Determinants of unintended pregnancies among nursing mothers in southwest Nigeria

Abstract

Background/aims
Studies have identified risks for unintended pregnancies, globally and in Nigeria, which include ineffective contraception, strong opposition to family planning by partners, number of living children and birth interval. These factors have contributed to the increasing rate of unintended pregnancy and the high rate of induced abortion, with associated consequences such as obstetric haemorrhage, infection and increased maternal morbidity and mortality. However, there is a paucity of information regarding the influence of culture and religion on pregnancy intentions. This study aimed to examine the influence of culture, religion, sociodemographic characteristics, and reproductive characteristics on nursing mothers' perception of unintended pregnancy in southwest Nigeria.

Methods
This study used a sequential explanatory mixed-method approach, with both quantitative and qualitative elements. A conceptual hierarchical model was used to analyse the influence of three levels of characteristics (sociodemographic, religious and cultural, reproductive) on unintended pregnancy in southwest Nigeria. A total of 400 nursing mothers attending either a postnatal, immunisation, infant welfare or under-five clinic were selected via multistage sampling from primary healthcare centres. Quantitative data were collected from these participants using a semi-structured questionnaire, administered by a researcher. These data were analysed using both bivariate and multivariate analysis. First, they were analysed with either a chi-squared or Fisher exact test, then subjected to a regression model analysis. Qualitative data were collected and subjected to content analysis via focus group discussions with a total of 32 purposively selected participants.

Results
Approximately 36.5% participants reported their index pregnancy as being unintended. With regression analysis, age (25–34 years: relative risk ratio=0.42, P=0.02; 35–44 years: relative risk ratio=0.21, P=0.003), parity (relative risk ratio=10.38, P<0.00), ethnicity (relative risk ratio=0.13, P=0.002) and religion (relative risk ratio=0.26, P=0.048) were found to be significant risk factors for unintended pregnancy.

Conclusions
Age, parity, ethnicity and religion were the main determinants of unintended pregnancies. Intervention programmes should therefore be aware of these variables and address myths and misconceptions about pregnancy intentions.

Full text

1 African Journal of Midwifery and Women’s Health | 2020 | https://doi.org/10.12968/ajmw.2019.0010
RESEARCH
© 2020 MA Healthcare Ltd
Introduction
Globally, unintended pregnancies pose a major public health challenge to women of
reproductive age, especially in low income countries (Bishwajit et al, 2017). It has been
estimated that the global prevalence of unintended pregnancy between 2010 and 2014
was 44% (Bearak et al, 2018; Guttmacher Institute, 2018), accounting for approximately
62 unintended pregnancies per 1000 women of reproductive age. Approximately 56% of these
unintended pregnancies result in induced abortion (Guttmacher Institute, 2018), contributing
to the high incidence of induced abortion, which has associated consequences such as
obstetric haemorrhage, infection, and increased maternal morbidity and mortality, especially
in developing countries. In Africa, approximately 14 million unintended pregnancies occur
How to cite this article:
Afolabi AO, Olaogun A, Afolabi
KA, Afolabi EK. Determinants
of unintended pregnancy
among nursing mothers in
southwest Nigeria. African
Journal of Midwifery and
Women’s Health. 2020.
https://doi.org/10.12968/
ajmw.2019.0010
Determinants of unintended pregnancies among
nursing mothers in southwest Nigeria
Adebukunola Olajumoke
Afolabi1
Adenike Ayobola Olaogun2
Kolade Afolayan Afolabi3
Esther Kikelomo Afolabi4
Author details can be found
at the end of this article
Correspondence to:
Adebukunola Olajumoke
Afolabi; bukieafolabi@
yahoo.com
Abstract
Background/Aims Studies have identied risks for unintended pregnancies, globally
and in Nigeria, which include ineffective contraception, strong opposition to family
planning by partners, number of living children and birth interval. These factors have
contributed to the increasing rate of unintended pregnancy and the high rate of induced
abortion, with associated consequences such as obstetric haemorrhage, infection and
increased maternal morbidity and mortality. However, there is a paucity of information
regarding the inuence of culture and religion on pregnancy intentions. This study aimed
to examine the inuence of culture, religion, sociodemographic characteristics, and
reproductive characteristics on nursing mothers’ perception of unintended pregnancy in
southwest Nigeria.
Methods This study used a sequential explanatory mixed-method approach, with both
quantitative and qualitative elements. A conceptual hierarchical model was used to
analyse the inuence of three levels of characteristics (sociodemographic, religious and
cultural, reproductive) on unintended pregnancy in southwest Nigeria. A total of 400
nursing mothers attending either a postnatal, immunisation, infant welfare or under-ve
clinic were selected via multistage sampling from primary healthcare centres. Quantitative
data were collected from these participants using a semi-structured questionnaire,
administered by a researcher. These data were analysed using both bivariate and
multivariate analysis. First, they were analysed with either a chi-squared or Fisher exact
test, then subjected to a regression model analysis. Qualitative data were collected and
subjected to content analysis via focus group discussions with a total of 32 purposively
selected participants.
Results Approximately 36.5% participants reported their index pregnancy as being
unintended. With regression analysis, age (25–34 years: relative risk ratio=0.42, P=0.02;
35–44 years: relative risk ratio=0.21, P=0.003), parity (relative risk ratio=10.38, P<0.00),
ethnicity (relative risk ratio=0.13, P=0.002) and religion (relative risk ratio=0.26, P=0.048)
were found to be signicant risk factors for unintended pregnancy.
Conclusions Age, parity, ethnicity and religion were the main determinants of unintended
pregnancies. Intervention programmes should therefore be aware of these variables and
address myths and misconceptions about pregnancy intentions.
Key words: Determinants; Nursing mothers; Unintended pregnancy;
Sociodemographic factors; Culture and religion
Submitted: 17 April 2019; accepted following double-blind peer review: 22 August 2019

2 African Journal of Midwifery and Women’s Health | 2020 | https://doi.org/10.12968/ajmw.2019.0010
RESEARCH
© 2020 MA Healthcare Ltd
annually; a multi-national evaluation of demographic and health survey data from African
countries found a prevalence rate of 29% for unintended pregnancy (Ameyaw et al, 2019).
Approximately 70.5% of women with a previous record of induced abortion in sub-Saharan
Africa are less than 30 years old (Lamina, 2015).
In Nigeria, the proportion of pregnancies that are unintended remains high, with an
estimated rate of 59 per 1000, out of which approximately 56% end in an induced abortion,
32% end in an unplanned birth and 12% end in a miscarriage (Bankole et al, 2015). A
study carried out by Lamina (2015) in southwest Nigeria of women of reproductive age
revealed that the prevalence of unintended pregnancy was 35.9%. Nigeria is considered to
be a nation with diverse cultural and religious views, where the use of contraceptives and
other reproductive choices are inuenced by the relationship between ethnicity, religion
and other demographic characteristics (Obasohan, 2015).
Several studies have attempted to investigate the predisposing factors to the high incidence
of unintended pregnancy and their consequences in some African regions; a study in Nairobi,
Kenya by Fotso et al (2014) observed that unintended pregnancy was a consequence of
strong opposition to family planning by their partners. Another study in Central Kenya
revealed that unintended pregnancy was statistically associated with maternal age, wealth
index, marital status, number of living children and birth interval (Kaaria, 2012). Similar
ndings were observed in a Nigerian study by Izugbara (2014), who observed that age
and sex of the household head, family size and household wealth index were signicantly
related to unintended pregnancy.
However, there is a paucity of information on the inuence of culture and religion
on pregnancy intentions, the incidence of unintended pregnancy and the acceptance of
pregnancy. This gap in previous studies on unintended pregnancy presents a challenge for
nurses and midwives, as information in this area would inform proper planning strategies
to assist women with unintended pregnancies and other reproductive health choices.
This study aims to identify determinants of unintended pregnancies among nursing
mothers and to evaluate the inuence of culture and religion on pregnancy intentions in
two selected local government areas in Osun State, southwest Nigeria, using a conceptual
hierarchical model adapted from Hall et al (2016). Nursing mothers were selected as a high
incidence of unintended pregnancy has been observed by the authors in this population
in the study area.
Methods
Application of a conceptual hierarchical model to determinants
of unintended pregnancies
The conceptual hierarchical model was introduced by Victora et al (1997) as a tool in
the analysis of determinants of an outcome in epidemiological studies. Hall et al (2016)
adapted this model and further grouped dependent variables into hierarchical levels,
beginning with the most distal and working down through increasingly more proximate
determinants. Variables in level one are sociodemographic factors, including age, education,
and occupation. Variables in level two include religious and cultural factors. Variables in
level three are reproductive characteristics and include age at rst marriage, parity (number
of children), menstrual history and contraceptive use. Variables higher in the hierarchy
inuence those below them, either directly or indirectly. The complete model, as adapted
in this study, explains the direct and indirect interaction between and within variables in
each level and unintended pregnancy, as shown in the theoretical framework (Figure 1).
Study design
The study adopted a sequential explanatory mixed-method design, using both quantitative
and qualitative methods.
Study setting
The study was conducted in primary healthcare centres in the Ife Central and Irewole Local
Government Areas of Osun State. The local government areas were selected through a
multistage sampling technique.

3 African Journal of Midwifery and Women’s Health | 2020 | https://doi.org/10.12968/ajmw.2019.0010
RESEARCH
© 2020 MA Healthcare Ltd
Eligibility criteria
The eligibility criteria for this study were nursing mothers who were between 15–49
years old, with a child less than 2 years old, who were attending either a postnatal,
immunisation, infant welfare or under-ve clinic. Nursing mothers with a child older than
2 years were excluded to reduce the possibility of recall bias in respect to the pregnancy
history concerning the index child.
Sample size determination and sampling
The sample size was calculated using the Cochrane formula for sample size estimation
(Cochrane, 1977): n=Z2pq/d2, where n is the desired sample size, Z is the standard normal
deviate at 95% condence level (at 95% condence level, Z=1.96), p=35.9%, being the
prevalence of unplanned pregnancy from Lamina (2015), q=1–p, q=1–0.395=0.605,
d is the degree of accuracy, taken as 0.05. This resulted in n=367. Assuming a 10%
non-response rate, an estimated sample of 404 nursing mothers was obtained and selected
using a multistage sampling technique. In stage one, two senatorial districts (Osun East and
Osun West) out of the three districts in Osun State were selected by simple random sampling.
In stage two, purposive selection was used to select the Ife Central local government area from
the Osun East senatorial district and the Irewole local government area from the Osun West
senatorial district. The two Local Government Areas were purposively selected because of
their close geographical proximity to each other. In stage three, two primary healthcare centres
were purposively selected from rural and urban communities in each of the selected local
government areas, to give a total of four primary healthcare centres. The primary healthcare
centres selected were those with the highest number of attendees. The total estimated sample
size was equally distributed among the selected primary healthcare centres. In stage four, a
list of nursing mothers in the daily attendance register attending each clinic in the selected
primary centres (postnatal, immunisation, infant welfare and under-ve clinics) were obtained.
Mothers were selected by simple random sampling (balloting). Daily selection continued
at each clinic until the required number of nursing mothers for each centre was obtained.
Data collection
Quantitative data were collected using a semi-structured interviewer-administered questionnaire.
The questionnaire consisted of three sections, labelled A–C. Section A contained 12 questions
on the sociodemographic characteristics of the participants. Section B was adapted from the
Nigeria Demographic and Health Survey 2013 questionnaire (National Population Commission
and ICF International, 2014) and asked for information on the participants’ reproductive
characteristics. Section C consisted of a modied London Measure of Unplanned Pregnancy
(Hall et al, 2017), which evaluated the proportion of nursing mothers whose most recent
Level 2 religious
and
cultural factors
Direct effect
of distal level
on unintended
pregnancy
Direct effect
of level 2
on unintended
pregnancy
Theoretical Framework
b
a c
Level 1
sociodemographic
characteristics of
nursing mothers
d
eLevel 3
reproductive
characteristics
fg
Unintended
pregnancies
Fig ure 1. Conceptual hierarchical model for analysing determinants of outcomes in
epidemiological studies. Adapted from Hall et al (2016).

4 African Journal of Midwifery and Women’s Health | 2020 | https://doi.org/10.12968/ajmw.2019.0010
RESEARCH
© 2020 MA Healthcare Ltd
child was the result of an unintended pregnancy. For the purpose of this study, unintended
pregnancy was dened as a pregnancy that occurs without the intention of the couple or one
that was not desired at the time of conception. The questionnaire was translated from English
to Yoruba before distribution by a language editor.
Qualitative data were collected using a focus group discussion guide with seven
open-ended questions (Table 1). The guide was translated into the local language (Yoruba)
and recorded responses were transcribed into English by a language editor. Four focus
group discussion sessions were conducted, with eight participants in each session, giving
a total of 32 participants. Participants were selected based on age group (20–29 years and
30–39 years). The discussion explored participants’ perception of unintended pregnancy, and
the inuence of reproductive characteristics, culture and religion on unintended pregnancy. The
discussion was carried out in Yoruba and lasted approximately 60–90 minutes. Handwritten
notes were also taken during the discussion and were used to supplement the audio recordings.
Data analysis
Quantitative data was processed using the Statistical Package for Social Sciences version
20. Analysis was carried out at univariate, bivariate and multivariate levels.
Univariate analysis was conducted using the frequency and percentage distribution of
participants’ background characteristics. Pregnancy intentions among the nursing mothers
were determined and categorised as intended, unintended and ambivalent. The proportion
of nursing mothers with previous experience of unintended pregnancies was determined
using a modied London Measure of Unplanned Pregnancy (section C of the questionnaire)
with six items on women’s experience of unintended pregnancy. Each item of the measure
is scored 0, 1, or 2. These are summed to create an ordinal scale of 0–12, with each increase
in score reecting an increase in pregnancy intention. Nursing mothers with a total score of
0–3 were categorised as unplanned, 4–9 categorised as ambivalent and 10–12 categorised
Table 1. Interview guide for focus group discussions
Question
1 How would you describe unintended pregnancy?
2 In your own view and with circumstances around you, what causes
unintended pregnancy?
OR
What kind of people do think are exposed to unintended pregnancy?
3 In a situation you just discovered you are pregnant; would you consider the
pregnancy as unintended? (Probe: why do you say that?)
4 Does your religion afliation allow unintended pregnancy?
OR
How does your religion consider unintended pregnancy?
5 Does your cultural background allow unintended pregnancy?
OR
How does your cultural background consider unintended pregnancy?
6 How do the following reproductive characteristics inuence unintended pregnancy?
• Age at marriage
• Age at rst birth
• Number of children
• Others
7 What do you think a woman should do if she discovers that she has an unintended
pregnancy? (Probe: give a reason for your response)

5 African Journal of Midwifery and Women’s Health | 2020 | https://doi.org/10.12968/ajmw.2019.0010
RESEARCH
© 2020 MA Healthcare Ltd
as planned. Factors associated with unintended pregnancies among nursing mothers were
identied using a conceptual hierarchical model.
Bivariate analysis was conducted to examine the relationship between unintended pregnancy
and potential determinants at each of the three levels of the conceptual hierarchical model.
Specic sociodemographic characteristics were selected for analysis of association, based
on their previously documented association with unintended pregnancy (Kaaria, 2012;
Izugbara, 2014; Eliason et al, 2014; Lamina, 2015). The signicance of the relationship was
assessed using Chi-square and Fisher’s exact statistic and corresponding P values. Multinomial
regression analysis was conducted to assess the simultaneous effect of independent variables
on the dependent variable using relative risk ratio. P<0.05 was assumed to be signicant.
The qualitative study explored similarities and differences in views regarding nursing
mothers’ perception about unintended pregnancies. Responses were analysed using Nvivo
software and ndings presented thematically and by content analysis.
Ethical considerations
Ethical clearance (number: IPH/OAU/12/961) was obtained from the Ethics and Research
Committee of the Obafemi Awolowo University, Ile-Ife. Permissions were also obtained
from the authorities of the Local Government Areas where this study was conducted. Written
consent was obtained from each participant before data collection.
Results
Quantitative ndings
Figure 2 shows the overall proportion of participants that reported unintended pregnancies,
based on the results of Section C of the questionnaire, the London Measure of Unplanned
Pregnancy. The majority (52%) reported that they were ambivalent, 11.5% reported that their
index pregnancy was intended and 36.5% of participants reported unintended pregnancy.
Sociodemographic characteristics
Table 2 shows the sociodemographic characteristics of participants. Table 3 shows
the breakdown of women by sociodemographic characteristic and whether they had
experienced an unintended pregnancy. The majority (87%) of the women who were less
than 20 years old had an unintended pregnancy, much higher than the women who were
30–39 years old or 40–49 years old (28.2% and 40.0%, respectively). For both monogamous
and polygamous relationships, a minority of women had experienced unintended pregnancy
36.5%
11.5%
52%
 Unintended
 Ambivalent
 Intended
Figure 2. Proportion of nursing mothers with unintended pregnancies.

6 African Journal of Midwifery and Women’s Health | 2020 | https://doi.org/10.12968/ajmw.2019.0010
RESEARCH
© 2020 MA Healthcare Ltd
Table 2. Participants’ sociodemographic characteristics
Sociodemographic characteristic Frequency, n=400 (%)
Age (years)
<20 16 (4.0)
20–29 204 (51.0)
30–39 170 (42.5)
40–49 10 (2.5)
Marital status
Married 400 (100.0)
Type of marriage
Monogamous 354 (88.5)
Polygamous 46 (11.5)
Ethnicity
Yoruba 344 (86.0)
Igbo 40 (10.0)
Hausa 10 (2.5)
Other (Urhobo, Igala) 6 (1.5)
Religion
Catholic Christian 34 (8.5)
Orthodox Christian 32 (8.0)
Pentecostal Christian 244 (61.0)
Islam 90 (22.5)
Highest level of education
No formal 10 (2.5)
Primary 14 (3.5)
Secondary 242 (60.5)
Tertiary 134 (33.5)
Occupation
Unemployed 14 (3.5)
Self-employed 294 (73.5)
Government employed 46 (11.5)
Private sector 46 (11.5)
Average income (naira)
<18 000 216 (56.0)
≥18 000 170 (44.0)
Place of residence
Urban 200 (50.0)
Rural 200 (50.0)

7 African Journal of Midwifery and Women’s Health | 2020 | https://doi.org/10.12968/ajmw.2019.0010
RESEARCH
© 2020 MA Healthcare Ltd
(37.3% and 30.4%, respectively). The majority of women with primary education
had experienced unintended pregnancy (57%), compared to those with secondary or
tertiary education (45% and 28%, respectively). A higher proportion of unemployed or
self-employed women experienced unintended pregnancy (42.9% and 41.5%, respectively)
Table 3. Bivariate analysis of the influence of sociodemographic characteristics on
unintended pregnancy
Sociodemographic
characteristic
Unintended
pregnancy Ambivalent
Intended
pregnancy Test
Age group (years)
Fisher’s
exact P value
<20 14 (87.0%) 2 (12.5%) 0 (0.0%)
29.28 0.03*
20–29 80 (39.2%) 102 (50.0%) 22 (10.8%)
30–39 48 (28.2%) 102 (60.0%) 20 (11.8%)
40–49 4 (40.0%) 2 (20.0%) 4 (40.0%)
Type of marriage Chi square df P value
Monogamous 132 (37.3%) 180 (50.8%) 42 (11.9%)
1.67 1 0.44
Polygamous 14 (30.4%) 28 (60.9%) 4 (8.7%)
Highest level of education Chi square df P value
Primary† 8 (57.1%) 14 (28.6%) 2 (14.3%)
48.44 2 0.04*Secondary 110 (45.5%) 118 (48.8%) 14 (5.8%)
Tertiary 28 (20.9%) 76 (56.7%) 30 (22.4%)
Occupation
Fisher ’s
exact P value
Unemployed 6 (42.9%) 8 (57.1%) 0 (0.0%)
21.36 0.001*
Self-employed 122 (41.5%) 140 (47.6%) 32 (10.9%)
Government employed 6 (13.0%) 30 (65.2%) 10 (21.7%)
Private sector 12 (26.1%) 30 (65.2%) 4 (8.7%)
Place of residence Chi square df P value
Urban 70 (35.0%) 110 (55.0%) 20 (10.0%) 1.72 1 0.42
Rural 76 (38.0%) 98 (52.0%) 26 (13.)%)
Ethnicity Chi square df P value
Yoruba 134 (39.0%) 172 (50.0%) 38 (11.0%) 13.98 2 0.03*
Ibo 8 (20.0%) 26 (65.0%) 6 (15.0%)
Other tribes (Hausas,
Urhobos, Igalas)
4 (25.0%) 10 (62.5%) 2 (12.5%)
Religion Chi square df P value
Catholic Christian 12 (35.3%) 16 (47.1%) 6 (17.6%) 2.54 3 0.86
Orthodox Christian 10 (31.2%) 18 (56.2%) 4 (12.5%)
Pentecostal Christian 88 (36.1%) 128 (52.5%) 28 (11.5%)
Islam 36 (40.0%) 46 (51.1%) 8 (8.9%)
* denotes signicance, † multiple zero responses in the ‘no formal education’ category produced indenite results and so these women were included in
the primary education category

8 African Journal of Midwifery and Women’s Health | 2020 | https://doi.org/10.12968/ajmw.2019.0010
RESEARCH
© 2020 MA Healthcare Ltd
compared to those who were employed by the government or in the private sector (13.0%
and 26.1%, respectively).
Table 3 also shows the results of bivariate analysis of the inuence of sociodemographic
characteristics on pregnancy intention. Age (P<0.05), education (P<0.05) and occupation
(P=0.001) were signicantly associated with unintended pregnancies.
The results of the regression analysis of the inuence of sociodemographic characteristics
are presented in
Table 4
. This analysis found that ethnicity (P=0.007, relative risk ratio=0.19)
and religion (Pentecostal Christian: P=0.014, relative risk ratio=0.023; Islam: P=0.015, relative
risk ratio 0.21) had a signicant inuence on unintended pregnancy. This analysis also showed
that non-government employment (P=0.03, relative risk ratio=2.38) and urban residence
(P=0.02, relative risk ratio=2.43) were signicantly associated with intended pregnancy.
Table 4. Regression analysis of sociodemographic characteristics of participants and
unintended pregnancy
Sociodemographic
characteristic
Unintended pregnancy Intended pregnancy
Relative
risk ratio P value
Confidence
interval
Relative
risk ratio P value
Confidence
interval
Age (years)
<25 (reference) 1 1
25–34 0.55 0.040 0.31–0.97 3.49 0.11 0.76–16.37
35–44 0.31 0.001* 0.15–0.63 3.41 0.14 0.68–17.17
Highest level of education achieved
Primary (reference) 1 1
Secondary 0.80 0.690 0.28–2.34 0.61 0.59 0.10–3.69
Tertiary 0.50 0.230 0.16–1.60 2.67 0.28 0.45–15.73
Occupation
Government employment
(reference)
1 1
Non-government employment 1.76 0.076 0.94–3.27 2.38 0.03* 1.05–5.37
Place of residence
Rural (reference) 1 1
Urban 0.90 0.670 0.55–1.47 2.43 0.02* 1.18–5.01
Ethnicity
Yoruba (reference) 1 1
Igbo 0.19 0.007* 0.06–0.63 0.59 0.46 0.15–2.38
Other tribes 0.50 0.300 0.14–1.86 0.92 0.92 0.15–5.69
Religion
Catholic Christian (reference) 1 1
Orthodox Christian 0.27 0.070 0.07–1.09 0.42 0.35 0.07–2.51
Pentecostal Christian 0.23 0.014* 0.07–0.74 0.37 0.19 0.09–1.64
Islam 0.21 0.015* 0.06–0.73 0.32 0.19 0.06–1.76
Constant 5.35 0.043 0 . 86 – 3 3 .11 0.49 0.043 0.002– 0.91
Model statistics: n=400, P value=0.000, R square=0.1004
* denotes signicance, base outcome is ambivalent

9 African Journal of Midwifery and Women’s Health | 2020 | https://doi.org/10.12968/ajmw.2019.0010
RESEARCH
© 2020 MA Healthcare Ltd
Reproductive characteristics
A summary of participants’ reproductive characteristics is shown in Table 5. Participants’
age at rst marriage ranged from 17–38 years, with a mean of 24±4 years. The majority
of participants had their rst sexual intercourse between ages 20–29 years old (70.5%), were
multiparous (66.5%) and were not using any contraception at the time of the study (62.0%).
Nearly half (48.5%) of participants had not yet resumed menses at the time of the study.
Table 6 shows that more than half (57.1%) of participants who had their rst marriage
when they were <20 years old experienced unintended pregnancies. Many (45.6%)
participants who experienced sexual intercourse for the rst time when they were
<20 years old reported unintended pregnancies, while only 33.3% of respondents who
experienced sexual intercourse for the rst time when they were between 20–29 years
old had unintended pregnancies. Less than half (43.3%) of respondents who were yet
to resume menstruation reported unintended pregnancies. Only 34.2% of respondents
who currently use contraception reported unintended pregnancies, in comparison to
37.9% of respondents who were not using any form of contraception.
Table 5. Participants’ reproductive characteristics
Reproductive characteristic Frequency, n=400 (%)
Age at first marriage (years)
<20 28 (7.0)
20–29 332 (83.0)
30–39 40 (10.0)
Age at rst sexual intercourse (years)
<20 114 (28.5)
20–29 282 (70.5)
30–39 4 (1.0)
Parity
Primipara 98 (24.5)
Multipara 266 (66.5)
Grand multipara 36 (9.0)
Length of menstrual cycle (days)*
<21 42 (10.5)
21–35 164 (41.0)
Not yet resumed menses 194 (48.5)
Current contraceptive method used
Condom 34 (8.5)
Intrauterine Contraceptive device 8 (9.5)
Hormonal pills 16 (4.0)
Hormonal injections 32 (8.0)
Implants 26 (6.5)
Calendar method 4 (1.0)
None 248 (62.0)
Traditional methods 2 (0.5)
*The length of menstruation within this cycle was 3-5 days

10 African Journal of Midwifery and Women’s Health | 2020 | https://doi.org/10.12968/ajmw.2019.0010
RESEARCH
© 2020 MA Healthcare Ltd
Table 6 also shows the result of bivariate analysis of the association between reproductive
characteristics and pregnancy intention. Age at rst marriage (P=0.007), parity (P=0.006),
length of menstrual cycle (P=0.03) and current use of contraception (P<0.05) were all
found to be signicantly associated with unintended pregnancy.
Table 7 shows the results of conducting regression analysis on the inuence of
participants’ reproductive characteristics on pregnancy intention. Age at rst marriage
(P<0.0001, relative risk ratio=0.34) and multiparity (P=0.007, relative risk ratio=3.47) had
a signicant inuence on unintended pregnancies. The relative risk ratio of respondents
developing unintended pregnancies whose age at rst marriage was between 25–34 years
old (0.34) was less than that for respondents whose age at rst marriage was <25 years old.
The relative risk ratio for grand multiparous respondents developing unintended pregnancies
(3.47) was higher than that for primiparous or multiparous mothers. The results also show
that being multiparous (P=0.01, relative risk ratio=4.88) and contraceptive use (P=0.02,
relative risk ratio=0.36) were signicantly associated with intended pregnancy.
Regression analysis: full model
The regression analysis in Table 8 shows the full model of the factors inuencing
unintended pregnancies among the respondents. This shows the simultaneous effects
Table 6. Bivariate analysis of the influence of participants’ reproductive
characteristics on unintended pregnancy
Reproductive
characteristic
Unintended
pregnancy Ambivalent
Intended
pregnancy Test
Age at first marriage (years)
Chi-
square df
P
value
<20 16 (57.1%) 10 (35.7%) 2 (7.1%)
14.23 4 0.007*20–29 124 (37.3%) 172 (51.8%) 36 (10.8%)
30–39 6 (15.0%) 26 (65.0%) 8 (20.0%)
Age at rst sexual intercourse (years)
Fisher
exact
P
value
<20 52 (45.6%) 56 (49.1%) 6 (5.3%)
12.04 0.0920–29 94 (33.3%) 148 (52.5%) 40 (14.2%)
30–39 0 (0.0%) 4 (100.0%) 0 (0.0%)
Parity
Chi-
square df
P
value
Primipara 40 (40.8%) 54 (55.1%) 4 (4.1%)
14.53 4 0.006*Multipara 86 (32.3%) 142 (53.4%) 38 (14.3%)
Grand multipara 20 (55.6%) 12 (33.3%) 4 (11.5%)
Length of menstrual cycle (days)
Chi-
square df
P
value
<21 16 (38.1%) 20 (47.6%) 6 (14.3%)
10.68 4 0.03*
21–35 46 (28.0%) 94 (57.3%) 24 (14.6%)
Not yet resumed
menses
84 (43.3%) 94 (48.5%) 16 (8.2%)
Current use of contraception
Chi-
square df
P
value
Yes 52 (34.2%) 84 (55.3%) 16 (10.5%)
43.90 14 0.04*
No 94 (37.9%) 124 (50.0%) 30 (12.1%)

11 African Journal of Midwifery and Women’s Health | 2020 | https://doi.org/10.12968/ajmw.2019.0010
RESEARCH
© 2020 MA Healthcare Ltd
of all variables in all three levels of the conceptual hierarchical model on pregnancy
intention. The analysis shows that age (25–34 years: P=0.02, relative risk ratio=0.42;
35–44 years: P=0.003, relative risk ratio=0.21), Igbo ethnicity (P=0.002, relative risk
ratio=0.13), Islam religion (P=0.048, relative risk ratio=0.26) and grand multiparity
(P<0.000, relative risk ratio=10.38) have a signicant inuence on unintended pregnancy.
Focus group discussions
The qualitative data analysis found ve themes under determinants of unintended
pregnancy, with a total of 13 sub-themes. These themes and sub-themes are
summarised in Table 9.
Perception of unintended pregnancy
Not many of the participants who were aged 20–29 years old described unintended pregnancy
as a pregnancy that occurs unexpectedly or as a pregnancy that the couple is unprepared
for, while the majority of participants aged 30–39 years old expressed this view. Similarly,
participants expressed views that unintended pregnancy is a pregnancy that occurs at
the wrong time, by mistake, or as a result of not using contraceptive methods or the
failure of their chosen method.
‘I think unintended pregnancies are pregnancies that occur when a woman is
not prepared’. (A 32-year-old from a rural primary healthcare centre)
‘In my own view, it is a kind of pregnancy that happen unexpectedly’. (A 33-year-
old from an urban primary healthcare centre)
‘(Unintended pregnancies are) pregnancies that occur when a woman is careless
due to non-use of contraceptive methods or when family planning fails’. (A
23-year-old from a rural primary healthcare centre)
Table 7. Regression analysis of influence of participants’ reproductive characteristics on
unintended pregnancy
Reproductive
characteristic
Unintended Intended
Relative risk ratio P value
Confidence
interval Relative risk ratio P value
Confidence
interval
Age at rst marriage (years)
<25 (reference) 1 1
25–34 0.34 <0.001* 0.21–0.56 0.74 0.40 0.38–1.48
Parity
Primipara
(reference)
1 1
Multipara 1.06 0.85 0.60–1.85 4.88 0.01* 1.59–15.01
Grand multipara 3.47 0.007* 1.41–8.53 4.38 0.07 0.88–21.60
Current use of contraception
No (reference) 1 1
Yes 0.96 0.87 0.57–1.61 0.36 0.02* 0.16–0.82
Constant 1.46 0.35 0.66– 3.26 0.08 0.002 0.12–0.34
Model statistics: n=400, P value=0.000, R square=0.0.826
* denotes signicance, base outcome is ambivalent

12 African Journal of Midwifery and Women’s Health | 2020 | https://doi.org/10.12968/ajmw.2019.0010
RESEARCH
© 2020 MA Healthcare Ltd
Table 8. Regression analysis of factors influencing unintended pregnancies: full model
Characteristic
Unintended Intended
Relative
risk ratio P value
Confidence
interval
Relative
risk ratio P value
Confidence
interval
Age (years)
<25 (reference) 1 1
25–34 0.42 0.020* 0.20–0.87 2.66 0.260 0.49–14.50
35–44 0.21 0.003* 0.08–0.59 2.67 0.320 0.38–18.42
Highest level of education
Primary (reference) 1 1
Secondary 1.19 0.760 0.38–3.74 0.81 0.120 0.12–5.44
Tertiary 0.96 0.950 0.27–3.37 5.52 0.780 0.78–38.86
Occupation
Government (reference) 1 1
Non-government 1.66 0.140 0.84–3.25 2.68 0.030* 1.08–6.62
Place of residence
Rural (reference) 1 1
Urban 0.96 0.920 0.44–2.09 2.82 0.210 0.56–14.24
Ethnicity
Yoruba (reference) 1 1
Igbo 0.13 0.002* 0.03–0.48 0.70 0.650 0.15–3.00
Other tribes 0.43 0.220 0.11–1.67 0.35 0.340 0.04–3.04
Religion
Catholic (reference) 1 1
Orthodox 0.33 0.170 0.07–1.56 0.59 0.620 0.07–4.68
Pentecostal 0.34 0.095 0.10–1.20 0.51 0.440 0.09–2.81
Islam 0.26 0.048* 0.07–0.99 0.51 0.490 0.07–3.60
Age at rst marriage (years)
17–24 (reference) 1 1
25–38 0.61 0.120 0.32–1.14 0.42 0.070 0.17–1.07
Parity
Primipara (reference) 1 1
Multipara 1.91 0.060 0.97–3.75 3.95 0.040* 1.10–14.15
Grand multipara 10.38 <0.000* 3.27–32.92 4.73 0.100 0.75–29.92
Current use of contraception
No (reference) 1 1
Yes 1.12 0.79 0.48–2.61 0.69 0.670 0.12–3.92
Constant 3.62 0.26 0.39 –33.76 0.01 0. 017 0.00–0.43
Model statistics: n=40 0, P value=0.000, R square=0.1662
* denotes signicance, base outcome is ambivalent

13 African Journal of Midwifery and Women’s Health | 2020 | https://doi.org/10.12968/ajmw.2019.0010
RESEARCH
© 2020 MA Healthcare Ltd
‘To me, unintended pregnancies are pregnancies that happen at the wrong time.
Unintended pregnancies are pregnancies by mistake, which occur when a girl
lacks basic needs and has to fend for herself’. (A 21-year-old from an urban
primary healthcare centre)
Perception of the causes of unintended pregnancy
Responses from participants aged 20–29 years old indicated that many believed that not
using contraception is a cause of unintended pregnancy. However, few of the participants
aged 30–39 years old expressed the opinion that unintended pregnancies are due to failure
of family planning methods.
‘Unintended pregnancies can occur when a woman is having sexual intercourse
without using family planning’. (A 21-year-old from an urban primary
healthcare centre)
‘Unintended pregnancies that occur when a woman does not use any form of
family planning to prevent pregnancy. In my opinion, unintended pregnancies
is caused by failure of family planning method.’ (A 23-year-old from a rural
primary healthcare centre)
All participants were aware about family planning methods, though none of the
participants currently use any form of modern contraception.
Table 9. Thematic analysis of responses from focus group discussions
Themes Sub-themes
Perception of unintended pregnancy Pregnancies that occur unexpectedly
Pregnancies that occur at the wrong time
Pregnancies that occur by mistake
Perception of the causes of
unintended pregnancy
Pregnancies occur as a result of not
using contraception
Pregnancies occur as a result of
contraception failure
Perception of the inuence of culture
on unintended pregnancy
My culture allows all forms of contraception
My culture believes that all pregnancies and
children are gifts from God
Perception of the inuence of religion
on unintended pregnancy
Islam does not consider pregnancies to be
unintended if you are married
My religion encourages a woman to have
children as long as she is married
Perception of the consequences
of unintended pregnancy
Unintended pregnancies can result
in abortion
Abortion can cause infertility and death
If I become pregnant, I will abort the
pregnancy because I do not want
more children
I will keep the pregnancy if I become
pregnant because I don’t want to die

14 African Journal of Midwifery and Women’s Health | 2020 | https://doi.org/10.12968/ajmw.2019.0010
RESEARCH
© 2020 MA Healthcare Ltd
‘My husband is currently not around, so I do not need family planning. even
if he comes around, I can’t be pregnant’. (A 39-year-old from a rural primary
healthcare centre)
‘I cannot use family planning because my elder sister used and could not be
pregnant again for a long time. My mother warned all her children not to use
any family planning method because of this experience’. (A 32-year-old from an
urban primary healthcare centre)
‘My friend continue to bleed after family planning, I don’t want to have similar
problem. I cannot use any family planning method now because I am still young
and have not completed my family’. (A 28-year-old from an urban primary
healthcare centre)
‘A women who is mature before marriage will use family planning to prevent
unintended pregnancies’. (A 31-year-old from an urban primary healthcare centre)
Perception of the inuence of culture on unintended pregnancy
The participants’ responses during the discussions on the inuence of culture on
unintended pregnancy revealed that some cultures encourage women to give birth, using
polygamy as a practice to allow a woman who has recently given birth time to recover,
and that others view women with more children as superior.
‘In the Yoruba culture, the practice of polygamy reduces incidence of unintended
pregnancies because women who just gave birth are allowed to recuperate while
the husband focuses on other wives. The Yoruba culture in the olden days have
different traditional methods of birth control thereby reducing unintended
pregnancies’. (A 32-year-old from an urban primary healthcare centre)
‘Some Igbo culture encourages higher births as women with higher parities are
considered superior’. (A 38-year-old from a rural primary healthcare centre)
Perception of the inuence of religion on unintended pregnancy
The participants’ responses on the subject of the inuence of religion on unintended
pregnancy showed that Christianity and Islam do not encourage unplanned pregnancy,
especially when the woman is not married. Some Christian sects encourage the use
of contraception, while some such as Catholicism, forbids the use of some methods.
‘As a Catholic Christian, women are not encouraged to have unintended pregnancies
and as such, calendar method of contraception is allowed as birth control’.
(A 33-year-old from a rural primary healthcare centre)
‘Islam does not consider pregnancy as unintended if a woman is married.
Pregnancy is an act of God. Islam does not support family planning except coital
interruption’. (A 30-year-old from a rural primary healthcare centre)
‘Islam forbids the use of modern contraception. The withdrawal method also
known as coital interruption and prolonged breastfeeding are recommended as
forms of birth control’. (A 35-year-old from an urban health centre)

15 African Journal of Midwifery and Women’s Health | 2020 | https://doi.org/10.12968/ajmw.2019.0010
RESEARCH
© 2020 MA Healthcare Ltd
Perception of the consequences of unintended pregnancy
Participants identied abortion as a possible consequence of unintended pregnancy, and the
belief that this can result in infertility in the woman or death. However, some women expressed
the opinion that they would seek an abortion if they felt it was necessary.
‘I will not abort any unplanned pregnancies because it can lead to death. If am pregnant
now with the present age of my baby, I will keep the pregnancy because I don’t want
to die from abortion’. (A 32-year-old from an urban primary healthcare centre)
‘Why will I keep such a pregnancy, it’s a shameful one, I will definitely abort it and
I will bear the consequences’. (A 38-year-old from a rural primary healthcare centre)
Discussion
The results showed that participants generally perceived unintended pregnancies as
pregnancies that occur unexpectedly, either as a result of not using contraception or
contraceptive failure. Just over one-third (36.5%) of nursing mothers reported an unintended
pregnancy, while the majority (52%) were ambivalent. When examining the inuence of
the three levels of the conceptual hierarchical model (sociodemographic characteristics,
religion and culture, and reproductive characteristics), regression analysis found that age,
ethnicity, religion and parity were signicant inuences on unintended pregnancy. The
relative risk ratio of having an unintended pregnancy for mothers aged 35–44 years old
(0.21) was less than the relative risk ratio for nursing mothers aged 25–34 years old (0.42),
which was also less than the relative risk ratio of mothers who were less than 25 years
old. Similarly, the relative risk ratio for Islamic mothers (0.26) was less than the risk for
mothers who are Catholic Christians (1).
This study found that 36.5% of participants had unintended pregnancies, 52% had
ambivalent pregnancies, while only 11% planned their previous pregnancies. This
prevalence is similar to the ndings of Lamina (2015), who conducted a study on the
prevalence and determinants of unintended pregnancy in women of reproductive age
in south west Nigeria and reported the prevalence of unintended pregnancy as 35.9%.
A study in the US by Ayoola (2015) examined the factors associated with late
recognition of pregnancy among women reporting unintended pregnancy and
found a similar prevalence; 42.2% of the women studied classied their pregnancies
as unplanned.
The regression analysis found that participants’ age, ethnicity, religion and parity were
signicant inuences on unintended pregnancy. This is similar to the observations by Eliason
et al (2014), who examined the determinants of unintended pregnancy in rural Ghana
and reported that parity, among other factors, was a signicant risk factor for unintended
pregnancy. Izugbara (2014) also observed that age and sex of household head, family size
and household wealth index were signicantly related to unintended pregnancy in Nigeria.
The present study revealed that some participants perceived unintended pregnancies as
the result of not using contraception or contraceptive failure. The majority (62%) of the
participants were not using any form of contraception at the time of this study, despite
widespread awareness of modern contraceptive methods. The focus group discussions
revealed the inuence of religion on contraception choices. Islamic mothers reported the
belief that Islam forbids the use of modern contraception and that prolonged breastfeeding
is accepted as a form of birth. This was backed by a ‘hadith’ in the Koran. This view is
consistent with the ndings of a study by Fadeyi and Oduwole (2016), which examined the
effect of religion on reproductive health issues in Nigeria. The authors reported that Islam
forbids limiting the number of children a woman can have and that coital interruption is
permitted, provided the woman consents. Similarly, some Catholic Christian participants
reported the belief that all forms of modern contraception are not permitted by their
religion, except coital interruption. This nding is supported by the work of Jones and
Dreweke (2011), who examined religion and contraceptive use among women in the US,
and reported that the Catholic leadership strongly opposes the use of modern contraception.

16 African Journal of Midwifery and Women’s Health | 2020 | https://doi.org/10.12968/ajmw.2019.0010
RESEARCH
© 2020 MA Healthcare Ltd
Conclusions
The participants generally perceived unintended pregnancies as those that occur
unexpectedly, as a result of not using contraception or contraceptive failure. The
prevalence of unintended pregnancy among the participants was 36.5%. Mothers’ age,
ethnicity, religion and parity were inuencers of unintended pregnancy. These ndings
show that intervention programmes towards prevention should target mothers aged 25–44
years old and grand multiparous women. Advocacy towards prevention of unintended
pregnancies should also take cognisance of the cultural and religious context of mothers.
Limitations
This study is not a national study; therefore, ndings may not be generalisable. However,
major ethnic and religious groups in this study setting were represented when selecting
participants for this study.
Recommendations
In view of the signicance of the multi-cultural and multi-religious context that is typical
of most African communities and the implication of unintended pregnancies on maternal
and child health, future research efforts should be directed towards conducting similar
studies on a national scale.
The ndings from this study should be used to inform midwives on how to develop
learning packages for the prevention of unintended pregnancy. Midwives should encourage
women of reproductive age to take responsibility for their reproductive health. In addition,
this study should highlight the importance of recognising the inuence of culture and
religion when midwives are caring for women with unintended pregnancies.
Author details
1Clinical Nursing Services, Obafemi Awolowo University Teaching Hospitals Complex, Ile-
Ife, Nigeria
2Department of Nursing Science, Obafemi Awolowo University, Ile-Ife, Nigeria
3Medical and Health Services, Obafemi Awolowo University, Ile-Ife, Nigeria
4Department of Nursing Science, Obafemi Awolowo University, Ile-Ife, Nigeria
Acknowledgements
The authors hereby acknowledge the Ethics and Research Committee of the Institute of
Public Health, Obafemi Awolowo University, Ile-Ife, the authorities of Ife Central and
Irewole Local Government Areas of Osun State, South West Nigeria where this study
was conducted.
Key points
■The prevalence of unintended pregnancy in Africa, and specically Nigeria is high and
there is a paucity of information on the inuence of sociodemographic characteristics
on unintended pregnancy.
■This study analysed data on the inuence of sociodemographic characteristics on
unintended pregnancy and the perception of it from nursing mothers attending clinics
in Ife Central and Irewole Local Government Areas of Osun State.
■Age, ethnicity, parity and religion were found to be signicantly associated with
unintended pregnancy.
■These ndings should be used to encourage midwives to improve their awareness
of these sociodemographic characteristics when caring for women with
unintended pregnancies.

17 African Journal of Midwifery and Women’s Health | 2020 | https://doi.org/10.12968/ajmw.2019.0010
RESEARCH
© 2020 MA Healthcare Ltd
Conict of interest
The authors declare that there are no conicts of interest.
References
Ameyaw EK, Budu E, Sambah F et al. Prevalence and determinants of unintended pregnancy in
sub-Saharan Africa: a multi-country analysis of demographic and health surveys., PLoS One.
2019;14(8):e0220970. https://doi.org/10.1371/journal.pone.0220970
Ayoola AB. Late recognition of unintended pregnancies. Public Health Nurs. 2015;32(5):462–470. https://
doi.org/10.1111/phn.12182
Bankole A, Adewole IF, Hussean R et al. Incidence of abortion in Nigeria. Int Perspect Sex Reprod
Health. 2015;41(4):170–181. https://doi.org/10.1363/intsexrephea.41.4.0170
Bearak J, Popinchal A, Alkema L, Sedgh G. Global, regional, and subregional trends in
unintendedpregnancy and its outcomes from 1990 to 2014: estimates from a Bayesian hierarchical
model. The Lancet Global Health. 2018;6(4):e380–e389. https://doi.org/10.1016/S2214-
109X(18)30029-9
Bishwajit G, Tang S, Yaya S, Feng Z. Unmet need for contraception and its association with unintended
pregnancy in Bangladesh. BMC J Pregnancy Childbirth. 2017;17(1):186. https://doi.org/10.1186/
s12884-017-1379-4
Cochrane WG. Sampling techniques (3rd edition). Wiley. 1977
Eliason S, Baiden F, Yankey BA, Awusabo–Asare K. Determinants of unintended pregnancies in rural
Ghana. BMC Pregnancy Childbirth. 2014;14(1):261. https://doi.org/10.1186/1471-2393-14-261
Fadeyi AO, Oduwole TA. Effect of religion on reproductive health issues in Nigeria. Int J Innov Healthc
Res. 2016;4(1):17–33
Fotso JC, Izugbara C, Saliku T, Ochako R. Unintended pregnancy and subsequent use of modern
contraceptive among slum and non-slum women in Nairobi, Kenya. BMC J Pregnancy Childbirth.
2014;14:224. https://doi.org/10.1186/1471-2393-14-224
Guttmacher Institute. Unintended pregnancy rate decline globally from 1990 to 2014. 2018. https://
guttmacher.org/news-release/2018/unintended-pregnancy-rates-declined-globally-1990-2014 (accessed
27 April 2020)
Hall J A, Barrett G, Phiri T, Copas A et al. Prevalence and determinants of unintended pregnancy
in Mchinji District, Malawi; using a conceptual hierarchy to inform analysis. PLoS One.
2016;11(10):e0165621. https://doi.org/10.1371/journal.pone.0165621
Hall JA, Barrett G, Smith S, Copas A, Stephenson J. London Measure of Unplanned Pregnancy: guidance
for its use as an outcome measure. Dove Press. 2017;8:4356. https://doi.org/10.2147/PROM.S122420
Izugbara C. Household characteristics and unintended pregnancy among ever-married women in Nigeria.
Journal of Social Medicine. 2014;8(1)
Jones RK, Dreweke J. Countering conventional wisdom: new evidence on religion and contraceptive use.
Guttmacher Institute. 2011. https://www.guttmacher.org/report (accessed 20 June 2018)
Kaaria M. Correlates of unintended childbearing: a comparative study of Nyanza and Central Kenya.
2012. http://erepository.uonbi.ac.ke/bitstream/handle/11295/11385/Abstract.pdf (accessed 12
August 2018)
Lamina MA. Prevalence and determinants of unintended pregnancy among women in South-Western
Nigeria. Ghana Med J. 2015;49(3):187–194. https://doi.org/10.4314/gmj.v49i3.10
National Population Commission and ICF International. Nigeria Demographic and Health Survey 2013.
2014. https://dhsprogram.com/pubs/pdf/FR293/FR293.pdf (accessed 27 April 2020)
Obasohan PE. Religion, ethnicity and contraceptive use among reproductive age women in Nigeria. Int J
Mch AIDS. 2015;3(1):63–73
Sedgh G, Singh S, Hussain R. Intended and unintended pregnancies worldwide in 2012 and recent trends.
Stud Fam Plann. 2014;45(3):301–314. https://doi.org/10.1111/j.1728-4465.2014.00393.x
Victora CG, Huttly SR, Fuchs SC, Olinto M. The role of conceptual frameworks in epidemiological
analysis: A hierarchical approach. Int J Epidemiol. 1997;26(1):224–227. https://doi.org/10.1093/
ije/26.1.224