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Abstract

Objective

To examine the association between Health Information System (HIS) performance and maternal health service (MHS) utilization in the Oromia and Gambella regions, Ethiopia.

Methods

A comparative cross-sectional study was conducted (15–25 October 2023) among 840 mothers in catchment areas of health facilities categorized as model (high HIS performance) or candidate (lower HIS performance). HIS performance was evaluated based on infrastructure (30%), data quality (30%), and data use (40%). MHS utilization was measured using a modified composite coverage index (CCI) integrating 10 essential interventions. Multivariable logistic regression (Stata/MP 17.0) identified predictors, reporting adjusted odds ratios (AORs) and 95% confidence intervals (CIs).

Results

MHS utilization was 60.3%, with higher crude odds in model facility areas (COR = 2.03, 95% CI [1.4–3.0]). After adjustment, this association attenuated (AOR = 1.4, 95% CI [0.92–2.15]). Key barriers included poverty (poorest quintile AOR = 0.45, 95% CI [0.30–0.68]) and limited transport access (AOR = 0.21, 95% CI [0.15–0.29]), which were associated with significantly lower MHS utilization. Sensitivity analyses confirmed robustness, and transport access modified the effect of facility type.

Conclusion

HIS performance alone did not independently predict MHS utilization after accounting for structural inequities. Transportation and economic barriers disproportionately hinder access, even in high-performing systems. Integrating HIS strengthening with poverty-sensitive interventions (e.g., transport support, financial protection) is critical to achieving equitable maternal health outcomes

Keywords

Health information systems maternal health services composite coverage index health services utilization socioeconomic factors Ethiopia cross-sectional study

Background

Maternal health encompasses women’s health during pregnancy, childbirth, and the postnatal period. Skilled care throughout these stages is vital to reducing maternal morbidity and mortality. Despite progress, it remains a critical global health priority. The World Health Organization is committed to improving maternal health by promoting timely, high-quality care to address the leading causes of death and illness worldwide.^1–3

Building on this global concern, maternal mortality remains a significant public health concern, especially in low- and middle-income countries (LMICs). In 2020 alone, an estimated 287,000 women died from preventable maternal causes, with nearly 70% in sub-Saharan Africa (SSA) and 94% in LMICs.⁴ The Sustainable Development Goal (SDG) 3.1 aims to reduce global maternal mortality to less than 70 per 100,000 live births by 2030.^5,6 This global commitment highlights the urgent need to examine both systemic and country-level challenges that hinder progress, particularly in LMICs such as Ethiopia.

In many developing countries, the delivery of MHS is severely hampered by a dual challenge of weak physical infrastructure and ineffective health information systems (HIS). Inadequate facilities, limited transportation, and unreliable utilities directly restrict access to timely and quality care, particularly in remote areas. Concurrently, fragmented and paper-based data systems result in incomplete, inaccurate, or delayed information, which undermines effective planning, resource allocation, and monitoring of services. These two deficiencies create a vicious cycle where a lack of data masks the true scale of need, leading to continued underinvestment and the perpetuation of a neglected and inefficient mental healthcare system.^7,8 Despite the systemic challenges of weak infrastructure and HIS that hinder maternal care; Ethiopia is demonstrating a proactive response by implementing coordinated health system initiatives.⁹ These efforts are strategically aimed at strengthening the delivery of MHS, specifically to leverage well-established, evidence-based interventions that have the proven potential to significantly reduce maternal and neonatal mortality.

Despite Ethiopia's commendable efforts to strengthen its MHS through the Health Extension Program, expansion of health facilities (HFs), and maternity waiting homes.^10,11 However, maternal mortality remains high; in 2020, Ethiopia was among three SSA countries with over 10,000 maternal deaths, accounting for 3.6% of the global total.^7,12 This persistent burden underscores that structural initiatives alone are insufficient, highlighting the urgent need to investigate and address the complex underlying barriers such as socioeconomic factors, cultural norms, and quality of care issues—that continue to limit the utilization of these life-saving services.

MHS utilization is shaped by a complex interplay of factors operating at multiple levels. Facility-level barriers include shortages of skilled providers, inadequate infrastructure (e.g. lack of reliable electricity, water, or delivery rooms), and limited capacity for emergency obstetric care.¹³ At the health system level, constraints such as financial limitations, shortages of trained staff, irregular supplies of essential commodities (e.g. oxytocin, magnesium sulfate, and delivery kits), and inconsistent application of clinical guidelines further challenge service delivery.^14,15 Client-side barriers encompass low educational attainment, poverty, cultural norms, low awareness of pregnancy-related risks, and dissatisfaction with services due to perceived poor provider attitudes or long waiting times.^16,17 Addressing these multifaceted barriers is essential for developing effective, targeted interventions to enhance MHS uptake and achieve national health coverage targets.

Building on this understanding, recent national surveys provide further insight into the current state of maternal health in Ethiopia. The Ethiopian Mini Demographic Health Survey indicates progress but also highlighted significant gaps in MHS utilization. While initial antenatal care (ANC) attendance has improved (74%), coverage of skill delivery attendance remains low (48%), and postnatal care (PNC) utilization is particularly inadequate (34%).¹⁸ In response, the Ethiopian government has established an ambitious target to increase ANC4 + coverage from 43% to 81%, skilled delivery from 50% to 76%, and early PNC within two days from 34% to 76%.¹⁰ Achieving these goals will require not only expanded service availability but also stronger systems for data management and decision-making.

Strong HIS are fundamental to achieving these national targets. A well-functioning HIS ensures the availability of high-quality, timely data, which is essential for monitoring progress, evaluating program effectiveness, and identifying gaps in maternal health service (MHS) delivery.^19,20 By enabling data-driven decision-making, supporting equitable resource allocation, and strengthening accountability mechanisms at all levels of the health system, HIS provides the foundational evidence required to improve the quality, efficiency, and utilization of MHS.^21–23

Despite their importance, HIS performance remains suboptimal in many LMICs

Common challenges include poor data quality, inadequate technological infrastructure, and limited use of information for decision-making due to constrained technical capacity.^24–26 This weak HIS performance is associated with low MHS utilization, particularly in rural and underserved areas.^27,28 In Ethiopia, persistent issues with routine data quality have hampered efficient resource allocation and undermined confidence in the health system's performance. These challenges underscore the urgent need to strengthen national HIS as a critical foundation for achieving sustainable improvements in maternal health outcomes.

Well-designed HIS interventions have the potential to address these critical gaps. By improving data quality, streamlining management processes, and promoting data utilization, such interventions can enhance health worker performance, optimize service delivery, and ultimately improve health outcomes,^29,30 Specific benefits include more efficient patient records management, improved tracking of service coverage, enhanced resource allocation, and better access to accurate, up-to-date information for healthcare providers.^31,32 Despite this potential, many LMICs continue to lack functional and reliable health management information systems. Implementation remains challenging due to weak infrastructure, limited technical capacity, and inadequate funding.^25,33,34 Consequently, HIS in these settings often fails to deliver the necessary management support required to drive meaningful improvements in health system performance.^35,36

Recognizing this, the 2030 Agenda for SDGs highlights digital health, including strong HIS, as vital to achieving health-related goals.^37,38 In Ethiopia, strengthening HIS is a central objective of the Health Sector Transformation Plans (I and II). A cornerstone of this effort is the Information Revolution (IR) Roadmap, launched by the Ministry of Health in 2016 to enhance the national HIS by promoting data quality, accessibility, and use for evidence-based decision-making. The subsequent IR Implementation Guideline (2021–2025) operationalizes these aims by institutionalizing a culture of data use, enhancing digital infrastructure, and reinforcing data governance and capacity across all health system levels.

A key mechanism of the IR is the quarterly assessment of HFs using a standardized checklist that evaluates three core components: HIS Structure and Implementation (30%), Data Quality (30%), and Administrative Data Use (40%). Based on their scores, facilities are classified into five performance tiers: Emerging (<65%), Candidate (65–89%), Model (≥90%), and Digital Model. This study leveraged this tiered system to examine the association between HIS performance and MHS utilization. Twelve facilities were categorized into three groups: Candidate (n = 4, consistently candidates), mixed (n = 4, fluctuating between candidate and model), and Model (n = 4, consistently models). While previous studies have focused on the effect of HIS on data quality and use, this study extends the evidence by evaluating the association between different levels of IR performance status and MHS utilization^39,40

Conceptual framework

This study uses a conceptual framework adapted from the PRISM model and previous studies, illustrating how HIS performance affects MHS utilization. It highlights the role of technical, organizational, and behavioral determinates in improving data quality and use, which strengthens decision-making and service delivery at HFs. Enhanced HIS performance ultimately contributes to better maternal health outcomes by increasing the likelihood of women receiving essential care at antenatal, delivery, and postnatal. The model also considers user characteristics like education and access, highlighting how community context shapes outcomes (Figure 1).^41–43

Figure 1.

Conceptual framework of the association between health information system performance and maternal health service utilization, adapted from the PRISM model.

Methods

Setting area

This study was conducted as part of a broader initiative by Jimma University (JU), which was one of six universities in Ethiopia tasked with implementing HIS interventions in the Oromia and Gambella regions. The JU-led Capacity-Building and Mentorship Partnership project's initial implementation sites were distributed across seven zones in the Oromia Region and two zones in the Gambella Region. The study sites included four woredas within the Oromia region Omo Nada, Metu Zuria, Bosot, and Digalo/Tijo and Gambella Zuria woreda in the Gambella region.

Study design and period

A cross-sectional comparative study was conducted from 15–25 October 2023, to evaluate the association between HIS performance and MHS utilization. Health facilities were classified using the Ministry of Health's IR framework into three tiers: emerging (<65%, requiring capacity building), candidate (65–90%, established but needing improvement), and model (>90%, excellence in HIS implementation).⁴³ An additional mixed category was introduced for facilities transitioning between tiers (e.g. candidate for two quarters and model for two quarters).

A population-based survey was administered to 840 women who had given birth within the past year, selected from households across 12 kebeles linked to four purposively sampled health centers from each performance category. While this design efficiently enabled comparison of MHS utilization across different HIS performance levels at a single point in time, the authors acknowledge its limitation in establishing causal or temporal relationships between HIS strengthening and health outcomes.

Health centers were categorized according to the Ministry of Health's IR tier framework, based on quarterly performance assessments conducted from July 2022 to June 2023 in HIS intervention sites (Supplemental Material S1).

Study population and eligibility criteria

The study population consisted of women who had given birth within the 12 months preceding the data collection period. Eligibility required residence in the selected kebeles and receipt of MHS from HFs implementing HIS interventions. Women who were unwilling to participate or too ill to respond were excluded from the study.

Sample size and sampling techniques

The sample size was calculated using the double population proportion formula, based on an expected absolute difference in MHS utilization between low and high HIS-performing facilities. Drawing from a previous study in HIS intervention sites that reported ANC4 + coverage rates,⁴⁴ we assumed a utilization rate of 55% in low-performing facilities and 65% in high-performing ones, yielding a 10% absolute effect size to maximize the required sample size. The calculation incorporated a 95% confidence level (Zα/2 = 1.96), 5% margin of error, and used the average of the two proportions (p) in the formula. This conservative approach ensured adequate power to detect significant differences in MHS utilization across HIS performance categories.

N = \frac{16 \times p (1 - p)}{(p 0 - p 1) 2}

The initial sample size was calculated as 384 women per group using standard statistical assumptions. To address the complex multistage sampling design and potential clustering effects within kebeles, a design effect of 2.0 was applied, increasing the sample to 768 women. Finally, to account for a 10% nonresponse rate, the total sample size was further adjusted to 840 participants. This final sample was proportionally allocated with 280 participants each from candidate, mixed, and model HFs to ensure balanced representation across all HIS performance categories.

A multistage sampling technique was employed to ensure representative selection of participants. In the first stage, 12 health centers were purposively selected to represent the spectrum of HIS performance categories (candidate, mixed, and model). During the second stage, two kebeles were randomly selected from each health center's catchment area, resulting in a total of 24 kebeles. In the final stage, 840 eligible women—those who had given birth in the preceding 12 months—were systematically sampled from updated household lists within each kebele. The sampling frame was constructed using existing health post records. Kebeles, typically comprising approximately 1000 households, providing a robust units for community-level sampling.

Data collection procedures and tool

A standardized, pretested questionnaire was adapted from the Demographic and Health Survey (DHS) and programed into SurveyCTO for digital data collection. The tool was translated into Oromiffa, Amharic, and Anuak to ensure linguistic and cultural appropriateness across study sites. A team of 24 data collectors (two per kebele) and five supervisors (one per district)—all with prior survey experience—underwent a three-day training covering study protocols, ethical procedures, and digital tool operation. The translated instrument was pretested by the field team before deployment. Data were collected electronically using mobile devices, enabling real-time entry and quality monitoring.

Operational definition

HIS intervention packages are a set of activities to be carried out in intervention facilities, including capacity building training, mentoring and supportive supervision, and routine performance monitoring and evaluation.

IR is one of the transformation agendas, which was aimed to bring about the radical change on data quality and use by cultivating a data-use culture and leveraging digital information systems and tools. It refers to the phenomenal advancement in the methods and practice of collecting, analyzing, presenting, and disseminating information that can influence decisions in the process of transforming economic and social sectors.⁴³

Emerging: A health unit that performed low in HIS performance (<65% of the assessment criteria)

Candidate level: a facility or administrative health office that scored 65% to 90% on the assessment criteria

Model: High-performing health unit with a score of more than 90% (> 90% of the assessment criteria)

Mixed health centers: Facilities that demonstrated inconsistent performance across quarterly assessments, qualifying as model for two quarters and candidate for the other two quarters within the annual evaluation period. This category captures health centers in a state of transition between the two tiers

Maternal health: It is the health of women during pregnancy, childbirth, and the postpartum period.

Wealth index: The wealth index was calculated using eight different household assets and living conditions, including: refrigerator, landline telephone, electricity, source of drinking water, ownership of agricultural land, membership in health insurance, type of roof, and type of toilet facility. These variables reflect household economic status and are commonly used in composite wealth indices. Based on the scores, we categorized them into five quintiles.

Poorest: Participants with wealth index scores in the lowest quintile.

Poor: Participants with wealth index scores in the second quintile.

Medium: Participants with wealth index scores in the middle quintile.

Rich: Participants with wealth index scores in the fourth quintile.

Richest: Participants with wealth index scores in the highest quintile

Variables

Dependent variable

The outcome variable was the composite coverage index (CCI). The CCI was the weighted average of the percentage coverage of ten essential maternal health (MH) interventions along the continuum of care. It includes the following ten indicators: early antenatal care (EANC), ANC4+, TTN, iron and folic acid (IFA), and syphilis test, facility delivery (FD), skilled birth attendant (SBA), early postnatal care, PNC, and Family Planning (FP). The index provides a single summary measure to identify gaps in service delivery and guide improvements in MH outcomes. We used the modified CCI formula adapted from a recent study by the Ethiopia Data Use Partnership, and other studies that evaluate MHS based on the composite indicators.⁴⁵ All indicators were equally weighted. Women were asked whether they received each service, and their answers were coded “yes” or “no.” This was followed by entering one for yes and zero for no into the modified CCI formula for each intervention use. For our analysis, we calculated the mean score of MHS utilization at the individual level. The overall mean score is considered a cutoff point to have dichotomous outcome variables for the logistic regression analysis. The modified CCI is composed of essential MH interventions, which are defined below.

M o d i f i e d C C I = \frac{1}{5} \frac{(E A N C + A N C 4 + T T N + I F A + S y p h i l i s T e s t)}{5} + (\frac{F D + S B A}{2}) + \frac{(E P N C + P N C}{2}) + F P

ANC was defined as the care that pregnant women received from skilled healthcare providers; EANC was initiated within the first trimester. ANC4 is at least 4 ANC visits. Tetanus toxoid (TT) immunization was defined when either woman received two TT injections during pregnancy. IFA was defined when pregnant women attending ANC received an iron folic acid supplement. The syphilis test was defined when pregnant women attending ANC were tested for syphilis (a blood sample was taken). Facility-based delivery refers to a birth occurring in an HF. A skilled birth attendant was defined as a birth attended by skilled health personnel. At an HF. Early PNC was defined when women and newborns received a health check within 24 h of delivery. PNC defined when women and newborns received a health check after birth up to six weeks (42 days) of postpartum. FP was defined as the percentage of women using any modern contraceptive method at the time of the study.

Independent variables

The household and women's questionnaire was adapted from the Ethiopian DHS (EDHS) and previous studies. The independent variables included socioeconomic and demographic factors such as women's age, marital status, educational and occupational status of mother and partner/husband, and wealth quintile (constructed from 10 asset variables), and maternal characteristics such as parity, pregnancy intention, and ANC initiation. Factors related to health service accessibility, such as catchment HF (residence), HIS performance status of HFs, health insurance membership, and perceived distance from the nearest HF, were also included.

Data processing and statistical analysis

Data were collected using SurveyCTO software, downloaded, and rigorously checked for consistency and completeness. A comprehensive review confirmed that the dataset contained no missing values for any of the key variables included in this analysis. As all study variables were categorical, a complete-case analysis was employed, and no imputation techniques were required. The cleaned dataset was exported to Stata/MP 17.0 for all analyses. Descriptive statistics were computed to summarize participant characteristics and are presented as frequencies and percentages. To assess the relationship with MHS utilization, all independent variables with a p-value < .25 in the bivariable logistic regression analysis were selected as candidates for the multivariable model. A backward stepwise logistic regression approach was then employed to identify the most significant predictor variables while adjusting for potential confounders. The final model was evaluated for multicollinearity by examining Variance Inflation Factor (VIF) and Tolerance statistics; all VIF values were well below the conservative threshold of 10, indicating that multicollinearity was not a significant concern. Ultimately, variables with an adjusted odds ratio (AOR), a 95% confidence interval, and a p-value < .05 in the final multivariable model were declared statistically significant predictors of MHS utilization.

Results

Descriptive summary statistics

Background characteristics of the respondent

All 840 invited women participated in the study, resulting in a 100% response rate. The majority (52%) was aged 25–34 years, and almost all were married (98%). Nearly one-third of mothers (29%) and one-quarter of partners (23%) had no formal education. Farming was the dominant occupation for both women (74%) and their partners (88%). The study participants were distributed fairly evenly across wealth quintiles, with approximately 19–21% in each category (Table 1).

Table 1.

Background characteristics of participants and association between HIS performance status of health centers and maternal health service utilization, Ethiopia, October 2023.

Characteristics	N = 840	%
HIS performance status of HCs
Model 280 33.3
Mixed 280 33.3
Candidate 280 33.3
Mother's age at birth years
15–24	300	36
25–34	434	52
> 35	106	13
Marital status
Married	820	98
Not married	20	2.4
Mothers’ educational status
No education	240	29
Primary (1–8)	423	50
Secondary or higher level	177	21
Partner's educational status
No education	193	23
Primary (1–8)	380	45
Secondary or higher level	267	31.8
Religion
Muslim	389	46
Orthodox	191	23
Protestant	227	27
Other	33	4
Mother's occupational status
Farmer	621	74
Unemployed	166	19.84
Merchant	53	6.3
Partner's occupational status
Employed	86	10
Farmer	735	88
Merchant	19	2.3
Wealth categorized
Poorest	158	19
Poor	177	21
Medium	175	21
Rich	151	18
Richest	178	21

HIS:

Health Information System.

MHS utilization

Service utilization of individual MHS was generally high. Most women (97.4%) attended at least one ANC visit, but only two-thirds (66.8%) achieved the recommended four or more visits. Early ANC initiation (within four months) was reported by 70% of women. Service coverage varied by component: while nearly all received tetanus toxoid (95%) and iron-folic acid (91%), sustained supplementation for ≥90 days was reported by only 40%.

FD was reported by 82% of participants, with 80.5% assisted by a skilled provider. The main reasons cited for home delivery included lack of transport (31%) and distance (18%). PNC uptake was 78%, and nearly all PNC checks occurred within the first 24 h (Table 2).

Table 2.

Magnitude of maternal health service utilization and its association with HIS performance status of health centers, Ethiopia, October 2023.

Variables	N	No. (%)	[95% CI]
ANC visit with skilled provider	840
Yes		818 (97.4)	[96.1, 98.3]
No		22 (2.6)	[1.7, 3.9]
Number of ANC visits in the last pregnancy	818
1–3 visits		257 (30.6)	[27.6, 33.8]
≥ANC 4		561 (66.8)	[63.6, 69.9]
Early ANC (≤4 months)	818
1–4 months		570 (69.7)	[66.5, 72.8]
5–9 months		248 (30.3)	[27.2, 33.5]
Length of IFA use	818
0–29 days		91 (11.1)	[9.1, 13.4]
30–59 days		165 (20.2)	[17.5, 23.0]
60–89 days		239 (29.2)	[26.2, 32.4]
90+ days		329 (39.5)	[36.3, 42.9]
TT vaccination	818
Yes		777 (95%)	[93.3, 96.3]
No		41 (5%)	[3.7, 6.7]
Facility delivery	840
Yes		687 (81.8%)	[79.1, 84.3]
No		153 (18.2%	[15.7, 20.9]
SBA	840
Yes		676 (80.5%)	[77.7, 83.0]
No		164 (19.5%)	[16.9, 22.3]
PNC checkups	840
Yes		656(78.1%)	[75.2, 80.8]
No		184 (21.9)	[19.2, 24.8]
PNC within 24 h	840
Yes		624 (95%)	[93.1, 96.5]
No		33 (5%)	[3.5, 6.9]
Modern CFP	840
Yes		453 (53.9%)	[50.5, 57.3]
No		387 (46.1%)	[42.7, 49.5]
Mean MHS composite coverage indicator	840	59.0	[55.7, 62.3]
Below mean		334 (39.8%)	[36.5, 43.1]
Above mean		506 (60.2%)	[56.9, 63.5]

ANC: antenatal care; IFA: iron and folic acid; TT: tetanus toxoid; PNC: postnatal care; FP: family planning; MHS: maternal health services; HIS: Health Information System.

MHS CCI

Overall, 60.3% of women achieved above-average coverage on the composite MHS index. Utilization varied by health center category: model HCs had the highest above-average coverage, 68.2% (95% CI [62.3–73.6%]), followed by mixed HCs, 61.1% (95% CI [55.3–66.6%]), whereas candidate HCs recorded the lowest 51.4% (95% CI [45.5–57.3%]). These findings suggest that stronger HIS performance is positively associated with higher MHS uptake, reinforcing the value of HIS readiness in advancing equitable care (Table 3).

Table 3.

Maternal health service utilization by health center HIS performance category, Ethiopia, October 2023.

HIS category	Below average n (%)	[95% CI]	Above average n (%)	[95% CI]
Model (n = 280)	89 (31.8)	[26.4, 37.7]	191 (68.2)	[62.3, 73.6]
Mixed (n = 280)	109 (38.9)	[33.4, 44.7]	171 (61.1)	[55.3, 66.6]
Candidate (n = 280)	136 (48.6)	[42.7, 54.5]	144 (51.4)	[45.5, 57.3]
Total (N = 840)	334 (39.8)	[36.6, 43.2]	506 (60.2)	[56.8, 63.4]

Note: CCI, Composite Coverage Index; HIS: Health Information System; IR: Information Revolution.

Combination of model and candidate refers model HC for two assessment quarters and a candidate for the remaining two assessment quarters during IR performance assessment (p-value <.001; Chi-square statistic = 16.5908).

Factors associated with MHS utilization

Bivariable logistic regression analysis revealed that MHS utilization was significantly associated with the IR performance status of health centers, household wealth index, access to transportation, pregnancy wantedness, family size, and distance to HFs. Specifically, women residing in Model health center catchments had twice the odds of utilizing MHS than those in Candidate catchments (COR = 2.0; 95% CI [1.4–3.0]). Similarly, women in mixed catchments had 1.5 times higher odds of MHS utilization than those in Candidate catchments (COR = 1.5; 95% CI [1.06–2.07]).

Pregnancy intention was significantly associated with MHS utilization: women with unwanted pregnancies had 50% lower odds of utilizing MHS compared to those with wanted pregnancies (COR = 0.50; 95% CI [0.31–0.79]). Similarly, smaller household size was associated with lower service uptake; women from households with fewer than five members had 45% lower odds of MHS utilization compared to those from larger households (COR = 0.55; 95% CI [0.41–0.73]).

Geographic and socioeconomic disparities were significantly associated with MHS utilization. Women residing more than 10 km from their catchment health center had 43% lower odds of accessing services compared to those living within 10 km (COR = 0.57; 95% CI [0.36–0.92]). Similarly, wealth-based inequalities were evident: women in the poor wealth quintile had 61% lower odds (COR = 0.39; 95% CI [0.28–0.56]), and those in the medium quintile had 53% lower odds (COR = 0.47; 95% CI [0.33–0.67]) of MHS utilization compared to women in the rich wealth quintile. These findings underscore the imperative for targeted policies that concurrently address structural access barriers and strengthen HIS to advance equitable service coverage.

Multivariable logistic regression analysis identified wealth index and transport access as the strongest independent predictors of MHS utilization. After adjusting for potential confounders, women in the poor wealth quintile had 55% lower odds (AOR = 0.45; 95% CI [0.30–0.68]), and those in the medium quintile had 50% lower odds (AOR = 0.50; 95% CI [0.33–0.75]) of utilizing MHS compared to women in the rich quintile. Furthermore, lack of transport access was associated with a 79% reduction in the odds of service utilization (AOR = 0.21; 95% CI [0.15–0.29]), highlighting the critical role of geographic accessibility as a barrier to care (Table 4).

Table 4.

Factors associated with maternal health service utilization in catchment areas of health centers with varied HIS performance, Ethiopia, October 2023 (multivariate logistic regression analysis).

Variables	Good utilization* n (%)	Low utilization n (%)	COR [95% CI]	AOR [95% CI]	p-value
IR performances status of HCs N = 840
Model	191 (68.2)	89 (31.8)	2.03 [1.4, 3.0]	1.41 [0.92, 2.15]	.116
Mixed	171 (61.1)	109 (38.9)	1.45 [1.06, 2.07]	1.20 [0.9, 1.81]	.388
Candidate (Ref.)	144 (51.4	136 (49,6)	1.00	1.00
Wealth index N = 840
Poor	133 (50.4%)	131 (49.6%)	0.40 [0.28, 0.60]	0.45 [0.30, 0.68]	< .001
Medium	135 (55.0%)	111 (45.0%)	0.45 [0.33, 0.67]	0.50 [0.33, 0.75]	< .001
Rich (Ref.)	237 (72.0)	92 (28.0%)	1.00	1.00	< .006
Access to transport
No	130 (38.9)	204 (61.1)	0.18 [0.13, 0.24]	0.21 [0.15, 0.29]	< .001
Yes (Ref.)	385 (78.1)	111 (21.9)	1.00
Number of family size
≤5 family members	161 (48.2)	173 (51.8)	0.55 [0.41, 0.73]	0.75 [0.54, 1.04]	.087
> 5 family members (Ref.)	171 (33.8)	335 (66.2)	1.00	1.00
Distance travel to HFs less than 1 km
> 10 km	293 (88)	40 (12)n	0.57 [0.36, 0.92]	1.02 [0.57, 1.83]	.948
< 10 km (Ref.)	459 (92.7)	36 (7.3)	1.00	1.00
Wantedness of pregnancy
Wanted (Ref.)	437 (63.2)	245 (36.8)	1.00	1.00
Wanted but mistimed	33 (46.5)	38 (53.5)	0.50 [0.31, 0.83]	0.57 [0.33, 0.99]	.048
Unwanted	36 (46.2)	42 (53.8)	0.50 [0.31, 0.83]	0.85 [0.49, 1.48]	.569

Note: COR: crude odds ratio; AOR: adjusted odds ratio; Ref: reference category.

Good utilization defined as above mean composite coverage index (CCI).

Other examined variables, including the IR performance status of health centers, family size, distance to the nearest facility, and pregnancy wantedness, were not retained as significant predictors in the final adjusted model. Although a clear gradient was observed in crude utilization with Model facilities showing higher uptake (68.2% above mean) than Candidate facilities (51.4%) this association was attenuated and lost statistical significance after adjustment for wealth and transport access (AOR = 1.41; 95% CI [0.92–2.15]; p = .116). The exception was pregnancy intention: women with wanted-but-mistimed pregnancies had significantly lower odds of utilization than those with intended pregnancies (AOR = 0.57; 95% CI [0.33–0.99]; p = .048). The diminished association of other factors after adjustment underscores the dominant role of socioeconomic status and transport accessibility as the primary determinants of MHS utilization in this setting.

We conducted sensitivity analyses to assess the robustness of our findings and to explore potential effect modification between HIS performance and other key variables. Tests for interaction indicated that the association between HIS performance and service utilization was modified by socioeconomic and access-related factors. For instance, the association was stronger among women from the richest households compared to those from medium-wealth households, suggesting the benefits of residing in a model facility catchment were not uniform across wealth quintiles. Furthermore, transport access was a consistently strong predictor; women without access to transportation had significantly lower service utilization regardless of the HIS performance status of their catchment facility. Sensitivity analyses confirmed the overall stability of the main model estimates. The attenuation of the HIS effect size after adjustment for covariates further underscores that the observed relationship is confounded by and operates in conjunction with wealth, transport access, and pregnancy intention.

Discussion

Despite high coverage of individual MHS including ANC (97.4%), skilled birth attendance (80.5%), and PNC (78.1%) comprehensive utilization across the full care continuum remained moderate (60.3% by CCI). This gap highlights significant care fragmentation and underscores that high single-service coverage does not guarantee integrated care. Programs must strengthen service continuity and care coordination to ensure women receive complete, timely interventions across pregnancy, childbirth, and postpartum.

Utilization of MHS varied substantially according to health center performance. Model health centers, characterized by strong HIS performance scores exceeding 90%, and achieved a CCI of 68.2%. This was notably higher than the CCI of mixed health centers (61.1%) and candidate centers (51.4%). This gradient underscores that robust HIS performance encompassing structure and resources (30%), data quality (30%), and data use (40%) is strongly associated with more effective MHS delivery.

However, strong HIS performance alone was insufficient to overcome profound structural barriers. Low wealth status and lack of transportation access emerged as major determinants of utilization, independent of facility-level capabilities. Women in the poorest wealth quintile were 55% less likely to utilize services, and those without access to transportation were 79% less likely to do so.

These findings emphasize a dual pathway to improving maternal health outcomes: strengthening HIS infrastructure is essential for enhancing internal facility operations, planning, and resource allocation. Yet, to fully realize these benefits and achieve equitable care, it is equally critical to implement parallel strategies that address underlying socioeconomic and logistical barriers, such as poverty and transportation limitations, which continue to restrict access for the most vulnerable populations.

Sensitivity analyses affirmed the robustness of the primary findings, confirming a persistent positive association between HIS performance and MHS utilization even after adjustment for confounders. However, the attenuation of effect sizes in adjusted models indicates that socioeconomic and access-related factors—particularly wealth and transportation—partially mediate this relationship.

Effect modification analyses further revealed that the benefits of HIS strengthening were not equitably distributed. Women from wealthier households derived greater advantage from improved HIS performance, whereas those from medium-wealth households showed diminished returns in high-performing facilities. Additionally, reliance on walking or public transport was associated with consistently lower service utilization across all facility performance levels. These results underscore that while HIS enhancements are associated with improved service uptake, they do not inherently benefit all subgroups equally. Without targeted efforts to mitigate structural barriers, HIS improvements may inadvertently exacerbate existing inequities in maternal health care access.

The attenuation of HIS effects after adjustment underscores that while strong HIS are necessary for improving service delivery, they are insufficient to overcome deep-rooted structural barriers. Rather, HIS enhancements appear to amplify existing advantages for already privileged groups, potentially widening inequities unless explicitly paired with complementary interventions. These findings highlight the urgent need for integrated policies that combine HIS strengthening with targeted investments in transportation infrastructure, financial protection mechanisms, and community-based support programs. Future research should prioritize identifying implementation strategies that explicitly link HIS improvements to equity-oriented interventions, ensuring that advancements in health system performance benefit all population subgroups fairly.

Our findings align with a growing body of evidence from LMICs on the role of HIS and digital tools in improving MHS utilization. Systematic reviews from SSA and Southern Asia confirm that HIS and mHealth interventions—particularly SMS and voice reminders—significantly increase ANC attendance, skilled birth attendance, and PNC coverage.^46,47 These technologies enhance service accessibility, patient health literacy, and overall health system performance, as demonstrated in Kenya.^48,49

Evidence from Ethiopia further supports these trends. A quasi-experimental study showed that a comprehensive HIS intervention package improved 17 of 19 maternal and child health indicators including ANC and skilled delivery through strengthened data-driven decision-making, accountability, and resource allocation.^44,50 Similarly, the national IR initiative was associated with improved quality and coverage of ANC⁵¹ Digital platforms such as District Health Information System 2, eCHIS, and community-based HIS have also contributed to better data quality, timeliness, and usability, facilitating more responsive MHS delivery.^19,52

Despite these advances, significant challenges in data quality and use persist. Studies in Ethiopia highlight that gaps in data completeness, accuracy, and utilization often linked to insufficient training, weak digital infrastructure, and inadequate supervision limit the effectiveness of HIS^52,53 technology alone is insufficient; sustainable improvements require concurrent investments in human capacity, institutional support, and the integration of data-driven practices into routine health system operations.

While these studies highlight the potential of HIS to strengthen health system efficiency and service coverage, our research adds an important equity dimension: the benefits of HIS are not automatically evenly distributed. To maximize their impact, HIS enhancements must be consciously linked to strategies that address structural barriers such as poverty, transportation, and educational disparities. This integrated approach is essential for ensuring that digital and data-driven advancements translate into equitable health gains for all women.

The magnitude of improvement in MHS utilization observed in this study was more modest than that reported in earlier Ethiopian research, likely reflecting differences in the specific services measured, methodological approaches, and study design. Despite these variations, our findings consistently affirm that strengthening HIS remains essential for enhancing MHS utilization. Improved HIS performance supports data-informed decision-making, enhances resource targeting, and contributes to broader global goals for digital health and universal health coverage.^54,55

Our findings confirm significant wealth-based disparities in MHS utilization, consistent with evidence from LMICs. Women in the highest wealth quintile were significantly more likely to access ANC, skilled birth attendance, and PNC services compared to those in the lowest quintile.^16,56 This pattern aligns with studies across SSA and South Asia, where socioeconomic status consistently predicts care access.^57,58 In particular, analyses from 23 African countries and multiple South Asian settings demonstrate that women from wealthier households are more likely to complete the full continuum of maternal care, while economic disadvantage remains associated with home delivery and fragmented service use.^59,60 Studies in Nigeria and Tanzania further support that wealth significantly increases the odds of MHS.^61,62

These persistent inequalities highlight that financial barriers—including both direct and indirect costs—continue to limit care access for the most vulnerable populations, even in contexts with user fee exemption policies. Reducing these disparities will require deliberate strategies that address not only financial constraints, but also the geographic, educational, and social barriers that disproportionately affect poor women.

Wealth status was strongly associated with MHS utilization in Ethiopia, with women in the highest wealth quintile demonstrating over 20 percentage point's greater coverage of key services (ANC4+, SBA, and PNC) compared to the poorest women.⁶³ National data show that only one third of women have adequate access to services, with cost likely including indirect and opportunity costs disproportionately affecting the poorest.⁶⁴ Household wealth alone explains nearly two-thirds of observed inequalities in recommended ANC utilization.⁶⁵ Despite user fee exemption policies, wealthier women maintain better access, confirming that fee removal alone is insufficient without addressing broader socioeconomic, infrastructure, and supply-side barriers.⁶⁶ These findings underscore wealth's persistent role in shaping maternal healthcare disparities.

Limited access to transportation services was a significant barrier to MHS utilization, as women facing long distances or poor infrastructure were significantly less likely to use services. This finding is consistent with other studies; research on rural healthcare service delivery identified long travel distances and poor infrastructure as key barriers to accessing maternal healthcare.^67,68 A cross-sectional study in Western Uganda and Kenya confirmed that limited transportation access was a major constraint for pregnant women.^69,70 Furthermore, despite the provision of free maternal services, transportation costs and other indirect expenses remains a primary barrier to equitable care utilization, as evidenced in the Democratic Republic of the Congo.⁷¹

Despite Ethiopia's introduction of a national ambulance service, its effectiveness remains constrained by poor road infrastructure and low community awareness, limiting timely access to emergency obstetric care. Qualitative evidence from West Shoa and Bahir Dar underscores that long travel distances, unreliable emergency transport, and delayed referrals persistently reduce MHS utilization and contribute to adverse outcomes.^72,73 Cross-sectional data from Jimma Zone further highlight how poor road conditions and high travel costs directly limit access to services,^74,75 while longitudinal studies confirm that geographic distance to HFs remains a critical barrier to care.⁶³ These findings collectively demonstrate that transportation barriers systematically exclude women from ANC, FD, and PNC, underscoring the urgent need for integrated strategies that combine emergency transport systems with road infrastructure improvements and community engagement to ensure equitable access to MHS.

Unintended pregnancy was significantly associated with reduced MHS utilization in our study. Women with unwanted pregnancies had lower odds of utilizing services, including delayed ANC initiation and reduced skilled birth attendance, compared to those with intended pregnancies. This pattern aligns with broader literature attributing lower health-seeking behavior in unplanned pregnancies to financial constraints, social stigma, and lack of awareness or support. This pattern aligns with global evidence: a systematic review across LMICs found unintended pregnancies associated with 25–39% lower utilization of ANC, delivery, and PNC,⁷⁶ while a pooled analysis of 48 Demographic and Health Surveys showed that unwanted pregnancies reduced the probability of attending four or more ANC visits by 3.6%.⁷⁷ Moreover, evidence from SSA shows that intended pregnancies more than double the likelihood of completing maternity,⁷⁸ and an analysis of 32 SSA countries further revealed that mistimed and unwanted pregnancies are significantly associated with delayed and inadequate ANC utilization.⁷⁹ These findings highlight the need to integrate reproductive health counseling and unintended pregnancy support into maternal care programs to improve engagement across the care continuum.

In the Ethiopian context, the negative association between unintended pregnancy and MHS utilization is particularly pronounced. Evidence demonstrates that women with unintended pregnancies are over twice as likely to initiate ANC late and have significantly fewer ANC visits and lower rates of skilled delivery attendance compared to those with intended pregnancies.⁸⁰ A systematic review of 22 Ethiopian studies confirmed that planned pregnancy substantially increases ANC utilization,⁸¹ while longitudinal research showed that women with intended pregnancies were significantly more likely to complete the full continuum of maternity care.⁸² These findings highlight that addressing unintended pregnancies through comprehensive reproductive health education, expanded FP access, and integration of pregnancy intention screening into maternal health programs is essential for improving service utilization and achieving better maternal health outcomes in Ethiopia.

Overall, this study underscores that strengthening HIS in health centers is vital for improving MHS utilization. Robust HIS facilitates data-driven decision-making, enhances resource allocation, and improves monitoring of service quality and coverage. However, HIS improvements alone are insufficient to achieve equitable MHS uptake. Complementary efforts addressing socioeconomic disparities particularly wealth-based inequalities improving transportation infrastructure, and reducing unintended pregnancies through expanded reproductive health services are equally critical. Sustainable progress requires integrated strategies that simultaneously strengthen HIS performance and tackle the structural barriers that limit access for the most vulnerable populations, ensuring that systemic enhancements translate into broader, more equitable health gains.

Strengths and limitations

This study examines the association between HIS performance and MHS utilization in Ethiopian health centers. Using a PRISM-based framework and multivariate regression, we found that HIS functionality particularly data quality and use was associated with service uptake, though socioeconomic and access factors were stronger predictors.

Key limitations include possible recall and social desirability bias in self-reported data, selection bias due to purposive sampling, and potential measurement bias in HIS classification, as better-resourced facilities may be rated higher irrespective of HIS-specific performance. The cross-sectional design prevents causal inference, and the lack of qualitative data limits insight into behavioral or institutional mechanisms affecting utilization. Future studies should use longitudinal mixed-methods designs to better establish causality and context.

Conclusion and recommendation

The study found that strong HIS performance in model health centers significantly improved MHS utilization by enhancing data quality, accountability, and evidence-based decision-making. To translate these findings into practice, the study recommends strengthening HIS infrastructure in underperforming facilities, scaling the model health center approach through mentorship and incentives, and addressing structural barriers like poverty and transportation access. Integrating HIS metrics into national dashboards and supervision frameworks can sustain accountability, while further longitudinal and mixed-methods research should clarify causal pathways. These interventions collectively aim to leverage HIS advancements for equitable and sustainable improvements in maternal healthcare delivery.

Supplemental Material

sj-docx-1-dhj-10.1177_20552076261417851 - Supplemental material for Impact of Health Information System Interventions on Maternal Health Service Utilization in Oromia and Gambella Regions, Ethiopia: A Comparative Cross-Sectional Study

Supplemental material, sj-docx-1-dhj-10.1177_20552076261417851 for Impact of Health Information System Interventions on Maternal Health Service Utilization in Oromia and Gambella Regions, Ethiopia: A Comparative Cross-Sectional Study by Kunuz Hajibedru Abadula, Abebaw Gebeyehu Worku, Gurmesa Tura Debelew and Muluemebet Abera Wordofa in DIGITAL HEALTH

Footnotes

Abbreviations

Acknowledgments

We would like to express our gratitude to the Doris Duke Charitable Foundation, the regional health bureau, Zonal and District health offices, data collectors, supervisors, and study participants for their support and collaboration.

ORCID iDs

Kunuz Hajibedru Abadula

Abebaw Gebeyehu Worku

Gurmesa Tura Debelew

Muluemebet Abera Wordofa

Ethics approval

Ethical clearance was obtained from Jimma University Institute of Health Review Board (Ref No.: JUIH/RB/586/23, Date: 23 August 2023). In addition, letters of support were obtained from the Oromia and the Gambela RHBs, the respective zonal district health offices. Prior to data collection, the study objectives and procedure were clearly explained to all participants. Confidentiality and privacy were guaranteed. All participants provided verbal informed consent and were informed that their participation was entirely voluntary.

Authors’ contributions

KH conceptualized and designed the study, conducted the study, analyzed and interpreted the data, and wrote the original draft of the manuscript. AW, GT, and MA have performed the study, analyzed and interpreted the data, and reviewed and edited the manuscript. All authors read and approved the final manuscript.

Funding

This work would not be possible without the financial support of the Doris Duke Charitable Foundation (Grant No. 2017187). The mission of the Doris Duke Charitable Foundation is to improve the quality of people's lives through grants supporting the performing arts, environmental conservation, medical research, and child well-being, and through preservation of the cultural and environmental legacy of Doris Duke’s properties.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Supplemental material

Supplemental material for this article is available online.

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Impact of Health Information System Interventions on Maternal Health Service Utilization in Oromia and Gambella Regions,Ethiopia: A Comparative Cross-Sectional Study

Abstract

Objective

Methods

Results

Conclusion

Keywords

Background

Despite their importance, HIS performance remains suboptimal in many LMICs

Conceptual framework

Methods

Setting area

Study design and period

Study population and eligibility criteria

Sample size and sampling techniques

Data collection procedures and tool

Operational definition

Variables

Dependent variable

Independent variables

Data processing and statistical analysis

Results

Descriptive summary statistics

Background characteristics of the respondent

MHS utilization

MHS CCI

Factors associated with MHS utilization

Discussion

Strengths and limitations

Conclusion and recommendation

Supplemental Material

sj-docx-1-dhj-10.1177_20552076261417851 - Supplemental material for Impact of Health Information System Interventions on Maternal Health Service Utilization in Oromia and Gambella Regions, Ethiopia: A Comparative Cross-Sectional Study

Footnotes

Abbreviations

Acknowledgments

ORCID iDs

Ethics approval

Authors’ contributions

Funding

Declaration of conflicting interests

Availability of data and materials

Supplemental material

References

Supplementary Material