Harmful alcohol and illicit drug use significantly contribute the burden of disease.
Aim
This study aimed to assess the prevalence and correlates of hazardous, harmful or dependent alcohol (HHDA) use and drug use amongst persons 15 years and older in South Africa.
Setting
Population-based survey.
Method
In a national cross-sectional 2017 survey, 39 210 persons 15 years and older (Median = 34 years) responded to a questionnaire on substance and health variables. The prevalence of HHDA use was 10.3% and past 3-month drug use 8.6%.
Results
In adjusted logistic regression analysis, men of middle age (25–34 year olds) with higher education, urban residence, drug use and psychological distress were positively associated and Indian or Asian and white population groups were negatively associated with HHDA. Women of middle age (25–34 year old) and mixed race, residing on rural farms and urban areas, with drug use and psychological distress were positively associated and older age (55 years and older) and Indians or Asians were negatively associated with HHDA. In adjusted logistic regression analysis, men, having Grade 8–11 education, mixed race, being unemployed, and the HHDA used were positively associated and middle and older age (25 years and older) and being a student or learner were negatively associated with past 3-month any drug use. Women, who were mixed race, Indians or Asians, with the HHDA use were positively associated and older age (45 years and older) were negatively associated with the past 3-month drug use.
Conclusion
About one in 10 participants with several sociodemographic and health indicators was identified to be associated with HHDA and any drug use.
Harmful alcohol and illicit drug use is a significant contributor to the global burden of disease.1,2,3 Globally, alcohol use contributed to 5.3% of all deaths and 5.0% of all disability-adjusted life years (DALYs) in 2016.1 The corresponding figures for South Africa in 2000 were 7.1% and 7.0%, respectively.4 Diverse alcohol use patterns have been observed in African countries.5,6,7,8 In a 2015 national survey in Kenya, 6.7% of the adult population engaged in hazardous or harmful alcohol use.9 In a 2008 national population-based survey of persons 15 years and older in South Africa, the prevalence of hazardous, harmful or dependent alcohol (HHDA) use was 9.0%, 17.0% amongst men and 2.9% amongst women.10
The estimated global past-year prevalence of illicit drug use was 5.3% in 20142 and 3.8% for cannabis, 0.77% for amphetamines, 0.37% for opioid and 0.35% for cocaine use in 2015.11 In the 25 country World Mental Health Survey, ‘lifetime drug use disorders prevalence increased with country income: 0.9% in low-/lower-middle income countries, 2.5% in upper-middle income countries and 4.8% in high-income countries’.12 In Nigeria, the past-year prevalence of illicit drug use amongst adults was 14.4%.13 In a 2012 national population-based survey in persons 15 years and older in South Africa, the prevalence of past 3-month drug use was 4.4% (4.0% for cannabis use, 0.4% sedatives, 0.3% opiates, 0.3% amphetamines, 0.2% inhalants and 0.1% hallucinogens use in the past 3 months).14 There is a lack of more recent national population-based data on the prevalence and correlates of HHDA and drug use in South Africa.
As previously reviewed,14,15 factors associated with HHDA and/or drug use may include male sex, middle adulthood, specific ethnic groups, lower socioeconomic status, unemployed, urban residence and other substance use. In addition, several studies have shown the comorbidity of HHDA with drug use and psychological distress,16,17 as well as the comorbidity of drug use with HHDA and psychological distress.6 Epidemiological population-based surveys are needed to target interventions to prevent HHDA and drug use. The study aimed to assess the prevalence and correlates of HHDA and drug use amongst persons 15 years and older in South Africa.
MethodsStudy design and participants
The data utilised in this study were obtained from a cross-sectional, nationally representative household-based survey conducted in 2017 in South Africa. The multistage stratified random cluster sampling approach of the survey is described elsewhere.18 In summary, the mid-year population estimates19 were utilised to select 1000 small area layers (SALs) that were stratified by province, locality type and race groups. A maximum of 15 households were randomly selected from each of the 1000 SALs. In each household, all household members, who resided in that household the previous night, were eligible to participate.18
Study procedure
All eligible household members had to individually complete an informed consent form in private with the study fieldworker prior to being enrolled into the study. All questions that the respondent had during consent or interview were answered by the fieldworker or team supervisor. The respondent had the option to end the interview at any time without consequence. The household head or delegated household authority completed a household questionnaire, which captured demographic and household situation information and each individual in the household completed an individual questionnaire.18 The survey questionnaire was captured electronically by the fieldworker on a Mercer A105 tablet utilising Census and Survey Processing System (CSPro) software. Data were collected from December 2016 to February 2018. For this paper, data from the household and individual questionnaires were used. We restricted the sample to those who were 15 years and older and who completed the alcohol use measurement.
MeasuresSubstance use variables
Hazardous, harmful or dependent alcohol was assessed using the Alcohol Use Disorders Identification Test (AUDIT)20 and was scored as in a previous survey in South Africa.10 Amongst adults (20 years and above), the cut-off score is 8 or more20 and amongst adolescents (15–19 years) 5 or more21 for classifying HHDA use. Cronbach’s alpha for the AUDIT was 0.87 in this sample.
Drug use in the past 3 months was assessed with seven items of the ‘Alcohol, Smoking and Substance Involvement Screening Test (ASSIST)’, for example, ‘In the past 3 months, how often have you used cannabis (dagga, marijuana, pot, grass, hash, etc.?’.22 One item was added ‘Whoonga (mixture of heroin, dagga = cannabis and antiretrovirals)’ and classified under opiates.14 ‘Response options ranged from 1 = never to 5 = almost daily. Any drug used in the past 3 months was coded as 1 and never as 0’.14 ‘All items were added together to indicate the prevalence of any drug use in the past 3 months’.14 Cronbach alpha for the ASSIST in this sample was 0.91.
Sociodemographic factors included age, sex, highest educational level, population group (African black, Coloured, Indian or Asian, white and others), employment status, province and residence status.18 Statistics South Africa asks people to describe themselves in the census in terms of five racial population groups, which is useful because of relevant differences between these population groups in terms of various health and other indicators.10
Psychological distress was assessed with the Kessler Psychological Distress Scale (K10), with scores 20 or more, indicating psychological distress.23 Cronbach’s alpha for the K10 was 0.92 in this sample.
Ethical considerations
Approval for the survey was granted by the ‘Human Sciences Research Council (HSRC) Research Ethics Committee (REC: 4/18/11/15)’. Approval was also granted by the Centers for Disease Control and Prevention (CDC’s) Center for Global Health (CGH). Written informed consent was obtained from all participants.
Data analysis
All statistical analyses were conducted using Statistics and data (STATA) software version 14.0 (Stata Corporation, College Station, TX, United States). The data were weighted to make the sample representative of the target population in South Africa. Descriptive statistics were used to summarise the sample and substance use prevalence characteristics. Unadjusted and adjusted (including variables significant at p < 0.05 in univariate analysis) logistic regression stratified by sex was used to predict HHDA and past 3-month drug use prevalence. Taylor linearisation methods were applied to account for the complex study design and the sampling weight. Results from logistic regression analyses are reported as odds ratios (ORs) and 95% confidence intervals (CIs). Missing values were excluded and p < 0.05 was considered significant.
ResultsCharacteristics of the sample and substance use
The sample comprised 39 210 persons 15 years and older (Median = 34 years, interquartile range = 25–48): 48.3% were men and 51.7% were women, 36.1% had Grade 12 or more education education and 79.3% were African black by population group or ethnicity. More than one in three participants (36.0%) were employed or self-employed, 69.0% lived in urban areas and 20.4% reported psychological distress. More than one in 10 respondents (10.3%) engaged in HHDA, 16.5% amongst males and 4.6% amongst females, and past 3-month drug use was 8.6%, 13.3% amongst males and 4.1% amongst females (see Table 1).
Sample characteristics and distribution of substance use.
Variable
Sample
Hazardous, harmful or dependent alcohol use
Any drug use
Total
Male
Female
Total
Male
Female
N
%
%
%
%
%
%
%
All
39 210
-
10.3
16.5
4.6
8.6
13.3
4.1
Sex
Female
23 112
51.7
-
-
-
-
-
-
Male
16 098
48.3
-
-
-
-
-
-
Age in years
15–17
3852
7.1
4.4
6.1
2.7
6.3
8.4
4.0
15–24
10 863
24.1
9.5
13.5
5.5
10.2
15.4
5.1
25–34
8749
27.2
14.9
23.0
6.8
11.1
17.9
4.5
35–44
6523
19.2
10.4
16.6
4.1
7.9
12.0
3.7
45–54
5315
13.1
9.0
14.7
3.8
6.4
9.6
3.5
55 or more
7760
16.5
5.1
10.2
1.6
4.4
6.2
3.1
Education
Grade 0–7
18 901
37.3
7.8
11.8
4.2
7.0
10.5
4.0
Grade 8–11
9871
26.5
13.2
20.8
5.6
11.3
19.1
3.7
Grade 12 or more
12 362
36.1
11.0
17.9
4.3
8.1
12.0
4.4
Population group
African black people
30 675
79.3
10.4
17.2
4.2
8.4
13.7
3.6
mixed race
4303
8.8
13.5
19.4
10.9
10.8
17.2
6.4
Indian or Asian people
2310
2.9
5.5
5.7
1.1
8.9
8.9
4.5
White people
1922
8.9
7.4
11.2
3.3
7.4
8.6
5.9
Employment status
Employed/self-employed
11 931
36.0
12.2
17.4
4.8
8.9
12.3
4.1
Unemployed
20 649
50.2
9.7
17.5
4.5
8.8
15.9
4.0
Student/pupil/learner
5400
12.4
8.0
10.9
5.1
6.7
8.9
4.6
Sick/disabled/unable/others
748
1.4
8.7
14.6
2.6
6.7
9.7
3.7
Residence
Rural informal
13 675
26.0
6.2
11.6
1.9
7.1
11.7
3.6
Rural farms
4263
5.0
9.6
12.3
5.8
8.8
12.2
4.2
Urban
21 372
69.0
12.0
18.5
5.7
9.1
14.0
4.3
Province
Western Cape
2860
12.2
14.4
19.7
9.2
9.9
14.9
5.1
Eastern Cape
2970
10.7
8.0
12.6
4.0
4.8
8.3
1.7
Northern Cape
2030
2.0
15.3
22.7
7.9
11.3
16.5
6.0
Free State
1753
5.1
15.2
23.6
7.2
10.3
17.0
4.1
KwaZulu-Natal
13 512
18.6
4.5
7.8
1.9
8.9
14.9
4.0
North-West
2498
6.8
13.0
22.3
4.5
8.3
11.3
5.6
Gauteng
6183
27.2
13.0
20.3
5.3
9.3
14.4
4.0
Mpumalanga
5054
7.9
7.8
11.7
3.7
7.0
8.7
5.4
Limpopo
2350
9.5
8.3
15.3
2.3
8.3
13.9
3.5
Psychological distress
No
31 307
79.6
9.8
15.6
4.1
8.3
12.8
3.8
Yes
7750
20.4
12.2
20.6
6.3
9.5
16.0
5.0
Distribution of past 3-month drug use pattern
The most common drug used was cannabis (7.8%), 12.4% amongst males and 3.5% amongst females. The prevalence of cocaine use was 1.8%, followed by sedeatives 1.7%, amphetamine 1.5%, inhalents 1.3%, hallucinogens 1.2% and opiates 1.2% (see Table 2).
Demographic distribution of the prevalence of past 3 month drug use.
Variable
Cannabis
Cocaine
Amphetamine
Inhalants
Sedatives
Hallucinogens
Opiates
%
%
%
%
%
%
%
All
7.8
1.8
1.5
1.3
1.7
1.2
1.2
Sex
Female
3.5
1.5
1.4
1.4
1.7
1.3
1.5
Male
12.4
2.1
1.5
1.2
1.7
1.2
2.0
Age in years
15–17
6.0
1.4
1.2
1.2
1.4
1.2
1.8
15–24
9.5
2.1
1.8
1.6
1.7
1.6
2.2
25–34
10.0
1.9
1.6
1.3
2.0
1.2
1.7
35–44
7.0
1.6
1.2
1.1
1.5
1.0
1.6
45–54
5.8
1.5
1.3
1.4
1.8
1.3
1.6
55 or more
4.1
1.3
1.1
1.1
1.5
1.0
1.2
Population group
African black people
7.8
1.8
1.5
1.4
1.7
1.3
1.9
mixed race
10.3
1.1
1.6
0.7
1.9
0.4
0.7
Indian or Asian people
6.1
2.5
1.5
1.4
2.1
1.6
1.7
White people
6.3
1.5
1.2
1.1
1.8
1.2
1.0
Residence
Rural informal
6.6
2.2
1.7
1.6
1.6
1.5
1.9
Rural farms
7.7
1.4
1.2
1.4
1.6
1.2
1.6
Urban
8.3
1.6
1.4
1.2
1.8
1.2
1.6
Province
Western Cape
8.9
1.5
1.4
0.6
1.6
0.7
1.1
Eastern Cape
4.4
0.6
0.7
0.4
0.6
0.4
0.9
Northern Cape
10.8
2.4
1.9
1.6
2.3
1.6
2.4
Free State
9.6
1.1
0.9
1.0
1.9
1.0
1.2
KwaZulu-Natal
8.2
2.4
2.1
2.0
2.2
1.9
2.2
North-West
7.3
2.6
2.7
2.8
3.1
2.5
3.4
Gauteng
8.5
0.9
0.6
0.6
1.1
0.5
1.0
Mpumalanga
6.4
3.3
3.0
3.2
2.9
2.9
3.3
Limpopo
7.4
3.0
1.6
1.6
1.7
1.4
2.1
Associations with hazardous, harmful or dependent alcohol
In adjusted logistic regression analysis, amongst men who are middle age (25–34 year olds) with higher education, urban residence, drug use and psychological distress were positively associated, whereas Indian or Asian and white population groups were negatively associated with HHDA. Amongst women who are middle age (25–34 year olds), mixed race and residing on rural farms and urban areas with drug use and psychological distress were positively associated and older age (55 years and older) and Indians or Asians were negatively associated with HHDA (see Tables 3 and 4).
Associations with hazardous, harmful or dependent alcohol consumption amongst men.
Variable
Simple logistic regression
Multiple logistic regression
Crude OR
95% CI
p
Adjusted OR
95% CI
p
Age in years
15–24
1
Reference
-
1
Reference
-
25–34
2.33
1.91, 2.85
< 0.001
1.82
1.42, 2.32
< 0.001
35–44
1.55
1.22, 1.97
< 0.001
1.29
0.96, 1.74
0.092
45–54
1.34
1.03, 1.75
0.030
1.23
0.87, 1.74
0.233
55 or more
0.88
0.68, 1.15
0.345
0.89
0.65, 1.22
0.468
Education
Grade 0–7
1
Reference
-
1
Reference
-
Grade 8–11
2.18
1.75, 2.71
< 0.001
1.52
1.18, 1.97
< 0.001
Grade 12 or more
1.79
1.44, 2.33
< 0.001
1.40
1.05, 1.87
0.024
Population group
African black people
1
Reference
-
1
Reference
-
mixed race
1.04
0.83, 1.30
0.747
0.97
0.77, 1.51
0.823
Indian or Asian people
0.50
0.37, 0.69
< 0.000
0.40
0.28, 0.57
< 0.001
White people
0.62
0.46, 0.83
< 0.001
0.52
0.35, 0.78
< 0.001
Employment status
Employed/self-employed
1
Reference
-
1
Reference
-
Unemployed
1.00
0.83, 1.19
0.960
1.14
0.92, 1.42
0.230
Student/pupil/learner
0.41
0.30, 0.54
< 0.001
1.93
0.82, 4.53
0.133
Sick/disabled/unable to work/others
0.82
0.47, 1.43
0.476
1.24
0.63, 2.42
0.530
Residence
Rural informal
1
Reference
-
1
Reference
-
Rural farms
1.18
0.83, 1.67
0.360
1.03
0.70, 1.51
0.889
Urban
1.84
1.44, 2.34
< 0.001
1.70
1.29, 2.23
< 0.001
Drug use (past 3 months)
No
1
Reference
-
1
Reference
-
Yes
3.11
2.54, 3.80
< 0.001
2.79
2.25, 3.46
< 0.001
Psychological distress
No
1
Reference
-
1
Reference
-
Yes
1.46
1.20, 1.78
< 0.001
1.42
1.16, 1.75
< 0.001
OR, odds ratio; CI, confidence interval.
Associations with hazardous, harmful or dependent alcohol consumption amongst women.
Variable
Simple logistic regression
Multiple logistic regression
Crude OR
95% CI
p
Adjusted OR
95% CI
p
Age in years
15–24
1
Reference
-
1
Reference
-
25–34
1.72
1.24, 2.36
< 0.001
1.72
1.25, 2.36
< 0.001
35–44
1.00
0.69, 1.47
0.982
1.01
0.69, 1.47
0.962
45–54
0.93
0.64, 1.34
0.68
0.88
0.61, 1.27
0.502
55 or more
0.38
0.24, 0.59
< 0.001
0.4
0.25, 0.63
< 0.001
Education
Grade 0–7
1
Reference
-
1
Reference
-
Grade 8–11
1.54
1.17, 2.02
0.002
1.13
0.83, 1.53
0.455
Grade 12 or more
1.15
0.89, 1.48
0.285
0.82
0.62, 1.09
0.174
Population group
African black people
1
Reference
-
1
Reference
-
mixed race
2.32
1.77, 3.02
< 0.001
1.98
1.49, 2.63
< 0.001
Indian or Asian people
0.31
0.15, 0.61
< 0.001
0.27
0.14, 0.55
< 0.001
White people
0.9
0.56, 1.44
0.668
0.95
0.58, 1.56
0.854
Employment status
Employed/self-employed
1
Reference
-
-
-
-
Unemployed
0.90
0.68, 1.19
0.460
-
-
-
Student/pupil/learner
0.71
0.43, 1.18
0.189
-
-
-
Sick/disabled/unable to work/others
0.54
0.18, 1.65
0.280
-
-
-
Residence
Rural informal
1
Reference
-
1
Reference
-
Rural farms
3.52
2.13, 5.82
< 0.001
2.57
1.51, 4.38
< 0.001
Urban
3.28
2.35, 4.57
< 0.001
2.87
1.99, 4.14
< 0.001
Drug use (past 3 months)
No
1
Reference
-
1
Reference
-
Yes
3.18
2.15, 4.69
< 0.001
2.91
1.94, 4.36
< 0.001
Psychological distress
No
1
Reference
-
1
Reference
-
Yes
1.69
1.29, 2.20
< 0.001
1.68
1.27, 2.22
< 0.001
OR, odds ratio; CI, confidence interval.
Associations with drug use
In adjusted logistic regression analysis, men having Grade 8–11 education, who are mixed race and unemployed and have HHDA use were positively and middle and older age (25 years and older) and a student or learner were negatively associated with past 3-month drug use. Women who are mixed race, Indians or Asians and have HHDA use were positively and older age (45 years and older) were negatively associated with past 3-month drug use (see Tables 5 and 6).
Associations with drug use amongst men.
Variable
Simple logistic regression
Multiple logistic regression
Crude OR
95% CI
p
Adjusted OR
95% CI
p
Age in years
15–24
1
Reference
-
1
Reference
-
25–34
1.19
0.95, 1.50
0.126
0.81
0.62, 1.04
0.102
35–44
0.75
0.59, 0.95
0.019
0.53
0.41, 0.70
< 0.001
45–54
0.58
0.43, 0.78
< 0.001
0.4
0.29, 0.56
< 0.001
55 or more
0.36
0.27, 0.48
< 0.001
0.26
0.19, 0.35
< 0.001
Education
Grade 0–7
1
Reference
-
1
Reference
-
Grade 8–11
2.01
1.64, 2.47
< 0.001
1.52
1.20, 1.92
< 0.001
Grade 12 or more
1.16
0.95, 1.42
0.141
0.93
0.74, 1.19
0.581
Population group
African black people
1
Reference
-
1
Reference
-
mixed race
1.31
1.03, 1.67
0.027
1.32
1.03, 1.69
0.029
Indian or Asian people
0.63
0.43, 0.93
0.019
0.79
0.52, 1.18
0.242
White people
0.59
0.41, 0.85
0.005
0.83
0.56, 1.24
0.362
Employement status
Employed/self-employed
1
Reference
-
1
Reference
-
Unemployed
1.34
1.11, 1.62
0.002
1.27
1.02, 1.58
0.034
Student/pupil/learner
0.7
0.52, 0.93
0.015
0.52
0.35, 0.76
< 0.001
Sick/disabled/unable to work/others
0.77
0.43, 1.36
0.363
0.99
0.51, 1.90
0.965
Residence
Rural informal
1
Reference
-
-
-
-
Rural farms
1.05
0.75, 1.46
0.778
-
-
-
Urban
1.24
0.98, 1.55
0.072
-
-
-
Hazardous/harmful/dependent alcohol use
No
1
Reference
-
1
Reference
-
Yes
3.12
2.57, 3.78
< 0.001
2.81
2.29, 3.46
< 0.001
Psychological distress
No
1
Reference
-
1
Reference
-
Yes
1.29
1.02, 1.62
0.031
1.22
0.96, 1.53
0.098
OR, odds ratio; CI, confidence interval.
Associations with drug use amongst women.
Variable
Simple logistic regression
Multiple logistic regression
Crude OR
95% CI
p
Adjusted OR
95% CI
p
Age in years
15–24
1
Reference
-
1
Reference
-
25–34
0.87
0.64, 1.18
0.367
0.85
0.63, 1.16
0.308
35–44
0.72
0.50, 1.03
0.069
0.71
0.50, 1.01
0.054
45–54
0.68
0.45, 1.02
0.061
0.64
0.43, 0.96
0.032
55 or more
0.6
0.43, 0.84
0.003
0.57
0.40, 0.80
< 0.001
Education
Grade 0–7
1
Reference
-
-
-
-
Grade 8–11
0.94
0.68, 1.26
0.624
-
-
-
Grade 12 or more
1.09
0.83, 1.44
0.523
-
-
-
Population group
African black people
1
Reference
-
1
Reference
-
mixed race
1.84
1.36, 2.62
< 0.001
1.70
1.25, 2.30
< 0.001
Indian or Asian people
1.25
0.61, 2.57
0.548
1.41
0.68, 2.91
0.355
White people
1.68
1.08, 1.62
0.022
1.94
1.22, 3.07
0.005
Employment status
Employed/self-employed
1
Reference
-
-
-
-
Unemployed
0.99
0.74, 1.31
0.922
-
-
-
Student/pupil/learner
1.14
0.79, 1.63
0.482
-
-
-
Sick/disabled/unable to work/others
0.89
0.41, 1.95
0.775
-
-
-
Residence
Rural informal
1
Reference
-
-
-
-
Rural farms
1.17
0.63, 2.19
0.615
-
-
-
Urban
1.2
0.85, 1.71
0.301
-
-
-
Hazardous/harmful/dependent alcohol use
No
1
Reference
-
1
Reference
-
Yes
3.43
2.37, 4.95
< 0.001
3.08
2.11, 4.49
< 0.001
Psychological distress
No
1
Reference
-
-
-
-
Yes
1.33
0.98, 1.81
0.071
-
-
-
OR, odds ratio; CI, confidence interval.
Discussion
Compared to previous national population-based surveys in 2008 (9.0% HHDA10) and 2012 (4.4% past 3-month drug use14) and a national survey in Kenya (6.7% HHDA),9 this national survey in 2017 showed higher rates of HHDA (10.3%) and any past 3-month drug use (8.6%) (see Table 7). Similarly, Harker et al.24 found an increase of opioid use disorder treatment admissions from 16.1% in 2012 to 20.0% in 2017 in South Africa. Reasons for the overall slight increase of HHDA and increase in any drug use in South Africa from 2008 or 2012 to 2017 need further research.25
Prevalence of hazardous, harmful or dependent alcohol consumption and drug use across national surveys in South Africa.
Study year
Hazardous, harmful or dependent alcohol use
Any drug use
Total
Male
Female
Total
Male
Female
%
%
%
%
%
%
200815
9.0
17.0
2.9
3.326
-
-
201214
-
-
-
4.4
7.9
1.3
2017
10.3
16.5
4.6
8.6
13.3
4.1
Prevalence of hazardous, harmful or dependent alcohol consumption and drug use across national surveys in South Africa.
Study year
Drug use
Cannabis
Cocaine
Amphetamine
Inhalants
Sedatives
Hallucinogens
Opiates
Total
Total
Total
Total
Total
Total
Total
%
%
%
%
%
%
%
200826
3.2
0.7
0.8
0.8
0.7
0.7
0.7
201214
4.0
0.3
0.3
0.2
0.4
0.1
0.1
2017
7.8
1.8
1.5
1.3
1.7
1.2
1.2
In agreement with previous studies,6,7,8,14,26,27 this study found that male sex increased the odds and older age decreased the odds of HHDA and drug use. Sex-specific role expectations and norms, such as associating drinking alcohol and drug use with masculinity, may be related to the male preponderance of HHDA and drug use.7,28 In older age, in this study amongst women, a reduction of HHDA may be expected because the tolerance towards alcohol reduces with ageing.29
Amongst different population or ethnic groups in South Africa, mixed race women had significantly higher odds for HHDA and drug use. This result concurs with previous studies in South Africa for both mixed race women and men.10,14 It is possible that people of mixed race (coloured) are exposed to more stressors than other population groups contributing to higher rates of substance use. Whilst previous research showed an association between lower education and lower socioeconomic status,10,30,31 this study did not find that educational level was associated with HHDA and drug use amongst women, whilst drug use amongst men with higher education was positively associated with HHDA and drug use. The findings amongst women are interesting and warrant further investigation to fully understand the change. Amongst men with higher education, the positive association could be explained because of rapid modernisation, which strongly correlates with drug use.32 As South Africa progresses from apartheid, there are an increasing number of people entering the higher education and middle-upper income bracket.
On the other hand, amongst men, unemployment increased the odds of drug use in this study, which is in line with a previous study in South Africa.26 As stated by Peltzer et al:32
[U]se of drugs may be functional as it provides a form of release or escape not only for large numbers of unemployed (especially young men) who may also feel they are unemployable. (p. 2228)
Consistent with previous research findings,16,17,33 this study found strong associations between drug use, psychological distress and HHDA and drug use. This confirms the comorbity between HHDA and drug use and psychological distress, but comorbidity between drug use and HHDA but not psychological distress. Reasons for the comorbidity between HHDA and drug use may lie in the codependence risk of the substances used. Public health interventions should be directed at integrating drug use and psychological distress prevention in persons with HHDA.
Study limitations
This study was limited by its cross-sectional design and self-report of data, including substance use. A further limitation was that in this household survey, populations using heavy substance, such as military personnel, homeless or institutionalised persons, were not included.34
Conclusion
In this large national population-based survey amongst persons 15 and older in 2017 in South Africa, about one in 10 participants engaged in HHDA and drug use, with several sociodemographic (male sex, middle age, higher education, being unemployed, mixed race, urban residence) and health indicators (substance use and psychological distress), was identified to be associated with HHDA and/or any drug use.
Acknowledgements
We acknowledge Human Sciences Research Council; South African National HIV Prevalence, HIV Incidence, Behaviour and Communication Survey (SABSSM) 2017: Combined – All provinces. (Data set). SABSSM 2017 Combined. Version 1.0; Pretoria South Africa: Human Sciences Research Council (producer) 2017; and Human Sciences Research Council (distributor) 2020. https://doi.org/doi:10.14749/1585345902.
Competing interests
The authors declare that they have no financial or personal relationships, which may have inappropriately influenced them in writing this article.
Authors’ contributions
All authors fulfil the criteria for authorship. S.P., S.R. and K.P. conceived and designed this research, performed the statistical analysis, drafted this manuscript and made critical revision of this manuscript for key intellectual content. All authors have read and agreed to the published version of this manuscript.
Funding information
Bill and Melinda Gates Foundation, Centers for Disease Control and Prevention Human Sciences Research Council, President’s Emergency Plan for AIDS Relief (Emergency Plan), South African National AIDS Council and United Nations Children’s Fund funded this work.
Data availability
The data are available at Human Sciences Research Council (distributor) 2020. http://dx.doi.org/doi:10.14749/1585345902.
Disclaimer
The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policy or position of any affiliated agency of the authors.
ReferencesShieldK, MantheyJ, RylettM, et al. National, regional, and global burdens of disease from 2000 to 2016 attributable to alcohol use: A comparative risk assessment study. Lancet Public Health. 2020;5(1):e51–e61. https://doi.org/10.1016/S2468-2667(19)30231-2United Nations Office on Drugs and Crime. World drug report 2016. New York, NY: United Nations Publication, Sales No. E.16.XI.7; 2016.DegenhardtL, WhitefordHA, FerrariAJ, et al. Global burden of disease attributable to illicit drug use and dependence: Findings from the global burden of disease study 2010. Lancet. 2013;382(9904):1564–1574. https://doi.org/10.1016/S0140-6736(13)61530-5SchneiderM, NormanR, ParryC, BradshawD, PlüddemannA, South African Comparative Risk Assessment Collaborating Group. Estimating the burden of disease attributable to alcohol use in South Africa in 2000. S Afr Med J. 2007;97(8 Pt 2):664–672.ClausenT, RossowI, NaidooN, KowalP. Diverse alcohol drinking patterns in 20 African countries. Addiction. 2009;104(7):1147–1154. https://doi.org/10.1111/j.1360-0443.2009.02559.xAgoudaviK, DalmayF, LegleyleS, et al. Epidemiology of alcohol use in the general population of Togo. Addict Behav Rep. 2015;2:1–5. https://doi.org/10.1016/j.abrep.2015.03.004KabwamaSN, NdyanabangiS, MutungiG, WesongaR, BahendekaSK, GuwatuddeD. Alcohol use among adults in Uganda: Findings from the countrywide non-communicable diseases risk factor cross-sectional survey. Glob Health Action. 2016;9:31302. https://doi.org/10.3402/gha.v9.31302PiresJ, PadrãoP, DamascenoA, Silva-MatosC, LunetN. Impact of different reference period definitions in the quantification of alcohol consumption: Results from a nationwide STEPS survey in Mozambique. Alcohol Alcohol. 2012;47(3):328–333. https://doi.org/10.1093/alcalc/ags003PengpidS, PeltzerK. Alcohol use among adults in Kenya: Results from the national non-communicable diseases risk factor survey, 2015. J Psychol Afr. 2019;29(1):49–53. https://doi.org/10.1080/14330237.2019.1568728PeltzerK, DavidsA, NjuhoP. Alcohol use and problem drinking in South Africa: Findings from a national population-based survey. Afr J Psychiatry. 2011;14(1):30–37. https://doi.org/10.4314/ajpsy.v14i1.65466PeacockA, LeungJ, LarneyS, et al. Global statistics on alcohol, tobacco and illicit drug use: 2017 status report. Addiction. 2018;113(10):1905–1926. https://doi.org/10.1111/add.14234DegenhardtL, BharatC, GlantzMD, et al. The epidemiology of drug use disorders cross-nationally: Findings from the WHO’s world mental health surveys. Int J Drug Policy. 2019;71:103–112. https://doi.org/10.1016/j.drugpo.2019.03.002ObotIS. Drugs in the Nigerian population: Availability, use, consequences and policy implications. Bull Narc [serial online]. 2019 [cited 2020 Jan 5];LXII:1–9. Available from: https://www.crisaafrica.org/wp-content/uploads/2020/08/Bulletin-19-11671_ebook.pdf#page=13. https://doi.org/10.18356/4a466a75-enPeltzerK, Phaswana-MafuyaN. Drug use among youth and adults in a population-based survey in South Africa. S Afr J Psychiatr. 2018;24:1139. https://doi.org/10.4102/sajpsychiatry.v24i0.1139PeltzerK, PengpidS. Alcohol use and problem drinking in South Africa: Results from a national-population-based survey 2014–2015. J Psychol Afr. 2018;28(2):147–151. https://doi.org/10.1080/14330237.2017.1375230LaiHM, ClearyM, SitharthanT, HuntGE. Prevalence of comorbid substance use, anxiety and mood disorders in epidemiological surveys, 1990–2014: A systematic review and meta-analysis. Drug Alcohol Depend. 2015;154:1–13. https://doi.org/10.1016/j.drugalcdep.2015.05.031Jané-LlopisE, MatytsinaI. Mental health and alcohol, drugs and tobacco: A review of the comorbidity between mental disorders and the use of alcohol, tobacco and illicit drugs. Drug Alcohol Rev. 2006;25(6):515–536. https://doi.org/10.1080/09595230600944461SimbayiLC, ZumaK, ZunguN, et al. South African national HIV prevalence, incidence, behaviour and communication survey, 2017. Cape Town: HSRC Press; 2019.Statistics South Africa. Mid-year population estimates 2017. Pretoria: Stats SA; 2017.BaborTF, Higgens-BiddleJC, SaundersJB, MonteiroMG. AUDIT: The alcohol use disorders identification test. Guidelines for use in primary care. Geneva: World Health Organization, Department of Mental Health and Substance Dependence; 2001.LiskolaJ, HaravuoriH, LindbergN, et al. AUDIT and AUDIT-C as screening instruments for alcohol problem use in adolescents. Drug Alcohol Depend. 2018;188:266–273. https://doi.org/10.1016/j.drugalcdep.2018.04.015HumeniukRE, Henry-EdwardsS, AliRL, PoznyakV, MonteiroM. The alcohol, smoking and substance involvement screening test (ASSIST): Manual for use in primary care. Geneva: World Health Organization; 2010.KesslerRC, AndrewsG, ColpeLJ, et al. Short screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychol Med. 2002;32(6):959–976. https://doi.org/10.1017/S0033291702006074HarkerN, LucasWC, LaubscherR, DadaS, MyersB, ParryCD. Is South Africa being spared the global opioid crisis? A review of trends in drug treatment demand for heroin, nyaope and codeine-related medicines in South Africa (2012–2017). Int J Drug Policy. 2020;83:102839. https://doi.org/10.1016/j.drugpo.2020.102839RamlaganS, PeltzerK, PengpidS. Prevalence and correlates of non-daily and daily cannabis use among persons 15 years and older in South Africa: Results of a national survey in 2017. Preprint from Research Square, 2020Dec3. https://doi.org/10.21203/rs.3.rs-116225/v1PeltzerK, RamlaganS. Illicit drug use in South Africa: Findings from a 2008 national population-based survey. S Afr J Psychiatr. 2010;16(1):8–15. https://doi.org/10.4102/sajpsychiatry.v16i1.230PhillipsMR, ChengHG, LiX, et al. Prevalence, correlates, comorbidity, and age of onset of alcohol use disorders in adult males from five provinces in China. Drug Alcohol Depend. 2017;173:170–177. https://doi.org/10.1016/j.drugalcdep.2016.12.026. https://www.niaaa.nih.gov/alcohols-effects-health/special-populations-co-occurring-disorders/older-adultsKassaA, TaddesseF, YilmaA. Prevalence and factors determining psychoactive substance (PAS) use among Hawassa University (HU) undergraduate students, Hawassa Ethiopia. BMC Public Health. 2014;14:1044. https://doi.org/10.1186/1471-2458-14-1044Institute of Alcohol Studies. Older people’s drinking habits, 2017 [homepage on the Internet]. [cited 2020 Aug 30]. Available from: https://www.niaaa.nih.gov/alcohols-effects-health/special-populations-co-occurring-disorders/older-adultsAllenL, WilliamsJ, TownsendN, et al. Socioeconomic status and non-communicable disease behavioural risk factors in low-income and lower-middle-income countries: A systematic review. Lancet Global Health. 2017;5(3):e277–e289. https://doi.org/10.1016/S2214-109X(17)30058-XAssariS, FarokhniaM, MistryR. Education attainment and alcohol binge drinking: Diminished returns of Hispanics in Los Angeles. Behav Sci. 2019;9(1):9. https://doi.org/10.3390/bs9010009PeltzerK, RamlaganS, JohnsonBD, Phaswana-MafuyaN. Illicit drug use and treatment in South Africa: A review. Subst Use Misuse. 2010;45(13):2221–2243. https://doi.org/10.3109/10826084.2010.481594PeltzerK, ChaoLW, RamlaganS, SzrekH. Daily tobacco use and problem drinking among urban adults in South Africa: A longitudinal study. Pan Afr Med J. 2019;32:51. https://doi.org/10.11604/pamj.2019.32.51.17256RehmJ, CrépaultJF, WettlauferA, MantheyJ, ShieldK. What is the best indicator of the harmful use of alcohol? A narrative review. Drug Alcohol Rev. 2020;39(6):624–631. https://doi.org/10.1111/dar.13053
How to cite this article: Pengpid S, Peltzer K, Ramlagan S. Prevalence and correlates of hazardous, harmful or dependent alcohol use and drug use amongst persons 15 years and older in South Africa: Results of a national survey in 2017. Afr J Prm Health Care Fam Med. 2021;13(1), a2847. https://doi.org/10.4102/phcfm.v13i1.2847