substance abuse in south africa essay

• Open access
• Published: 27 May 2024

Trends in socio-demographic characteristics and substance use among high school learners in a selected district in Limpopo Province, South Africa

• Linda Shuro 1 &
• Firdouza Waggie 2  

BMC Public Health volume  24 , Article number:  1407 ( 2024 ) Cite this article

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Substance use is an escalating public health problem in South Africa resulting in risky behaviours and poor educational attainment among adolescents. There is a huge battle to overcome substance use among learners as more drugs become easily available with the mean age of drug experimentation reported to be at 12 years of age. It is important to continuously understand the trends in substance use in order to assess if there are positive changes and provide evidence for the development of context-specific effective interventions. This paper outlines the prevalence of substance use among selected high schools in a district in Limpopo province.

To determine the prevalence of substance use among selected high school learners in a district in Limpopo Province, a cross-sectional school survey of 768 learners was conducted. Data was analysed using SPSS v 26. Descriptive analysis was used to describe the independent and dependent variables and Chi-Square test was used to investigate associations between demographic characteristics and substance use among high school learners.

The most abused substances by learners were alcohol (49%), cigarettes (20.8%) and marijuana (dagga/cannabis) (16.8%). In a lifetime, there was a significant difference ( P  < 0.05) in cigarette smoking with gender, school, and grade; with more use in males (14.2%) than females (7.6%); in urban schools (14.6) than peri-urban (6.7%) and more in Grade 12 (6.4%). There was a significant difference ( P  < 0.05) in alcohol use with more use in Grade 10 (12.6%) and varied use among male and female learners but cumulatively more alcohol use in females (27.7%). Drug use varied, with an overall high drug use in urban schools (20.7%).

Conclusions

Substance use is rife among high school learners in the district and health promotion initiatives need to be tailored within the context of socio-demographic characteristics of learners including the multiple levels of influence such as peer pressure, poverty, unemployment and child headed families. Additional research is required to investigate the factors leading to a notable gradual increase in use among female learners and into the environmental and family settings of learners in influencing substance use.

Peer Review reports

Substance use and abuse is a major public health concern among female and male adolescents with prevalence varying in different contexts. Drug consumption in South Africa is twice the global average [ 1 ]. South Africa is ranked in the top 10 narcotics and alcohol abusers in the world. For every 100 people, 15 have a drug problem and for every 100 Rands in circulation, 25 Rands is linked to substance use [ 2 ]. Many schools in South Africa continue to battle with the problem of substance use among learners. The most experimented substances by adolescents in South Africa is tobacco and alcohol [ 3 ]. A majority of these adolescents are found in schools. The use of substances at an early age, especially among learners results in negative health and social outcomes such as school dropouts, and early onset of sexual behavior which may lead to teenage pregnancy and sexually transmitted infections [ 4 , 5 ]. A review of studies on impact of substance use on school performance and public health indicate that use of substances is significantly associated with negative school outcomes such as truancy, low motivation to learn, regular sickness, increasing school abstenteeism, decreasing marks and high chances of skipping school [ 6 , 7 , 8 ].

The mean age of drug experimentation in South Africa is 12 years and this is rapidly decreasing [ 9 ]. Globally 1 in 4 learners (13–15 years old) had their first smoke before the age of 10 and the percentage of use is greater than 10% for any tobacco product by 13-15-year-old learners [ 10 ]. The increased availability and variety of drugs available to South African teenagers is a cause for alarm, for example, marijuana (known as dagga/cannabis), cocaine, glue, methamphetamine known as TIK and whoonga known as “nyaope”-a street name for a mixture of mainly dagga and low-grade heroin [ 11 ]. South Africa is also experiencing an up rise in drugs and gangsterism labelled the “twin evils of our time”, especially found among youth in previously marginalized communities. The problem is viewed as an indication of the many socio-economic challenges faced by working class communities [ 12 ].

Global initiatives such as the WHO Global School Health initiative promote health promoting schools (HPS) to improve the health of the school community. An HPS is a “school constantly strengthening its capacity as a healthy setting for living, learning and working” [ 13 ]. The HPS approach was implemented in South Africa in 1994 in efforts to redress inequalities created during apartheid in the education and health sector and also the policy context was favourable for its acceptance [ 14 ]. There are many public health initiatives to address health issues among adolescents in South Africa which are integrated as part of the current health reforms such as re-engineering primary health care, National Drug Master Plan 2013–2017 [ 15 ] and the revised Integrated School Health Policy (ISHP). With a focus on school health, the ISHP was launched in 2012, as a collaboration between the Department of Health (DoH) and the Department of Basic Education (DBE) [ 16 ] as a framework for the new school health programme (grade 0 to 12 learners), implemented at sub-district level. It is therefore invested at the primary level and aligned to several government commitments such as the United Nations Convention on the Rights of the Child and Bill of Rights of the South African Constitution [ 17 ]. The above initiatives require a more integrated approach for effective change and to address the social determinants of substance use among learners [ 18 ].

Schools in Limpopo face multiple social challenges that affect effective teaching and learning such as crime and violence, sexual assault/abuse, substance use and bullying [ 19 ]. There was a recent outcry for action by learners to the Education Member of Executive Council (MEC) to address these social challenges [ 20 ]. A review of prevalence studies [ 21 , 22 , 23 , 24 , 25 ] in Limpopo high schools shows that male learners abuse drugs more than female learners. The review also showed past month low prevalence rates in rural high schools but with progression of studies and lifetime use, a gradual increase in prevalence rates in schools was noted. Some of the contributing factors to substance use noted include more access to finances by the males, the presence of liquor stores near the learners’ homes; certain demographic characteristics such as being male, urban versus rural learners; substance use among parents and friends. The major determinants of alcohol use found in students include, “gender, age, ever having smoked a cigarette, ever damaged property, walking home alone at night, easy availability of alcohol, thinking alcohol use was wrong, attending religious services and number of friends who used alcohol” [ 21 ]. A similar study identified the following five community level factors linked to use of home prepared alcohol by learners: i) subjective adult norms around substance use in the community, ii) negative opinions about one’s neighbourhood, iii) perceived levels of adult antisocial behavior in the community, iv) community affirmations of adolescents, and v) perceived levels of crime and violence in the community (derelict neighbourhood)” [ 26 ]. In one district in Limpopo, learners identified alcohol, tobacco, marijuana, petrol, glue and jeyes fluid mixed with spirit as the commonly used substances and other learners experimented on heroin and cannabis as they had friends with access to the drugs in town [ 27 ] which is consistent with other studies [ 28 , 29 ]. Youth in Limpopo are engaged in different substances (tobacco, alcohol, hard core drugs) with cannabis, inhalants, bottled wine, home, and commercially brewed beer as commonly abused substances [ 30 ]. This highlights the need for more monitoring studies to review the escalating situation of substance use among learners to create a wider data baseline for evidence-based initiatives. One of the research sessions at the 47th annual meeting of the Society for Epidemiologic Research on to tobacco and smoking showed that continued publication of health effects leads to reduction in smoking [ 31 ].

This study adds on to recent prevalence studies on substance use in high schools and adolescents in Limpopo province. Additionally, it contributes to baseline information which assists in the development of evidence-based initiatives. In line with the aims of international surveys [ 32 ] from which this study adopts, the findings support reporting of comparable data on drug use trends in Limpopo as well as having data from 1 of the 6 districts helps comparison within the province and supports targeted intervention and not a one size fits all approach. The main researcher was involved in anti-substance use clubs in some schools in Limpopo as part of health promotion and in light of many existing policies, it is worrying to note a gradual increase in prevalence rates of substance use amongst learners noting the gap between what is on paper and actual implementation (Lenkokile, 2016; Madikane, 2018; Mokwena et al., 2020). This study was an important process in Limpopo focused at providing current evidence towards developing effective context specific anti-substance use initiatives in high schools. The aim of this study was therefore to determine the prevalence of substance use among selected high school learners in schools in one district in Limpopo Province.

Study design

A cross-sectional survey (quantitative) was conducted among 768 high school learners from four high schools in the district.

Study population and sampling

The study population was all high school learners (N-13 244) enrolled in the period 2019–2020, Grade 8 to 12 [ 33 ]. Fifteen high schools within the Polokwane circuit were stratified into two strata according to socioeconomic and geographical divide: Urban and Peri-Urban. Simple random sampling was used to select two schools from each stratum. Once the four schools were identified, simple random sampling was used to select one class each from Grade 8 to 12 for participation in the cross-sectional survey. Consideration was taken to include the whole class so that learners are treated equally and excluding some students could affect anonymity perceptions and lead to disturbances [ 32 ]. However, due to the pressure of the announcement of the lockdown due to Covid 19 in March 2020 and schools closing, random sampling of classes was a bit limited to available classes in each grade.

Research sites

The study took place in four public high schools in the Polokwane circuit, Capricorn district, Polokwane local Municipality. The two peri urban schools selected are in the Seshego cluster on the north-west outskirts of the Polokwane city which is divided into 8 residential zones. Seshego is diverse with both extremes (poverty and wealth) located about 5kms from the CBD and most people must commute to the city for work. The urban schools are found more adjacent to the Polokwane city in Nirvana and Flora Park (formerly “coloured” and white” suburbs) but now quite diverse [ 34 ]. Polokwane is found in the Limpopo Province, South Africa. Limpopo is a rural province with 5 district municipalities: Capricorn, Sekhukhune, Waterberg, Vhembe, and Mopani. Within the Capricorn district are four local municipalities: Polokwane, Blouberg, Molemole and Lepelle-Nkumpi [ 35 ].

Data Collection

The school management and the heads of department for Life Orientation were instrumental to grant permission to conduct the survey and to randomly select one class per each grade to participate in the schools. The questionnaire was distributed to the learners to fill in and the researcher was present to explain the purpose of the research and address any clarifications. The survey took place in March 2020, a few weeks, and days before the national lockdown due to COVID 19. The self-administered questionnaire used in this study, is a modified instrument adapted from the UNODC Global Assessment Programme on Drug Abuse (GAP) Toolkit questionnaire on Conducting School Surveys on Drug Abuse. This tool is deemed valid and reliable as it was used and adapted in previous studies [ 36 , 37 , 38 ]. The original questionnaire was developed to build local level capacity among member states to collect data that can guide reduction activities in schools and therefore better fits the purpose for this study. The questionnaire was adapted to the local context using SA based terms and removing terms not relevant to the local context. A pilot study was conducted in a different circuit and adjustments made to the questionnaire and the process of data collection.

Ethical considerations

Permission to conduct the study was granted by the Limpopo Department of Education (Ref: 2/2/2) and the University of Western Cape (HS19/9/12). Information sheets, consent, and assent forms to participate in the study and seek permission from a guardian or parent were given to the learners prior to the data collection date. The researcher went with the invitations, information sheets and consent forms to the education circuit and these were sent to each school via the circuit office. The researcher also went with copies of the information sheets for the parents and learners to each school before the data collection. Therefore, the learners were informed of the study by providing them with an information sheet and explaining the purpose of the research and what is expected of them. An information sheet was also provided for the parents or guardians. Parents received information sheets and parental consent sought for learners to participate in the school survey. The signed forms from the parents/guardians were collected before administering the questionnaire to learners. Before administering the questionnaire, an explanation was provided again and learners above 18 received the consent forms and assent forms for learners under 18 to agree to participate in the study once they fully understood the purpose of the research.

Data analysis

Microsoft Excel was used to capture the data and exported to IBM SPSS v 26 for analysis to obtain baseline information about substance use in high schools (Briggs, 2016). Descriptive analysis was used to describe the independent and dependent variables using percentages, means, and standard deviation and inferential analysis such as correlation between sociodemographic characteristics and substance use, was used as well [ 39 ]. Percentages of gender, age, school, grades, level of parent’s education and person living with the learner were described. The frequency of substance use (alcohol, cigarette smoking and drugs) was presented to show lifetime, during the last 12 months and past 30 days (previous month) substance use. The Chi-Square test was used to investigate associations between demographic characteristics and substance use (cigarette smoking, alcohol use and drug use). The different p-values of less than 0.05 at a 5% significance level obtained, suggested that either grade, school, and gender have an influence and the differences on substance use depending on the substance. Percentages on awareness of substances, disapproval of substance use, friends who used substances, access to substances, perceived risk and associated behaviours under influence of substance use.

Demographic characteristics

Seven hundred and sixty-eight ( N  = 768) learners from four high schools participated in the survey. 54.2% ( n  = 416) were female and 45.8% ( n  = 352) male learners, with a mean average age of 16 years. There were 286 participants from two schools in the peri-urban (school 2 and 3) and 482 participants from the urban environment (school 1 and 4). The percentage of grades was distributed proportionally with a slightly increased percentage among the Grade 10s. The break-down of the participants is represented in Table  1 . The percentage of participation in each grade in each school varied due to class size variation. For purpose of this article, only geographical location (urban and peri-urban), gender, age and school are reported in relation to substance use.

Perceived availability and awareness of substances

In the four participating schools, learners responded to availability of several substances with cigarettes indicated as the most easily accessible substance 46.5% ( n  = 357) and mandrax the least accessible (see Table  2 ). The results indicate a wide variety of substances available to learners.

When learners were asked if they ever heard of the drugs listed on the questionnaire, learners had mostly heard of marijuana 72.1% ( n  = 554), nyaope 69.8% ( n  = 536), and least heard of ecstasy 22.3% ( n  = 171) and other drugs 22.5% ( n  = 173). Learners went on to mention the other drugs which included petrol, vape, tretamines and names that seemed mostly to be street names such as weed, Bluetooth, globe, flakka, hubbly, hashishka, cat, lollipop, ice pace and soil pill.

Trends in cigarette smoking and alcohol use

The overall lifetime prevalence of cigarette smoking among the learners was 20.8% (split according to the number of occasions (from 1 to 2 times to 40+) as seen in Table  3 ). 11.4% of the learners responded to have smoked during the last 12 months. In the last 30 days there was an overall prevalence of 7.1% further broken down by number of occasions. There was a high lifetime overall utilisation of alcohol with 49% of the learners having drunk alcohol (split according to the number of occasions (from 1 to 2 times to 40+) in Table  3 . 37.1% indicated alcohol consumption during the last 12 months with 13.7% who did not indicate. In the last 30 days the overall prevalence was at 20.9% with 16.3% who did not indicate. 9.6% of the learners indicated that they had five or more drinks in a row at least once and 4.4%, 10 or more times in the last 30 days.

An attempt was made to establish whether the learners’ socio-demographic characteristics were associated with cigarette smoking and alcohol use in a lifetime. The results as shown in Table  4 shows Chi-square test with a P- value of 0.000 at a 5% significance level suggesting that grade, school and gender have an influence on lifetime cigarette smoking. Schools in the urban area had an overall higher prevalence of cigarette smoking (14.6%) than schools in the Peri-Urban (7%) as cumulative effect. The results also show a significant difference in use for cigarette smoking by gender with more males (14.2%) than females (7.6%). The results in Table  4 also shows Chi-square test with a P- value of 0.001 for grade and 0.000 for gender suggest an association between lifetime alcohol use and these two socio-demographic characteristics with most alcohol use found to be in grade 10 (12.8%) and least in grade 8 (8%) as a cumulative effect. The differences in use among male and female learners varied with the number of occasions with overall high use in females (27.7%). There was no significant difference in alcohol use with schools.

Results showed that the age at first use of alcohol (beer, wine) and cigarette smoking was quite low at 13 years or less with a significant percentage even below 15 years of age. An age of 13 years or younger was taken as an indicator of early onset. At the age of 13 years or younger: 18.5% ( n  = 142) of the learners had drunk beer, 18.8% ( n  = 144) drank wine and 11.1% ( n  = 85) had smoked cigarettes. There was a percentage decline in use with increasing age.

Trends in drug use

Learners who tried drugs.

When asked if they had ever tried the listed drugs on the questionnaire, 19.3% male learners and 13.9% of the female learners said “Yes” to marijuana. The results for the other drugs had lower percentages which varied but indicate more males had tried out substances than females.

Lifetime use of drugs, during 12 months and last 30 days

Lifetime use of drugs varied with the type of drugs (see Fig.  1 ). A percentage overall of 16.8% of the learners had used marijuana in a lifetime, 13% in the last 12 months, and 8.4% in the last 30 days (see Fig.  1 ). Results also showed that the most common drug first tried was marijuana (dagga) (12.1%).

Drug use- Lifetime, During last 12 months and last 30 days

The Chi-square test was applied to investigate the association between socio-demographic characteristics and lifetime use of drugs. There was a significant association between school and the use of drugs prescribed by medical workers ( P  = 0.03), ecstasy (0.01), nyaope/whoonga (0.025) and mandrax (0.03) with higher use in urban (20.7%) compared to peri-urban (16.9%) schools. There was an association between grade and the use of marijuana (dagga) (0.000) with overall high use from Grade 10 to 12 and gender on the use of marijuana (0.000) and nyaope (0.018) with more males (25.1%) than females (14.1%).

Risk of substance use

46% ( n  = 352) of learners perceived great risk with smoking one or more packs of cigarettes per day, 40% ( n  = 307) perceived great risk by having four or five drinks in a row nearly every day, 24% ( n  = 183) of the learners perceived smoking cigarettes occasionally as a great risk but closely 22% ( n  = 169) perceived no risk with smoking occasionally, 19% ( n  = 147) no risk with having one or two drinks nearly every day and 16% ( n  = 125) no risk with trying marijuana.

This study investigated the prevalence of substance use among high school learners and highlighted how certain socio-demographic characteristics such as gender, grade, and school influence substance use patterns.

Notably, our findings reveal gender disparities in substance use. Whilst alcohol use varied with the number of occasions among female and male learners, there was an overall high alcohol use in females (27.7%) compared to their male counterparts (24.5%). It is essential to recognize that our study, primarily focused on prevalence, and did not delve into the determinants of substance use specifically within gender groups. Nonetheless, contextual factors such as poverty and gender-based violence prevalent in South African communities may contribute to the elevated substance use rates observed among female learners. Additionally, delays in accessing social assistance could potentially exacerbate risky behaviors among this demographic. Additional research is needed into factors influencing a gradual increase in uptake among female learners which was beyond the scope of this study.

The study also shows further gender disparity with male learners demonstrating higher levels of experimentation and use of drugs, particularly marijuana and nyaope (whoonga). This trend aligns with existing research, which consistently indicates a higher prevalence of substance abuse among male learners. A review of past and present prevalence studies conducted in Limpopo high schools corroborates this observation, with multiple studies consistently reporting higher rates of drug abuse among male learners [ 21 , 22 , 23 , 24 , 25 , 26 ]. Assumptions from observation can be made that this could be linked to societal settings and friends in which boys “hang out” with more than girls and may have ease of access to drugs. Males tend to be found more on the corners of streets, shops and stay out late. The phenomenon of having more male users than females is found in many prevalence studies [ 20 , 29 , 48 , 49 , 50 ] suggesting the need for male-oriented initiatives. To show the magnitude of the problem “Harmful use of alcohol is accountable for 7.1% and 2.2% of the global burden of disease for males and females respectively” [ 51 ]. However, several studies are beginning to show that there is little difference in use between genders [ 55 ]. This suggests the need for gender-specific initiatives to ensure effective programs among adolescents.

The results of the present study indicate that substance use is rife in both peri-urban and urban environments among high school learners in the district. In a lifetime, cigarette smoking (20.8%), alcohol (49%), and marijuana (16.8%) were identified as the commonly used substances among the learners, mirroring trends observed in past studies conducted in Limpopo and Sub-Saharan Africa. A baseline study among youth conducted in Limpopo in 2013 showed percentage use of inhalants at 39%, marijuana (49%) and alcohol (54,8%) as the most used substances [ 30 ]. Similarly, a systematic review of 27 studies in Sub-Saharan Africa among 143 201 adolescents shows that alcohol (32.8%), tobacco products (23.5%), khat (22%) and cannabis (15.9%) were the most commonly used substances [ 53 ].

Despite these similarities, the present study indicates a decrease in the percentage use of these substances compared to past studies. This discrepancy could potentially be attributed to the present study’s focus on selected high schools within a district, as opposed to previous studies that may have had broader sampling across the entire province or region. Nonetheless, overall this data is comparable to a certain extent to results of national surveys that have been conducted using a similar instrument which show trends in use in a lifetime, annually and in the past month and correlations across demographic characteristics and substance use. Examples include the annual drug national survey of 2020 (Monitoring the Future) in the United States [ 38 ] and an older survey in Kenya on patterns of drug use in public secondary schools [ 36 ]. Therefore, the findings add to a wider data baseline for evidence-based initiatives and more specific to Limpopo towards evidence-based context-specific anti-substance use initiatives in high schools.

A worrying phenomenon observed in this study and other previous studies is the decreasing age of onset of substance use at the age of 13 years or less. Despite the legal restrictions in South Africa setting the minimum age purchasing alcohol and cigarettes at 18, adolescents are gaining access to these substances, as indicated by the study findings. The findings are consistent with the study conducted in Limpopo in 2013, which shows the age of first use of cannabis/marijuana as early as 10 years or less [ 29 ]. There have been policy discussions to amend the age to 21 but this may seem not to be effective considering that alcohol is easy to access by a 13-year-old or younger suggesting a “ thriving illegal market” [ 40 ]. A similar finding by the Southern Africa Alcohol Policy Alliance (SAAPA) showed that 12% of those under 13 years were said to have drunk alcohol in the past month and 25% of young people binge drinking [ 40 ]. The rise in underage use may also be linked to the ongoing alcohol advertising which is prominent in the neighbourhoods and entices adolescents. With mixed views on the introduction of the new Limpopo tobacco bill, it’s crucial to monitor any changes in adolescent access to substances. Calls for the bill to enforce strict measures on the sale of tobacco products to minors highlight the urgency of addressing this issue [ 52 ].

According to the WHO Global status report on alcohol and health [ 41 ], Alcohol is the leading risk factor for premature mortality and disability among those aged 15 to 49 years, accounting for 10% of all deaths in this age group. This highlights a huge public health challenge and the need for preventive strategies that target lower grades before the onset of substance use. Whilst learners are aware of the different types of substances there is still a significant number of learners who do not perceive the risk of smoking and drinking occasionally and trying marijuana. To improve the perceived risk associated with substance use among learners, as part of integration in education, consistent awareness programmes in all the subjects of the curriculum could assist in improving the level of perceived risk and not limited to the Life Orientation subject in the school curriculum only as recommended that health education should be established in all school topics [ 42 ]. . This approach aligns with recommendations from the U.S. Department of Health & Human Services [ 56 ], which emphasize the significance of prevention programs at different life stages and involving the community.

The increased lifetime utilisation of alcohol, cigarettes, and marijuana is linked to the ease of access of these substances as first experimental substances, providing a gateway for the introduction of other substances [ 30 ]. In line with the ecological model on determinants of health, some of the multiple factors for increased use and availability of marijuana among the learners could be that South Africa is ranked among the countries in the region where cannabis cultivation and production occur to a large extent and marijuana use is legalised for adults to cultivate and smoke in their homes. A contributing factor to use by learners is substance use by parents or adults they live with. In 2017, 3.8% of the global population aged 15 to 64 years used cannabis at least once and cannabis use increased significantly between 2010 and 2017 in Africa [ 46 ]. Cannabis (marijuana/dagga) is highly used globally with approximately 3.8% between 15 and 64 years, about 188 million people having used it once or more times in 2017 (UNODC, 2019). Cannabis is ranked among the most used substances among adolescents attributed to its ease of access and a low and drop in the percentage of the perceived risk of using it, as evidenced in Western countries (UNODC, 2018; UNODC, 2014; UNODC, 2021).

In terms of geographical determinants of substance use and abuse, there were higher percentages of cigarette smoking and drug use in the urban schools compared to peri-urban schools. There is a diverse group of learners attending schools in urban areas coming from different areas including from the peri-urban and rural areas. The majority of the learners commute to attend schools and are exposed to access and use of substances when traveling which contributes as a factor to the ongoing public health concern of substance use among adolescents. This phenomenon of learners commuting long distances is also highlighted by a study in New York where older students, girls, and higher attaining students are likely to commute to distant schools despite schools close to them [ 54 ]. As a result, the road to school exposes learners to many risky situations including access to substances. A collaboration between the Department of Education and the Department of Transport should exist to ensure safe transport systems for learners whilst, at the same time, more work needs to go into improving local schools working together with the local municipality.

There is a need for policy coherence in all sectors to address substance use especially in the health, education, justice, transport, trade and social development sectors. Policies in the trade sectors which ensure strict adherence to the sale of substances, harsher sentences in the justice sector for the illegal market, improvement in quality of local schools and improved learner transport, if adequately implemented can curb the access and exposure of substances among minors. Key policies like the National Drug Master Plan, which aims for a drug-free society through collaboration with other national departments such as health, justice, and education, need strengthening and universal implementation, including in all schools [ 47 ]. While numerous health-oriented public policies exist, there’s a noticeable gap in their implementation, highlighting the necessity for increased resources allocated toward their effective execution [ 43 ].

The revised ISHP has a component on health education on prevention of substance use. If implemented in all schools, it has a potential to reach learners and raise awareness at an early age and across all phases (foundation, intermediate and senior) on the dangers of use and may contribute more effectively to the reduction of substance use and early onset [ 44 ]. The School Safety Programme which derives itself from many of the policies including the South African Constitution, School Health Promotion, and Children’s Justice Act should also be strengthened to assist in the prevention and control of drugs in schools. The programme has a component of building capacity among school stakeholders [ 45 ]. Training should be escalated to all including educators to assist with the timely identification and intervention of learners who use drugs. Parents or guardians need to be included in the training and implementation process for prevention to support behavior change among the learners.

Limitations

There are two major limitations in this study that could be addressed in future research. Firstly, the analysis was restricted to specific socio-demographic variables, overlooking the potential influence of parents’ educational attainment and the composition of the learners’ living environment. Due to constraints in resources and time, this study did not delve into the association between substance use and these factors. However, exploring the impact of parents’ level of education and the presence of individuals residing with the learners could offer a more comprehensive understanding of the underlying determinants of substance use. Future research endeavors should prioritize investigating these associations to enrich the existing knowledge base.

Secondly, the selection of classes was not randomized but based on availability, primarily due to logistical challenges exacerbated by the unforeseen onset of the Covid-19 pandemic and subsequent lockdown measures. Schools were compelled to adapt their operations swiftly, hindering the feasibility of a randomized sampling approach. Despite this limitation, the selected classes adequately represented the target population and facilitated the attainment of the study’s objectives. It is important to note that the deviation from randomization was a pragmatic adjustment necessitated by external circumstances and did not compromise the integrity or validity of the research findings.

Addressing these limitations in future studies will enhance the comprehensiveness of investigations into the complex dynamics of substance use among learners.

The findings of the study indicate that substance use is rife in high schools and utilization varies with sociodemographic characteristics. A more robust approach should be implemented which supports consistent health education integrated within all subjects of the curriculum from an early age. This should explore interactive means to reach the different social and educational platforms for learners to be informed, perceive the risk of substances from an early age and receive support. Findings also suggest the need for tailored health promotion programmes depending on the demographics of the learners (gender, grade and location of school) while also addressing the social determinants of substance use. One size does not fit all. The findings of this study contribute to the body of updated evidence on the prevalence of substance use in Limpopo Province among high school learners.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Central Drug Authority

Department of Basic Education

Department of Education

Department of Health

Department of Social Development

Department of Corporate Governance and Traditional Affairs

Human Immuno-deficiency Virus

Head of Department

Health Promoting Schools

Integrated School Health Policy

National School Safety Framework

Primary Health Care

South African Medical Research Council

South African National Council on Alcoholism and Drug Dependence SACENDU: South African Community Epidemiology Network on Drug Use

Statistics South Africa

School Governing Body

School Management Team

Crystal Methamphetamine

United Nations Office of Drugs and Crime

United Nations Population Fund

United Nations Children’s Fund

World Health Organisation

Youth Risk Behaviour Survey

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Acknowledgements

This work is based on the research supported by The Belgian Directorate- General for Development Cooperation, through its Framework Agreement with the Institute for Tropical Medicine (Grant Ref: FA4 DGD-ITM 2017–2020). The authors would also like to acknowledge funding from the South African Research Chairs Initiative of the Department of Science and Technology and National Research Foundation of South Africa (grant no. 82769)’. Opinions expressed and conclusions arrived at, are those of the authors and are not necessarily to be attributed to the funders.

The Belgian Directorate- General for Development Cooperation, through its Framework Agreement with the Institute for Tropical Medicine (Grant Ref: FA4 DGD-ITM 2017–2020), The South African Research Chairs Initiative of the Department of Science and Technology and National Research Foundation of South Africa (grant no. 82769) funded the involvement of LS in this study.

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The research proposal for the study received ethical clearance from the University of Western Cape (UWC) Humanities and Social Sciences Research Ethics Committee from 18 November 2019 to 18 November 2020, ethics reference number: HS19/9/12. An application to the ethics committee for additional ethical amendments was made to include conducting interviews virtually in July 2020 due to the Covid-19 lockdown regulations. Ethics renewal was also sought and granted with an extension from 21 April 2021 to 21 April 2023. Permission was granted to conduct the study by the Limpopo Department of Education within the selected schools. The study was conducted in accordance with the general ethical guidelines and regulations of the ethics committee and Department of Education. Informed consent, anonymity, confidentiality and right to withdraw was assured to all the participants. Informed consent ensured that the participants were fully informed of what the study entailed, what is expected from them, for them to decide whether they will participate in the research or not. The researcher visited each of the schools and explained the study to the principals and HOD. The researcher also visited the NGO that participated to present the study and ask for participation. The researcher went with the invitations, information sheets and consent forms to the circuit and these were sent to each school via the circuit office. The researcher also went with copies of the information sheets for the parents and learners to each school before the data collection. Therefore, the learners were informed of the study by providing them with an information sheet explaining the purpose of the research and what is expected of them. They also received the consent forms and assent forms to agree to participate in the study once they fully understood the purpose of the research. Parents/legal guardians received information sheets and parental informed consent sought for learners to participate in the school survey. Anonymity was assured by the fact that participants’ information/responses were not ascribed to them specifically. It was assured that no names of individuals or the schools were written on the transcripts or in the report or publications. A preliminary report was made available to all relevant participants to verify the accuracy of the information before submission of the thesis for marking. An explanation was provided that the research is for academic purposes only and that there are no foreseeable risks from this research. Counselling and referral services to a social worker were arranged if participants experienced emotional discomfort during the data collection. However, during data collection, no participants required such services. Participants were informed that they are free to not participate further in the study at any time during the data collection process. It was explained that the research is not designed to help the participants personally, but the results may help the investigator learn more about factors linked to substance abuse in high schools and develop informed strategies. It was explained that the study provided valuable information and resources (Anti-Substance Abuse Initiative and situational analysis of the problem) which can benefit the high school community to develop skills to tackle substance abuse. This in turn could improve educational attainment and reduce risky behaviours.

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Shuro, L., Waggie, F. Trends in socio-demographic characteristics and substance use among high school learners in a selected district in Limpopo Province, South Africa. BMC Public Health 24 , 1407 (2024). https://doi.org/10.1186/s12889-024-18927-7

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• Substance use
• High School Learners
• Adolescents
• School health
• Limpopo, South Africa
• Cross-sectional survey
• Socio-ecological model

BMC Public Health

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Heavy drinking and contextual risk factors among adults in South Africa: findings from the International Alcohol Control study

• Pamela J. Trangenstein 1 , 2 ,
• Neo K. Morojele 3 , 4 , 5 ,
• Carl Lombard 6 ,
• David H. Jernigan 2 &
• Charles D. H. Parry   ORCID: orcid.org/0000-0001-9787-2785 7 , 8  

Substance Abuse Treatment, Prevention, and Policy volume  13 , Article number:  43 ( 2018 ) Cite this article

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There is limited information about the potential individual-level and contextual drivers of heavy drinking in South Africa. This study aimed to identify risk factors for heavy drinking in Tshwane, South Africa.

A household survey using a multi-stage stratified cluster random sampling design. Complete consumption and income data were available on 713 adults. Heavy drinking was defined as consuming ≥120 ml (96 g) of absolute alcohol (AA) for men and ≥ 90 ml (72 g) AA for women at any location at least monthly.

53% of the sample were heavy drinkers. Bivariate analyses revealed that heavy drinking differed by marital status, primary drinking location, and container size. Using simple logistic regression, only cider consumption was found to lower the odds of heavy drinking. Persons who primarily drank in someone else’s home, nightclubs, and sports clubs had increased odds of heavy drinking. Using multiple logistic regression and adjusting for marital status and primary container size, single persons were found to have substantially higher odds of heavy drinking. Persons who drank their primary beverage from above average-sized containers at their primary location had 7.9 times the odds of heavy drinking as compared to persons who drank from average-sized containers. Some significant associations between heavy drinking and age, race, and income were found for certain beverages.

Rates of heavy drinking were higher than expected giving impetus to various alcohol policy reforms under consideration in South Africa. Better labeling of the alcohol content of different containers is needed together with limiting production, marketing and serving of alcohol in large containers.

In 2011, South African adults (aged 15 years and older) consumed 9.5 l of absolute alcohol each year -- higher than the average for Africa (6.0 l) and the world (6.2 l) [ 1 ]. In 2015, alcohol was the fifth leading cause of death and disability in South Africa [ 2 ], which is likely attributable to alcohol’s role in causing sexually transmitted infections and interpersonal violence, the two leading causes of death in South Africa [ 3 , 4 , 5 , 6 ]. In addition, community-based samples repeatedly show atypically high prevalence of fetal alcohol spectrum disorders, ranging up to 29% [ 7 , 8 , 9 ]. Altogether, alcohol caused 7.1% of all deaths and 7.0% of all disability-adjusted life years in South Africa in 2000 [ 10 ], and harmful alcohol use is estimated to cost R249–280 billion each year, 10–12% of South Africa’s gross domestic product [ 11 ].

Drinking patterns shape the association between alcohol consumption and related harms, because they determine the dose of toxic effects (which cause chronic disease) and the level of intoxication (which determines risk for injuries and social problems) [ 12 ]. Many alcohol-related conditions show a dose-response relationship between volume of alcohol consumption and risk of adverse outcomes [ 13 ], suggesting that heavy drinking occasions have higher risk for both toxic effects and greater intoxication [ 14 , 15 , 16 ].

Heavy drinking is a pattern of consumption involving consuming large volumes of alcohol during one occasion or in a short period of time. There is no universal definition for heavy drinking, but studies generally define it using one of two thresholds: 1) 60 g of absolute alcohol for men and 40 g for women or 2) 100 g of absolute alcohol for men and 60 g for women [ 13 ]. While there are criticisms of implementing a one-size-fits-all cutoff to define heavy drinking across diverse people and cultures [ 17 ], the convergent validity of heavy drinking measures is established by their use as a proxy for identifying persons who have alcohol use disorders (AUDs) [ 13 ]. Heavy drinking works as a proxy for AUDs, because the association between the two drinking patterns is almost linear and questions about number of drinks consumed may avoid the social desirability bias that problematizes other types of questions to assess alcohol-related problems [ 18 ].

Monitoring heavy drinking trends over time may help researchers predict treatment and prevention needs at the population level. In addition, drinking patterns modify the association between per capita consumption and related harms such that countries with riskier drinking patterns tend to experience increased levels of harms from increases in consumption [ 19 ]. This implies that researchers may need to monitor drinking patterns in order to accurately anticipate the potential outcomes of policies that could alter per capita consumption.

South Africa is currently considering a liquor amendment bill to reduce per capita consumption, including provisions that would raise the national minimum legal purchase age from 18 to 21 years, establish a minimum 500-m buffer between alcohol outlets and other outlets or sensitive locations (e.g., schools, places of worship), and hold alcohol manufacturers and suppliers of alcohol to unlicensed alcohol outlets liable for damages resulting from consumption of their products. The proposed bill also brings some informally and illicitly produced alcohol into the regulated sector by lowering the threshold used to define alcoholic beverages from 1.0% alcohol to 0.5% alcohol [ 20 ].

However, there is an incomplete picture of heavy drinkers in South Africa. To date, the literature begins to assemble demographic profiles of heavy drinkers: they tend to be young, male, Black African or Coloured, and reside in urban areas [ 21 , 22 ]. However, researchers need more than a simplistic analysis of demographics to understand the possible push factors that promote heavy drinking and serve as potential points of intervention. To the best of our knowledge, there is no research about the contextual factors surrounding heavy drinking in South Africa. Given this, the present analysis aims to describe the demographic characteristics of heavy drinkers, where they primarily drink, the type of alcohol they consume most often, and the container size that they typically drink from in the Tshwane Metropole. Secondary analyses aim to determine the distribution of consumption by decile of drinker and identify characteristics of heavy drinking occasions.

Materials and methods

Sample and data collection.

Data for this study are from the South African arm of the multi-country International Alcohol Control (IAC) study [ 23 ]. This cross-sectional study was conducted during 2014 in the Tshwane Metropole, located around the executive capital, Pretoria. It is located mainly within the province of Gauteng and overlaps into part of North West province. It consists of five regions and 76 wards. The estimated population of Tshwane is 3.3 million [ 24 ].

The study used a multi-stage stratified cluster random sampling design. There were four stages to the cluster random sampling involving sampling of wards (Stage 1), enumeration areas (EAs) within selected wards (Stage 2), households within selected EAs (Stage 3), and study participants within selected households (Stage 4). This is described in detail elsewhere [ 25 ]. Data were weighted to take into account the underlying structure of the realized sample and the sample frame to ensure a random selection of respondents. Eligible participants had to have consumed alcohol in the past six months and be 18 to 65 years old. The target sample size of 2000 was determined by the IAC Study [ 23 ]. The overall response rate was 78% [ 25 ].

Measures used in this analysis

We adapted the standard (English) IAC questionnaire, then translated and back-translated it into seTswana and Afrikaans. It included various items, with those relevant to this paper being demographic factors (e.g., age, gender, total annual personal income, and marital status) and alcohol consumption.

Sociodemographic variables

Participants’ ages were categorized as: 18–19, 20–24, 25–34, 35–44, 45–54, and 55–65 (reference group). Annual personal income was categorized into low (<R30,000 - reference group), medium (>R30,000 but ≤R200,000), and high (>R200,000).

• Heavy drinking

Heavy drinking was defined as consuming 96 g of absolute alcohol (AA) or more (roughly 8 standard drinks, or 120 ml) for men or 72 g or more (roughly 6 standard drinks, or 90 ml) for women at any location at least monthly. This definition, used by the IAC study [ 26 ], is higher than typically used in surveys and by the WHO, but reflects a growing questioning of the validity of the 4+/5+ binge or heavy drinking criterion [ 17 ]. The questionnaire asked quantity and frequency of typical alcohol consumption at each of 16 locations (i.e., your home, someone else’s home, nightclubs, other clubs, restaurants, theatres, workplaces, planes, motor vehicles, sports events, outdoors, shebeens, bars, hotels, special events, and other) over the past six months. We then calculated absolute alcohol for each beverage type as (number of containers)*(container size)*(percent absolute alcohol) by location. The absolute alcohol for each beverage type and location was then summed to determine average consumption of AA by location. The heavy drinking variable was dichotomous and separated persons who reported consuming more than 96 g (for men) or 72 g (for women) of AA on an average occasion at least monthly from those who did not (reference group).

Heavy drinking occasions

Each typical drinking occasion at each location was classified as low risk or heavy drinking based on the usual quantity of alcohol consumed at least monthly.

Occasions that did not include heavy drinking (96 g AA for men and 72 g AA for women) were defined as low risk.

Primary drinking location

Primary drinking location was defined as the location in which the participant reported drinking most frequently. If the participant drank at two locations with the same maximum frequency, then the location where the participant consumed a greater quantity of absolute alcohol was selected. If there were two locations with the same maximum frequency and quantity, then the more exotic location was selected (e.g., nightclubs and special events are more exotic than homes and restaurants). The primary drinking location variable was categorical with 12 of the 16 original drinking locations included: own home, someone else’s home, nightclubs, sports clubs, other clubs, restaurants, motor, sports events, outdoors, shebeen, pub, hotels, special events, and other. No participants primarily drank in theaters, planes, workplaces, hotels, or at sports events.

Primary beverage

The primary beverage consumed at the primary drinking location was selected by determining the beverage the participant drank with maximum quantity (of AA) at that location. The primary beverage variable was categorical with 12 of the original 14 beverage types: beer; low alcohol beer; home brew beer; stout; wine; spirits; cocktails; liqueur; shooters; sherry, port, or vermouth; cider; alcopops (a ready-mixed drink that resembles a soft drink but contains alcohol), and other beverages. No participants primarily drank other beverages or sherry, port, or vermouth.

• Container size

Container size was determined as the usual container size of the primary beverage at the primary drinking location, and was categorized into average, below average, or above average. Average container size was defined as the container size closest to a standard drink (i.e., 330 ml for beer; 330 ml for low alcohol beer; 500 ml for home brew beer; 330 ml for stout; 150 ml for wine; 30 ml for spirits; 30 ml for cocktails; 50 ml for liqueur; 25 ml for shooters; 50 ml for sherry, port, or vermouth; 330 ml for cider; 330 ml for alcopops; and 330 ml for other alcohols).

After obtaining informed consent, participants were interviewed in their homes by trained interviewers. Interviews were administered on a tablet. This approach was adopted due to the complexity of the questionnaire. After the interview, participants received a resource card for alcohol-related problems as well as a shopping or a cellular telephone recharge voucher worth R30. The study was approved by the Research Ethics Committee of the South African Medical Research Council.

Statistical analyses

Taylor series linearization approximations [ 27 ] were used to account for the complex multi-stage sampling as implemented in the “svy” prefix in Stata version 14.0 [ 28 ]. As part of exploratory analyses, deciles of drinkers were obtained using the total amount of absolute alcohol the participant consumed across all locations and beverage types over a six-month period. The total amount of absolute alcohol consumed by each decile was summed and divided by the total amount of alcohol consumed by the entire sample to determine the percent of consumption by decile. Corrected weight chi-square tests were used to detect significant relationships between heavy drinking and the sociodemographic and alcohol consumption characteristics.

The analysis then used multivariate logistic regression to test the hypotheses that heavy drinking differs by demographics and alcohol consumption characteristics. Variables with significant relationships to the outcome variables and key demographic variables (i.e., age, gender, race/ethnicity, and total annual personal income) were selected using best subset variable selection methods with no variables forced into the model. The model included age, race/ethnicity, marital status, and container size. While other variables explained variability better than primary beverage in the main model, simple logistic regressions of heavy drinking on primary beverage type and primary drinking location were performed to investigate the impact of beverage and location choices. The multiple logistic regression model was then repeated for the persons who consumed the four main types of alcohol (i.e., beer, wine, spirits, and cider) to determine whether the associations differed by beverage. Multicollinearity was assessed by examining correlations between predictors. No two predictors had a correlation >  0.5. Model fit was checked using an adaptation of Hosmer Lemeshow’s Goodness of Fit Test, and all models indicated appropriate fit. P values less than 0.05 were considered statistically significant.

Nine hundred and eighty-seven participants did not report frequency data for all drinking locations, and seven participants did not report enough consumption information for their primary drinking location to determine primary beverage and/or primary container size. In addition, 449 participants did not provide a total annual personal income. These participants were excluded from the analyses. The final sample size included 713 adults.

Participants with missing consumption data did not differ from the sample on race/ethnicity ( F 2.06, 39.22  = 2.18, p  = 0.12), income ( F 1.81, 34.48  = 0.02, p  = 0.97), or urbanicity ( F 1, 19  = 1.17, p  = 0.29). Participants with missing consumption data were more likely to be younger ( F 3.85, 73.06  = 4.67, p  < 0.01) and female ( F 1, 19  = 4.71, p  = 0.04), and missingness differed by marital status ( F 4.04, 76.74  = 5.17, p  < 0.001). Participants with missing personal income data did not differ on gender ( F 1, 19  = 0.01, p  = 0.92), urbanicity ( F 1, 19  = 0.32, p  = 0.58), heavy drinking status ( F 1, 19  = 0.58, p  = 0.46), primary beverage ( F 5.97, 113.36  = 1.96, p  = 0.08), primary drinking location ( F 6.44, 122.38  = 1.57, p  = 0.16), or primary beverage container size ( F 1.44, 27.32  = 2.92, p  = 0.09). Participants with missing personal income data were more likely to be younger ( F 2.26, 42.99  = 7.24, p  = 0.001) and less likely to be Black African ( F 2.10, 39.97  = 10.07, p  < 0.001), and missingness differed by marital status ( F 3.61, 68.62  = 49.11, p  < 0.001).

Demographics & drinking characteristics

The mean age in the sample was 36.3 years, 65.8% were male, 79.1% were Black African, and 77.0% were low-income (see Table  1 ). Fifty-three percent of the sample were heavy drinkers. Heavy drinking did not vary by gender ( F 1, 19  = 3.96, p  = 0.06), age ( F 3.86, 73.33  = 1.07, p  = 0.37), race/ethnicity ( F 1.70, 32.34  = 2.51, p  = 0.10) or total annual personal income ( F 1.82, 34.4  = 0.11, p  = 0.87). Heavy drinking differed by marital status ( F 2.48, 47.11  = 3.09, p  = 0.04).

Homes (59.9%), pubs (15.3%), someone else’s home (14.8%), nightclubs (2.2%), outdoors (1.9%), shebeens (1.7%), other clubs (1.3%), and restaurants (1.1%) were the most common primary drinking locations. Heavy drinking differed by primary drinking location ( F 6.42, 122.04  = 2.48, p  = 0.02). Among the commonly reported primary drinking locations, persons who primarily drank at special events (91.8%), in motor vehicles (87.2), other clubs (77.5), nightclubs (79.8%), shebeens (61.5%), someone else’s home (65.8%), and pubs (57.3%) had the highest percentages of heavy drinking. Persons who primarily drank at restaurants (19.5%) had the lowest percentages of heavy drinking.

Beer (48.4%), cider (17.9%), wine (14.4%), and spirits (12.6%) were the most commonly reported primary beverages consumed at the primary drinking location. Heavy drinking did not differ by primary beverage ( F 4.92, 93.50  = 1.89, p  = 0.10). High percentages of persons who primarily drank beer (62.5%), wine (50.7%), cider (45.8%), and spirits (42.7%) were heavy drinkers.

The container size of the primary beverage at the primary drinking location was also associated with heavy drinking ( F 1.72, 32.76  = 34.72, p  < 0.001). Fifty-eight percent of the sample primarily drank from above-average sized containers, 34.0% drank from average-sized containers, and 7.7% drank from below average-sized containers. Seventy-two percent of persons who drank from above average-sized containers were heavy drinkers, while only 26.7% of persons who drank from average-sized, and 19.0% of persons who drank from below average-sized containers were heavy drinkers.

Drinks by decile

The top 10% of drinkers drank 70.3% of the absolute alcohol, and the top 20% of drinkers drank 82.3% of the absolute alcohol (see Fig.  1 ). Together, heavy drinkers drank 93.9% of the absolute alcohol.

Percent of absolute alcohol consumed by decile of drinkers

Table  2 summarizes the results of the simple logistic regression predicting heavy drinking by the primary beverage. Primarily drinking cider was found to lower the odds of heavy drinking when compared to persons who primarily drank beer (OR = 0.51). While non-significant, the trend from this analysis shows persons who primarily drank beer at their primary drinking location had the highest odds for heavy drinking, except for people who primarily drank stout (OR = 1.26).

Table  3 summarizes the results of the simple logistic regression predicting heavy drinking by primary drinking location. As compared to people who primarily drank in their own home, people who primarily drank at someone else’s home (OR = 2.22), nightclubs (OR = 4.58), or sports clubs (OR = 15.32) had increased odds for heavy drinking.

Table  4 summarizes the results from the main multiple logistic regression. Heavy drinking did not differ by age, race/ethnicity, or total annual personal income after adjusting for marital status and primary container size. Persons who never married had 2.91 times the odds of heavy drinking as persons who were married, and persons who were separated have 4.45 times the odds of heavy drinking as persons who were married. Primary container size proved to have a strong association with heavy drinking. Persons who drank their primary beverage from above average-sized containers at their primary location had 7.91 times the odds of heavy drinking as compared to  persons who drank from average-sized containers.

Heavy drinking by beverage type

Table  5 summarizes heavy drinking by the four main beverage types. The relationship between age and heavy drinking differed by beverage type. As compared to persons aged 55–65, persons aged 35–44 had 5.93 times the odds of heavy drinking among wine drinkers. High-income persons who primarily drank beer at their primary drinking location had higher odds of heavy drinking (AOR = 7.71). An association between heavy drinking and marital status was only present among persons who primarily drank beer at their primary drinking location. Among beer drinkers, persons who were never married had 2.44 times the odds of heavy drinking as persons who were married. There were consistently strong associations between primary container size and heavy drinking across all beverage types. Drinking from an above average-sized container predicted heavy drinking among beer drinkers (AOR = 6.94), wine drinkers (AOR = 38.26), spirits drinkers (AOR = 14,657.39), and cider drinkers (AOR = 7.52). However, persons who primarily drank beer from below average-sized containers also had increased odds of heavy drinking when compared to their counterparts who typically drank from average-sized containers (AOR = 4.02).

A large proportion (53%) of drinkers in Tshwane Metropole, South Africa drank heavily (70% of men and 30% of women), even when using a conservative definition of heavy drinking. These heavy drinkers drank the vast majority (93.9%) of the absolute alcohol sold. Primary beverage container size emerged as having the most consistent association with heavy drinking, and it held across four of the most common beverage types. Drinkers who primarily drank from above average-sized containers had nearly 8 times the odds of heavy drinking compared to persons who primarily drank from average-sized containers after adjusting for demographics like age, sex/gender, race/ethnicity and income level. Surprisingly heavy drinking did not differ by gender ( p  = 0.06), but other studies conducted among drinkers in South Africa, have similar levels of risky drinking at weekends among male and female drinkers [ 29 ].

Overall, one of the most significant findings is that heavy drinking appears to be a common occurrence among drinkers in Tshwane Metropole. Given the level of harms associated with this drinking pattern, researchers and practitioners should place greater focus on monitoring and preventing heavy drinking because it may foreshadow needs for chronic health services. Our prevalence estimates are similar but higher than those of previous estimates from South Africa, such as 47.5% in 2002–2004 [ 30 ] and 48.2% among males and 22.8% among females in 2014–2015 [ 22 ]. They are likely to be more accurate given that the innovative location-specific alcohol consumption questions in the IAC cover 94% of taxable alcohol sales [ 31 ] while the standard quantity-frequency measures used in most alcohol research only cover 40–60% of these sales [ 32 ].

While in other IAC countries less than 70% of the absolute alcohol was consumed during heavy drinking occasions (e.g., Mongolia and Thailand reported 62% and St. Kitts and Nevis reported 57%), 93% of the absolute alcohol was consumed during heavy drinking occasions in Tshwane, South Africa [ 33 ]. South Africa’s political and economic history history, and the associated demographic nuances all provide clues to understanding this difference. At the end of apartheid, South Africa inherited a large number of informal alcohol outlets (“shebeens”) existing outside of the formally regulated business sector [ 34 ]. As informal outlets, shebeen owners were often undeterred by consequences established using the regulatory framework. In addition, shebeens were an integral part of the social fabric of South Africa, as there were often few recreational opportunities outside of these establishments. In examining the larger legislative framework, South Africa’s national alcohol policy was last revised in June 2013, and the current version contains few effective mechanisms to control the harmful use of alcoholic beverages [ 34 ]. As of mid 2018, South Africa does not have national restrictions on the days, hours, location, or density of alcohol outlets, and it used voluntary/self-regulation for most types of advertising and product placement. Other factors likely to play a role in South Africa’s extremely high levels of heavy drinking include high levels of poverty and social inequality, and experience of and exposure to interpersonal violence [ 35 ].

The results from this analysis also imply that there is another important contextualizing factor at play in the South African drinking environment: the alcohol industry. Our finding that 93.9% of the absolute alcohol is consumed by heavy drinkers in the Tshwane Metropole suggests that the alcohol industry’s revenues in South Africa depend on heavy drinking. The alcohol industry often argues that alcohol-related problems only affect a subset of drinkers and the majority of drinkers consume alcohol “responsibly” [ 36 ], but these data strongly contradict that conclusion.

Another key finding from this study is the strong association between primary container size and heavy drinking in Tshwane Metropole. This suggests that container size may promote risky drinking and that the liquor industry may well be driving heavy drinking and hence be a major contributor to alcohol’s high burden of death and disability in South Africa. Beer containers in South Africa often contain two or more standard drinks (i.e., 750 ml and 660 ml containers). In 2017, South African Breweries introduced a larger version (500 ml) of Carling Black Label beer, and priced it the same as their previous 440 ml version [ 37 ]. That same year, their launch of “Ama 1 litre” Black Label beer also introduced more affordable, large containers of beer, which are likely to be the major vehicle for beer sales in future. The authors are unaware of research about the association between beer container sizes and related harms. However, increased wine container sizes are associated with increased alcohol consumption [ 38 ] and symptoms of alcohol-related problems [ 39 ].

Despite the IAC’s innovative survey design, this analysis has limitations. These data are specific to Tshwane, and may not generalize to other parts of South Africa. The analyses only included data from adult participants, so the results may not generalize to youth drinkers. Future research should expand to other areas in South Africa to determine whether these trends are local, regional, or national, and should include youth to detect age-related trends early in life. Data for this study are cross-sectional, so we are unable to rule out reverse causation, which would suggest, for example, that heavy drinkers choose extra-large containers.

The definition of heavy drinking used in this study is extreme. Considering that the threshold of heavy drinking is designed to separate drinkers who cause/experience harm from others, such a definition may result in false negatives. However, this choice in operational definitions also increases the likelihood that the drinkers identified as problematic using this definition really are suffering/causing alcohol-related problems. Relatedly, the IAC questionnaires ask about “typical” drinking occasions, and it is possible that respondents overestimate the amount of alcohol consumed when they average across drinking events. However, this analysis capped reports of alcohol consumption at roughly 2500 g of absolute alcohol, which would limit the effects of any extreme overestimation. Further, the previous studies that demonstrate the IAC has high coverage of alcohol sales [ 31 ] suggests that this overestimation is likely small if it exists.

Finally, a sizable portion of respondents had missing data and were excluded from the analysis. The net effect of this missingness may have been to increase the width of some of our confidence intervals, rendering some differences insignificant. This may explain non-significant findings for differences in heavy drinking prevalence by sex/gender, and in the odds of heavy drinking in the simple linear regression for beverage type and the multiple regressions that stratified by beverage type.

Conclusions

Heavy drinking is common among current drinkers in South Africa, and heavy drinkers consume most of the alcohol sold. Primary container size emerged as the most robust correlate of heavy drinking. South Africa is currently contemplating alcohol policy reform, and this study underscores the importance of these draft policies. The draft liquor amendment bill of 2016 proposes several evidence-based policies that could help reduce these heavy drinking occasions. Rigorous monitoring of the heavy drinking environment may also serve to establish baseline data to evaluate the effects of any future policy changes.

Abbreviations

Absolute alcohol

Adjusted odds ratio

Alcohol use disorder

Confidence interval

Enumeration area

International Alcohol Control

Milliliters

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Acknowledgements

We also thank the Research Coordinator (Elmarie Nel), the project assistants (Naledi Kitleli, Frans Masango, Shirley Hlope and Chantal Graca-Correia), as well as all the field supervisors and interviewers for their role in data collection for the survey. Finally, we express our appreciation to all the participants who gave of their time to take part in this research.

This study was supported by the International Development Research Centre (IDRC) Canada (Grant number 107198–001). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the IDRC. The South African Medical Research Council is acknowledged for its support for CDHP, NM, and CL in their preparation of the manuscript for publication. PT was supported by Award Numbers T32AA007240, Graduate Research Training in Alcohol Problems: Alcohol-related Disparities and P50AA005595, Epidemiology of Alcohol Problems: Alcohol-Related Disparities from the National Institute on Alcohol Abuse and Alcoholism. The content is solely the responsibility of the authors and does not necessarily represent the official view of the National Institute on Alcohol Abuse and Alcoholism or the National Institutes of Health.

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The datasets generated and/or analysed during the current study are not publicly available as the dataset is not yet final but are available from the corresponding author on reasonable request.

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CP and NM directed the study, including conceptualizing the study design, data collection, analysis and interpretation. PT analyzed the data and wrote the first draft of the manuscript under the mentorship of CP and DJ. CP, NM, DJ, and CL reviewed the final manuscript, providing intellectual content to help interpret the results. CL calculated sample weights and provided statistical support. All authors gave final approval of the version to be published.

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Trangenstein, P.J., Morojele, N.K., Lombard, C. et al. Heavy drinking and contextual risk factors among adults in South Africa: findings from the International Alcohol Control study. Subst Abuse Treat Prev Policy 13 , 43 (2018). https://doi.org/10.1186/s13011-018-0182-1

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BOOK REVIEW

Substance use and abuse in south africa: insights from brain and behavioural sciences.

George F R Ellis, Dan J Stein, Kevin G F Thomas, Ernesta M Meintjies, editors. UCT Press. 2012. ISBN: 978-1-91989-529-1.

The book is an edited collection written by experienced scholars as well as practitioners which makes it especially useful in providing an evidence-based approach to understanding substance use and abuse. The fields of expertise range from psychiatry, clinical and neuro-psychology, and human genetics to economics and mathematics. As such, it represents an active cohort of researchers and practitioners working in the area of substance use and abuse in South Africa. The volume is divided into three sections, Epidemiology and Symptomatology, Neuroscience and Psychology and Intervention and Policy , focusing on the most salient issues related to substance use and abuse. The introductory chapter provides a very good overview of what each chapter deals with and therefore helps the reader navigate to the chapters that are of immediate interest. The book is very useful in bringing together strands of research distributed over many sources. It therefore serves not only as an overview of the most pertinent issues related to substance use and abuse in South Africa, but as a helpful reference for the most relevant scientific work to emerge on this issue over the last 20 years. For example, while chapter 1 deals with prevalence issues, chapter 2 is focused on the clinical presentation of substance-related disorders, and in relation to mental disorders. Overall, most of the chapters are written in an accessible way and tend to avoid the practice of writing chapters densely packed with information in which the reader may have little interest.

Given that substance use is characterised as a public health issue in broad terms, it would have been useful to provide an overview of the best approaches to dealing with the problem of substance use as encountered in the practice of general medicine and not only in relation to evaluated school-based interventions (chapter 14). For example, the utility of screening brief intervention and referral (SBIRT) would have been a useful addition to the chapters under the section dealing with intervention and policy, even though the South African evidence for such interventions is only recently under way.

Nevertheless, no single volume is able to cover the vast range of issues and approaches to the problem of substance use and abuse on its own, and this is a valuable contribution to the field and practice of substance use and abuse in South Africa. It is strongly recommended to a broad readership.

Arvin Bhana

Human and Social Development

Human Sciences Research Council

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Patterns of substance use in South Africa: Results from the South African Stress and Health study

Margaretha s van heerden.

Department of Psychiatry, Stellenbosch University, Tygerberg, W Cape

Anna T Grimsrud

School of Public Health and Family Medicine, University of Cape Town

Soraya Seedat

MRC Stress and Anxiety Disorders Unit, Department of Psychiatry, Stellenbosch University, Tygerberg, W Cape

Landon Myer

David r williams.

Department of Society, Human Development and Health, Harvard School of Public Health, and Department of African and African American Studies, Harvard University, Cambridge, Mass., USA

Dan J Stein

Department of Psychiatry and Mental Health, University of Cape Town

There are limited data on substance use in South Africa. We describe patterns of substance use based on recent, nationally representative data.

Data were derived from the 2002 – 2004 South African Stress and Health (SASH) study. A nationally representative household probability sample of 4 351 adults was interviewed using the paper and pencil version of the World Health Organization Composite International Diagnostic Interview (CIDI). Data are reported for lifetime use, socio-demographic correlates of use, and age of cohort predicting lifetime use for four classes of drugs.

The estimate for cumulative occurrence of alcohol use was 38.7%, of tobacco smoking 30.0%, of cannabis use 8.4%, of other drug use 2.0%, and of extra-medical psychoactive drug use 19.3%. There were statistically significant associations between male gender and alcohol, tobacco, cannabis and other drug use. Coloureds and whites were more likely than blacks to have used alcohol, tobacco and other drugs. Clear cohort variations existed in the age of initiation of drug use; these were most marked for other drugs and for extra-medical drug use. Use of all drug types was much more common in recent cohorts, with a similar cumulative incidence of tobacco, alcohol and cannabis use across age cohorts.

Conclusions

Epidemiological patterns of use for alcohol, tobacco, cannabis, other drugs and extra-medical drugs provide the first nationally representative data. New findings on race and exploratory data on time trends provide a foundation for future epidemiological work on drug use patterns across birth cohorts and population subgroups in South Africa.

During the apartheid years South Africa was relatively isolated from the rest of the world and substance use primarily revolved around locally produced substances, notably alcohol, tobacco and cannabis. During the 1990s and early 2000s South Africa went through major social and political transformation. During this period links and trade with the rest of the world opened. Law authorities, social services and service providers agree that substance-related problems have increased dramatically over the past 10 years. These include road traffic accidents, mental illness and, most worrying, violence and severe crime committed under the influence of substances.

Historically substance abuse data in South Africa have been limited. Until the late 1990s information came mostly from ad hoc cross-sectional studies, often conducted in a single location, and from information on police arrests and drug seizures, mortuaries and school surveys. This has since been supplemented by national surveys. 1 Recently, several more reliable systems have been initiated, most notably the South African Community Epidemiology Network on Drug Use (SACENDU) project, which meets biannually to present and discuss information about substance abuse patterns. 1 Alcohol is by far the major substance of abuse, while cannabis is still the most common illicit drug used, especially among youths attending treatment centres. Cape Town continues to experience a dramatic increase in the use of crystal methamphetamine (known as Tik), which has become the primary substance of abuse. Substance misuse is most prevalent among males, with trends suggesting roughly an 80/20% male/female split across the country. Whites appear to be the highest users of substances, followed by blacks, coloureds and Indians in Gauteng and Mpumalanga, while coloureds are the highest users, relative to other race groups, in Port Elizabeth and Cape Town. Black substance abusers far outnumber any other group in the East London area. 2

Although systems such as SACENDU provide valuable information on substance abuse trends, there have been no systematic data available that are fully representative of the diverse South African population.

This paper aims to present: ( i ) cumulative incidence proportions of alcohol, tobacco, cannabis, other drugs (lysergic acid diethylamide (LSD), cocaine, heroin, opium, glue, any other drugs) and any extra-medical drug use for the population as a whole; and ( ii ) cumulative incidence proportions for major population subgroups, defined with reference to ( a ) year of birth, ( b ) gender, ( c ) race/ethnicity, and ( d ) the following characteristics (which may vary across time) as measured at the time of assessment: educational attainment, marital status, employment status, household income, assets owned by household, and location of residence (rural or urban).

Data were derived from the 2002 – 2004 South African Stress and Health (SASH) study. Briefly, SASH was an epidemiological survey of mental illness, and part of the World Health Organization (WHO)’s World Mental Health (WMH) 2000 initiative which sought to obtain population-based data on the prevalence and severity of psychiatric disorders, their demographic and psychosocial correlates, and the level of adequacy of mental health service utilisation. 3

Study population

A sample of 4 351 adults aged 18 and older, drawn from a nationally representative, household probability sample, were interviewed. Households and hostel quarters were included. Sampled residences were stratified into 10 diverse household categories, including rural-commercial, agricultural, rural traditional subsistence areas, black townships, informal urban or peri-urban shack areas, coloured townships, Indian townships, general metropolitan residential areas, general large metropolitan residential areas and urban domestic servant accommodation. Within each of these strata, 600 households were listed from maps, census data or aerial photographs. A probability sample of households was selected and screened to determine eligibility. A singe adult respondent from each selected dwelling was drawn randomly using the Kish method.

Survey instrument

Fieldwork in SASH utilised the paper and pencil version of the WHO Composite International Diagnostic Interview Version 3.0 (WMH-CIDI 3.0). The CIDI is a fully structured interview used by trained lay interviewers and can generate diagnoses according to the ICD-10 ( International Statistical Classification of Diseases and Related Health Problems , 10th revision) and DSM-IV ( Diagnostic and Statistical Manual of Mental Disorders , 4th edition) diagnostic systems. The translation of the English version of the CIDI into six other South African languages (Afrikaans, Zulu, Xhosa, Northern Sotho, Southern Sotho and Tswana) used in the SASH study was carried out according to WHO recommendations of iterative back-translation procedures conducted by panels of bilingual and multilingual experts. Discrepancies found in the back-translation were resolved by an expert consensus panel.

‘Extra-medical’ drug taking encompasses alcohol, tobacco and illegal drug use and the use of psychoactive prescription or over-the-counter (OTC) drug compounds, when used ‘to get high’ or for other reasons beyond the boundaries approved for legitimate prescribing and dispensing. 4 Our primary response variable of interest is the cumulative occurrence of drug use, as observed through, and up to, the age of respondents born between 1927 and 1984 at the time of assessment. Evaluated in cross-section, this is a cumulative incidence proportion among cohort members who have survived to the age of assessment – i.e. the cumulative incidence proportion among survivors (CIPAS). 5 These parameters were estimated for: ( i ) alcohol; ( ii ) tobacco; ( iii ) cannabis; ( iv ) other drugs; and ( v ) any extra-medical drug use (excluding tobacco and alcohol).

Alcohol use was defined as ever had a drink, and age of onset as the age at which the respondent had his or her first drink, and the age at which he or she started drinking at least 12 drinks a year. The response rate to the question of age of first drink was very low, with answers missing or refused for 62%. The combined number of respondents for the above questions was used to determine the prevalence of alcohol use.

Tobacco users were defined as those reporting smoking more than 100 cigarettes in their lifetime. Onset of use was defined as the age at which a respondent started smoking.

Cannabis use was defined as having ever used cannabis and its onset as at what age use commenced. Extra-medical drug use comprised the use of sedatives or tranquillisers, stimulants, analgesics or any other psychoactive over-the-counter compound. The age of onset was defined as the age at which the first of these drugs was used.

The category ‘other drugs’ included cocaine, LSD, heroin, opium, glue, or any other drug ever used during the respondent’s lifetime. Age of onset of use was defined as the age of first use.

Covariates include three time-fixed variables: sex, race/ethnicity (black, white, coloured and Indian/Asian), and birth cohort. The birth cohorts were 1975 – 1986 (18 – 29 years at time of assessment), 1965 – 1974 (30 – 39 years), 1955 – 1964 (40 – 49 years), and 1912 – 1954 (≥50 years).

Time-varying covariates were studied, including: ( i ) completed level of education (grouped as none, Grades 1 – 7, Grades 8 – 11, Matric and Matric+ levels); ( ii ) marital status (married, previously married or not married); ( iii ) employment (employed or unemployed); and ( iv ) family income (zero, low, low average, high average and high). Similar to other WMH countries, our measure of income was calculated by dividing household income by the number of household members and defining four income categories. The two lowest quartiles of per capita income were called low income and low-average income. High average income was defined as income between one and two times the median per capita income and high income was defined as more than twice the median; ( v ) residence in an urban or rural area; ( vi ) asset index (we used an asset index based on 17 items reflecting individual and household wealth. This was based on household ownership of material goods (refrigerator/freezer, vacuum/floor cleaner, television, video cassette recorder, radio, microwave, and washing machine), ownership or use of other household resources (telephone, running water in the home, kitchen sink, flush toilet, automobile, domestic servant, and stove/hotplate) and financial activities participants engaged in (shopping at a supermarket, using financial services such as a bank account or credit card, and having an account at a retail store). This index has been shown to have excellent reliability (Cronbach’s alpha, 0.92). These measures of asset ownership were used to construct an aggregate asset score, which was categorised into categories for low, medium and high assets. 6

Analysis methods

To account for the stratified multi-stage sample design, the data were weighted to adjust for differential probability of selection within households as a function of household size and clustering of the data, and for differential non-response. A post-stratification weight was also used to make the sample distribution comparable to the population distribution in the 2001 South African census for age, sex, and province. The weighting and geographical clustering of the data were taken into account by using the Taylor series linearisation method in the SUDAAN statistical package (Research Triangle Institute, Research Triangle Park, NC, USA). Logistic regression analysis was used to study socio-demographic correlates. Logistic regression coefficients and their design-corrected standard errors (SEs) were exponentiated and are reported here as odds ratios (ORs) and 95% confidence intervals (CIs). Statistical significance was consistently evaluated using 0.05 level two-sided tests.

Sample characteristics

Table I presents frequency distributions for covariates and response variables. Unweighted sample sizes are followed by (weighted) estimated proportions and Taylor series linearisation derived SEs for the proportions. Aside from the unweighted sample frequencies, all results are based on conventional analytical methods for complex survey data.

Description and summary of sample

The SE of a method of measurement or estimation is the estimated SD of the error in that method. Namely, it is the SD of the difference between the measured or estimated values and the true values. Notice that the true value is, by definition, unknown and this implies that the SE of an estimate is itself an estimated value.

More than half the sample was female, only 23.5% had completed high school, half was married and more than 60% lived in an urban setting. Slightly more than three-quarters was black, 69% were unemployed and 13.7% had no income at all. Use of alcohol was most common (38.7%), followed by tobacco (30%), extra-medical drugs (19.3%), cannabis (8.4%) and lastly other drugs (2%).

Cumulative occurrence of drug use across birth cohorts

Table II shows the estimated cumulative incidence proportions and ORs of cumulative occurrence. Alcohol was used by the majority of participants with the proportions using alcohol similar among younger birth cohorts (38.3% and 39.1%). These were slightly lower than estimates for the older 1955 – 1964 cohort (42.8%). The 1955 – 1964 birth cohort was 1.4 times more likely to report ever trying alcohol compared with the 1912 – 1954 cohort.

Estimated cumulative occurrence of drug use by birth cohort, and estimates from discrete time survival analysis models

Estimated cumulative incidence proportions for cannabis were lowest for the oldest cohort, born 1912 – 1954 (5.9%). Larger proportions were observed in the most recent cohort, born 1975 – 1986: 10.6% of this cohort had become users by the time of the interview. The most recent cohort was 1.9 times more likely to report having tried cannabis compared with the oldest cohort (OR 1.9 compared with OR 1.0 for the 1912 – 1954 cohort, CI 1.2 – 3.0).

Although not statistically significant, the cumulative incidence proportions for other drugs, including cocaine, and extra-medical drug use were again slightly higher in the two more recent cohorts. Relatively high rates of extra-medical drug use were reported across age cohorts, ranging from a cumulative incidence of 16.6% to 20.3%.

Correlates of drug use

Table III presents estimated ORs for selected covariates (bivariate) and cumulative occurrence of drug use, and Table IV shows covariate-adjusted (multivariate) estimates of the strength of these associations. While trends showed an increase in the use of cannabis, other drugs and extra-medical drugs in younger age groups, this was only significant for cannabis use on bivariate analysis ( Table III ). While the same trends were observed on multivariate analysis, there were no significant associations between age and drug use ( Table IV ).

Estimated strength of association between selected covariates and cumulative occurrence of drug use

Covariate-adjusted estimates of strength of association between selected covariates and cumulative occurrence of drug use

On both bivariate and covariate estimates, gender was the most significant indicator of substance use – males were generally 8 – 9 times more likely than females to have become users of all drug types, except for extra-medical drugs.

On bivariate analysis, participants identified as black were less likely than whites and coloureds to be users of alcohol (OR 3.1 and 2.6), tobacco (OR 3.2 and 3.4) and other drugs (OR 2.2 and 3.9), and also less likely to have used tobacco than Indians/Asians (OR 1.7) ( Table III ). The association between race/ethnicity and alcohol use persisted, and became more robust in certain cases, on covariate-adjusted estimates. After adjustment for other demographic and socio-economic factors, coloureds were 3.9 times more likely to have tried alcohol, 5.3 times more likely to be smokers and 8.4 times more likely to have used ‘other drugs’ compared with blacks. Whites used significantly more alcohol, tobacco, cannabis and other drugs compared with blacks.

Estimated associations with educational attainment differed across drug types. Based upon estimates from the bivariate analyses, persons with post-matric education were more likely to have used cannabis (OR 2.8). Other drug use (e.g. cocaine) was more prevalent in the Grade 7 – 11 group (OR 3.3) and in the post-matric education group (OR 3.1), compared with the group with primary school education only. These crude associations did not persist in multivariate models.

On bivariate analysis, those who were employed were more likely to have tried all classes of drugs compared with the unemployed ( Table III ). This was only significant for alcohol and tobacco use. After adjustment the employed were still more likely to have tried alcohol and be smokers, but there was no association between employment status and cannabis, ‘other drugs’, or extra-medical drug use.

On bivariate analysis only, respondents with low income used significantly more alcohol compared with those with no income. No association was found between marital status and drug use.

Strong associations were found on bivariate analysis ( Table III ) between urban residence and alcohol, tobacco, cannabis and other drug use, but not extra-medical drug use. Those living in urban areas were more than twice as likely to have used cannabis (OR 2.2) and other (OR 2.5) drugs. On multivariate analysis, cannabis, ‘other drugs’ and extra-medical drugs were used more in urban areas, but only cannabis use reached statistical significance.

On bivariate analysis, possessing more assets was associated with a higher prevalence of substance use. On multivariate analysis, respondents with an average number of assets showed statistically higher use of alcohol compared with those with no income.

Initiation of drug use across birth cohorts

Fig. 1, a – e , presents the cumulative occurrence of drug use by age, and according to birth cohort. Of those in the youngest cohort (18 – 29 years) who had used alcohol, 89.4% had used it by the time of turning 20; in contrast, only 53.6% of the oldest cohort (≥50) who used alcohol had done so by age 20. Both tobacco smoking and cannabis use show similar trends, where at age 20 more than 80% of the youngest cohort (18 – 29 years) had used these two drugs compared with just over 50% of the oldest cohort (≥50).

Estimated age-specific cumulative occurrence of drug use by birth cohort.

More pronounced cohort-associated variations were found when cumulative proportions of those who had used other drugs (stimulants, cocaine, etc.) by the age of 17 years were examined. In both the two younger cohorts, of those who used this group of drugs, more than 60% had already used them by age 17, whereas no one had used them by age 17 in the oldest cohort (≥50 years) and only 25% had used them by age 17 in the second-oldest cohort (40 – 49 years). In the youngest cohort (18 – 29 years), 66% of those who used extra-medical drugs had used them by age 18, compared with only 20% in the oldest cohort.

These findings are in keeping with countrywide estimates of alcohol, tobacco and cannabis use. Alcohol remains the substance most often used by South Africans (38.7%), which is consistently lower than prior data from less representative reports and surveys. 1 , 2 The prevalence of tobacco use (30.0%) is consistent with data on lifetime tobacco use in South Africa (27% in 2007 and 37.6% for South African high-school students in 2002). 8 The rate of cannabis use (8.3%) is also in keeping with prior data on annual prevalence from the World Drug Report (8.4%). 9 At 2% the use of other drugs, including methamphetamine, might be an underestimation of drug use trends in South Africa. During 2005, after this survey was conducted, methamphetamine was documented as the primary drug of abuse in the Western Cape, replacing alcohol and overtaking cannabis. 2

Limitations of this study deserve mention. First, a cross-sectional survey does not include drug-related deaths, i.e. persons who have died secondary to substance use were not included. 5 However, it is improbable that drug-related mortality could explain these differences. For cannabis, there is no proven increased mortality risk and in this study there were large differences in the cumulative occurrence of use by age 15 years between adjacent cohorts (see, for example, the ‘other drugs’ estimates for the two oldest birth cohorts). Even if those who began use early had substantially increased mortality rates, this increased mortality would be unlikely to account for cumulative incidence proportions of cannabis use by age 20 years (around 15% lower in the oldest cohort compared with the youngest cohort). In addition, tobacco-associated mortality should have been especially high; however, this was the drug with the smallest cohort-associated variations.

Second, the report of first use of drugs might be ‘right censored’: 10 because younger birth cohorts have not yet reached an older age, their reported drug use necessarily occurs at a younger age. However, such a bias is not relevant for estimates of the cumulative incidence proportion for ages through which all cohorts have passed, since comparisons are made across cohorts for a given age in the lifespan (e.g. age 15 years).

A third possible bias is that older respondents may have struggled to remember events long ago. 5 However, this cannot account for all the differences in age of onset observed here, since the cumulative incidence of alcohol use was lower for the two more recent cohorts (1965 – 1974 and 1975 – 1986) than it was for the next older one (1955 – 1964). It is therefore unlikely that response or other biases completely account for the trends observed here. Similar birth cohort trends in the age of initiation of illegal drug use have been observed in surveys in the USA 11 and Australia, 12 some of which used data collected across time rather than relying solely on retrospective reports. The trends are also consistent with data concerning illegal drug markets in South Africa. There is good evidence that drug availability and drug use in the general population co-vary. For example, since the first democratic elections in South Africa in 1994 there has been an increase in the trafficking and use of heroin, cocaine, and amphetamine-type stimulants in the country. The trends found by the SACENDU project are largely replicated in this study.

A fourth limitation might be that drug availability simply changes patterns of use in a given population. Since there is no simple relationship between drug availability and drug use in a population, drug availability cannot be the sole cause for changes in the cumulative incidence of drug use. 5 There have been many changes in South Africa in the past 14 years. The country has gone through major political, economic and cultural changes. There has been an influx of foreign people, trade and culture into and through South Africa with transitions in several provinces from a predominantly rural-cultural society into an urban westernised society, all of which could play a role in the trends described here. Finally, these data do not capture the dramatic increase in the use of methamphetamine and other drugs. Since 2004, methamphetamine has become the primary drug of abuse in the Western Cape. 2 The use of heroin has steadily increased and so has the use of methcathinone (CAT). It is therefore vital to replicate the present survey.

Despite these limitations, the following findings are notable. Males as well as whites and coloureds had a considerably higher prevalence of substance use compared with females and blacks and Indians. Compared with blacks, coloureds were 8.4 times more likely to have used ‘other drugs’, 3.9 times as likely to have used alcohol, and 5.3 times more likely to have used tobacco. Whites were consistently the second-highest users, with the exception of cannabis, for which they were the highest users. Indians were least likely to have used any type of substance. These and other socio-demographic correlates – substance use being more common in males 13 and in urban populations 14 – are consistent with previous reports.

The use of all drug types has increased in younger populations, with younger cohorts starting to use ‘harder’ drugs at a younger age. Whereas early cohorts had a particularly higher prevalence of alcohol use, more recent cohorts demonstrated a particularly high prevalence of cannabis use. More recent cohorts were much more likely to start drug use, particularly extra-medical and other drug use, in childhood and in early to mid-adolescence. Of those who had used any substance, the age of onset was earlier for younger cohorts, and this was more evident for ‘other drugs’ than for alcohol and tobacco.

Substance use has multiple adverse consequences for individuals and for society in general. Prior work has demonstrated associations between substance use and health, crime and sexual behaviour. Substance abuse and its many interactions with the aforementioned and with mental illness must be taken into account in the allocation of resources and in the planning of health services.

Acknowledgments

The South African Stress and Health study was carried out in conjunction with the World Health Organization World Mental Health (WMH) Survey Initiative. We thank the WMH staff for assistance with instrumentation, fieldwork, and data analysis. These activities were supported by the US National Institute of Mental Health (R01MH070884), the John D and Catherine T MacArthur Foundation, the Pfizer Foundation, the US Public Health Service (R13-MH066849, R01-MH069864 and R01 DA016558), the Fogarty International Center (FIRCA R01-TW006481), the Pan American Health Organization, Eli Lilly and Company, Ortho-McNeil Pharmaceutical, Inc., GlaxoSmithKline, and Bristol-Myers Squibb. The South African Stress and Health study was funded by grant R01-MH059575 from the National Institute of Mental Health and the National Institute of Drug Abuse with supplemental funding from the South African Department of Health and the University of Michigan. Dan Stein and Soraya Seedat are also supported by the Medical Research Council of South Africa. A complete list of WMH publications can be found at http://www.hcp.med.harvard.edu/wmh/

Ms K Cloete, science writer, Department of Psychiatry, Stellenbosch University, is thanked for her contribution to the preparation and formatting of this manuscript.

Contributor Information

Margaretha S van Heerden, Department of Psychiatry, Stellenbosch University, Tygerberg, W Cape.

Anna T Grimsrud, School of Public Health and Family Medicine, University of Cape Town.

Soraya Seedat, MRC Stress and Anxiety Disorders Unit, Department of Psychiatry, Stellenbosch University, Tygerberg, W Cape.

Landon Myer, School of Public Health and Family Medicine, University of Cape Town.

David R Williams, Department of Society, Human Development and Health, Harvard School of Public Health, and Department of African and African American Studies, Harvard University, Cambridge, Mass., USA.

Dan J Stein, Department of Psychiatry and Mental Health, University of Cape Town.

Substance Use Disorders in the South Asian Community

An interview with vasavi kumar and jyoti chand on south asian substance abuse..

Updated May 26, 2024 | Reviewed by Devon Frye

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“Outside of my immediate family, there was a general thought that maybe I wasn’t an alcoholic enough to quit drinking. I didn’t fit the mold. I drank like everyone else and if I was calling myself an alcoholic, then what does that make everyone else? My sobriety was negatively internalized by many early on.” —Jyoti Chand, author ( A Fitting Indian , 2025) and sobriety advocate.

There is an unspoken belief among many in the South Asian (SA) diaspora that mental health issues and substance use disorders (SUDs) or alcohol use disorders (AUD) are primarily a Western construct, not something “our people” deal with. However, given the harmful notion in many Asian communities that negative emotions are a sign of weakness, it is not too surprising that many SAs turn to substances to cope. This belief manifests in these issues being ignored or excused by others, especially female partners, who may fear backlash or shame .

According to addiction expert and media personality Dr. Lipi Roy, “ stigma is profound in most ethnic communities and acts as a major barrier to treatment and care. In SA communities, women specifically face unique challenges that prevent access to treatment. These barriers include deep cultural stigma, shame, and judgement, as well as long-standing patriarchal traditions where women experience a strong sense of powerlessness.”

Coping in a Bicultural World

For Vasavi Kumar (author of Say it Out Loud ), alcohol entered her life at the age of 14 when she used it to cope with anxiety . “We went to a typical Indian party, and I was served a drink, and I loved the way it made me feel.” She adds, “I was a pretty anxious kid growing up in my household, which was pretty chaotic. There was a lot of fighting and instability with my mother, who was a very inconsistent person emotionally.”

Unfortunately, mental health issues in immigrant parents are often inconsistently monitored, and help is often deferred until circumstances are dire. They may seek treatment solely to appease partners, take medications irregularly, and drop therapy after a session or two. This is often to the detriment of their children. While Vasavi doesn’t blame her mother, a cardiologist, who she recognized had her own mental health challenges, she admits that her mother’s issues impacted her mental health.

Jyoti Chand, author of Fitting Indian , was also 14 when she tried her first drink. “Alcohol was the main character in my life for a very long time. He was invited to everything I did and without him, it just wasn’t fun. It allowed me to feel included and a ‘part of the crowd.’ High school is awkward and difficult as it is and all I wanted was to fit in.” SA teens are often expected to toe the line at home concerning schoolwork and strict curfews, so daily life in high school can be a challenging contrast.

Academic performance is a tangible barometer for SA immigrant parents seeking to ensure their children are on the right path. For “model minority” youth, slacking off in school is rarely an option. If grades slip, the first culprit in parents’ eyes is excessive socialization, which in some cases causes SA teens to lose the benefits of a supportive group of friends.

Vasavi, who was diagnosed with bipolar disorder at the age of 19, experienced manic episodes in high school, “I would seek out situations where I could get f***ed up… all my friends drank and used and so I would just be hanging out with them. My parents were very strict; they did not like me leaving the house because they knew something was up.” Vasavi admits that she didn’t tell her parents anything and became good at lying to cover her tracks.

SA parents may also blame the school system. Many immigrant parents fear their children are susceptible to negative influences and may send children back to their homeland for schooling, or to local private or boarding schools. Vasavi's grades suffered, she recalls. “My dad thought the solution was to take me out of my school and put me in a private school, and the change was so hard… my anxiety was so high that I didn't stop using substances.”

For SA bicultural youth, college can be a time of deliverance. Young adults find themselves suddenly free to party openly or finally partake in taboos such as dating , alcohol, and drugs.

In college, Jyoti was drinking almost daily. ‘We partied and drank when we were happy or sad or, honestly, always. It was part of the lifestyle. To me, blacking out and being carried out of the club was a rite of passage I would chuckle about later in life. But when college was over, I was continuing to drink like I was in college.”

Vasavi started using cocaine her sophomore year; her senior year brought harder drugs such as ecstasy. She was a heavy substance user by the time she was 28 years old.

“Many South Asian individuals—like the general public—are unaware that SUD is a chronic illness affecting the brain, as opposed to a moral weakness or failing” Dr. Lipi Roy explains.

While Jyoti’s parents didn’t understand her addiction, they remained supportive and over time came to understand her story. Yet a lack of knowledge can stand in the way of support. Vasavi, whose mother was physically abusive towards her, spoke of living with shame. “I was berated by her and made to feel like everything was a moral issue.” Her parents didn’t understand how mental health and SUDs go hand-in-hand.

“For the longest time, I felt like I was the only Indian girl with anxiety and depression ," Jyoti says. "I felt like if I talked about it openly, I would be judged harshly. It was as if everyone was OK, and I wasn’t, and it was scary to face it all alone."

Disclosing issues even to close friends can be daunting, due to an underlying and often valid concern of how one will be viewed. Religious and cultural systems play a huge supporting role in collectivist cultures. However, they are not always well-equipped to handle mental health issues, let alone substance use challenges.

“My mom would bring me to temple or concerts," Vasavi recalls. "She thought if she could just immerse me in the Indian community and if I just had more Indian friends, that I would be better.” However, Vasavi was turned off by how judgmental she found the community to be—an unfortunate drawback of diasporic culture that can make leaning on the larger community more disheartening than helpful.

Getting Help

“When I became a mom, I started to use alcohol to cope with motherhood," Jyoti says. "Only then did it start to look like a problem to me. After several attempts at quitting and then gaslighting myself into believing I didn’t have a problem, I finally became sober on January 3, 2021, and never looked back.”

Dr. Roy shares how her own family members dealing with alcohol use disorders “not only did not reach out for help, they also never acknowledged having a problem.” Many of her SA patients wait far too long to seek help. “They feel too ashamed to speak up.” She argues that mental health professionals need to do more educational outreach and hire more SA staff. “People who feel stigmatized are more likely to open up to professionals who look like them.”

Jyoti shares that while working on her sobriety, she read a lot of literature, but noted that “it didn’t represent me, my upbringing, my community, or my story. 'Sober lit' is primarily written by white women who cannot touch upon the guilt and shame that we feel culturally.” What helped her was a combination of reading, using a sobriety app, and connecting with her sober community.

Given their challenges, both Vasavi and Jyoti have become leading advocates for sobriety support in the SA community. As a mom of three, Jyoti feels there is still much to do. She believes that it is by having open conversations, especially with her own children when they are ready, that there can be awareness and then change.

Vasavi started therapy at age 12 after telling her parents that she needed to speak to someone. In her late 20s, after using cocaine for several years and on the heels of an unhealthy relationship and a recent miscarriage , she entered rehab. Her parents finally realized that she had a problem. However, they never attended the groups and gained the support they needed, which she feels could have made a difference. Thankfully, rehab helped Vasavi tremendously. Now a licensed therapist and five years sober, Vasavi openly shares her story in the hopes of helping others.

Jyothsna S. Bhat, Psy.D. , is a clinical psychologist and mental health advocate specializing in the treatment of anxiety, depression, relationship and family issues.

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