Access to this full-text is provided by Springer Nature.
Content available from Substance Abuse Treatment Prevention and Policy
This content is subject to copyright. Terms and conditions apply.
R E S E A R C H Open Access
Family management risk and protective
factors for adolescent substance use in
South Africa
Beatrice Wamuyu Muchiri
*
and Monika M. L. dos Santos
Abstract
Background: An increasingly recognised prevention approach for substance use entails a reduction in risk factors,
and the enhancement of promotive, or protective factors in individuals and the environment surrounding them
during their growth and development.
Methods: This exploratory study evaluated the effect of potential risk and protective factors associated with family
management relating to adolescent substance use in South Africa. Exploratory analysis and cumulative odds ordinal
logistic regression modelling was performed on the data, while controlling for the influence of demographic and
socio-economic characteristics on adolescent substance use.
Results: The most frequently used substances were cannabis, followed by other illicit substances and alcohol in
decreasing order of use intensity. The specific protective, or risk effect of family management factors, varied according
to substance. Risk factors associated with demographic and socio-economic factors included being male, of a younger
age, lower education grades, of a coloured ethnicity, adolescents from divorced parents, and unemployed or fully
employed mothers. Several family management factors, categorised as parental monitoring, discipline, behavioural
control and rewards, demonstrated either risk or protective effects on adolescent substance use.
Conclusions: This exploratory study demonstrated that various risk and protective factors associated with family
management may affect adolescent substance use. Interaction amongst risk or protective factors, as well as the type of
substance, should be considered when further considering interventions based on these risk or protective factors.
Keywords: Risk factors, Protective factors, Substance use, Adolescents, Family management, Discipline and behavioural
control, Parental monitoring, Parental discipline, Parental rewards, South Africa
Background
Substance use among adolescents has been reported to sig-
nificantly affect the health and various facets of individual
well-being [1]. With close to half of the South African popu-
lation consisting of youth 20 years old or younger [2], it is
important to pay attention to the use of Alcohol and other
Drugs (AODs) by this group due to the potential implica-
tions for the country’s socio-economic development [3].
Negative health consequences are increasingly being
addressed by prevention science, which involves redu-
cing risk and enhancing promotive or protective factors
in individuals and the environment surrounding them
during their growth and development [4]. Risk factors
predict enhanced likelihood of problems, while protect-
ive factors mediate or moderate exposure to the risk [5].
Protective factors buffer adolescents from exposure to
risks leading to a reduced likelihood of acquiring such
problematic behaviours [6]. Additionally, promotive fac-
tors play a further role in the decreased likelihood of
health problems [7]. Protective factors are distinguished
from promotive factors because the later moderates the
negative effects of risks for predicting negative out-
comes, and therefore only compensates for risk exposure
[8]. An understanding of these risk and protective fac-
tors is important in the development of effective inter-
ventions. Risk and protective factors affecting substance
use can be categorized as contextual, variable and indi-
vidual risk, and protective factors - which have been
* Correspondence: wamuyu.muchiri@gmail.com
University of South Africa, Pretoria, South Africa
© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Muchiri and dos Santos Substance Abuse Treatment, Prevention, and Policy
(2018) 13:24
https://doi.org/10.1186/s13011-018-0163-4
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
extensively reviewed [1]. Fixed markers include gender,
biological indicators, income, family substance history,
parent psychopathology, parental marital status and in-
come/ social economic status. Contextual variables in-
clude factors such as law, availability, social norms and
community order. Examples of individual variables in-
corporate family relations, family management, educa-
tion factors, a positive attitude or expectancies, social
competence, peer relations, religious involvement, con-
formity or moral order, living situation, stressful events,
individual psychopathology and adolescent substance
use [1]. Among individual and interpersonal risk and
protective factors, family environment influences the
likelihood of substance abuse problems significantly.
Family environment is viewed in terms of family rela-
tions and family management [1]. Modification of risk
and protective factors may ameliorate harms from sub-
stance abuse prior to birth, and continue through to
young adulthood. These developmental periods are pre-
dominantly spent in the family context [1].
Family relations and their influence on substance use
can be viewed either in terms of connectedness or conflict
[1]. Increase in either parent to parent conflict, or parent
to offspring conflict, has been shown to increase the risk
of developing a substance use disorder [9]. The level of
family bonding and support by parents to their offspring
are a predictor of alcoholism and drug use amongst the
youth [1]. Favourable family bonds or relationships may
also reduce the likelihood of substance use problems, even
amongst those with personality problems [10]. The social
development model postulates that children learn behav-
ioural patterns from their social environment - including
family, school, peers and community institutions either in
a pro or an antisocial pathway. The dynamic nature of so-
ciety and new trends in substance use necessitate the
identification of risk factors as an on-going process. Treat-
ment programmes and models too should be revised ac-
cording to the patterns of risk elements in different
cultures and social groups in society [11]. Mitigation mea-
sures are not universal and risk factors are influenced by
cultural groupings which have called for culturally rele-
vant programmes [12]. An increasing number of studies
have therefore identified factors influencing substance use
in industrialised nations, however, there are few studies in
South Africa and other developing countries that explore
these facets [12].
From a review of published literature, it is evident that
there is a general lack of studies focusing on family pre-
dictors of substance use based on family management
and relations. Brook et al. [12] assessed the effect of two
types of parental factors in South Africa: parental drug
use and adolescent’s identification with the parent. How-
ever, no investigators have focused on how family fac-
tors, aside from the parent–child relationship, predict
adolescent substance use in South Africa and other de-
veloping countries [12]. These factors, and their interac-
tions, would provide more insight into possible family
environment based intervention strategies. Such interac-
tions include risk, through to protective interaction (for
example, risk factor of family substance drug use being
ameliorated by a good family environment, leading to
less drug use), to protective factor interactions (for ex-
ample, the protective factor of low family substance use
being enhanced by good family environment, leading to
less drug use).
The study of risk and protective family management risk
and protective factors for adolescent substance use is pro-
jected to support evidence based treatment and interven-
tion programmes by policy makers. Treatment and
intervention programmes and studies should account for
the patterns of risk elements in different cultures and so-
cial groups in society [11,13]. Such programmes can be
founded based on the social development model, which is
a theory of causation and prevention, and an important
prerequisite to an intervention strategy seeking to mitigate
risk factors, while at the same time enhancing protective
factors [5]. Theory-driven intervention elements based on
this model include (i) creation of opportunities for
pro-social activities for the adolescents; (ii) offering of em-
powerment towards successful performance of these activ-
ities; and (iii) offering positive reinforcement for
successful contribution. Protective factors buffer adoles-
cents from exposure to risks and reduce the likelihood of
acquiring such behaviours [5,13].
This study offers a pilot exploration of important fam-
ily management risk and protective factors that affect al-
cohol and other drugs use problems amongst
adolescents in South Africa.
Methods
Participants
The principal investigator personally interviewed adoles-
cent participants with a history of substance use. Partici-
pants were sourced from rehabilitation centres in
Pretoria, namely Staanvaas and Castle Carey Clinic, be-
tween September 2014 and June 2015, and were con-
tacted upon ethical approval of the study. Ethical
approval was provided by the Ethics Committee of the
Department of Psychology at the University of South Af-
rica (UNISA) with special reference to the requirements
of the Code of Conduct for Psychologists of the Health
Professions Council of South Africa (HPCSA) and the
UNISA Policy on Research Ethics. The 54 respondents
consisted of 48 males and six females between the ages
of 14 and 20 years from different socio-economic back-
grounds. Respondents were selected by stratified random
sampling, with rehabilitation centres serving as the
stratum.
Muchiri and dos Santos Substance Abuse Treatment, Prevention, and Policy (2018) 13:24 Page 2 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Procedure
Data was collected by using a structured pre-tested
paper based questionnaire after acquisition of informed
consent from the institutional directors, parents or
guardians of the adolescents below age 17, as well as the
adolescents. Both the respondent and interviewer
swapped booklets, and both marked the responses dir-
ectly on the questionnaire. The interviewer then pro-
ceeded to cross-check the responses after the interview.
Table 1displays the variables and components of the
scales used.
Measures
Background variables
Background variables (or socio-economic variables) such
as ethnicity, gender, parental education, parental marital
status and income / socioeconomic status have been
shown to influence substance use and abuse [1]. These
background variables were factored in during data ana-
lysis, and variables influencing the results significantly
were controlled for their effect on study variables.
Youth and parental substance use
The frequency and intensity of which alcohol, cannabis,
and other illicit drugs are used were measured. The fre-
quency of illicit drug use was measured using an open
ended questionnaire that explored over the preceding
two years the non-medical and purpose of use. Illicit
drug used amongst participants include amphetamines,
barbiturates, cocaine, heroin, LSD or other psychedelics
and tranquilizers. Response categories included: 7 =
everyday or almost every day; 6 = 3 to 5 days a week; 5 =
1 or 2 days a week; 4 = 2 or 3 days a month; 3 = once a
month or less; 2 = 1 or 2 days in the past 12 months; 1
= never [14]. Response categories for intensity included
the number of substance units used where categories in-
cluded: 1 = none; 2 = 1 or 2; 3 = 3 or 4; 4 = 5 or 6, 5 = 7
or 8, 6 = 9 or 10; 7 = 10 or more.
Family management
Family management is a broad concept which encom-
passes (i) parental monitoring, (ii) discipline, (iii) behav-
ioural control, and also (iv) the reward system set in
place by parents to reinforce good behaviours [1].
Parental monitoring was assessed using the parental
monitoring measurement tool developed by Arria et al.
[15] consisting of nine questions on a five point scale,
namely: “level 1 = never”,“level 2 = rarely”,“level 3 =
sometimes”,“level 4 = often”and “level 5 = always”. Ad-
olescents were asked to recall their high school experi-
ences and rate on a four point scale responses to
questions such as: i) when one gets home from school,
how often was an adult there within an hour of you get-
ting home, ii) when one went to parties, how often was
a supervising adult present at the party and iii) when
one wanted to go to a party, how often did parents con-
firm that an adult would supervise the party. This tool
was modified to include predictors of delinquency in ad-
olescents as proposed by Steinberg et al. [16]. The scale
contains items that include questions on the child’s per-
ception of parental rule-setting, supervision, conse-
quences and monitoring which were scored on
five-point scale per item.
Parental discipline and behavioural control was mea-
sured using the Children’s Report of Parental Behaviour
Inventory [17] that assesses the consistency of discipline
and rule enforcement (30 items each for both the
mother and father). Correlation coefficients were ana-
lysed between maternal and paternal support. The use of
power-assertive techniques by parents to control their
Table 1 Family relations and management variables and their measures
Variable Measures Reference
Background Variables Gender, age, level of education, cultural
background, parental marital status,
parental education, parental socio-economic status
[1]
Family Management [1]
Parental monitoring Monitoring [15]
Delinquency [16]
Discipline and Behavioural Control Sharing, control through guilt, strictness,
expression of affection, emotional support,
parental direction, sharing, moderate autonomy,
lax discipline, positive evaluation, negative evaluation,
irritability, extreme autonomy, laissez-faire family style
[17,18]
Parental rewards Good behaviour, achievement, [31]
Substance use
Adolescent and parental substance use Intensity and frequency of alcohol use, intensity
and frequency of alcohol use, frequency of other
substance use, age at initiation of use
[14]
Muchiri and dos Santos Substance Abuse Treatment, Prevention, and Policy (2018) 13:24 Page 3 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
children was also measured as the sum of paternal and
maternal scores on the five-item maternal and paternal
discipline scales [18]. Discipline and behavioural control
was measured either as “level 1 = not like”,“level 2 =
somewhat like”or “level 3 = a lot like”. The higher the
score, the greater the degree of disciplinary measures
used. Parental rewards was measured by asking how
often parents rewarded good behaviour and achievement
and responses were: “level 1 = never”,“level 2 = often”
and “level 3 = always”.
Data analyses
Frequency and intensity of adolescent alcohol use were
used to compute legal substance use. Combined scores
representing “other illicit substance use”were calculated
from the frequency of the use of amphetamines, barbitu-
rates, cocaine, heroin, LSD or other psychedelics, tran-
quilizers and other substances. Parental substance use
was calculated either as legal substance (alcohol) use, or
illicit substance use with combined scores from the rest
of the substances. Reliability of the different dimensions
or constructs in the questionnaire is indicated by their
Cronbach’s Alpha values [19]. Items of the scale were ei-
ther retained or removed based on their Cronbach
Alpha values [19].
Exploratory data analysis was performed by cross tabu-
lation of predictor and response variables and explor-
ation of their interrelations. Cumulative odds ordinal
logistic multivariate regressions with proportional odds
were run to determine the effects of family management
and relation variables controlling for demographic and
socio-economic characteristics on adolescent substance
use. Modelling was first performed for each independent
variable against adolescent alcohol, cannabis and other
illicit substance use. Variables that were significantly dif-
ferent at a screening p-value ≤0.1 were entered into mul-
tiple ordinal logistic regression models controlling for
significant demographic and socio-economic characteris-
tics [20]. The model was further considered to be of
statistical significance insofar an association with the
dependent variable over and above the intercept-only
model whenever p-values were ≤0.05. Adjusted odds ra-
tios with p-values and 95% confidence intervals were ob-
tained to compare the influence of the family
characteristics.
In the second part of the study, all family management
variables and controlled variables were incorporated into
a single logistic regression model for each of the descrip-
tors of the family management variables in an explora-
tory manner. A backward elimination was applied to
remove those variables with less explanatory power to-
wards the substance use, according to their p-values.
The final model was one in which all remaining family
factors were significant [21,22].
The proportional odds assumptions were assessed
using a full likelihood ratio test comparing the fitted
model to a model with varying location parameters
where p-values greater than 0.05 are considered accept-
able. Deviance and Pearson goodness-of-fit tests were
performed with an indication that the model was a good
fit to the observed data whenever p- value was greater
than 0.05.
Results
Table 1reports all the studied constructs. After variables
selection and dropping of most of the variables from the
final models, this section as well as Tables 2and 3out-
lines statistically significant results of the specific items
of the constructs that are measures of the broad study
constructs.
Demographic and socio-economic characteristics
The odds of cannabis use by males were statistically
higher and 5 times that of females. An increase in age of
the adolescents was associated with 1.4 times decrease
in odds of higher cannabis use (Table 2).
Alcohol use significantly differed according to adoles-
cent ethnicity, whereby the odds of higher frequency of
Table 2 Results from ordinal logistic regression predicting substance use in adolescents given demographic and socio-economic
characteristics
Variable Substance Odds (95% CI) Model Fit p-value
Gender Cannabis 5.035 (1.012–25.05) χ2(1) = 3.9 0.048
Age Cannabis 0.738 (0.536–1.016) χ2(1) = 3.968 0.046
Ethnicity
Coloured versus white Alcohol 15.637 (2.880–84.9) χ2(1) = 10.149 0.001
Coloured versus black Alcohol 13.578 (2.763–66.735) χ2(1) = 10.310 0.001
Parental Employment Maternal
Unemployed versus self employed Cannabis 15.449 (1.398–170.8) χ2(1) = 4.987 0.026
Full time employed versus self employed Cannabis 12.764 (1.331–122.4) χ2(1) = 4.876 0.027
Part time employed versus self employed Illicit 28.888 (1.251–66.18) χ2(1) = 4.409 0.036
Muchiri and dos Santos Substance Abuse Treatment, Prevention, and Policy (2018) 13:24 Page 4 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
alcohol use for colored respondents was 16 times and 14
times higher than that of white and black respondents
respectively (Table 2). The odds of higher frequency of
cannabis use for adolescents from unemployed and full
time employed mothers were 16 and 13 times higher
than those from self-employed mothers. The odds of
illicit substance use for adolescents from part time
employed mothers were 29 times higher than those from
self-employed mothers (Table 2).
Family management outcomes
The results from ordinal logistic regression assessing the
effect of family management variables on adolescent
substance use are presented in Tables 3. This section
presents those results that were statistically significant.
Parental monitoring
Table 3shows results from ordinal logistic regression
predicting substance use in adolescents with changes in
parental monitoring. The odds of using alcohol more
frequently with parental monitoring as measured by par-
ental knowledge of adolescent activities were 3.9 at the
lowest category than those of level 3.
Parental monitoring as measured by parental know-
ledge of adolescent activities controlling for ethnicity
significantly predicted higher adolescent alcohol use
(Table 3). The final model significantly explained the
dependent variable over and above the intercept-only
model. The odds of using alcohol more frequently indi-
cated a 1.8 times decrease in odds of using alcohol more
frequently with each increase in the level of parental
knowledge of adolescent activities.
Effect of after school parental monitoring on use of
other illicit substance was tested controlling for maternal
employment status. The final model statistically signifi-
cantly predicted the dependent variable over and above
the intercept-only model (Table 3). The odds of using al-
cohol more frequently decreased 2.3 times with every in-
crease in parental knowledge of adolescent activities.
Discipline and Behavioural control
Table 3displays results from ordinal logistic regression
predicting substance use in adolescents as influenced by
discipline and behavioural control.
Discipline and behavioural control against alcohol use
Sharing, control through guilt, strictness and affection
statistically significantly predicted adolescent alcohol use
even when ethnicity was controlled for.
The odds of consuming alcohol more frequently when
sharing was at lowest category sharing were 6.5 times
than when sharing was at the second higher category.
The odds of being in a higher category of alcohol use
when behavioural control through guilt was at category
were 12.8 times when compared to level 2 (Table 3).
The lowest category of parental strictness increased
the odds ratio of more frequent consumption of alcohol
by 3.7 times more than when strictness was in category
2. The odds ratio of being in a higher frequency of alco-
hol consumption when affection was at lowest category
was 3.4 more than when affection was at category 2
(Table 3).
Discipline and behavioural control against cannabis use
Adolescent cannabis use as influenced by emotional sup-
port and positive evaluation was assessed controlling for
gender, age, marital status of parent and maternal em-
ployment status. The odds of higher frequency of using
cannabis when emotional support was at lowest level
were 3.7 times more than those when emotional support
was at level 2. The odds of using cannabis more fre-
quently when positive evaluation was at lowest were 3.7
Table 3 Results from ordinal logistic regression predicting substance use in adolescents given family management variable parental
monitoring
Family management variable Measure Substance Cronbach Aplha Odds (95% CI) Model Fit p-value
Parental monitoring Parental knowledge Alcohol 0.84 0.556 (0.312 to 0.991) χ2(1) = 3.964 0.046
Adolescent recall Illicit substance 0.84 0.428 (0.238 to 0.975) χ2(4) = 11.323 0.023
Discipline and behavioural control Sharing Alcohol 0.73 6.447 (1.642 to 25.313) χ2(1) = 7.131 0.008
Control through guilt Alcohol 0.6 12.782 (1.418–115.217) χ2(1) = 5.159 0.023
Strictness Alcohol 0.82 3.646 (1.204–11.039) χ2(1) = 5.239 0.022
Affection Alcohol 0.75 3.349 (1.092–10.275) χ2(1) = 4.467 0.035
Emotional support Cannabis 0.85 3.7 (0.966–14.16) χ2(1) = 3.648 0.05
Positive evaluation Cannabis 0.87 3.723 (1.027–13.492) χ2(1) = 4.005 0.045
Negative evaluation Illicit substances 0.64 0.184 (0.028–1.192) χ2(4) = 10.176 0.038
Rewards Parental rewards Alcohol 0.72 4.164 (1.133–15.302) χ2(1) = 4.616 0.032
Parental use Parental legal substance Illicit substance 0.78 0.108 (0.012–1.000) χ2(1) = 3.841 0.05
Parental illicit substance Alcohol 0.88 0.073 (0.010–0.525) χ
2
(1) = 6.751 0.009
Muchiri and dos Santos Substance Abuse Treatment, Prevention, and Policy (2018) 13:24 Page 5 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
more than when positive evaluation was at level 2 (Table
3).
Discipline and behavioural control against other illicit
substance use
The odds of adolescents using illicit substances more
frequently when negative evaluation was at the lowest
level were 5.3 times than when negative evaluation was
at level 2.
The effect of negative evaluation by parents on adoles-
cent illicit substance use was assessed, controlling for
maternal employment status. The final model signifi-
cantly explained maternal employment status over and
above the intercept-only model. There was a 5.4 de-
crease in the frequency of illicit substance use with each
unit increase in negative evaluation.
The effect of discipline and behavioural control on
adolescent cannabis use was tested controlling for gen-
der, age, marital status of parent and maternal employ-
ment status. The final model indicated that discipline
and behavioural control when maternal employment and
marital status is controlled for did not statistically sig-
nificantly predict higher adolescent illicit substance use.
Parental rewards
Table 3depicts results from ordinal logistic regression
predicting the influence of parental rewards on sub-
stance use in adolescents. The odds of using alcohol
more frequently when parental rewards were rated at
lowest category were 4.2 times more than when parental
rewards was at category 3. The effect of parental rewards
on adolescent alcohol use was assessed controlling for
ethnicity. However, parental rewards did not statistically
significantly predict higher adolescent alcohol use when
ethnicity was controlled for.
Parental substance use
Results from the ordinal logistic regression assessing the
effect of parental substance use on adolescent substance
use are presented in Table 3. When parental legal sub-
stance use was considered, there was a 13.7 and 9.26 de-
crease in adolescent illicit substance at the lowest
parental legal substance use categories 1 and 2 respect-
ively when compared with parental legal substance use
category 6.
Considering the influence of parental legal substance
use on adolescent illicit substance use controlling for
ethnicity, the model statistically significantly predicted
higher adolescent alcohol substance use. The odds of be-
ing in a higher category of alcohol use increased 1.5
times with each increase in the category of parental
illicit substance use.
Discussion
The age of respondents in this study ranged between 14
and 20. This stage is characterized by a rapid change to
a new social phase where individuals have greater free-
dom and less social control when compared to the ex-
perience during childhood [1].
Cannabis was the most highly used illicit substance as
reported by 63% of the adolescents. This may be a re-
flection of a higher societal tendency towards an accept-
ance of cannabis use in comparison to other illicit
substances of abuse, though cannabis use might be asso-
ciated with more deviance among adolescents and adults
users than those who do not initiate use [23,24].
Study outcomes suggest that the increased alcohol use
by parents was a risk factor for illicit substance use by
adolescents. Risk factors associated with demographic
and socio-economic factors for substance use among the
adolescents included being male, younger age, being in
lower education grades, coloured ethnicity, adolescents
from divorced parents and unemployed or fully
employed mothers. Such factors are fixed implying that
they cannot demonstrate change but mitigation efforts
can be focused on adolescent demographic groups in
categories at higher risk [1].
Results further indicate a relationship between the
working status of mothers and the risk of cannabis as well
as other illicit substance use. Controlling for maternal em-
ployment status also resulted in changes in the signifi-
cance of the relationship of other variables with substance
use. This should be interpreted with caution due to the
fact that caregiving in mainly maternal in South Africa,
which may moderate the maternal care availability versus
adolescent alcohol use. Primary caretakers of children in
South Africa are predominantly female, and at least 92%
of primary caretakers of children in poor households are
females. Further evidence can be derived from child sup-
port grant system where the primary caregiver of the
minor child receives the grant regardless of their gender.
In this respect, studies from the initial years of the grant
recipients indicates that only 0.2% of the caretakers were
men, though this has slightly increased to 3–8%. However,
behavioral problems in childhood and later in life, espe-
cially in adolescence, have been in many studies associated
with mothers who are distant emotionally or physically
[25,26]. Furthermore, severe maternal deprivation has
been regarded as a key contributor to juvenile delinquency
[27]. Alternative caregiving by nannies often leads to ex-
posure to different caretakers which has also been associ-
ated, especially during the first few years, with antisocial
behaviour [28]. However, the quality of alternative care,
especially daycare rather than the effect of maternal care,
may be more important. Hausfather, Toharia, LaRoche,
and Engelsmann [29] for instance report beneficial effects
of longer-term exposure to high-quality child care centers,
Muchiri and dos Santos Substance Abuse Treatment, Prevention, and Policy (2018) 13:24 Page 6 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
but detrimental effects of longer-term exposure to
poor-quality child care centers, with respect to noncom-
pliant behaviour in children.
The history of family management may predict current
substance use [30]. The significance of various factors in
this study varied with the type of substance. These fac-
tors were classified as parental monitoring, discipline,
behavioural control and rewards.
Demographic and socio-economic factors associated
with increased substance use among the adolescents in-
cluded being male, younger age, being in lower education
grades, coloured ethnicity, adolescents from divorced par-
ents and unemployed or fully employed mothers.
Parental monitoring
Low parental monitoring was associated with increased
likelihood of engagement in alcohol use in adolescents.
In similar results, a study of eight to ten year old chil-
dren over a three-year period reported a 1.6-fold reduc-
tion in substance use initiation with increased levels of
parental monitoring and supervision [15].
Even when age was controlled for, the odds of using
alcohol more frequently decrease with increasing paren-
tal knowledge of adolescent activities. Increased parental
monitoring of adolescent activities was also associated
with decreased illicit substance use. Childhood and ado-
lescent risk of later alcohol abuse and dependence may
be reduced and protection enhanced by early establish-
ment and maintenance of close parental or other adult
monitoring and supervision activities [15,31,32]. More
parental monitoring and supervision also leads to a delay
in substance use initiation, as well as less frequency and
intensity of substance use [14,15,33,34]. Lastly, en-
hanced parental monitoring and supervision is corre-
lated with less high school alcohol consumption,
independent of gender, ethnicity and religiosity [15].
Discipline and Behavioural control
Parental sharing and control through guilt and affection
were significantly associated with adolescent alcohol use
even when ethnicity was controlled for. Adolescents
whose parents scored low in sharing were more likely to
use alcohol than those with more sharing parents. Less
employment of behavioural control through guilt by par-
ents was associated with more likely to use alcohol. A
similar trend was observed for parental strictness, where
less parental strictness was associated with increased con-
sumption of alcohol. Adolescents who received less affec-
tion from parents were more likely to use alcohol than
those receiving more affection. This implies that among
factors related to discipline and behavioural control, risk
factors influencing substance use included lower levels of
sharing, control through guilt, parental strictness, affec-
tion, emotional support, positive evaluation and negative
evaluation. Decision making by parents, setting of rules
and limits, as well as monitoring and defining behavioural
control - which is a socialisation dimension associated
with reduced adolescent substance use, deviance and en-
gagement in early sexual intercourse - were also
highlighted as key risk aspects [14]. Parental permissive-
ness to substance use in childhood or early adolescence
also increases the risk of early age initiation of substance
use [33]. Parent-child interactions devoid of closeness in-
fluence substance initiation and they are a predictor of
substance use. Emotional and inter-personal sharing, on
the other hand, offers a protective effect as it supports the
growth of adolescents in families characterized by feelings
of parental trust, warmth, and involvement [35].
Higher levels of behavioural control through guilt and
strictness were associated with less adolescent alcohol use.
Parental strictness is most firmly associated with lessened
youth antisocial behaviour when compared to other major
protective aspects against youth antisocial behaviour in-
cluding positive peer relations and behavioural control
[14]. Clear censure of underage drinking has been re-
ported among other effective parenting practices with an
effect on adolescent drinking reduction [15].
Lower affection received from parents was associated
with increased alcohol use and affection showed an inter-
active effect with sharing and behavioural control through
guilt. Nurturance/warmth and demands for responsible
behaviour have been found to be important determinants
of effect of parenting. High nurturance and more demands
by parents lead to more authority, which is a predictor of
better developmental outcomes in children [33]. Indirect
control, which involves parent-child closeness, may have a
significantly higher effect on the prevalence of delinquent
behaviour than direct control involving parental involve-
ment and monitoring [6].
An increase in parental emotional support and positive
evaluation was associated with decreased intensity of
cannabis use in adolescents. Conversely, adolescents
were more likely to use cannabis when they received less
positive support by parents. There was a decrease in the
frequency of illicit substance use with increased negative
evaluation. Among reported parental socializing prac-
tices associated with less substance use and other ado-
lescent deviant behaviours include: emotional and
instrumental support, as well as moderate levels of con-
trol [34]. Parental socialisation aspects nurturing positive
behavioural development in adolescents include positive
evaluation through enhanced autonomy [14].
Increased adolescents negative evaluation by parents
was associated with a decrease in the frequency of illicit
substance even when maternal employment status was
controlled for. In this study, negative evaluation at higher
levels, therefore, appeared to have a protective effect on
adolescent substance. This effect may be explained by a
Muchiri and dos Santos Substance Abuse Treatment, Prevention, and Policy (2018) 13:24 Page 7 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
possible similar effect to that of discipline and behavioural
control measures [36].
In conclusion, discipline and behavioural control, nur-
turance of behaviour, creating boundaries and the setting
of clear rules, are some of the universal prevention strat-
egies within the family that may be employed to reduce
incidences and onset of delinquency, including substance
abuse through family based interventions [6].
Parental rewards
Lower levels of parental rewards were associated with
higher risk of alcohol use. However, when the effect of
ethnicity was controlled for, parental rewards were not
significantly associated with higher adolescent alcohol
use. The risk of later childhood and adolescence alcohol
abuse and dependence may be reduced and protection
enhanced by providing appropriate parental rewards for
good behaviour in children [31]. Conversely, family man-
agement typified by limited and inconsistent rewards for
positive behaviour is characterized by increased risk of
substance use, violence, and delinquency [32].
Parental substance use
Lower parental legal substance (alcohol) use had a pro-
tective effect against higher illicit substance use among
the adolescents. Prior evidence indicates that children
develop positive attitudes about alcohol use when their
parents, or other family members, drink more and hold
positive alcohol-related expectancies [15,31,36]. Con-
versely, adolescents whose parents have negative atti-
tudes toward alcohol and disapprove of underage
drinking, show lower levels of alcohol use, are more
likely to engage with peers who do not drink and have a
higher level of self-efficacy for alcohol refusal [15].
The effect of parental influence on substance use may
be equivalent to that of peer influence [6,37]. Parental al-
coholism has also been linked to less than optimal family
management. For instance, less parental discipline is in-
stilled by fathers with alcohol use problems when com-
pared to non-alcoholic fathers [38]. Lower levels of
emotional support and parental monitoring have also been
reported by older children of alcoholic parents [39].
Study limitations
The focus on respondents from rehabilitation centres may
be both advantageous and disadvantageous. Studies involv-
ing information rich cases have been associated with useful
manifestations of the concepts being studied thereby re-
vealing useful insights while avoiding mere empirical gener-
alizations [40,41]. The comparatively more informative
categorical data allowing for ordinal regression models were
used owing to the fact that respondents already had a his-
tory of substance use. Various studies have however re-
ported either “protective but reactive interactions”or
“classic buffering”effect of protective factors where the dif-
ferent levels of factors may manifest varying extent of risk
among respondents [9]. In the current study, instances of
non-significant protective effects where other studies report
significant associations may, therefore, be attributed to a
greater representation of the highest risk levels among the
rehabilitation centre participants which may yield protect-
ive but reactive interactions whereas lower-risk samples
may produce classic buffering effects. For instance, Woot-
ton et al. [42] reported a protective but reactive interaction
in their study on a clinical sample of young children, such
that the protective role of effective parenting against con-
duct problems diminished among children with high per-
sonality risk [9]. Further sampling is recommended
covering clusters of differing socio-demographics and more
balanced gender representation. This will enhance the
generalizability of these results to other adolescent popula-
tions from other geographic regions with different demo-
graphic characteristics.
Children responses concerning parent behaviour may
also constitute a limitation. It has been postulated that
the perception of a child concerning parental behaviour
may be more related to the child’s adjustment than is
the actual behaviour of his parents. This aspect has how-
ever provoked a large quantity of research on children’s
perceptions of parental behaviour [17].
Conclusions
In conclusion, several family management factors with ei-
ther risk or protective effect on adolescent substance use
were outlined. Some factors had either interactive risk or
significant protective effect on substance use or lost sig-
nificance when analysed jointly together with other factors
such as controlled variables. It can be surmised that family
based prevention programmes based upon significant risk
and protective factors reported here may form a cost ef-
fective and practical way of dealing not only with preven-
tion of single behaviours but a range of problems
emanating from substance abuse such as harder sub-
stances, antisocial behaviours and problematic substance
use [6]. Other factors should however also be taken into
account such as peers, communities, workplace, govern-
ment policies and services, and the broader economic and
social environment which all affect family well-being in an
“ecological”manner [6].
Acknowledgements
The authors highly appreciate Prof. Edmund Njagi, London School of
Hygiene and Tropical Medicine for the data analysis and interpretation. We
thank Dr. Patrick Njage, University of Pretoria, for proof reading the
manuscript and assistance in developing the measurement tools and data
management. The rehabilitation centres, Castle Carey Clinic and Stabilis in
Pretoria, are also highly appreciated for permitting the implementation
study, and in assisting in accessing parents for their consent signatures. The
centers also provided a superb interviewing environment which encouraged
an open dialogue with the respondents.
Muchiri and dos Santos Substance Abuse Treatment, Prevention, and Policy (2018) 13:24 Page 8 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Funding
This study was supported by University of South Africa student bursary. The
bursary does not play any role in the design of the study, or the collection,
analysis, interpretation of data, or in drafting the manuscript.
Availability of data and materials
The datasets used and/or analysed during the current study are available
from the corresponding author on reasonable request.
Authors’contributions
BWM conceptualized the study objectives, design, analyses, interpretation of
the data, and drafted the paper. MMLDS supervised the study, and assisted
in data interpretation and finalisation of the paper. All authors read and
approved the final manuscript.
Authors’information
Mrs Beatrice Wamuyu Muchiri is currently a doctoral candidate in the
Department of Psychology at the University of South Africa –with a research
focus area on substance use disorders in youth. She completed her MA degree
in psychology with distinction in 2016.
Prof Monika dos Santos is an associate professor in the Department of Psychology
at the University of South Africa. She is focused on the translation and syntheses
of research and clinical-based knowledge into policy and service developments in
South Africa and internationally, and in academic development. Prof dos Santos
was consulted to provide specialist input regarding the injection drug use (IDU)
risk population group for the revised South African National Strategic Plan for HIV/
AIDS, STIs and TB (2012–2016), and served as a member on the IDU Technical
Working Group for the National Department of Health. She also served on the
Key Population Technical Working Group that guided the development of the
National Guidelines for HIV, Prevention, Care and Treatment for Key Populations in
South Africa. She previously worked in various therapeutic capacities. She served
as a Massachusetts Institute of Technology (MIT) Climate CoLab Fellow, Center for
Collective Intelligence, MIT Sloan School of Management, and is currently a reader
in sustainable urban development at the University of Oxford, Harris Manchester
College.
Ethics approval and consent to participate
Ethical clearance was granted by the Ethics Committee of the Department of
Psychology, University of South Africa, in August 2015. Consent prior to the
study was obtained from the institutional directors, parents or guardians of
the adolescents below age 17, as well as the adolescents.
Consent for publication
“Not applicable”.
Competing interests
The authors declare that they have no competing interests.
Publisher’sNote
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Received: 28 March 2018 Accepted: 6 June 2018
References
1. Stone AL, Becker LG, Huber AM, Catalano RF. Review of risk and protective
factors of substance use and problem use in emerging adulthood. Addict
Behav. 2012;37:747–75.
2. Census. Statistical release ( Revised ) Census 2011. 2012; . doi:P0301.4.
3. Parry CDH, Bennetts A. Alcohol policy and public health in South Africa.
Cape Town: Oxford University Press; 1998.
4. O’Connell ME, Boat T, Warner KE. Preventing mental, emotional, and
behavioral disorders among young people: Progress and possibilities. 2009.
doi:https://doi.org/10.17226/12480.
5. Hawkins JD, Catalano RF, Miller JY. Risk and protective factors for alcohol
and other drug problems in adolescence and early adulthood: implications
for substance abuse prevention. Psychol Bull. 1992;112:64–105.
6. Centre for Suicide Research and Prevention U of HK (CSRP). A study on
drug abuse among youths and family relationship. LC Pap. 2011. http://
www.legco.gov.hk/yr11-12/chinese/panels/ws/papers/ws0312cb2-1546-1-ec.
pdf#page=1&zoom=auto,-178,842.
7. Sameroff AJ. Developmental systems and psychopathology. Dev
Psychopathol. 2000;12:297–312.
8. Zimmerman MA, Stoddard SA, Eisman AB, Caldwell CH, Aiyer SM, Miller A.
Adolescent resilience: promotive factors that inform prevention. Child Dev
Perspect. 2013;7:215–20.
9. Zhou Q, King KM, Chassin L. The roles of familial alcoholism and adolescent
family harmony in young adults’substance dependence disorders:
mediated and moderated relations. J Abnorm Psychol. 2006;115:320–31.
10. Morojele NK, Brook JS. Adolescent precursors of intensity of marijuana
and other illicit drug use among adult initiators. J Genet Psychol. 2001;
162:430–50.
11. Maddahian E, Newcomb MD, Bentler PM. Risk factors for substance use:
ethnic differences among adolescents. J Subst Abus. 1988;1:11–23.
12. Brook JS, Morojele NK, Pahl K, Brook DW. Predictors of drug use among
south African adolescents. J Adolesc Health. 2006;38:26–34.
13. Muisener PP. Family and peer relationship factors: the adolescent’s
interpersonal environment. In: Sourcebooks for the Human Services Series:
Understanding and treating adolescent substance abuse. Thousand Oaks:
SAGE Publications, Inc.; 1994. p. 76–97.
14. Roche KM, Ahmed S, Blum RW. Enduring consequences of parenting for risk
behaviors from adolescence into early adulthood. Soc Sci Med. 2008;66:2023–34.
15. Arria AM, Kuhn V, Caldeira KM, O’Grady KE, Vincent KB, Wish ED. High school
drinking mediates the relationship between parental monitoring and
college drinking: a longitudinal analysis. Subst Abuse Treat Prev Policy. 2008;
3(6) https://doi.org/10.1186/1747-597X-3-Received.
16. Steinberg L, Fletcher A, Darling N. Parental monitoring and peer influences
on adolescent substance use. Pediatrics. 1994;93(6 Pt 2):1060–4.
17. Schaefer ES. Children’s reports of parental behavior: an inventory. Child Dev.
1965;36:413–24. https://doi.org/10.2307/1126465.
18. Avgar A, Bronfenbrenner U, Henderson CR. Socialization practices of
parents, teachers, and peers in Israel : kibbutz, Moshav, and City. Child Dev.
1977;48:1219–27.
19. Nunnally JC, Bernstein I. Psychometric theory. 1994.
20. Hosmer DW, Lemeshow S. Applied Logistic Regression; 2000. https://doi.
org/10.2307/2074954.
21. Agresti A. Categorical Data Analysis; 2002. https://doi.org/10.1198/
tech.2003.s28.
22. Molenberghs G, Verbeke G. Models for discrete longitudinal data; 2005.
https://doi.org/10.1007/0-387-28980-1.
23. Kandel DB, Davies M, Glantz MD, Pickens RW, Glantz MD. In: Pickens RW,
editor. Progression to regular marijuana involvement: Phenomenology and
risk factors for near-daily use. Vulnerability to drug Abus; 1992. p. 211–53.
https://doi.org/10.1037/10107-009.
24. Yamaguchi K, Kandel DB. Patterns of drug use from adolescence to young
adulthood: II. Sequences of progression. Am J Public Health. 1984;74:668–72.
25. Belsky J, Woodworth S, Crnic K. Trouble in the second year: three questions
about family interaction. Child Dev. 1996;67:556–78.
26. McCartney K, Owen MT, Booth CL, Clarke-Stewart A, Vandell DL. Testing a
maternal attachment model of behavior problems in early childhood. J
Child Psychol Psychiatry. 2004;45:765–78. https://doi.org/10.1111/j.1469-
7610.2004.00270.x.
27. Karen R. Becoming attached: unfolding the mystery of the infant‐mother
bond and its impact on later life. New York: Oxford University Press; 1998.
28. Cadoret RJ, Cain C. Sex differences in predictors of antisocial behavior in
adoptees. Arch Gen Psychiatry. 1980;37:1171–5.
29. Hausfather A, Toharia A, LaRoche C, Engelsmann F. Effects of age of entry,
day-care quality, and family characteristics on preschool behavior. J Child
Psychol Psychiatry. 1997;38:441–8.
30. Beyers JM, Toumbourou JW, Catalano RF, Arthur MW, Hawkins JD. A cross-
national comparison of risk and protective factors for adolescent substance
use: the United States and Australia. J Adolesc Health. 2004;35:3–16. https://
doi.org/10.1016/j.jadohealth.2003.08.015.
31. Guo J, Hawkins JD, Hill KG, Abbott RD. Childhood and adolescent predictors
of alcohol abuse and dependence in young adulthood. J Stud Alcohol.
2001;62:754–62.
32. Arthur MW, Hawkins JD, Pollard JA, Catalano RF, Baglioni AJ. Measuring risk
and protective factors for substance use, delinquency, and other adolescent
problem behaviors: the communities that care youth survey. Eval Rev. 2002;
26:575–601.
Muchiri and dos Santos Substance Abuse Treatment, Prevention, and Policy (2018) 13:24 Page 9 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
33. Loxley W, Toumbourou JW, Stockwell T, Haines B, Scott K, Godfrey C, et al.
The prevention of substance use, risk and harm in Australia: a review of the
evidence. Drugs Educ Prev Policy. 2005:334. https://doi.org/10.1080/
09687630500070037.
34. Engels RC, Vermulst AA, Dubas JS, Bot SM, Gerris J. Long-term effects of
family functioning and child characteristics on problem drinking in young
adulthood. Eur Addict Res. 2005;11:32–7.
35. Locke TF, Newcomb MD. Adolescent predictors of young adult and adult
alcohol involvement and dysphoria in a prospective community sample of
women. Prev Sci. 2004;5:151–68.
36. Kliewer W, Murrelle L. Risk and protective factors for adolescent substance
use: findings from a study in selected central American countries. J Adolesc
Health. 2007;40:448–55.
37. Baumann M, Spitz E, Predine R, Choquet M, Chau N. Do male and female
adolescents differ in the effect of individual and family characteristics on
their use of psychotropic drugs? Eur J Pediatr. 2007;166:29–35.
38. DeLucia C, Belz A, Chassin L. Do adolescent symptomatology and family
environment vary over time with fluctuations in paternal alcohol
impairment? Dev Psychol. 2001;37:207–16.
39. King KM, Chassin L. Mediating and moderated effects of adolescent
behavioral undercontrol and parenting in the prediction of drug use
disorders in emerging adulthood. Psychol Addict Behav. 2004;18:239–49.
40. Neuman WL. Social research methods: qualitative and quantitative
approaches. 2000.
41. Patton M. Qualitative Research and Evaluation Methods. US Patent. 2001;
2(561):882–06. 806
42. Wootton JM, Frick PJ, Shelton KK, Silverthorn P. Ineffective parenting and
childhood conduct problems: the moderating role of callous-unemotional
traits. J Consult Clin Psychol. 1997;65:301–8.
Muchiri and dos Santos Substance Abuse Treatment, Prevention, and Policy (2018) 13:24 Page 10 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
not:
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
control;
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
onlineservice@springernature.com
Available via license: CC BY 4.0
Content may be subject to copyright.