moderate loneliness [18] . ...
... P: Expected proportion in population-based on previous studies' prevalence of loneliness in elderly =9.9 according to Phaswana, et al.[17] . ...
... The overall prevalence of self-reported loneliness is 9.9% in the SA elderly. 50,51 This often happens when they lose their loved ones, or retire and become pensioners living in isolation. 52,53 Loneliness has been found to be a risk factor for excessive alcohol intake. ...
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The use and misuse of alcohol has become a public health problem among the South African (SA) elderly population, among whom risky drinking is a common practice. Previous publications encouraging alcohol use have referred to two supposedly beneficial effects of alcohol, categorised as: (1) cardioprotective and haemostatic; and (2) promoting a positive balance in iron status. However, more recent evidence has weakened these assertions for all age groups as the disadvantages of alcohol use far outweigh these benefits. Some of these disadvantages can cause severe medical and physical harm to the elderly. Attempts to curb risky drinking among the SA elderly must be adopted through screening by clinicians during consultations, use of various screening and diagnostic tools available for addressing alcohol use and abuse, and exploiting the channels of alcohol exposure for appropriate interventions. Elderly populations are vulnerable to alcohol misuse irrespective of their consumption patterns or levels of use because of their ageing condition and the interaction of alcohol with medication. Therefore, there is a need to sensitise the SA elderly population on the risk posed by alcohol use, misuse or abuse, hence the FBDG ‘If you drink alcohol, drink sensibly’.
... The World Health Organization's Study on global AGEing and adult health (SAGE) included a report on loneliness in older persons in South Africa (Phaswana-Mafuya & Peltzer, 2017), but limited research has been done on the social isolation of older persons living in residential care in South Africa. This further compounds the paucity of studies on loneliness and social isolation in lower middle-income countries. ...
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Introduction Residential care settings have shown high social isolation rates with incumbent risks necessitating measurement to formulate health promotion policies. Objective To measure social isolation levels in older persons living in a lower socio-economic residential care setting in South Africa. Method A cross sectional survey with older persons from four inner city residential care facilities. A researcher-administered questionnaire was developed based on the Working Paper No.66, Oxford Poverty and Human Development Initiative. Data were analysed to describe social isolation and assess the influence of demographics. Results The response rate was 72.14% (n=277) and representative of the residential care population for age and gender. Nearly half of the respondents (47.3%) met criteria for social isolation in terms of social network support and density and almost 20% for perceived isolation through decreased levels of friendship. Conclusion Although residential care does not prevent social isolation, the residents in the setting may provide a buffering in the provision of some social support.
... raised in The Times of India (Devidayal, Das, Ghosh, & Dhawan, 2018) along with The Indian Express (Ali & Barnagarwala, 2018) seeking to reassure readers that: ''You are not the only one: India stares at a loneliness epidemic.'' Studies of loneliness and the life cycle have reported a relatively greater proportion of loneliness among adolescents and youths (Mushtaq et al., 2014;West, Kellner, R., & Moore-West, 1986;Anderson, 1999, Chen et al., 2004, Liu, Li, Purwono, Chen, & French, 2015, Stickley et al., 2016 while also noting a high prevalence for elderly populations, including sub-Saharan African countries (Phaswana-Mafuya & Peltzer, 2017;Nzabona, Ntozi, & Rutaremwa, 2016;Roos & Klopper, 2010;Waweru, Kabiru, Mbithi, & Some, 2003;Van Der Geest, 2004). Loneliness has been noted as commonplace among Nigerian and Japanese college students (Ishaku, Terao, Takai, Karuri, & Matsumoto, 2018). ...
Article
This study examined the correlation between loneliness and four dimensions of psychological well-being (PWB) of old people in South Africa as they vary by their socio-demographic characteristics. Respondents were a non-probability/random sample of 301 elderly in Buffalo City, South Africa (female = 69.1%, male = 30.9%; mean age (old age home /private home) = 75.43 /74.39 years, SD (old age home /private home) = 7.25 /7.68 years; black = 57.9%, white = 42.1%) were used. The respondents completed the Ryff Psychological Well-being Scale (RPWBS) and the University of California Loneliness Scale (UCLALS). Hierarchical regression analysis results indicate ethnicity is predictive of loneliness. Specifically, black people had both lower levels of loneliness and autonomy compared to the white elderly. Moreover, white elderly with low levels of loneliness had higher levels of self-acceptance compared to the black elderly. These findings suggest ethnic affiliation to moderate self-acceptance, environmental mastery, and autonomy; except for purpose in life. Interventions to support the elderly with loneliness should prioritise building their social capital base for psychological well-being.
Article
Objectives: Loneliness may negatively impact on health outcomes. The study aimed to estimate the associations between loneliness and poor physical health, poor mental health, and health risk behaviours in middle-aged and older adults in a national population survey in India. Methods: The sample included 72,262 middle-aged and older adults from a cross-sectional national community dwelling survey in India in 2017-2018. Results: Results indicate that the prevalence of moderate loneliness was 20.5%, and severe loneliness was 13.3%. In the adjusted logistic regression analysis, moderate and/or severe loneliness was significantly positively associated with fair or poor self-rated health status, and significantly negatively associated with life satisfaction and cognitive functioning. Furthermore, loneliness was associated with stroke, angina, physical injury, difficulty of Activities of Daily Living (ADL), difficulties of Instrumental Activities of Daily Living (IADL), and multimorbidity. Loneliness increased the odds of major depressive disorder and insomnia symptoms. The associations between loneliness and current tobacco use and Body Mass Index (BMI) were negative and between loneliness and physical inactivity and underweight were positive. Conclusions: Loneliness is associated with poor physical health, poor mental health and health risk behaviour (physical inactivity), emphasising the need to consider loneliness in various physical and mental health contexts. This article is protected by copyright. All rights reserved.
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Background: Research supports an association between smoking and negative affect. Loneliness is a negative affective state experienced when a person perceives themselves as socially isolated and is associated with poor health behaviors and increased morbidity and early mortality. Objectives: In this article, we systematically review the literature on loneliness and smoking and suggest potential theoretical and methodological implications. Methods: PubMed and PsycINFO were systematically searched for articles that assessed the statistical association between loneliness and smoking. Articles that met study inclusion criteria were reviewed. Results: Twenty-five studies met inclusion criteria. Ten studies were conducted with nationally representative samples. Twelve studies assessed loneliness using a version of the UCLA Loneliness Scale and nine used a one-item measure of loneliness. Seventeen studies assessed smoking with a binary smoking status variable. Fourteen of the studies were conducted with adults and 11 with adolescents. Half of the reviewed studies reported a statistically significant association between loneliness and smoking. Of the studies with significant results, all but one study found that higher loneliness scores were associated with being a smoker. Conclusions/Importance: Loneliness and smoking are likely associated, however, half of the studies reviewed did not report significant associations. Studies conducted with larger sample sizes, such as those that used nationally representative samples, were more likely to have statistically significant findings. Future studies should focus on using large, longitudinal cohorts, using measures that capture different aspects of loneliness and smoking, and exploring mediators and moderators of the association between loneliness and smoking.
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This study explores country differences in late-life loneliness in Europe among men and women and establishes the role of micro-level differences in socioeconomic status, health, and social variables in these patterns. We use cross-sectional, nationally representative data from the Generations and Gender Survey. The analysis comprises 33,832 Europeans aged 60–80 from 11 countries. A six-item short version of the de Jong-Gierveld Scale is used to measure loneliness, yet we employ a different method of calculating loneliness scores than in prior work. Findings show considerable between-country heterogeneity in late-life loneliness, especially among women. The rate of a quite severe level of loneliness is 30–55 % among men and women in Eastern Europe, compared with 10–20 % among their peers in Western and Northern Europe. Loneliness is strongly associated with lower socioeconomic status, poorer health, and not having a partner. More than half of the country variance in loneliness is mediated by health, partnership status, and socioeconomic disparities across countries. Differences in societal wealth and welfare and cultural norms may account for some of the unexplained country variance in loneliness.
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Objective. To investigate cognitive functioning and associated factors in a national probability sample of older South Africans who participated in the Study of Global AGEing and Adult Health (SAGE) in 2008. Methods. In 2008 we conducted a national population-based cross-sectional study with a sample of 3 840 adults aged ≥50 years in South Africa. We administered a questionnaire surveying socio-demographic characteristics, health variables, and anthropometric and blood pressure measurements. Multivariate regression analyses were used to assess the association of socio-demographic factors and health variables with cognitive functioning. Results. Mean variables in the sample were: 5.9 recalled words, a verbal fluency of 9.9 words in a specified category (animals), a forward and backward digit span of 5.2 and 3.2, respectively, and an overall mean cognition score of 48.5. Higher overall cognitive functioning (a combination of memory and executive functioning) was positively associated with: younger age; white, Indian/Asian or coloured ethnicity; being married; a higher level of education; greater wealth; a higher level of physical activity; a greater quality of life; and a better subjective health status. Conclusions. Our findings can be used to refine future projections of cognitive function and healthcare needs in ageing middle-income societies such as those in South Africa.
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Loneliness is a prevalent and global problem for adult populations and has been linked to multiple chronic conditions in quantitative studies. This paper presents a systematic review of quantitative studies that examined the links between loneliness and common chronic conditions including: heart disease, hypertension, stroke, lung disease, and metabolic disorders. A comprehensive literature search process guided by the PRISMA statement led to the inclusion of 33 articles that measure loneliness in chronic illness populations. Loneliness is a significant biopsychosocial stressor that is prevalent in adults with heart disease, hypertension, stroke, and lung disease. The relationships among loneliness, obesity, and metabolic disorders are understudied but current research indicates that loneliness is associated with obesity and with psychological stress in obese persons. Limited interventions have demonstrated long-term effectiveness for reducing loneliness in adults with these same chronic conditions. Future longitudinal randomized trials that enhance knowledge of how diminishing loneliness can lead to improved health outcomes in persons with common chronic conditions would continue to build evidence to support the translation of findings to recommendations for clinical care.
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Most people have experienced loneliness and have been able to overcome it to reconnect with other people. In the current review, we provide a life-span perspective on one component of the evolutionary theory of loneliness-a component we refer to as the reaffiliation motive (RAM). The RAM represents the motivation to reconnect with others that is triggered by perceived social isolation. Loneliness is often a transient experience because the RAM leads to reconnection, but sometimes this motivation can fail, leading to prolonged loneliness. We review evidence of how aspects of the RAM change across development and how these aspects can fail for different reasons across the life span. We conclude with a discussion of age-appropriate interventions that may help to alleviate prolonged loneliness. © The Author(s) 2015. Access to on-line version of the paper at http://pps.sagepub.com/content/10/2/250.full.pdf+html
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Background: Loneliness is a significant concern among the elderly, particularly in societies with rapid growth in aging populations. Loneliness may influence cognitive function, but the exact nature of the association between loneliness and cognitive function is poorly understood. The purpose of this systematic review was to synthesize current findings on the association between loneliness and cognitive function in older adults. Method: A comprehensive, electronic review of the literature was performed. Criteria for inclusion were original quantitative or qualitative research, report written in English, human participants with a mean age ≥ 60 years, and published from January 2000 through July 2013. The total number of studies included in this systematic review was ten. Results: Main findings from the ten studies largely indicate that loneliness is significantly and negatively correlated with cognitive function, specifically in domains of global cognitive function or general cognitive ability, intelligence quotient (IQ), processing speed, immediate recall, and delayed recall. However, some initial correlations were not significant after controlling for a wide range of demographic and psychosocial risk factors thought to influence loneliness. Conclusions: Greater loneliness is associated with lower cognitive function. Although preliminary evidence is promising, additional studies are necessary to determine the causality and biological mechanisms underlying the relationship between loneliness and cognitive function. Findings should be verified in culturally diverse populations in different ages and settings using biobehavioral approaches.
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Research suggests that the prevalence of loneliness varies between countries and that feeling lonely may be associated with poorer health behaviours and outcomes. The aim of the current study was to examine the factors associated with loneliness, and the relationship between feeling lonely and health behaviours and outcomes in the countries of the former Soviet Union (FSU) - a region where loneliness has been little studied to date. Using data from 18,000 respondents collected during a cross-sectional survey undertaken in nine FSU countries - Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Moldova, Russia and Ukraine - in 2010/11, country-wise logistic regression analysis was conducted to determine: the factors associated with feeling lonely; the association between feeling lonely and alcohol consumption, hazardous drinking and smoking; and whether feeling lonely was linked to poorer health (i.e. poor self-rated health and psychological distress). The prevalence of loneliness varied widely among the countries. Being divorced/widowed and low social support were associated with loneliness in all of the countries, while other factors (e.g. living alone, low locus of control) were linked to loneliness in some of the countries. Feeling lonely was connected with hazardous drinking in Armenia, Kyrgyzstan and Russia but with smoking only in Kyrgyzstan. Loneliness was associated with psychological distress in all of the countries and poor self-rated health in every country except Kazakhstan and Moldova. Loneliness is associated with worse health behaviours and poorer health in the countries of the FSU. More individual country-level research is now needed to formulate effective interventions to mitigate the negative effects of loneliness on population well-being in the FSU.
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Alcohol abuse poses special risks for increased morbidity and mortality among older adults. Little attention has focused on assessing alcohol use and associated factors among older adults in transitional societies such as South Africa. This study aimed to determine the prevalence of alcohol use and associated factors in older South Africans who participated in the Study of Global Ageing and Adults Health (SAGE) in 2008. We conducted a national population-based cross-sectional study with a sample of 3840 aged 50 years or older in South Africa in 2008. In this study we analysed data from all 2144 participants who were over 60 years old. The questionnaire included socio-demographic characteristics, alcohol intake as well as comorbidity. Risky drinking was defined in two ways: heavy drinkers (>7 drinks/week) and binge drinkers (>3 drinks/one occasion/week). Four percent of participants reported heavy drinking and 3.7% binge drinking. Male gender (Odds Ratio (OR) =3.79, Confidence Interval (CI) =1.38-10.37) and white population group (OR=3.01, CI=1.31-6.89) were associated with risky drinking in multivariate analysis; as well as tobacco use (OR=5.25, CI=2.20-12.52) and not being obese (OR=0.14, CI=0.05-0.35). Hypertension, diabetes and depression were not associated. This study reveals moderate rates of risky drinking among older adults (60 years and more) in South Africa that puts them at risk of morbidity. Alcohol problems among older adults are commonly under-recognized, indicating a need for health care worker intervention.
Article
Background: Older persons are particularly vulnerable to loneliness because of common age-related changes and losses. This paper reviews predictors of loneliness in the older population as described in the current literature and a small qualitative study. Methods: Peer-reviewed journal articles were identified from psycINFO, MEDLINE, and Google Scholar from 2000–2012. Overall, 38 articles were reviewed. Two focus groups were conducted asking older participants about the causes of loneliness. Results: Variables significantly associated with loneliness in older adults were: female gender, non-married status, older age, poor income, lower educational level, living alone, low quality of social relationships, poor self-reported health, and poor functional status. Psychological attributes associated with loneliness included poor mental health, low self-efficacy beliefs, negative life events, and cognitive deficits. These associations were mainly studied in cross-sectional studies. In the focus groups, participants mentioned environmental barriers, unsafe neighborhoods, migration patterns, inaccessible housing, and inadequate resources for socializing. Other issues raised in the focus groups were the relationship between loneliness and boredom and inactivity, the role of recent losses of family and friends, as well as mental health issues, such as shame and fear. Conclusions: Future quantitative studies are needed to examine the impact of physical and social environments on loneliness in this population. It is important to better map the multiple factors and ways by which they impact loneliness to develop better solutions for public policy, city, and environmental planning, and individually based interventions. This effort should be viewed as a public health priority.
Article
To examine which factors are associated with feeling lonely in Moscow, Russia, and to determine whether loneliness is associated with worse health. Cross-sectional study. Data from 1190 participants were drawn from the Moscow Health Survey. Logistic regression analysis was used to examine which factors were associated with feeling lonely and whether loneliness was linked to poor health. Almost 10% of the participants reported that they often felt lonely. Divorced and widowed individuals were significantly more likely to feel lonely, while not living alone and having greater social support reduced the risk of loneliness. Participants who felt lonely were more likely to have poor self-rated health (odds ratio [OR]: 2.28; 95% confidence interval [CI]: 1.38-3.76), and have suffered from insomnia (OR: 2.43; CI: 1.56-3.77) and mental ill health (OR: 2.93; CI: 1.88-4.56). Feeling lonely is linked to poorer health in Moscow. More research is now needed on loneliness and the way it affects health in Eastern Europe, so that appropriate interventions can be designed and implemented to reduce loneliness and its harmful impact on population well-being in this setting. Copyright © 2015 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.