Introduction

Maternal healthcare referral systems are the cornerstone of effective healthcare delivery, particularly in resource-constrained settings serving vulnerable populations [1–3]. Globally, effective and timely referral mechanisms are essential components of comprehensive emergency obstetric care (EmOC) designed to ensure timely access to appropriate levels of care for pregnant women experiencing complications [4–6].

The three-tiered public healthcare system in India, comprising Primary Health Centres (PHCs), Community Health Centres (CHCs), and District Headquarters Hospitals (DHHs), forms the backbone of maternal healthcare delivery in rural and tribal regions[7,8]. This hierarchical system relies on effective referral mechanisms to ensure that women with obstetric complications identified at peripheral facilities are stabilized and promptly referred to higher-level centres, such as CHCs or DHHs, which are designated as First Referral Units (FRUs) capable of providing EmOC [9,10].

Tribal populations in India face unique and compounded challenges in accessing maternal healthcare services. These communities often experience geographical isolation, socio-cultural beliefs that may discourage institutional care, transportation difficulties, and economic constraints that complicate healthcare-seeking behaviours[10–13]. The "three delays" framework, which identifies delays in deciding to seek care, reaching appropriate facilities, and receiving adequate and appropriate quality care upon arrival, provides a useful lens for understanding maternal mortality patterns in these settings[2,14].

Odisha, with a 22.85% tribal population, is home to 13 of India’s 75 Particularly Vulnerable Tribal Groups (PVTGs)[15]. These communities represent some of the most marginalized populations in the country, facing significant healthcare disparities, including significantly poorer maternal health indicators than mainstream populations[16,17]. Despite government initiatives, such as the Janani Shishu Suraksha Karyakram (JSSK), to improve maternal health outcomes, tribal communities continue to experience challenges in accessing and utilizing quality maternal healthcare services[18–20].

Recent studies have highlighted that while some PVTG populations in Odisha have achieved utilization rates comparable to the national averages for certain maternal health indicators, significant gaps remain in institutional delivery rates and quality of care. The persistence of these disparities indicates that tailored approaches are required to effectively address maternal health challenges in tribal communities[19,21].

Understanding referral patterns and their temporal variations is crucial for optimizing maternal healthcare delivery systems[22]. However, limited research has examined the specific dynamics of obstetric referrals in tribal districts, particularly regarding day-night variations in referral patterns and the quality of pre-referral stabilization and management provided at lower-level facilities before transfer[23–25].

This study addresses critical knowledge gaps by examining maternal referral patterns in the Gajapati district of Odisha, a predominantly tribal district with a significant PVTG population. The specific objectives were to (1) analyse temporal patterns of obstetric referrals, including day-night variations; (2) assess correlations between delivery volumes and referral dynamics; (3) evaluate the quality of pre-referral management for common obstetric complications; and (4) identify system-level factors influencing referral effectiveness in tribal healthcare contexts.

Materials and Methods

Study Design and Setting

This prospective observational study was conducted at the Maternal and Child Health (MCH) unit of the District Headquarters Hospital (DHH), Paralakhemundi, Gajapati District, Odisha, India. The study period extended from July 2024 to August 2025, encompassing 14 months of continuous data collection.

DHH Paralakhemundi serves as the sole First Referral Unit (FRU) for this tribal-dominated district, functioning as a 161-bedded facility within the apex tier of the district's healthcare network. The Gajapati district comprises 30 government healthcare facilities, including one DHH, eight CHCs, and 21 PHCs, serving a population with significant representation of PVTGs [26].

Study Population

The study population included all pregnant women who delivered at the Maternal Child Health (MCH) unit during the study period, as well as those referred to or from the facility for obstetric care. During the 14-month observation period, 2,759 deliveries were conducted at the facility, with 909 cases referred from lower-level facilities and 451 cases referred to higher-level tertiary care centres (referrals-out).

Data Collection

Data were collected prospectively using a structured checklist designed specifically for this study to systematically document referral parameters. The checklist captured: demographic information of referred patients; temporal data (date and time of referral, categorised as day [08:00–20:00] or night [20:00–08:00]); source facility for referrals-in and destination facility for referrals-out; primary reason for referral; clinical status at referral; pre-referral management provided at the referring facility; mode of transport; transfer time; and maternal and foetal outcomes.

Variables and Measurements

The primary variables were: total monthly deliveries at the MCH unit; referral-in count (cases referred to the DHH from lower-level facilities); referral-out count (cases referred from the DHH to higher-level centres); and the day-night distribution of referrals.

The pre-referral management was assessed against the minimum essential intervention expected before referral for each complication, namely: antibiotics for premature rupture of membranes (PROM); partograph use for monitoring labour; antenatal corticosteroids for preterm labour; magnesium sulphate for eclampsia/pre-eclampsia; initial management of obstetric haemorrhage; neonatal resuscitation for birth asphyxia; initiation of treatment for maternal and neonatal sepsis; and preliminary management of severe anaemia. For each complication, a pre-referral management score was derived as the mean monthly number of referred cases in which the corresponding minimum essential intervention was documented as having been provided, with the standard deviation summarising month-to-month variability.

Data Analysis

Data analysis was performed using the Statistical Package for Social Sciences (SPSS) version 26.0[27]. Descriptive statistics (frequencies, percentages, means, standard deviations) characterised monthly delivery volumes and referral patterns. Pearson’s correlation coefficients examined relationships between total deliveries and total referrals (in and out), between daytime and nighttime referral patterns, and between referral-in and referral-out volumes; Spearman’s rank coefficients were computed in parallel to assess monotonic associations robust to non-normality. Asymptotic standard errors and approximate T-values are reported. A trend analysis was conducted to describe monthly delivery and referral dynamics over the 14-month period, with model fit summarised by stationary R-squared, RMSE, MAPE, MaxAPE, MAE, MaxAE, and Normalised BIC. Statistical significance was set at p<0.05. Given the modest number of monthly observations (n=14) and the multiple correlations tested, associations are interpreted as exploratory.

Ethical Considerations

The study was conducted according to the Declaration of Helsinki and approved by the Institutional Ethics Committee of Medipulse Hospital, Jodhpur (approval no. MH/IEC/2024/BHR/0007, September 11, 2024). Permission was obtained from the Chief District Medical and Public Health Officer (CDM andPHO) of the Gajapati district. All data were anonymized to protect the patients’ confidentiality. As this was an observational study of routine clinical data, the requirement for individual informed consent was waived by the ethics committee.

Results

Overview of Deliveries and Referrals

During the 14-month study period, 2,759 deliveries were conducted at the MCH unit of the District Headquarters Hospital. The monthly delivery count ranged from 88 to 230, with a mean of 197.07 (SD=33.269). Throughout this period, the facility received 909 referrals (referred in) and transferred 451 patients to higher-level facilities (referred out). The referral-in and referral-out rates were 32.9% and 16.3% of total deliveries, respectively.

Temporal Analysis of Referral Patterns

Analysis of day-night referral patterns revealed significant temporal variations in the referral dynamics (Tables 2-5; Figure 1).

Daytime referrals-in (08:00 to 20:00) showed a strong positive correlation with total referrals-in (Pearson's R=0.862, p<0.001). The Spearman correlation coefficient (0.855, p<0.001) further confirmed this strong relationship, indicating that variations in daytime referrals strongly predicted the overall referral-in pattern at the facility.

Nighttime referrals-in (20:00 to 08:00 ) demonstrated a moderate positive correlation with total referrals-in (Pearson's R=0.466), although this relationship was not statistically significant (p=0.093). The Spearman correlation coefficient (0.310, p=0.281) also showed a weak positive relationship, suggesting that nighttime referrals contribute less consistently to the overall referral-in pattern than daytime referrals.

A significant positive correlation was observed between nighttime referrals-in and nighttime referrals-out (Pearson's R=0.571, p=0.033), indicating that increases in nighttime referrals-in are associated with increases in nighttime referrals-out, suggesting a potential "pass-through" phenomenon during the night hours.

The facility received more referrals during the daytime (M=41.07, SD=9.352) than during the nighttime (M=24.57, SD=4.847). Similarly, more patients were referred during the daytime (M=16.29, SD=4.159) than during the nighttime (M=15.93, SD=6.545), although this difference was smaller (Figure 1).

Figure 1: Day–night distribution of obstetric referrals (referrals-in and referrals-out), District Headquarters Hospital, Gajapati District, Odisha (July 2024 – August 2025). Bars show mean monthly referrals; whiskers denote ±1 SD.

Correlation Between Deliveries and Referrals

Total deliveries showed weak, non-significant correlations with total referrals-in (r=−0.144, p=0.625) and total referrals-out (r=−0.153, p=0.602). Referrals-in and referrals-out were weakly and non-significantly correlated (r=0.166, p=0.571). Delivery volume was therefore not strongly related to referral activity.

Time Series Analysis Results

The trend model of monthly deliveries and referrals showed a stationary R-squared of 0.725, explaining 72.5% of the variance, with RMSE=18.973, MAPE=6.311, MaxAPE=26.909, MAE=10.570, MaxAE=37.506, and Normalised BIC=6.452. Given the short series (14 monthly points), these estimates are descriptive rather than predictive.

Pre-referral Management Quality

Pre-referral management varied considerably across complications (Figure 2). It was highest for birth asphyxia (mean=8.0, SD=5.0), followed by PROM (mean=4.0, SD=2.0) and eclampsia (mean=4.0, SD=3.0). Severe pre-eclampsia also received substantial management (mean=3.4, SD=3.0). Moderate management was seen for obstructed labour (mean=2.0, SD=3.0) and preterm labour (mean=2.0, SD=2.0); lower levels for foetal distress, neonatal sepsis, and severe anaemia (each mean=1.0). No pre-referral management was recorded for delayed labour progress or postpartum haemorrhage (both mean=0.0, SD=0.0), and maternal sepsis received minimal management (mean=0.0, SD=1.0). The wide standard deviations indicate inconsistent application of pre-referral protocols across months.

Figure 2: Pre-referral management by obstetric complication, District Headquarters Hospital, Gajapati District, Odisha (July 2024 – August 2025). Bars show the mean monthly pre-referral management score (±1 SD); lower whiskers are truncated at zero because scores cannot be negative.

Discussion

This prospective analysis of obstetric referral patterns in a tribal district of Odisha provides important insights into the functioning of maternal healthcare delivery systems that serve vulnerable populations. The findings revealed significant temporal disparities, quality gaps in pre-referral management, and concerning patterns that suggest inadequate emergency obstetric care capacity during critical periods[28,29].

Temporal Disparities in Referral Patterns

The strong correlation between daytime referrals-in and total referrals-in (r=0.862, p<0.001) contrasted markedly with the weaker correlation observed during nighttime hours (r=0.466, p=0.093). These findings highlight pronounced diurnal variations in healthcare-seeking behavior and referral decision-making processes. Such temporal disparities likely reflect multiple factors, including transportation availability, family decision-making patterns, and provider readiness at referring facilities during different periods[30].

The significant correlation between nighttime referrals-in and nighttime referrals-out (r=0.571, p=0.033) suggests a concerning "pass-through" phenomenon, in which patients referred during night hours are subsequently transferred to higher facilities without substantial intervention at the district hospital level. This pattern indicates potential limitations in the facility's capacity to manage complex obstetric emergencies during nighttime hours, raising important questions about staffing patterns, resource availability, and provider confidence in managing critical cases during night shifts[31].

Quality Gaps in Pre-referral Management

Substantial variations in the quality of pre-referral management represent a critical finding with immediate clinical implications. Birth asphyxia received the highest management scores (mean=8.0, SD=5.0), likely reflecting the widespread recognition of neonatal resuscitation as an essential clinical skill. However, the high standard deviation indicates inconsistent application, even for this well-recognized emergency condition.

Figure 2 starkly visualizes the quality disparities in pre-referral management. The complete absence of pre-referral management for postpartum haemorrhage and delayed labour progress (both mean=0.0, SD=0.0) is particularly concerning, given that these conditions are among the leading direct causes of maternal mortality globally[32]. The inability to provide initial stabilization measures before referral significantly compromises patient outcomes and suggests fundamental gaps in the emergency obstetric care capacity at peripheral facilities.

The color-coded representation in Figure 2 immediately highlights the critical quality gaps: while birth asphyxia, PROM, and eclampsia receive relatively good management (green bars), conditions such as postpartum haemorrhage show virtually no pre-referral intervention (dark red minimal bars). This visualization powerfully demonstrates the need for urgent interventions in the referral system.

Independence of Referral Patterns from Delivery Volumes

The absence of significant correlations between total deliveries and referral patterns challenges the conventional assumptions regarding healthcare utilization dynamics. This finding suggests that referral decisions are influenced more by case complexity, provider capacity, and institutional factors than by patient volumes. Such independence indicates that the effectiveness of the referral system depends on qualitative factors, including provider competency, facility readiness, and case-specific clinical decision-making, rather than simple patient load considerations[21,33].

Implications for Health System Strengthening

The temporal disparities identified highlight the urgent need for enhanced night-shift capabilities, including improved staffing patterns, targeted training initiatives, and resource allocation, to ensure 24-hour readiness for complex obstetric cases[1]. The variability in pre-referral management suggests that standardization of protocols, coupled with regular training and supportive supervision, could significantly improve the quality of initial care before referral[34].

Enhanced communication systems between referring and receiving facilities could improve coordination and ensure appropriate preparation for incoming referrals[23]. The significant correlation between nighttime referrals-in and referrals-out suggests that improved night-shift capacity at the district level could reduce unnecessary onward referrals and improve overall system efficiency[33].

Context-Specific Considerations for Tribal Healthcare

These findings must be interpreted within the specific context of tribal healthcare delivery, where communities face multiple intersecting barriers, including geographical isolation, cultural preferences, economic constraints, and historical marginalization from mainstream healthcare services[35,36]. The achievement of relatively high referral utilization rates suggests that institutional barriers may be more amenable to intervention than previously assumed, provided that appropriate strategies are implemented[37].

Limitations

As a single-center study, its generalizability to other tribal districts or healthcare settings may be limited. The observational nature of the study precludes definitive causal inferences regarding the observed associations. The 14-month observation period, although substantial, may not capture longer-term trends or seasonal variations. Additionally, the study documents referral patterns and quality gaps but does not provide direct insights into healthcare provider decision-making processes or patient experiences in the referral system.

Future Research

Future work should examine provider decision-making across shifts, patient-centred experiences of referral, and intervention studies testing standardised pre-referral protocols and enhanced communication systems. Comparative studies across tribal districts could identify transferable best practices for referral-system strengthening.

Conclusions

This analysis reveals temporal disparities in the obstetric referral system serving a tribal population in Eastern India. The strong daytime but weaker nighttime referral correlations indicate a need for enhanced night-shift capability, and the association between nighttime referrals-in and referrals-out is consistent with a possible pass-through of cases during critical hours. The wide variation in pre-referral management from comprehensive care for birth asphyxia to no recorded intervention for postpartum haemorrhage underscores the need for standardised protocols, while the independence of referral patterns from delivery volume indicates that factors beyond caseload drive referrals. For tribal populations, strengthening maternal referral systems will require attention to both supply-side factors (provider capacity, facility readiness) and demand-side considerations (cultural appropriateness, geographical access). These findings offer concrete targets addressing the nighttime pass-through through enhanced district-level EmOC and implementing pre-referral protocols for life-threatening conditions relevant to maternal-health programmes in tribal regions.

Conflict of Interest:

The authors declare no conflict of interest.

Funding:

This research received no specific grant from any funding agency.

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