«Suggested citation: Panday, S., Makiwane, M., Ranchod, C., & Letsoalo, T. (2009). Teenage pregnancy in South Africa - with a speciﬁc focus on ...»
Source: South African Demographic and Health Survey, 1998 Urban-Rural Overview of the World Fertility Surveys (WFS) and the DHS programmes indicate that fertility declines began in urban areas due to economic development, and increasing access to education and contraception, and later moved onto rural areas. However, few studies have conducted rural/urban comparisons of teenage pregnancy (Snyder, n.d.) and inconsistencies in how urban and rural areas are deﬁned make interpretation and comparability difﬁcult. The 1998 SADHS reported almost double the fertility rate among teenagers in rural settings (99 per 1000) than among those in urban settings (56 per 1000) (DOH, MRC & Measure DHS, 2002).
The KZN Transitions to Adulthood study also reported higher rates of pregnancies in rural areas than in urban areas (Manzini, 2001).
Rapid urbanisation in SA has meant that a large percentage of the most disadvantaged sectors of society live in informal settlements on the fringes of urban areas. The most mobile sector of South African society is young people, migrating to urban centres in search of educational and work opportunities (Budlender, 2007). Over 15% (16.7%) of 15-24 year olds can be found in informal settlements (Harrison, 2008a; Stats SA, 2005). Figures increase to more than 30% in the urban hubs of Gauteng. Although the 1998 SADHS did not differentiate 36 Teenage pregnancy in South Africa - with a speciﬁc focus on school-going learners pregnancy by urban informal areas, data from the 2003 RHRU survey is indicative of the concentration of risky sexual behaviour in informal settlements. HIV prevalence among young people living in urban informal areas is double that of other geotypes (see Figure 7 below). Given the common antecedents of HIV and pregnancy, further investigation of pregnancy rates in urban informal areas is warranted.
Figure 7: HIV prevalence by geography type among 15-24 year olds in South Africa, 2003
20% 17.4% 13.5% 15% 9.8% 10% 8.1% 5%
Source: Pettifor et al., 2004 Population group The South African history of racial classiﬁcation accompanied by gross inequalities in access to education and economic opportunities as well as health services is reﬂected in the teenage fertility rates. The teenage fertility rates of White (14 per 1000) and Indian (22 per 1000) South Africans mirror that of developed countries, while higher rates are reported among Coloured (60 per 1000) and African (71 per 1000) teenagers for the year 2001 (Moultrie & McGrath, 2007). However, as shown in Figure 8 below, fertility among 15-19 year olds declined in all population groups between 1996 and 2001. The largest decline was registered among the White population (-29.8%), followed by African (-16.8%), Coloured (-12.7%) and Indian (-7.8%) teenagers (Moultrie & Dorrington, 2004).
Figure 8: Teenage fertility by population group, 1996-2001
86 81 81 78 76 71 68 65 60 26 24 22 19 20 14
Education Increasing access to education among women has been identiﬁed as one of the main reasons for the systematic decline in fertility since the 1970s. One of the greatest achievements since democracy in SA is the massive expansion in access to education, especially in the enrolment of African youth and women. Access to primary schooling is universal (104%) and secondary school enrolment (80%) is high (Schindler, 2008). Data from the 1998 SADHS shows a strong inverse relationship between education and teenage fertility (DOH, MRC & Measure DHS, 2002). Teenage mothers are concentrated among those with only primary education (38.5%) but declines progressively among those with some secondary education (12.9%), matric (7.9%) and those with higher education (4.0%) (see Table 2).
Despite the protective effect that schooling exerts over sexual behaviour (girls in school are less like to be sexually active than girls out of school and are more likely to use contraception (NRC & IOM, 2005), the risk of pregnancy during ages at which girls are attending school increases. Data from 28 demographic and health surveys showed that countries in which enrolment were high were more likely to report pregnancy as a reason for school dropout (NRC & IOM, 2005). The converse is true for countries reporting low enrolment; girls will not be in school during their reproductive years. This relationship is clear when enrolment is below 20% but a mixed effect is evident when enrolment increases above 50%, suggesting that schooling, depending on the context, can have a mediating effect on reproductive health behaviour (NRC & IOM, 2005).
The South African schooling system is characterised by both high enrolment and high rates of repetition, dropout, late entry and re-entry meaning that a signiﬁcant number of older learners, well past the onset of puberty, can be found in lower grades (Schindler, 2008). As a result, the system has had to accommodate traditionally high rates of teenage fertility. Studies have reported that over a third of girls below 19 years of age who had an early pregnancy were attending school in 1993 (Maharaj, Kaufman & Richter, 2000). A similar trend was evident in KZN in 2001 (Hallman & Grant, 2003).
But the relationship that teenagers have with school can inﬂuence their sexual behaviour and as a result, early pregnancy. When teenagers feel a sense of attachment or connection to school and are successful at school, they are less likely to fall pregnant. School attachment, academic achievement and higher aspirations for education offer incentives to teenagers to avoid pregnancy (Kirby, 2002, Santelli, Lowry, Brener & Robin, 2000).
On the other hand, when the relationship with schooling is tenuous, either through dislike of school (Imamura et al., 2007), poor academic achievement (Cassell, 2002) or poor expectations of furthering education (Imamura et al., 2007) girls are more likely to become pregnant.
While many studies report on pregnancy as the reason for school dropout, recent studies are contesting the direction of this relationship (Cassell, 2002). The KZN Transitions study reported that for males, the inability to pay school fees (31%) and the need to work (22%) were the main reason for dropout, while for females, pregnancy (39%) and the inability to pay school fees (30%) were cited as reasons for dropout (Rutenberg et al., 2001). But the implicit assumption that girls who dropout of school because of pregnancy would have continued their education may not be valid (NRC & IOM, 2005). Pregnancy may be the endpoint most directly associated with dropout but is often not the cause. Pregnancy and school dropout in fact share many common social and economic antecedents (Lloyd & Mensch, 2008), the most signiﬁcant of which are poverty and poor academic achievement (Cassell, 2002). Lloyd and Mensch (1999) contend that “rather than pregnancy causing girls to drop out, the lack of social and economic opportunities for girls and women and the domestic demands placed on them coupled with the gender inequities of the education system, may result in unsatisfactory 38 Teenage pregnancy in South Africa - with a speciﬁc focus on school-going learners school experiences, poor academic performance, and acquiescence in or endorsement of early motherhood’.
Using the KZN Transitions data, Grant and Hallman (2006) showed that poor school performance is a strong marker of the increased likelihood of experiences a pregnancy while enrolled in school and of dropping out of school at the time of pregnancy. Poor school performance also limits the likelihood that girls who experience a pregnancy would ever return to school.
Although the timing of school dropout and pregnancy coincide for some girls, for the most, pregnancy follows school dropout (Imamura et al., 2007) often due to poor academic performance resulting in a lost of interest in school (Manlove, 1998). In fact, Lloyd and Mensch (2008) demonstrated that the risk of leaving school due to childbearing and marriage has diminished signiﬁcantly over time in Africa alluding to the protective effect that schooling exerts on the social outcomes of young people. A similar trajectory of increased risk of pregnancy following school dropout is evident in SA. Harrison (2008b) demonstrated the increasing risk of pregnancy amongst those who were no longer at school by combining Census data with the 2003 RHRU survey (see Figure 9). The analysis showed that pregnancy increased signiﬁcantly among 17 and 18 year olds outside of the school system.
Figure 9: Increasing teenage pregnancy rates among those who are not at school
Source: Harrison, 2008b Similar evidence is available from rural SA. The odds of being pregnant among 14-19 year old school girls in Bushbuckridge were one tenth that of girls who had left the schooling system (Hargreaves et al., 2008).
Dropping out of school not only increases risk for pregnancy, it also signiﬁcantly increases risk for HIV. The 2003 RHRU survey showed that young women who did not complete their secondary school education were four times more likely to be HIV positive compared to those who had completed high school (Pettifor et al., 2008). The study concluded that structural factors such as lack of education may play a more fundamental role in exacerbating risk for HIV, more so than individual level factors (Pettifor et al., 2008).
While SA is able to retain learners during the compulsory years of schooling, dropout increases dramatically from grade 9 onwards, particularly for Coloured and Black learners (DOE, 2007b). In a context of pervasive poverty, economic barriers play a signiﬁcant role in school dropout. But two important and related markers of a high risk of dropping out are grade repetition and higher age for grade. These factors are also concentrated 39 Teenage pregnancy in South Africa - with a speciﬁc focus on school-going learners among Coloured and Black learners. Data from the Cape Area Panel Study showed that by age 16-17 years, 49 per cent of African males and 27 per cent of Coloured males were two or more years behind the appropriate grade for age compared to only 7 per cent of White males (Lam, Leibbrandt & Mlatsheni, 2008). Without a system of remediation to improve school performance, learners become disillusioned and disengaged from the school environment, increasing the risk of dropout. In addition, as learners mature biologically, other nonschool related goals (such as earning an income, forming relationships and raising a family) overtake education as their sole focus (DOE, 2007b). In the absence of age or developmentally appropriate (rather than grade appropriate) life skills education on sexuality (Jewkes & Christoﬁdes, 2008), the risk of early pregnancy is heightened.
While a liberal school policy on teenage pregnancy has mitigated some of the consequences of early childbearing in SA, not all teen mothers remain in school or return to school. This may stem from uneven implementation of the school policy resulting in suspension or expulsion of pregnant teens, poor academic performance prior to pregnancy, few child-caring alternatives, poor support from families, peers and the school environment and the social stigma of being a teenage mother (Cassell, 2002). The KZN Transitions study reported that 74% of girls aged 14-19 years dropped out of school at the time of pregnancy and only 29% returned to school following pregnancy-related dropout (Grant & Hallman, 2006). What is more, for every year that passes after pregnancyrelated school dropout, young women are signiﬁcantly less likely to return to school (Grant & Hallman, 2006).
The odds of returning to school among 14-24 year olds declines signiﬁcantly by 60% two years post-dropout, by 70% three years post-dropout, and by 80% four years post-dropout.
Instituting strategies to retain learners in school by addressing both ﬁnancial and school performance reasons, as well as ensuring early return post-pregnancy, may be the most effective contributions that the education system can make to prevent and mitigate the impact of early pregnancy. When learners do dropout of school, concerted effort is required to re-enrol them in school or in alternative systems of education.
Secondary analysis EMIS data Using EMIS data for 2004-2008, the number of pregnant learners per 1000 registered learners was estimated.
For example, in 2004, the Education Department registered 51 pregnancies for every 1000 female learners.
The table shows that there was a steady increase in the proportion of learners who had become pregnant during the period. It is not clear whether this represents a real increase in teenage pregnancy, or an improvement in the reporting process. While most national surveys have reported a decline in teenage fertility during the period, it should be noted that termination of pregnancy by all South African women increased during the period. According to Department of Health records, over 70 000 termination of pregnancies were reported in South African public health facilities during the year 2003, representing a 200% increase in terminations since 1997 (Makiwane, 2009). About 30% of terminations were among women aged 15 to 19. It is therefore possible that the national decline in teenage fertility may be in part accounted for by an increase in termination of pregnancy rather than a decline in teenage pregnancy. However, this would need to be conﬁrmed empirically.
It is not known, though, if the pregnancy statistics reported in the EMIS data includes pregnancies that were terminated. Our assessment of the EMIS data is that it most likely approximates fertility (that is live births) as pregnancies are more likely to be reported/discovered late into the gestation period, well past the period for safe termination.
The table below provides a provincial breakdown of the number of pregnancies per 1000 learners. A consistent pattern of high pregnancy rates are reported for provinces that are poor and mostly rural (Eastern Cape, KwaZulu-Natal and Limpopo), and a reverse is evident for the most afﬂuent and urban provinces (Gauteng and Western Cape). This contrast is more distinct than the teenage fertility data reported in the 1998 SADHS.
Table 5: Learner pregnancy rates per province, 2004-2008