Inequalities in the prevalence of Caesarean section and associated factors in rural and urban Nigeria: insights from the 2018 Nigeria Demographic and Health Survey

doi:10.21203/rs.3.rs-3439762/v1

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Research Article

Inequalities in the prevalence of Caesarean section and associated factors in rural and urban Nigeria: insights from the 2018 Nigeria Demographic and Health Survey

https://doi.org/10.21203/rs.3.rs-3439762/v1

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published 14 Aug, 2024

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Introduction

When medically indicated, caesarean section (CS) can be a lifesaving intervention for mothers and their newborns. This study assesses the within-population CS prevalence and associated factors with an emphasis on inequalities between rural and urban areas in Nigeria.

Methods

We disaggregated the 2018 Nigeria Demographic Health Survey and performed analysis separately for the overall, rural, and urban residences in Nigeria. Data were summarised using frequency tabulations, and factors associated with CS were identified through multivariable binary logistic regression analysis.

Results

The prevalence of CS was 2.7% in Nigeria (overall), 5.2% in urban and 1.2% in rural areas. The North-West region had the lowest prevalence, with 0.7% overall, 1.5% in urban, and 0.4% in rural areas, respectively. Mothers with higher education demonstrated a greater CS prevalence of 14.0% overall, 15.3% in urban and 9.7% in rural residences. Frequent internet use increased the prevalence of CS in Nigeria (14.3%), urban (14.1%) and rural (10.1%) residences. Across all residences, rich wealth index, maternal age ≥35, 8+ antenatal contacts and lower birth order increased the odds of a CS. In rural Nigeria, husbands' education, spouses’ joint healthcare decisions, birth size, and unplanned pregnancy were associated with higher CS odds. In urban Nigeria, multiple births, Christianity, frequent internet use, and ease of healthcare permission were associated with greater CS odds.

Conclusion

CS utilisation remains low and varies across rural-urban, regional, and socioeconomic divides in Nigeria. Targeted interventions are imperative for uneducated and socioeconomically disadvantaged mothers across all regions and Muslim mothers in urban areas. Health insurance was not a significant predictor; hence, rather than merely subsidising costs, a holistic intervention that confronts social inequalities (between rural and urban residents), fosters high-quality education, promotes access to the Internet, guarantees equal opportunities, and empowers individuals and communities to improve their socio-economic well-being, is likely to yield more impactful and sustainable outcomes.

cesarean section

childbirth

emergency obstetric care

inequalities

maternal-child health

Nigeria

rural-urban differences

Caesarean section (CS) is a critical obstetric intervention that involves surgically delivering a baby through incisions in the mother's abdomen and uterus ^1,2. It is an essential component of specialised health facilities and interventions designed to enhance positive outcomes for pregnant women and their newborns, collectively termed ‘comprehensive emergency obstetric care’ (CEmOC) ^1,2. CS is commonly recommended for high-risk pregnancies, including instances of multiple foetuses, breech presentations, obstructed labour, transmissible infections, and uterine rupture, among several other factors ³. When medically indicated, CS can contribute to the survival and well-being of mothers and their newborns ⁴. However, there is an ongoing debate about the appropriate population-based CS prevalence ^1,5,6, highlighting the complexity involved in determining the optimal CS prevalence, globally. Regardless of the contentions, a growing unanimity among stakeholders indicates that population-based CS rates below 5% signify unmet needs for necessary interventions—a contributor to maternal and newborn morbidity and mortality—while rates exceeding 10–15% may not yield any additional benefits for maternal and newborn survival ^1,5,7.

The World Health Organisation (WHO) recommends universal access to CS for all women in need of it, regardless of the prevailing population prevalence ^1,6. However, Nigeria's CS prevalence has consistently remained low in comparison to several other countries ^8–10, even as the global estimates show an increasing trend ¹¹. This low prevalence suggests underutilisation and is akin to the situation in many low-to-middle-income countries where maternal and neonatal mortality rates are high ^8,12. Notably, with over 82,000 maternal deaths in 2020, representing about 28.5% of the global burden, Nigeria ranks as the country with the highest number of maternal mortality, worldwide ¹³. This statistic is concerning considering that Nigeria's population is less than 3% of the world's total ¹³. There is a well-established relationship between high maternal or neonatal mortality rates and poor access to or non-/underutilisation of healthcare services ^14,15. The persistent stagnation of population-based CS rates in Nigeria, thus, raises concern about its impact on maternal and neonatal health outcomes, particularly in the face of the country's high maternal mortality burden ¹³.

Numerous observational studies have investigated CS rates and their associated factors in Nigeria ^9,16–23, underscoring the significance and priority accorded this subject in the country. However, prior to Adewuyi and colleagues’ nationally representative study ⁹, research on CS in Nigeria primarily relied on institutional data ^16–19. These institutional-based studies were recently summarised in a systematic review and meta-analysis ²⁴. While the studies provide invaluable perspectives, their reliance on healthcare facility records limits their capacity to capture the complete picture of CS rates across the broader Nigerian population. On the other hand, a few studies have enhanced this discourse by harnessing nationally representative data from the highly regarded Nigerian Demographic and Health Survey (NDHS) 2013^9,25. This approach enables a more comprehensive understanding of population-based CS utilisation across the country. Building on this evidence, two nationally representative studies have further advanced this subject using the most recent 2018 NDHS, providing additional insights into CS utilisation in the country’s context ^26,27. A notable limitation of the available population-based studies to date, however, lies in their ‘one-size-fits-all’ approach, focussing on the entire Nigerian population using aggregated datasets. This approach is valuable but can inadvertently mask within-population inequities and inequalities ^9,12,28–31.

Inequities in CS utilisation represent systematic disparities between various subpopulation groups, characterised by their ‘unjust, unfair, and avoidable’ nature. Inequality, on the other hand, serves as a measurable metric for assessing these inequities, reflecting the observed differences across subpopulation groups ³¹—a phenomenon well pronounced in maternal healthcare services utilisation ^12,28,30. Inequality can, for instance, manifest across various domains, including geographic divides (e.g., rural versus urban dwellings)^28,32, socioeconomic status (wealth index or educational attainment levels)^29,30,32, age differentials ^33,34, ethnicity ^12,28, etc. Understanding these disparities is vital for designing effective and target-oriented public health interventions, policies, and healthcare services. Moreover, tailoring interventions to address the unique challenges and needs of specific geographic subpopulations is essential for achieving healthcare access equity, and contributing towards realising the key targets of the Sustainable Development Goals (SDGs)—reducing maternal mortality (target 3.1), ending preventable newborn deaths (target 3.2), and achieving universal health coverage (target 3.8)³⁵. Thus, there is a pressing need for research based on disaggregated data to illuminate disparities in CS utilisation within different population subgroups in Nigeria.

This research investigates CS prevalence and associated factors in Nigeria with a priority focus on the inequality between rural and urban residences in the country. New insights from the study promise to inform policy formulation or initiatives geared towards enhancing better maternal and neonatal care outcomes in Nigeria. Additionally, findings have the potential to facilitate equitable access to healthcare services on a national scale, aligning with the global drive towards realising targets 3.1, 3.2, and 3.8 of the SDGs ³⁵.

Data source

The dataset used in this study was derived from the NDHS of 2018, a national survey conducted every five years with globally validated and locally adapted methodology ¹⁰. The survey was implemented by the Nigerian National Population Commission in collaboration with the ‘ICF’ and other development partners to provide up-to-date and nationally representative basic health and demographic indicator estimates ¹⁰. The 2018 NDHS is the sixth edition in the series, and its sampling process, which used a sampling frame from the 2006 census, is detailed in the official report ¹⁰. The survey was conducted using a two-stage stratified cluster sample selection involving 42,000 households and 1,400 clusters, and it was successfully administered in 40,427 households and 1,389 clusters ¹⁰. Data were collected from eligible men, women, and households using predetermined selection criteria and validated questionnaires ¹⁰.

A total of 41821 women aged 15–49 years (16,984 in urban, 24,837 in rural areas) who were either permanent residents or visitors staying in the selected households the night before the survey, responded for themselves and their children ¹⁰. The eligible women's response rate was 99.3% (99.2 in urban and 99.4 in rural residence) ¹⁰. We used the 'children recode (KR)' dataset in the present study and analysed a weighted sample of 34057 mothers (13096 in urban, 20963 in rural areas) who had complete information on their mode of delivery for the descriptive analysis. We included a weighted sample of 21157 most recent live childbirths (8258 in urban, 12899 in rural areas) in the five years before the survey for the adjusted analysis. Our analysis included single and multiple births.

Variables

Outcome variable

CS was the outcome variable for our study. The NDHS data includes information on whether childbirth was caesarean, asking, ‘was (NAME) delivered by caesarean, that is, did they cut your belly open to take the baby out?’ ¹⁰. However, it does not disaggregate the type of CS deliveries whether as elective or emergency. Therefore, all CS were included in the present study as in previous studies ^9,25–27. The mode of childbirth was dichotomised as ‘CS = 1’ and ‘non-CS’ i.e., vaginal delivery = 0’ to report the prevalence and facilitate association with the key independent variables.

Factors

We selected independent variables for this study based on key regional and global literature ^{8–10,25−27}. Maternal and husband’s education was included as: higher, secondary, primary and no education ^9,28,30. Maternal and husband’s working status was re-coded as ‘working’, if reported as working in paid work, and ‘not working’, if not in paid work. Household wealth was based on asset possession and was included as poorest, poor, middle, rich and richest. This was re-categorised into poor (poorest and poor), middle and rich (rich and richest) ^9,28,30 to facilitate our analysis. Having the final say on their health was included as women’s ability to decide for themselves and was recorded as respondent alone, respondent and partner/husband, and partner/someone else.

Many bio-demographic factors were also included in this study. Maternal age was documented in the dataset as 15–19, 20–24, 25–29, 30–34, 35–39, 40–44, and 45–49; these categories were maintained in our study, to provide specific insights across age differences. Furthermore, birth order was originally recorded as a continuous variable which was further categorised into 1, 2–3 and 4 or more ^9,28,30. Birth sizes were categorised into large, average, and small. Birth size is used as a proxy for birth weight as a substantial number of mothers could not report the birth weight of their children. Birth type was included as single and multiple births. Preceding birth interval was also a continuous variable which was categorised into < 24 months and 24 or more months ^9,28,30. Antenatal care (ANC) contacts were documented as a continuous variable and were subsequently categorised here into seven or fewer (underuse) and 8 or more (optimal use) ANC contacts. Place of deliveries were re-categorised into three: private health facility, public health facility and home, to facilitate analysis.

Other key sociodemographic factors were also included in our study. Maternal religion was included as Christianity (Catholic and other Christians), Traditionalist, and Islam ^9,28,30. Residence was documented as rural and urban. The regions of residence were used as provided in the data: North-Central, North-East, North-West, South-East, South-South and South-West ¹⁰. Nigeria has a diverse ethnic group ¹⁰; this was categorised into four major groups for this analysis: Hausa/Fulani, Yoruba, Igbo, and Others, as in previous studies ^9,28,30. Distance to the health facility; permission to visit health facility, getting money for health services and companionship to health facility were documented in the survey as ‘a big problem’, and ‘not a big problem’; we maintain the same categorisation in the present study. Health insurance coverage was categorized as yes or no, while access to information was collected as frequency of reading newspapers/magazines, frequency of listening to radio, frequency of watching television ^9,28,30, and frequency of internet use. These variables were categorised as ‘not at all’, ‘less than once a week’ and ‘≥ once a week’ ^10,28.

Statistical analysis

We performed analysis by first summarising the data using a frequency distribution table. Subsequently, we conducted descriptive statistics, disaggregating the NDSH data (overall Nigerian population) into rural, and urban residences (Table 1) as in previous studies ^15,28,30. To explore the relationship between CS and various explanatory factors, we initially employed the Chi-square (χ2) test. Significant variables identified from this test were then subjected to multivariable binary logistic regression analysis to determine their independent association with the outcome variable. To account for potential interactions among numerous independent variables, we utilised the backward likelihood ratio method. Adjusted odds ratios (AOR) along with their corresponding 95% Confidence Intervals (CI) were computed and reported. The multivariable binary logistic regression analysis was initially conducted on the entire Nigerian population (using the aggregated dataset) and then replicated for data segmented into rural and urban residences. Our analysis excluded missing data and was conducted with adjustments for the study design and sample weights. We utilised the complex sample function of SPSS version 21 software for the implementation of these statistical procedures. Results were deemed statistically significant at the 5% level.

Table 1

Sample characteristics and prevalence of Cesarean delivery in Nigeria
Factors	Urban Nigeria			Rural Nigeria			Overall Nigeria
	Weighted sample (%)^a	Prevalence of CS		Weighted sample (%)^a	Prevalence of CS		Weighted sample (%)^a	Prevalence of CS
	Weighted sample (%)^a	95% CI	p-values	Weighted sample (%)^a	95% CI	p-values	Weighted sample (%)^a	95% CI	p-values
Outcome variable
Mode of delivery			< 0.001*			< 0.001*			< 0.001*
Caesarean section (CS)	687	5.2 (4.5–6.1)		244	1.2 (1.0–1.4)		930	2.7 (2.4–3.1)
Vaginal delivery	12409	94.8 (93.9–95.5)*		20719	98.8 (88.6–99.0)*		33127	97.3 (96.9–97.6)*
Geographic/environmental factors
Rural-urban residence									< 0.001*
Rural				20962 (100.0)	1.2 (1.0–1.4)		20962 (61.5)	1.2 (1.0–1.4)
Urban	13095 (100.0)	5.2 (4.5–6.1)					13095 (38.5)	5.2 (4.5–6.1)
Region of residence			< 0.001*			< 0.001*			< 0.001*
North-Central	1450 (11.1)	4.3 (3.2–5.8)		3167 (15.1)	1.9 (1.4–2.7)		4617 (13.6)	2.7 (2.2–3.3)
North-East	1474 (11.3)	1.9 (1.2–3.0)		4716 (22.5)	0.6 (0.4–1.1)		6190 (18.2)	0.9 (0.2–1.3)
North-West	3115 (23.8)	1.5 (1.0–2.2)		9442 (45.0)	0.4 (0.3–0.7)		12557 (36.9)	0.7 (0.5–0.9)
South-East	2428 (18.5)	7.0 (5.1–9.5)		919 (4.4)	3.2 (2.3–4.5)		3347 (9.8)	6.0 (4.5–7.8)
South-South	1206 (9.2)	8.5 (5.3–13.3)		1753 (8.4)	2.8 (2.1–3.9)		2959 (8.7)	5.1 (3.7–7.2)
South-West	3422 (26.1)	8.1 (6.4–10.2)		966 (4.6)	3.3 (1.8–6.1)		4388 (12.9)	7.0 (5.7–8.7)
Socio-demographic factors
Maternal education level			< 0.001*			< 0.001*			< 0.001*
Higher	2171 (16.6)	15.3 (12.8–18.1)		622 (3.0)	9.7 (6.9–13.4)		2793 (8.2)	14. 0 (12:0-16.4)
Secondary	6019 (46.0)	4.8 (4.1–5.7)		4311 (20.6)	2.4 (1.9–3.0)		10330 (30.3)	3.8 (3.4–4.4)
Primary	2014 (15.4)	1.9 (1.3–2.8)		3073 (14.7)	1.0 (0.6–1.5)		5087 (14.9)	1.3 (1.0 -1.8)
None	2891 (22.1)	0.8 (0.5–1.5)		12957 (61.8)	0.4 (0.3–0.6)		15847 (46.5)	0.5 (0.3–0.7)
Maternal working status			0.091			< 0.001*			< 0.001*
Working	9561 (73.0)	5.6 (4.7–6.5)		13441 (64.1)	1.5 (1.2–1.8)		23003 (67.5)	3.2 (2.8–3.6)
Not working	3534 (27.0)	4.4 (3.3–5.7)		7521 (35.9)	0.6 (0.4–0.9)		11054 (32.5)	1.8 (1.4–2.3)
Husband education level			< 0.001*			< 0.001*			< 0.001*
higher	3145 (25.9)	10.2 (8.2–12.5)		1561 (7.9)	4.8 (3.5–6.6)		4706 (14.7)	8.4 (7.0–10.1)
Secondary	5414 (44.5)	4.5 (3.7–5.4)		5347 (27.0)	1.9 (1.5–2.4)		10761 (33.7)	3.2 (2.7–3.7)
Primary	1579 (13.0)	4.2 (3.0–5.8)		2942 (14.8)	0.9 (0.5–1.5)		4521 (14.1)	2.0 (1.5–2.7)
None	2017 (16.6)	0.8 (0.4–1.4)		9974 (50.3)	0.3 (0.2–0.5)		11991 (37.5)	0.4 (0.3–0.6)
Husband working status			0.045**			0.427			0.018**
Not working	335 (2.7)	2.7 (1.3–5.3)		702 (3.5)	0.8 (0.3–2.1)		1037 (3.2)	1.4 (0.8–2.4)
Working	11933 (97.3)	5.4 (4.6–6.3)		19424 (96.5)	1.2 (1.0–1.4)		31357 (96.8)	2.8 (2.4–3.1)
Wealth index			< 0.001*			< 0.001*			< 0.001*
Rich	8680 (66.3)	7.1 (6.0–8.3)		3015 (14.4)	4.1 (3.2–5.2)		11695 (34.3)	6.3 (5.5–7.2)
Middle	2665 (20.3)	2.0 (1.4–2.7)		4357 (20.8)	1.4 (1.1–1.9)		7022 (20.6)	1.6 (1.3–2.0)
Poor	1750 (13.4)	1.1 (0.6–2.3)		13591 (64.8)	0.4 (0.3–0.6)		15341 (45.0)	0.5 (0.4–0.7)
Decision on own health			< 0.001*			< 0.001*			< 0.001*
Respondent alone	1652 (13.4)	8.8 (6.5–11.7)		1351 (6.7)	1.9 (1.2–2.9)		3003 (9.2)	5.7 (4.4–7.3)
Respondent and Husband	5038 (41.0)	6.8 (5.6–8.2)		4571 (22.6)	2.6 (2.1–3.3)		9609 (29.6)	4.8 (4.1–5.6)
Husband alone/someone else	5603 (45.6)	2.9 (2.3–3.6)		14269 (70.7)	0.6 (0.5–0.8)		19871 (61.2)	1.3 (1.1–1.5)
Maternal marital status			0.786			0.602			0.742
Never married	277 (2.1)	4.4 (2.5–7.3)		304 (1.4)	1.7 (0.7–4.1)		581 (1.7)	3.0 (1.9–4.6)
Formerly married	526 (4.0)	4.9 (2.8–8.4)		467 (2.2)	1.2 (0.6–2.4)		993 (2.9)	3.1 (1.9–5.0)
Currently married	12292 (93.9)	5.3 (4.5–6.2)		20191 (96.3)	1.2 (1.0–1.4)		32484 (95.4)	2.7 (2.4–3.1)
Maternal age (years)			< 0.001*			0.010**			< 0.001*
15–19	285 (2.2)	2.8 (1.2–6.2)		1169 (5.6)	1.4 (0.6–3.2)		1454 (4.3)	1.7 (0.9–3.1)
20–24	2005 (15.3)	2.5 (1.7–3.5)		4661 (22.2)	0.8 (0.5–1.2)		6666 (19.6)	1.3 (1.0–1.7)
25–29	3785 (28.9)	4.0 (3.1–5.3)		5766 (27.5)	0.8 (0.6–1.1)		9551 (28.0)	2.1 (1.6–2.6)
30–34	3436 (26.2)	6.3 (4.8–8.2)		4329 (20.7)	1.5 (1.1–2.1)		7765 (22.8)	3.6 (2.9–4.5)
35–39	2362 (18.0)	8.0 (6.5–9.9)		3054 (14.6)	1.5 (1.1–2.2)		5416 (15.9)	4.4 (3.6–5.3)
40–44	883 (6.7)	5.6 (3.9–8.0)		1438 (6.9)	1.9 (1.2–2.9)		2321 (6.8)	3.3 (2.5–4.3)
45–49	339 (2.6)	6.3 (3.3–11.6)		545 (2.6)	0.8 (0.3–2.0)		883 (2.6)	2.9 (1.7–5.1)
Maternal religion			< 0.001*			< 0.001*			< 0.001*
Christianity	6243 (47.7)	8.4 (7.2–9.9)		5960 (28.4)	2.8 (2.3–3.5)		12203 (35.8)	5.7 (5.0–6.5)
Traditional/other	50 (0.4)	2.3 (0.6–8.0)		131 (0.6)	0 (0–0)		181 (0.5)	0.6 (0.2–2.6)
Islam	6802 (51.9)	2.3 (1.8–2.9)		14871 (70.9)	0.5 (0.4–0.7)		21673 (63.6)	1.1 (0.9–1.3)
Birth order			< 0.001*			< 0.001*			< 0.001*
1	2810 (21.5)	8.2 (6.8–10.0)		3775 (18.0)	1.9 (1.5–2.5)		6585 (19.3)	4.6 (4.0–5.4)
2–3	4940 (37.7)	6.2 (5.1–7.5)		6513 (31.1)	1.3 (1.0–1.7)		11453 (33.6)	3.4 (2.9–4.0)
4 or more	5346 (40.8)	2.8 (2.2–3.4)		10674 (50.9)	0.8 (0.6–1.1)		16019 (47.0)	1.5 (1.2–1.7)
Birth size			0.097			0.031**			0.011**
Large	4293 (33.4)	6.0 (5.0–7.2)		6940 (33.6)	1.5 (1.1–1.9)		11233 (33.5)	3.2 (2.8–3.7)
Average	6962 (54.1)	4.9 (4.0–5.9)		10662 (51.6)	0.9 (0.7–1.2)		17624 (52.6)	2.5 (2.1–2.9)
Small	1605 (12.5)	4.9 (3.7–6.4)		3064 (14.8)	1.2 (0.8–1.9)		4669 (13.9)	2.5 (1.9–3.1)
Birth interval (preceding)			0.795			0.262			0.407
< 24 months	2503 (24.4)	4.2 (3.0–5.9)		4322 (25.2)	0.8 (0.5–1.2)		6825 (24.9)	2.1 (1.6–2.7)
24 or more months	7755 (75.6)	4.4 (3.8–5.1)		12823 (74.8)	1.0 (0.8–1.3)		20578 (75.1)	2.3 (2.0–2.6)
Birth type			< 0.001*			< 0.001*			< 0.001*
Multiple	466 (3.6)	13.8 (9.4–19.9)		805 (3.8)	3.2 (1.9–5.3)		1271 (3.7)	7.1 (5.2–9.7)
Single	12629 (96.4)	4.9 (4.2–5.8)		20157 (96.2)	1.1 (0.9–1.3)		32786 (96.3)	2.6 (2.3–2.9)
Ethnicity			< 0.001*			< 0.001*			< 0.001*
Hausa/Fulani	3986 (30.4)	1.7 (1.2–2.3)		11748 (56.0)	0.4 (0.3–0.7)		15735 (46.2)	0.7 (0.6–1.0)
Yoruba	2973 (22.7)	5.1 (4.0–6.7)		759 (3.6)	4.7 (2.7–8.2)		3732 (11.0)	5.1 (4.0–6.4)
Igbo	3061 (23.4)	9.7 (7.7–12.1)		1184 (5.6)	3.4 (2.5–4.5)		4246 (12.5)	7.9 (6.4–9.7)
Others	3075 (23.5)	5.5 (4.3–7.0)		7270 (34.7)	1.6 (1.3–2.1)		10345 (30.4)	2.8 (2.3–3.3)
Health enabling factors
Antenatal contact			< 0.001*			< 0.001*			< 0.001*
Use 8 or more	2958 (35.3)	10.4 (8.8–12.3)		1355 (10.4)	4.6 (3.5–6.1)		4313 (20.1)	8.6 (7.4–10.0)
Underuse 7 or less	5427 (64.7)	3.3 (2.7–3.9)		11728 (89.6)	1.0 (0.8–1.3)		17155 (79.9)	1.7 (1.5–2.0)
Health insurance coverage			< 0.001*			0.053			< 0.001*
Yes	488 (3.7)	17.1 (12.4–23.1)		239 (1.1)	2.7 (1.1–6.5)		727 (2.1)	12.4 (8.9–16.9)
No	12607 (96.3)	4.8 (4.1–5.6)		20723 (98.9)	1.1 (1.0–1.4)		33331 (97.9)	2.5 (2.2–2.8)
Place of delivery			< 0.001*			< 0.001*			< 0.001*
Private health facility	3074 (24.1)	13.6 (11.3–16.3)		1244 (6.0)	7.5 (5.7–9.7)		4318 (12.9)	11.8 (10.1–13.8)
Public health facility	4893 (38.4)	5.5 (4.6–6.5)		4115 (19.8)	3.7 (3.0–4.5)		9008 (26.9)	4.7 (4.1–5.3)
Home	4787 (37.5)	0 (0–0)		15388 (74.2)	0 (0–0)		20175 (60.2)	0 (0–0)
Distance to health facility			0.374			< 0.001*			< 0.001*
Big problem	2205 (16.8)	4.7 (3.5–6.1)		7366 (35.1)	0.7 (0.5–1.0)		9572 (28.1)	1.6 (1.3–2.0)
Not a big problem	10890 (83.2)	5.4 (4.5–6.3)		13596 (64.9)	1.4 (1.1–1.7)		24486 (71.9)	3.2 (2.8–3.6)
Permission to visit health facility			< 0.001*			0.002*			< 0.001*
Big problem	1024 (7.8)	2.2 (1.3–3.6)		2999 (14.3)	0.6 (0.3–0.9)		4022 (11.8)	1.0 (0.7–1.4)
Not a big problem	12072 (92.2)	5.5 (4.7–6.4)		17964 (85.7)	1.3 (1.1–1.5)		30035 (88.2)	3.0 (2.6–3.4)
Getting money for health services			< 0.001*			0.004*			< 0.001*
Big problem	5044 (38.5)	3.9 (3.2–4.8)		11526 (55.0)	0.9 (0.7–1.1)		16570 (48.7)	1.8 (1.5–2.1)
Not a big problem	8051 (61.5)	6.1 (5.1–7.3)		9436 (45.0)	1.5 (1.2–1.9)		17487 (51.3)	3.6 (3.1–4.2)
Need factor
Desire for pregnancy			0.617			< 0.001*			< 0.001*
Then	11417 (87.2)	5.1 (4.4–6.0)		19180 (91.5)	1.0 (0.9–1.2)		30597 (89.8)	2.6 (2.3–2.9)
Later	1269 (9.7)	5.9 (3.9–8.7)		1328 (6.3)	2.5 (1.6–3.9)		2597 (7.6)	4.2 (3.1–5.6)
No more	409 (3.1)	6.1 (3.9–9.3)		454 (2.2)	2.8 (1.6–5.0)		864 (2.5)	4.4 (3.1–6.2)
Information access factors
Frequency of reading newspaper/magazine			< 0.001*			< 0.001*			< 0.001*
Not at all	10446 (79.8)	4.0 (3.3–4.8)		19783 (94.4)	1.0 (0.8–1.2)		30230 (88.8)	2.0 (1.8–2.3)
< Once a week	1915 (14.6)	10:1 (8.1–12.6)		857 (4.1)	3.0 (1.8–4.9)		2773 (8.1)	7.9 (6.4–9.7)
≥ Once a week	734 (5.6)	10.2 (7.1–14.4)		321 (1.5)	7.9 (4.8–12.8)		1055 (3.1)	9.5 (7.0–12.6)
Frequency of listening to radio			0.001*			< 0.001*			< 0.001*
Not at all	4124 (31.5)	3.3 (2.5–4.4)		12121 (57.8)	0.6 (0.5–0.8)		16245 (47.7)	1.3 (1.1–1.6)
< Once a week	3759 (28.7)	6.6 (5.2–8.4)		4451 (21.2)	1.7 (1.3–2.3)		8210 (24.1)	4.0 (3.2–4.8)
≥ Once a week	5212 (39.8)	5.8 (4.7–7.1)		4390 (20.9)	2.0 (1.5–2.7)		9602 (28.2)	4.1 (3.4–4.8)
Frequency of watching television			< 0.001*			< 0.001*			< 0.001*
Not at all	4094 (31.3)	1.6 (1.1–2.3)		15434 (73.6)	0.6 (0.4–0.8)		19528 (57.3)	0.8 (0.6–1.0)
< Once a week	2944 (22.5)	4.8 (3.7–6.3)		2812 (13.4)	2.2 (1.6–3.1)		5755 (16.9)	3.6 (2.9–4.4)
≥ Once a week	6057 (46.3)	7.9 (6.7–9.3)		2716 (13.0)	3.3 (2.6–4.4)		8774 (25.8)	6.5 (5.6–7.5)
Frequency of Internet use			< 0.001*			< 0.001*			< 0.001*
Not at all	10578 (80.8)	3.2 (2.7–3.7)		20444 (97.5)	1.0 (0.8–1.1)		31022 (91.1)	1.7 (1.5–1.9)
< Once a week	413 (3.2)	7.7 (4.6–12.5)		105 (0.5)	6.9 (2.9–15.2)		518 (1.5)	7.5 (4.8–11.5)
≥ Once a week	2104 (16.1)	15.1 (12.3–18.4)		413 (2.0)	10.1 (6.9–14.6)		2517 (7.4)	14.3 (11.8–17.1)
* Prevalence of vaginal delivery, CI: confidence interval, Significant at < 1% level, * Significant at < 5% level

Sample characteristics

Table 1 provides an overview of the participants’ characteristics. In the overall Nigerian population, only 8.2% of the mothers had attained higher education, while almost half (46.5%) had received no formal education. One-third of the mothers were not working, and 45% came from poor households. Additionally, most mothers (61.5%) were from rural areas. Most of the births were multiparous, and around 13.5% of children were born smaller in size. Only 3.7% of births were multiple births. One in five mothers had the recommended 8 or more ANC contacts. For 88.2% of the mothers, permission to visit health facilities was not a major problem.

After disaggregating the overall data into rural and urban residences, we observed considerable variations in respondents’ characteristics between the two groups. Urban women notably exhibit a more favourable profile (Table 1), revealing substantial rural-urban differences. In the rural residence, for instance, higher education attainment was only three percent while the percentage was over five times higher (16.6%) in urban residence. Similarly, the percentage of women with no education was nearly three times higher in rural residence (61.8%) than in urban residence (22.1%) (Fig. 3 and Table 1). Furthermore, 64.8% of respondents from rural residence belonged to the poor wealth status compared to only 13.4% in urban residence (Table 1).

Prevalence of caesarean section in Nigeria

In terms of the prevalence of CS and rural-urban differences, 2.7% (95% CI: 2.4, 3.1%, P < 0.001) of mothers reported giving birth through caesarean delivery in Nigeria (Fig. 1 and Table 1). The proportion of CS in urban areas was 5.2% (95% CI: 4.5, 6.1%, P < 0.001), while it was nearly four times lower in rural areas (1.2%; 95% CI: 1.0, 1.4%, P < 0.001) (Fig. 1). Several independent variables were assessed for the unadjusted association with CS (Table 1). These factors remained significant even when the data was disaggregated by urban and rural areas (Table 1). The prevalence of CS reflects notable disparities across geographic (rural and urban, as well as region of residence, Figs. 1 and 2) and socioeconomic (maternal education levels and wealth index, Figs. 3 and 4) divides. In all residences, the prevalence of CS was substantially higher in private facilities, 11.8%, 13.6% and 7.5% in the overall, urban, and rural residences, respectively. Also, respondents in the southern regions, those with higher levels of education and those in the rich wealth index category had higher CS prevalence in all residences (Table 1).

Factors associated with caesarean section in the overall Nigerian population

Table 2 presents the outcomes of our multivariable analysis. Several factors had a significant association with the odds of a CS in Nigeria, including maternal education level, rural-urban residence, wealth index, maternal age, antenatal contact, birth order, birth type, birth size, frequency of internet use, maternal religion, and permission to access healthcare services. Mothers with a higher education level had greater odds (AOR: 2.60, 95%CI: 1.51, 4.45) of a CS compared to those with no education. Similarly, mothers living in urban areas had higher odds of having their childbirth through CS compared to their rural counterparts (AOR: 1.28, 95% CI: 1.01, 1.63). The odds and significance of a CS increased substantially in urban residence once ‘wealth index’ was excluded from the model, suggesting wealth strongly influenced CS utilisation in Nigeria. Mothers from rich households (AOR: 2.92, 95% CI: 1.74, 4.90) or middle wealth status (AOR: 1.97, 95% CI: 1.16, 3.33) had more likelihood of CS compared to their counterparts from poor households. Likewise, mothers aged 35–39 years (AOR: 2.55, 95% CI: 1.13, 5.75) and 40–44 years had higher odds (AOR: 3.11, 95% CI: 1.34, 7.25) of CS compared to mothers aged 15–19 (the reference category).

Table 2

Factors associated with caesarean section in Nigeria and across urban and rural residences
Factors	Urban Nigeria			Rural Nigeria			Overall Nigeria
	AOR	95%CI	P-value	AOR	95%CI	P-value	AOR	95%CI	P-value
Maternal education			< 0.001			0.037			< 0.001
Primary	1.00	0.46–2.17	0.997	0.96	0.41–2.24	0.925	1.08	0.63–1.88	0.772
Secondary	1.61	0.79–3.32	0.193	1.38	0.64–3.00	0.409	1.57	0.93–2.67	0.092
Higher	2.54	1.23–5.26	0.012	2.53	1.13–5.68	0.025	2.60	1.51–4.45	0.001
No education	1.00	Reference	-	1.00	Reference	-	1.00	Reference	-
Residence									0.042
Urban							1.28	1.01–1.63	0.042
Rural							1.00	Reference	-
Husband education level						0.018
Higher				3.16	1.43–6.98	0.004
Primary				2.01	0.74–5.42	0.169
Secondary				1.94	0.91–4.14	0.085
No education				1.00	Reference	-
Wealth index			0.008			0.037			< 0.001
Rich	2.70	1.16–6.29	0.021	2.51	1.22–5.14	0.012	2.92	1.74–4.90	< 0.001
Middle	1.66	0.70–3.91	0.25	1.98	0.96–4.09	0.065	1.97	1.16–3.33	0.012
Poor	1.00	Reference	-	1.00	Reference	-	1.00	Reference	-
Maternal age			< 0.001			0.006			0.002
20–24	0.89	0.36–2.20	0.801	1.70	0.48–6.03	0.410	0.66	0.29–1.50	.325
25–29	1.27	0.51–3.16	0.608	1.31	0.65–2.66	0.448	0.98	0.44–2.18	.956
30–34	1.91	0.76–4.80	0.169	2.98	1.45–6.13	0.003	1.56	0.70–3.47	.274
35–39	3.36	1.31–8.64	0.012	3.76	1.75–8.10	0.001	2.55	1.13–5.75	.024
40–44	3.45	1.29–9.23	0.014	4.97	1.96–12.64	0.001	3.11	1.34–7.25	.008
45–49	7.81	2.78–21.96	< 0.001	2.98	0.97–9.15	0.056	4.06	1.64–10.07	.002
15–19	1.00	Reference	-	1.00	*Reference	-	1.00	Reference	-
ANC contacts			< 0.001			0.021			< 0.001
ANC (8 or more contacts)	1.78	1.37–3.32	< 0.001	1.54	1.07–2.21	0.021	1.69	1.36–2.09	< 0.001
Underuse (7 or less contacts)	1.00	Reference	-	1.00	Reference	-	1.00	Reference	-
Birth order			< 0.001			0.004			< 0.001
1	3.24	2.17–4.81	< 0.001	3.30	1.64–6.64	0.001	3.16	2.23–4.48	< 0.001
2–3	2.09	1.50–2.91	< 0.001	1.96	1.12–3.44	0.019	1.98	1.49–2.62	< 0.001
≥ 4	1.00	Reference	-	1.00	Reference		1.00	Reference	-
Birth type			< 0.001						< 0.001
Multiple	3.96	2.15–7.31	< 0.001				3.17	1.96–5.13	< 0.001
Singe	1.00	Reference	-				1.00	Reference	-
Birth size						0.004			0.01
Large				1.96	1.31–2.92	0.001	1.38	1.11–1.71	.004
Small				1.88	1.06–3.35	0.032	1.33	0.98–1.80	.068
Average				1.00	Reference	-	1.00	Reference	-
Frequency of using the Internet			0.008						0.003
< Once a week	1.12	0.59–2.12	0.198				1.07	0.61–1.86	0.814
≥ Once a week	1.72	1.22–2.43	0.002				1.70	1.25–2.30	0.001
Not at all	1.00	Reference	-				1.00	Reference	-
Maternal religion			0.006						< 0.001
Christianity	1.73	1.24–2.42	0.001				1.76	1.32–2.34	< 0.001
Traditionalist/others	1.43	0.46–4.41	0.537				0.82	0.24–2.80	0.755
Islam	1.00	Reference	-				1.00	Reference	-
Getting permission for healthcare			0.006						0.002
Not a big problem	1.94	1.04–3.61	0.006				2.06	1.31–3.22	0.002
Big problem	1.00	Reference	-				1.00	Reference	-
Desire for pregnancy						0.056
No more				1.73	0.84	0.134
Later				1.77	1.01	0.047
Then				1.00	Reference	-
Decision on own health						0.03
Respondent alone				1.44	0.82	0.201
Respondent and spouse				1.68	1.14	0.008
Spouse alone/someone else/others				1.00	Reference	-
AOR: adjusted odds ratio, Significant at P < 0.05 level, *Age 20–24 was chosen as the reference category in rural Nigeria to clarify the significance of the association of maternal age with CS odds. Note: We reported only the significant factors in each of the residences. A factor may be significant in one residence but not in the other, explaining why some variables could have vacant spaces in one or more residences. To prevent over-fitting and or low statistical power, we included only factors significant at P < 0.05 (in Table 1) in our multivariable modelling.

Mothers with eight or more antenatal contacts had higher odds of CS compared to those with seven or fewer contacts (AOR: 1.69, 95% CI: 1.36, 2.09). Additionally, compared to mothers with a birth order ≥ 4, we found increased odds of CS among those with a birth order of one (AOR: 3.16, 95% CI: 2.23, 4.48), and those with a birth order of 2–3 (AOR: 1.98, 95% CI: 1.49, 2.62). Furthermore, multiple births (AOR: 3.17, 95% CI: 1.96, 5.13), and larger birth size (AOR: 1.38, 95% CI: 1.11, 1.71) increased the odds of a CS. Mothers who used the internet at least once a week (AOR: 1.70, 95% CI: 1.25, 2.30), those who identified as Christian (AOR: 1.76, 95% CI: 1.32, 2.34) and those with no difficulty in getting permission for healthcare services (AOR: 2.06, 95% CI: 1.31, 3.22) had higher odds of a CS.

Factors associated with CS in urban residence in Nigeria

Table 2 presents factors associated with CS in urban Nigeria. Mothers with higher education (AOR: 2.54, 95% CI: 1.23, 5.26), those from rich wealth category (AOR: 2.70, 95% CI: 1.16, 6.29), and those aged 35–39 years (AOR: 3.36, 95% CI: 1.31, 8.64) or 40–44 years (AOR: 3.45, 95% CI: 1.29, 9.23) had elevated odds of undergoing CS, compared to their respective counterparts. Similarly, mothers who recorded eight or more ANC sessions were more likely to have a CS (AOR: 1.78, 95% CI: 1.37, 2.32) compared to those with fewer ANC contacts. A birth order of one (AOR: 3.24, 95% CI: 2.17, 4.81), and 2–3 (AOR: 2.09, 95% CI: 1.50, 2.91) significantly increased the likelihood of CS when compared to a birth order of four or more. Mothers of multiple births (AOR: 3.96, 95% CI: 2.15, 7.31), those who identified as Christians (AOR: 1.73, 95% CI: 1.24, 2.42), who used the internet at least once a week (AOR: 1.72, 95% CI: 1.22, 2.43), or those for whom obtaining consent to access a healthcare facility posed no challenges (AOR: 1.94, 95% CI: 1.04, 3.61) had higher odds of a CS in urban Nigeria.

Factors associated with CS in rural residence in Nigeria

Table 2 shows the factors associated with CS in rural Nigeria. These factors include a higher level of education for mothers (AOR: 2.53, 95%CI: 1.13, 5.68) and their husbands (AOR: 3.16, 95% CI: 1.43, 6.98), compared to no education, respectively. Mothers in the rich wealth index (compared to poor wealth category, AOR: 2.51, 95%CI: 1.22, 5.14), and those having ANC of at least eight times (compared to seven or fewer contacts, AOR:1.54, 95% CI: 1.07, 2.21) had increased odds of CS. Similarly, mothers aged 30–34 years demonstrated nearly 3 times higher odds of CS (AOR: 2.98, 95% CI: 1.45, 6.13) compared to those aged 20–24 years. Similar trends were observed for older age groups, including mothers aged 35–39 years (AOR: 3.76, 95% CI: 1.75, 8.10) and 40–44 years (AOR: 4.97, 95% CI: 1.96, 12.64).

Compared to the birth order ≥ 4, birth order of one (AOR: 3.30, 95% CI: 1.64, 6.64), and 2–3 (AOR: 1.96, 95% CI: 1.12, 3.44), respectively, were associated with increased odds of having a CS. Additionally, large birth size (AOR: 1.96, 95% CI: 1.31, 2.92), and small birth size (AOR: 1.88, 95% CI: 1.06, 3.35) increased the odds of a CS, compared to average birth size. In comparison to those who desired pregnancy at the time they conceived, mothers who had such desire later (potentially, unplanned pregnancy) demonstrated higher odds of a CS (AOR: 1.77, 95% CI: 1.01, 3.11). Finally, mothers who jointly made decisions with their spouse on their health had increased odds of a CS (AOR: 1.68, 95% CI: 1.14, 2.46), compared to their counterparts for whom such decisions were made without their involvement.

Comparing factors associated with CS across rural and urban Nigeria

In both rural and urban Nigeria, several common factors were associated with the odds of a CS. First is a higher level of maternal education with comparable effect sizes (rural: AOR 2.53, 95% CI: 1.13, 5.68; urban: AOR 2.54, 95% CI: 1.23, 5.26). Secondly, mothers from wealthier households had elevated odds of a CS, regardless of whether they resided in rural or urban areas in Nigeria. Estimates of effect sizes are similarly comparable (rural: AOR 2.51, 95% CI: 1.22, 5.14; urban: AOR 2.70, 95% CI: 1.16, 6.29). Thirdly, in both settings, having optimal ANC contacts (8+) increased the odds of a CS (rural: AOR 1.54, 95% CI: 1.07, 2.2; urban: AOR 1.78, 95% CI: 1.37, 2.32). Moreover, in both rural and urban contexts, older mothers, particularly those aged 30–44, were more likely to undergo a CS. Lastly, lower birth order was associated with increased CS odds in both rural and urban areas (rural: AOR 3.30 [birth order of one], AOR 1.96 [birth order of 2–3]; urban: AOR 3.24 [birth order of one], AOR 2.09 (birth order of 2–3]).

Despite these shared factors, disparities exist between rural and urban Nigeria in terms of factors associated with CS. In rural Nigeria, birth size was a significant factor, with both larger and smaller birth sizes increasing the odds of CS compared to average-sized births. This factor did not exhibit a significant association in urban settings. The timing of ‘desire for pregnancy’ revealed differences. In rural areas, mothers who desired pregnancy later (potentially unplanned pregnancy) had higher odds of a CS, whereas this factor was not significant in urban Nigeria. Spousal involvement in health decisions emerged as a noteworthy factor in rural Nigeria, where mothers who made joint healthcare decisions with their husbands had increased CS odds. Conversely, this factor did not show a significant association in urban Nigeria. While older maternal ages of 30–44 years were significantly associated with CS in both residences, maternal age of 45 years and above was associated with CS only in rural Nigeria. In rural Nigeria, mothers whose husbands had higher education levels demonstrated increased odds of a CS; this association was not observed in urban areas. Notably, several unique factors were found to be associated with CS in urban Nigeria, including multiple births, religious affiliation (with mothers professing Christianity having higher odds), frequency of internet use, and the ease of obtaining permission to visit health facilities.

We present a comprehensive exploration of CS prevalence and associated factors in Nigeria with a focus on rural-urban inequalities. Our analysis reveals an overall CS prevalence of 2.7% (95%CI: 2.4, 3.1, P < 0.001) which is consistent with previous Nigerian studies ^26,27. Compared to the 2.0% reported in 2008 ³⁶ and the 2.1% estimated in 2013 ⁹, the present finding indicates a marginal increase; nonetheless, our study supports an underutilisation, indicating unmet needs, and emphasising the necessity for improved access to life-saving CS in Nigeria. Consistent with this position, the CS prevalence in our study is considerably lower than the pooled estimate for sub-Saharan Africa (6.04%)³² and those of similar African countries, including Rwanda 15.6% (in 2019–2020) ³⁷, Ghana (12.80% in 2014)³⁸, Liberia (5.48 in 2019–2020)³², and Togo (7.0 in 2013–2014)³². Our estimated CS prevalence also differs from findings in healthcare facility-based studies, an example being a recent systematic review reporting a prevalence of 17.6% across Nigeria ²⁴. Institutional CS prevalence represents a proportion of cesarean births relative to the total number of deliveries at a healthcare facility, and this can vary widely, not necessarily reflecting the population-level prevalence ^9,24. The WHO's position that CS rates exceeding 10–15% have no additional benefits, relates to population-based estimates ^1,6,7.

We assess the within-population differences in CS prevalence and associated factors across rural and urban residences in Nigeria. To our knowledge, this is the first study to provide this critical insight into CS utilisation in Nigeria. We notably uncovered a substantial gap between rural and urban areas, with rural respondents having a nearly fourfold lower CS prevalence (1.2%) compared to their urban counterparts (5.2%). CS prevalence similarly differs between rural and urban Nigeria across geographic, educational, and socioeconomic factors. The substantial divergence in CS utilisation observed in our study strongly suggests limited access or potential barriers in rural Nigerian settings—consistent with the concept of social inequities occasioned by geographic and socioeconomic inequalities ³⁹. This observation gains further support across regions in the country. For instance, CS prevalence was generally higher in the southern compared to the northern regions. Even in rural areas, CS prevalence in the southern regions exceeded that of any northern region, be it urban, rural, or overall, except urban North-Central. These significant rural-urban and regional variations likely mirror Nigeria’s healthcare facility distribution. The WHO recommends a minimum of five emergency obstetric care facilities per 500,000 people, with at least one capable of comprehensive services (that is CEmOC), evenly spread across all subpopulations ⁴⁰. There is no convincing evidence to suggest this level of CEmOC facility and service coverage, particularly, in rural areas, in Nigeria ^41–44. Contrariwise, inadequate, or sparsely distributed CEmoC facilities and expertise are commonly reported ^41–45, and citing of facilities is largely influenced by donor funding and political pressure rather than data-driven needs assessments ⁴⁴.

In all residences, higher maternal education was strongly associated with increased CS odds, echoing previous evidence of maternal education’s centrality to healthcare services utilisation ^9,26,28. Urban residency also demonstrated a significant association with higher odds of CS (overall population), similar to findings in Nigeria and elsewhere ^9,27,32,38, potentially reflecting the ‘urban advantage’ in access to services ^28,30,32. Furthermore, mothers from rich households had elevated CS odds across overall, rural, and urban settings in Nigeria, reflecting socioeconomic disparities in CS access in Nigeria ⁴⁶. By excluding ‘wealth index’ from our adjusted model, we observed a substantial increase in the disparity of CS odds between rural and urban residences, indicating socioeconomic inequalities indeed play a pivotal role in CS utilisation in Nigeria. Thus, addressing socioeconomic inequalities represents a notable intervention implicated in our study. The finding of increased CS odds among mothers aged ≥ 35, in all residences, suggests age-related complications or maternal preferences in this demographic. The link of optimal ANC with increased CS odds also cuts across all residences in Nigeria, possibly signifying the heightened medical surveillance that ANC services promise ²⁸. Optimal ANC may expose pregnant women to health information, and contribute potentially to debunking myths about CS. Lastly, primiparous mothers and those with a birth order of 2–3 had elevated CS odds, suggesting higher childbirth risks necessitating surgical delivery among first-time mothers or those with fewer prior pregnancies. Current findings notably align with the 2013 NDHS results ⁹, except for 'husband education' and 'health insurance coverage,' which did not retain significance in the overall and urban residence.

Factors associated with CS in urban areas include multiple births, Christianity, frequency of internet use, and ease of obtaining permission for healthcare services. These factors may be explained by urban advantages such as healthcare infrastructure, access to information, employment opportunities, cultural or religious diversity, technology, social amenities, and autonomy or likely support systems for health decision-making. The finding that internet access is associated with CS in urban residence, is particularly interesting, given its potential to contribute to the socioeconomic well-being of the population ^47,48. Internet access is considered a fundamental human right ^47,48, and the United Nations supports its unrestricted access ⁴⁸. Further recognising its potential, Internet connectivity and digital literacy have been termed the “super social determinants of health” because of their capacity to influence other social determinants of health ^49,50. However, the proportion of mothers using the internet is low, and extremely so in rural Nigeria (< 3%), indicating poor access, a likely reason the factor was not significant in rural areas. Improved internet access can facilitate information accessibility, telemedicine, online prenatal education, networking, socialising and virtual meetings, ultimately enhancing timely access to resources, cost-saving, informed decision-making and contributing to more effective healthcare service utilisation ^49–51, including CS, indicated in the present study. Our finding, thus, provides evidence-informed support for enhanced internet access towards improving maternal healthcare service use in Nigeria.

In rural Nigeria, the likelihood of a CS was uniquely associated with disproportionate birth size (large and small), aligning with a recognised CS indication ³. This finding agrees with similar observations in many countries ³² and echoes results from the 2013 NDHS analysis conducted on the broader Nigerian population ⁹. The significance of this factor in rural Nigeria may relate to limited interventions, facilities, resources, or expertise for managing anticipated complicated delivery of disproportionately sized babies in rural relative to urban areas. Other factors such as unplanned pregnancy and spousal involvement in healthcare decision-making increased CS odds exclusively in rural residence. The results regarding unplanned pregnancies might signal limited knowledge or access to family planning services in rural areas—a crucial entry point for targeted interventions.

On the other hand, collaborative healthcare decision-making that harnesses the autonomy of mothers and incorporates spouses’ inputs may be an important contributor to utilising life-saving CS (and perhaps other healthcare services) in the context of rural Nigeria. Furthermore, mothers whose husbands attained higher education demonstrated increased odds of CS in rural areas, lending support for the influence of education on healthcare decision-making. Identifying this factor in rural residences possibly underscores the considerable gap in husband’s higher educational attainments between rural (7.9%) and urban (25.9%) residences. Providing opportunities for rural mothers and their husbands to achieve their full educational capacity, thus, comes across as an important entry point for improving CS utilisation in rural Nigeria. Furthermore, higher spousal education attainment, especially in rural areas, may be indicative of a higher socioeconomic status, which can contribute to greater access to healthcare resources and services, including, CS.

Implications and recommendations

Current findings have far-reaching implications for CS utilisation in Nigeria. Firstly, addressing the rural-urban disparities requires immediate attention through targeted interventions. These interventions may focus on enhancing the availability and accessibility of CS facilities and services, promoting ANC utilisation, raising awareness about the life-saving benefits of CS, and improving the expertise of healthcare providers. Secondly, the link between higher maternal education and increased CS prevalence brings to the fore the importance of prioritising formal education for girls in Nigeria. This recommendation gains further significance given the relatively low levels of higher educational attainment in the country (8.2%), especially among rural mothers (3.0%). The result implicating ‘husband’s education’ in rural residence further supports prioritising rural Nigeria for educational-related interventions. Providing an enabling environment for rural dwellers to achieve their educational potential can foster socioeconomic empowerment, poverty reduction, gender equality, informed decision-making ability and improved health-seeking behaviours.

Thirdly, with ‘wealth index’ as a significant predictor of CS in all residences, socioeconomic barriers to healthcare utilisation persist in Nigeria. Given that health insurance did not demonstrate significance as a CS predictor in any location (multivariable analyses), merely subsidising costs or eliminating user fees may not necessarily boost CS utilisation. This position aligns with the findings of a study on user fee exemption in Nigeria, which revealed lower CS rates among low-income women, notwithstanding the availability of free maternal healthcare services ²³. Thus, a multifaceted intervention strategy, addressing systemic issues (social injustices, for example), promoting high-quality education, ensuring equal opportunities, creating job prospects, and empowering individuals and communities to enhance their economic well-being, is likely to yield more effective and enduring results ³⁹. Moreover, the distinct urban factor related to religious affiliations underscores the need for faith-based and culturally sensitive health communication strategies. Lastly, the association between internet use and CS in urban areas (where access is relatively better) suggests the potential effectiveness of digital health campaigns and online platforms as tools for telemedicine, health awareness or education. Providing equal access to the Internet may, thus, contribute to bridging CS (and potentially, other maternal healthcare services) utilisation inequalities, across rural and urban Nigeria.

Strengths and limitations

This study has several notable strengths that enhance the credibility and relevance of its findings. We used recent, nationally representative datasets with high response rates and a rigorous rural-urban data disaggregation approach. Accordingly, our findings are current and generalisable, providing valuable insights into the prevalence of CS and the associated factors across geographic divides in Nigeria. With a large sample size, data disaggregation does not undermine the study's generalisability. The study maintains a low rate of missing data and uses complex sample statistics during data analysis, thereby enhancing the representativeness, accuracy, and unbiasedness of its estimates. Remarkably, this study represents the first nationally representative endeavour in Nigeria to comprehensively investigate the prevalence of CS and its associated factors with data disaggregated for rural and urban contexts.

However, it is important to consider the limitations of this study when interpreting its results. Firstly, the study's cross-sectional design restricts its capacity to establish causal relationships between the outcome and predictor variables. Secondly, the dataset employed relies on self-reported information collected retrospectively, which makes it susceptible to potential biases associated with social desirability and information recall. Furthermore, due to a significant proportion of missing data for body mass index, the study did not include obesity among the factors analysed. Additionally, the "place of delivery" was not included in the adjusted analysis, as a substantial number of mothers delivered at home. Notably, the WHO recommends the use of the Robson classification for CS within and between healthcare facilities; hence, it was not applied in this population-based study. Lastly, due to data constraints, the study was unable to stratify CS into emergency or elective categories.

This study provides new insights into the use of CS and the associated factors in Nigeria, with a focus on the inequalities between rural and urban areas. Our findings revealed approximately four times higher prevalence and increased odds of CS in urban compared to rural Nigeria. The prevalence of CS was also disproportionately lower among respondents in northern regions and those in the low socioeconomic stratum, highlighting geographic and socioeconomic inequalities in access to the life-saving obstetric care services in Nigeria. Predictors of CS utilisation showed varying degrees of significance in rural and urban residences. While some factors were consistent across both settings, such as maternal education, wealth status, maternal age, and ANC contacts, others displayed notable differences. In rural areas, abnormal birth size, unplanned pregnancy, spousal involvement in health decisions, older maternal age > 45 years, and husbands' higher education levels increased the odds of CS. In contrast, multiple births, Christianity, frequency of internet use, and ease of getting permission increased the odds of CS in urban areas. Our study highlights the imperatives of addressing inequalities in CS access, particularly in rural areas and the northern regions. Targeted interventions are needed for Muslim mothers (in urban areas), uneducated mothers (in all residences), and socioeconomically disadvantaged mothers (in all residences). Bridging socioeconomic inequalities between rural and urban areas, ensuring adequate numbers of well-equipped facilities across geographic divides, providing equal access to the internet, and encouraging ANC use is crucial in addressing inequitable utilisation of life-saving CS in Nigeria.

Ethics approval and consent to participate: We utilised publicly accessible, de-identified datasets from the Demographic and Health Survey program. The survey received ethical approval from the ICF Institutional Review Board in the USA, and the National Ethics Board in Nigeria. Mothers provided informed consent both for themselves and on behalf of their children ¹⁰. Given this research only involved a secondary analysis of completely anonymised data, there was no need for additional ethical clearance. Authors received approved access to utilise the data.

Consent for publication: Not applicable.

Availability of Data and materials: Access to the data underlying this study is freely granted through online requests on the DHS's website at www.dhsprogram.com.

Competing interests: The authors declare no conflicts of interest.

Funding: This research did not receive any funding.

Author Contributions: EOA and VK conceived and designed this study. EOA carried out statistical analysis, AAP extracted data and populated Tables, and EOA, VK and MIA contributed to interpreting the results. EOA, WA, VO, AAP, RO, MIA, AA, and VK contributed to writing various sections of the first manuscript draft. All authors reviewed the manuscript for intellectual content and approved the final copy for submission.

Acknowledgments: The authors thank DHS/ICF, USA, for freely providing the NDHS dataset that made this research possible.

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No competing interests reported.

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Inequalities in the prevalence of Caesarean section and associated factors in rural and urban Nigeria: insights from the 2018 Nigeria Demographic and Health Survey

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Abstract

Figures

Introduction

Methods

Data source

Variables

Outcome variable

Factors

Statistical analysis

Results

Sample characteristics

Prevalence of caesarean section in Nigeria

Factors associated with caesarean section in the overall Nigerian population

Factors associated with CS in urban residence in Nigeria

Factors associated with CS in rural residence in Nigeria

Comparing factors associated with CS across rural and urban Nigeria

Discussion

Implications and recommendations

Strengths and limitations

Conclusion

Declarations

References

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