Exploring the Determinants of High-risk Fertility Behavior among Child bearing Women in Somaliland: Insights from Somaliland Demographic and Health Survey, 2020

doi:10.21203/rs.3.rs-4705560/v1

Download PDF

Research Article

Exploring the Determinants of High-risk Fertility Behavior among Child bearing Women in Somaliland: Insights from Somaliland Demographic and Health Survey, 2020

https://doi.org/10.21203/rs.3.rs-4705560/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Purpose

The purpose of this study was to investigate the determinants of high-risk fertility behaviors in Somaliland using data from the nationally representative Somaliland Demographic and Health Survey (SLHDS). This was done in accordance with Sustainable Development Goal (SDG) 3, specifically Indicator 3:1 which aims to reduce the maternal mortality ratio to less than 70 per 100,000 live births.

Patients and Methods:

The study utilized the SLHDS data, which provided information on a wide range of health and demographic indicators. The data were collected through self-reported surveys conducted in both urban and rural areas. Descriptive statistics, including mean, frequency, and percentage, were computed, and bivariate and multivariable binary logistic regression analyses were performed to assess the associated factors of high-risk fertility behaviors.

Results

The analysis revealed significant associations between high-risk fertility behaviors and various individual-level and community-level variables. Individual-level factors included maternal age group, maternal and husband education levels, contraceptive use, and child-related variables. Community-level factors encompassed residence, region, household size, mass media exposure, household wealth status, toilet facility, water source, and access to healthcare services. However, the study acknowledged the presence of social desirability and recall biases due to the self-reported nature of the survey. Some important variables had to be removed from the analysis due to data incompleteness and inappropriateness.

Conclusion

This study sheds light on the determinants of high-risk fertility behaviors in Somaliland, providing valuable insights for policymakers and healthcare professionals. The findings underscore the need for targeted interventions and reproductive health programs that address individual-level and community-level factors. Future research should employ rigorous data collection methods to minimize biases and explore additional factors that may influence high-risk fertility behaviors in the context of Somaliland.

Sustainability

High-risk fertility

déterminants

Somaliland

modelling

High-risk Fertility Behavior (HRFB) among women is defined as women that have narrow birth intervals (less than 24 months), a larger number of live births (3 or more), and an age at birth that is either too early (less than 18 years) or too late (more than 34 years) (1). A bio-demographic risk factor known as maternal HRFB makes it more difficult to reduce morbidity and death in mothers and children (2). Understanding fertility in a particular nation or civilization is crucial because marriage is a key indicator of a woman's introduction to the danger of getting high-risk fertility. According to Somaliland’s first ever demographic health survey 2020 data, the total fertility rate (TFR) of Somaliland is high (5.7), whereas maternal mortality is also high (396/100,000 live births), which is below the United Nations Sustainable Development Goals (UNSDG). By the year 2030, every nation is expected to decrease their maternal mortality ratio (MMR) to at least two-thirds of the levels recorded in 2010. The global objective is to achieve an MMR lower than 70 deaths per 100,000 live births by 2030. This target falls under Sustainable Development Goal 3, target 3:1, which aims to reduce the MMR below 70 deaths per 100,000 live births by the end of 2030. Additionally, each country has a supplementary goal to ensure that their MMR does not exceed 140 deaths per 100,000 live births (twice the global target) by 2030 (3).

Individual characteristics and geographical location can influence high-risk fertility behavior. Previous studies have indicated that factors such as religion, education level, marital status, unintended pregnancies, successful previous births, lack of antenatal care visits, non-use of contraceptives, and specific regions are correlated with high-risk fertility behavior. Similarly, reproductive health factors like previous child mortality, giving birth in a healthcare facility, and utilization of family planning methods play a role in determining high-risk fertility behavior (4, 5).

On a global scale, the high occurrence of high-risk fertility behavior (HRFB) is a significant concern for public health and serves as a measure of both a country's economic status and its healthcare conditions, particularly in developing nations. Implementing effective strategies to control birth rates through the use of contraceptive methods in countries with high fertility rates has the potential to reduce the number of deaths among children and mothers. Consequently, the planning of births becomes a crucial approach for addressing economic and healthcare issues, and mitigating health-related difficulties (6).

The total fertility rate is falling globally, but it is falling more slowly in Sub-Saharan Africa (SSA), where the rate dropped from 5.37 in 2008 to 4.69 children per woman in 2018. In fact, 1.2 billion people are expected to live there by 2025, and they don't have a population growth strategy in place to control or monitor fertility rates (7).

Numerous studies have identified that late maternity is linked to increased risks for miscarriage, chromosomal abnormalities, stillbirth, fetal growth restriction, preterm birth, pre-eclampsia, gestational diabetes mellitus, and caesarean section (8, 9). Childbearing at an early age (< 18 years) is connected to growing restriction, child malnutrition, preterm birth, and infant mortality; on the other hand, late motherhood (34 years) is related to preterm births, intrauterine growth restriction, stillbirths, amniotic fluid embolism, chromosomal abnormalities, and low birth weight in newborns (10). HRFB in mothers is also associated with the neonatal and under five mortality (2, 4), and childbearing at a young age was also found to be linked to neonatal mortality (11).

The authors have found that there is a scarcity of information regarding the factors that contribute to high-risk fertility behavior in Somaliland. As a result, this study was conducted with the purpose of assessing the determinants of such behavior among women of reproductive age in the region. The findings of this study will be beneficial to health planners and policymakers, as they aim to reduce instances of high-risk fertility behavior in Somaliland. Moreover, this research will serve as a fundamental resource for other researchers by providing them with essential baseline information.

Study Area

The present study was conducted in Somaliland, situated in East Africa and bordered by Djibouti, Ethiopia, Somalia, and the Gulf of Aden. Somaliland encompasses an area of 176,119.2 km2 and experiences a combination of wet and dry climates. It is divided into five geopolitical regions: Awdal, Waqooyi Galbeed, Togdheer, Sanaag, and Sool. The population of Somaliland is estimated to be 4.2 million, predominantly consisting of Somali ethnic groups who practice Islam. Despite some progress in economic growth following its claim for independence, Somaliland has faced challenges in its economic development. The lack of international recognition as an independent nation has hindered investments and international aid. Livestock production plays a significant role in the livelihoods of both rural and urban dwellers in Somaliland.

Study Design and Study Period

The SLDHS was conducted in 2019 to gather data on health and demographic indicators in Somaliland. The survey covered various topics, including reproductive health, family planning, immunization, and HIV/AIDS awareness. The findings from the survey were compiled into the 2020 SLDHS report, which provides valuable insights for policymakers and healthcare professionals. The report serves as a resource to guide evidence-based decision-making and targeted interventions to improve health outcomes in Somaliland.

Sample Size and Sampling of SLDHS Data

The study involved the analysis of data from 5,688 women between the ages of 15 and 49, which was extracted from the SLHDS data set. The sampling process took into account five different geographic regions as strata, and the type of residence (urban, rural, and nomadic) was also considered. For urban and rural areas, Geographic Information System (GIS) software was utilized to choose specific areas for enumeration. A total of 2,806 dwelling structures (1,869 in urban areas and 937 in rural areas) were included in the sampling frame. The selection of 35 enumeration areas (EA) was based on the probability proportionate to the size of the dwelling structures. Subsequently, households within these 35 EAs were listed, and 10 primary sampling units (PSU) were randomly selected using the probability proportional sampling technique. In order to include the nomadic residents, temporary nomadic settlements (TNS) were used to create a sampling frame. The list of TNS served as the sampling frame, and the estimated number of households in each TNS was used as a measure of size. A total of 1,448 dwelling structures in TNS were identified, and the same procedure as urban and rural areas was followed to select the EAs. The final sampling unit consisted of households, which were selected using systematic sampling techniques.

Conceptual Framework

Our conceptual framework aims to examine the relationship between fertility preferences and various individual and community level factors among child bearing women aged 15–49 in Somaliland. Drawing on previous research on influencing factors of high risk fertility behavior (12, 16–20), we incorporate individual and community level factors as key variables in our analysis. These factors were selected based on their relevance and availability in the 2020 SLDHS (Somaliland Demographic and Health Survey) dataset, which is the first-ever dataset of its kind in Somaliland. Figure 1 illustrates the adapted constructs of our conceptual framework.

Variables

Outcome variable

This study focuses on the examination of high-risk fertility behaviors among married women who are of reproductive ages. The DHS data does not directly include information on high-risk fertility behaviors. Therefore, this paper introduces three criteria to create the outcome variable for identifying high-risk fertility behaviors. A woman is considered to exhibit high-risk fertility behavior if she meets any of the following conditions: (i) giving birth before the age of 18, or (ii) giving birth at or after the age of 34, or (iii) having a birth order of 3 or higher, or (iv) having a birth spacing of less than 24 months. If a woman meets any of these conditions, she is classified as having high-risk fertility behavior. It is important to note that the definition and conceptualization of high-risk fertility behaviors may vary in the existing literature. Some scholars have distinguished between single and multiple risks in order to capture and model high-risk fertility behaviors (6, 12–15). In this study, the outcome variable is coded as 1 if a woman has at least one of these high-risk fertility behavior-related conditions, and 0 if she does not have any of these conditions.

Independent variables

Previous literature has identified a number of factors associated with high-risk fertility behaviors in sub-Saharan Africa (12, 16–20). This paper examines the determinants of high-risk fertility behaviors and categorizes them into two groups: individual-level and community-level variables. The individual-level variables include maternal age group, maternal education level, maternal occupation, husband education level, husband occupation, contraceptive use, sex of household head, child sex, child twin, and child survival status. Additionally, the study reviews the literature on community-level factors, such as residence, region, household size, mass media exposure, household wealth status, toilet facilities, water sources, and access to healthcare services.

Data Source

The study utilized data from the SLDHS, which were collected by skilled interviewers using the CSPro Android platform. Surveys were conducted in both urban and rural areas. Within each region stratum, 30 households were selected from 10 enumeration areas for data collection. Likewise, in nomadic areas, 30 households were chosen from each enumeration area. To ensure the accuracy and completeness of the household list, it was cross-checked one day before data collection in each TNS.

Data Quality Assurance

Prior to collecting survey data, data collectors received training, and a pretest was conducted to ensure their preparedness. To facilitate georeferenced data collection, GPS tracking of field operations was utilized. The data collection process was closely supervised to ensure accuracy and quality.

Data Processing and Analysis

The data used in this study were obtained from the SLDHS and underwent a cleaning process. Participants who did not have outcome variables in the dataset were excluded from the analysis. The data were then exported to STATA version 17 software for analysis and R software version 4.3.3. Descriptive statistics, such as mean, frequency, and percentage, were calculated. Bivariate and multivariable binary logistic regression analyses were conducted to assess the factors associated with high-risk fertility behavior. Variables with a p-value below a certain threshold (e.g., 0.05) were considered statistically significant.

Ethical Clearance

The study utilized publicly available secondary data, which can be accessed at no cost through the website https://microdata.nbs.gov.so/index.php/catalog/50. The data collection and survey process were approved by the National Statistics Office and the Ministry of Health, eliminating the need for additional ethical approval. Since the study relied on pre-approved data sources that were already ethically obtained for primary investigations, no further ethical approval was necessary.

Individual Level Factors of the Study Participants

Table 1 presents the individual-level factors of the study participants in Somaliland, based on the SLDHS 2020 dataset. The table provides valuable insights into various aspects of the participants' characteristics. It reveals that the majority of the participants fell within the age groups of 25–34 (21.11%), 30–34 (21.69%), and 35–39 (25.58%). Regarding education, a significant proportion of the participants (90.15%) had no formal education, while smaller percentages had primary (6.31%), secondary (2.87%), or higher education (0.67%). The employment status of the participants showed that the vast majority (98.66%) were not employed. When considering the husbands' education level, 81.36% had no education, while smaller percentages had primary (5.52%), secondary (7.07%), or tertiary education (6.05%). In terms of husband employment, 41.05% were employed, and 58.95% were not. The data also revealed low contraceptive use among the participants, with only 3.16% reporting its use. In terms of child-related factors, most participants had a parity of more than four children (76.97%) and lived in households headed by females (67.84%). The child sex distribution was relatively balanced, with 47.54% female children and 52.46% male children. The majority of participants had single children (98.38%), and the child survival rate was relatively high, with 96.24% of children reported as alive. These findings provide a comprehensive understanding of the individual-level factors that contribute to high-risk fertility behavior among childbearing women in Somaliland and will be instrumental in further analysis and modelling to predict and address these behaviors effectively.

Table 1

Individual level factors of the Study Participants in Somaliland, SLDHS 2020 (n = 5688)
Variables	Categories	Frequency (n)	Percentage %
Maternal age group	15–19	44	0.77
	20–24	500	8.79
	25–34	1201	21.11
	30–34	1234	21.69
	35–39	1455	25.58
	40–44	842	14.80
	45–49	412	7.24
Maternal education level	No education	5128	90.15
	Primary education	359	6.31
	Secondary education	163	2.87
	Higher education	38	0.67
Maternal employment status	Yes	76	1.32
Maternal employment status	no	5612	98.66
Husband education level	No education	4628	81.36
	Primary education	314	5.52
	Secondary education	402	7.07
	Tertiary education	344	6.05
Husband employment status	yes	2335	41.05
Husband employment status	No	3353	58.95
Contraceptive use	Yes	180	3.16
Contraceptive use	No	5508	96.84
Parity	0–4	1,310	23.03
Parity	More than 4	4,378	76.97
House hold head	Female	3,859	67.84
House hold head	Male	1829	32.16
Child sex	Female	2704	47.54
Child sex	Male	2984	52.46
Child twin	Single	5596	98.38
Child twin	Multiple	92	1.62
Child survival status	Alive	5474	96.24
Child survival status	Dead	214	3.76

Community Level Factors of the Study Participants

Table 2 presents the community-level factors of the study participants in Somaliland, based on the SLDHS 2020 dataset. This table provides insights into various factors related to the participants' community and living conditions. Firstly, it reveals the distribution of participants based on their place of residence, showing that the majority resided in nomadic areas (55.64%), followed by rural areas (25.53%) and urban areas (18.83%). The table also displays the frequency and percentage distribution of participants across different regions of Somaliland. For example, Awdal had 668 participants (11.74%), Waqooyigalbeed had 905 participants (15.91%), Togdheer had 985 participants (17.32%), Sool had 1386 participants (24.37%), and Sanaag had 1744 participants (30.66%). Household size was categorized into two groups, with 59.27% of participants having a household size of 0–5, and 40.73% having more than 5 members. On the other hand, the table provides information on mass media exposure, indicating that 11.18% of participants were exposed to mass media at least once a week, 2.46% were exposed less than once a week, and 86.36% had no exposure. Household wealth status was categorized based on quintiles, with the lowest quintile representing 34.19% of participants, followed by the second (11.99%), middle (15.01%), fourth (18.41%), and fifth quintiles (20.39%). The table also reveals the type of toilet facility and water source available to participants. Around 20.46% had improved toilet facilities, while 79.54% had not. In terms of water sources, 46.91% had improved sources, while 53.09% did not. Lastly, the table presents data on health access, showing that 52.73% of participants had access to health services, while 47.27% did not. These community-level factors provide crucial contextual information that can help in understanding the social and environmental influences on high-risk fertility behavior among the study participants in Somaliland.

Table 2

Community level factors of the Study Participants in Somaliland, SLDHS 2020 (n = 5688)
Variables	Categories	Frequency	Percentage
Place of residence	Rural	1452	25.53
	Urban	1071	18.83
	Nomadic	3165	55.64
Region	Awdal	668	11.74
	Waqooyi-galbeed	905	15.91
	Togdheer	985	17.32
	Sool	1386	24.37
	Sanaag	1744	30.66
Household size	0–5	3,371	59.27
	More than 5	2,317	40.73
Mass media exposure	At Least Once a week	636	11.18
	Less than once a week	140	2.46
	Not at all	4912	86.36
Household wealth status	Lowest	1945	34.19
	Second	682	11.99
	Middle	854	15.01
	Fourth	1047	18.41
	Fifth	1160	20.39
Toilet facility	Improved	1,164	20.46
Toilet facility	Not improved	4,524	79.54
Water source	Improved	2,668	46.91
Water source	Not improved	3020	53.09
Health access	Yes	2999	52.73
Health access	No	2689	47.27

Magnitude of Fertility Preference

The magnitude of high-risk fertility behavior among child-bearing women in Somaliland in the current study was 83.19% (95% CI: 82.20–84.14) (Fig. 2)

Determinants of High-risk Fertility

The bivariate analysis conducted on high-risk fertility behaviors among women of reproductive age in the SLDHS revealed several significant associations, as shown in Table 3. Firstly, age was found to be strongly associated with high-risk fertility behaviors. The 35–39 age group had the highest percentage of single high-risk fertility behaviors, while the 15–19 age group had the highest percentage of multiple high-risk fertility behaviors.

Secondly, husband characteristics showed significant associations. Lower education levels of husbands were associated with higher percentages of single high-risk fertility behaviors, and having a husband with no occupation was also associated with a higher percentage of single high-risk fertility behaviors.

Other factors, such as maternal education, maternal occupation, contraceptive use, and health access, did not show significant associations with high-risk fertility behaviors. However, characteristics like parity, residence, region, household wealth status, and toilet facility were found to be significantly associated with high-risk fertility behaviors, with different categories within these factors showing varying percentages of single and multiple high-risk fertility behaviors.

Table 3

Bivariate analysis of High-risk fertility behaviors among women in reproductive age respondents in SLDHS
	High risk fertility behaviors		Chi-square	Df	P-value
Characteristics	Frequency - percentage
Age in 5-year groups	Single	Multiple
15–19	1 (2.27)	43 (97.73)	1260.63	6	0.0000
20–24	0 (0.00)	500 (100.00)
25–29	0 (0.00)	1201(100.00)
30–34	0 (0.00)	1234(100.00)
35–39	526 (36.15)	929 (63.85)
40–44	289 (34.32)	553 (65.68)
45–49	140 (33.98)	272 (66.02)
Maternal	883 (17.22)	4245 (82.78)
Maternal education
No Education	883 (17.22)	4245 (82.78)	6.61	3	0.0853
Primary	45 (12.53)	314 (87.47)
Secondary	22 (13.50)	141 (86.50)
Higher	6 (15.79)	(84.21)
Maternal occupation
Yes	12 (15.79)	64 (84.21)	0.06	1	0.8112
No	944 (16.82)	4668 (83.18)
Husband education
No Education	749 (16.18)	3879 (83.82)	8.23	3	0.0415
Primary	66 (21.02)	248 (78.98)
Secondary	80 (19.90)	322 (80.10)
Higher	61 (17.73)	283 (82.27)
Husband occupation
Yes	345 (14.78)	1990 \|(85.22)	11.70	1	0.0006
No	611 (18.22)	2742 (81.78)
Contraceptive use
Yes	30 (16.67)	150 (83.33)	0.00	1	0.9591
No	926 (16.81)	4582 (83.19)
Parity
0–4	21 (1.60)	1289 (98.40)	281.39	1	0.000
More than 4	935 (21.36)	3443 (78.64)
Sex of house hold head
Female	667	3192	1.95	1	0.1623
Male	289	1540
Child sex
Male	489 (16.39)	2495 (83.61)	0.79	1	0.3736
Female	467 (17.27)	2237 (82.73)
Child twin
Single	940 (16.80)	4656 (83.20)	0.02	1	0.8800
Multiple	16 (17.39)	76 (82.61)
Child survival status
Alive	899 (16.42)	4575 (83.58)	15.36	1	0.0001
Dead	57 (26.64)	157 (73.36)
Residence
Rural	273 (18.80)	1179 (81.20)	19.36	2	0.0001
Urban	212 (19.79)	859 (80.21)
Nomadic	471 (14.88)	2694 (85.12)
Region
Awdal	87 (13.02)	581 (86.98)	40.94	4	0.0000
Woqooyi/gabeed	125 (13.81)	780 (86.19)
Togdheer	148 (15.03)	837 (84.97)
Sool	304 (21.93)	1082 (78.07)
Sanaag	292 (16.74)	1452 (83.26)
Household size
0–5	567 (16.82)	2804 (83.18)	0.00	1	0.9755
More than 5	389 (16.79)	1928 (83.21)
Mass media exposure
At Least Once a week	101 (15.88)	535 (84.12)	5.38	2	0.0680
Less than once a week	14 (10.00)	126 (90.00)
Not at all	841 (17.12)	4071 (82.88)
Household wealth status
Lowest	384 (19.74)	1561 (80.26)	20.54	4	0.0004
Second	107 (15.69)	575 (84.31)
Middle	116 (13.58)	738 (86.42)
Fourth	164 (15.66)	883 (84.34)
Fifth	185 (15.95)	975 (84.05)
Toilet facility
improved	239 (20.53)	925 (79.47)	14.53	1	0.0001
unimproved	717 (15.85)	3807 (84.15)
Water source
improved	446	2222	0.03	1	0.8635
unimproved	510	2510
Health access
Yes	529 (17.64)	2470 (82.36)	3.14	1	0.0764
No	427 (15.88)	2262 (84.12)

The multivariate logistic regression analysis of the determinants of HRFB among women in Somaliland, based on data from the SLDHS 2020, reveals several significant findings, as presented in Table 4. Age is a crucial factor, with women in the 35–39 age group having a higher likelihood of HRFB compared to younger women. Maternal education and occupation do not show a significant association with HRFB, suggesting that other factors may be more influential. However, husband education has a significant impact, with husbands who have primary education showing a lower likelihood of HRFB. Parity is also a significant determinant, with women having more than four children being at higher risk. The sex of the household head and child survival status also play a role, as women in male-headed households and those who have experienced child deaths have an increased likelihood of HRFB. The region of residence and the type of toilet facility are marginally associated with HRFB, highlighting the potential influence of contextual and environmental factors.

In summary, this analysis demonstrates that age, husband education, parity, sex of the household head, child survival status, region of residence, and toilet facility type are significant determinants of high-risk fertility behaviors among women in Somaliland. Maternal education and occupation, as well as contraceptive use, do not show a significant association with HRFB. These findings emphasize the importance of considering multiple factors and tailoring interventions to address the unique challenges faced by women in different age groups, household dynamics, and regional contexts. Further research is needed to gain a comprehensive understanding of the underlying factors influencing HRFB in Somaliland.

Table 4

Multivariate Logistic Regression Estimates of the determinants with HRFB by reproductive age group of Somaliland, SLDHS 2020
Variables	Levels	AOR	Coefficient (S.E)	95% CI	P-value
Age in 5-year groups	15–19	ref
	20–24	.5946242	.3503581	.1873729 1.887028	0.008
	25–29	.6529478	.1078384	.4723862 .9025259	0.010
	30–34	.1976567	.0471405	.1238519 .3154425	0.000
	35–39	.0942233	.0973039	.0124488 .7131641	0.022
	40–44	.0994672	.102911	.0130921 .7557049	0.026
	45–49	.108657	.1127595	.014214 .8306114	0.032
Maternal education	No Education	ref
	Primary	1.019961	.2231283	.664314 1.566007	0.928
	Secondary	.8486691	.2681623	.4568524 1.576525	0.604
	Higher	.5946242	.3503581	.1873729 1.887028	0.378
Maternal occupation	Yes	Ref
Maternal occupation	No	.9349761	.3662525	.4338749 2.014821	0.864
Husband education	No Education	Ref
	Primary	.659183	.1256336	.4537071 .9577153	0.029
	Secondary	.7455091	.1351975	.5225024 1.063696	0.105
	Higher	.7589598	.156696	.5063822 1.13752	0.182
Husband occupation	Yes	Ref
Husband occupation	No	.9589442	.102137	.7782731 1.181557	0.694
Contraceptive use	Yes	ref
Contraceptive use	No	.9424283	.2281189	.9183595 1.288217	0.330
Parity	0–4	Ref
Parity	More than 4	.1976567	.0471405	.1238519 .3154425	0.000
Sex of house hold head	Female	Ref
Sex of house hold head	Male	1.233545	.1172805	1.023826 1.486222	0.027
Child sex	Male	Ref
Child sex	Female	.9647322	.0799583	.8200835 1.134894	0.665
Child twin	Single	Ref
Child twin	Multiple	.7380441	.2544958	.3754629 1.450767	0.378
Child survival status	Alive	Ref
Child survival status	Dead	.4215366	.0873556	.2808274 .6327483	0.000
Residence	Rural	Ref
	Urban	.9301891	.1158204	.7287622 1.18729	0.561
	Nomadic	.8936367	.1477349	.6463108 1.235608	0.496
Region	Awdal	Ref
	Woqooyi/gabeed	1.02255	.1823366	.7209445 1.450331	0.900
	Togdheer	.7941953	.1419529	.559481 1.127377	0.197
	Sool	.6529478	.1078384	.4723862 .9025259	0.010
	Sanaag	.7848383	.1367987	.5577175 1.10445	0.165
Household size	0–5	Ref
Household size	More than 5	.9698637	.0849868	.8168118 1.151594	0.727
Mass media exposure	At Least Once a week	Ref
	Less than once a week	1.129098	.416803	.5476632 2.327824	0.742
	Not at all	.9813756	.1709899	.6974711 1.380843	0.914
Household wealth status	Lowest	ref
	Second	.9803653	.1602893	.7115688 1.3507	0.903
	Middle	1.103714	.2023597	.7705371 1.580955	0.590
	Fourth	.9414383	.1759678	.6526654 1.357979	0.747
	Fifth	1.172421	.2476709	.7749426 1.773772	0.451
Toilet facility	improved	Ref
Toilet facility	unimproved	1.260725	.1554663	.9900436 1.605411	0.060
Water source	improved	ref
Water source	unimproved	.8967867	.0782971	.7557394 1.064158	0.212
Health access	Yes	Ref
Health access	No	1.087679	.0939043	.9183595 1.288217	0.330
Constant		122.8802	142.963	12.56509 1201.707	0.000

The aim of the present study was to examine the factors influencing high-risk fertility behaviors by utilizing the 2020 SLDHS dataset. The findings of this multivariate logistic regression analysis provide valuable insights into the determinants of HRFB among women in Somaliland. The results confirm and align with previous studies on reproductive health behaviors while also shedding light on unique factors specific to the region. The discussion will focus on how the significant factors identified in this analysis align with previous research.

Age has consistently been identified as a significant determinant of reproductive health behaviors in various contexts. The finding that women in the 35–39 age group have a higher likelihood of engaging in HRFB is consistent with previous studies highlighting the association between older age and increased risk-taking behaviors in reproductive health. This may be attributed to factors such as desired family size, perceived fertility decline, or other socio-cultural factors that influence reproductive decision-making among women in this age group (16, 18, 20, 21).

The significant association between husband education and HRFB supports previous research emphasizing the importance of spousal education in reproductive decision-making. Studies have shown that higher levels of husband education are associated with increased knowledge and awareness of reproductive health, leading to more informed decision-making and reduced risk-taking behaviors. The finding that primary education level is particularly influential suggests that even basic education can have a positive impact on reproductive health outcomes (12, 16, 17, 21).

The significant association between parity and HRFB aligns with previous research highlighting the relationship between high parity and increased risk-taking behaviors. Women with more than four children may face challenges related to limited resources, increased maternal health risks, and reduced access to family planning services. Addressing the specific needs of women with multiple children and providing tailored family planning interventions can contribute to reducing HRFB (22).

The findings related to the sex of the household head and child survival status corroborate previous studies on gender dynamics and reproductive decision-making. Women in male-headed households may experience limited autonomy and decision-making power, which can impact their reproductive health choices. Similarly, the association between child mortality and HRFB suggests that the experience of losing a child may influence subsequent reproductive decisions, potentially due to emotional factors or socio-cultural norms surrounding childbearing (19, 21).

While this analysis did not find a significant association between maternal education and occupation with HRFB, these results should be interpreted with caution. Previous studies have shown that maternal education plays a crucial role in improving reproductive health outcomes, including the reduction of high-risk fertility behaviors. The lack of significance in this analysis may be due to various factors, such as the specific context of Somaliland or limitations in the dataset. Further research is needed to explore the complex relationship between maternal education, occupation, and HRFB in this context.

In conclusion, the findings of this study provide valuable insights into the determinants of high-risk fertility behaviors among women in Somaliland. The results confirm the importance of age, husband education, parity, sex of the household head, child survival status, region of residence, and toilet facility type in influencing reproductive health behaviors. These findings align with previous research and highlight the need for tailored interventions and policies that address the unique challenges faced by women in Somaliland. Further research is warranted to deepen our understanding of the factors shaping high-risk fertility behaviors and to inform effective strategies for improving reproductive health outcomes in the region.

This paper examined the determinants of HRFB among women in Somaliland using multivariate logistic regression analysis. The study identified several significant factors that influence HRFB, including age, husband education, parity, sex of the household head, child survival status, region of residence, and toilet facility type. These findings contribute to the existing body of knowledge on reproductive health behaviors and shed light on the specific context of Somaliland. While some results align with previous studies, such as the impact of age and husband education, the study also highlights unique factors that are particularly relevant to the region, such as the influence of the sex of the household head and child survival status.

Recommendations

In conclusion, the recommendations for addressing HRFB among women in Somaliland include developing targeted interventions for different age groups, enhancing spousal education to improve reproductive decision-making, addressing challenges faced by women with high parity, empowering women in male-headed households, tailoring interventions to regional contexts, improving sanitation facilities, and conducting further research to explore socio-cultural factors, enhance access to reproductive health services, and strengthen the healthcare system. These recommendations aim to promote informed decision-making, reduce barriers to reproductive healthcare, and enhance overall reproductive health outcomes in Somaliland.

Future Work

In summary, future research on HRFB among women in Somaliland should consider incorporating qualitative methods to explore socio-cultural factors, conduct longitudinal studies to analyze temporal relationships, assess the accessibility of reproductive health services, investigate the impact of socio-economic factors, and evaluate the healthcare system. Qualitative research can provide deeper insights into the lived experiences and decision-making processes of women and their families. Longitudinal studies would enable the examination of changes in HRFB over time and the identification of additional significant predictors. Understanding the barriers to accessing reproductive health services and the influence of socio-economic factors can inform targeted interventions, while evaluating the healthcare system can guide improvements in the delivery of reproductive health services.

By addressing these areas of future research, a more comprehensive understanding of HRFB among women in Somaliland can be achieved. This knowledge will help in developing evidence-based interventions, policies, and programs to improve reproductive health outcomes and reduce the risks associated with high-risk fertility behaviors.

Limitations and Strength of the Study

The present study utilized data from the nationally representative SLDHS, marking the first research of its kind conducted in Somaliland. However, it is important to note that the data collection relied on self-reported surveys, which may introduce social desirability and recall bias. Additionally, certain important variables had to be excluded from the analysis due to issues related to incomplete or inappropriate data.

Author Contributions

Hodo Abdikarim conceived the study. Abdisalam Hassan Muse, Christophe Chesneau, and Hodo Abdikarim drafted the manuscript. All authors (Hodo Abdikarim, Abdisalam Hassan Muse, and Christophe Chesneau) contributed to the interpretation of data, revised the article critically for important intellectual content, and approved the final version of the manuscript. Hodo Abdikarim supervised the study. Hodo Abdikarim, the corresponding author, attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Disclosure Statement

The authors confirm that there are no conflicts of interest regarding this work.

Data Availability Statement

The dataset used in this study is publicly accessible on the Somalia National Bureau of Statistics website at https://microdata.nbs.gov.so/index.php/catalog/50. Derived data supporting the findings of this study are available from the corresponding author upon request.

Ethical Consideration

This study is based on secondary data analysis, and permission was obtained from the microdata page to download and utilize the data for the study's purpose. Therefore, ethical approval and participants' consent are not applicable to this specific study. The dataset is publicly available in the official database of the Somali National Bureau of Statistics (NBS) without any personal identification.

Competing interests

The authors affirm that there are no conflicts of interest pertaining to the publication of this article.

Funding

Not applicable

Acknowledgment

The authors would like to express their sincere gratitude to the Ministry of Planning and all other institutions involved in the collection and publication of the Somaliland Demographic and Health Survey (DHS) data. Their valuable contributions and support were instrumental in the successful execution of the survey and the availability of the data used in this study. The authors acknowledge the collaborative efforts of these organizations, which significantly contributed to the advancement of research and knowledge in the field.

Woldeamanuel BT, Gessese GT, Demie TG, Handebo S, Biratu TD. Women’s education, contraception use, and high-risk fertility behavior: A cross-sectional analysis of the demographic and health survey in Ethiopia. Front Glob Women’s Heal. 2023;4(March):1–9.
Amir-ud-Din R, Naz L, Rubi A, Usman M, Ghimire U. Impact of high-risk fertility behaviours on underfive mortality in Asia and Africa: evidence from Demographic and Health Surveys. BMC Pregnancy Childbirth. 2021;21(1):1–14.
Kumar S, Kumar N, Vivekadhish S. Millennium development goals (MDGS) to sustainable development goals (SDGS): Addressing unfinished agenda and strengthening sustainable development and partnership. Indian J Community Med. 2016;41(1):1–4.
Howlader MH, Roshid HO, Kundu S, Halder HR, Chanda SK, Rahman MA. Determinants associated with high-risk fertility behaviours among reproductive aged women in Bangladesh: a cross-sectional study. Reprod Health [Internet]. 2022;19(1):1–10. https://doi.org/10.1186/s12978-022-01333-w.
Tsala Dimbuene Z, Tadesse Tessema Z, Wang Sonne SE. High-risk fertility behaviours among women of reproductive ages in the Democratic Republic of the Congo: Prevalence, correlates, and spatial distribution. PLoS One [Internet]. 2023;18(3):e0283236. http://dx.doi.org/10.1371/journal.pone.0283236.
Rahman M, Haque SE, Zahan S, Islam J, Rahman M, Asaduzzaman MD et al. Maternal high-risk fertility behavior and association with chronic undernutrition among children under age 5 y in India, Bangladesh, and Nepal: Do poor children have a higher risk? Nutrition [Internet]. 2018;49:32–40. https://www.sciencedirect.com/science/article/pii/S0899900717302174.
Ogundari K, Awokuse T. Human capital contribution to economic growth in Sub-Saharan Africa: Does health status matter more than education? Econ Anal Policy [Internet]. 2018;58:131–40. https://www.sciencedirect.com/science/article/pii/S0313592616302041.
Camara BS, Delamou A, Grovogui FM, de Kok BC, Benova L, El Ayadi AM et al. Interventions to increase facility births and provision of postpartum care in sub-Saharan Africa: a scoping review. Reprod Health [Internet]. 2021;18(1):1–22. https://doi.org/10.1186/s12978-021-01072-4.
Claramonte Nieto M, Meler Barrabes E, Garcia Martínez S, Gutiérrez Prat M, Serra Zantop B. Impact of aging on obstetric outcomes: Defining advanced maternal age in Barcelona. BMC Pregnancy Childbirth. 2019;19(1):1–10.
Magnus MC, Wilcox AJ, Morken NH, Weinberg CR, Håberg SE. Role of maternal age and pregnancy history in risk of miscarriage: Prospective register based study. BMJ. 2019;364:1–8.
Neal S, Channon AA, Chintsanya J. The impact of young maternal age at birth on neonatal mortality: Evidence from 45 low and middle income countries. PLoS ONE. 2018;13(5):1–16.
Tamirat KS, Tesema GA, Tessema ZT. Determinants of maternal high-risk fertility behaviors and its correlation with child stunting and anemia in the East Africa region: A pooled analysis of nine East African countries. PLoS One [Internet]. 2021;16(6 June 2021):1–15. http://dx.doi.org/10.1371/journal.pone.0253736.
Rahman M, Islam MJ, Haque SE, Saw YM, Haque MN, Duc NHC et al. Association between high-risk fertility behaviours and the likelihood of chronic undernutrition and anaemia among married Bangladeshi women of reproductive age. Public Health Nutr [Internet]. 2016/09/09. 2017;20(2):305–14. https://www.cambridge.org/core/article/association-between-highrisk-fertility-behaviours-and-the-likelihood-of-chronic-undernutrition-and-anaemia-among-married-bangladeshi-women-of-reproductive-age/417774E2F700939353CE5489ED0A23B7.
Adewuyi EO, Auta A, Khanal V, Bamidele D, Akuoko CP, Adefemi K et al. Prevalence and factors associated with underutilization of antenatal care services in Nigeria: A comparative study of rural and urban residences based on the 2013 Nigeria demographic and health survey. PLoS ONE. 2018;1–21.
Salawu MM, Afolabi RF, Gbadebo BM, Salawu AT, Fagbamigbe AF, Adebowale AS. Preventable multiple high-risk birth behaviour and infant survival in Nigeria. BMC Pregnancy Childbirth. 2021;21(1):1–12.
Rahman M, Hosen A, Khan MA. Association between maternal high-risk fertility behavior and childhood morbidity in Bangladesh: A nationally representative cross-sectional survey. Am J Trop Med Hyg. 2019;101(4):929–36.
Tessema ZT, Tamirat KS. Determinants of high-risk fertility behavior among reproductive-age women in Ethiopia using the recent Ethiopian Demographic Health Survey: a multilevel analysis. Trop Med Health. 2020;48(1):1–9.
Ahinkorah BO, Seidu AA, Armah-Ansah EK, Ameyaw EK, Budu E, Yaya S. Socio-economic and demographic factors associated with fertility preferences among women of reproductive age in Ghana: evidence from the 2014 Demographic and Health Survey. Reprod Health [Internet]. 2021;18(1):1–10. https://doi.org/10.1186/s12978-020-01057-9.
Tessema ZT, Azanaw MM, Bukayaw YA, Gelaye KA. Geographical variation in determinants of high-risk fertility behavior among reproductive age women in Ethiopia using the 2016 demographic and health survey: A geographically weighted regression analysis. Arch Public Heal. 2020;78(1):1–12.
Merdad L, Ali MM. Timing of maternal death: Levels, trends, and ecological correlates using sibling data from 34 sub-Saharan African countries. PLoS ONE. 2018;13(1):1–13.
Tessema ZT, Tamirat KS. Determinants of high-risk fertility behavior among reproductive-age women in Ethiopia using the recent Ethiopian Demographic Health Survey: a multilevel analysis. Trop Med Health [Internet]. 2020;48(1):93. https://doi.org/10.1186/s41182-020-00280-1.
Seidu AA, Ahinkorah BO, Anjorin SS, Tetteh JK, Hagan JE Jr, Zegeye B et al. High-risk fertility behaviours among women in sub-Saharan Africa. J Public Health (Bangkok) [Internet]. 2023;45(1):21–31. https://doi.org/10.1093/pubmed/fdab381.

No competing interests reported.

Download PDF

Editorial decision: Revision requested
11 Jul, 2024
Editor assigned by journal
08 Jul, 2024
Submission checks completed at journal
08 Jul, 2024
First submitted to journal
08 Jul, 2024

You are reading this latest preprint version

Exploring the Determinants of High-risk Fertility Behavior among Child bearing Women in Somaliland: Insights from Somaliland Demographic and Health Survey, 2020

Status:

Version 1

Abstract

Purpose

Patients and Methods:

Results

Conclusion

Figures

Introduction

Methods

Study Area

Study Design and Study Period

Sample Size and Sampling of SLDHS Data

Conceptual Framework

Variables

Outcome variable

Independent variables

Data Source

Data Quality Assurance

Data Processing and Analysis

Ethical Clearance

Results

Individual Level Factors of the Study Participants

Community Level Factors of the Study Participants

Magnitude of Fertility Preference

Determinants of High-risk Fertility

Discussion

Conclusion

Recommendations

Future Work

Limitations and Strength of the Study

Declarations

References

Additional Declarations

Status:

Version 1