Adolescent fertility and its determinants in Kenya: Evidence from Kenya demographic and health survey 2014

Background Adolescent fertility in Kenya is vital in the development and execution of reproductive health policies and programs. One of the specific objectives of the Kenyan Adolescent Sexual Reproductive Health (ASRH) policy developed in 2015 is to decrease early and unintended pregnancies in an attempt to reduce adolescent fertility. We aimed to establish determinants of adolescent fertility in Kenya. Methods The Kenya Demographic and Health Survey (KDHS) 2014 data set was utilized. Adolescent’s number of children ever born was the dependent variable. The Chi-square test was utilized to determine the relationship between dependent and independent variables. A Proportional-odds model was performed to establish determinants of adolescent fertility at a 5% significance level. Results Over 40% of the adolescent girls who had sex below 17 years had given birth i.e, current age 15–17 years (40.9%) and <15 years (44.9%) had given birth. In addition, 70.7% of the married adolescents had given birth compared to 8.1% of the unmarried adolescents. Moreover, 65.1% of the adolescents who were using contraceptives had given birth compared to only 9% of the adolescents who were not using a contraceptive. Approximately 29.4% of the adolescents who had no education had given birth compared to 9.1% who had attained secondary education. Age at first sex (18–19 years: OR: 0.221, 95% CI: 0.124–0.392; 15–17 years: OR: 0.530, 95% CI: 0.379–0.742), current age (18–19 years: OR: 4.727, 95% CI: 3.318–6.733), current marital status (Not married: OR:0.212, 95% CI: 0.150–4.780), and current contraceptive use (Using: OR 3.138, 95% CI: 2.257–4.362) were associated with adolescent fertility. Conclusion The study established that age at first sex, current age, marital status, and contraceptive use are the main determinants of adolescent childbearing. The stated determinants should be targeted by the government to control the adolescent birth rate in Kenya. Consequently, delaying the age at first sex, discouraging adolescent marriage, and increasing secondary school enrollment among adolescent girls are recommended strategies to control adolescent fertility in Kenya.

Faktor Determinan Fertilitas Remaja di Provinsi Nusa Tenggara Timur

Introduction: Adolescent fertility caused various negative consequences starting from the occurrence of adolescent fertility include health, social and economic impacts. Purpose: to study the determinants of adolescent fertility in the Province of East Nusa Tenggara. Methods: This study uses secondary survey data in the 2017 IDHS. The sample was adolescents aged 15-19 years who were recorded as selected samples in East Nusa Tenggara Province as many as 401 adolescents. Analysis using multiple logistic regression with predictive factor model. Results: The highest aOR value is marital status 17.25 (95% CI 6.60-45.04). Education only finished elementary school aOR 4.94 (95%CI 1.08-22.45). Never accessed the internet aOR 3.37 (95%CI 0.85-13.37). For every 1 year increase in adolescent age, the odds increased by 3.32 (95% CI 1.71-6.46) or 19 years old had the highest odds. Didn’t work aOR 3.00 (95% CI 0.71-12.66). Living in rural areas aOR 2.55 (95%CI 0.53-12.13). Low knowledge of family planning methods has an AOR of 0.26 (0.05-1.22). Conclusion: The determinants of adolescent fertility in NTT Province are age, education, occupation, marital status, place of residence, internet use and knowledge of family planning methods.

Young motherhood: levels and socioeconomic determinants of adolescent fertility in Ghana

Adolescent fertility levels have shown considerable improvements globally over the past decades. However, adolescent childbearing remains high in developing countries, particularly in sub-Saharan Africa. This study, thus, examines the levels and socioeconomic factors associated with adolescent fertility in Ghana. The study drew on data from the 2003, 2008, to the 2014 Ghana Demographic and Health Surveys to perform a logistic regression analysis of socioeconomic factors associated with adolescent fertility. The results show that adolescent childbearing levels have not shown any considerable improvements over the study periods (10%, 10%, and 11% for 2003, 2008, and 2014, respectively). Socioeconomic factors such as household wealth status, working status, employer status, and employment period were associated with adolescent fertility. Female adolescents from poor households, employed and self-employed adolescents, as well as regular workers, were linked to higher adolescent fertility risks. Older adolescents, and ever married adolescents also show significantly higher childbearing risks while the risk levels steadily increased over time. Promoting economic empowerment among female adolescents and targeting employed female adolescents in fertility control measures may have considerable positive implications for adolescent fertility levels in Ghana.

Association between socioeconomic status and fertility among adolescents aged 15 to 19: an analysis of the 2013/2014 Zambia Demographic Health Survey (ZDHS)

Background Adolescents face significant barriers to access and utilization of sexual and reproductive health services in many low-income settings, which in turn may be associated with adverse consequences such as early pregnancy, sexually transmitted infections, unsafe abortion and mortality. There is evidence suggesting that limited access to sexual and reproductive health information and services among adolescents contributes to these outcomes. We aimed to find out the factors that affect the fertility of adolescents aged 15 to 19 years in Zambia and to identify possible drivers of adolescents’ fertility. Methods Secondary analysis of the ZDHS 2013/14 data was carried out to find out the factors that affect the fertility rate of adolescents aged 15 to 19 years using multivariate logistic regression (n = 3666). Results Overall, 23.1% of adolescents had given birth at least once in the 5 years leading to the survey (n = 3666, 99.4% response), and 49.8% were rural-based while 50.2% were urban-based. The median number of schooling was 8 years (IQR 6–10). About 52% of the adolescents were in the poorer, poor and medium wealth quintiles while the other 48% were in the rich and richer quintiles. Factors found to affect fertility include residence, wealth status, educational attainment, marriage and abortion. An urban-based adolescent with a lower socioeconomic status was 2.4 times more likely to give birth compared to rural-based poorer adolescents (aOR = 2.4, 95% CI: 1.5, 3.7, p < 0.001). Although odds of giving birth were much higher among rural-based married adolescents (aOR = 8.0, 95% CI: 5.4, 11.9, p < 0.001) compared to urban married adolescents (aOR = 5.5, 95% CI: 8.3, 16.0, p < 0.001), and these relationships both statistically significant, higher educational attainment (aOR = 0.7, 95% CI: 0.6, 0.8 p < 0.001) and abortion (aOR = 0.3, 95% CI: 0.1, 0.8, p = 0.020) reduced these odds, particularly for rural-based adolescents. Conclusion Despite response aimed at reducing adolescent fertility, low wealth status, low educational attainment and early marriage remain significant drivers of adolescent fertility in Zambia. There is a need to address sexual and reproductive health needs of urban-based adolescents with a lower socioeconomic status.

Travelling for abortion services in Mexico 2016–2019: community-level contexts of Mexico City public abortion clients

ObjectiveTo describe the community context of women who travel to access Mexico City’s public sector abortion programme and identify factors associated with travelling from highly marginalised settings.MethodsWe used data from the Interrupción Legal de Embarazo (ILE) programme (2016–2019) and identified all abortion clients who travelled from outside Mexico City. We merged in contextual information at the municipality level and used descriptive statistics to describe ILE clients’ individual characteristics and municipalities on several measures of vulnerability. We also compared municipalities that ILE clients travelled from with those where no one travelled from. We used logistic regression to identify factors associated with travelling to access ILE services from highly marginalised versus less marginalised municipalities.ResultsOur sample included 21 629 ILE clients who travelled to Mexico City from 491 municipalities within all 31 states outside Mexico City. The majority of clients travelled from the least marginalised (81.9%) and most populated (over 100 000 inhabitants; 91.3%) municipalities. Most (91.2%) ILE clients came from municipalities with adolescent fertility rates in the bottom three quintiles. Clients with a primary or secondary education (vs high school or more) and those from a municipality with a high adolescent fertility rate (top two quintiles) had higher odds of travelling from a highly marginalised (vs less) municipality (adjusted odds ratio (aOR) 1.46, 95% CI 1.35 to 1.58 and aOR 1.89, 95% CI 1.68 to 2.12, respectively).ConclusionILE clients travel from geographically and socioeconomically diverse communities. There is an unmet need for legal abortion across Mexico.

Socio-economic and geographical inequalities in adolescent fertility rate in Ghana, 1993–2014

Background Despite public health interventions to control adolescent fertility, it remains high in sub-Saharan Africa. Ghana is one of the countries in sub-Saharan Africa with the highest adolescent fertility rates. We examined the trends and socio-economic and geographical patterns of disparities in adolescent fertility in Ghana from 1993 to 2014. Methods Using the World Health Organization’s (WHO) Health Equity Assessment Toolkit (HEAT) software, data from the 1993–2014 Ghana Demographic and Health surveys were analyzed. First, we disaggregated adolescent fertility rates (AFR) by four equity stratifiers: wealth index, education, residence and region. Second, we measured the inequality through summary measures, namely Difference (D), Population Attributable Risk (PAR), Ratio (R) and Population Attributable Fraction (PAF). A 95 % confidence interval was constructed for point estimates to measure statistical significance. Results We observed substantial absolute and relative wealth-driven inequality in AFR (PAR=-47.18, 95 % CI; -49.24, -45.13) and (PAF= -64.39, 95 % CI; -67.19, -61.59) respectively in favour of the economically advantaged subpopulations. We found significant absolute (D = 69.56, 95 % CI; 33.85, 105.27) and relative (R = 3.67, 95 % CI; 0.95, 6.39) education-based inequality in AFR, with higher burden of AFR among disadvantaged subpopulations (no formal education). The Ratio measure (R = 2.00, 95 % CI; 1.53, 2.47) indicates huge relative pro-urban disparities in AFR with over time increasing pattern. Our results also show absolute (D, PAR) and relative (R, PAF) inequality in AFR across subnational region, between 2003 and 2014. For example, in the 2014 survey, the PAR measure (D=-28.22, 95 % CI; -30.58, -25.86) and the PAF measure (PAF=-38.51, 95 % CI; -41.73, -35.29) indicate substantial absolute and relative regional inequality. Conclusions This study has indicated the existence of inequality in adolescent fertility rate in Ghana, with higher ferlitiy rates among adolescent girls who are poor, uneducated, rural residents and those living in regions such as Northern, Brong Ahafo, and Central region, with increasing disparity over the time period of the study. There is the need for policy interventions that target adolescent girls residing in the rural areas and those in the low socioeconomic subgroups to enable the country to avert the high maternal/newborn morbidity and mortality usually associated with adolescent childbearing.

Contribution of Contraceptive Use on Fertility Reduction in Rwanda

Objectives: In this study, the contribution of contraceptive use on fertility rate reduction in Rwanda was assessed using the data from RDHS 2014/2015 and the sample was constituted with 5,954 household respondents where the targeted population were male between 15-59 years and female between 15-49 years of age, multinomial logistic regression was used to prove the full contribution of contraceptive use to the fertility reduction in the country in order to improve Rwandan’s welfare.Results: Compared to the traditional method and modern method it was found that 48.54% of male use modern method, while 5.39% uses traditional method; otherwise, 33.70% of female use modern method while 1.24% of female use traditional method, finally sexual and reproductive health programs should be encouraged and the parents must teach their children about the sexual and reproductive health (SRH) which will reduce the adolescent fertility which is rising day to day.

Summary measures of socioeconomic and area-based inequalities in fertility rates among adolescents: evidence from Ethiopian demographic and health surveys 2000–2016

Background One of the highest rates of adolescent pregnancies in the world is in sub-Saharan Africa. Most adolescent pregnancies in the region are unintended or unwanted, due to poor access to information and services on sexual and reproductive health for adolescents. Ethiopia has high adolescent fertility rates (AFR) with disparities across socioeconomic subgroups and regions. This study assessed the magnitude and trends of socioeconomic and area-based AFR inequalities in Ethiopia. Methods The 2000 and 2016 Ethiopia Demographic and Health surveys (EDHS) was analyzed using the World Health Organization’s (WHO) Health Equity Assessment Toolkit (HEAT) software. Adolescent fertility rates were disaggregated using three equity stratifiers (economic status, education and residence) and analyzed through four summary measures (Difference (D), Population Attributable Risk (PAR), Ratio (R) and Population Attributable Fraction (PAF)) to assess inequality. To measure statistical significance, point estimates were constructed using a 95% Uncertainty Interval (UI). Results Large socio-economic and urban-rural inequalities were observed within the 16-year period. Adolescents in less well-off socio-economic groups (PAF: -62.9 [95% UI; − 64.3, − 61.4], D: 96.4 [95% UI; 47.7, 145.1]), uneducated (R: 8.5 [95% UI; 4.8, 12.2], PAR: -76.4 [95% UI;-77.7, − 75.0]) and those from rural areas (D: 81.2 [95% UI; 67.9, 94.6], PAF: -74.2 [95% UI, − 75.7, − 72.7]) had a higher chance of pregnancy and more births than their counterparts. Conclusions Socioeconomic (education and economic status) and place of residence determine adolescents’ pregnancy and childbearing. Policies and programs should be directed at preventing child marriage and early fertility so that adolescents continue to access education, sexual and reproductive health care as well as access employment opportunities. More emphasis should be placed on subpopulations with disproportionately higher adolescent pregnancy and childbirth.