From Pandemic to Progress: Maternal Health Resilience in the post COVID-19 era in Tamil Nadu, India

doi:10.21203/rs.3.rs-5238242/v1

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Research Article

From Pandemic to Progress: Maternal Health Resilience in the post COVID-19 era in Tamil Nadu, India

https://doi.org/10.21203/rs.3.rs-5238242/v1

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Background and Objectives

The COVID-19 pandemic considerably impacted emergency medical services (EMS), particularly in the context of maternal care. In response, the government made significant investments in both EMS and maternal health during the pandemic. This study aims to evaluate maternal and childbirth outcomes, specifically during the resilient period, by analyzing the long-term effects of the pandemic on healthcare delivery.

Data and Methods

The research analyzed key metrics related to emergency medical services for pregnancy, including call volume, response and transfer times, hospital handoff times, and ambulance travel distances. Maternal outcomes assessed included mortality rates, institutional childbirth, home deliveries, miscarriages, vaginal complications, and C-section rates.

Data was sourced from the Tamil Nadu State Control Room registry, covering historical data from Jan 2017 including the pandemic phases in 2020–2022 and the subsequent resilient period in 2023-24. This study employs time-series analysis to compare the distribution of daily key metrics of EMS during eight pandemic phases with the average daily frequency during the pre-pandemic period. An effect size measure is then used to quantify the improvement in maternal healthcare outcomes and EMS metrics.

Results

Throughout the various stages of the pandemic, there was a notable increase in call volume related to women. Despite this, there were significant improvements in response times, transfer times, and hospital handoff times. In comparison to the corresponding period before the pandemic, maternal and childbirth outcomes saw marked enhancements during the post pandemic phase in 2023 and resilient phase in 2024. Specifically, the maternal mortality rate dropped by 19%, with 37 deaths per 100,000 live births, significantly lower than the national average of 97 deaths per 100,000 live births. Additionally, the rates of infant mortality, neonatal mortality, miscarriages, complicated vaginal births, and home deliveries decreased by 19.35%, 17.03%, 28.02%, 19.23%, and 36.05%, respectively.

Conclusions: Government investments during the pandemic, along with the sustained focus on maternal health programs, appear to have provided substantial support to pregnant women and newborns. The reproductive health of women in Tamil Nadu does not seem to have been adversely impacted by the pandemic.

Maternal Mortality Rate

Infant Mortality Rate

Home Deliveries

Pandemic

C- section

Emergency Medical Services

The global COVID-19 pandemic, which began in early 2020, brought unprecedented challenges to healthcare systems worldwide. The pandemic’s impact was especially pronounced in maternal and child healthcare, with disruptions to routine services, emergency care, and public health interventions. In India, one of the most populous countries in the world, the healthcare system faced significant strain, particularly in Tamil Nadu, a state with an estimated population of 84 million. As the pandemic unfolded in three distinct waves from March 2020 to March 2022, followed by a post-pandemic phase from April 2022 to December 2022 and a resilient recovery period from January to November 2023, the focus on maternal health outcomes became increasingly critical.

This research seeks to assess maternal and childbirth outcomes, particularly during the post-pandemic and resilient recovery phases, following the disruptions caused by COVID-19. Specific outcomes of interest include the maternal mortality rate (MMR), infant mortality rate (IMR), the prevalence of caesarean sections (C-sections), incidences of home deliveries, and complications during vaginal births. This study will examine the years 2023 and 2024 to understand how maternal health has changed in the post-pandemic era. It will also assess the reproductive health of the general population in Tamil Nadu, India. The main goal is to determine if the pandemic has had a lasting effect on reproductive health outcomes, especially for mothers and infants. The pandemic disrupted routine health services and necessitated a reevaluation of how emergency medical services (EMS), including ambulance services, adapted to meet the needs of pregnant women.

During the first wave of the pandemic, the Tamil Nadu government implemented several emergency measures to strengthen healthcare delivery. These included increasing the state's ambulance fleet, hiring temporary medical and paramedical staff, and improving emergency department infrastructure in government hospitals. Since maternal healthcare often requires timely interventions, including EMS, the availability and efficiency of ambulances were crucial for maternal health outcomes during this period.

The research will investigate whether pregnant women received adequate EMS during different phases of the pandemic. Key metrics such as call volume (CV) for emergency services, response time (RT) of ambulances, transfer time (TT) from the emergency site to the hospital, hospital handoff time (HT), and total distance traveled by ambulances (DT) will be used to assess the continuity and quality of care. The study will analyze how these metrics influenced maternal health outcomes during both the pandemic and post-pandemic periods. Additionally, it will examine whether government-run maternal health schemes aimed at providing uninterrupted care to pregnant women during the pandemic were successfully maintained.

Ultimately, this study aims to provide a comprehensive understanding of the COVID-19 pandemic's impact on maternal health outcomes in Tamil Nadu. It will offer insights into the effectiveness of emergency medical interventions and identify areas for improvement in future healthcare crises. By focusing on the intersection of EMS and maternal care, this research hopes to inform policy decisions and healthcare strategies to better protect maternal and child health in India during public health emergencies.

The COVID-19 pandemic has greatly affected maternal health services globally, exposing weaknesses and highlighting the need for resilient health systems. As nations transition from crisis to recovery, several key aspects emerge regarding maternal health resilience in the post-pandemic era. The care of pregnant women during the COVID-19 pandemic globally has evolved significantly, reflecting both challenges and adaptations in healthcare delivery. This literature will be further elaborated through the following subsections: Adverse pandemic impact on maternal care and childbirth, and Innovative strategies – Better emergency care and maternal and childbirth outcomes.

Adverse pandemic impact on maternal care and childbirth

The studies reviewed indicate a global pattern of disrupted maternal healthcare during the COVID-19 pandemic, with regional differences in extent and causes. Financial constraints, fear of infection, and barriers to healthcare access significantly impeded maternal health service use. Disparities, especially in low-resource settings, emphasize the need to strengthen healthcare systems and prepare for future crises.

Changes in hospital practices during the pandemic revealed that maternity care often did not meet WHO standards. Women and families voiced the need for support systems and consistent maternity care, highlighting healthcare system shortcomings [1]. The pandemic further revealed significant disparities in healthcare access and quality, stressing the need for resilient maternal healthcare systems [2]. The PregCovid registry noted increased maternal complications during the Delta variant wave, including higher preterm birth rates and low birth weights [3].

In the USA, maternal care in South Carolina was disrupted across multiple socio ecological levels. Women faced personal fears, reduced access to group antenatal care, and language barriers, particularly affecting African American and Hispanic women. The study recommended the integration of telehealth and culturally tailored education to address these challenges [4]. During the pandemic in Brazil, disruptions in maternal care led to an increase in caesarean deliveries and maternal mortality rates [5], with a significant rise in stillbirths (4.8%) and maternal deaths (71.6%) [6].

Russia’s Far Eastern Federal District (FEFD) also saw maternal mortality rates surpass national averages, driven primarily by non-obstetric causes, particularly extragenital diseases (EGD), highlighting the need for better pre-pregnancy care and rural healthcare access [7]. This study done in Rio de Janeiro found that COVID-19 significantly increased the risk of severe maternal morbidity and mortality, especially during the third trimester. ICU admissions and maternal deaths were more frequent following caesarean delivery. Notably, maternal health outcomes were worse during the Gamma wave compared to the Delta wave [8]

The studies conducted in Africa, highlighted disruptions in maternal healthcare utilization due to the COVID-19 pandemic. For instance, the utilization of maternal health services declined in northern region in Nigeria, from 65.8–42.4%, largely due to fear of contracting COVID-19, transportation challenges, and harassment by security personnel [9]. The study from the cross-sectional survey of households in Lubumbashi, Democratic Republic of Congo (DRC), found that only 36% of women completed the continuum of maternal care, with barriers like vaccine hesitancy and financial constraints limiting access [10]. In Ethiopia, a systematic review and meta-analysis revealed a decline in family planning services (26.62%), antenatal care (19.30%), and institutional deliveries (12.82%), driven by fear of infection, poor care quality, and resource shortages [11]. Additionally, [12] emphasized the need for home healthcare and community worker involvement to support pregnant women in low-resource settings. In Egypt, significant disruptions in maternal health services led to unintended pregnancies and increased health risks for women [13].

In Malaysia, maternal and child health services were heavily disrupted by the pandemic, although infant immunization largely remained stable, highlighting the need for targeted strategies to maintain service continuity [14]. Similarly, Southeast Asia experienced major setbacks in reproductive and maternal health, with antenatal care and facility-based deliveries decreasing by up to 69.6% and 52.4%, respectively [15].

In Sri Lanka, reduced income due to the pandemic pushed many families into poverty, further limiting access to maternal health services [16]. In West Bengal, India, while most women delivered in health facilities, only 37.6% received postnatal care, exacerbated by financial constraints due to job losses [17]. Anxiety and fear of infection during the pandemic further deterred women from seeking care [18].

Innovative strategies – Better emergency care and maternal and childbirth outcomes

Though the COVID-19 pandemic led to widespread disruptions in maternal healthcare services, some regions demonstrated resilience and innovation in maintaining care. These variations underscore both the challenges and successes in safeguarding maternal health during the crisis. The following literature examines how different regions were impacted, with some areas experiencing severe disruptions while others adapted to sustain services.

The studies below relating to different regions in the world demonstrated succeeded in combating the pandemic by proper adaptation to sustain services and ensure continuum in the antenatal care for pregnant women. A systematic review highlighted that however, found that despite disruptions in antenatal care globally, many women displayed resilience, with no significant increase in severe maternal morbidity or stillbirth rates [19]. A study found that 88.4% of pregnant women believed they were at increased risk of COVID-19, with 81.2% fearing infection during hospital visits. Despite this, many women maintained knowledge of necessary antenatal practices, although only 50.8% adhered to supplement intake regularly [20]. However, overall neonatal outcomes, such as birth weight, remained stable despite the increased maternal risks [21].

It is reported that the overall health service utilization remained stable in Global Network sites, with no significant decline in pregnancy outcomes despite increased COVID-19 infections, [22]. In Brazil, the pandemic led to increased rates of caesarean sections and maternal mortality, particularly due to disruptions in maternal care [5]. Research from Sweden indicated a decline in births during the pandemic, highlighting different regional impacts on maternal health services [23]. In Kenya, the MomCare platform significantly improved care-seeking behaviors among expectant mothers, ensuring continued access to services despite lockdowns, thus maintaining maternal care quality during the pandemic [24].

A study in Indonesia found no significant difference in maternal stress levels between adverse and good pregnancy outcomes during the pandemic, with 70.6% of mothers reporting normal stress levels [25]. Additionally, innovative solutions helped sustain maternal healthcare in Indonesia. Telehealth services allowed women to receive care despite government advice to postpone visits [26]. Similarly, smartphone applications provided online health classes and guidelines, enhancing self-management among pregnant women [27].

In Iran, a national maternal health network was created to ensure continuous care during the pandemic. This network provided guidelines for managing COVID-19 in pregnant women, helping to reduce risks and maintain essential services [28].

A study by Jain and others found a significant decline in maternal healthcare utilization in northern India, with institutional deliveries dropping by 30% and antenatal visits by 25%. However, government initiatives like Janani Suraksha Yojana (JSY) and Pradhan Mantri Surakshit Matritva Abhiyan (PMSMA) continued to provide essential services, although some areas saw reductions [29]. Similarly, Singh and Chand (2024) highlighted various coping strategies adopted by pregnant women, such as seeking social support, despite the pandemic's challenges.[30] The healthcare system in Tamil Nadu, India, also adapted effectively during the pandemic waves. Emergency services, particularly for antenatal care, saw a 62% rise in ambulance calls, reflecting increased attention to maternal health [31]. Additionally, the availability of healthcare services in Tamil Nadu was high, with 98% of women expressing satisfaction with maternal health services, indicating that government initiatives were successful in maintaining access [32].

The proposed study addresses the significant gap in the literature by undertaking an empirical study of long time window from pre-pandemic era through different waves of pandemic to post pandemic phases and lastly to the resilient period in an under researched region by exploring the relationship how the emergency medical services that were provided to pregnant women during the pandemic phases has impacted on maternal outcomes and childbirths especially in the post-pandemic period. By analyzing outcomes after two or more years of the pandemic, this study provides a comprehensive understanding of how resilient healthcare systems have adapted, a topic that has scope for a thorough investigated. This research will offer critical insights into both the immediate and long-term impacts on maternal and child healthcare services, filling a crucial gap in the current body of knowledge.

Data

The research draws on data from Emergency Medical Services (EMS) in Tamil Nadu, one of India's largest states, with a population of approximately 84 million. The state comprises 42 districts, including the Chennai Corporation, which functions as part of its public health administration. The study relies on data collected from ambulance service calls made through the 108 emergency number, which is activated during medical crises. Dispatchers coordinate ambulance responses based on the type and location of emergencies, ensuring timely transportation of patients. The ambulances are stationed at designated base locations across the state to enable quick access to emergency sites.

The study identifies several key variables related to ambulance services: Response Time (RT), which is the time taken for an ambulance to reach the emergency site from its base location; Transfer Time (TT), the time taken to transport patients from the scene to a hospital; and Handover Time (HT), which refers to the time taken by ambulance personnel to transfer patient care to the hospital's emergency department.

Ambulance travel distances (DT) are divided into three segments: from the base location to the emergency site, from the emergency site to the hospital, and from the hospital back to the base location. Emergency calls are categorized by their origin, with "Inter-Facility Transfer" (IFT) calls used for transferring patients between medical facilities, and non-IFT calls typically originating from locations such as a pregnant woman’s home. RT and TT measure the ambulance's response efficiency, while Call Volume (CV) and DT provide insight into the ambulance workload. HT indicates hospital preparedness for patient admission, excluding travel time.

The second data which is more important one relates to the maternal and childbirth outcomes. These metrics including institutional and home deliveries, complicated vaginal births, caesarean sections, maternal mortality rates (MMR), and infant mortality rates for every month beginning 2013–2014 to 2023–2024 for the state of Tamil Nadu. This data has been sourced from the National Health Mission, Department of Health and Family Welfare, Government of India. This data is all inclusive of entire population of Tamil Nadu where the pregnant women and newborns would have availed either the government or private hospital facilities or at home.

Method

In evaluating the impact of any intervention, randomized control trials are typically the most reliable approach. However, in the case of population health studies, such as this one, it is impractical to randomly assign individuals to treatment or control groups. While counterfactual analysis could be a viable method, it is less applicable here due to the research’s focus on the pandemic’s impact, extending through post-pandemic and resilient phases over a span of more than two years. To address this, the researchers employed various techniques ranging from statistical methods like ARIMA to machine learning approaches such as Generalized Additive Models, and advanced deep learning models including transformers. Despite experimenting with these techniques, the validation error in the test data exceeded acceptable limits.

The data for this study spans different periods for Emergency Medical Services (EMS) and maternal outcomes, starting in 2016 for EMS and 2013 for maternal data. However, the primary focus of the analysis is on pandemic phases, including Wave-1 (W-1), post-Wave-1 (post-W-1), Wave-2 (W-2), post-Wave-2 (post-W-2), Wave-3 (W-3), post-Wave-3 (post-W-3), and the resilient period (RP). EMS metrics include Call Volume (CV), Response Time (RT), Transport Time (TT), Hospital Handover Time (HHT), and Total Distance Travelled by the ambulance for one call (TDT). For maternal outcomes, the study considers Institutional Deliveries in Government (IDG) and Private hospitals (IDP), Home Deliveries (HD), Miscarriages (MC), Cesarean Sections (CS), Complicated Vaginal Births (CVB), Maternal Mortality (MM), Neonatal Mortality (NNM), Neonatal Mortality within 7 days (NN-7), and Infant Mortality (IM). The study also calculates maternal and neonatal mortality rates: MMR, NMR, NMR-7, and IMR.

EMS data is analyzed as daily mean values, while maternal outcomes are considered as monthly averages. To compare pandemic-phase data with the pre-pandemic period, for instance, the response time (RT) during Wave-1 (March 23, 2020, to September 30, 2020) is compared with the pre-pandemic data from March 23 to September 30 in 2016, 2017, 2018, and 2019. This results in two distributions with 191 data points each: one for the pandemic period and the other for the corresponding pre-pandemic days. A simple comparison of the means of these distributions may overlook the variability in the data, leading to inaccurate conclusions. Therefore, the study compares the full distribution of each metric during different pandemic phases with their respective pre-pandemic counterparts.

First, the normality of the data is assessed. If the distribution is normal, a t-test is applied to check for significant differences between the two periods, and Cohen's D is used to compute the effect size. In most cases, the distributions are non-Gaussian, prompting the use of the Wilcoxon test to assess statistical differences, with Cliff's Delta applied to measure the effect size. The Cliff’s Delta value indicates the degree of change, whether low, medium, high, or very high, based on values ranging from − 1 to + 1.

The first section will present the results of EMS response, evaluated using five metrics: CV, RT, TT, HHT, and TDT. This will be followed by a discussion of maternal outcomes, which are assessed using twelve different metrics.

EMS-Metrics

In terms of CV, four key trends were observed in non-IFT calls. First, there was a moderate decline in non-IFT calls during W-1 and post-W-1 (with a medium effect size). The second trend was a slight increase in the W-2 phase, which coincided with the peak of the pandemic, characterized by the highest rates of COVID-19 infections and fatalities. Third, in the subsequent pandemic phases—post-W-2, W-3, and post-W-3—there was a consistent rise in CV, with a reported large effect size. Finally, the fourth trend was a sharp decline in CV during the resilient period (RP), marked by a significant effect size reduction. As for IFT CV, there was a continuous upward surge across most pandemic phases, with effect sizes ranging from large to very large, except during W-2, where the increase was minimally significant. The distinct W-2 phase only showed an insignificant rise in IFT calls, standing out from the other periods. (See Table-1)

Table 1

Percentage Difference between Actual and Pre-Pandemic Total Calls, IFT Calls & NIFT Calls related to pregnancy along with Effect Size(Cliffs Delta; Cohen's D) and respective Confidence Intervals during various phases of lockdowns in all three waves in 2020–2022 and Resilience Period in 2023 in Tamil Nadu.
Period	Actual	Reference	% Change	Avg. Ref. Daily Call	Avg Actual Daily Call	N- test Actual	N- test- Pre-Panemic	Wilcoxon Test	Effect Size	Confidence Interval - Effect Size
IFT
Wave 1	96256	88397	8.89	460	501	< 0.001	< 0.001	< 0.001	0.63 Medium*	[0.43, 0.83]
Post Wave 1	99149	78012	27.09	429	545	< 0.001	< 0.001	< 0.001	0.49 large	[0.38, 0.59]
Wave 2	92299	85709	7.69	468	504	< 0.001	< 0.001	< 0.001	0.21 small	[0.08, 0.32]
Post Wave 2	49946	44023	13.45	543	617	0.446	< 0.001		0.97 Large*	[0.63, 1.29]
Wave 3	39622	34184	15.91	422	489	0.893	0.879		1.05 Large*	[0.74, 1.36]
Post Wave 3	165250	137063	20.57	465	560	< 0.001	< 0.001	< 0.001	0.57 large	[0.49, 0.63]
Resilience Period	107256	153684	-30.21	460	321	< 0.001	< 0.001	< 0.001	-0.73 large	[-0.78, -0.68]
Non-IFT
Wave 1	41588	48606	-14.44	253	217	< 0.001	< 0.001	< 0.001	-0.45 medium	[-0.55, -0.32]
Post Wave 1	29310	38835	-24.53	213	189	< 0.001	< 0.001	< 0.001	-0.36 medium	[-0.45, -0.21]
Wave 2	46161	38602	19.58	211	252	< 0.001	< 0.001	< 0.001	0.08 negligible	[-0.06, 0.2]
Post Wave 2	26781	18588	44.08	229	331	< 0.001	< 0.001	< 0.001	0.61 large	[0.44, 0.74]
Wave 3	21288	14941	42.48	184	263	< 0.001	< 0.001	< 0.001	0.66 large	[0.51, 0.78]
Post Wave 3	89938	70420	27.72	239	305	< 0.001	< 0.001	< 0.001	0.36 medium	[0.27, 0.44]
Resilience Period	51832	77476	-33.10	232	155	< 0.001	< 0.001	< 0.001	-0.64 large	[-0.7, -0.57]

Nearly all time-based metrics, including RT, TT, and HHT, saw a significant reduction during the pandemic period, from post-W-1 to RP for both IFT and non-IFT calls. However, the decrease was more pronounced during the phases of W-2, post-W-2, W-3, and post-W-3, while the declines in W-1, post-W-1, and RP were less substantial. These reductions were compared to corresponding pre-pandemic periods. Overall, the declines in time metrics were largely consistent across all phases, with only slight differences emerging during the W-3, post-W-3, and RP phases. All duration-related metrics such as RT, TT, and HHT showed a more significant overall reduction during the various pandemic phases for IFT category calls compared to non-IFT. In contrast, the distance metric, TDT, showed a larger distance covered from W-1 to W-3 for IFT-related pregnancy calls, while the trend for non-IFT calls mirrored that of the time-based metrics. As for IFT calls, there was a continuous upward surge across most pandemic phases, with effect sizes ranging from large to very large, except during W-2, where the increase was minimally significant. The distinct W-2 phase only showed an insignificant rise in IFT calls, standing out from the other periods (See Tables 2,3,4,5)

Table 2

Percentage Difference between Actual and Pre-Pandemic (IFT and Non-IFT Response Time (Mins) for pregnancy related calls with Effect Size (Cliffs Delta; Cohen's D) and respective Confidence Intervals during various phases of lockdowns in all three waves in 2020–2022 and Resilience Period in 2023 in Tamil Nadu.
Period	Actual	Reference	% Change	N- test Actual	N- test- Predicted	Wilcoxon Test	Effect Size	Confidence Interval - Effect Size
IFT
Wave 1	22.03	21.91	0.56	< 0.001	< 0.001		0.08 Very small*	[-0.12, 0.28]
Post Wave 1	15.98	20.74	-22.95	< 0.001	< 0.001	< 0.001	-0.71 large	[-0.78, -0.62]
Wave 2	14.68	20.58	-28.69	< 0.001	< 0.001	< 0.001	-0.82 large	[-0.88, -0.75]
Post Wave 2	14.66	22.58	-35.08	0.767	0.5		-4.14 Very Large*	[-4.66, -3.6]
Wave 3	14.93	19.98	-25.28	0.44	0.739		-3.0 Very Large*	[-3.4, -2.6]
Post Wave 3	15.36	20.96	-26.70	< 0.001	< 0.001	< 0.001	-0.97 large	[-0.98, -0.95]
Resilience Period	15.81	20.85	-24.21	< 0.001	< 0.001	< 0.001	-0.94 large	[-0.96, -0.92]
Non-IFT
Wave 1	23.98	24.26	-1.18	< 0.001	< 0.001	< 0.001	-0.06 negligible	[-0.17, 0.06]
Post Wave 1	18.09	23.27	-22.27	< 0.001	< 0.001		-0.5 large	[-0.64, -0.42]
Wave 2	12.44	22.27	-44.14	< 0.001	< 0.001	< 0.001	-0.72 large	[-0.79, -0.64]
Post Wave 2	11.28	23.85	-52.70	< 0.001	< 0.001	< 0.001	-0.85 large	[-0.92, -0.75]
Wave 3	12.28	22.84	-46.24	< 0.001	< 0.001	< 0.001	-0.84 large	[-0.92, -0.72]
Post Wave 3	12.30	21.87	-43.76	< 0.001	< 0.001	< 0.001	-0.8 large	[-0.85, -0.74]
Resilience Period	11.86	22.04	-46.19	< 0.001	< 0.001	< 0.001	-0.81 large	[-0.85, -0.75]

Table 3

Percentage Difference between Actual and Pre-Pandemic Non-IFT and IFT Travel Time (Mins) with Effect Size (Cliffs Delta; Cohen's D) and respective Confidence Intervals during various phases of lockdowns in all three waves in 2020–2022 and Resilience Period in 2023 in Tamil Nadu
Period	Actual	Reference	% Change	N- test Actual	N- test- Predicted	Wilcoxon Test	Effect Size	Confidence Interval - Effect Size
IFT
Wave 1	55	59.54	-7.63	< 0.001	< 0.001	< 0.001	-0.84 large	[-0.89, -0.78]
Post Wave 1	53	57.00	-7.82	< 0.001	< 0.001	< 0.001	-0.84 large	[-0.88, -0.77]
Wave 2	53	56.02	-5.43	< 0.001	< 0.001	< 0.001	-0.79 large	[-0.85, -0.72]
Post Wave 2	53	56.02	-5.39	< 0.001	< 0.001	< 0.001	-0.86 large	[-0.92, -0.75]
Wave 3	55	56.03	-1.83	0.003	< 0.001	< 0.001	-0.63 large	[-0.75, -0.48]
Post Wave 3	55	56.50	-2.65	< 0.001	< 0.001	< 0.001	-0.46 medium	[-0.54, -0.37]
Resilience Period	55	56.50	-2.65	< 0.001	< 0.001	< 0.001	-0.35 medium	[-0.43, -0.26]
Non-IFT
Wave 1	35	33.03	5.96	< 0.001	< 0.001	< 0.001	0.26 small	[0.14, 0.37]
Post Wave 1	29	30.24	-4.08	< 0.001	< 0.001		-0.24 small	[-0.37, -0.12]
Wave 2	25	30.02	-16.72	< 0.001	< 0.001	< 0.001	-0.45 medium	[-0.55, -0.34]
Post Wave 2	24	32.00	-25.00	< 0.001	< 0.001	< 0.001	-0.68 large	[-0.79, -0.54]
Wave 3	26	31.02	-16.18	< 0.001	< 0.001	< 0.001	-0.48 large	[-0.62, -0.3]
Post Wave 3	26	32.00	-18.75	< 0.001	< 0.001	< 0.001	-0.49 large	[-0.56, -0.4]
Resilience Period	27	31.99	-16.27	< 0.001	< 0.001	< 0.001	-0.44 medium	[-0.52, -0.36]

Table 4

Percentage Difference between Actual and Pre-Pandemic Non-IFT and IFT Hospital Handoff Time (Mins) with Effect Size (Cliffs Delta; Cohen's D) and respective Confidence Intervals during various phases of lockdowns in all three waves in 2020–2022 and Resilience Period in 2023 in Tamil Nadu
Period	Actual	Reference	% Change	N- test Actual	N- test- Predicted	Wilcoxon Test	Effect Size	Confidence Interval - Effect Size
IFT
Wave 1	15.00	15.03	-0.19	< 0.001	< 0.001	< 0.001	-0.69 large	[-0.76, -0.6]
Post Wave 1	15.03	16.02	-6.14	< 0.001	< 0.001	< 0.001	-0.32 small	[-0.43, -0.2]
Wave 2	15.00	17.02	-11.85	< 0.001	< 0.001	< 0.001	-0.97 large	[-0.99, -0.95]
Post Wave 2	15.00	18.02	-16.74	< 0.001	< 0.001	< 0.001	-0.96 large	[-0.98, -0.9]
Wave 3	15.58	17.02	-8.42	< 0.001	< 0.001	< 0.001	-0.63 large	[-0.75, -0.48]
Post Wave 3	17.01	18.00	-5.51	< 0.001	< 0.001	< 0.001	-0.33 small	[-0.41, -0.23]
Resilience Period	19.00	18.00	5.56	< 0.001	< 0.001	< 0.001	0.3 small	[0.21, 0.38]
Non-IFT
Wave 1	14.03	14.00	0.18	< 0.001	< 0.001	< 0.001	0.01 negligible	[-0.1, 0.13]
Post Wave 1	13.02	14.99	-13.15	< 0.001	< 0.001		-0.31 small	[-0.45, -0.21]
Wave 2	11.00	14.58	-24.53	< 0.001	< 0.001	< 0.001	-0.64 large	[-0.71, -0.54]
Post Wave 2	11.71	15.00	-21.94	< 0.001	< 0.001	< 0.001	-0.73 large	[-0.83, -0.61]
Wave 3	12.23	15.00	-18.44	< 0.001	< 0.001	< 0.001	-0.55 large	[-0.68, -0.39]
Post Wave 3	15.00	15.00	0.00	< 0.001	< 0.001	< 0.001	-0.19 small	[-0.28, -0.1]
Resilience Period	15.00	15.00	0.00	< 0.001	< 0.001	< 0.001	-0.07 negligible	[-0.15, 0.02]

Table 5

Percentage Difference between Actual and Pre-Pandemic Non-IFT and IFT Total Distance Travelled (in Kms) with Effect Size (Cliffs Delta; Cohen's D) and respective Confidence Intervals during various phases of lockdowns in all three waves in 2020–2022 and Resilience Period in 2023 in Tamil Nadu
Period	Actual	Reference	% Change	N- test Actual	N- test- Pre-Pandemic	Wilcoxon Test	Effect Size	Confidence Interval - Effect Size
IFT
Wave 1	60	57	5.26	< 0.001	< 0.001		1.15 Large*	[0.94. 1.37]
Post Wave 1	59	56	5.36	< 0.001	< 0.001	< 0.001	0.35 medium	[0.23, 0.46]
Wave 2	59	56	5.36	< 0.001	< 0.001	< 0.001	0.56 large	[0.46, 0.65]
Post Wave 2	57	55	3.64	0.266	< 0.001		0.54 Medium*	[0.21, 0.85]
Wave 3	57	56	1.79	0.23	< 0.001	< 0.001	0.16 small	[0.01, 0.33]
Post Wave 3	56	56	0	< 0.001	< 0.001	< 0.001	-0.16 small	[-0.25, -0.07]
Resilience Period	55.5	56	-0.89	< 0.001	< 0.001	< 0.001	`-0.01 negligible	[-0.08, 0.09]
Non-IFT
Wave 1	38	31	22.58	< 0.001	< 0.001	< 0.001	0.74 large	[0.65, 0.81]
Post Wave 1	31	31	0	< 0.001	< 0.001		-0.07 negligible	[-0.06, 0.2]
Wave 2	25	30	-16.67	< 0.001	< 0.001	< 0.001	0.2 small	[-0.32, 0.08]
Post Wave 2	23	32	-28.13	< 0.001	< 0.001	< 0.001	-0.52 large	[-0.66, -0.34]
Wave 3	25	32	-21.88	< 0.001	< 0.001	< 0.001	-0.44 medium	[-0.58, − 0.26]
Post Wave 3	23	33	-30.3	< 0.001	< 0.001	< 0.001	-0.58 large	[-0.66, -0.5]
Resilience Period	23	33	-30.3	< 0.001	< 0.001	< 0.001	-0.51 large	[-0.58, -0.44]

Maternal and childbirth Outcomes

Among various maternal and childbirth outcome metrics, HD, MC, CVB, MM, NM, MMR and NMR showed more significant changes than others. The most notable impact of the pandemic was the surge in HD during critical phases like post-W-1, W-1, and post-W-2, with increases of 66.3%, 15.9%, and 30.7% respectively, compared to pre-pandemic periods. MM and MMR saw a dramatic rise of 98.5% and 109.2% during W-2, and W-3 experienced significant increases of 53.7% and 60.4% respectively, compared to pre-pandemic times. NM, NM-7, NMR and NMR-7 consistently rose during the pandemic, though only moderately, hovering around a 10% increase. The IMR followed a similar trend, with slightly lower increases.

The other metrics, such as institutional births in both private and government sectors, as well as C-section deliveries, exhibited only marginal fluctuations in percentage across the different pandemic and post-pandemic phases. In contrast, both Infant Mortality and Infant Mortality Rate showed a moderate increase throughout the pandemic phases when compared to the corresponding pre-pandemic periods, except during the resilient period (RP), where there was a reduction of 14% and 9.7%, respectively

In contrast, CVB sharply declined across all pandemic phases, with percentage drops ranging from 19.2–37.8%. During the various phases of the pandemic, most metrics, except for complicated vaginal births (CVB), saw moderate to significant increases. However, in the resilient period (RP), all metrics experienced a downward trend compared to pre-pandemic levels, with home deliveries (HD) decreasing by 36.1%, maternal complications (MC) by 28.1%, CVB by 19.2%, maternal mortality (MM) by 19%, and neonatal mortality (NM) by 17%. (See Table 6). Figure 1 illustrates that most the lines representing most metrics converge well below the zero mark, emphasizing the significant improvements achieved. Figure 2 illustrates, across ten distinct panels, the trends observed in maternal outcome metrics throughout seven different phases, covering both the pandemic and post-pandemic periods. Notably, during the resilient phase, indices such as C-Section rates, maternal mortality rates (MMR), infant mortality rates (IMR), neonatal mortality rates (NMR), and NMR-7 were significantly lower than the rates observed in the pre-pandemic period.

Table-6 shows the percentage change in the different maternal and childbirth outcomes during the various phases of pandemic and post pandemic when compared with the corresponding pre-pandemic period

Category	W-1	Post-W-1	W-2	Post-W-2	W-3	Post-W-3	Resilient Period
% Change	% Change	% Change	% Change	% Change	% Change	% Change	% Change
Institutional Deliveries - Govt	-3.9	-4.9	-4.1	2.3	-0.5	-1.1	-7.4
Institutional Deliveries - Private	12.2	-1.7	-2.0	0.2	-8.4	-2.8	-2.1
Home Deliveries	-17.3	66.3	15.9	30.7	-24.5	-21.0	-36.1
Miscarriages	14.8	-4.7	13.0	-0.5	-6.2	-12.4	-28.0
Cesarean Section	10.5	4.0	3.0	5.2	-0.4	5.1	4.3
Complicated Vaginal Births	-20.7	-27.3	-22.9	-19.2	-37.8	-24.3	-19.2
Maternal Mortality	26.3	-12.9	98.5	8.6	53.7	-13.0	-19.0
Neonatal Mortality	10.6	3.8	11.4	9.3	6.9	7.3	-17.0
Neonatal Mortality − 7 Days	8.1	5.2	11.0	11.6	8.5	6.2	-19.3
Infant Mortality	5.6	0.6	7.3	8.6	5.1	7.3	-14.0
MMR	24.1	-9.8	109.2	8.0	60.4	-11.8	-14.0
NMR	8.6	8.7	14.7	7.6	11.0	9.5	-12.1
NMR − 7 Days	6.2	10.2	14.3	10.1	12.6	8.2	-14.5
IMR	3.6	6.0	10.7	7.1	9.1	9.6	-8.7

This study examines the impact of the COVID-19 pandemic on maternal and child health outcomes, particularly during the resilient phase in 2023–2024, considering the role of government interventions in healthcare services, especially those related to EMS. Focusing on the resilient phase following the third pandemic wave, the research explores the long-term effects of the pandemic on emergency healthcare access and its subsequent impact on maternal and childbirth outcomes.

Before discussing the results, it is essential to highlight some key developments that influenced maternal healthcare, including emergency services. First, the severity of the pandemic was notably higher during Wave 2 (W-2) compared to Wave 1 (W-1) and Wave 3 (W-3) [33, 34]. Additionally, the government made significant investments to enhance healthcare infrastructure, including upgrading medical facilities at hospitals and public health centers and expanding the ambulance fleet [35]. Moreover, the government implemented various initiatives to address the pandemic, such as hiring additional medical and paramedical personnel [36].

During the pandemic, call volumes for emergency services increased due to the emergence of new COVID-19 variants. Interestingly, there was a rise in pregnancy-related IFT calls, likely because certain hospitals were designated specifically for treating COVID-19 patients, and others aimed to protect pregnant women from infection [37]. As medical services were reallocated to cope with the pandemic, call volumes rose across all pandemic phases, from W-1 to post-W-3. A return to normalcy was indicated by a decline in call volumes during the resilient period of 2023 compared to pre-pandemic levels.

For non-IFT pregnancy-related calls (pregnant women at home), a decline was observed in W-1, post-W-1, and W-2, likely due to hospital avoidance behavior and government-imposed restrictions [38, 39, 40]. Time-based metrics, such as response time (RT), travel time (TT), hospital handoff time (HHT), and total ambulance distance travelled, saw significant reductions post-W-1, which can be attributed to the government’s substantial investment in improving hospital infrastructure, hiring new personnel, and expanding the ambulance fleet [41].

Home deliveries (HD) increased sharply during most pandemic phases, especially during the more severe waves, likely due to hospital avoidance. This trend was more pronounced early in the pandemic and during the more severe phases, when compliance with COVID-19 protocols was high. However, HD numbers continued to rise during later phases, possibly due to lingering pandemic-related concerns. Alongside the increase in HD, institutional births (IBs) in both public and private hospitals moderately declined. This decrease may have also contributed to the reduction in CVBs, as fewer IBs occurred during the pandemic compared to pre-pandemic phases.

Despite the government's efforts to enhance emergency medical services, maternal mortality rates (MMR) did not improve correspondingly. In fact, MMR increased during W-2 and W-3. However, a remarkable improvement in critical maternal and neonatal health indicators occurred during the resilient period, with significant reductions in HD (36.1%), miscarriages (MC, 28.1%), complicated vaginal births (CVB, 19.2%), maternal mortality (MM, 19%), and neonatal mortality (NM, 17%) compared to pre-pandemic levels. The surge in maternal mortality during W-2 can be attributed to reduced IBs and increased HD, which are associated with higher risks of maternal and neonatal deaths due to a lack of adequate medical care during childbirth. Additionally, COVID-19 infections may have exacerbated maternal health complications, contributing to the mortality rate.

While mortality-related metrics remained high throughout the pandemic, they were not significantly worse compared to global impacts. Notably, during the resilient period, all metrics, including MM, NM, NM-7, MMR, NMR, and NMR-7, showed drastic improvements beyond pre-pandemic levels. These improvements may be attributed to enhanced healthcare infrastructure, better antenatal care, and other contributing factors. The study recognizes that additional factors may have influenced these outcomes and suggests further investigation.

Limitations of the Study

The authors recognize that the observed improvements in maternal and childbirth outcomes, particularly during the post-pandemic and resilient periods, may be influenced by external factors unrelated to government-driven enhancements in medical infrastructure.

The study relies exclusively on quantitative data, which, while reliable, may not fully capture the ground realities, especially given potential underreporting or non-reporting of medical conditions and issues related to maternal healthcare, including mortality.

Furthermore, the research is focused solely on the state of Tamil Nadu, India, which limits the generalizability of the findings to other regions, as differences in population health indices, the balance between public and private healthcare services, and demographic-specific maternal challenges vary across regions.

Additionally, the study spans only the immediate post-pandemic period (2023–2024), which may not provide a sufficient timeframe to fully understand the long-term impact of the pandemic on maternal health outcomes.

Finally, a qualitative component, including patient experiences and insights from medical and paramedical professionals, as well as a more granular analysis of district- or region-specific variations within Tamil Nadu, could offer deeper insights into the factors driving the improvements in maternal health outcomes.

The study highlights observed improvements in maternal health outcomes during certain phases of the pandemic and after the COVID-19 pandemic, particularly during the resilient period in 2024, with a focus on government initiatives. The research involved an analysis of key emergency medical service (EMS) metrics and maternal health outcomes across various phases of the pandemic and the post-pandemic resilient period.

EMS efficiency saw significant improvement, with decreased response times (RT), transfer times (TT), and hospital handoff times (HHT) during critical pandemic phases, even with an increase in call volume, indicating a higher caseload for ambulance services. Despite the pandemic's challenges, the period from 2023 to 2024 showed a notable recovery in maternal outcomes, with a 19% reduction in maternal mortality rate (MMR), resulting in 37 deaths per 100,000 live births.

The most notable result was the MMR of 24 in June 2024, which is considerably lower than the national average and that of other emerging countries, typically around 100. Other critical metrics such as neonatal mortality, infant mortality, home deliveries, and complicated vaginal births also saw significant declines compared to pre-pandemic levels.

The study observed improvements in maternal healthcare during and after the pandemic, coinciding with investments in healthcare infrastructure, such as increased ambulance fleets, expanded medical personnel, and enhanced public health facilities. These developments may have contributed to sustained support for maternal health during this period. The findings suggest that Tamil Nadu's reproductive health systems exhibited resilience, with the long-term effects of the pandemic reflecting positive outcomes in healthcare delivery. Notably, maternal and neonatal health in the region did not appear to be adversely impacted by the pandemic.

sections–Caesarean Sections

Call Volume

CVB

Complicated Vaginal Births

Distance Travelled

EMS

Emergency Medical Services

Home Deliveries

HHT

Hospital Handoff Time

Institutional Births (Private and Public hospitals)

IFT

Interfacility Transfer

IMR

Infant Mortality Rate

Miscarriages

Maternal Mortality

MMR

Maternal Mortality Rate

Neonatal Mortality

7 Neonatal Mortality within 7 days

NMR

Neonatal Mortality Rate

non

IFT–Non Interfacility Transfer (Direct transfer from home to facility)

post

W–1–Post wave–1 of the pandemic

post

W–2–Post wave–2 of the pandemic

post

W–3–Post wave–3 of the pandemic

Resilient Period

Travel Time

1–Wave − 1 of the pandemic

2–Wave − 2 of the pandemic

3–Wave − 3 of the pandemic

Ethics approval and consent to participate

Not applicable

Consent for publication

We hereby give consent for this research to be published,

Availability of data and material

The data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request.

Competing interests

The authors have no competing interests or other interests that might be perceived to influence the results and/or discussion reported in this paper

Funding

This research did not receive any financial support or funding.

Authors' contributions

KP: Conceptualisation, Methodology, Formal Analysis, Investigation, Writing Original Draft, Reviewed the final.

AP: Methodology, Software, Formal Analysis, Investigation, Visualisation, Reviewed the final manuscript.

Acknowledgements

The authors would like to express their sincere gratitude to the Emergency Medical Services and the National Health Mission, Tamil Nadu, under the Department of Health and Family Welfare, Government of Tamil Nadu, India, for providing the necessary data for this study. Special thanks are extended to Mr. M. Selvakumar, State Head of Operations, Tamil Nadu EMRI Green Health Services, for his invaluable support in providing data throughout the research.

Authors’ Information

Dr Kandaswamy Paramasivan, formerly Director General of Police, Vigilance and Anti-Corruption, presently Professor of Practice, Department of Management Studies, Indian Institute of Technology, Madras @ Chennai, India.

Ashwin Prakashis an Associate Programmer Analyst in Moody’s Analytics, Bengaluru, India.

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No competing interests reported.

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From Pandemic to Progress: Maternal Health Resilience in the post COVID-19 era in Tamil Nadu, India

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Abstract

Figures

Introduction

Literature Review

Adverse pandemic impact on maternal care and childbirth

Innovative strategies – Better emergency care and maternal and childbirth outcomes

Data and Method

Data

Method

Results

EMS-Metrics

Maternal and childbirth Outcomes

Discussion

Limitations of the Study

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1