Assessment of quality of maternal and newborn care and its determinants: a national study of primary health care facilities in Nigeria

Abstract

Background

Reducing the global burden of maternal morbidity and mortality necessitates focusing on the quality of maternal and newborn care (MNC). This study aimed to assess the quality of MNCs among 25,840 public primary healthcare centers (PHCs) across Nigeria, analyze subnational variations, and determine the influence of contextual variables on the quality of care (QoC) by PHCs.

Methods

Data from a nationwide PHC assessment conducted in 2022 were utilized to create the composite index of care for PHC facilities in Nigeria. Summary statistics were then generated for the composite of care scores across PHC facilities. Subnational differences in QoC among the 36 states and the Federal Capital Territory and the influence of four contextual variables (Basic Health Care Provision Fund-status of PHC, activity of ward development committees, implementation of quality improvement plan, and activity of facility management committees) on QOC were determined using a one-way analysis of variance and multiple linear regression respectively.

Results

PHCs in Nigeria had QoC scores ranging from 0.06-4.0 and a mean QoC score of 2.07. There were significant variations in the mean PHC facility QoC across states, with Katsina and Ondo states having the lowest (1.35) and highest (2.98) QoC, respectively. The regression model showed that the contextual variables analyzed accounted for 31.5% of the variation in QoC, with varying statistically significant relationships with the QoC.

Conclusion

The quality of maternal and newborn healthcare in Nigeria’s PHC facilities is unsatisfactory, with noticeable subnational variation in the QoC. To achieve significant improvements in the quality of care provided by PHC facilities in the country, targeted interventions, such as empanelment of more PHCs as BHCPF facilities, strengthening implementation of quality improvement plans and strengthening activities of ward development committees, and facility management committees, are required to improve the quality of maternal and newborn care, and reduce maternal and infant mortality rates.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-025-12957-6.

Background

Quality of care is increasingly recognized as a crucial component in achieving universal health coverage (UHC) worldwide [1]. Poor quality care has been linked to adverse patient outcomes such as excess and inappropriate care, iatrogenic deaths, and antimicrobial resistance [2]. Evidence exists that maternal mortality has decreased in the last decade in other world regions, such as Central and South Asia, and some Oceania countries (like Australia and New Zealand) [3]. Unfortunately, quality of care remains low in many low- and middle-income countries (LMICs), especially sub-Saharan Africa, despite its growing recognition [4], and a revised quality of maternal and neonatal care framework (QMNC) [5]. This is particularly critical in the context of maternal and newborn care, where poor quality of maternal care services contributes to a high rate of morbidity and mortality [6, 7]. Unlike their LMIC counterparts, several European countries have established an enhanced surveillance system to optimize lessons learned from maternal death [8], fitting experts’ assumption that a high-quality health system will reduce the maternal mortality rate (MMR) [9].

Quality of care is a determinant of maternal and child health outcomes, which needs to be monitored and improved for better results [10]. Ensuring high quality of maternal and childcare in countries with a high burden of maternal and infant mortality has been established to have the potential to reduce maternal deaths, stillbirths, and neonatal deaths by 28% with the existing level of care [11]. Over the years, quality of care has constantly remained in a state of deterioration in many LMICs, including Nigeria [12] despite its high potential. Therefore, there is a need to improve both the coverage and the quality of maternal and childcare services throughout the continuum of care [13]. Quality of maternal and newborn care is a multidimensional concept consisting of structural, process, and outcome measures, which are all important in improving the quality of care [14]. Due to the multidimensional nature of the quality of care, composite indices allow for simultaneous consideration of multiple dimensions and comparison of performance across facilities or systems [15].

To ensure the monitoring of quality of care, the World Health Organization (WHO) has developed a comprehensive framework consisting of eight domains to capture the multidimensional nature of quality care for pregnant women and newborns in health facilities [12]. Each of these domains consists of a set of indicators that are fundamental to assessing, improving, and monitoring the quality of care [16]. Although monitoring the quality of care delivered across health facilities using long lists of indicators can be challenging, the use of a composite index allows for comparing, tracking, and monitoring complex concepts like the quality of care by combining multiple indicators into a single index while reflecting the true nature of the underlying concept [15, 17]. A composite index has been applied in tracking maternal service coverage and quality at various levels, including facility [15], district [17], and other types of subnational levels [18, 19]. A composite index has also been applied to research endeavours seeking to assess the quality of care in LMICs, including Nigeria [20, 21].

Global evidence of MMR indicates that there are 223 deaths per 100,000 live births [22], which is three times more than Sustainable Development Goal 3 (focus 1) to attain < 70 per 100,000 live births before 2030 [23]. Onambele et al. [24] highlighted that MMR in sub-Saharan Africa declined by nearly 40% between 2000 and 2017. Nigeria, after South Sudan and Chad, currently ranks third among countries with the highest rate of maternal mortality in the world [25], with 1,047 per 100,000 live births [26], and approximately 50,000 women lose their lives annually due to preventable conditions [27]. According to UNICEF [28], the average Neonatal mortality rate (NMR) is 17 deaths per 1000 live births, which is less than the sub-Saharan Africa NMR of 27 deaths per 1000 live births [29]. Multiple factors have been reported to contribute to this high rate of maternal mortality, and they include medical causes, social determinants, cultural factors, behavioral factors, and healthcare-related factors [30]. Studies have also highlighted that poor quality of care, such as the non-use of labour monitoring tools and the absence of companions during labour, predicts maternal and perinatal deaths in healthcare facilities across the country [31]. Furthermore, the perception of poor quality of care has reduced the utilization of healthcare facilities for antenatal clinic visits, child delivery, and postnatal care services [32, 33].

In Nigeria, the provision of primary healthcare (PHC) services is considered a core strategy to improve maternal health outcomes due to its accessibility [34]. Only a few studies have assessed the multidimensional nature of the quality of maternal care delivery among primary healthcare facilities in Nigeria [35–37]. Most of these studies, however, did not utilize a composite index to evaluate the quality of care. However, a previous study conducted across the six geopolitical zones in Nigeria [21] assessed the influence of the quality of maternal care offered at healthcare facilities (in the selected sites) on the uptake of such services using a composite index, and observed the influence of quality on demand for obstetric care.

Since the passage of the National Health Act in 2014, Nigeria has intensified efforts to strengthen its PHC system, including the introduction of direct facility financing mechanisms [38]. The flagship DFF initiative, the Basic Healthcare Provision Fund (BHCPF), allocates at least 1% of the consolidated revenue fund to support the delivery of a basic minimum package of healthcare services nationwide. Given the limited financial resources available, the initial phase of BHCPF implementation prioritizes apex PHC facilities at the ward level [39]. Beyond improving financing, the BHCPF model emphasizes community-led management of healthcare facilities and facility-driven, community-supported quality improvement initiatives, with a particular focus on enhancing maternal and child health services [40].

In this study, we aimed to assess the quality of MNC services among 25,840 public PHCs and examined the effects of four contextual factors closely aligned with the BHCPF framework that may influence the quality of maternal and newborn care at the facility level: (1) BHCPF accreditation status of facilities, (2) the existence and use of quality improvement plans, (3) the recent activity of Facility Management Committees, and (4) the activity of Ward Development Committees. These factors were selected because they reflect both the operationalization of direct facility financing and the community engagement structures intended to improve service delivery outcomes.

Furthermore, this study seeks to contribute to the knowledge and understanding of public health practitioners (especially MNC enthusiasts) and academia on the quality of care towards maternal and neonatal care using a composite index please see Supplementary material A) through primary health care (PHC) facilities across Nigeria. Additionally, the study aims to understand the underlying determinants of healthcare quality by assessing the effects of four relevant contextual variables on the quality of care delivered among these PHC facilities. Hence, findings from this study will generate new insights geared towards informing policymakers and healthcare stakeholders about the current state of care and identifying lacunas to enhance maternal and neonatal quality of care for mothers and newborns.

Methodology

To assess the quality of maternal and newborn care in PHC facilities in Nigeria, we used data from a comprehensive national assessment survey of PHCs conducted across the Thirty-six (36) states of Nigeria and the Federal Capital Territory (FCT) between August and September 2022. The assessment evaluated the state of primary healthcare facilities in the country across seven thematic areas: service delivery, financial management, human resources for health, logistics and commodities, infrastructure, ward mechanism and governance, and health management information system. The choice of these thematic areas was guided by the National Primary Health Care Development Agency’s established criteria for assessing PHC functionality [41]. One hundred and forty-eight (148) trained research assistants collected data (across the 36 + 1 states) through direct observation of facilities, interviews with facility managers (one hundred and eleven across the 36 + 1 states), and exit interviews with patients (one hundred and eleven across the 36 + 1 states) in 25,840 health facilities nationwide. The survey data was collected using a computer-assisted personnel interview (CAPI) through an Open Data Kit (ODK) called KoboCollect country-wide.

Study framework and statistical technique

To assess the quality of maternal and newborn care in each primary healthcare facility using a composite index, we adapted the approach of Wilhelm et al. [15] who adapted a stepwise approach for developing a composite index for high-income countries outlined by the Organization for Economic Cooperation and Development (OECD) for low and middle-income countries. The guideline describes the steps for the development of composite indicators as follows: (i) Identification of appropriate framework (ii) metric selection (iii) imputation of missing data (iv) initial data analysis (v) normalization (vi) weighting and aggregating selected variables (vii) uncertainty and sensitivity analysis (viii) deconstruction of the score into components.

Our approach to use these steps to develop a composite index is detailed below;

We adapted the list of indicators identified by Wilhelm et al. [15], which were categorized using the WHO quality of care matrix. Due to the exclusion of direct observation of labour and delivery from the data collection method of the PHC assessment survey, we substituted these indicators related to direct observation of labour and delivery with related indicators from direct facility observation and interviews with health facility managers and excluded indicators that cannot be substituted. Our final list of indicators was assigned to the different cells of the quality-of-care matrix: effectiveness, accessibility and timeliness, patient-centeredness, and safety. We included 45 indicators from the dataset to compute the composite maternal and newborn care index based on available data. This included indicators across four dimensions of the quality of care– safe, effective, accessible/timely, patient-centered/acceptable. Equity and efficiency dimensions were not included because of inadequate indicators that captured these dimensions in our original dataset. The full list of indicators is found in Supplementary material A.

Imputation of missing data

From the literature, the approaches to handling missing data in the development of composite indexes are case deletion, single imputation, and multiple imputation [42]. Due to the use of a well programmed data collection software to ensure data validation and appropriate skip patterns, thereby preventing data entry errors, we had high data quality with minimal cases of missing data. Skipped cells automatically assigned a computer-generated value of “blank”, were replaced with a value of 0, which showed they were unobserved. We also did case deletion for one healthcare facility, which had multiple missing variables, this ensured a complete case analysis of our datasets.

Initial data analysis

For most variables in our datasets, responses were binary, and we assigned a value of 0 for negative responses and 1 for positive responses. Using the national standard of care and findings from the literature, we dichotomized variables with multiple options. The water source to the facility was categorized as a clean or unclean source of water based on whether the water source was protected or unprotected. The number of staff required for skilled birth services was dichotomized as adequate or inadequate based on the assumption that at least two nurses/midwives are required for a 24 − 7 maternal and newborn care service provision. Details of the final list of variables selected for the study are presented in Supplementary material A.

Normalization, weighting, and aggregation

We adopted equal weight indices for weighing indicators in our datasets to allow for some level of discrimination between our datasets while staying true to the principle of weighting [43]. Most of the variables in our datasets are binary with values of 0 and 1, however, the few non-binary variables were not dichotomized as they represent different levels of quality on the continuum of care but were rescaled to values between 0 and 1. Indicators within the matrix were aggregated using the addictive approach; the total score obtainable varies between cells depending on the number of indicators within each matrix.

In the next step, we rescaled each matrix score using two approaches, rescaling each cell score to a range of 0 to 1. Normalization ensured their equal contribution to the overall facility composite index score. Following normalization, we additively aggregated the scores within the matrix to create a facility composite score.

Uncertainty and sensitivity analysis

To account for the assumptions we made in the creation of our composite index, we created alternative scenarios for these assumptions at three stages of our analysis: (i) initial data analysis (ii) normalization, and (iii) aggregation.

Alternative A

To account for the uncertainty of our decision to dichotomize all variables in our datasets, we created an alternative where variables were treated ordinally based on their order of preference from most preferred to least preferred.

Alternative B

To normalize cells within matrices, we considered an alternative approach of standardization using z-scores, which resulted in normally distributed values with a mean of 0 and a standard deviation of 1.

Alternative C

We geometrically aggregated the normalized matrix scores to generate our facility composite scores.

Spearman’s rank correlation was used to assess the consistency of facility performance rankings across the four index construction scenarios. Spearman’s rank correlation was used for sensitivity analyses.

Data analysis

Summary statistics were obtained for the quality of care across each geopolitical zone and state of the federation. A one-way analysis of variance was used to assess the variation between the quality of care, geopolitical zones, and states.

Using multiple linear regression, we also examined the relationship between selected management practices and maternal and newborn care quality among the 25,840 health facilities. ANOVA on mean scores and linear regression on score levels rely on the central limit theorem and were confirmed to meet OLS residual assumptions; with n ≈ 25 839, these parametric tests are robust to modest non‑normality.

The dependent variable was each facility’s composite quality of care score, while the independent variables were four selected management practices. The management practices were:

• I)Basic Healthcare Provision Fund (BHCPF) status of health facilities: The BHCPF is a direct facility financing mechanism of the federal government that aims to strengthen the demand and supply of primary healthcare services in Nigeria. The fund in its first phase aims to reduce the rate of maternal and neonatal mortality rates in the country by 31% and 33% respectively [44]. To achieve this goal, health facilities undergo accreditation to ensure they provide quality healthcare services before they are designated BHCPF facilities [45].
• II)Utilization of quality improvement plan: Quality improvement is a rigorous and systematic process that applies continuous efforts and activities to achieve measurable improvement of health outcomes [46]. Quality improvement plans are formal documents that show a facility’s proactiveness in its awareness of its quality status and strategies to improve it.
• III)Activity of the facility management committee in the past month: Facility management committees are a part of the ward development committee system, constituted to support managers in facility management activities and initiatives.
• IV)Activity of the ward development committee in the past quarter: Ward development committees are community ownership structures set up by the National Primary Health Care Development Agency (NPHCDA) to enhance community participation in PHC delivery and performance [47]. These committees are expected to perform the demand functions of creating awareness, promoting health within the community, and monitoring the quality of services received by community members [48].

Because of the possible effects of the BHCPF status of facilities on other contextual variables in our analysis, we included BHCPF as an interaction between terms with other contextual variables to explore potential moderation effects.

Results

This section presents the outcomes of the composite maternal and newborn care index across the assessed PHC facilities. We begin with a sensitivity analysis of index construction, followed by subnational variations in facility scores and the relationship between selected management practices and quality of care.

Uncertainty and sensitivity analysis

To test the robustness of our composite index, we conducted sensitivity analyses by applying three alternative methodological assumptions: dichotomization of variables (Alt A), z-score normalization (Alt B), and geometric aggregation (Alt C). There was a high degree of correlation between the base scores and each alternative, with Spearman correlation coefficients ranging from 0.988 to 0.999. Geometric aggregation (Alt C) showed the lowest correlation with the base (ρ = 0.988), while z-score standardization (Alt B) had the highest (ρ = 0.999).

Alt A (Dichotomization)

Figure 1 compares base scores with those derived from dichotomized variables. The two are strongly correlated (ρ = 0.990), indicating that binary recoding had a minimal impact on facility rankings. However, dichotomization slightly lowered the mean score (2.045 vs. 2.067; − 1.1%) and reduced variability (SD: 0.945 vs. 0.982; − 3.8%). The distribution became more dispersed at the tails, particularly among high- and low-performing facilities, suggesting that binary thresholds may amplify performance differences at the extremes.

Fig. 1.

Base composite score vs. Alt A composite score of PHCs

Alt B (Z-score normalization)

Figure 2 shows results using z-score transformation. As expected, the distribution was centered around zero (mean ≈ 0) and exhibited greater spread (SD: 3.539 vs. 0.982; +260%). The score range expanded (–7.245 to 7.036), enhancing outlier visibility. Despite this, the ranking of facilities remained essentially unchanged, as evidenced by the near-perfect correlation with base scores (ρ = 0.999).

Fig. 2.

Base composite score vs. Alt B composite score of PHCs

Alt C (Geometric aggregation)

Figure 3 presents scores generated using geometric aggregation. This method produced the lowest average score (mean: 0.276; − 86.6%) and the most compressed distribution (SD: 0.242; − 75.4%), with the range narrowing to 0.003–1. This pattern reflects a range-compression effect, in which extreme scores are pulled toward the center. While geometric aggregation still maintained a strong correlation with the base index (ρ = 0.988), it showed the most substantial shifts in individual facility scores.

Fig. 3.

Base composite score vs. Alt C composite score of PHCs

Subnational variation in the quality of care

The analysis of variance of facility index scores across different states revealed significant variations in the mean facility index scores (F = 130.46, p < 0.001). The mean facility index score for the entire dataset was 2.07, with a standard deviation of 0.98. Among the states, Katsina state had the lowest mean facility index score (1.35), indicating relatively lower facility conditions, while Ondo state had the highest mean facility index score (2.98), suggesting better facility conditions. The distribution across the state is shown in Fig. 4.

Fig. 4.

Distribution of quality of care across the states

The analysis of variance (ANOVA) indicated a significant difference in facility index scores between the groups (F = 130.46, p < 0.001), suggesting that the average facility z-composite index score varied significantly across different states. The post-hoc comparisons using the Bonferroni correction revealed significant differences in facility index scores between several state pairs. For example, Adamawa state had significantly lower facility index scores compared to Abia state (p < 0.001), while Lagos state had significantly higher facility index scores compared to Kwara state (p = 0.043). The results of Bartlett’s test for equal variances indicated a significant difference in variances between the groups (chi2 = 1.3e + 03, p < 0.001), suggesting heterogeneity of variances (Supplementary Material B).

Relationship between management practices of phcs and quality of care

The regression model accounted for a 31.5% variation in the quality of care among the healthcare facilities. The adjusted R-squared was 31.46%, indicating a reasonable fit of the model. F-statistics were significant (F (4, 25835) = 2965.48), P < 0.001), which suggests our entire model was statistically significant (Table 1).

Table 1.

Regression coefficients of contextual variables and facility quality of care

Independent variable	Regression coefficient	Standard error	P– value
BHCPF status	0.678	0.011	0.000
Quality improvement plan	0.370	0.013	0.000
Facility management committee	0.357	0.012	0.000
Ward development committee	0.304	0.018	0.000

All independent variables in our model were statistically significant and positively related to the dependent variable. The BHCPF status of health facilities has the highest regression coefficient among the independent variables. Details of the correlation coefficient and corresponding p-values are presented in Table 1.

Moderation effect

Table 2 shows that the interaction between the BHCPF status of health facilities and activities of Ward Development Committees (WDC) in the past quarter was found to be statistically significant (p < 0.001, beta = 0.266), suggesting that the effect of WDC activities in the past quarter on the quality of maternal and newborn care was stronger when the facility provides care to BHCPF enrollees. There was no statistically significant interaction between the BHCPF status of facilities and utilization of the quality improvement plan (p = 0.351) and the BHCPF status of facilities and activities of the facility management committee in the past month (p = 0.419) (Supplementary Material C).

Table 2.

Table of moderation effects

Moderating variables	Regression coefficient	Standard error	P– value
BHCPF status and WDC activities	0.266	0.030	0.000
BHCPF status and facility management	-0.202	0.810	0.419
BHCPF status and quality improvement	0.012	0.038	0.751

Discussion

This study aimed to assess the quality of care among PHCs in Nigeria using a composite index and evaluate the effect of selected contextual variables on the quality of care. Findings from our study reveal that sensitivity analysis is useful in quantifying the assumptions and uncertainties of the scores and rankings of the composite index scores. The alternative scenarios of our analysis are highly correlated, with coefficients varying from 98.8 to 99.9%. This suggests that the different scenarios did not lead to significant differences in the composite index scores; however, the use of geometric aggregation led to substantial differences in the rank of average composite index scores across states in the country. The difference between geometric and addictive aggregation resulted because geometric aggregation, unlike addictive aggregation, does not allow for offsetting a good indicator with a bad one [14, 49].

Our study revealed that the quality of care in primary healthcare facilities, as assessed by the composite index, was relatively low. However, there were significant variations in the quality of care at subnational levels, specifically by region and state. This finding is consistent with previous literature on the quality of care in primary healthcare facilities in Nigeria [21, 36, 50]. The low quality of care observed in these facilities should be a matter of concern for policymakers, as it is associated with a reduced demand for obstetric services [21] and contributes to poor maternal health outcomes [51]. The statistically significant variations in the quality of care across regions and states indicate disparities in access to quality care in the country, highlighting the presence of inequalities in healthcare provision [52, 53]. The subnational variation in quality can also be attributed to the decentralized mode of governance of PHC facilities in Nigeria. Different state governments implement various policies and attract different levels of support from development partners, which can influence the quality of care provided [54, 55]. Our analysis shows that states investing deliberately in PHC quality and maternal and child health over the past decade consistently achieved the highest composite scores, whereas those facing chronic underinvestment, staffing shortfalls, and security or governance disruptions lag. For example, Ondo State’s Abiye Safe Motherhood Initiative (2009–2016) combined free maternal and child services, dedicated referral hospitals, and real-time mortality surveillance efforts credited with an 84.9% reduction in maternal mortality and marked improvements in facility readiness and community trust [56]. Likewise, Lagos State’s integration of quality-assurance requirements into its Ilera Eko health insurance scheme has driven measurable gains in PHC performance across the state [57].

In contrast, states with constrained health budgets and human-resource shortages see poorer outcomes. In Katsina State, persistent workforce gaps, frequent stock-outs, and low public confidence have hampered service delivery [58]. Similarly, security-related disruptions and under-utilized community health funds in Adamawa State have resulted in inconsistent drug supplies and uneven care quality at the PHC level [59]. To improve maternal and newborn health outcomes in Nigeria, targeted, state-level strategies are needed to strengthen the quality of care at primary health centres. These approaches should be modeled on successful interventions already implemented in other states or other similar settings across LMICs.

Findings from our study showed that all four contextual variables significantly affected the quality of care in primary healthcare facilities. Notably, the BHCPF designation of PHCs had the most substantial effect on the quality of care. This finding is unsurprising, given that all BHCPF facilities undergo an accreditation process; facilities are assessed and certified to provide the basic minimum package of health services, including maternal and newborn care, before they can provide services to BHCPF beneficiaries [45]. Although there is a paucity of data on the effects of BHCPF on health facility performance, our findings corroborate that of Esomonu et al. [56], which reported a high satisfaction rate among patients who access care in BHCPF PHCs.

Facility management practices have been shown to contribute to health facility performance in low- and middle-income countries [57, 58]. Our study showed that the meeting of Ward Development Committees (WDCs) in the past quarter significantly influenced the quality of care, with a more pronounced effect observed in BHCPF facilities. This finding suggests that community participation through WDC activity has a positive effect on the quality of care in primary health facilities. Our findings align with previous studies indicating that community engagement is an important element in improving the quality of health facilities, maternal and newborn care [59], and quality of care generally [60, 61]. The effect of WDC meetings on the quality of care might have been limited because these committees do not have decision-making powers and oftentimes have to resort to endlessly appealing to the government [62]. Further research will be necessary to identify the skills and composition of WDCs that led to the increased quality of care observed in this study.

The use of quality improvement plans among PHCs allows for continuous quality improvement. Our study found that the utilization of quality improvement plans among healthcare facilities improved the quality of maternal and newborn care. This improvement may be attributed to increased awareness of quality among healthcare workers in these facilities [63]. However, the small coefficient of this variable could be due to the effectiveness of the quality improvement plan relying on the technical capacity and commitment of facility managers [36]. This highlights the importance of policymakers mandating quality improvement plans among primary health centers and providing regular technical support to facility managers to effectively develop and implement quality improvement plans. To maximize the gains of the quality improvement plan, there is a need to ensure that all PHCs have a minimum level of infrastructure and staff competency [64].

Facility management committees are integral components of the ward development committee, constituted to support managers in facility management activities and initiatives. Our study revealed a positive relationship between the recent activity of facility management committees and the quality of care in PHCs. This finding might be due to elements of community support and participation within these committees [60].

Policy implications

The findings from this study have important policy implications.

Addressing low levels of quality of maternal and newborn care

The study highlights the urgent need for stakeholders in the healthcare sector to prioritize improving the quality of maternal and newborn care in PHCs in Nigeria. This can be achieved through targeted interventions, capacity building, and resource allocation to enhance health facilities’ infrastructure, staffing, and service delivery.

Inclusive approach

While the BHCPF facilities showed better quality of care, it is important to ensure that non-BHCPF facilities are not overlooked in efforts to improve primary healthcare delivery and performance. Policy initiatives should extend support and resources to all PHCs, focusing on enhancing their capabilities to deliver high-quality maternal and newborn care services.

Strengthening community structures

The study highlights the significant role of community structures, such as Ward Development Committees (WDCs), in ensuring quality of care. To improve the quality of care, it is crucial to intensify strategies that enhance the functionality and effectiveness of these community structures.

Limitation

The non-inclusion of direct observation of labor in the data collection process, which resulted in the exclusion of related indicators from the composite index, was a limitation of our study [62, 63]. This limited the comprehensiveness of the index. Future studies should strive to incorporate direct observation of labor to provide a more comprehensive assessment of the quality of care in maternal and newborn services. To mitigate this limitation arising from excluding certain indicators, the study substituted them with elements of direct observation and interviews with facility managers where possible. However, such substitutions involve subjective judgment at the imputation stage and can introduce measurement biases, potentially distorting facility rankings and failing to capture the full nuance of the excluded indicators [64]. Researchers should consider methods to minimize measurement biases and enhance the validity of indicator substitution in future studies.

Conclusion

Improving the quality of maternal and newborn care is crucial for enhancing maternal health outcomes in Nigeria. Using a composite index provides a valuable tool for measuring and comparing the performance of health facilities in delivering high-quality maternal and childcare services. This study revealed poor quality of care in PHCs, with significant subnational variations. Policy interventions should focus on addressing the determinants of quality of care, including the BHCPF status of PHCs, the activities of WDCs, facility management committees, and the utilization of quality improvement plans. By implementing targeted policies and interventions, Nigeria can make significant strides toward improving the quality of maternal and newborn care and reducing maternal and infant mortality rates.

Abbreviations

MNC

Maternal and Newborn Care

PHC

Primary Health Care

QoC

Quality of Care

UHC

Universal Health Coverage

BHCPF

Basic Health Care Provision Fund

LMICs

Low and Middle-Income Countries

WHO

World Health Organization

CAPI

Computer-Assisted Personnel Interview

ODK

Open Data Kit

OECD

Organization for Economic Cooperation and Development

NPHCDA

National Primary Healthcare Development Agency

WDC

Ward Development Committees

Authors’ contributions

TO and OA conceptualized the article. TO developed the original draft that OA initially reviewed. SDI and HIO subsequently reviewed and refined the original manuscript draft. OWS conducted the final review of the manuscript draft and TO and SDI incorporated the feedback. SS and HE conducted a dataset review of the data utilized for analysis. FO managed the project implementation process. HE and OO led the team of program analysts on the project implementation. SN, OD, DA, OO, LK, HU, and SA were all program analysts who played major roles in data curation from the implementation of the project.

Funding

This study did not receive any external funding or grant from any funding agency in the public, commercial, or non-profit sectors.

Data availability

The data supporting the findings will be available from the corresponding author upon request following a 1-year embargo from the publication date. Requests will be examined and considered on a case-by-case basis.

Declarations

Ethics approval and consent to participate

This research adhered to the principles outlined in the Helsinki Declaration. Prior to the commencement of the study, informed consent was obtained from each participant. Additionally, the research protocols were approved by the Federal Ministry of Health (FMoH) and the National Primary Health Care Development Agency (NPHCDA) in Abuja, Nigeria. Having reviewed the protocol (being an essential part of the assessment team), the body waived the need for ethical approval.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.