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

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 & 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.

Full text

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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