Article Text

Improving quality of care during childbirth in primary health centres: a stepped-wedge cluster-randomised trial in India
1. Ramesh Agarwal1,
2. Deepak Chawla2,
3. Minakshi Sharma3,
4. Shyama Nagaranjan4,
5. Suresh K Dalpath5,
6. Rakesh Gupta5,
7. Saket Kumar5,
9. Premananda Mohanty3,
10. Mari Jeeva Sankar1,
11. Krishna Agarwal6,
12. Shikha Rani7,
13. Anu Thukral1,
14. Suksham Jain2,
16. Geeta Gathwala9,
17. Praveen Kumar10,
18. Jyoti Sarin11,
19. Vishnubhatla Sreenivas12,
20. Kailash C Aggarwal13,
21. Yogesh Kumar11,
23. Surender Singh Bisht15,
24. Gopal Shridhar16,
25. Raksha Arora17,
26. Kapil Joshi18,
27. Kapil Bhalla9,
28. Aarti Soni19,
29. Sube Singh5,
30. Prischillal Devakirubai16,
31. Ritu Samuel16,
33. Rajiv Bahl21,
34. Vijay Kumar3,
35. Vinod Kumar Paul1
36. for the QI Haryana Study Collaboration
1. 1 Division of Neonatology, Department of Pediatrics, All India Institute of Medical Sciences, New Delhi, India
2. 2 Department of Neonatology, Government Medical College Hospital, Chandigarh, India
3. 3 Survival for Women and Children Foundation (SWACH), Panchkula, India
4. 4 Saha Manthran Pvt Ltd, Gurugram, India
5. 5 National Health Mission (Haryana), Government of India, Panchkula, India
6. 6 Maulana Azad Medical College and LNJP Hospital, New Delhi, India
7. 7 Department of Obstetrics and Gynecology, Government Medical College Hospital, Chandigarh, India
8. 8 National Institute of Malaria Research, New Delhi, India
9. 9 Pt BD Sharma PGIMS, Rohtak, India
10. 10 PGIMER, Chandigarh, India
11. 11 MM College of Nursing, Mullana, Ambala, India
12. 12 Department of Biostatistics, All India Institute of Medical Sciences, New Delhi, India
13. 13 Safadarjung Hospital and Vardhman Mahavir Medical College, New Delhi, India
14. 14 Government Medical College, Kannauj, India
15. 15 Swami Dayanand Hospital, New Delhi, India
16. 16 Western Command Hospital, Panchkula, India
17. 17 Santosh Medical College, Gaziabad, India
18. 18 UNICEF Rajasthan, Jaipur, India
19. 19 UNICEF Chhattisgarh, Raipur, India
20. 20 Lady Hardinge Medical College, New Delhi, India
21. 21 WHO, Geneva, Switzerland
1. Correspondence to Dr Vijay Kumar; 1940kumarv{at}gmail.com; Professor Vinod Kumar Paul; vinodkpaul{at}gmail.com

## Abstract

Background Low/middle-income countries need a large-scale improvement in the quality of care (QoC) around the time of childbirth in order to reduce high maternal, fetal and neonatal mortality. However, there is a paucity of scalable models.

Methods We conducted a stepped-wedge cluster-randomised trial in 15 primary health centres (PHC) of the state of Haryana in India to test the effectiveness of a multipronged quality management strategy comprising capacity building of providers, periodic assessments of the PHCs to identify quality gaps and undertaking improvement activities for closure of the gaps. The 21-month duration of the study was divided into seven periods (steps) of 3  months each. Starting from the second period, a set of randomly selected three PHCs (cluster) crossed over to the intervention arm for rest of the period of the study. The primary outcomes included the number of women approaching the PHCs for childbirth and 12 directly observed essential practices related to the childbirth. Outcomes were adjusted with random effect for cluster (PHC) and fixed effect for ‘months of intervention’.

Results The intervention strategy led to increase in the number of women approaching PHCs for childbirth (26 vs 21 women per PHC-month, adjusted incidence rate ratio: 1.22; 95% CI 1.17 to 1.28). Of the 12 practices, 6 improved modestly, 2 remained near universal during both intervention and control periods, 3 did not change and 1 worsened. There was no evidence of change in mortality with a majority of deaths occurring either during referral transport or at the referral facilities.

Conclusion A multipronged quality management strategy enhanced utilisation of services and modestly improved key practices around the time of childbirth in PHCs in India.

Trial registration number CTRI/2016/05/006963.

• health systems
• maternal health
• cluster-randomised trial
• paediatrics

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## Statistics from Altmetric.com

### Key questions

• The childbirth and first 24 hours after birth contribute to nearly half of the total stillbirths and maternal and neonatal deaths.

• Improvement in quality of care during childbirth and postnatal period is necessary to meet the Sustainable Development Goals.

#### What are the new findings?

• A provider-led multipronged quality management programme with external facilitation was successful in improving key care practices during childbirth and immediate postnatal period.

• A large number of deficits in the quality of care were due to structural issues which need system strengthening and are difficult to address by healthcare providers themselves.

#### What do the new findings imply?

• Healthcare providers at small facilities should be trained in the methods of monitoring and addressing quality gaps and they should be fully supported with needed resources to identify and address the lacunae in quality of care.

## Introduction

Despite significant decline in maternal and under-5 child deaths over last two decades, 275 000 maternal death and 5.9 million under-5 deaths including 2.7 million neonatal deaths still occur every year, over 90% of them in the low/middle-income countries (LMIC).1 2 Due to slower decline of neonatal deaths, its contribution to under-5 deaths has increased from 36% in 1990 to 46% in 2015.1 3 Additionally, a burden of approximately 2.1 million stillbirths every year, unnoticed so far, has also become a major concern.3 The childbirth and first 24 hours thereafter is the most critical period as nearly half of total stillbirths and maternal and neonatal deaths occur during this period.4 5

Increase in institutional deliveries in India from 38.7% in 2005–2006 to 78.9% in 2015–2016 has not translated into commensurate gains in maternal and neonatal survival but has largely shifted the mortality burden from the community to the facilities.6–8 This has been attributed to low quality of care (QoC) in the facilities and improving the QoC in addition to universal coverage of evidence-based interventions has therefore been identified as a key strategy to achieve Sustainable Development Goals of significantly reducing maternal, fetal and neonatal mortality by 2030.9 Improved QoC is expected to save nearly 113 000 maternal deaths, 531 000 stillbirths and 1 325 000 neonatal deaths every year globally.10

Most of the evidence on improvement in QoC has emanated from high-income countries, which have strong and functional health systems. Systematic reviews demonstrate dearth of good-quality evidence on interventions, which can improve QoC of maternal and newborn health in LMICs with weaker health systems.10 Recently, a large cluster-randomised trial (BetterBirth study) undertaken in Uttar Pradesh state of India focused on implementation of coaching-based WHO safe childbirth checklist (BetterBirth checklist) as a tool to drive QoC in small facilities.11 The trial reported a modest improvement in adherence to essential practices around the time of childbirth but no change in perinatal deaths, maternal deaths or maternal severe complications.

The QoC is a multifaceted concept which envisions that the clients receive good-quality evidence-based care (provision of care) and are also treated well (experience of care). Providing good QoC which is safe, timely, effective, efficient, equitable and patient centred requires a robust health system. Improvement in healthcare processes needs ‘structure’ to be in place in the form of skilled and committed human resource, infrastructure, information system, equipment and supplies, financing, and leadership/governance. The Donabedian and WHO models emphasise the importance of both robust ‘structure’ and ‘processes’ in order to achieve desirable health outcomes.6 12

We hypothesised that quality management activities driven by facility teams with external facilitation and comprising activities to build capacity of the providers, regular review of key inputs and outcomes to identify gaps and closing those gaps through facility teams’ improvement efforts will increase the uptake of essential practices around the time of childbirth in primary health centres (PHC). The improved QoC would enhance the faith of community and families, which would improve utilisation of the PHC services.

## Methods

### Case studies

Most of the PHCs showed improvement in key practices related to childbirth during the study. However, contextual factors such as local leadership, adequate availability of human resources and incidental activities influenced the effect of intervention. We illustrate three case studies highlighting such influences (Box 1 and figure 3). In the first case study, a poorly performing PHC showed significant improvement due to strong leadership of the MO in charge. In the second case study, QoC declined due to transfer out of three of the five nurses from a high case load PHC. In the third case study, an already well-performing PHC deteriorated following a maternal death.

Box 1

### Case studies

Case study 1

PHC A, located 12 km away from the district headquarter, had deployment of one to five nurses and two MOs during the study period. This PHC was one of the poorly performing PHCs and it initiated intervention in the fourth month of the study. Motivated by the quality management process, the MO in charge provided exceptional leadership to the team resulting in significant improvement in the PHC performance over the remaining study period (figure 3). The number of deliveries increased from an average of 6 per month in the control period to 17 per month in the intervention period. There was a significant improvement in composite care score. The MO in charge took keen interest in the quality management activities by supervising the work of nurses, closing gaps in the staff deployment by interacting with district hospital administration. The PHC added an extra labour table, constructed a toilet in labour room and put up curtains in postnatal ward to ensure privacy.

Case study 2

PHC B, located in a township, had two MOs including one female MO, two to five nurses and adequate physical infrastructure. The PHC initiated intervention in the 15th month of the study. It had a high case load (average 36 deliveries per month) during the study period. The composite care score of the PHC was high indicating good performance of the PHC during initial 12 months. At 12 months, there were major changes in the deployment of health providers in the PHC: reduced strength of nurses (from five to two) due to transfer of three nurses and the additional responsibility given to the MO at the DH making him unavailable for good part of his time for the PHC. As a result, there was a significant decline in composite care score in ensuing months (figure 3). The two nurses were unable to deliver optimum childbirth services due to excessive workload and the MO was not able to provide leadership to the PHC team. There was inadequate uptake of quality management activities when intervention started at the PHC from the 15th month of the study.

Case study 3

PHC C, located about 11 km away from the district headquarter, had one to two MOs including a female MO and two to five nurses during the study period. The PHC had labour room and postnatal care facilities as per the state norms. This PHC initiated intervention in the fourth month of the study. The MO in charge was a motivated and committed person and led the team to provide good services with a large number of deliveries (n=15 per month) occurring in the PHC. During the 11th month of study, a maternal death occurred, which led to an enquiry by the district administration. The MO got intimidated and proceeded on leave. Thereafter, she was irregular and distracted from her work. The staff got demoralised and the community behaved in a hostile manner. The number of deliveries declined. Quality management team made persistent efforts to raise the morale of the staff and continued to visit the PHC and interact with staff. The MO resumed work only after about 4 months and there was improvement in services thereafter.

• DH, district hospital; MO, medical officer; PHC, primary health centre.

Figure 3

Performance of three primary health centres (PHC). Y-axis represents the care score derived from adding the number of 12 childbirth practices received by a patient. Dots represent the care score of individual observed patient. Solid line with shaded area represents the smoothed trend in care score and its 95% CI.

## Discussion

The intervention strategy in our study has shown a modest effectiveness: increase in number of deliveries signifying improved utilisation of PHC and modest improvement of 6–12 key practices. There was no suggestion of any change either in stillbirth or neonatal mortality rates (secondary outcomes). Importantly, the study highlights two very important issues with serious implication to future research and policy: (1) despite substantial strengthening of the public health system in past two decades, there exists a significant degree of fragility and weakness at all levels potentially undermining any improvement efforts; (2) a majority of stillbirths and neonatal deaths occurred in women and their babies during referral transport or at the referral facilities.

Five randomised controlled studies have investigated impact of interventions to improve QoC at the time of childbirth at PHCs.20–25 Taking cues from the evidence of efficacy of use of the surgical checklist in settings of strong health systems, the BetterBirth study focused on providers using a checklist tool to improve essential practices during childbirth and reduce mortality in settings of weak health systems (Uttar Pradesh, India). The study intervention was limited to providing intense coaching (43 visits in 8 months) to the facility staff for use of the checklist for improving the uptake of life-saving practices and did not specifically focus on strengthening of the health system or provider skills. While the study did find a modest improvement in the practices (73% in intervention arm vs 42% in the control arm) with use of checklist at 4 months of intervention, there was no effect on the perinatal death, maternal deaths and maternal severe complications. Also, the improvement in practices too dwindled in a few months of cessation of the intervention. Unfortunately, this study did not provide concrete data as to why the intervention was modestly effective in improving practices and did not improve mortality and other health outcomes. A study with factorial design from Malawi showed no reduction in neonatal and perinatal mortality with improvement intervention at facility level unless it was combined with community mobilisation.20 There was 22% reduction in neonatal mortality with combined facility and community intervention and 16% reduction in perinatal mortality with community intervention alone. Another cluster-randomised trial in PHCs of India reported improved provider preparedness, facility readiness to deal with childbirth and related complications, and improved practices as reviewed from records.22

Our study focused on a multipronged approach targeted at improvement of all the important pillars of the health systems and systematically measured the existing gaps in health systems and barriers to quality improvement. The facility staff drove the improvement process with external mentoring and with the involvement of government functionaries. Though our study results are in agreement with BetterBirth study, it provides deeper insight as to why improvement efforts in these two important studies may not have yielded the expected results and what could be the way forward to improve the QoC and health outcomes in small facilities.

Our study reinforces the WHO notion, which is based on Donabedian model, that provision of optimum QoC requires existence of a robust health system including leadership/governance, accountability, adequate financing, skilled and committed human resource and physical resources.6 12 The improvement processes in our study identified a large number of serious gaps (n=479) in almost every essential domain of the health system. Moreover, low level of motivation and a lower level of accountability, and vulnerability of the health systems to a variety of contextual factors further hindered the improvement process. Many of identified gaps (n=322/479, 67.2%) could not be closed despite multiple improvement activities, undertaken by the facility staff and efforts made by the project team as these required concrete actions at the higher level of health system, which was also seriously limited. The effective closure of gaps in skills requires a high level of motivation for learning of the providers as well as availability of enough opportunities for honing of the skills. For effective functioning of the PHCs, we feel multiple factors have to be addressed that include but not limited to health systems made more resilient by ensuring stability of the leadership, easy fund flow, the issue of availability of skilled and motivated providers and creating a framework of accountability at all levels. There is a need to explore if the available resources can be used in a more efficient manner—likely strengthening of better performing PHCs and downscaling low-volume PHCs. While the governments and policymakers are understandably looking for the scalable models of improving QoC that work in the health system with all its weaknesses, our study strongly highlights the need to have stronger, accountable and responsive health system as an essential prerequisite for significant improvement in QoC at health facilities. In our study, implementation of the intervention package was led by PHC healthcare workers with periodic external facilitation. More intensive support or mentoring by EQM teams could have made more impact on the study outcomes. However, sustainability of improvement achieved by such an approach has not been demonstrated.11 Large number of gaps in healthcare infrastructure identified in the study indicates the intervention package should have stronger quality assurance component.

PHCs cater to low-risk conditions. Linking these institutions to referral facilities through optimal referral transport is critical to improve health outcomes of its clients. But, this critical link is often not as effective as it should be. There are delayed recognitions of conditions requiring referral and suboptimal transport (second delay) and significant third delay in overburdened referral facilities. VA data revealed that 49 of 152 stillbirths or neonatal deaths, families had to visit at least two referral facilities for the want of care. Nearly two-thirds (112/171) of deaths occurred in patients during referral transport or at referral facilities. A large number of stillbirths occurred even before women presenting to the PHC highlighting a poor quality of antenatal care including optimum counselling of women to recognise danger signs. In face of existence of such critical weakness in the health system, the bad outcomes cannot be effectively averted no matter how good QoC is provided at the smaller facilities. It is therefore imperative that the QoC programmes target improvement of services at all levels starting from first contact facilities to the referral centres.

Our study has several limitations. It was a small study involving only 15 PHCs with relatively low case load in two districts and not powered for mortality and other important health outcomes. As the perinatal, neonatal and maternal mortality outcomes can only be influenced if a range of services (processes) are improved, we took 13 outcomes to be studied as the primary outcomes. We calculated the sample size for each of these 13 practices and took the largest sample size. However, the analysis does not account for multiple hypotheses testing. The primary outcome of number of deliveries depended on several other factors apart from QoC but we believe that study design addressed this issue. The study investigators could not have blinded. However, we employed robust independent outcome measurement. We did not evaluate costing and the sustainability of the model. Lastly, the SW design does not fully compensate for secular trend but is a design of choice for such interventions. The effect of contextual factors on the success of QM approach was demonstrated by three different case studies. However, this before-and-after analysis does not preclude the potential effect of secular trend.

In conclusion, our study reports modest effectiveness of a quality management strategy driven by the provider teams with external facilitation in small health facilities of Haryana, India. We noted several serious and difficult-to-close quality gaps in the health system as the limiting factors for desirable level of improvement in QoC and potentially health outcomes. Two-thirds of deaths occurred in women and their babies during the transport or at the referral facilities. We recommend that a stronger and functional health system and a responsive and accountable mechanism to identify and effectively close the quality gaps in health system must be an integral part of any QoC programmes.

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## Footnotes

• Handling editor Valery Ridde

• Collaborators We are grateful to the study coinvestigators, and the implementation and management teams, including: Mamta Jajoo, MD; Vinay Kulkarni, Neeraj Gupta, DM; Anju Huria, MS; Levis Murry, Prahlad Agarwal, Herbaksh Kaur, Amit Duggal, Jaidev Khatri, Vinod Gupta, Mangat Ram Passi, VP Mann, Alaknanda Malik, Bela Jain, VK Jain, Deepshikha Sharma.

• Contributors Writing committee: RA, DC, VK, VKP, RB. Investigators: NHM, Haryana: RG, SK, SKD, KJ, AS, SS, AD (NHM), JK; SWACH, Chandigarh: VK, PM, MS; AIIMS, New Delhi: VKP, RA, SC, MJS, AT, VS, CPY; GMCH, Chandigarh: DC, ST, SJ; Saha Manthran, Gurugram, Haryana: SN, PA; Maulana Azad Medical College, New Delhi: KA; PGIMER, Chandigarh: PK; PGIMS, Rohtak: GG, KB; MM College of Nursing, Mullana, Ambala: JS, YK; WHO, Geneva: RB. Protocol development: VKP, VK, RB, DC, MJS. Study implementation: VK, PM, MS, Gagandeep, DS. Study resources development: SC, VKP, VK, AT, KA, RA, DC, MJS, RB. Study data team: DC. Study external facilitation team: RA, VPM, SC, SN, Shikha Taneja, KA, AT, GG, PK, JS, SJ, KCA, Yogesh Dhankar, PK, SSB, GS, RA, PD, RS, RY, KB, RB. Data analysis: MJS, RB, DC, VK, RA, VKP, CPY, VS.

• Funding The study was funded through a grant to the WHO by USAID. The WHO team participated in the protocol development, and provided technical support to the investigators in implementation, analysis of data, interpretation of findings and preparation of the manuscript. The corresponding authors had full access to all the data in the study and bear the final responsibility for deciding about the publication.

• Disclaimer RB is a staff member of the World Health Organization. The expressed views and opinions in this paper do not necessarily express the policies of the World Health Organization.

• Competing interests None declared.

• Patient consent Obtained.

• Ethics approval The institutional ethics committees of the World Health Organization, All India Institute of Medical Sciences and the Government Medical College, Chandigarh.

• Provenance and peer review Not commissioned; externally peer reviewed.

• Data sharing statement The deidentified dataset is available upon request.

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