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Prevalence and risk factors of maternal near miss in India: A systematic review and meta-analysis
For correspondence: Dr Aravind P. Gandhi, Department of Community Medicine, All India Institute of Medical Sciences, Nagpur 441 108, Maharashtra, India e-mail: aravindsocialdoc@gmail.com
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Received: ,
Accepted: ,
How to cite this article: Rathod PG, Kakade SV, Deshmukh KP, Deshmukh PR, Aparnavi P, S P, et al. Prevalence and risk factors of maternal near miss in India: A systematic review and meta-analysis. Indian J Med Res. 2026;163:69-77. DOI: 10.25259/IJMR_1590_2025.
Abstract
Background and objectives
Maternal near miss (MNM) is a marker of severe maternal complications and reflects the quality of obstetric care systems. The present systematic review and meta-analysis aimed to estimate the prevalence of MNM and identify common risk factors among pregnant women in India.
Methods
In accordance with PRISMA 2020 guidelines, “MEDLINE, Scopus, Embase, and Web of Science” database search was carried out until November 21, 2024. Observational studies from India that detailed the risk factors and prevalence of MNM were included. The Newcastle Ottawa Scale (NOS) and the JBI checklist were used to evaluate quality. Prevalence and heterogeneity were determined through meta-analysis using a random-effects model.
Results
There were 6,606 MNM cases in 39 studies with 242,015 pregnant women. The pooled prevalence of MNM was 3.9% (95% CI: 1.6%–7.2%) with substantial heterogeneity (I2=99.4%). The prevalence of MNM varied between the states, with Rajasthan reporting as low as <1% to West Bengal reporting 38%. Anemia, sepsis, haemorrhage, and hypertensive disorders were the most frequently mentioned risk factors.
Interpretation and conclusions
The prevalence of MNM varies greatly throughout India. The study emphasizes the necessity of integrating MNM surveillance into national health systems.
Keywords
India
Maternal near miss
Meta-analysis
Risk factors
Severe maternal morbidity
An estimated 260,000 maternal deaths occurred worldwide in 2023, a 40% decrease from 2000. Despite these advancements, there are still significant differences; 70% of maternal deaths worldwide occur in Sub-Saharan Africa, while 17% occur in Southern Asia.1,2 The World Health Organization (WHO) defines a maternal near miss (MNM) as ‘a woman who nearly died but survived a life-threatening complication during pregnancy, childbirth, or within 42 days of termination of pregnancy’.3,4 The fact that MNM cases are 20–30 times more common than maternal deaths provides important information about the standard of obstetric care and the efficacy of emergency measures.5 Studies have demonstrated a strong correlation between the occurrence of adverse perinatal outcomes, such as stillbirth, and preterm birth, among others and the MNM events.6 Although India has achieved notable progress in reducing maternal mortality through policies like LAQSHYA, ‘Janani Suraksha Yojana (JSY)’, maternal morbidity—especially MNM—continues to be underreported and insufficiently recognized.7-9 In India, MNM rates vary across states and settings, ranging from 8 to 39 per 1,000 live births in hospital-based studies.10-12 Common determinants include anaemia, prior caesarean delivery, lack of antenatal care (ANC), hypertensive disorders, and delays in seeking or receiving care.13 Common medical causes include severe bleeding and hypertension, but the cases are worsened by poor antenatal care, delay in receiving care, and lack of critical resources like blood or trained staff. Social issues such as limited awareness, transport challenges, and financial hardship also play a major role.14
There are few attempts to examine severe maternal morbidity in order to find the determinants that can be prevented and scope to enhance the maternity care that hospitals and health systems provide.15 Currently, India lacks pooling of data on MNM and a national surveillance is crucial for guiding policy and improving maternal health outcomes.14 This systematic review and meta-analysis (SRMA) aims to generate pooled estimates of maternal near miss and its associated factors in Indian context.
Methods
‘Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA)’ 2020 guidelines were adhered to for this SRMA (Supplementary Table I). Study was registered with PROSPERO. The primary research question was: ‘What is the prevalence of maternal near miss among pregnant women in India?’ While secondary research question was: ‘What are the underlying risk factors and causes for maternal near miss among pregnant women in India?’ Detailed eligibility criteria as per PECO is enumerated in Supplementary Table II.
Search strategy
Search was done on “MEDLINE (PubMed), Scopus and Web of Science” databases till November 21, 2024. The initial search strategy was developed by one of the reviewers (APG), which was peer reviewed by two other reviewers (PR and PD). Database specific search strings are given in Supplementary Table III. Reference lists of the included studies were manually examined to identify any additional relevant studies.
Study selection process
The study selection was done in two stages- title/abstract and full text screening. Two reviewers (PR, SVK, PA and PS) independently carried out the screening at title/abstract and full text screening stages (KD, AY, SS, CG). The articles found relevant were advanced for full text review, where they were again assessed by both the reviewers according to the predefined eligibility criteria. The reviewers arrived at the consensus after discussion with the third reviewer APG and disagreements if any, were resolved
Data extraction
Data of the studies included after consensus was extracted. Two reviewers (PR, SVK, PA and PS) independently extracted the data in a preformed Microsoft Excel sheet which included study location, study period/publication year, study design, diagnostic criteria, incidence of MNM and/or its risk/causes. A final consensus sheet was then used for analysis and review purpose.
Quality assessment
Two reviewers (KD, AY, SS, CG) independently assessed the quality of the included studies. JBI checklist was used for cross-sectional studies and New Castle Ottawa scale was used for analytical observational studies. Consensus was met after discussion with third reviewer (AGP)
Statistical analysis
A random-effects model employing maximum likelihood estimators was utilized to derive pooled outcome estimates (proportions). Inter-study heterogeneity was assessed using the I2 statistic. Freeman–Tukey double-arcsine (PFT) transformation (sm = “PFT” in the R meta package) was applied to stabilize variances when pooling proportions. The pooled results were back-transformed to the proportion scale to simplify interpretation. To evaluate publication bias, Doi plots and the LFK index were applied. Leave-one-out analysis was undertaken to determine the sensitivity of pooled estimate. Meta-regression and bubble plot were made to explore the heterogeneity. All statistical analyses were performed in R Studio.
Results
Study selection
A systematic search yielded 498 studies and after removing the duplicates (n=235), 232 unique articles were received from 4 databases [Embase(n=218), Scopus (n=123), PubMed (n=66) and Web of Science (n=91)]. Following two stage screening, 39 studies were found eligible, (Fig. 1) from which 20 studies were included in meta-analysis. The most common reasons for exclusion at full text review were that they reported outcomes other than maternal near miss (n=66), including populations that did not fit our criteria (n=69), or used study designs that were not eligible for this review.
- PRISMA flowchart for identification of studies.
Study characteristics
The demographics of individual studies are mentioned in Table.16-33,35-48,51-53,55-58
| No. | Authors, yr | Location | Study period | Study design | MNM | Sample size |
|---|---|---|---|---|---|---|
| 1 | Chhabra et al16, 2019 | Delhi | January 2013 to June 2015 | Case-control | 261 | 38111 |
| 2 | Grover et al17, 2022 | Punjab | March 2015 to July 2016 | Observational study | 53 | 413 |
| 3 | Yasmin et al18, 2016 | Uttar Pradesh | July 2015 to July 2016 | Prospective Observational study | 122 | - |
| 4 | Madan et al19, 2021 | Punjab | Duration - 20 months | Retrospective | 63 | - |
| 5 | Nath et al20, 2020 | Delhi | October 2015 to December 2016 | Cross sectional observational study | 249 | |
| 6 | Pandit et al21, 2019 | Chandigarh | July 2015 to February 2016 | Prospective observational study | 174 | - |
| 7 | Nanda et al22, 2016 | Haryana | September 2012 to February 2014 | prospective observational study | 184 | 15170 |
| 8 | Sangeeta et al23, 2014 | North India | January 2012 to March 2013 | Prospective observational study | - | - |
| 9 | Bakshi et al24, 2015 | Uttarakhand | 12 months | Cross sectional epidemiological study | 51 | |
| 10 | Pandey et al25, 2014 | Uttar Pradesh | May 2011 to April 2012 | Retrospective case record study | 633 | - |
| 11 | Vandana et al26, 2021 | Karnataka | October 2016 to June2018 | Prospective observational | 31 | 300 |
| 12 | Reena et al27, 2018 | Kerala | January 2016 to December 2016 | cross-sectional | 32 | 3451 |
| 13 | Roopa PS et al28, 2013 | Karnataka | Jan 2011 to Dec 2012 | Observational study | 131 | 7330 |
| 14 | Shiva et al29, 2023 | Karnataka | September 2018 and August 2019 | prospective observational study | 218 | 16016 |
| 15 | Sunanda et al30, 2023 | Karnataka | October 2021 to September 2022 | retrospective analysis | 164 | 8791 |
| 16 | Padmavathi et al31, 2023 | Andhra Pradesh | January 2022 to September 2023 | prospective study | 109 | 4536 |
| 17 | Doshi et al32, 2023 | Maharashtra | September 2017 to August 2019 | Prospective observational | 220 | - |
| 18 | Ingole et al33, 2021 | Not reported | January 2010 to September | retrospective observational study | 315 | - |
| 19 | Rathod et al35, 2016 | Maharastra | January 2011 to December 2013 | Retrospective cohort study | 161 | - |
| 20 | Parmar et al36, 2016 | Gujarat | May 2012 to September 2012 | Cross sectional observational study | 46 | - |
| 21 | Maity et al37, 2022 | West Bengal | April 2018 to December 2020 | Descriptive observational | 249 | 26332 |
| 22 | Kumar et al38, 2018 | West Bengal | May–June 2016 | Prospective observational | 126 | 332 |
| 23 | Kalra et al39, 2015 | Rajasthan | May 2011 to October 2012 | Cross-sectional observational study | 112 | 26734 |
| 24 | Bhattacharjee et al40, 2024 | Gujarat | January 2014 to December 2021 | retrospective observational | 280 | 9133 |
| 25 | Bhadra et al41, 2024 | West Bengal | June 2023 to June 2024 | Observational, record‐based cross‐sectional | 234 | 6582 |
| 26 | Thakur et al42, 2023 | Madhya Pradesh | January 2015 to June 2015 | hospital based cross sectional study | 63 | 6198 |
| 27 | Sayyed et al43, 2023 | Rajasthan | January2020 to December 2020 | prospective observational study | 170 | 12127 |
| 28 | Desai et al44, 2013 | Gujarat | April 2011 to October 2011 | Retrospective case record study | - | - |
| 29 | Meravi et al45, 2024 | Madhya Pradesh | August 2019 to August 2020 | Observational | 262 | 2481 |
| 30 | Thakur et al46, 2023 | Chandigarh | November 2018 to 31st October 2019 | prospective study | 258 | 5994 |
| 31 | Chaudhary et al47, 2018 | West Bengal | April 2013 to October 2014 | Prospective observational study | 177 | 4200 |
| 32 | Abha et al48, 2016 | Chattisgarh | September 2013 to August 2015 | Prospective observational study | 211 | - |
| 33 | Khan et al51, 2017 | Delhi | September 2009 to August 2011 | retrospective cross-sectional study | 302 | - |
| 34 | Singh et al52, 2023 | Jharkhand | April 2018 toSeptember 2019. | Observational study | 200 | - |
| 35 | Kulkarni et al53, 2023 | Maharashtra | July 26, 2018 to November 25, 2020 | Prospective observational | 228 | 26091 |
| 36 | Balachandran et al55, 2022 | Puducherry | May 2018 to April 2021 | Prospective observational | 323|280 | 37590 |
| 37 | Bavdekar et al56, 2023 | Maharashtra | 18 months | Retroprospective observational study | 120 | 3488 |
| 38 | Keepanasseril et al57, 2024 | Puducherry | May 2018 and September2021 | Prospective observational study | 323 | 1833 |
| 39 | Nanda et al58, 2016 | Haryana | September 2012 to February 2014 | Prospective observational study | - | - |
PIH, pregnancy induced hypertension; HTN, hypertension; BPL, below poverty line; CVD, cardiovascular disorders; MNM, maternal near miss
Out of 242015 pregnant females reported across the selected studies, 6606 were MNM. Data were collected from either face-to-face interviews or hospital records. The study periods ranged from 2009 to 2023.The included studies represented diverse geographical regions of India- from the North,16-25 South,26-31West,32-44 and East/Central regions.14,45,46
The prevalence and incidence of MNM per 1000 live births and mortality ratio were given by various studies.18,19,21-24,26,33,35,36,43,47-53 Maternal near miss incidence ratio of 45.2 per 1000 was reported by Yasmin et al.18 MNM IR of 24.2/1000 live births and a mortality ratio of 2.4:1 was reported by Grover et al17. The prevalence of MNM varied between the states, with Rajasthan reporting as low as <1% to West Bengal reporting 38%.38,39
Neonatal mortality was reported at 21.5 per 1,000 live births, while the maternal mortality ratio was 832.8 per 100,000 live births. The most common determinant of maternal near-miss (MNM) events was severe pregnancy-induced hypertension (PIH), which had a relatively low mortality index of 5.8 per.44 Another study reported an MNM ratio of 23.8 per 1,000 live births and an MNM rate of 20.6 per 1,000 pregnant women admitted.36 A hospital-based analysis of 220 MNM cases found that 58.18% of women underwent caesarean delivery, while 40% delivered vaginally.32 Hypertensive disorders of pregnancy were identified as the leading cause of near-miss events (53.2%), followed by anaemia (19.09%), heart disease (9.09%), and abruptio placentae (6.36%) (Doshi et al,32). Approximately 52% of the women exhibited vascular and haematological dysfunction.32
A study identified haemorrhage as the leading cause for maternal mortality.26 In another study 36,366 deliveries were reviewed, and 315 cases of MNM were identified.33 Hypertensive disorders were the most common cause, accounting for 133 cases (42.22%). This was followed by obstetric haemorrhage (97 cases, 30.79%), severe anaemia (36 cases, 11.42%), and sepsis (30 cases, 9.52%).
Prevalence of maternal near miss
A meta-analysis of 20 studies assessing maternity near miss events showed a pooled prevalence of 0.04 (95% CI: 0.02; 0.07%).16,26-28,31,37-42,45,46,48,54,55 High heterogeneity was observed across the studies (I2=99.4%, P< 0.001). The prediction interval (0.00–0.25) is also suggestive of variability (Fig. 2). The sensitivity analysis revealed that omitting any single study did not substantially affect the findings. The pooled prevalence of MNM was found to be 3.9% (95% CI: 1.6%–7.2%) (Supplementary Fig. 1). In DOI plot, the LFK index was 4.53, thus indicating major asymmetry and substantial publication bias (Supplementary Fig. 2).
- Prevalence of maternal near miss.
Risk factors of maternal near miss
Hypertensive disorders of pregnancy were reported among MNM cases in a total of 38 studies, with specific mention on eclampsia in 12, pre-eclampsia in 12, and PIH in 6 studies. Anaemia was cited among MNM cases in 16 studies and other haematological conditions including coagulation disorders, sickle cell disease, and HELLP syndrome in 7 studies. Cardiac conditions and hepatic diseases (including hepatitis and hepatic coma) complicating pregnancy among MNM women were reported in 13 studies each. Renal problems, respiratory diseases, and neurological conditions were noted among MNM cases in 6, 9, and 3 studies, respectively. Multi-organ failure among MNM was seen in 6 studies. Sepsis or infection was identified in 26 studies, of which malaria, chorioamnionitis, and endometritis were reported in onestudy each. Complications of pregnancy mentioned in MNM were any type of haemorrhage (31 studies), abortion and related complications (6 studies), ectopic pregnancy (9), placental abruption (10), rupture of uterus/dystocia (8), obstructed labour (2), and caesarean/previous C-section (3). Type II diabetes mellitus was mentioned in 1 study and other endocrine disorders in another. Socio-demographic variables such as parity, rural residence, and below poverty line (BPL) status were mentioned as frequent occurrences in MNM in a single study and gravida, and age in 2 studies. A meta-regression was performed, and it demonstrated no significant association between maternal age and the prevalence of maternal near miss (β = 0.0141, P= 0.69) (Supplementary Fig. 3).
Quality assessment
Out of the 39 included studies, 29 were observational cohort, 1 was a case-control, and 9 cross-sectional studies. Among the cohort studies, 6 were rated as poor quality,18,19,26,28,33 7 as fair,23,31,41-43,45 and the remaining were graded as good quality.21,29,35,37,38,40,47,49,50,55,55-60. The JBI critical appraisal tool was used for the cross-sectional studies. Of the 9 cross-sectional studies, 4 were assessed as having low risk of bias,18,27,39,51 while 5 were found to be of moderate quality20,24,36,50,52. The case-control study was assessed to be of high quality.
Discussion
This systematic review revealed high variability in MNM prevalence across studies, (4%). A global meta-analysis of MNM on 2011 to 2018 on 60 studies showed a lower pooled prevalence of 1.4%.61 The studies included in the current meta-analysis showed that MNM ranged from 0.1% to 10.6% with an exception of one study that showed a high prevalence of 37.9% from West Bengal.38 A similar range of MNM (0.58% to 12%) was also reported from the 11 studies based on WHO MNM criteria included from India in the previous meta-analysis and the highest 12% was from a study in Uttar Pradesh.61 Only two to three studies were available from other central and southern Asian countries for the global review in 2018, and they showed high variations, Afghanistan (0.12%,), Iran (0.5%- 2.5%), Nepal (0.3%-1.5%), Pakistan (0.7-5.2%), Sri Lanka (0.4%) and Thailand (0.5%).61 Notably, even currently, Uttar Pradesh and West Bengal have the third and sixth leading MMR among Indian states with values above the national average.62 Variation were observed in the reported maternal near miss ratios (MNMR) across these regions. Studies conducted in relatively resource-limited or geographically difficult areas, such as rural Madhya Pradesh, and parts of Uttar Pradesh, generally reported higher MNMRs, likely reflecting barriers to timely access and limited availability of comprehensive emergency obstetric care.14,16 In contrast, studies from urban tertiary centers such as Delhi and West Bengal tended to report comparatively lower MNMRs, suggesting better access to specialized maternal health services.20,38 A national survey from Brazil showed an MNM of 1%.63 In the current study a very high heterogeneity was observed (I2=99.4%).This might be explained by the existing inequities in healthcare services in India. Apart from the determinants of healthcare received, variability might be also be explained by the different criteria and approaches used to diagnose MNM.3,64
Overall, the most commonly reported risk factors for maternal near miss were hypertensive disorders, followed by haemorrhage, sepsis/infection, and anaemia. Jena et al65 had reported that hypertensive disorders followed by haemorrhage were common in MNM and anaemia increased the risk of MNM pre-existing medical problems. Among the 16 studies of MNM reporting anaemia to be frequently occurring, 14 had haemorrhagic conditions including abruptio placenta and this can be explained by the established relation of anaemia with obstetric haemorrhage.66,67Among the 13 studies where hepatitis was reported in MNM, Sahijwani et al50, specifically mentioned Hepatitis E as a risk factor for MNM, but notably the study period coincided with an epidemic of the same.68 None of the included studies reported about the level of antenatal care or the availability of services in determining MNM. A survey from Brazil listed that ANC visits and search for health care services were strongly associated with MNM.63
In addition to the obstetric determinants, social factors also have a significant influence on MNM. Social determinants such as poverty, rural residence, low literacy, and limited awareness of maternal danger signs play a critical role in maternal near miss events.69 Barriers like inadequate transport, financial constraints, and gender-related inequities often delay timely access to skilled obstetric care, thereby worsening outcomes even when medical causes are otherwise manageable.
This meta-analysis revealed a considerable variation between studies (design, quality and geography) which limits the comparability and interpretability of pooled estimates and so the strength of conclusions drawn from the same. Differences in how MNM was defined, diagnosed, and reported across various settings may have introduced misclassification bias. Inconsistent reporting on antenatal care and health service availability or systemic delays made it harder to analyze risk factors in a meaningful way. Owing to the hospital setting of the included studies, the community level MNM may be underestimated. The analysis showed major asymmetry in the DOI plot and a high LFK index, suggesting possible publication bias. The major limitation stems from the indicator itself, as the diagnosis of MNM can be based either on clinical criteria or the level of intervention, making it difficult to achieve uniformity. Future research and health systems should prioritise the standardized use of WHO-defined criteria to improve consistency and comparability across studies.64,70
The study emphasises the need for standardized MNM diagnostic criteria and their uniform application across institutions. Routine MNM monitoring should be integrated into national health surveys, with regular case audits. Strengthening antenatal and emergency obstetric care, improving referral systems, and implementing targeted interventions for haemorrhage and hypertensive disorders are crucial to reduce MNM and enhance maternal outcomes.
Acknowledgment
Authors acknowledge the contribution of the Department of Health Research supported SARANSH-1 (Systematic Reviews And Networking Support in Health) workshop organised by the Department of Community medicine, All India Institute of Medical Sciences, Nagpur, India and the Technical Resource Centre (Centre for evidence-based guidelines), AIIMS Nagpur for developing their capacity and providing resources to undertake the systematic review.
Authors contributions
PR: Design, methodology, data acquisition, manuscript writing; SK: Design, methodology, data acquisition, manuscript writing; KD: Methodology, data acquisition, analysis, manuscript writing; PD: Conception or design of the work, data curation, manuscript writing; PA: Methodology, data acquisition, manuscript writing; PS: Methodology, data acquisition, manuscript writing; AY: Methodology, data acquisition, manuscript writing; SS: Methodology, data acquisition, manuscript writing; CG: Methodology, data acquisition, manuscript writing; APG: Conception or design of the work, methodology, supervision, data curation, manuscript writing. All authors have read and approve the final edited version of the manuscript.
Financial support and sponsorship
None.
Conflicts of Interest
None.
Use of Artificial Intelligence (AI)-Assisted Technology for manuscript preparation
The authors confirm that there was no use of AI-assisted technology for assisting in the writing of the manuscript and no images were manipulated using AI.
References
- World Health Organization. Trends in maternal mortality estimates 2000 to 2023: estimates by WHO, UNICEF, UNFPA, World Bank Group and UNDESA/Population Division. Available from: https://iris.who.int/handle/10665/381012, accessed on October 7, 2025.
- United Nations. Population Division. Inter-agency child and maternal mortality estimates. Available from: https://www.un.org/development/desa/pd/content/inter-agency-child-and-maternal-mortality-estimates, accessed on October 7, 2025.
- WHO working group on maternal mortality and morbidity classifications. Maternal near miss--towards a standard tool for monitoring quality of maternal health care. Best Pract Res Clin Obstet Gynaecol.. 2009;23:287-96.
- [CrossRef] [PubMed] [Google Scholar]
- World Health Organization. Evaluating the quality of care for severe pregnancy complications: the WHO near-miss approach for maternal health. Available from: https://iris.who.int/handle/10665/44692, accessed on October 7, 2025.
- WHO maternal death and near-miss classifications. Bull World Health Organ.. 2009;87:734.
- [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
- Maternal near-miss and the risk of adverse perinatal outcomes: A prospective cohort study in selected public hospitals of Addis Ababa, Ethiopia. BMC Pregnancy Childbirth.. 2018;18:345.
- [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
- National Health Mission. Government of India. Janani Suraksha Yojana. Available from: https://nhm.gov.in/index1.php?lang=1&level=3&sublinkid=841&lid=309, accessed on October 7, 2025.
- National Health Mission. Government of India. Labor room quality improvement initiative (LaQshya). Available from: https://nhm.gov.in/index1.php?lang=1&level=3&sublinkid=1307&lid=690, accessed on October 7, 2025.
- National Health Mission. Government of India. Pradhan Mantri Surakshit Matritva Abhiyan. Available from: https://nhm.gov.in/index1.php?lang=1&level=3&sublinkid=1308&lid=689, accessed on October 8, 2025.
- Near miss: Determinants of maternal near miss and perinatal outcomes: A prospective case control study from a tertiary care center of India. J Matern Fetal Neonatal Med.. 2022;35:5909-16.
- [CrossRef] [PubMed] [Google Scholar]
- Maternal near-miss as a surrogate indicator of the quality of obstetric care: A study in a tertiary care hospital in Eastern India. Cureus.. 2021;13:e12548.
- [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
- Retrospective analysis of maternal near miss and the applicability of previous caesarean section delivery as a predictor of risk at a tertiary level hospital of India. Hamdan Med J.. 2023;16:14.
- [CrossRef] [Google Scholar]
- Maternal near-miss audit: Lessons to be learnt. Int J Appl Basic Med Res.. 2017;7:85-7.
- [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
- Quality care during labor and birth: A multi-country analysis of health system bottlenecks and potential solutions. BMC Pregnancy Childbirth.. 2015;15:S2.
- [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
- A global view of severe maternal morbidity: Moving beyond maternal mortality. Reprod Health.. 2018;15:98.
- [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
- Severe maternal morbidity and maternal near miss in a tertiary hospital of Delhi. Natl Med J India.. 2019;32 :270-6.
- [CrossRef] [PubMed] [Google Scholar]
- A study of severe maternal outcome at government medical college of Punjab. JK Practitioner.. 2022;27:1-2.
- [Google Scholar]
- Maternal near miss events: A prospective observational study in a tertiary care centre. Int J Reprod Contracept Obstet Gynecol. 2016:3088-93.
- [CrossRef] [Google Scholar]
- Prevalence of near miss and maternal deaths and its maternal and perinatal outcomes: a retrospective study. Asian J Pharm Clin Res.. 2021;14:62-5.
- [CrossRef] [Google Scholar]
- Why some mothers could be saved and not others? Evaluating different phases of delay in causing maternal near misses and maternal deaths. Journal of Clinical and Diagnostic Research. 2020;14:QC01-QC05.
- [CrossRef] [Google Scholar]
- Using near miss model to evaluate the quality of maternal care at a tertiary health-care center: A prospective observational study. J Obstet Gynaecol India.. 2019;69:405-11.
- [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
- A prospective observational study of near-miss events and maternal deaths in obstetrics. J South Asian Fed Obstet Gynaecol.. 2016;8:261-5.
- [CrossRef] [Google Scholar]
- Evaluation of severe maternal outcomes to assess quality of maternal health care at a tertiary center. J Obstet Gynaecol India.. 2015;65:23-7.
- [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
- Indicators of maternal near miss morbidity at different levels of health care in North India: A pilot study. Bangladesh J Med Sci.. 2015;14:254-7.
- [Google Scholar]
- Evaluation of obstetric near miss and maternal deaths in a tertiary care hospital in north India: Shifting focus from mortality to morbidity. J Obstet Gynaecol India.. 2014;64:394-9.
- [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
- A prospective study of severe acute maternal morbidity and maternal near miss in a tertiary care hospital. J Obstet Gynaecol India.. 2022;72:19-25.
- [CrossRef] [Google Scholar]
- Factors associated with maternal near miss: A study from Kerala. Indian J Public Health.. 2018;62:58-60.
- [CrossRef] [PubMed] [Google Scholar]
- “Near Miss” obstetric events and maternal deaths in a tertiary care hospital: An audit. J Pregnancy.. 2013;2013:1-5.
- [Google Scholar]
- Maternal near-miss in tertiary referral center: A prospective observational study from Urban Bengaluru. J South Asian Fed Obstet Gynaecol.. 2023;15:683-5.
- [CrossRef] [Google Scholar]
- Analysis of maternal near miss cases in a tertiary care hospital. Int J Reprod Contracept Obstet Gynecol.. 2023;12:1248-52.
- [CrossRef] [Google Scholar]
- Analysis of “maternal near miss” cases in a tertiary care hospital, GGH, Srikakulam. J Cardiovasc Dis Res.. 2023;14:11.
- [Google Scholar]
- Study of clinical profile of maternal near miss cases at a tertiary hospital. Eur J Cardiovasc Med.. 2023;13:598-603.
- [Google Scholar]
- Maternal near miss events in tertiary care hospital: A retrospective observational study. J Clin Diagn Res.. 2021;15:QC14-QC17.
- [CrossRef] [Google Scholar]
- Evaluation of the incidence and common causes of maternal near miss among the cases admitted in tertiary health care, Jharkhand, India. Int J Pharm Clin Res.. 2023;15:1452-73.
- [Google Scholar]
- Analysis of near-miss and maternal mortality at tertiary referral centre of rural India. J Obstet Gynaecol India.. 2016;66:295-300.
- [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
- Incidence of maternal “Near-Miss” events in a tertiary care hospital of central Gujarat, India. J Obstet Gynecol India.. 2016;66:315-20.
- [Google Scholar]
- An observational study on maternal mortality and maternal near miss in a selected facility of West Bengal. Indian J Public Health.. 2022;66:371-4.
- [CrossRef] [PubMed] [Google Scholar]
- “Near-Miss obstetric events” and its clinico-social correlates in a secondary referral unit of Burdwan District in West Bengal. Indian J Public Health.. 2018;62:235.
- [CrossRef] [PubMed] [Google Scholar]
- Obstetric near miss morbidity and maternal mortality in a tertiary care centre in Western Rajasthan. Indian J Public Health.. 2014;58:199-201.
- [CrossRef] [PubMed] [Google Scholar]
- A retrospective comparative analysis of the trends of near miss mortality and maternal mortality in a rural tertiary care center. Int J Med Public Health.. 2024;14:200-6.
- [Google Scholar]
- An observational study on maternal near miss cases in a tertiary care hospital in Kolkata draining Howrah and Hooghly districts between June 2023‐2024. Res J Med Sci.. 2024;18:285-9.
- [Google Scholar]
- A study on maternal near miss: The submerged iceberg, in a tertiary care hospital of central India. Indian J Public Health Res Dev.. 2023;14:17-22.
- [Google Scholar]
- A descriptive study of maternal near miss mortality in the department of obstetrics and gynaecology at SMS Medical College. Int J Pharm Clin Res.. 2023;15:99-101.
- [Google Scholar]
- Analysis of near miss cases as a reflection of emergency obstetric services and need of obstetric ICCU. J South Asian Fed Obst Gynae.. 2013;5:99-101.
- [CrossRef] [Google Scholar]
- An observational, cross-sectional, hospital-based study of maternal near miss in tertiary care centre of Bastar district. Int J Pharm Clin Res.. 2024;16:725-733.
- [Google Scholar]
- Acute kidney injury and it’s outcome following maternal near miss event: A prospective observational study from a tertiary care hospital. Obstet Med.. 2023;16:48-51.
- [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
- Life-threatening complications in pregnancy in a teaching hospital in Kolkata, India. J Obstet Gynaecol India.. 2019;69:115-22.
- [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
- Maternal near miss: A valuable contribution in maternal care. J Obstet Gynaecol India.. 2016;66:217-22.
- [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
- Physical growth, morbidity profile and mortality among healthy late preterm neonates. Indian Pediatr.. 2017;54:629-34.
- [CrossRef] [PubMed] [Google Scholar]
- Analysis of near miss cases as a reflection of emergency obstetric services and need of obstetric ICCU. J South Asian Fed Obst Gynae.. 2013;5:99-101.
- [CrossRef] [Google Scholar]
- Prognostic factors of maternal near miss events and maternal deaths in a tertiary healthcare facility in India. Int J Gynaecol Obstet.. 2017;138:171-6.
- [CrossRef] [PubMed] [Google Scholar]
- A phenomenological qualitative exploration of mind-body therapy use and effectiveness among young, middle, and older adult cancer survivors. Integr Cancer Ther.. 2024;23:15347354241253847.
- [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
- Incidence factors influencing maternal near miss events in tertiary hospitals of Maharashtra, India. Indian J Med Res.. 2023;158:66-74.
- [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
- Comparative evaluation of three different behavior management techniques among children aged 6-12 years in dental practice: A single-center, double-blind, randomized controlled trial. Dent Med Probl.. 2024;61:641-50.
- [CrossRef] [PubMed] [Google Scholar]
- Indicators for maternal near miss: An observational study, India. Bull World Health Organ.. 2022;100:436-4.
- [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
- Analytical study of near-miss cases at tertiary care center. J South Asian Fed Obstet Gynaecol.. 2023;15:440-4.
- [CrossRef] [Google Scholar]
- Impact of social determinants of health on progression from potentially life-threatening complications to near miss events and death during pregnancy and post partum in a middle-income setting: An observational study. BMJ Open.. 2024;14:e081996.
- [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
- A prospective observational study of near-miss events and maternal deaths in obstetrics. JSAFOG.. 2016;8:261-5.
- [CrossRef] [Google Scholar]
- Observing live fish improves perceptions of mood, relaxation and anxiety, but does not consistently alter heart rate or heart rate variability. IJERPH.. 2019;16:3113.
- [CrossRef] [Google Scholar]
- Experience of mindfulness meditations based on stress reduction among undergraduate nursing students, Chitwan, Nepal. J Nepal Health Res Counc.. 2024;21:439-44.
- [CrossRef] [PubMed] [Google Scholar]
- Global and regional estimates of maternal near miss: A systematic review, meta-analysis and experiences with application. BMJ Glob Health.. 2022;7:e007077.
- [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
- Census India. India - Sample registration system (SRS)-special bulletin on maternal mortality in India 2019-21. Available from: https://censusindia.gov.in/nada/index.php/catalog/45561, accessed on June 8, 2025.
- Factors associated with maternal near miss in childbirth and the postpartum period: Findings from the birth in Brazil National Survey, 2011-2012. Reprod Health.. 2016;13:115.
- [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
- Maternal near miss: Reaching the last mile. J Obstet Gynaecol.. 2021;41:675-83.
- [CrossRef] [PubMed] [Google Scholar]
- Maternal near miss: An analysis of severe maternal morbidity and its determinants in a tertiary care hospital. Int J Pharm Clin Res. 2024;16:1500-05.
- [Google Scholar]
- Prenatal anemia and postpartum hemorrhage risk: A systematic review and meta-analysis. J Obstet Gynaecol Res.. 2021;47:2565-76.
- [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
- Maternal anaemia and the risk of postpartum haemorrhage: A cohort analysis of data from the WOMAN-2 trial. Lancet Glob Health.. 2023;11:e1249-5.
- [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
- National Centre for Disease Control (NCDC), Ministry of Health & Family Welfare, Government of India. Disease alerts/outbreaks reported and responded by States/UTs through integrated disease surveillance project (IDSP). 29th week (ending 24 July 2011). 2011 Jul 24. Available from: https://ncdc.mohfw.gov.in/.../29th_wk11.pdf, accessed on October 8, 2025.
- Determinants of maternal near miss events among women admitted to tertiary hospitals in Mogadishu, Somalia: A facility-based case-control study. BMC Pregnancy Childbirth.. 2022;22:658.
- [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
- Obstetric audit in resource-poor settings: Lessons from a multi-country project auditing “near miss” obstetrical emergencies. Health Policy Plan.. 2004;19:57-66.
- [CrossRef] [PubMed] [Google Scholar]