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1. Introduction
Anyone aged between 10 and 19 is considered an adolescent according to the World Health Organization (WHO) [1]. The WHO considers individuals between the ages of 10 and 24 when defining young people. The risks and realities of teenage motherhood are well known, with effects beginning with pregnancy and childbirth and continuing throughout the mother’s and child’s lives. Adolescent pregnancies continue to be a global issue that affects more high-income, middle-income, and then low-income countries, with the latter experiencing the majority of cases [2].
Teenagers in poor nations confront several difficulties such as restrictive laws and policies that limit the use of contraceptives based on a woman’s age or marital status, service providers’ prejudiced treatment of adolescents’ sexual health needs, a lack of autonomy to ensure proper and consistent contraceptive use, lack of information, access to transportation, and financial resources [3]. Many different factors can affect adolescent childbearing, and they can differ from one country or location to another. However, these elements can also be influenced by the parents of the affected girls, their friendships with their peers, and their parents’ socioeconomic and educational position [4].
Globally, 16 million girls between the ages of 15 and 19 and 1 million girls under the age of 15 give birth each year, with a teen birth rate of 49 live births per 1000 women [2, 5]. The biggest cause of death for young women [6–10] is problems during pregnancy, childbirth, and postpartum (42 days following childbirth) [11].
Low-income countries have the greatest birth rates (97 live births per 1000) while high-income countries have the lowest rates (12 live births per 1000 teenage girls) [12]. Regionally, the rate of adolescent births is highest in Africa (99 live births per 1000), while it is lowest in the WHO Western Pacific Region (14 live births per 1000) [1]. The results of studies on “parent/child sexual communication and adolescent pregnancy risk” have been uneven, but they have consistently shown that females with a family history of teenage childbearing are at considerably higher risk of teenage pregnancy and childbearing themselves [13].
Ghana’s population is younger than most other countries, with about 57% of people under the age of 25. During the 1980s and 1990s, its overall fertility rate drastically decreased but has recently reached a plateau of about four children per woman [14]. In Ghana, teenage pregnancy rates are high (17.8%), and 1 in 5 and 1 in 20 girls get married before turning 18 and 15, respectively [15]. According to studies done in Ghana, teens gave birth to nearly 30% of all babies recorded [16]. In 2014, teenage pregnancy climbed from 13% to 14%, with 16.7% of occurrences happening in rural Ghana and 11.5% in urban Ghana [5].
Although the rate of adolescent pregnancy has decreased globally, it is still rising in emerging nations, with sub-Saharan Africa showing the greatest prevalence [2]. The sexual and reproductive health of young adolescent males and girls between the ages of 10 and 14 is also little understood, as is their fertility. The topic is challenging to investigate methodically due to the cultural sensitivity of the subject, the fact that young girls under the age of 15 are often less sexually active than older adolescents or women, and the fact that they very seldom have children at such young ages [17]. Therefore, the current study looked into the prevalence and variables predicting adolescent childbearing in Ghana among the age group 15–19 years.
2. Materials and Methods
2.1. Study Design and Data Source
Data from the Ghana Multiple Indicator Cluster Survey (MICS) 2017–2018 was used to conduct an analytical cross-sectional study. Through a formal request, this study’s researcher was able to get the 2017–2018 Ghana MICS data from UNICEF. UNICEF gave its consent for the anonymized data to be used. In all ten of Ghana’s previous administrative regions, the 2017/2018 MICS used computer-assisted personal interviewing (CAPI) to gather data on population and health indicators. The Ghana Health Service Ethics Review Committee gave its approval to the MICS protocols for the years 2017–2018. All adults who participated in the study verbally agreed to participate before any questionnaires were given out. Consent was sought from parents or guardians for participants between the ages of 15 and 17. All participants received guarantees regarding their free will to leave the interview at any time, as well as voluntary participation, information confidentiality, and anonymity.
2.2. Population and Sampling
The 2010 Ghanaian Population and Housing Census (PHC) was used as the sampling frame. Ghanaians between the ages of 15 and 49 were the target demographic for the 2017/2018 MICS. To choose the participants, a two-stage sampling procedure was used. First, 660 enumeration areas/clusters were chosen proportionally to size from the 2010 PHC list. The selection of 13,202 households for the next step of the hiring process required rigorous random sampling, out of which 12,886 were chosen for interviews. Women in the chosen households between the ages of 15 and 49 were eligible to participate in the women’s survey. In the chosen households, exactly 14,609 women were found, and 14,374 of them were questioned, for a response rate of 98.4%. Fieldworkers who had received training collected the data. Data collection lasted from 15 October 2017 to 15 January 2018, inclusive.
For the purpose of this current study, all females between the ages of 15 and 19 years, who remained in certain homes the night before the survey (2974), whether they were tourists or permanent residents, were used for this study analysis.
2.3. Study Variables
2.3.1. Dependent Variable
The main dependent or outcome variable in this study is adolescent childbearing, thus adolescents with childbirth history.
2.3.2. Independent Variables
The independent variables included the participants’ socioeconomic characteristics such as age, education status, region, residence type, marital status, health insurance status, functional difficulties, and wealth index quintile status. Also included as independent variables are participant exposure to the Internet and media such as newspaper, radio, television (TV), computer or tablet, and Internet use.
2.4. Statistical Analysis
The results were examined with SPSS Version 20 (IBM Corp., 2011, and NY). The effects of categorical variables were explained using frequency and percentage tables. The chi-square test was used to ascertain how the dependent and independent variables related to one another. A binary logistic regression model was used to determine the predictive variables of adolescent childbearing in Ghana. A
2.5. Ethical Consideration
The Ghana MICS 2017/2018 dataset was approved for use in this study by the MICS team from UNICEF. Since this analysis needed a secondary look at a dataset without revealing the identity of the participants and their houses, ethical approval was not necessary.
3. Results
3.1. Characteristics of Study Participants
The total number of adolescents isolated from the 2017 Ghana MICS dataset for this study analysis was 2974. The mean age of the study participants was
Table 1
Socioeconomic characteristics of study participants.
Frequency ( | Percentage | |
Age of woman | ||
15 | 698 | 23.5% |
16 | 533 | 17.9% |
17 | 615 | 20.7% |
18 | 583 | 19.6% |
19 | 545 | 18.3% |
Ever attended school | ||
Yes | 2865 | 96.3% |
No | 109 | 3.7% |
Educational achievement ( | ||
Primary | 515 | 18.0% |
JHS/JSS | 1637 | 57.1% |
Secondary/tech/voc/comm | 694 | 24.2% |
Higher | 19 | 0.7% |
Region | ||
Western | 265 | 8.9% |
Central | 297 | 10.0% |
Greater Accra | 302 | 10.2% |
Volta | 268 | 9.0% |
Eastern | 301 | 10.1% |
Ashanti | 404 | 13.6% |
Brong Ahafo | 264 | 8.9% |
Northern | 314 | 10.6% |
Upper East | 262 | 8.8% |
Upper West | 297 | 10.0% |
Area | ||
Urban | 1372 | 46.1% |
Rural | 1602 | 53.9% |
Marital status | ||
Married | 98 | 3.3% |
Cohabitation | 131 | 4.4% |
Not in union | 2745 | 92.3% |
Ethnicity | ||
Akan | 1114 | 37.5% |
GA/Dangme | 225 | 7.6% |
Ewe | 347 | 11.7% |
Guan | 105 | 3.5% |
Gruma | 149 | 5.0% |
Mole Dagbani | 642 | 21.6% |
Grusi | 143 | 4.8% |
Mande | 23 | 0.8% |
Other | 226 | 7.6% |
Health insurance | ||
With insurance | 1609 | 54.1% |
Without insurance | 1365 | 45.9% |
Functional difficulties (age 18–49 years) ( | ||
Has functional difficulty | 58 | 5.1% |
Has no functional difficulty | 1070 | 94.9% |
Wealth index quintile | ||
Poorest | 800 | 26.9% |
Second | 522 | 17.6% |
Middle | 595 | 20.0% |
Fourth | 530 | 17.8% |
Richest | 527 | 17.7% |
Note: Data source: GMICS 2017/2018.
3.2. Respondents’ Exposure to the Internet and Media
There was zero frequency of newspaper or magazine reading among the majority (84.3%) of the participants. Most (42.5%) of the participants were not listening to the radio. Only 32.4% of the participants were not at all exposed to watching TV. The majority (76.1%) of the participants never used a computer or tablet before. And Internet ever use was only 14.8% (
Table 2
Respondents exposure to Internet and media.
Frequency | Percentage | |
Frequency of reading newspaper or magazine | ||
Not at all | 2508 | 84.3% |
Less than once a week | 244 | 8.2% |
At least once a week | 190 | 6.4% |
Almost every day | 32 | 1.1% |
Frequency of listening to the radio | ||
Not at all | 1264 | 42.5% |
Less than once a week | 501 | 16.8% |
At least once a week | 636 | 21.4% |
Almost every day | 573 | 19.3% |
Frequency of watching TV | ||
Not at all | 963 | 32.4% |
Less than once a week | 337 | 11.3% |
At least once a week | 628 | 21.1% |
Almost every day | 1046 | 35.2% |
Ever used a computer or a tablet | ||
Yes | 711 | 23.9% |
No | 2263 | 76.1% |
Ever used Internet ( | ||
Yes | 422 | 14.8% |
No | 2427 | 85.2% |
Note: Data source: GMICS 2017/2018.
3.3. Socioeconomic Predictors of Adolescent Childbearing
The prevalence of adolescent childbearing according to this study analysis was 12.3%. The regions with the highest prevalence of adolescent childbearing were 17.6% and 17.2% for the Eastern Region and Volta Region, respectively. And the lowest prevalence was recorded among adolescents in the Northern Region (Table 3). The first chi-square analysis was done to identify factors associated with the dependent variable (adolescent childbearing). At this stage, all the studied socioeconomic characteristics except functional difficulties had a significant association with adolescent childbearing with chi-square analysis (Table 4). Also, exposure to the Internet and media factors such as frequency of newspaper or magazine reading and computer and Internet use experience was significantly associated with adolescent childbearing (Table 5).
Table 3
Prevalence of adolescent childbearing in Ghana.
Ever given birth | Total | Test statistics | |||
No | Yes | ||||
Region | |||||
Western | 231 | 34 | 265 | 31.175 | |
87.2% | 12.8% | 100.0% | df | 9 | |
Central | 253 | 44 | 297 | Sig. | < 0.001 |
85.2% | 14.8% | 100.0% | |||
Greater Accra | 276 | 26 | 302 | ||
91.4% | 8.6% | 100.0% | |||
Volta | 222 | 46 | 268 | ||
82.8% | 17.2% | 100.0% | |||
Eastern | 248 | 53 | 301 | ||
82.4% | 17.6% | 100.0% | |||
Ashanti | 352 | 52 | 404 | ||
87.1% | 12.9% | 100.0% | |||
Brong Ahafo | 228 | 36 | 264 | ||
86.4% | 13.6% | 100.0% | |||
Northern | 290 | 24 | 314 | ||
92.4% | 7.6% | 100.0% | |||
Upper East | 240 | 22 | 262 | ||
91.6% | 8.4% | 100.0% | |||
Upper West | 267 | 30 | 297 | ||
89.9% | 10.1% | 100.0% | |||
Total | 2607 | 367 | 2974 | ||
87.7% | 12.3% | 100.0% |
Note: Data source: GMICS 2017/2018.
Table 4
The association between respondents’ socioeconomic characteristics and birth history.
Ever given birth | Test statistics | |||||
No | Yes | |||||
Age of woman | ||||||
15 | 689 | 98.7% | 9 | 1.3% | 236.270 | |
16 | 502 | 94.2% | 31 | 5.8% | df | 4 |
17 | 550 | 89.4% | 65 | 10.6% | Sig. | 0.000 |
18 | 466 | 79.9% | 117 | 20.1% | ||
19 | 400 | 73.4% | 145 | 26.6% | ||
Ever attended school | ||||||
Yes | 2526 | 88.2% | 339 | 11.8% | 18.635 | |
No | 81 | 74.3% | 28 | 25.7% | df | 1 |
Sig. | 0.000 | |||||
Educational achievement | ||||||
Primary | 412 | 80.0% | 103 | 20.0% | 77.130 | |
JHS/JSS | 1429 | 87.3% | 208 | 12.7% | df | 3 |
Secondary/tech/voc/comm | 666 | 96.0% | 28 | 4.0% | Sig. | 0.000 |
Higher | 19 | 100.0% | 0 | 0.0% | ||
Region | ||||||
Western | 231 | 87.2% | 34 | 12.8% | 31.175 | |
Central | 253 | 85.2% | 44 | 14.8% | df | 9 |
Greater Accra | 276 | 91.4% | 26 | 8.6% | Sig. | 0.000 |
Volta | 222 | 82.8% | 46 | 17.2% | ||
Eastern | 248 | 82.4% | 53 | 17.6% | ||
Ashanti | 352 | 87.1% | 52 | 12.9% | ||
Brong Ahafo | 228 | 86.4% | 36 | 13.6% | ||
Northern | 290 | 92.4% | 24 | 7.6% | ||
Upper east | 240 | 91.6% | 22 | 8.4% | ||
Upper west | 267 | 89.9% | 30 | 10.1% | ||
Area | ||||||
Urban | 1254 | 91.4% | 118 | 8.6% | 32.929 | |
Rural | 1353 | 84.5% | 249 | 15.5% | df | 1 |
Sig. | 0.000 | |||||
Marital status | ||||||
Yes, currently married | 32 | 32.7% | 66 | 67.3% | 702.557 | |
Yes, living with a partner | 42 | 32.1% | 89 | 67.9% | df | 2 |
No, not in union | 2533 | 92.3% | 212 | 7.7% | Sig. | 0.000 |
Ethnicity | ||||||
Akan | 958 | 86.0% | 156 | 14.0% | 24.829 | |
GA/Damgme | 187 | 83.1% | 38 | 16.9% | df | 8 |
Ewe | 293 | 84.4% | 54 | 15.6% | Sig. | 0.002 |
Guan | 98 | 93.3% | 7 | 6.7% | ||
Gruma | 136 | 91.3% | 13 | 8.7% | ||
Mole Dagbani | 579 | 90.2% | 63 | 9.8% | ||
Grusi | 128 | 89.5% | 15 | 10.5% | ||
Mande | 23 | 100.0% | 0 | 0.0% | ||
Other | 205 | 90.7% | 21 | 9.3% | ||
Health insurance | ||||||
With insurance | 1388 | 86.3% | 221 | 13.7% | 6.306 | |
Without insurance | 1219 | 89.3% | 146 | 10.7% | df | 1 |
Sig. | 0.012 | |||||
Functional difficulties (age 18–49 years) | ||||||
Has functional difficulty | 43 | 74.1% | 15 | 25.9% | 0.238 | |
Has no functional difficulty | 823 | 76.9% | 247 | 23.1% | df | 1 |
Sig. | 0.626 | |||||
Wealth index quintile | ||||||
Poorest | 680 | 85.0% | 120 | 15.0% | 50.060 | |
Second | 435 | 83.3% | 87 | 16.7% | df | 4 |
Middle | 512 | 86.1% | 83 | 13.9% | Sig. | 0.000 |
Fourth | 475 | 89.6% | 55 | 10.4% | ||
Richest | 505 | 95.8% | 22 | 4.2% |
Note: Data source: GMICS 2017/2018.
Abbreviations:
Table 5
The association between respondents’ exposure to Internet and media and birth history.
Ever given birth | Test statistics | |||||
No | Yes | |||||
Frequency of reading newspaper or magazine | ||||||
Not at all | 2170 | 86.5% | 338 | 13.5% | 20.492 | |
Less than once a week | 229 | 93.9% | 15 | 6.1% | df | 3 |
At least once a week | 176 | 92.6% | 14 | 7.4% | Sig. | 0.000 |
Almost every day | 32 | 100.0% | 0 | 0.0% | ||
Frequency of listening to the radio | ||||||
Not at all | 1095 | 86.6% | 169 | 13.4% | 7.505 | |
Less than once a week | 444 | 88.6% | 57 | 11.4% | df | 3 |
At least once a week | 575 | 90.4% | 61 | 9.6% | Sig. | 0.057 |
Almost every day | 493 | 86.0% | 80 | 14.0% | ||
Frequency of watching TV | ||||||
Not at all | 835 | 86.7% | 128 | 13.3% | 7.302 | |
Less than once a week | 306 | 90.8% | 31 | 9.2% | df | 3 |
At least once a week | 562 | 89.5% | 66 | 10.5% | Sig. | 0.063 |
Almost every day | 904 | 86.4% | 142 | 13.6% | ||
Ever used a computer or a tablet | ||||||
Yes | 665 | 93.5% | 46 | 6.5% | 29.768 | |
No | 1942 | 85.8% | 321 | 14.2% | df | 1 |
Sig. | 0.000 | |||||
Ever used Internet | ||||||
Yes | 394 | 93.4% | 28 | 6.6% | 16.766 | |
No | 2091 | 86.2% | 336 | 13.8% | df | 1 |
Sig. | 0.000 |
Note: Data source: GMICS 2017/2018.
Abbreviations:
The predictors of adolescent childbearing were identified using a binary logistics regression model. This was done using the variables’ significant association at the chi-square analysis stage. The age of the adolescent predicted childbearing; as their ages increased from 15 to 19 years, the likelihood of childbearing increased (
Table 6
Binary logistics regression for predictors of adolescent childbearing.
AOR | 95% C.I. for AOR | ||||
Lower | Upper | ||||
Age of woman | |||||
15 | |||||
16 | 1.569 | 4.802 | 2.211 | 10.427 | |
17 | 2.241 | 9.403 | 4.525 | 19.540 | |
18 | 2.985 | 19.779 | 9.591 | 40.786 | |
19 | 3.393 | 29.762 | 14.336 | 61.788 | |
Educational achievement | |||||
Primary | |||||
JHS/JSS | −0.889 | 0.411 | 0.287 | 0.588 | |
Secondary/TECH/VOC | −2.350 | 0.095 | 0.053 | 0.171 | |
Higher | −20.453 | 0.999 | 0.000 | 0.000 | |
Region | |||||
Western | 0.010 | ||||
Central | 0.068 | 0.828 | 1.071 | 0.578 | 1.984 |
Greater Accra | 0.357 | 0.358 | 1.429 | 0.667 | 3.063 |
Volta | 0.838 | 0.039 | 2.311 | 1.043 | 5.120 |
Eastern | 0.352 | 0.280 | 1.422 | 0.751 | 2.692 |
Ashanti | 0.589 | 0.054 | 1.802 | 0.989 | 3.281 |
Brong Ahafo | −0.055 | 0.871 | 0.947 | 0.490 | 1.831 |
Northern | −0.813 | 0.077 | 0.444 | 0.180 | 1.092 |
Upper East | −0.857 | 0.078 | 0.424 | 0.164 | 1.100 |
Upper West | −0.548 | 0.224 | 0.578 | 0.239 | 1.399 |
Area | |||||
Urban | |||||
Rural | 0.201 | 0.263 | 1.222 | 0.860 | 1.737 |
Marital status | |||||
Married | |||||
Cohabitation | −0.395 | 0.300 | 0.674 | 0.319 | 1.421 |
Not in union | −2.569 | 0.077 | 0.041 | 0.142 | |
Ethnicity | |||||
Akan | 0.002 | ||||
GA/Damgme | −0.033 | 0.910 | 0.967 | 0.544 | 1.720 |
Ewe | −0.774 | 0.015 | 0.461 | 0.247 | 0.859 |
Guan | −1.525 | 0.003 | 0.218 | 0.080 | 0.595 |
Gruma | −0.883 | 0.059 | 0.414 | 0.165 | 1.035 |
Mole Dagbani | −0.760 | 0.019 | 0.467 | 0.247 | 0.883 |
Grusi | −0.979 | 0.037 | 0.376 | 0.150 | 0.944 |
Mande | −20.506 | 0.998 | 0.000 | 0.000 | |
Other | −1.356 | 0.258 | 0.127 | 0.524 | |
Health insurance | |||||
With insurance | |||||
Without insurance | −0.608 | 0.545 | 0.404 | 0.735 | |
Wealth index quintile | |||||
Poorest | 0.014 | ||||
Second | −0.177 | 0.422 | 0.838 | 0.544 | 1.290 |
Middle | −0.262 | 0.260 | 0.769 | 0.488 | 1.214 |
Fourth | −0.654 | 0.017 | 0.520 | 0.303 | 0.891 |
Richest | −1.071 | 0.001 | 0.343 | 0.178 | 0.660 |
Frequency of reading newspaper or magazine | |||||
Not at all | 0.795 | ||||
Less than once a week | 0.180 | 0.579 | 1.198 | 0.633 | 2.266 |
At least once a week | 0.311 | 0.362 | 1.365 | 0.700 | 2.660 |
Almost every day | −17.604 | 0.998 | 0.000 | 0.000 | |
Ever used a computer or a tablet | |||||
Yes | |||||
No | 0.210 | 0.338 | 1.234 | 0.803 | 1.898 |
Ever used Internet | |||||
Yes | |||||
No | 0.147 | 0.593 | 1.158 | 0.676 | 1.983 |
Note: Data source: GMICS 2017/2018.
4. Discussion
Regionally, the rate of adolescent births is highest in Africa (99 live births per 1000), while it is lowest in the WHO Western Pacific Region (14 live births per 1000) [1]. Adolescent pregnancy rates are very high. Thirty percent (30%) of all births recorded in Ghana in 2014 were by adolescents, and 14% of teenagers between the ages of 15 and 19 had started having children [18]. In this current study, the prevalence of adolescent childbearing according to this study analysis was 12.3%. Trends in the proportion of teenagers (15–19 years) who had begun childbearing decreased from 22% in 1993 to 13% in 2008 and then increased to 14% in 2014 [19]. In this study, prevalence of adolescent childbearing is even lower as compared to the global prevalence of 14% from the UNICEF report [6]. However, this is still very significant given their vulnerability to health consequences of pregnancy and child delivery [6].
Many different factors can affect adolescent childbearing, and they can differ from one country or location to another [4]. In this present study, all the studied socioeconomic characteristics except functional difficulties had a significant association with adolescent childbearing with chi-square analysis. Also, exposure to the Internet and media factors such as frequency of newspaper or magazine reading and computer and Internet use experience was significantly associated with adolescent childbearing. However, the independent predictive factors identified were increasing age, decreasing educational level, regional originality, ethnic originality of the study participants, and low economic status.
The age of the adolescent predicted childbearing; as their ages increased from 15 to 19 years, the likelihood of childbearing increased. Maybe this is so because the increasing age of the adolescent is associated with sexual activeness and fertility [17].
Also, increasing educational status was a protective factor against adolescent childbearing in this study. Those with JHS/JSS educational levels were 59% less likely to engage in adolescent childbearing as compared to those with primary educational levels. Again, with education, those with senior secondary education were 90% less likely to engage in adolescent childbearing when compared to those with primary educational levels. This is in line with an earlier study in Sunyani Municipality of Ghana where being in school predicted the risk of adolescent pregnancy as compared to those not in school [7]. Also, according to Nguyen, Chengshi, and Farber, girls in households with higher formal education have a lower adolescent pregnancy rate [8]. Again, according to research done in West and Central Africa, a girl’s educational level and geographic location have a strong positive correlation with adolescent pregnancy [9]. This may due to the fact that higher educational attainment is associated with higher knowledge on reproductive services.
Location is a significant demographic factor that makes difference in our health issues. Adolescent pregnancy can be influenced positively or negatively by the community in which one lives. A constructive conversation about sex is prohibited and seen as evil because so many traditional houses in Ghana are highly conservative [10]. With this present study, in terms of regional prediction, those from the Volta Region were 131% more likely to engage in adolescent childbearing when compared to those from the Western Region. Meanwhile, this did not transfer to the ethnicity of the participants. The dominant tribe in Volta Region is Ewe, but Ewes were 54% less likely to engage in adolescent childbearing as compared to those from the Akan ethnic group. The possible explanation is that the Volta Region is a multiethnic and multilingual, including groups such as the Ewe, the Guan, and the Akan people.
More so, the ethnicity of the adolescent can influence them getting pregnant. In the United State, both non-Hispanic Black teens’ (25.8%) and Hispanic teens’ (25.3%) birth rates in 2019 were more than twice as high as non-Hispanic White teens’ rate (11.4%). The greatest birth rate (29.2%) among all racial/ethnic groups was among American Indian/Alaska Native teenagers [20]. Also, in this study, ethnic originality of the study participants had a significant association with adolescent childbearing. Those from the Ewe ethnic group were 54% less likely to engage in adolescent childbearing as compared to those from the Akan ethnic group. Also, those of the Guan ethnic group were 88% less likely to engage in adolescent childbearing as compared to those from Akan ethnic group. Again, adolescents from the Mole Dagbani tribe were 53% less likely to bear children as compared to those from the Akan tribe. More so, those with Grusi ethnic backgrounds were 96% less likely to engage in adolescent childbearing as compared to those from the Akan tribe. The other tribes not listed in this study were 76% less likely to engage in adolescent childbearing as compared to those Akan ethnic groups. However, this finding negates the finding of an earlier study that was done in Sunyani Municipality Ghana; their study indicated no significant association between ethnicity and adolescent pregnancy [7]. This may be due to the fact that their study was not national study and has to do municipality.
In addition to the factors identified in this study, the better economic status of adolescent girls influences their childbearing; those from the fourth wealth index quintile were 48% less likely to engage in adolescent childbearing as compared to those from the poorest quintile. Also, those from the richest quintile were 66% less likely to engage in adolescent childbearing as compared to that from the poorest quintile. This supports a previous study in Sunyani Municipality of Ghana that found that adolescents with lower socioeconomic status had a 4.1 times higher chance of adolescent pregnancy than those with better socioeconomic status [7]. Young girls from low-income families are more likely to have children as teenagers, according to research by Moore, Jones, and Meador. The authors concluded that an adolescent girl’s living circumstances are a strong indicator of her likelihood of getting pregnant at an earlier stage of development [21]. Also, in A.R. Alhassan, Abdulai, and M.A. Alhassan’s study in Ghana, lower wealth status was predictor of earlier sexual debut among women [22]. This may be due to the fact that adolescents from low economic background are more likely to exchange sex for favors.
Finally, according to empirical research, teen marriage is a significant predictor of early sexual intercourse and subsequent teen pregnancy [23]. In rural sub-Saharan Africa, where poverty is becoming more common, girls have very few options for their daily survival except from marriage, which can lead to relatively quick marriage transactions with both families. Teenagers in sub-Saharan African traditional cultures have a poor social status, which is portrayed as an economic burden because it prevents them from being employable wage workers [23]. Also, with this current study, those not in unions were 93% less likely to engage in adolescent childbearing when compared to those in a married union. It is obvious in Africa that after marriage, childbearing is what is expected immediately and this explains this finding. And also, those not in marriage relationship were more likely to be involved in abortion practice as it was reported in a Ghana study by Alhassan and Adolipore [24].
Although this study offers essential information about adolescent childbearing, it is not without limitations. Survey results should be regarded cautiously since they cannot fully capture the participants’ diverse and nuanced points of view. However, because the data were nationally representative, the study’s conclusions can be applied to the whole Ghanaian adolescent population.
5. Conclusion
The prevalence of adolescent childbearing in this study was significant that needed the attention of all, given their vulnerability to health consequences of pregnancy and child delivery. Factors such as educational attainments, child marriage, ethnicity, region of originality, and economic status predicted adolescent childbearing. The needs, environment, and history of teenagers should be taken into account while developing interventions and policies. Programs to improve adolescent reproductive health must take into account multiple levels of elements, such as the individual, family, community, institutions, national, and international challenges that have an impact on such programs.
Funding
Funding for this study was accomplished by Ghana Organization for Maternal and Child Health (GOMaCH).
Acknowledgments
The Ghana Organization for Maternal and Child Health (GOMaCH), which sponsored the costs of carrying out this research, deserves special recognition. Also, we are thankful to the employees and management of Hasbi Research Consultancy (HasbiRC) for their numerous contributions to this study.
[1] WHO, "World Health Organization," 2019. https://www.who.int/health-topics/adolescent-health#tab=tab_1
[2] WHO, Adolescent pregnancy, 2020.
[3] N. Todd, A. Black, "Contraception for adolescents," Journal of Clinical Research in Pediatric Endocrinology, vol. 12 no. 1, pp. 28-40, DOI: 10.4274/jcrpe.galenos.2019.2019.S0003, 2020.
[4] S. Neal, Z. Matthews, M. Frost, H. Fogstad, A. V. Camacho, L. Laski, "Childbearing in adolescents aged 12–15 years in low resource countries: a neglected issue. New estimates from demographic and household surveys in 42 countries," Acta Obstetricia et Gynecologica Scandinavica, vol. 91 no. 9, pp. 1114-1118, DOI: 10.1111/j.1600-0412.2012.01467.x, 2012.
[5] UNESCO, Early and unintended pregnancy & the education sector; Evidence Review And Recommendations, 2017.
[6] UNICEF, Early childbearing can have severe consequences for adolescent girls, 2021.
[7] B. Y. A. Asare, D. Baafi, B. Dwumfour-Asare, A. R. Adam, "Factors associated with adolescent pregnancy in the Sunyani Municipality of Ghana," International Journal of Africa Nursing Sciences, vol. 10, pp. 87-91, DOI: 10.1016/j.ijans.2019.02.001, 2019.
[8] H. Nguyen, S. Chengshi, N. Farber, Prevalence and factors associated with teen pregnancy in Vietnam: results from two national surveys, 2016.
[9] N. S. Fenn, J. Edmeades, H. Lantos, O. Onovo, Child marriage, adolescent pregnancy and family formation in West and Central Africa: patterns, trends and drivers of change, 2015.
[10] J. K. Krugu, F. Mevissen, M. Münkel, R. Ruiter, "Beyond love: a qualitative analysis of factors associated with teenage pregnancy among young women with pregnancy experience in Bolgatanga, Ghana," Culture, Health & Sexuality, vol. 19 no. 3, pp. 293-307, DOI: 10.1080/13691058.2016.1216167, 2017.
[11] G. M. Kassa, A. O. Arowojolu, A. A. Odukogbe, A. W. Yalew, "Prevalence and determinants of adolescent pregnancy in Africa: a systematic review and meta-analysis," Reproductive Health, vol. 15 no. 1,DOI: 10.1186/s12978-018-0640-2, 2018.
[12] World Health Statistics, World Health Statistics 2019: monitoring health for the SDGs, sustainable development goals, 2019.
[13] W. W. Elizabeth, L. R. Leslie, C. N. Nathan, "Teenage pregnancy: the impact of maternal adolescent childbearing and older sister’s teenage pregnancy on a younger sister," BMC Pregnancy and Childbirth, vol. 16 no. 1,DOI: 10.1186/s12884-016-0911-2, 2016.
[14] Index mundi, "Index mundi," 2021. https://www.indexmundi.com/ghana/demographics_profile.html
[15] R. de Groot, M. Y. Kuunyem, T. Palermo, "Child marriage and associated outcomes in northern Ghana: a cross-sectional study," BMC Public Health, vol. 18 no. 1,DOI: 10.1186/s12889-018-5166-6, 2018.
[16] A. S. Yussif, A. Lassey, G. GYk, E. J. Kantelhardt, H. Kielstein, "The long-term effects of adolescent pregnancies in a community in Northern Ghana on subsequent pregnancies and births of the young mothers," Reproductive Health, vol. 14 no. 1,DOI: 10.1186/s12978-017-0443-x, 2017.
[17] United Nations, Department of Economic and Social Affairs, Population Division, Fertility among very young adolescents aged 10-14 years (ST/ESA/SER.A/448), 2020.
[18] Graphic online, Teenage pregnancy in Ghana: assessing situation and moving forward, 2016.
[19] GSS, GHS, and ICF International, Ghana demographic and health survey 2014, 2015.
[20] J. Martin, B. Hamilton, M. Osterman, A. Driscoll, "Births: final data for 2019," National Vital Statistics Reports, vol. 70 no. 2, 2021.
[21] B. Moore, E. Jones, J. Meador, "Teenage pregnancy rates: a multiple regression analysis of teen pregnancy rates and the correlates for Texas counties in the year 2010," Politics, Bureaucracy, and Justice, vol. 3 no. 2, 2010.
[22] A. R. Alhassan, K. Abdulai, M. A. Alhassan, "Early sexual debut among Ghanaian women: correlates and psychological effect," BioMed Research International, vol. 2021,DOI: 10.1155/2021/5838510, 2021.
[23] UNICEF, Child marriage: latest trends and future prospects, 2018.
[24] A. R. Alhassan, J. N. A. Adolipore, "Prevalence and predictive factors of induced abortion among women in Ghana: data analysis of maternal health survey, 2017," Journal of Clinical Research and Reports, vol. 9 no. 3,DOI: 10.31579/2690-1919/204, 2021.
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Abstract
According to studies done in Ghana, teens gave birth to nearly 30% of all babies recorded [16]. The topic is challenging to investigate methodically due to the cultural sensitivity of the subject, the fact that young girls under the age of 15 are often less sexually active than older adolescents or women, and the fact that they very seldom have children at such young ages [17]. [...]the current study looked into the prevalence and variables predicting adolescent childbearing in Ghana among the age group 15–19 years. 2. Ethical Consideration The Ghana MICS 2017/2018 dataset was approved for use in this study by the MICS team from UNICEF. Since this analysis needed a secondary look at a dataset without revealing the identity of the participants and their houses, ethical approval was not necessary. 3. Frequency ( n=2974) Percentage Age of woman 15 698 23.5% 16 533 17.9% 17 615 20.7% 18 583 19.6% 19 545 18.3% Ever attended school Yes 2865 96.3% No 109 3.7% Educational achievement ( n=2865) Primary 515 18.0% JHS/JSS 1637 57.1% Secondary/tech/voc/comm 694 24.2% Higher 19 0.7% Region Western 265 8.9% Central 297 10.0% Greater Accra 302 10.2% Volta 268 9.0% Eastern 301 10.1% Ashanti 404 13.6% Brong Ahafo 264 8.9% Northern 314 10.6% Upper East 262 8.8% Upper West 297 10.0% Area Urban 1372 46.1% Rural 1602 53.9% Marital status Married 98 3.3% Cohabitation 131 4.4% Not in union 2745 92.3% Ethnicity Akan 1114 37.5% GA/Dangme 225 7.6% Ewe 347 11.7% Guan 105 3.5% Gruma 149 5.0% Mole Dagbani 642 21.6% Grusi 143 4.8% Mande 23 0.8% Other 226 7.6% Health insurance With insurance 1609 54.1% Without insurance 1365 45.9% Functional difficulties (age 18–49 years) ( n=1128) Has functional difficulty 58 5.1% Has no functional difficulty 1070 94.9% Wealth index quintile Poorest 800 26.9% Second 522 17.6% Middle 595 20.0% Fourth 530 17.8% Richest 527 17.7% Note: Data source: GMICS 2017/2018.
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Details

1 Department of Surgery Tamale Teaching Hospital P.O. Box TL 16, Tamale Ghana; Hasbi Research Consultancy Tamale Ghana; Ghana Organization for Maternal and Child Health (GOMaCH) Tamale Ghana
2 Nursing and Midwifery Training College P.O. Box Gu 13, Gushegu Northern Region Ghana
3 Department of Clinical Nutrition and Dietetics School of Allied Health Sciences University of Cape Coast Cape Coast Ghana
4 Nurses and Midwives Training College Tamale Ghana
5 Department of Health Services Policy Planning Management and Economics School of Public Health University for Development Studies Tamale Ghana