Exploring the spatial patterns of cesarean section delivery in India: Evidence from National Family Health Survey-4

Journal Pre-proof Exploring the spatial patterns of Cesarean Section Delivery in India: Evidence from National Family Health Survey-4 Shobhit Srivastava, Himanshu Chaurasia, KH Jiten Kumar Singh, Pratishtha Chaudhary PII:

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Clinical Epidemiology and Global Health

Received Date: 3 April 2019 Revised Date:

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Please cite this article as: Srivastava S, Chaurasia H, Kumar Singh KJ, Chaudhary P, Exploring the spatial patterns of Cesarean Section Delivery in India: Evidence from National Family Health Survey-4, Clinical Epidemiology and Global Health, https://doi.org/10.1016/j.cegh.2019.09.012. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier, a division of RELX India, Pvt. Ltd on behalf of INDIACLEN.

Exploring the spatial patterns of Cesarean Section Delivery in India: Evidence from National Family Health Survey-4 Authors Information

Shobhit Srivastava*, Himanshu Chaurasia** , KH Jiten Kumar Singh***, Pratishtha Chaudhary* Mr. Shobhit Srivastava is an M.Phil. Scholar of International Institute for Population Sciences, Mumbai-400088. His research interests include ageing issues, maternal and child health, reproductive and child health, fertility, and gender. Mr. Himanshu Chaurasia is working as Scientist-B (Statistician) at National Institute for Research in Reproductive Health, ICMR, Parel, Mumbai. His research interests include population and development; ageing issues and health; fertility; public health and mortality; maternal and child health, reproductive and child health, migration and urbanization. Mr. KH Jiten Kumar Singh is Scientist D at National Institute of Medical Statistics, New Delhi. His research interests include population and development; fertility; public health and mortality; maternal and child health, reproductive and child health. Ms. Pratishtha Chaudhary is an M.Phil. Scholar of International Institute for Population Sciences, Mumbai400088. Her research interests include ageing issues, maternal and child health, reproductive and child health, fertility, and gender

Shobhit Srivastava [email protected] **Himanshu Chaurasia (Corresponding Author) [email protected] KH Jiten Kumar Singh [email protected] Pratishtha Chaudhary [email protected]

*

International Institute for Population Sciences, Govandi Station Road, Deonar, Mumbai, India

** National Institute for Research in Reproductive Health, ICMR, Parel, Mumbai, India *** National Institute of Medical Statistics, New Delhi, India

Author Contributions Conceived and designed the research paper: SS, and HC; analysed the data: SS, HC, and PC; Contributed agents/materials/analysis tools: PC, and HC; Wrote the manuscript: SS, HC, Refined the manuscript: HC, SS, PC, and JKS.

Compliance with Ethical Standards Conflict of Interest The authors declare no conflict of interest. Informed Consent Informed consent was obtained from all individual participants included in the study. Ethical Treatment of Experimental Subjects (Animal and Human) Disclosure of potential conflicts of interest has been provided. This study was based on a large dataset that is publicly available on DHS website (https://dhsprogram.com/data/) conducted by the MOHFW and International Institute for Population Sciences (IIPS) in India with ethical standards being complied with including informed consent obtained from participants. India digital map The district

level

shape

file

of

India

was

acquired

from

GitHub

at

https://github.com/datameet/maps/tree/master/Districts. The digital map has been used under the Creative Commons Attributions 2.5 India license. The shape file was created using the administrative atlas of Census 2011, India. And the map was projected in WGS 1984 UTM zone 43N.

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Exploring the spatial patterns of Cesarean Section Delivery in India: Evidence from

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National Family Health Survey-4

3

Abstract

4

Introduction: Almost every day, 800 women die from pregnancy or childbirth-related

5

complications around the world. The risks and costs associated with C-section deliveries are

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significant, mainly where there was no medical indication. Past research has shown a positive

7

and significant association with C-section and maternal death.

8

Objective: The paper attempts to throw light on the pattern of C-section delivery in India at

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district levels as the increasing use of medical technologies during childbirth is a matter of

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concern.

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Methods: Bivariate, logistic regression and spatial analyses techniques have been used for

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analysis purpose, using the fourth round of the National Family Health Survey (NFHS-4) data

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conducted in 2015-16.

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Results: C-section have shown variability across all the states, and shifting from public to

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private is associated with an increase in the number of deliveries.

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educational status of women, wealth, ANC, and OOPE were significantly associated with C-

17

section.

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Conclusions: There should be the provision of maternity benefits should be given to women

19

who belong to below poverty line (BPL). Routine monitoring and evaluation of emergency

20

obstetric services should be carried out. Further research to improve the quality of care in

21

public health institutions should be made.

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Keywords: C-section, delivery, spatial analyses, below the poverty line

Variables like the

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1

24

Introduction

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Caesarean section (C-section) delivery is a major surgical procedure and obstetric

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intervention (Teguete et al 2012; & Betrán et al 2016). Over the past three decades, the world

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is witnessing a dramatic rise in the rate of caesarean deliveries in both developed and

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developing countries (Wilkinson et al 1998; Villar et al 2006; & Betrán et al 2016). C-

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section is an obstetric intervention introduced in late Nineteenth century which aimed at

30

saving lives of both the mother and the foetus/ their new-borns by preventing poor obstetric

31

outcomes from life-threatening pregnancy and childbirth-related complications (WHO 1985;

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Teguete et al 2012). Earlier World Health Organization (WHO) has recommended that the

33

rate of C-Section utilization should lie between 5 -15 per cent in any population to have an

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optimal impact and has medical indications for C-section (WHO 1985; Dumont et al 2001;

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Althabe et al 2006; Betrán et al 2007; Lauer et al 2010; & Ostovar et al 2010). A rate below 5

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percent indicates that a substantial proportion of women don’t have access to surgical,

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obstetric care and unmet need for skilled delivery services (Maine et al 1997). On the other

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hand, a rate higher than 15 percent implies overutilization of the procedure for other than life-

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saving reasons (WHO 1985; WHO 1994). The rates above 15 percent are unsuitable and

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unnecessary, imposing a financial burden and clinical risks on patients and healthcare

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systems (UNICEF 2008; & Berhan and Berhan 2014). However, WHO has recently

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suggested that they don’t recommend a specific rate at either a hospital-level or a country-

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level as the extremely high or low cesarean delivery rate is an important quality of care issue.

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It may also indicate the mismatch between evidence and training/ practice in obstetrics

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(Betrán et al 2007; & Gabbe et al 2016).

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Numerous studies have found the high rate of C-section delivery rate throughout the world

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and have become a serious public health threat for health systems and does not contribute to

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maternal health and pregnancy outcome (Bertollini et al 1992; Biswas et al 2005; Van 2009; 2

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& Gibbons et al 2010). It is an important issue in many parts of the world, not only because

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of the additional short and long term health hazards it causes but also due to increased costs

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associated with caesarean births (Souza et al 2016). In fact, WHO also underscores the

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importance of focusing on the wants of the pregnant mothers and discourages performing C-

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sections with no need. C-section delivery without a medical need places both mothers and

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their babies at a higher risk of short- and long-term health consequences (Betrán et al 2016).

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Worldwide, C-section rates have increased and varied across different countries and albeit

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unequally. According to a study based on 150 countries, 18.6 percent of all births occurred by

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C-Section, ranging from 0.6 percent to 27.2 percent in the least and most developed regions,

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while in some countries, C-Section rates are up to 50 percent, mainly in the private sector

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resulting in millions of women undergoing unnecessary surgery respectively (Betrán et al

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2016). Another study based on 26 South Asian and sub-Saharan African countries using

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Demographic and Health Survey (DHS) data, found that rates were lowest among the ‘rural

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poor’ in 18 countries and highest among the ‘urban rich’ in all countries (Betrán et al 2007; &

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Cavallaro et al 2013). Moreover, it has also been reported in studies that high cost of C-

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section may result in catastrophic health expenditure (CHE) for families and exert extra

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pressure upon overburdened health systems mostly in low- and middle-income countries

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(WHO and UNICEF 1994; & Festin et al 2009).

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There is an evidence that C-section without medical indication is associated with increased

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maternal and neonatal mortality and morbidity, which can be reduced when indicated. Also,

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the rate of C-section delivery was positively related to postpartum, antibiotic treatment,

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stillbirths, anomalies of the placenta, neonatal survival, obstructed labour, selected breech

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delivery, mal-presentation of the foetus and foetal distress, even after adjustment for risk

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factors (Weil and Fernandez 1992; Villar et al 2006; & Chu et al 2012). In many countries, it

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is seen that millions of women who need surgical procedures do not have access to them, 3

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putting their and their children's lives at risk, while in some countries, unnecessary overuses

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of surgical practices are common (Langer and Villar 2002; & Maine 2007). Many studies

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have investigated the trends and inequities in use of MCH care services; there is a scarcity of

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information on clinical indications for C-section particularly from population-based studies,

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essential for more in-depth understanding of why C-section delivery rate is increasing and

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what strategies are needed to control its epidemic (Anwar et al 2008; Anwar et al 2015; &

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Collin et al 2007). This high and rising C-section rate is a reason for concern. However, little

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information on how or why C-section rate is increasing and what should be done, both

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demand and supply side factors, attributed for this rapid rise in population-based C-section

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are important in the contexts (Anwar et al 2008; & Anwar et al 2015).

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It is often seen that patients request the obstetricians to perform the C-section delivery, and

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from physician’s point of view, it is much more convenient and quicker than normal vaginal

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delivery, less painful and less time consuming (Pai 2000). In India, giving birth to a baby at a

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predetermined auspicious time and day are driving the patient (women) to go for a C-section,

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thus increasing the demand for C-sections (Kabra et al 1994; & Mishra and Ramanathan

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2002). In India, the rate of C-section delivery has increased from 8.5 per cent to 17.2 percent

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between 2005-06 and 2015-16 (IIPS NFHS-4 Report 2015) which is lower compared to

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developing countries like Brazil and China. If we follow the guidelines of WHO, the present

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rate of C-section seems to be alarming both at the national and regional level. The percentage

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distribution of state-wise C-section deliveries over time (2005 to 2016) and in rural and urban

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areas in India (2015–16) are given in the appendix (Table 1). In all the states of India, C-

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section rates increased from 2005 to 2016 and the highest increase was in Jammu and

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Kashmir (19.6%). It also showed that there were more C-section deliveries in urban as

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compared to rural areas in almost all the states. The distribution of C-section by type of

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health facility are given in Table 2 (appendix). Table 2 shows the percentage of women 4

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undergoing C-section by type of Health Facility by states, India, 2015–16. It clearly showed

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that C-section deliveries were higher in private health care facilities as compared to public

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health care facilities in almost all the states of India. In private health care facilities, highest

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C-sections were performed in Jammu and Kashmir (75.5%) followed by Telangana (74.9%),

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Tripura (73.7%) and West Bengal (70.9%). The states with the lowest C-section deliveries

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were Rajasthan (23.2%), followed by Haryana (25.3%) and Gujrat (26.6%). A similar pattern

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was observed in public health care facilities, except for Telangana in which C-sections were

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highest (40.6%) followed by Jammu and Kashmir and other states. Both the tables show

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variation across all the states, assuming that there might be variations across the districts too.

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Although the study has reported a rising rate of caesarean births, the reasons remain unknown

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(Radhakrishnan et al 2017). The present study aims to investigate the relationship between C-

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section deliveries and its associated complications, along with their socio-demographic

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determinants at district levels to inform policy-makers about equitable and focused strategies

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to end preventable maternal mortality and to improve MCH services in India. To the best of

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our knowledge, no previous study has conducted a spatial analysis of C-section delivery at

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the district level in India. However, we have not overlooked the spatial patterns of

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inequalities in the public-private sector and rural-urban area.

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Data Source and Sampling design

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The analysis is based on the National Family Health Survey (NFHS-4) conducted during

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2015-16. All four NFHS surveys have been done under the stewardship of the Ministry of

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Health and Family Welfare (MoHFW), Government of India (GOI). Total eligible women

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taken for the analysis were 259,627 which includes 255,726 married women. The NFHS-4

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sample is a stratified two-stage sample. The 2011 census served as the sampling frame for the

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selection of PSUs. PSUs were villages in rural areas and Census Enumeration Blocks (CEBs)

5

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in urban areas. In NFHS 2015-16 data is collected at district level and unit of analysis is

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individual as from every household, individuals are being surveyed.

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Statistical analysis

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Bivariate and logistic regression analyses were used to study the socioeconomic differentials

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in caesarean section deliveries in India. To examine spatial dependence and clustering of

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caesarean section deliveries over various explanatory variables, Moran’s I, Univariate Local

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indicator of Spatial Association (LISA), Bivariate Local indicator of Spatial Association

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(LISA), LISA cluster map were produced. The Spatial weight matrix (w) of order 1 has been

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generated using the Queen’s contiguity method to quantify the spatial proximity between

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each possible pair of observational entities in the dataset (Getis and Ord 1992, 2010). A

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positive spatial autocorrelation indicates that points with similar attribute values are closely

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distributed in space whereas negative spatial autocorrelation indicates that closely associated

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points are more dissimilar. Moran’s I usually takes values in between −1 to +1, where

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positive values suggest the spatial clustering of the similar values and negative values

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indicate the clustering of different values. A zero value indicates a random spatial pattern

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with no spatial autocorrelation. Univariate LISA measures the correlation of neighbourhood

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values around a specific spatial location (Anselin 1995). It determines the extent of spatial

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randomness and clustering present in the data (Clark and Evans1954; & Cliff and Ord 1970).

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Four types of spatial autocorrelation were generated:

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1. Hot Spots: Locations with high values, with similar neighbours (High-High).

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2. Cold Spots: Locations with low values, with similar neighbours (Low-Low).

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3. Spatial Outliers: Locations with high values, but with low-value neighbours (High-Low).

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4. Spatial Outliers: locations with low values, but with higher values of neighbours (Low-

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High). 6

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To see the potential regional correlates of C-section deliveries we performed spatial

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regression analysis which includes ordinary least square (OLS) model, Spatial Lag Model

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(SLM) and Spatial Error Model (SEM) (Anselin et al 2006).

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Findings

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Map I indicates the prevalence of C-section deliveries in districts of India. The darker regions

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show a high incidence of C-section deliveries among women in various states. The high

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prevalence is visible in southern parts of India which includes Andhra Pradesh, Karnataka,

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Tamil Nadu and Kerala and also in the upper part of Northern India which provides for

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Himachal Pradesh and Jammu and Kashmir.

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Table 3 depicts binary logistic regression analysis for C-section deliveries in India, NFHS-4,

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2015–16. It shows the ORs for the association between C-section and the background

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characteristics. Women of age-group 25-29 and 30-49 were 1.15 [95% CI: 1.118-1.196] and

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1.37 [95% CI: 1.310-1.448] times more likely to undergo C-section as compared to women of

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age group 15-24. Women of rural areas were 9% [95% CI: 0.884-0.939] less likely to

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undergo C-section as compared to women of urban areas. The odds ratios were significantly

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higher among educated women compared to uneducated women. The women from poorer

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HHs (OR=1.27; 95% CI: [1.211-1.348]), middle-income HHs (OR=1.69; 95% CI: [1.610-

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1.789]), richer HHs (OR=1.73; 95% CI: [1.643-1.834]) and richest HHs (OR=1.56; 95% CI:

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[1.471-1.659]) were significantly more likely to go for C-section than those from poorest

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HHs. Religion, caste, birth order and CEB were significantly associated with C-section. As

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compared to underweight women, normal, overweight and obese women 1.27 [95% CI:

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1.225-1.318], 2.16 [95% CI: 2.064-2.261] and 3.31 [95% CI: 3.104-3.547] times more likely

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to go for C-section. Size of the child at birth was also significant factors associated with C-

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section delivery. Women who go to a private health care facility for their delivery were 3.34

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[95% CI: 3.252-3.438] times more likely to undergo C-section. Women who went for ANC 7

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1-3 times and more than four times were 18% [95% CI: 1.115-1.259] and 84% [95% CI:

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1.740-1.951] respectively, more likely to undergo C-section as compared to women who

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never had any ANC visit.

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Table 4 indicates Univariate, and Bivariate Moran’s I for the dependent and independent

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variables. Moran’s I value for Caesarean-section delivery is 0.693 (p<0.001) which indicates

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that there is high spatial auto-correlation in Caesarean-section deliveries in districts of India.

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The value of Moran’s I ranges from 0.440 (p<0.001) for an urban place of residence to 0.685

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(p<0.001) for deliveries in private hospitals. The value of Bivariate Moran’s I indicates the

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spatial dependence of C-section delivery on other dependent variables. The value of Bivariate

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Moran’s I ranges from 0.089 (p<0.001) for respondents among SC/ST categories to 0.503

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(p<0.001) for obese women.

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Table 5 inspects the regional determinants and influential factors affecting caesarean-section

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delivery among women aged 15-49 in 640 districts of India. After establishing the significant

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bivariate spatial association between the dependent and independent variables, spatial OLS

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model, the spatial lag model and the spatial error model was fitted. Spatial error model was

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the best fit model as AIC value was least and hence it was considered an appropriate model

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for the study. The value of Lambda is 0.790 (p<0.001) which is highly significant and depicts

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that there is positive spatial autocorrelation among the regions of India having a high

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prevalence of caesarean-section deliveries. Age at first birth 35 and above, delivery in private

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facility, SC/ST respondents, multiple births, obesity and urban place of residence was

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significantly spatially associated with caesarean-section among women in districts of India.

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Obesity was most strongly related to caesarean-section delivery among women in India i.e.

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10 percentage points increase in obesity level would be significantly associated with 6.45

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percent increase in caesarean-section delivery. Similarly, 10 percentage points increase

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among deliveries in private facility was significantly associated with 3 percent point increase 8

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in caesarean-section delivery. The value of the lag coefficient from the spatial lag model

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suggested that a change in the caesarean-section delivery in a particular district may

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statistically lag the rate of caesarean-section delivery by 51.4 percent in the neighbouring

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districts.

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Map 2 shows the bivariate LISA cluster maps indicating the spatial auto-correlation of C-

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section delivery with various explanatory variables. Map A indicates 53 hop-spot regions

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(Moran’s I=0.215, p<0.001) depicting high regional dependence of C-section deliveries on

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women having age at first birth 35 and above which includes regions of Himachal Pradesh

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and Jammu & Kashmir in Northern India and parts of Maharashtra, Kerala, Andhra Pradesh,

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Karnataka and Tamil Nadu in Southern India. Map B shows 86 hot-spot regions (Moran’s

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I=0.417, p<0.001) indicating high regional dependence of C-section deliveries on deliveries

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in private facilities which includes regions of Himachal Pradesh, West Bengal and a major

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part of southern India including Maharashtra. Map C shows 63 hop-spot regions (Moran’s

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I=0.089, p<0.001) depicting high regional dependence of C-section deliveries on women

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belonging to SC/ST category which includes parts of Himachal Pradesh, West-Bengal,

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Andhra Pradesh, Karnataka, Tamil Nadu and Kerala. Map D indicates 52 hop-spot regions

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(Moran’s I=0.123, p<0.001) depicting high regional dependence of C-section deliveries on

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women giving multiple births which includes parts of Jammu and Kashmir, Himachal

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Pradesh, West Bengal and major region of Southern India including Maharashtra. Map E

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shows 85 hot-spot regions (Moran’s I=0.503, p<0.001) depicting high regional dependence of

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C-section deliveries on women having high BMI (obesity) which includes Jammu and

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Kashmir, Himachal Pradesh, West-Bengal, Maharashtra Andhra Pradesh, Karnataka, Tamil

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Nadu and Kerala. Map F indicates 65 hot-spot regions (Moran’s I=0.503, p<0.001) depicting

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high regional dependence of Caesarean-section deliveries on women belonging to Urban

9

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areas which include Jammu and Kashmir, Himachal Pradesh, West-Bengal, Maharashtra

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Andhra Pradesh, Karnataka, Tamil Nadu and Kerala.

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Discussion

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In India, the population-based proportion of C-section greatly exceeds the threshold of 5–

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15% recommended by WHO (Betrán et al 2016). C-section has increased over time with a

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wide heterogeneity in the incidence, and our findings are in line with many studies (Khawaja

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et al 2004; Ba'aqeel 2009; Subedi 2012; & Al Rifai 2014). This increase in C-section may be

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attributed to the fact that once a caesarean, always a caesarean and study have reported that in

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hospitals most of the cases are repeat C-sections. As per our findings, C-section is relatively

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high in some parts of the country like 34.1 percent in Tamil Nadu, 35.8 percent in Kerala,

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40.1 percent in Andhra Pradesh, and 58 percent in Telangana respectively. Few states in the

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country have shown a C-section r0ate below 15 percent. Interestingly, our findings are

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consistent with other studies that most of the southern states of India have recorded high C-

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section delivery in the country. The main reason for this transition is increase in institutional

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delivery for the inclination of caesarean delivery in all southern states. In most of the states,

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C-section is higher in urban areas. Many factors influence the use of C-section in urban areas

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such as advanced health facilities with technologically and advanced obstetric services,

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higher levels of women’s choice to opt for private facilities, high rates of maternal healthcare

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utilisation and its competition for profit (Rahman et al 2014; Diamond-Smith and

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Sudhinaraset 2015; Nazir 2015; Radha et al 2015; Khanal et al 2016; & Singh et al 2018).

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Women living in rural area are generally uneducated, lack of awareness, belongs to low

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socio-economic status, doesn’t receive proper antenatal care or counselling for pregnancy.

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Moreover, hospitals located in urban areas often deal with pregnancy complications which

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include both urban as well as rural patients (Radha et al 2015).

10

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Mothers living in high SES, obesity, pregnancy resulting in multiple babies, high-risk birth

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weight, were found to be significantly associated with C-section. Previous evidence shows

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that older mothers are more likely to use healthcare services, to experience complications

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during pregnancy and delivery, and likely to have C-section delivery compared to younger

249

ones even in the absence of complications (Webster et al 1992; Peipert and Bracken 1993;

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Padmadas et al 2000; Bell et al 2001; Mishra and Ramanathan 2002; Adageba et al 2008;

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Ajeet et al 2008; Lumbiganon et al 2010; & Tran et al 2013). Researchers have found that

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higher socio-economic status is positively associated with C-section giving rise to the rich-

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poor gap, this finding goes in line with other studies (Cavallaro et al 2013; Kaur et al 2013; &

254

Patel et al 2014). Also women with higher education are more likely to undergo C-section as

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compared to uneducated women. (Unnikrishnan et al 2010; Divyamol

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Guilmoto and Dumont 2019). There is a remarkable difference in C-section deliveries rates

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in urban areas compared to their rural counterparts. These differences are often observed

258

across different community groups and our findings is similar with other studies (Rahman et

259

al 2014; Diamond-Smith and Sudhinaraset 2015; Nazir 2015; Radha et al 2015; Khanal et al

260

2016; & Singh et al 2018).

261

Studies have also proven, the rural-urban, public-private, tribal/non-tribal gap also exist in the

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community (Shabnam 2001; Neuman et al 2014; Desai et al 2017; Radhakrishnan et al 2017;

263

& Khan et al 2018). In order to understand why these inequalities, exist, spatial analysis will

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be the most suitable technique to trap the insights in the society. Women going for more

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ANC check-ups might be facing some complications during pregnancy, due to which it is

266

more likely to have C-section delivery; thus positive association has been found between

267

ANC visit and C-section, reiterated by various studies also earlier (Bayou et al 2016; Begum

268

et al 2017). Various government schemes such as Janani Shishu Suraksha Karyakram (JSSK),

269

Rashtriya Swasthya Bima Yojana (RSBY), Mother-Child tracking system under the National

et al 2016; &

11

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Rural Health Mission (NRHM), and National Ambulance services has increased awareness

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about the health facilities as well as the strengthening of primary health centres (PHCs). It has

272

helped to improve transport facilities and increase institutional deliveries all over India. Most

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of the south Indian states have already reported a high number of institutional deliveries with

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a positive correlation with C-section and has been well documented (Potter et al 2001). Due

275

to the increase in health care coverage, the diagnosis has been better with ease of referral,

276

increased the rate of C-section deliveries at tertiary-care hospitals also.

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Findings of other study revealed that C-section is often attributed to the moderately higher

278

costs which are consistent with our results (Kamal 2013). High out-of-pocket (OOP)

279

expenditure for unnecessary C-section induced by physicians could also lead to financial

280

strain for the underprivileged. Thus, physicians play a crucial role is such cases as they have

281

opportunity to decide whether it should be vaginal delivery to C-section. The main factors

282

that influence the use of C-section facility are possible financial exploitation, reflected from

283

the economic status of the women, i.e. wealth index, and higher-educated women are more

284

informed about the costs and benefit of the use of maternal healthcare services (Kamal 2009;

285

& Kamal 2013). Secondly, poorer household fails to pay for the surgery and the extra cost

286

associated with C-section (Arsenault et al 2013). Additionally, rich are likely to be aware of

287

their existing illnesses, and this knowledge may prompt them and doctors to consider CS.

288

Sparse distribution of the necessary health facilities is another important reason (Montagu et

289

al 2011). In other countries, this phenomenon is called as ‘reverse equity’ where women with

290

higher socioeconomic status (wealth index), presumably with less medical risks, have higher

291

caesarean rates (Sakae et al 2009; & Kamal 2013).

292

Conclusion

293

Findings demonstrated that C-section rates have increased significantly in almost all the

294

states of India (Table 1) in recent years and has reached an alarming level. Shifting of birth 12

295

deliveries from the public to the private sector appears to be associated with an increase in the

296

number of deliveries that occurred (Table 2). It indicates the need for growth of the health

297

care delivery system and to encourage institutional deliveries in public sector in India. C-

298

section have shown variations among the population across geographical locations of

299

different socioeconomic status. Women in the south region reported a higher C-section than

300

women in other areas. Our study found significant interactive associations between C-section

301

with age, religion, place of residence, tribes, BMI and location of delivery (shown in

302

regression Table 3). Other variables like the educational status of women, wealth, ANC, and

303

OOPE were also significant in the regression table (Table 3) but were controlled while

304

performing spatial analysis.

305

Recommendations

306

Although schemes like Janani Suraksha Yojana (JSY) and RSBY have a significant impact

307

on poor women accepting institutional deliveries and improving financial risk protection

308

respectively, these schemes seem to be biased towards prioritizing surgical procedures over

309

normal deliveries, indicating a moneymaking process for the private healthcare providers

310

(Nandi et al 2016; Selvaraj and Karan 2012). Considering the current rising trend of

311

institutional deliveries as a reason for the increase uptake of C-section in India, measures

312

protecting the beneficiaries need to be put in place. Improvement through financial support

313

(provision of maternity benefits) should be given to those below poverty line (BPL) to reduce

314

out of pocket (OOP) expenditure (Mohanty and Srivastava 2012). Delivering high-quality

315

timely care and counselling measures throughout the gestation period as a measure to

316

minimise C-sections is the responsibility of every midwife healthcare provider, is an effective

317

way as suggested by professionals. Improving the quality of care component in public health

318

institutions will play a significant role in drawing women’s attention to seek maternal health

319

care. C-section without medical indication should be discouraged amongst the beneficiaries

13

320

and providers. There is an urgent need to monitor the deliveries in clinics and hospitals to

321

find the right balance between the demand and provisioning of adequate and high quality care

322

services. The government should inform practitioners and women of the unnecessary risks of

323

non-medically justified C-sections. India has to face a “double burden” of providing C-

324

sections to population that still have no access to it (because of poverty, remoteness or lack of

325

facilities, etc.) and at the same time curbing an overuse which is the key challenge for the

326

Indian healthcare system.

327

Additional research is needed to examine the attitude of service providers in future. C-section

328

deliveries have other serious implications on breastfeeding initiation, duration and difficulties

329

in the first four months of postpartum and post-natal care need to be studied in future. It is

330

also recommended to explore future trends in the magnitude of spatial inequalities in rural-

331

urban tribal/non-tribal and public-private sector institutions. Rigorous in-depth investigations

332

with routine monitoring and evaluation of emergency obstetric services should also be carried

333

out to address the overuse of C-section and its effect on maternal and child health (MCH)

334

outcomes.

335

Strengths and Limitations

336

The major strength of this study is that it is based on one of the largest-scale surveys

337

conducted in India covering the geographical regions at the district level. On the other hand,

338

some limitations of the study are also noted. The data being cross-sectional in nature didn’t

339

allow making causal inferences about the association between ANC and delivery care and the

340

risk factors. Various demographic, socioeconomic and cultural factors and costs of delivery

341

services have been included in this analysis, however women’s role in the decision-making

342

process also likely to influence the delivery practices of women. The survey didn’t cover

343

evidence about accessibility (i.e., distance to a health facility) and the quality of health- care

344

facility which might have influenced the use of health facility delivery. Nevertheless, we 14

345

believe that the conclusions of our study can still be relevant and beneficial for programme

346

planners and policy- makers not merely to encourage health facility-based deliveries but also

347

to address the high rates of C-sections among married women in India.

348

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India digital map The district level shape file of India was acquired from GitHub at https://github.com/datameet/maps/tree/master/Districts. The digital map has been used under the Creative Commons Attributions 2.5 India license. The shape file was created using the administrative atlas of Census 2011, India. And the map was projected in WGS 1984 UTM zone 43N. Table 4 Univariate and Bivariate Moran’s I for the dependent and independent variables Variables C-section (%) Women aged 35 and above (%) Age first birth 35 and above (%) Muslim (%) Delivery in Private facility (%) SC/ST (%) Multiple birth (%) BMI (obesity) (%) No colostrum feeding (%) Urban place of residence (%) Terminated the pregnancy (%)

Univariate Moran’s I 0.693* 0.676* 0.518* 0.680* 0.685* 0.651* 0.015 0.504* 0.675* 0.440* 0.482*

Bivariate Moran’s I N.A -0.016* 0.215* 0.156* 0.417* 0.089* 0.123* 0.503* -0.208* 0.292* 0.002

Interpretation: Values of I range from −1 (indicating perfect dispersion) to +1 (perfect correlation). A zero value indicates a random spatial pattern. *P≤0.05 N.A- Not Applicable Source: Authors’ calculations.

Table 5 Spatial Regression models for C-section among women in India, NFHS-4, 2015-16 Variables Age at first birth (35+) (%) Muslim (%) Delivery in Private facility (%) SC/ST (%) Multiple birth (%) BMI (obesity) (%) Urban place of residence (%) R square adj. R square AIC Rho Lambda Regions *P≤0.05 Source: Author’s calculations

Spatial OLS 0.360* (0.114) 0.088* (0.016) 0.231* (0.021) 0.048 (0.028) -0.122 (0.265) 1.148* (0.151) -0.009 (0.016) 0.706 0.701 4337

640

Spatial lag 0.394* (0.092) 0.050* (0.013) 0.167* (0.018) 0.027 (0.022) -0.348 (0.214) 0.666* (0.125) 0.016 (0.013) 0.805

Spatial error 0.241* (0.096) 0.018 (0.018) 0.304* (0.022) 0.071* (0.027) -0.458* (0.169) 0.645* (0.107) 0.051* (0.013) 0.862

4115 0.514

3963

640

0.790* 640

Appendix: Table 1 Percentage distribution of C-section delivery in India and states, NFHS-4, 2015-16 States/UTs India Andhra Pradesh Arunachal Pradesh Assam Bihar Chandigarh Chhattisgarh NCT Delhi Daman and Diu Dadra and Nagar Haveli Goa Gujarat Haryana Himachal Pradesh Jammu and Kashmir Jharkhand Karnataka Kerala Madhya Pradesh Maharashtra Manipur Meghalaya Mizoram Nagaland Odisha Punjab Puducherry Rajasthan Sikkim Tamil Nadu Telangana Tripura Uttar Pradesh Uttarakhand West Bengal

Total 2005-06 2015-16 8.5 27.5 2.9 5.3 3.1 na 4.1 13.7 na na 25.7 8.9 5.3 12.6 13.5 3.9 15.5 30.1 3.5 11.6 9.0 4.1 6.2 2.0 5.1 16.5 na 3.8 12.3 20.3 na 12.9 4.4 8.1 10.2

17.2 40.1 8.9 13.4 6.2 22.6 9.9 23.7 15.8 16.2 31.4 18.4 11.7 16.7 33.1 9.9 23.6 25.8 8.6 20.1 21.1 7.6 12.7 5.8 13.8 24.6 33.6 8.6 20.9 34.1 58.0 20.5 9.4 13.1 23.8

2015-16 Urban 28.3 48.4 20.1 36.9 13.9 na 18.9 23.7 14.9 26.7 33.5 27.8 13.6 29.6 53.1 22.4 29.2 37.1 19.1 26.3 33.0 20.5 19.0 12.4 24.1 25.8 30.9 16.4 28.8 36.1 63.2 45.8 18.9 19.4 36.6

Rural 12.9 37.1 5.8 10.8 5.4 na 7.5 25.9 17.7 8.7 27.7 12.0 10.6 15.6 26.9 7.0 19.9 34.6 5.1 15.2 15.2 5.6 5.7 3.4 12.1 23.7 39.8 6.5 17.1 32.3 53.4 12.2 6.9 10.2 18.9

Note: 1) na: data not available. 2) Telangana is a newly formed state, which was the part of Andhra Pradesh and hence CS rate of Andhra Pradesh up to 2005-06. UTs: Union Territories

Table 2 Percentage of women undergoing C-section by Type of Health Facility by States, India, NFHS-4, 2015–16

State/UTs India Andhra Pradesh Arunachal Pradesh Assam Bihar Chandigarh NCT Delhi Goa Gujarat Haryana Himachal Pradesh Jammu and Kashmir Jharkhand Karnataka Kerala

Private Health Facility 40.9 57.0 37.5 53.3 31.0 46.6 42.9 51.3 26.6 25.3 44.4 75.5 39.5 40.3 38.6

Public Health Facility 11.9 25.5 12.5 12.9 2.6 5.7 21.0 19.9 10.8 8.6 16.4 35.1 4.6 16.9 31.4

State/UTs Madhya Pradesh Maharashtra Manipur Meghalaya Mizoram Nagaland Odisha Punjab Rajasthan Tamil Nadu Telangana Tripura Uttar Pradesh Uttarakhand West Bengal

Private Health Facility 40.8 33.1 46.2 31.4 30.0 31.4 53.7 39.7 23.2 51.3 74.9 73.7 31.3 36.4 70.9

Public Health Facility 5.8 13.1 22.6 9.8 12.3 13.5 11.5 17.8 6.1 26.3 40.6 18.1 4.7 9.3 18.8

UTs: Union Territories

Table 3 Odds ratio of C-section delivery by selected background characteristics, India, NFHS-4, 2015-16 Background Variables Age of mother 15-24 25-29 30-49 Place of Residence Urban Rural Education No education Primary Secondary Higher Caste SC/ST non SC/ST Religion Hindu Muslim Others Wealth Poorest Poorer Middle Richer Richest

O.R

95 % C.I

1 1.156*** 1.378***

[1,1] [1.118,1.196] [1.310,1.448]

1 0.912***

[1,1] [0.884,0.939]

1 1.128*** 1.333*** 1.323***

[1,1] [1.070,1.190] [1.278,1.390] [1.253,1.396]

1 0.902*** 1 1.303*** 0.894*** 1 1.278*** 1.697*** 1.736*** 1.562***

Background Variables CEB 1 2 3 More than 3 BMI Underweight Normal Overweight Obese

O.R

95 % C.I

1 [1,1] 1.057 [0.948,1.179] 0.684*** [0.565,0.827] 0.345*** [0.256,0.466] 1 [1,1] 1.271*** [1.225,1.318] 2.160*** [2.064,2.261] 3.318*** [3.104,3.547]

Ever had a terminated pregnancy [1,1] No 1 [1,1] [0.871,0.934] Yes 1.187*** [1.147,1.228] Size of child at birth [1,1] Large 1 [1,1] [1.256,1.352] Average 0.818*** [0.792,0.845] [0.856,0.934] Small 0.834*** [0.795,0.874] Multiple birth [1,1] Single birth 1 [1,1] [1.211,1.348] Multiple birth 3.186*** [2.921,3.475] [1.610,1.789] Place of Delivery [1.643,1.834] Public 1 [1,1] [1.471,1.659] Private 3.344*** [3.252,3.438]

Age of respondent at 1st birth 15-24 1 25-29 1.345*** 30-49 2.011*** Birth Order 1 1 2 0.740*** 3 0.669*** More than 3 0.731*

[1,1] [1.291,1.401] [1.872,2.160] [1,1] [0.665,0.823] [0.551,0.811] [0.545,0.981]

Others ANC Visit No ANC 1-3 Full ANC OOPE No expense Up to 20000 More than 20000

1

[1,1]

1 [1,1] 1.185*** [1.115,1.259] 1.842*** [1.740,1.951] 1 [1,1] 0.877*** [0.820,0.938] 1.550*** [1.465,1.640]

Note: SC/ST- Scheduled Caste/ Scheduled Tribes, CEB- Children ever born, BMI- Body Mass Index, PNC- Postnatal Care, ANC- Antenatal Care, OOPE- Out of pocket expenditure *if p<0.01 **if p<0.05 and ***if p<0.1 O.R: Odds Ratio C.I: Confidence Interval

Map 1 Prevalence of C-section deliveries in districts of India, NFHS -4, 2015-16

Map 2 Bivariate LISA maps showing the spatial clustering and outliers of different independent variables across the districts of India, 2015–16 (A) (B) (C) (D) (E) (F)

Age at first birth deliveries in private facilities SC/ST category multiple births BMI (obesity) Urban Place of Residence