Adolescent sexual and reproductive health in sub-Saharan Africa: who is left behind?

Adolescent sexual and reproductive health (ASRH) continues to be a major public health challenge in sub-Saharan Africa where child marriage, adolescent childbearing, HIV transmission and low coverage of modern contraceptives are common in many countries. The evidence is still limited on inequalities in ASRH by gender, education, urban–rural residence and household wealth for many critical areas of sexual initiation, fertility, marriage, HIV, condom use and use of modern contraceptives for family planning. We conducted a review of published literature, a synthesis of national representative Demographic and Health Surveys data for 33 countries in sub-Saharan Africa, and analyses of recent trends of 10 countries with surveys in around 2004, 2010 and 2015. Our analysis demonstrates major inequalities and uneven progress in many key ASRH indicators within sub-Saharan Africa. Gender gaps are large with little evidence of change in gaps in age at sexual debut and first marriage, resulting in adolescent girls remaining particularly vulnerable to poor sexual health outcomes. There are also major and persistent inequalities in ASRH indicators by education, urban–rural residence and economic status of the household which need to be addressed to make progress towards the goal of equity as part of the sustainable development goals and universal health coverage. These persistent inequalities suggest the need for multisectoral approaches, which address the structural issues underlying poor ASRH, such as education, poverty, gender-based violence and lack of economic opportunity.


Modern contraceptive use for family planning (%)percentage of modern contraceptive
use was computed as the number of sexually active adolescent and young female population of age 15-24 years (identified as married or single) using any modern contraceptive method (implants, IUD, injectable, pills, emergency contraception, condom, sterilization, other) as the numerator divided by the total number of sexually active adolescent and young female population of age between 15-24 (identified as married or single) with a need of family planning (the denominator). Methods with less than 1% were grouped together in the 'others' category.
Results presented in this study were drawn from two main components of analyses: synthesis of data from 33 countries extracted from STATcompiler [4] and analysis of raw data from 10 countries to further triangulate results and better understand who is left behind in adolescents sexual and reproductive health (ASRH) in sub-Saharan Africa.

A. sub-Saharan Africasynthesis of data from 33 countries
This first component of the analysis involves critical review of published literature and synthesis of data from DHS and AIS from a total of 33 countries in sub-Saharan Africa, using the most recent survey since 2010 and national HIV surveys from a total of 30 countries. The surveys were on average conducted in 2014. The DHS provides standardized nationally representative data that can be disaggregated by major stratifiers, mainly by age group (15-19 vs. 20-24), urban-rural residence, education level (no education, primary vs. secondary or higher), wealth quintile (poorest (20%) vs. richest (20%)). In STATcompiler, data on the median age at first sex, marriage and birth are available for men and women aged 25 or above. Therefore, data on these events were based on the recall of these events by women and men aged 25-29 years, and in particular, data on age at first marriage from men aged 30-34 were used to impute if the data from 25-29 were missing.
HIV prevalence data were extracted from either DHS, AIS or PHIA. HIV prevalence data which were extracted from both STATcompiler and PHIA were used to synthesise inequality.
It is worth noting that DHS, AIS and PHIA are all cross-sectional, household, and nationally representative surveys of adults age 15 years and above.

B. Ten large-population countries analysis
The second component of the analysis involves the analysing of raw DHS and/or AIS data from the 10 large-population countries (Ethiopia, Ghana, Kenya, Malawi, Mozambique, Nigeria, Tanzania, Uganda, Zambia and Zimbabwe), with primary focus on adolescents of age 15-24 years. These countries have a total of over 15 million population and it is mainly to gain further insight into more recent trends, as well as to triangulate data and minimize the potential impact of recall bias on the key ASRH indicators.  Descriptive analyses were conducted to describe the distribution of the proportion of adolescents attending school and use of contraceptives, while survival analysis was utilized to compute median age at first sex, marriage and birth. All analyses were done by survey for each country to examine trends over time. Our analyses to examine inequality was informed by our conceptual framework of looking at similarities and differences by cross-cutting variables: gender, urban vs. rural as residential area, current marital status, education status and socioeconomic status. Marital status was categorized as never married, currently married or formerly married (i.e., widowed, divorced or separated), while education statuscategorized in three groups as no education, primary, or secondary or higherwas based on highest level of education completed during interview. Survival analysis (sts function in the statistical package of Stata version 15) was performed to examine trends in the distribution of age at first sex, marriage and childbearing, whereas descriptive analysis was utilized to assess the level and trends in inequalities for education and sexual behaviour and health service utilization. The median survival time for age at first sex, marriage or childbirth was obtained from a cumulated single-year percent distribution from the Kaplan-Meier product-limit estimates of the survival curve, computed using a Stata function stsum, disaggregated by gender, urban-rural residential area, marital status, education status and socioeconomic status as appropriate. The Kaplan-Meier failure function was also utilized to explore the distribution of the probability of experiencing any of the first life events (i.e., first sex, marriage or childbirth) by each single year of age as appropriate.
Analysis was performed by survey for each gender (male vs. female) and country.
The median age at first sex was determined based on whether or not the respondent ever had sex and, if applicable, recalled age at first sex. Reported age at first sex is set as the failure event and those who never had intercourse are censored at their current age during interview. We focused on data from all respondents 15-24 years and all analyses were conducted for each survey by sex of the respondent. The estimations of the distribution of age at first marriage and age at first childbirth were conducted in the same way as for age at first sex.
We focused on data from all respondents of age 15-24 years, except for median age at first marriage for men as the median was not reached by age 25 (analysis rerun using 15-29 years as reference). The median at first marriage for men was considerably later and not be computed for men 15-24 years for most countries. The analysis to estimate the distribution of median age at first birth was conducted only for adolescent women aged 15-24. The equity analyses involve triple disaggregation: age, sex and socioeconomic information, as well as several age-dependent indicators such as education status.
We also computed the median and weighted mean of the country values to describe the overall distribution of the key ASRH indicators and gain insight the big picture. The weighted mean was computed using data from UN country-year population estimates of respective age group adolescents. For each summary table, we provided country medians, and the weighted mean computed from country values.
Analysis of current school attendance among adolescent men and women aged 15-19 is based on data from all household members obtained through the household questionnaires.
The percentage distribution of school attendance was computed by using the number of year as numerator divided by the total number of adolescents in the respective age group.
The demand for family planning satisfying by modern methods among adolescent and young women aged 15-24 was computed as current contraceptive use divided by the sum of current use and unmet need for family planning, while modern contraceptive use among those who are sexually active was computed as the number of sexually active adolescent and young women using any modern contraceptive method (implants, IUD, injectable, pills, emergency contraception, condom, sterilization, other) as the numerator divided by the total number of sexually active adolescent and young women with a need of family planning (the denominator). Methods prevalence was computed by dividing the number of adolescent and young women using a particular method by total number of adolescent and young women who mentioned using any modern contraceptive method. Methods with less than 1% were grouped together in the 'others' category.
In this analysis we did not opt to show confidence intervals. For the median ages at specific events as it would imply having to introduce another measure such as the proportion who had experienced an event by age x. In this paper we look at the big picture and if the majority of the 10 countries are more or less uniformly moving in a certain direction, we make note of this in the main text. However, the confidence intervals of proportions from the life table analyses on sex, marriage and birth, and the trends in contraception, condom use and HIV have been computed and are available upon request. Note: Women are all 15-24 years; Men 15-24 were used to estimate age first sex; Men 15-29 were used to estimate age first marriage. c., which stands for circa, refers to median year of surveys of each country. *Absolute difference in median age between secondary+ (i.e., secondary or higher) and none (i.e., without education); **Absolute difference in median age between wealthiest and poorest wealth tercile; ***Absolute difference in median age between women and men; # Weighted by county-year population sizes of respective age group adolescents using data obtained from UN population database (https://population.un.org/wpp/).