Optimising Caesarean section use in Nepal through hospital generated and population-based data on Caesarean section rates to inform policy and practice

Optimising Caesarean section use in Nepal through hospital generated and population-based data on Caesarean section rates to inform policy and practice | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Optimising Caesarean section use in Nepal through hospital generated and population-based data on Caesarean section rates to inform policy and practice Maureen Dar Iang, Alison Dembo Rath, Madhu Dixit Devkota, Vishnu Sapkota, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5432594/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Background: The dual problem of lack of access to life-saving caesarean section (C-section) and the negative effects of over-intervention when it is not medically required, is well established. The health burden plus additional direct and indirect costs for households and the health system, create a ‘triple burden.’ In Nepal, whilst C-section rates have rapidly escalated, our data suggests an opportunity to avert a C-section epidemic and reduce unmet need for C-section among women with very low income. Methods We analysed C-section rates from 29 public and private hospitals across Nepal using the Robson Ten Group Classification System and assessed how each group compared with the Robson reference groups. Using the WHO C-Model we estimated the gap between observed and predicted facility C-section rates, adjusted for the obstetric case-mix at each facility. We generated estimates for four categories of hospitals: public with Aama (the government demand-side financing programme); public without Aama; private with Aama; private without Aama. Results We found that Robson groups 1 and 3 (nulliparous or multiparous women with single cephalic pregnancy, ≥ 37-week gestation, spontaneous onset of labour), together account for 66.5% of C-sections of total institutional deliveries. All four hospital categories had higher C-section rates than predicted rates. The percentage points difference between observed and predicted rates was greater for private hospitals (with Aama 18.6 percentage points and non-Aama 19.7 percentage points), than government hospitals (with Aama 10.8 percentage points and non-Aama 13.9 percentage points). The biggest gap was for private hospitals (non-Aama with observed C-section rate at 42% compared with 23% predicted. Conclusions Our hospital-based study data provides evidence that C-section rates are too high relative to need and are not just a function of late arrivals or complicated births, as is often claimed by facilities. C-section rates can be controlled by introducing measures in both public and private sectors. Policy needs to continue to focus on increasing uptake of the life-saving intervention among populations where C-section rates fall below optimal levels, while also ensuring that they are avoided if not indicated medically. Resources for maternal and newborn health need to be reallocated to achieve uptake of medically required C-sections equitably across population groups and steer Nepal towards optimal rates. Clinical trial number : Not applicable. Caesarean Section Equity Robson Ten Group Classification WHO C-Model Maternal Health Over-medicalisation Unmet need Health Services Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Nepal has spent more than 25 years in an organised effort to increase its population level caesarean section (C-section) rates from a very low base of 1% in 1996 {1] . It has formulated policies and strategies which provide an enabling environment for all women to access comprehensive emergency obstetric and neonatal care (CEONC), of which C-section is a major service component. Seminal policies such as the Safe Motherhood policy (1998) and the Skilled Birth Attendant (SBA) policy (2006) initiated it and were operationalised through the Safe Motherhood and Long-Term Health Plan 2006-2017. Further, Nepal was a forerunner in creating demand and supply side incentives for maternity care through the ongoing Aama Surakshya Programme (Aama), which started as the Maternity Incentive Scheme 2005. Aama supports women to access maternity care services by providing transport incentives, and free institutional childbirth services through supply side-payments to participating hospitals (most public and more than 60 private) for each vaginal and C-section childbirth conducted. The SBA policy and an earmarked CEONC fund ensured that at a time of need the country was able to place adequately skilled health personnel to meet human resource shortages in remote areas (Fig 1) Insert Figure 1 here Nepal transitioned into a federal state in September 2015, and over the near decade now the country has seen several important acts, standards and regulations which address citizen rights and responsibilities at each level of government (Fig 2); and many which have an effect on the provision of services for emergency obstetric care [2]. Insert Figure 2 here Supportive policies, strategies and programmes have resulted in improved availability of CEONC services from one-third in 1997 to nearly all (76 out of 77) formerly designated district areas in 2023 and an increase in the number of public CEONC hospitals from 41 in 2011 to 105 in 2023 (Fig 3). The expansion continues at municipality levels with 29 local level governments also offering CEONC services as of 2023. Overall, there are now more than 200 public and private hospitals [3] providing C-sections across almost all the country, with expected annual births of about 500,000 6 . Insert Figure 3 here Consequently, C-section rates increased as the Nepal Demographic Health Survey (NDHS) data from 2006 2016 showed, but geographic and socioeconomic disparities with C-section rates too had increased for many groups (Fig 4). Although proportion of births delivered by C-section had almost doubled from 5% in 2011 to 9% in 2016, they were still within the World Health Organization (WHO) reference of 5–15% [4] . But by 2016, while C-section rates among women in the highest wealth quintile had risen to 28%, they were just 2% among those in the lowest quintile. C-section rates were also considerably higher for births in private facilities (35%) than in public facilities (12%). This dual problem of lack of access to life-saving C-section for some women and the negative effects of over intervention when C-section is not medically required, is well established [5]. The latter increases health risks for mothers and infants, plus incurs additional direct and indirect health care costs for households and the health system [6, 7], creating a ‘triple burden’ from overuse. Insert Figure 4 here How the inequality of access noted across groups, translated to facility-level C-section rates was difficult to know because of differences in the client mix and obstetric profile of the women who seek care at the facility. In 2017, Nepal’s Ministry of Health and Population’s, Family Welfare Division requested its long-standing technical assistance partner, Options, funded by the UK government, to design a study to understand the drivers of institutional C-section rates in public and private hospitals. This paper presents findings from the first nation-wide study to determine the differences and possible drivers of facility-based C-section delivery in public and private hospitals of Nepal. Methods A cross-sectional quantitative retrospective study was conducted to determine and compare facility based C-section rate among four types of hospital in Nepal, using Robson Ten Groups Classification System (TGCS) of 10 mutually exclusive and totally inclusive classification categories based on obstetric characteristics. The study also used the World Health Organization’s mathematical model known as the C-model [8, 9] , to estimate the difference between actual and predicted C-section rates in the study population. The sample and data collection Births at hospitals with a C-section rate >10% and recording a minimum of 500 childbirths annually, formed the study population, using data from the health management information system (HMIS 2015/16). Four categories of hospitals were identified: Government hospitals with Aama programme Government teaching hospitals without Aama programme Private hospitals/ teaching hospitals with Aama programme Private hospitals/ teaching hospitals without Aama programme The sample size calculation was done in order to respond to the objective – to compare the actual C-section rate with the predicted C-section rate using the WHO C-model. For the sample size to have 80% power to detect the important differences in C-section rates according to hospital characteristics (e.g., government/private; with/without Aama), and for simplicity, we assumed the characteristic was binary and there were roughly equal number of hospitals with and without the characteristics. Power analysis and sample size calculation scheme for group comparison was carried out in Stata 12.1. The design effect (DE) was taken to be 1 + (n-1) ρ, the standard formula accounting for a two-stage clustered sample, where n is the number of cases per hospital. The intra-cluster correlation ρ was assumed to be 0.01. The total required sample of births was estimated to be 4680 births. A two-stage stratified cluster random sampling strategy was applied. The first stage included random selection of hospitals for each of the four categories, from a list that met the initial criteria of having a C-section rate >10% and recording a minimum of 500 childbirths annually. A total of 30 hospitals were randomly selected as below: Government hospitals with Aama programme - 9 hospitals Government teaching hospitals without Aama programme - 3 hospitals Private hospitals/ teaching hospitals with Aama programme - 9 hospitals Private hospitals/ teaching hospitals without Aama programme - 9 hospitals In the second stage, within each hospital, a systematic random sample of births recorded in the discharge register of 2015/16 (July 2015 to July 2016) were selected from the sampling frame. A fixed number of 140 cases were selected from each hospital. Due to the large number of births at government hospitals, the sampling frame covered only the cases of the latest three months, while among private hospitals, cases from the latest six-month were included. Among government hospitals without Aama programme, 300 cases were randomly selected, as there were only three hospitals in this stratum. A sample of 4680 cases were estimated and planned, and data on 4380 deliveries from 29 hospitals were collected (94%) (Table 1). Table 1 Sample Design, implementation and response rate Hospital Category (Strata) Hospital Planned (PSU) Hospital Surveyed (PSU) Cases Planned (SSU) Cases Surveyed (SSU) Percent Covered Average Sample Weight 7 Government Hospitals with Aama Programme (Govt Aama +) 9 8 1260 1120 89 154 Government Hospitals without Aama Programme (Govt Aama -) 3 3 900 740 82 29 Private Hospital with Aama Programme (Private Aama +) 9 9 1260 1260 100 30 Private Hospital without Aama Programme (Private Aama -) 9 9 1260 1260 100 16 Aggregate 30 29 4680 4380 94 57 The data collection tool was developed, with variables for Robson ten group classification and for C-model calculation, using open-source software. Pretesting of tools was conducted at two public and private hospitals each. Patient IDs were used to track each patient’s files to collect the demographic and obstetric information needed for this study. Data collection was completed between 11/2016 and 02/2017. Data collection was completed by experienced enumerators who were trained on the data collection tool and method, and use of the software. The differences resulting from under-sampling or over-sampling at specific hospitals due to this variation were addressed through weighting in the analysis to ensure findings were representative. Robson classification The Robson classification systems groups all women at hospital admission, for giving birth, into one of ten groups that are mutually exclusive and totally comprehensive based on five obstetric variables [4] : parity (nulliparous, multiparous with and without previous C-section), onset of labour (spontaneous, induced or pre-labour C-section), gestational age (preterm or term), foetal presentation (cephalic, breech or transverse), and number of foetuses (single or multiple) WHO C-model WHO’s C-model is the global referencing tool for benchmarking C-section rates at facility level, and through a customised estimate of C-section rates, the C-model may provide a locally relevant reference of what would be an optimal C-section rate. The variables needed for the C-model includes maternal age, parity, previous C-section, number of foetus, presentation, preterm birth, provider-initiated-childbirth, diagnosis of selected obstetric complications (placenta praevia, abruptio-placentae, pre-eclampsia), diagnosis of illnesses (chronic hypertension, renal disease and HIV), and presence of organ dysfunction or Intensive Care Unit admission [8] . The survey package in R software was used for statistical analysis. For the calculation of predicted C-section using C-model, we adopted variables and coefficients of v1.3 [ ibid ., pp433] . Results Demographic and Obstetric Characteristics of the Study population Total 4380 women’s information was obtained from 29 hospitals (94% of the estimated sample size) (Table 1). The average age of the pregnant women was 24.56 years (SD 4.66) and mean gestational age was 38.6 weeks (SD 2.17). Fifty five percent of mothers were nulliparous, 1.2% of deliveries were multiple pregnancies and 3% were breech presentation (Table 2), with similar distribution across all hospital types. Table 2 Maternal and foetal characteristics (n=4380) Levels Overall n (%) Govt Aama + n (%) Govt Aama - n (%) Private Aama+ n (%) Private Aama- n (%) Parity Nulliparous 2413 (55.1) 609 (54.4) 431 (58.3) 716 (56.8) 687 (54.5) Multiparous 1967(44.9) 511 (45.6) 309 (41.7) 544 (43.2) 573 (45.5) Number of Foetuses Multiple 54 (1.2) 14 (1.2) 9 (1.2) 14 (1.1) 20 (1.5) Single 4326 (98.8) 1106 (98.8) 731 (98.8) 1246 (98.9) 1240 (98.5) Foetal presentation Cephalic 4236 (96.7) 1090 (97.3) 707 (95.6) 1202 (95.4) 1201 (95.3) Breech 125 (2.9) 27 (2.4) 28 (3.8) 47 (3.7) 51 (4) Shoulder/oblique/transverse 20 (0.4) 3 (0.3) 5 (0.6) 11 (0.9) 9 (0.7) The overall C-section rate across all the sampled facilities was 26.6%, with the highest rate observed in private facilities that were not a part of the Aama programme and lowest rate in government facilities that were enrolled in the programme. The C-section rate also increased with maternal age (Table 3) Table 3 C-section rates Levels Total deliveries (n) Total C-section (n) C-section rate (%) Type of facility Govt: Aama+ 1120 242 21.6 Govt: Aama - 740 268 36.2 Private: Aama + 1260 446 35.4 Private: Aama - 1260 539 42.8 Aggregate 4380 1167 26.6 Age of mother in years <20 650 124 19.1 20 - 29 3195 836 26.2 30 - 39 518 194 37.5 40+ 16 14 87.5 Across hospitals an average of 8% of the deliveries were a result of induced labour, 14.2% of the C-sections were performed before labour began, 77.8% had spontaneous on set of labour. Private hospitals without Aama had the highest percentage of C-sections performed before labour (27.5%). (Table 4) Table 4: Management of Labour by types of hospitals (n=4380) Levels Overall Govt: Aama+ Govt: Aama- Private: Aama + Private: Aama - p-value Spontaneous 3407(77.8) 934(83.4) 449(60.6) 912(72.4) 736(58.4) <0.001 Induced 350(8.0) 71(6.3) 121(16.4) 96(7.6) 177(14) C-section Before labour 623(14.2) 115(10.3) 170(23.0) 252(20.0) 347(27.5) Classifying caesarean sections using Robson groups Robson TGCS allowed comparison of the C-section rate of the ten groups among different hospitals (Table 5). Group 1 (nulliparous women with single cephalic pregnancy, ≥37-week gestation had spontaneous onset labour) at 39.2% were the largest sample group across all types of hospitals at the time of admission. Groups 1 and 3 (nulliparous or multiparous women with single cephalic pregnancy, ≥37-week gestation had spontaneous onset of labour), together account for 66.5% of C-sections of total institutional deliveries (Tables 5 and 6 and Fig 5). Table 5: Description of the study deliveries according to Robson classification Group Robson group description C-section / women in Group [#] C-section rate (of Robson group) Reference population C-section rate [##] Relative size (of total deliveries) Robson group contribution to total C-section rate Number ( % ) ( % ) ( % ) ( % ) 1 Nulliparous women with single cephalic pregnancy, ≥37 weeks gestation in spontaneous labour 260/1716 15.1 9.8 39.2 5.94 2 Nulliparous women with single cephalic pregnancy, ≥37 weeks gestation who either had labour induced or were delivered by caesarean section before labour 281/414 67.9 39.9 9.5 6.42 3 Multiparous women without a previous uterine scar, with single cephalic pregnancy, ≥37 weeks gestation in spontaneous labour 95/1195 7.7 3.0 27.3 2.1 4 Multiparous women without a previous uterine scar, with single cephalic pregnancy, ≥37 weeks gestation who either had labour induced or were delivered by caesarean section before labour 107/195 54.7 23.7 4.5 2.44 5 All multiparous women with at least one previous uterine scar, with single cephalic pregnancy, ≥37 weeks gestation 217/297 84.8 74.4 5.9 4.95 6 All nulliparous women with a single breech pregnancy 50/62 80.3 78.5 1.4 1.14 7 All multiparous women with a single breech pregnancy including women with previous uterine scars 34/43 78.1 73.8 1 0.78 8 All women with multiple pregnancies, including women with previous uterine scars 18/35 52.3 57.7 0.8 0.41 9 All women with a single pregnancy with a transverse or oblique lie, including women with previous uterine scars 13/17 73.9 86.6 0.4 0.3 10 All women with a single cephalic pregnancy <37 weeks gestation, including women with previous scars 96/445 21.5 25.1 10.2 2.19 # The figures were produced using sample weight for complex survey design. Cell frequencies were rounded. So, the fraction may not exactly match the caesarean section rate and group size percent when frequencies are very low ## Souza J, Betran A, Dumont A, Mucio B, Pickens G, Deneux-Tharaux C, et al. A global reference for caesarean section rates (C-Model): a multi-country cross-sectional study. BJOG. 2016;123(3):427-36. C-sections among group 5 (all multiparous women with at least one previous uterine scar, with single cephalic pregnancy, ≥37-week gestation i.e., repeat C-section) was nearly universal for all hospital types except for government facilities without Aama (75%), which may be because this group includes medical college hospitals 8 . (Figure 5). Insert Figure 5 here Comparing observed versus predicted caesarean section rates using WHO C-model As laid out in the methods section, the WHO C-model includes a number of input variables relating to maternal and obstetric characteristics, including selected complications, chronic illness and presence of organ failure or admission to the Intensive Care Unit. Figure 6 shows results from the C-model analysis stratified across the four types of hospitals. Observed C-section rates were higher than the C-model predicted rates across all the four types. The ‘excess’ in C-section rates was greater for the private hospitals (with Aama was 18.63 percentage points and non-Aama was 19.7 percentage points), than the government hospitals (with Aama was 10.8 percentage points and non-Aama was 13.9 percentage points). Insert Figure 6 here Discussion Overall, the institutional C-section rate was almost double to WHO’s global reference rate among Robson groups 1,2,3 and4; Robson groups 1,2 and 5 account for 65% of total C-section rate among these hospitals (26.7%) while they represent 55% of total women who gave birth at these hospitals. Private hospitals have higher difference between predicted and actual C-section rate using WHO’s C-model compared to public hospitals. Robson classification allows comparison of C-section rate among the ten groups, as institutional C-section rate is influenced by the obstetric characteristics of the women. Robson categorisation of the deliveries and C-sections in this study indicated a significantly higher proportion of C-sections across certain groups of women in this study than the reference population (Table 5) and in some groups almost double the reference rates (Robson groups 1, 2, 3 and 4). Studies conducted using similar Robson classification and C-section rates among the ten groups show very high variation for each group in low- and middle-income countries – India, Ethiopia, Tanzania (hospital level) [10, 11, 12], and in Europe (country level) [13] rates from group 1 ranges from 14% to 28%, group 2 from 34% to 73% and group 5 from 63% to 90% in these studies. The C-section rates in Robson groups of our study lies within the range of the studies conducted in these hospitals or countries, however, the almost double rates compared to the WHO’s reference rate among group 1-4 needs addressing. Robson group 5 (women with singleton at term, previous C-section scar) in the study groups accounts for 5.9% of total women and 84.8% had undergone C-section compared to WHO’s reference C-section rate at 74.5% of this group. This, together with high C-section among the Nulliparous, Term, Singleton, Vertex (NTSV) (group 1 and 2) and an increasing proportion of hospitals delivery being women in their first pregnancy can have deleterious effects with a spiralling of C-section in a country like Nepal where the reported ideal number of children for 75% of women with one child is 2 or more) [14]. This can culminate in a triple burden when scarce resources are diverted away from priority programmes, adding avoidable morbidity and mortality, due to spending on non-medically indicated C-section while still having unequitable access of C-section [15] , further hampering Nepal’s efforts to reaching universal coverage and SDG goals. Calculation of C-section rates using C-model also show higher observed C-section rates among all four types of hospitals (Figure 6) compared to the predicted rates. These differences are even higher if C-section rates due to foetal indication are considered (which is 2-4%) [16] . This finding of higher C-section rate at private hospitals is concerning - the phenomena commonly observed in other countries including India [17] , Brazil [18] , and middle east and north Africa [19] . Studies investigating reasons for high C-section in other countries report complex intertwining reasons driving high C-section. A systematic review in China found easy accessibility and acceptability of C-section resulting from women preference for this mode of delivery which was shaped by perceived poor quality of care during vaginal birth including lack of pain relief, and a fear related to risk for the baby in vaginal birth, as well as financial incentives for providers [20] . A study at tertiary hospital in Tanzania found a complex interaction of different factors, including the role of private practice, dysfunctional teamwork, an auditing process which can encourage staff to prioritise safeguarding themselves, and all these factors can lead to unnecessary C-sections [21] . A study in Canada also found that while financial incentives contribute only a small percentage of high C-section rate, other factors such as improved foetal monitoring, delayed motherhood and defensive medicine contribute to higher C-section rates [22] . On the other hand, a study in Sweden found population belief in normal birth, teamwork and a health system with a clear pathway for maternity care including midwife led birthing were the reasons for maintaining low C-section rate in the country [23] . In China, women’s preference of delivery seemed to change over the course of pregnancy and was influenced by what was considered ‘safe’ and ‘unsafe’ by the care provider [24] , which only highlights the importance of quality antenatal counselling and of midwife led maternity care back-up with timely referrals to a hospital for any complication management. Limitations This study is not without limitations. Firstly, the study is based only on the patients' records available at the hospitals, and some records could not be identified as the study was conducted after a major earthquake in 2015. Secondly, the study relies on the quality of records maintained at the hospitals and missing parameters may cause biases in interpretation. Thirdly, the study does not identify the appropriateness of clinical decisions. Conclusion Our study highlighted high institutional C-section rate among private hospitals; high C-section rate among Robson group 1 and 2, with almost 100% C-section among women with previous C-section scar, and the increasing percentage of nulliparous women at hospital deliveries signal the impending spiralling effects of over-use of C-section. This trend impacts the health of both mothers, and their newborns delivered via C-section, while also diverting scarce financial resources from primary healthcare. Maternity care in Nepal is provided free to all women, which includes government subsidies for all-C sections. With an increasing trend of institutional childbirth among nulliparous women and declining fertility rate in Nepal, we can expect higher proportions of women who give birth at the hospital will be women in their first pregnancy (nulliparous women). In our study, both government and private hospitals implementing the Aama programme (which includes free maternity care and transport incentive), have a lower difference between observed versus predicted C-section rates when compared to their counterparts (government and private hospitals not implementing Aama programme). This indicates that universal access to C-sections may no longer appropriate now as there is an excess of potentially non-medically needed C-sections which absorb scarce public funds, whilst unmet need for life saving C-sections exists for other groups of women. This calls for an update of Nepal’s suite of maternal health policies. Steering health policy becomes more complex after early proven interventions have been implemented (e.g., expansion of CEONC), as policy development has to factor in changes in the demand-side (e.g., where women want to give birth [25] ) and respond to the health sector devolution in Nepal. The successes and challenges have become evident from the increases seen between NDHS 2016 and 2022. The proportion of births delivered by C-section had almost doubled from 5% in 2011 to 9% in 2016 but were still within the World Health Organization (WHO) reference of 5–15% [26] . With reports of 18.6% in NDHS 2022, these challenges now seem more urgent. Rising C-section rates can quickly spiral into a serious public health concern when it combines with inequalities in access and use of services. With the scaling-up of the National Health Insurance Scheme with 18,000 NRP re-imbursement for C-section (compared to 7000 NRP per C-section in Aama programme), policymakers in Nepal need to consider the possible financial effects of spiralling C-section rates on the insurance scheme unless effective intervention to cap un-necessary C-sections is implemented. Unnecessary C-section consumes large amounts of the national health budget diverting scarce resource from essential health care services, lead to catastrophic financial hardship to the families [6] and long-term implications for mothers and babies [7] , and this calls for better understanding of the drivers of increasing C-section trends and the need to introduce interventions to halt or reduce increasing trend of C-section rate in Nepal. Post-script Based on this study findings and WHO’s recommendation, the Family Welfare Division of Department of Health Services, Ministry of Health and Population, with the leadership of Nepal Society of Obstetricians and Gynaecologists (NESOG), introduced the Robson Ten Group Classification System in early 2020 starting from four referral hospitals, and expanded it to 33 hospitals, to monitor C-section rates across the ten Robson groups and recommended the hospitals to self-monitor and review the C-section rates among the ten Robson groups. Combining this Robson classification monitoring with clinical audit and feedback [27] and mandatory second opinion for C-section [28] may be used as part of follow up by the hospitals wanting to improve rational use of C-section. Further research on educating women on effects of non-medically indicated C-section combining with midwifes led maternity care providing quality antenatal intrapartum and postnatal care backed up by a timely referral to a hospital for management of complications may be appropriate as midwife led birthing units are in the immediate plan of the country’s “National Nursing and Midwifery Strategic Plan 2021-30”. Abbreviations CEONC - Comprehensive Emergency Obstetric and Neonatal Care NESOG - Nepal Society of Obstetricians and Gynaecologists NDHS – Nepal Demographic Health Survey NTSV - Nulliparous, Term, Singleton, Vertex SBA - Skilled Birth Attendant TGCS - Ten Groups Classification System WHO – World Health Organization Declarations Ethics approval and consent to participate The study received the ethics approval from the Nepal Health Research Council, and permissions from the Ministry of Health and Population to gather the hospital records data. No other separate consent was sought, as all hospital record data was gathered anonymised. No data gathering was undertaken directly from any women. Consent for publication Not applicable Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests : The authors declare that they have no competing interests Funding The study was conducted as a part of the technical support provided to the Ministry of Health and Population, through the UKAid funded Nepal Health Sector Support Programme. Authors' contributions MDI jointly conceived the original research idea, contributed to the development of the protocol and to the draft article. ADR co-ordinated the review of the literature, jointly reviewed the literature and developed the draft article. MD developed the research protocol including the tools, ensured the quality of data, initial analysis and the preliminary report SM led on the development of the research protocol, jointly reviewed the literature, developed the drafts and finalised the article. VS developed the methods, analysed the data and commented on drafts All authors have approved the submitted version Acknowledgements The authors acknowledge the support provided by the Family Welfare Division of the Ministry of Health and Population, with the technical oversight on the study in 2017, and for all the permissions that enabled this study to be conducted. References Pradhan, Ajit, Ram Hari Aryal, Gokarna Regmi, Bharat Ban, and PavalavaIli Govindasamy. 1997. Nepal Family Health Survey 1996. Kathmandu, Nepal: Ministry of Health/Nepal, New ERA/Nepal, and Macro International. Available at http://dhsprogram.com/pubs/pdf/FR78/FR78.pdf. 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Haider MR, Rahman MM, Moinuddin M, Rahman AE, Ahmed S, Khan MM. Ever-increasing Caesarean section and its economic burden in Bangladesh. PLoS One. 2018;13(12):e0208623. doi:10.1371/journal.pone.0208623. Souza J, Betran A, Dumont A, Mucio B, Pickens G, Deneux-Tharaux C, et al. A global reference for caesarean section rates (C-Model): a multi-country cross-sectional study. BJOG. 2016;123(3):427-36. World Health Organization. Tool to calculate caesarean section reference rates at health facilities is launched: the C-Model [Internet]. 2018 [cited 2022 Dec 9]. Available from: https://www.who.int/news/item/29-11-2018-tool-to-calculate-caesarean-section-reference-rates-at-health-facilities-is-launched-the-c-model. (Accessed 18 th October 2024) Dhodapkar, Sneha Badwe, Sindhu Bhairavi, Mary Daniel, Neelima Singh Chauhan, & Ramesh Chand Chauhan. "Analysis of caesarean sections according to Robson ten group classification system at a tertiary care teaching hospital in South India." International Journal of Reproduction, Contraception, Obstetrics and Gynecology [Online], 4.3 (2015): 745-749. Web. 14 Dec. 2022 Abubeker, F.A., Gashawbeza, B., Gebre, T.M. et al. Analysis of cesarean section rates using Robson ten group classification system in a tertiary teaching hospital, Addis Ababa, Ethiopia: a cross-sectional study. BMC Pregnancy Childbirth 20, 767 (2020). https://doi.org/10.1186/s12884-020-03474-x Tognon F, Borghero A, Putoto G, et al. Analysis of caesarean section and neonatal outcome using the Robson classification in a rural district hospital in Tanzania: an observational retrospective study. BMJ Open 2019;9:e033348. doi:10.1136/ bmjopen-2019-033348 Zeitlin J, Durox M, Macfarlane A, Alexander S, Heller G, Loghi M, Nijhuis J, Sol Olafsdottir H, Mierzejewska E, Gissler M, Blondel B; the Euro-Peristat Network. Using Robson Ten Group Classification System for comparing caesarean section rates in Europe: an analysis of routine data from the Euro-Peristat study. BJOG 2021;128:1444–1453. Ministry of Health, Nepal; New ERA; and ICF. 2017. Nepal Demographic and Health Survey 2016. Kathmandu, Nepal: Ministry of Health, Nepal. Betran AP, Ye J, Moller A-B, et al. Trends and projections of caesarean section rates: global and regional estimates. BMJ Global Health 2021;6:e005671. doi:10.1136/ bmjgh-2021-005671 Miller DA, Paul RH. Cesarean Section for Fetal Distress. In: Flamm BL, Quilligan EJ, editors. Cesarean Section. Clinical Perspectives in Obstetrics and Gynecology. New York: Springer; 1995. p. 95–114. doi:10.1007/978-1-4612-2482-2_7. Singh, P., Hashmi, G. & Swain, P.K. High prevalence of cesarean section births in private sector health facilities- analysis of district level household survey-4 (DLHS-4) of India. BMC Public Health 18, 613 (2018). https://doi.org/10.1186/s12889-018-5533-3 [ Marmitt LP, Machado AKF, Cesar JA. Recent trends in cesarean section reduction in extreme south of Brazil: a reality only in the public sector?. Cien Saude Colet. 2022;27(8):3307. doi:10.1590/1413-81232022278.05742022 McCall SJ, Semaan A, Altijani N, Opondo C, Abdel-Fattah M, Kabakian-Khasholian T. Trends, wealth inequalities and the role of the private sector in caesarean section in the Middle East and North Africa: A repeat cross-sectional analysis of population-based surveys. PLoS One. 2021;16(11):e0259791. Nov 2021. doi:10.1371/journal.pone.0259791 Long Q, Kingdon C, Yang F, Renecle MD, Jahanfar S, Bohren MA, et al. (2018) Prevalence of and reasons for women’s, family members’, and health professionals’ preferences for cesarean section in China: A mixed-methods systematic review. PLoS Med 15(10): e1002672. https://doi. org/10.1371/journal.pmed.1002672 Litorp H, Mgaya A, Mbekenga CK, Kidanto HL, Johnsdotter S, Essén B. Fear, blame and transparency: Obstetric caregivers' rationales for high caesarean section rates in a low-resource setting. Soc Sci Med. 2015;143:232-240. doi:10.1016/j.socscimed.2015.09.003 Allin, Sara and Baker, Michael and Isabelle, Maripier and Stabile, Mark, Accounting for the Rise in C-Sections: Evidence from Population Level Data (March 2015). NBER Working Paper No. w21022, Available at SSRN: https://ssrn.com/abstract=2578870 Panda, S., Daly, D., Begley, C. et al. Factors influencing decision-making for caesarean section in Sweden – a qualitative study. BMC Pregnancy Childbirth 18, 377 (2018). https://doi.org/10.1186/s12884-018-2007-7 Panda S, Begley C, Daly D. Influence of women's request and preference on the rising rate of caesarean section - a comparison of reviews. Midwifery. 2020;88:102765. doi:10.1016/j.midw.2020.102765 Family Health Division, NHSSP. Responding to Increased Demand for Institutional Childbirths at Referral Hospitals in Nepal: Situational Analysis and Emerging Options. Kathmandu: FHD/NHSSP; 2013. Available at https://www.nhssp.org.np/NHSSP_Archives/ehcs/Responding_to_demand_report_february2013.pdf (Accessed 18 th October 2024) Ministry of Health and Population [Nepal], New ERA, ICF. Nepal Demographic and Health Survey 2022. Kathmandu, Nepal: Ministry of Health and Population [Nepal]; 2023. Chaillet N, Dumont A, Abrahamowicz M, Pasquier JC, Audibert F, Monnier P, et al. QUARISMA Trial Research Group. A cluster‐randomized trial to reduce cesarean delivery rates in Quebec. New England Journal of Medicine 2015;372(18):1710‐21. Althabe F, Belizan JM, Villar J, Alexander S, Bergel E, Ramos S, et al. Mandatory second opinion to reduce rates of unnecessary caesarean sections in Latin America: a cluster randomised control trial. Lancet 2004;363(9425):1934‐40. Footnotes Calculated from the Nepal Health Management Information System data 2021/22 Sample weights were calculated for Two Stage Stratified Cluster Systematic Random Sampling. Note: As mentioned in the methods section, government hospitals without the Aama Programme included government owned medical colleges (teaching hospitals). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5432594","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":388548139,"identity":"f783e3d6-14fa-4658-b4fd-74c6a26286ca","order_by":0,"name":"Maureen Dar Iang","email":"","orcid":"","institution":"Options Consultancy Services","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Maureen","middleName":"Dar","lastName":"Iang","suffix":""},{"id":388548140,"identity":"494c5409-16dc-4459-b46d-79a2f258bf33","order_by":1,"name":"Alison Dembo Rath","email":"","orcid":"","institution":"Options Consultancy Services","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Alison","middleName":"Dembo","lastName":"Rath","suffix":""},{"id":388548141,"identity":"9ad9e0d1-dfd7-4f3b-acbb-2e7ca2e2c254","order_by":2,"name":"Madhu Dixit Devkota","email":"","orcid":"","institution":"Tribhuvan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Madhu","middleName":"Dixit","lastName":"Devkota","suffix":""},{"id":388548142,"identity":"4424ed7d-aaaa-4940-a396-fc93d66f9dce","order_by":3,"name":"Vishnu Sapkota","email":"","orcid":"","institution":"Tribhuvan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Vishnu","middleName":"","lastName":"Sapkota","suffix":""},{"id":388548143,"identity":"e49255b3-d053-49ef-a6e7-42935e6b8fec","order_by":4,"name":"Shanti Mahendra","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYDACCRBhAGXwMDDIgfgHHhDScgBJizFYSwJBLTAGUEtiA4iDT4v87OZnjz8UMETzz24+9uDtHrv0+WGHHwJtsZPTbcCuxeDOMXMDoMNyZ9w5lm4451ly7sbbaQZALcnGZgdwaJFIMJMAaWm4kWMmzXOAOXfj7ASQlgOJ23BokZ+R/g2sZf6N/G9ALfXphrPTP+DVwgA0HKxlw40cNqCWwwny0jn4bTG4kVMmccZAInfjjTQzyTkHjhtukM4pOJBggNsvQIdtk6j4Y5M770byM4k3B6rl5Wenb/7wocJODpcWKJBAshes0gCvcnR7G0hRPQpGwSgYBSMBAAB1z2Tp6KUwRwAAAABJRU5ErkJggg==","orcid":"","institution":"Options Consultancy Services","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Shanti","middleName":"","lastName":"Mahendra","suffix":""}],"badges":[],"createdAt":"2024-11-11 13:53:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5432594/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5432594/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71756987,"identity":"bcb5231e-f4a7-4336-9e6e-d80bbaebec5b","added_by":"auto","created_at":"2024-12-18 10:16:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":342844,"visible":true,"origin":"","legend":"\u003cp\u003eKey safe motherhood policies and strategies in Nepal to increase access to comprehensive emergency obstetric and neonatal care (CEONC), including C-section\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5432594/v1/ae9bad19d93edd28d945c169.png"},{"id":71758251,"identity":"9e39559a-4b99-41f3-b536-75f5074658b7","added_by":"auto","created_at":"2024-12-18 10:24:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":57231,"visible":true,"origin":"","legend":"\u003cp\u003ePolicy developments since 2017, that have an impact on CEONC service provision\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5432594/v1/fecbec58c34ee4a3999d007d.png"},{"id":71756988,"identity":"e94f0c6a-d105-4210-bd4f-c9151bdcff07","added_by":"auto","created_at":"2024-12-18 10:16:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":489283,"visible":true,"origin":"","legend":"\u003cp\u003eExpansion of emergency obstetric care services from 2010 to 2023 (in formerly designated districts\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5432594/v1/d3cdbb9dbdb3fc9c5bfc1bf3.png"},{"id":71758250,"identity":"6101bfdb-f8de-4ce4-8425-9cda02f7a4de","added_by":"auto","created_at":"2024-12-18 10:24:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":35673,"visible":true,"origin":"","legend":"\u003cp\u003eC-section deliveries for women who had a live birth, by wealth quintiles, 2006-2016\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5432594/v1/ef0594c8610e1847a3995952.png"},{"id":71756992,"identity":"25b72936-2635-4ba0-95f9-6d43606d9ad1","added_by":"auto","created_at":"2024-12-18 10:16:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":509970,"visible":true,"origin":"","legend":"\u003cp\u003eC-section rates among Robson ten groups at four types of hospitals\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5432594/v1/119eb5bbc89df2896c9c0995.png"},{"id":71756993,"identity":"b20b9854-529a-4fca-978c-0f542c1c00fd","added_by":"auto","created_at":"2024-12-18 10:16:34","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":444892,"visible":true,"origin":"","legend":"\u003cp\u003eObserved versus predicted caesarean section rates\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5432594/v1/cb20a102ab042f8839ab8841.png"},{"id":71758654,"identity":"fa8f0908-f14b-4e61-b19f-251c4d712c50","added_by":"auto","created_at":"2024-12-18 10:32:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2688273,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5432594/v1/d6692bf4-05ac-4779-8639-fff4422ef523.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimising Caesarean section use in Nepal through hospital generated and population-based data on Caesarean section rates to inform policy and practice","fulltext":[{"header":"Background","content":"\u003cp\u003eNepal has spent more than 25 years in an organised effort to increase its population level caesarean section (C-section) rates from a very low base of 1% in 1996 \u003cstrong\u003e{1]\u003c/strong\u003e. It has formulated policies and strategies which provide an enabling environment for all women to access comprehensive emergency obstetric and neonatal care (CEONC), of which C-section is a major service component. Seminal policies such as the Safe Motherhood policy (1998) and the Skilled Birth Attendant (SBA) policy (2006) initiated it and were operationalised through the Safe Motherhood and Long-Term Health Plan 2006-2017. Further, Nepal was a forerunner in creating demand and supply side incentives for maternity care through the ongoing Aama Surakshya Programme (Aama), which started as the Maternity Incentive Scheme 2005. Aama supports women to access maternity care services by providing transport incentives, and free institutional childbirth services through supply side-payments to participating hospitals (most public and more than 60 private) for each vaginal and C-section childbirth conducted. The SBA policy and an earmarked CEONC fund ensured that at a time of need the country was able to place adequately skilled health personnel to meet human resource shortages in remote areas (Fig 1)\u003c/p\u003e\n\u003cp\u003eInsert Figure 1 here\u003c/p\u003e\n\u003cp\u003eNepal transitioned into a federal state in September 2015, and over the near decade now the country has seen several important acts, standards and regulations which address citizen rights and responsibilities at each level of government (Fig 2); and many which have an effect on the provision of services for emergency obstetric care \u003cstrong\u003e[2].\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInsert Figure 2 here\u003c/p\u003e\n\u003cp\u003eSupportive policies, strategies and programmes have resulted in improved availability of CEONC services from one-third in 1997 to nearly all (76 out of 77) formerly designated district areas in 2023 and an increase in the number of public CEONC hospitals from 41 in 2011 to 105 in 2023 (Fig 3). The expansion continues at municipality levels with 29 local level governments also offering\u003c/p\u003e\n\u003cp\u003eCEONC services as of 2023. Overall, there are now more than 200 public and private hospitals \u003cstrong\u003e[3]\u0026nbsp;\u003c/strong\u003eproviding C-sections across almost all the country, with expected annual births of about 500,000\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e6\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInsert Figure 3 here\u003c/p\u003e\n\u003cp\u003eConsequently, C-section rates increased as the Nepal Demographic Health Survey (NDHS) data from 2006 2016 showed, but geographic and socioeconomic disparities with C-section rates too had increased for many groups (Fig 4). Although proportion of births delivered by C-section had almost doubled from 5% in 2011 to 9% in 2016, they were still within the World Health Organization (WHO) reference of 5\u0026ndash;15% \u003cstrong\u003e[4]\u003c/strong\u003e. But by 2016, while C-section rates among women in the highest wealth quintile had risen to 28%, \u0026nbsp;they were just 2% among those in the lowest quintile. C-section rates were also considerably higher for births in private facilities (35%) than in public facilities (12%). This dual problem of lack of access to life-saving C-section for some women and the negative effects of over intervention when C-section is not medically required, is well established \u003cstrong\u003e[5].\u0026nbsp;\u003c/strong\u003eThe latter increases health risks for mothers and infants, plus incurs additional direct and indirect health care costs for households and the health system \u003cstrong\u003e[6, 7],\u003c/strong\u003e creating a \u0026lsquo;triple burden\u0026rsquo; from overuse.\u003c/p\u003e\n\u003cp\u003eInsert Figure 4 here\u003c/p\u003e\n\u003cp\u003eHow the inequality of access noted across groups, translated to facility-level C-section rates was difficult to know because of differences in the client mix and obstetric profile of the women who seek care at the facility. In 2017, Nepal\u0026rsquo;s Ministry of Health and Population\u0026rsquo;s, Family Welfare Division requested its long-standing technical assistance partner, Options, funded by the UK government, to design a study to understand the drivers of institutional C-section rates in public and private hospitals. This paper presents findings from the first nation-wide study to determine the differences and possible drivers of facility-based C-section delivery in public and private hospitals of Nepal.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eA cross-sectional quantitative retrospective study was conducted to determine and compare facility based C-section rate among four types of hospital in Nepal, using Robson Ten Groups Classification System (TGCS) of 10 mutually exclusive and totally inclusive classification categories based on obstetric characteristics. The study also used the World Health Organization\u0026rsquo;s mathematical model known as the C-model \u003cstrong\u003e[8, 9]\u003c/strong\u003e, \u0026nbsp;to estimate the difference between actual and predicted C-section rates in the study population.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe sample and data collection\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBirths at hospitals with a C-section rate \u0026gt;10% and recording a minimum of 500 childbirths annually, formed the study population, using data from the health management information system (HMIS 2015/16). Four categories of hospitals were identified:\u0026nbsp;\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eGovernment hospitals with Aama programme\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eGovernment teaching hospitals without Aama programme\u0026nbsp;\u003c/li\u003e\n \u003cli\u003ePrivate hospitals/ teaching hospitals with Aama programme\u0026nbsp;\u003c/li\u003e\n \u003cli\u003ePrivate hospitals/ teaching hospitals without Aama programme\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe sample size calculation was done in order to respond to the objective \u0026ndash; to compare the actual C-section rate with the predicted C-section rate using the WHO C-model. For the sample size to have 80% power to detect the important differences in C-section rates according to hospital characteristics (e.g., government/private; with/without Aama), and for simplicity, we assumed the characteristic was binary and there were roughly equal number of hospitals with and without the characteristics. Power analysis and sample size calculation scheme for group comparison was carried out in Stata 12.1. The design effect (DE) was taken to be 1 + (n-1) \u0026rho;, the standard formula accounting for a two-stage clustered sample, where n is the number of cases per hospital. The intra-cluster correlation \u0026rho; was assumed to be 0.01. The total required sample of births was estimated to be 4680 births.\u003c/p\u003e\n\u003cp\u003eA two-stage stratified cluster random sampling strategy was applied. The first stage included random selection of hospitals for each of the four categories, from a list that met the initial criteria of having a C-section rate \u0026gt;10% and recording a minimum of 500 childbirths annually. A total of 30 hospitals were randomly selected as below:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eGovernment hospitals with Aama programme - 9 hospitals\u003c/li\u003e\n \u003cli\u003eGovernment teaching hospitals without Aama programme - 3 hospitals\u003c/li\u003e\n \u003cli\u003ePrivate hospitals/ teaching hospitals with Aama programme - 9 hospitals\u003c/li\u003e\n \u003cli\u003ePrivate hospitals/ teaching hospitals without Aama programme - 9 hospitals\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn the second stage, within each hospital, a systematic random sample of births recorded in the discharge register of 2015/16 (July 2015 to July 2016) were selected from the sampling frame. A fixed number of 140 cases were selected from each hospital. Due to the large number of births at government hospitals, the sampling frame covered only the cases of the latest three months, while among private hospitals, cases from the latest six-month were included. Among government hospitals without Aama programme, 300 cases were randomly selected, as there were only three hospitals in this stratum. A sample of 4680 cases were estimated and planned, and data on 4380 deliveries from 29 hospitals were collected (94%) (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eTable 1 Sample Design, implementation and response rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHospital Category (Strata)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHospital Planned (PSU)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHospital Surveyed (PSU)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCases Planned (SSU)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCases Surveyed\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(SSU)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercent Covered\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage Sample Weight\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e\u003cstrong\u003e\u003c/strong\u003e\u003c/a\u003e\u003csup\u003e7\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGovernment Hospitals with Aama Programme\u0026nbsp;\u003c/strong\u003e(Govt Aama +)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGovernment Hospitals without Aama Programme\u0026nbsp;\u003c/strong\u003e(Govt Aama -)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrivate Hospital with Aama Programme\u0026nbsp;\u003c/strong\u003e(Private Aama +)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrivate Hospital without Aama Programme\u0026nbsp;\u003c/strong\u003e(Private Aama -)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAggregate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e30\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e29\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4680\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4380\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e94\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e57\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe data collection tool was developed, with variables for Robson ten group classification and for C-model calculation, using open-source software. Pretesting of tools was conducted at two public and private hospitals each. Patient IDs were used to track each patient\u0026rsquo;s files to collect the demographic and obstetric information needed for this study. Data collection was completed between 11/2016 and 02/2017. Data collection was completed by experienced enumerators who were trained on the data collection tool and method, and use of the software. The differences resulting from under-sampling or over-sampling at specific hospitals due to this variation were addressed through weighting in the analysis to ensure findings were representative.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRobson classification\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe Robson classification systems groups all women at hospital admission, for giving birth, into one of ten groups that are mutually exclusive and totally comprehensive based on five obstetric variables \u003cstrong\u003e[4]\u003c/strong\u003e:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eparity (nulliparous, multiparous with and without previous C-section),\u003c/li\u003e\n \u003cli\u003eonset of labour (spontaneous, induced or pre-labour C-section),\u003c/li\u003e\n \u003cli\u003egestational age (preterm or term),\u003c/li\u003e\n \u003cli\u003efoetal presentation (cephalic, breech or transverse), and\u0026nbsp;\u003c/li\u003e\n \u003cli\u003enumber of foetuses (single or multiple)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cem\u003eWHO C-model\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWHO\u0026rsquo;s C-model is the global referencing tool for benchmarking C-section rates at facility level, and through a customised estimate of C-section rates, the C-model may provide a locally relevant reference of what would be an optimal C-section rate. The variables needed for the C-model includes maternal age, parity, previous C-section, number of foetus, presentation, preterm birth, provider-initiated-childbirth, diagnosis of selected obstetric complications (placenta praevia, abruptio-placentae, pre-eclampsia), diagnosis of illnesses (chronic hypertension, renal disease and HIV), and presence of organ dysfunction or Intensive Care Unit admission \u003cstrong\u003e[8]\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eThe survey package in R software was used for statistical analysis. For the calculation of predicted C-section using C-model, we adopted variables and coefficients of v1.3 \u003cstrong\u003e[\u003cem\u003eibid\u003c/em\u003e., pp433]\u003c/strong\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eDemographic and Obstetric Characteristics of the Study population\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTotal 4380 women\u0026rsquo;s information was obtained from 29 hospitals (94% of the estimated sample size) (Table 1). The average age of the pregnant women was 24.56 years (SD 4.66) and mean gestational age was 38.6 weeks (SD 2.17). Fifty five percent of mothers were nulliparous, 1.2% of deliveries were multiple pregnancies and 3% were breech presentation (Table 2), with similar distribution across all hospital types.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eTable 2 Maternal and foetal characteristics (n=4380)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevels\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGovt Aama +\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGovt Aama -\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrivate Aama+ n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrivate Aama- n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParity\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eNulliparous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2413 (55.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e609 (54.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e431 (58.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e716 (56.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e687 (54.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eMultiparous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1967(44.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e511 (45.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e309 (41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e544 (43.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e573 (45.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of Foetuses\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eMultiple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e54 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e14 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e9 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e14 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e20 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e4326 (98.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1106 (98.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e731 (98.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1246 (98.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1240 (98.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFoetal presentation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eCephalic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e4236 (96.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1090 (97.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e707 (95.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1202 (95.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1201 (95.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eBreech\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e125 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e27 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e28 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e47 (3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e51 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eShoulder/oblique/transverse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e20 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e3 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e5 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e11 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e9 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe overall C-section rate across all the sampled facilities was 26.6%, with the highest rate observed in private facilities that were not a part of the Aama programme and lowest rate in government facilities that were enrolled in the programme. The C-section rate also increased with maternal age (Table 3)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eTable 3 C-section rates \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevels\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal deliveries\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal C-section\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC-section rate\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of facility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003eGovt: Aama+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e21.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003eGovt: Aama -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e36.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003ePrivate: Aama + \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e35.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003ePrivate: Aama -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e42.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003eAggregate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e4380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e1167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e26.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge of mother in years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026lt;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e19.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e20 - 29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e3195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e26.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e30 - 39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e37.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e40+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e87.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAcross hospitals an average of 8% of the deliveries were a result of induced labour, 14.2% of the C-sections were performed before labour began, 77.8% had spontaneous on set of labour. Private hospitals without Aama had the highest percentage of C-sections performed before labour (27.5%). (Table 4)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"591\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 591px;\"\u003e\n \u003cp\u003eTable 4: Management of Labour by types of hospitals (n=4380)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevels\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGovt: Aama+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGovt: Aama-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrivate: Aama +\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrivate: Aama -\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpontaneous\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e3407(77.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e934(83.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e449(60.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e912(72.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e736(58.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInduced\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e350(8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e71(6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e121(16.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e96(7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e177(14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC-section Before labour\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e623(14.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e115(10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e170(23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e252(20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e347(27.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eClassifying caesarean sections using Robson groups\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eRobson TGCS allowed comparison of the C-section rate of the ten groups among different hospitals (Table 5). Group 1 (nulliparous women with single cephalic pregnancy, \u0026ge;37-week gestation had spontaneous onset labour) at 39.2% were the largest sample group across all types of hospitals at the time of admission. Groups 1 and 3 (nulliparous or multiparous women with single cephalic pregnancy, \u0026ge;37-week gestation had spontaneous onset of labour), together account for 66.5% of C-sections of total institutional deliveries (Tables 5 and 6 and Fig 5).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eTable 5: Description of the study deliveries according to Robson classification\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRobson group description\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC-section / women in Group\u0026nbsp;\u003c/strong\u003e[#]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC-section rate\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(of Robson group)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference population\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eC-section rate\u0026nbsp;\u003c/strong\u003e[##]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelative size\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(of total deliveries)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRobson group contribution to total C-section rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e(\u003cstrong\u003e%\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e(\u003cstrong\u003e%\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e(\u003cstrong\u003e%\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e(\u003cstrong\u003e%\u003c/strong\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eNulliparous women with single cephalic pregnancy, \u0026ge;37 weeks gestation in spontaneous labour\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e260/1716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e15.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e39.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e5.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eNulliparous women with single cephalic pregnancy, \u0026ge;37 weeks gestation who either had labour induced or were delivered by caesarean section before labour\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cpre\u003e281/414 \u003c/pre\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e67.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e39.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e9.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e6.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eMultiparous women without a previous uterine scar, with single cephalic pregnancy, \u0026ge;37 weeks gestation in spontaneous labour\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cpre\u003e95/1195\u003c/pre\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e27.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eMultiparous women without a previous uterine scar, with single cephalic pregnancy, \u0026ge;37 weeks gestation who either had labour induced or were delivered by caesarean section before labour\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e107/195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e54.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e23.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e2.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eAll multiparous women with at least one previous uterine scar, with single cephalic pregnancy, \u0026ge;37 weeks gestation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e217/297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e84.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e74.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e4.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eAll nulliparous women with a single breech pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e50/62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e80.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e78.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eAll multiparous women with a single breech pregnancy including women with previous uterine scars\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e34/43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e78.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e73.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eAll women with multiple pregnancies, including women with previous uterine scars\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e18/35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e52.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e57.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eAll women with a single pregnancy with a transverse or oblique lie, including women with previous uterine scars\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e13/17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e73.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e86.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eAll women with a single cephalic pregnancy \u0026lt;37 weeks gestation, including women with previous scars\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e96/445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e21.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e25.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e# The figures were produced using sample weight for complex survey design. Cell frequencies were rounded. So, the fraction may not exactly match the caesarean section rate and group size percent when frequencies are very low\u003c/p\u003e\n \u003cp\u003e## \u0026nbsp; Souza J, Betran A, Dumont A, Mucio B, Pickens G, Deneux-Tharaux C, et al. A global reference for caesarean section rates (C-Model): a multi-country cross-sectional study. BJOG. 2016;123(3):427-36.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eC-sections among group 5 (all multiparous women with at least one previous uterine scar, with single cephalic pregnancy, \u0026ge;37-week gestation i.e., repeat C-section) was nearly universal for all hospital types except for government facilities without Aama (75%), which may be because this group includes medical college hospitals\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e8\u003c/sup\u003e. (Figure 5).\u003c/p\u003e\n\u003cp\u003eInsert Figure 5 here\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eComparing observed versus predicted caesarean section rates using WHO C-model\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAs laid out in the methods section, the WHO C-model includes a number of input variables relating to maternal and obstetric characteristics, including selected complications, chronic illness and presence of organ failure or admission to the Intensive Care Unit.\u003c/p\u003e\n\u003cp\u003eFigure 6 shows results from the C-model analysis stratified across the four types of hospitals. Observed C-section rates were higher than the C-model predicted rates across all the four types. The \u0026lsquo;excess\u0026rsquo; in C-section rates was greater for the private hospitals (with Aama was 18.63 percentage points and non-Aama was 19.7 percentage points), than the government hospitals (with Aama was 10.8 percentage points and non-Aama was 13.9 percentage points).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInsert Figure 6 here\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOverall, the institutional C-section rate was almost double to WHO\u0026rsquo;s global reference rate among Robson groups 1,2,3 and4; Robson groups 1,2 and 5 account for 65% of total C-section rate among these hospitals (26.7%) while they represent 55% of total women who gave birth at these hospitals. Private hospitals have higher difference between predicted and actual C-section rate using WHO\u0026rsquo;s C-model compared to public hospitals.\u003c/p\u003e\n\u003cp\u003eRobson classification allows comparison of C-section rate among the ten groups, as institutional C-section rate is influenced by the obstetric characteristics of the women. Robson categorisation of the deliveries and C-sections in this study indicated a significantly higher proportion of C-sections across certain groups of women in this study than the reference population (Table 5) and in some groups almost double the reference rates (Robson groups 1, 2, 3 and 4). Studies conducted using similar Robson classification and C-section rates among the ten groups show very high variation for each group in low- and middle-income countries \u0026ndash; India, Ethiopia, Tanzania (hospital level) \u003cstrong\u003e[10, 11, 12],\u0026nbsp;\u003c/strong\u003eand in Europe (country level) \u003cstrong\u003e[13]\u0026nbsp;\u003c/strong\u003erates from group 1 ranges from 14% to 28%, group 2 from 34% to 73% and group 5 from 63% to 90% in these studies. The C-section rates in Robson groups of our study lies within the range of the studies conducted in these hospitals or countries, however, the almost double rates compared to the WHO\u0026rsquo;s reference rate among group 1-4 needs addressing. Robson group 5 (women with singleton at term, previous C-section scar) in the study groups accounts for 5.9% of total women and 84.8% had undergone C-section compared to WHO\u0026rsquo;s reference C-section rate at 74.5% of this group. This, together with high C-section among the Nulliparous, Term, Singleton, Vertex (NTSV) (group 1 and 2) and an increasing proportion of hospitals delivery being women in their first pregnancy can have deleterious effects with a spiralling of C-section in a country like Nepal where the reported ideal number of children for 75% of women with one child is 2 or more)\u003cstrong\u003e\u0026nbsp;[14].\u003c/strong\u003e This can culminate in a triple burden when scarce resources are diverted away from priority programmes, adding avoidable morbidity and mortality, due to spending on non-medically indicated C-section while still having unequitable access of C-section \u003cstrong\u003e[15]\u003c/strong\u003e, further hampering Nepal\u0026rsquo;s efforts to reaching universal coverage and SDG goals.\u003c/p\u003e\n\u003cp\u003eCalculation of C-section rates using C-model also show higher observed C-section rates among all four types of hospitals (Figure 6) compared to the predicted rates. These differences are even higher if C-section rates due to foetal indication are considered (which is 2-4%) \u003cstrong\u003e[16]\u003c/strong\u003e. This finding of higher C-section rate at private hospitals is concerning - the phenomena commonly observed in other countries including India \u003cstrong\u003e[17]\u003c/strong\u003e, Brazil \u003cstrong\u003e[18]\u003c/strong\u003e, and middle east and north Africa \u003cstrong\u003e[19]\u003c/strong\u003e\u0026nbsp; .\u003c/p\u003e\n\u003cp\u003eStudies investigating reasons for high C-section in other countries report complex intertwining reasons driving high C-section. A systematic review in China found easy accessibility and acceptability of C-section resulting from women preference for this mode of delivery which was shaped by perceived poor quality of care during vaginal birth including lack of pain relief, and a fear related to risk for the baby in vaginal birth, as well as financial incentives for providers \u003cstrong\u003e[20]\u003c/strong\u003e. A study at tertiary hospital in Tanzania found a complex interaction of different factors, \u0026nbsp;including the role of private practice, dysfunctional teamwork, an auditing process which can encourage staff to prioritise safeguarding themselves, and all these factors can lead to unnecessary C-sections \u003cstrong\u003e[21]\u003c/strong\u003e. A study in Canada also found that while financial incentives contribute only a small percentage of high C-section rate, other factors such as improved foetal monitoring, delayed motherhood and defensive medicine contribute to higher C-section rates \u003cstrong\u003e[22]\u003c/strong\u003e. On the other hand, a study in Sweden found population belief in normal birth, teamwork and a health system with a clear pathway for maternity care including midwife led birthing were the reasons for maintaining low C-section rate in the country \u003cstrong\u003e[23]\u003c/strong\u003e. In China, women\u0026rsquo;s preference of delivery seemed to change over the course of pregnancy and was influenced by what was considered \u0026lsquo;safe\u0026rsquo; and \u0026lsquo;unsafe\u0026rsquo; by the care provider \u003cstrong\u003e[24]\u003c/strong\u003e, which only highlights the importance of quality antenatal counselling and of midwife led maternity care back-up with timely referrals to a hospital for any complication management.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLimitations\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study is not without limitations. Firstly, the study is based only on the patients\u0026apos; records available at the hospitals, and some records could not be identified as the study was conducted after a major earthquake in 2015. Secondly, the study relies on the quality of records maintained at the hospitals and missing parameters may cause biases in interpretation. Thirdly, the study does not identify the appropriateness of clinical decisions.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study highlighted high institutional C-section rate among private hospitals; high C-section rate among Robson group 1 and 2, with almost 100% C-section among women with previous C-section scar, and the increasing percentage of nulliparous women at hospital deliveries signal the impending spiralling effects of over-use of C-section. This trend impacts the health of both mothers, and their newborns delivered via C-section, while also diverting scarce financial resources from primary healthcare.\u003c/p\u003e \u003cp\u003eMaternity care in Nepal is provided free to all women, which includes government subsidies for all-C sections. With an increasing trend of institutional childbirth among nulliparous women and declining fertility rate in Nepal, we can expect higher proportions of women who give birth at the hospital will be women in their first pregnancy (nulliparous women). In our study, both government and private hospitals implementing the Aama programme (which includes free maternity care and transport incentive), have a lower difference between observed versus predicted C-section rates when compared to their counterparts (government and private hospitals not implementing Aama programme). This indicates that universal access to C-sections may no longer appropriate now as there is an excess of potentially non-medically needed C-sections which absorb scarce public funds, whilst unmet need for life saving C-sections exists for other groups of women. This calls for an update of Nepal\u0026rsquo;s suite of maternal health policies.\u003c/p\u003e \u003cp\u003eSteering health policy becomes more complex after early proven interventions have been implemented (e.g., expansion of CEONC), as policy development has to factor in changes in the demand-side (e.g., where women \u003cem\u003ewant\u003c/em\u003e to give birth \u003cb\u003e[25]\u003c/b\u003e) and respond to the health sector devolution in Nepal. The successes and challenges have become evident from the increases seen between NDHS 2016 and 2022. The proportion of births delivered by C-section had almost doubled from 5% in 2011 to 9% in 2016 but were still within the World Health Organization (WHO) reference of 5\u0026ndash;15% \u003cb\u003e[26]\u003c/b\u003e. With reports of 18.6% in NDHS 2022, these challenges now seem more urgent. Rising C-section rates can quickly spiral into a serious public health concern when it combines with inequalities in access and use of services. With the scaling-up of the National Health Insurance Scheme with 18,000 NRP re-imbursement for C-section (compared to 7000 NRP per C-section in Aama programme), policymakers in Nepal need to consider the possible financial effects of spiralling C-section rates on the insurance scheme unless effective intervention to cap un-necessary C-sections is implemented. Unnecessary C-section consumes large amounts of the national health budget diverting scarce resource from essential health care services, lead to catastrophic financial hardship to the families \u003cb\u003e[6]\u003c/b\u003e and long-term implications for mothers and babies \u003cb\u003e[7]\u003c/b\u003e, and this calls for better understanding of the drivers of increasing C-section trends and the need to introduce interventions to halt or reduce increasing trend of C-section rate in Nepal.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePost-script\u003c/h2\u003e \u003cp\u003eBased on this study findings and WHO\u0026rsquo;s recommendation, the Family Welfare Division of Department of Health Services, Ministry of Health and Population, with the leadership of Nepal Society of Obstetricians and Gynaecologists (NESOG), introduced the Robson Ten Group Classification System in early 2020 starting from four referral hospitals, and expanded it to 33 hospitals, to monitor C-section rates across the ten Robson groups and recommended the hospitals to self-monitor and review the C-section rates among the ten Robson groups. Combining this Robson classification monitoring with clinical audit and feedback \u003cb\u003e[27]\u003c/b\u003e and mandatory second opinion for C-section \u003cb\u003e[28]\u003c/b\u003e may be used as part of follow up by the hospitals wanting to improve rational use of C-section. Further research on educating women on effects of non-medically indicated C-section combining with midwifes led maternity care providing quality antenatal intrapartum and postnatal care backed up by a timely referral to a hospital for management of complications may be appropriate as midwife led birthing units are in the immediate plan of the country\u0026rsquo;s \u0026ldquo;National Nursing and Midwifery Strategic Plan 2021-30\u0026rdquo;.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCEONC - Comprehensive Emergency Obstetric and Neonatal Care\u003c/p\u003e\n\u003cp\u003eNESOG - Nepal Society of Obstetricians and Gynaecologists\u003c/p\u003e\n\u003cp\u003eNDHS \u0026ndash; Nepal Demographic Health Survey\u003c/p\u003e\n\u003cp\u003eNTSV - Nulliparous, Term, Singleton, Vertex\u003c/p\u003e\n\u003cp\u003eSBA - Skilled Birth Attendant\u003c/p\u003e\n\u003cp\u003eTGCS - Ten Groups Classification System\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWHO \u0026ndash; World Health Organization\u003c/p\u003e"},{"header":"Declarations","content":"\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe study received the ethics approval from the Nepal Health Research Council, and permissions from the Ministry of Health and Population to gather the hospital records data. No other separate consent was sought, as all hospital record data was gathered anonymised. No data gathering was undertaken directly from any women.\u0026nbsp;\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e: \u0026nbsp;\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe study was conducted as a part of the technical support provided to the Ministry of Health and Population, through the UKAid funded Nepal Health Sector Support Programme.\u0026nbsp;\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003e\u003cem\u003eAuthors\u0026apos; contributions\u003c/em\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eMDI jointly conceived the original research idea, contributed to the development of the protocol and to the draft article.\u003c/p\u003e\n\u003cp\u003eADR co-ordinated the review of the literature, jointly reviewed the literature and developed the draft article.\u003c/p\u003e\n\u003cp\u003eMD developed the research protocol including the tools, ensured the quality of data, initial analysis and the preliminary report\u003c/p\u003e\n\u003cp\u003eSM led on the development of the research protocol, jointly reviewed the literature, developed the drafts and finalised the article.\u003c/p\u003e\n\u003cp\u003eVS developed the methods, analysed the data and commented on drafts\u003c/p\u003e\n\u003cp\u003eAll authors have approved the submitted version\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe authors acknowledge the support provided by the Family Welfare Division of the Ministry of Health and Population, with the technical oversight on the study in 2017, and for all the permissions that enabled this study to be conducted.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003ePradhan, Ajit, Ram Hari Aryal, Gokarna Regmi, Bharat Ban, and PavalavaIli Govindasamy. 1997. Nepal Family Health Survey 1996. Kathmandu, Nepal: Ministry of Health/Nepal, New ERA/Nepal, and Macro International. Available at http://dhsprogram.com/pubs/pdf/FR78/FR78.pdf. (Accessed 18\u003csup\u003eth\u003c/sup\u003e October 2024)\u003c/li\u003e\n \u003cli\u003eConsultative Review of CEONC monitoring at the provincial level: Findings and recommended strategic actions. NHSSP Technical Brief. October 2023.\u003c/li\u003e\n \u003cli\u003eNepal Safe Motherhood and Newborn Roadmap 2030 [Internet]. 2020 [cited 2022 Dec 9]. Available from: https://fwd.gov.np/cms/nepal-safe-motherhood-and-newborn-health-road-map-2030/ (Accessed 18\u003csup\u003eth\u003c/sup\u003e October 2024)\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;World Health Organization. WHO statement on Caesarean Section. WHO/RHR/15.02 [Internet]. 2015 [cited 2022 Dec 9]. Available from: https://apps.who.int/iris/bitstream/handle/10665/161442/WHO_RHR_15.02_eng.pdf (Accessed 18\u003csup\u003eth\u003c/sup\u003e October 2024)\u003c/li\u003e\n \u003cli\u003eStemming the global caesarean section epidemic. Lancet. 2018 Oct 13;392(10155):1279.\u003c/li\u003e\n \u003cli\u003eMori AT, Binyaruka P, Hangoma P, et al. Patient and health system costs of managing pregnancy and birth-related complications in sub-Saharan Africa: a systematic review. Health Econ Rev. 2020;10:26. doi:10.1186/s13561-020-00283-y.\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Haider MR, Rahman MM, Moinuddin M, Rahman AE, Ahmed S, Khan MM. Ever-increasing Caesarean section and its economic burden in Bangladesh. PLoS One. 2018;13(12):e0208623. doi:10.1371/journal.pone.0208623.\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Souza J, Betran A, Dumont A, Mucio B, Pickens G, Deneux-Tharaux C, et al. A global reference for caesarean section rates (C-Model): a multi-country cross-sectional study. BJOG. 2016;123(3):427-36.\u003c/li\u003e\n \u003cli\u003eWorld Health Organization. Tool to calculate caesarean section reference rates at health facilities is launched: the C-Model [Internet]. 2018 [cited 2022 Dec 9]. Available from: https://www.who.int/news/item/29-11-2018-tool-to-calculate-caesarean-section-reference-rates-at-health-facilities-is-launched-the-c-model. (Accessed 18\u003csup\u003eth\u003c/sup\u003e October 2024)\u003c/li\u003e\n \u003cli\u003eDhodapkar, Sneha Badwe, Sindhu Bhairavi, Mary Daniel, Neelima Singh Chauhan, \u0026amp; Ramesh Chand Chauhan. \u0026quot;Analysis of caesarean sections according to Robson ten group classification system at a tertiary care teaching hospital in South India.\u0026quot; International Journal of Reproduction, Contraception, Obstetrics and Gynecology [Online], 4.3 (2015): 745-749. Web. 14 Dec. 2022\u003c/li\u003e\n \u003cli\u003eAbubeker, F.A., Gashawbeza, B., Gebre, T.M. et al. Analysis of cesarean section rates using Robson ten group classification system in a tertiary teaching hospital, Addis Ababa, Ethiopia: a cross-sectional study. BMC Pregnancy Childbirth 20, 767 (2020). https://doi.org/10.1186/s12884-020-03474-x\u003c/li\u003e\n \u003cli\u003eTognon F, Borghero A, Putoto G, et al. Analysis of caesarean section and neonatal outcome using the Robson classification in a rural district hospital in Tanzania: an observational retrospective study. BMJ Open 2019;9:e033348. doi:10.1136/ bmjopen-2019-033348\u003c/li\u003e\n \u003cli\u003eZeitlin J, Durox M, Macfarlane A, Alexander S, Heller G, Loghi M, Nijhuis J, Sol Olafsdottir H, Mierzejewska E, Gissler M, Blondel B; the Euro-Peristat Network. Using Robson Ten Group Classification System for comparing caesarean section rates in Europe: an analysis of routine data from the Euro-Peristat study. BJOG 2021;128:1444\u0026ndash;1453.\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Ministry of Health, Nepal; New ERA; and ICF. 2017. Nepal Demographic and Health Survey 2016. Kathmandu, Nepal: Ministry of Health, Nepal.\u003c/li\u003e\n \u003cli\u003eBetran AP, Ye J, Moller A-B, et al. Trends and projections of caesarean section rates: global and regional estimates. BMJ Global Health 2021;6:e005671. doi:10.1136/ bmjgh-2021-005671\u003c/li\u003e\n \u003cli\u003eMiller DA, Paul RH. Cesarean Section for Fetal Distress. In: Flamm BL, Quilligan EJ, editors. Cesarean Section. Clinical Perspectives in Obstetrics and Gynecology. New York: Springer; 1995. p. 95\u0026ndash;114. doi:10.1007/978-1-4612-2482-2_7.\u003c/li\u003e\n \u003cli\u003eSingh, P., Hashmi, G. \u0026amp; Swain, P.K. High prevalence of cesarean section births in private sector health facilities- analysis of district level household survey-4 (DLHS-4) of India. BMC Public Health 18, 613 (2018). https://doi.org/10.1186/s12889-018-5533-3\u003c/li\u003e\n \u003cli\u003e[ Marmitt LP, Machado AKF, Cesar JA. Recent trends in cesarean section reduction in extreme south of Brazil: a reality only in the public sector?. Cien Saude Colet. 2022;27(8):3307. doi:10.1590/1413-81232022278.05742022\u003c/li\u003e\n \u003cli\u003eMcCall SJ, Semaan A, Altijani N, Opondo C, Abdel-Fattah M, Kabakian-Khasholian T. Trends, wealth inequalities and the role of the private sector in caesarean section in the Middle East and North Africa: A repeat cross-sectional analysis of population-based surveys. PLoS One. 2021;16(11):e0259791. Nov 2021. doi:10.1371/journal.pone.0259791\u003c/li\u003e\n \u003cli\u003eLong Q, Kingdon C, Yang F, Renecle MD, Jahanfar S, Bohren MA, et al. (2018) Prevalence of and reasons for women\u0026rsquo;s, family members\u0026rsquo;, and health professionals\u0026rsquo; preferences for cesarean section in China: A mixed-methods systematic review. PLoS Med 15(10): e1002672. https://doi. org/10.1371/journal.pmed.1002672\u003c/li\u003e\n \u003cli\u003eLitorp H, Mgaya A, Mbekenga CK, Kidanto HL, Johnsdotter S, Ess\u0026eacute;n B. Fear, blame and transparency: Obstetric caregivers\u0026apos; rationales for high caesarean section rates in a low-resource setting. Soc Sci Med. 2015;143:232-240. doi:10.1016/j.socscimed.2015.09.003\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Allin, Sara and Baker, Michael and Isabelle, Maripier and Stabile, Mark, Accounting for the Rise in C-Sections: Evidence from Population Level Data (March 2015). NBER Working Paper No. w21022, Available at SSRN: https://ssrn.com/abstract=2578870\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Panda, S., Daly, D., Begley, C. et al. Factors influencing decision-making for caesarean section in Sweden \u0026ndash; a qualitative study. BMC Pregnancy Childbirth 18, 377 (2018). https://doi.org/10.1186/s12884-018-2007-7\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Panda S, Begley C, Daly D. Influence of women\u0026apos;s request and preference on the rising rate of caesarean section - a comparison of reviews. Midwifery. 2020;88:102765. doi:10.1016/j.midw.2020.102765\u003c/li\u003e\n \u003cli\u003eFamily Health Division, NHSSP. Responding to Increased Demand for Institutional Childbirths at Referral Hospitals in Nepal: Situational Analysis and Emerging Options. Kathmandu: FHD/NHSSP; 2013. Available at https://www.nhssp.org.np/NHSSP_Archives/ehcs/Responding_to_demand_report_february2013.pdf (Accessed 18\u003csup\u003eth\u003c/sup\u003e October 2024)\u003c/li\u003e\n \u003cli\u003eMinistry of Health and Population [Nepal], New ERA, ICF. Nepal Demographic and Health Survey 2022. Kathmandu, Nepal: Ministry of Health and Population [Nepal]; 2023.\u003c/li\u003e\n \u003cli\u003eChaillet N, Dumont A, Abrahamowicz M, Pasquier JC, Audibert F, Monnier P, et al. QUARISMA Trial Research Group. A cluster‐randomized trial to reduce cesarean delivery rates in Quebec. New England Journal of Medicine 2015;372(18):1710‐21.\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Althabe F, Belizan JM, Villar J, Alexander S, Bergel E, Ramos S, et al. Mandatory second opinion to reduce rates of unnecessary caesarean sections in Latin America: a cluster randomised control trial. Lancet 2004;363(9425):1934‐40.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Calculated from the Nepal Health Management Information System data 2021/22\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Sample weights were calculated for Two Stage Stratified Cluster Systematic Random Sampling.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Note: As mentioned in the \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003emethods\u003c/span\u003e section, government hospitals without the Aama Programme included government owned medical colleges (teaching hospitals).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Caesarean Section, Equity, Robson Ten Group Classification, WHO C-Model, Maternal Health, Over-medicalisation, Unmet need, Health Services","lastPublishedDoi":"10.21203/rs.3.rs-5432594/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5432594/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dual problem of lack of access to life-saving caesarean section (C-section) and the negative effects of over-intervention when it is not medically required, is well established. The health burden plus additional direct and indirect costs for households and the health system, create a ‘triple burden.’ In Nepal, whilst C-section rates have rapidly escalated, our data suggests an opportunity to avert a C-section epidemic and reduce unmet need for C-section among women with very low income.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analysed C-section rates from 29 public and private hospitals across Nepal using the Robson Ten Group Classification System and assessed how each group compared with the Robson reference groups. Using the WHO C-Model we estimated the gap between observed and predicted facility C-section rates, adjusted for the obstetric case-mix at each facility. We generated estimates for four categories of hospitals: public with Aama (the government demand-side financing programme); public without Aama; private with Aama; private without Aama.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe found that Robson groups 1 and 3 (nulliparous or multiparous women with single cephalic pregnancy, ≥ 37-week gestation, spontaneous onset of labour), together account for 66.5% of C-sections of total institutional deliveries. All four hospital categories had higher C-section rates than predicted rates. The percentage points difference between observed and predicted rates was greater for private hospitals (with Aama 18.6 percentage points and non-Aama 19.7 percentage points), than government hospitals (with Aama 10.8 percentage points and non-Aama 13.9 percentage points). The biggest gap was for private hospitals (non-Aama with observed C-section rate at 42% compared with 23% predicted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur hospital-based study data provides evidence that C-section rates are too high relative to need and are not just a function of late arrivals or complicated births, as is often claimed by facilities. C-section rates can be controlled by introducing measures in both public and private sectors. Policy needs to continue to focus on increasing uptake of the life-saving intervention among populations where C-section rates fall below optimal levels, while also ensuring that they are avoided if not indicated medically. Resources for maternal and newborn health need to be reallocated to achieve uptake of medically required C-sections equitably across population groups and steer Nepal towards optimal rates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e: Not applicable.\u003c/p\u003e","manuscriptTitle":"Optimising Caesarean section use in Nepal through hospital generated and population-based data on Caesarean section rates to inform policy and practice","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-18 10:16:29","doi":"10.21203/rs.3.rs-5432594/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-09-09T11:50:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180587170332839588641944006782516036976","date":"2026-09-02T05:23:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"45422413284510667622298291334991479177","date":"2026-08-28T14:01:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"274401195374640957572889016509137722824","date":"2026-06-05T14:01:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-28T16:42:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191383748896315850344119731118023365664","date":"2026-03-16T07:03:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-23T06:47:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"133774507955100009553995315759068867089","date":"2025-01-06T19:04:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-12-23T16:44:42+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-11-19T10:24:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-15T08:51:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-15T08:48:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pregnancy and Childbirth","date":"2024-11-11T13:49:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"af829bc9-c920-4978-9b5a-e44b7ab2db1d","owner":[],"postedDate":"December 18th, 2024","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-09-09T11:50:43+00:00","index":174,"fulltext":""},{"type":"reviewerAgreed","content":"180587170332839588641944006782516036976","date":"2026-09-02T05:23:32+00:00","index":172,"fulltext":""},{"type":"reviewerAgreed","content":"45422413284510667622298291334991479177","date":"2026-08-28T14:01:16+00:00","index":166,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-12-18T10:16:29+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-18 10:16:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5432594","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5432594","identity":"rs-5432594","version":["v1"]},"buildId":"omnImTCwR2MFx8CMYfrG7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}