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

Srivastava, Shobhit; Chaurasia, Himanshu; Singh, Kh Jiten Kumar; Chaudhary, P. Chaudhary S. K.

doi:10.1016/j.cegh.2019.09.012

Cited by 21 publications

(27 citation statements)

References 59 publications

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“…27 The main reason for this transition is increase in institutional delivery increases the caesarean delivery rates in all southern states. This finding corroborates the findings of Srivastava S et al 28…”

Section: Discussionsupporting

confidence: 93%

Regional Disparities and Determinants of Caesarean Deliveries in India

Kathuria

Tp²

2021

IJoYAH

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Background: The caesarean rate in India has been increased from 8.5% (NFHS-III) during 2005-06 to 17.2% (NFHS-IV) during 2015-16. Objective: The main objective of the study is to assess the regional disparities and determinants of caesarean deliveries in India. Material and Method: It is a descriptive study. The fourth round of National Family Health Survey, India (NFHS-4), conducted in 2015-16 has been analysed for the purpose of this study. Result: The study found that the C-section deliveries are significantly higher in southern region followed by western region in India. Social factors such as higher education of mother, residing in urban area, belong to richest wealth group, mothers belong to non-SC/ST category are more likely to opt for C-section delivery. Evidence also indicates that risk factors like high age of mother, high birth order, complicated pregnancy, multiple births, size of child at birth, weight of child at birth significantly influences the decision of C-section deliveries. Institutional and Individual Behavioural Factors such as number of ANC visit, place of delivery, access to mass media are also significant determinants of opting C-section delivery. Delivering high quality timely care and counselling measures throughout the gestation period as a measure to minimise C-section deliveries is the responsibility of every midwife healthcare provider, is an effective way. There is an urgent need to monitor the deliveries in clinics and hospitals to find out the right balance between demand and provisioning of high quality health care services.

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Section: Discussionsupporting

confidence: 93%

Regional Disparities and Determinants of Caesarean Deliveries in India

Kathuria

Tp²

2021

IJoYAH

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“…Negative (positive) values indicate a negative (positive) spatial autocorrelation. Positive autocorrelation indicates that points with similar attribute values are closely distributed in space, whereas negative spatial autocorrelation indicates that closely associated points are more dissimilar [45,48,50,51].…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, it also varies across geographic regions and states in India [6,20,22]. Spatial disparity matters in the MCH services coverage and its utilization [23,[42][43][44][45]. Studies show that there is a strong correlation between the proximate determinants of spatial clustering and service coverage [43][44][45].…”

Section: Introductionmentioning

confidence: 99%

Regional inequality in the Janani Suraksha Yojana coverage in India: a geo-spatial analysis

Mishra

Kumar

Srivastava

2021

Int J Equity Health

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Introduction Although India has made significant progress in institutional delivery after the implementation of the National Rural Health Mission under which the Janani Suraksha Yojana (JSY) is a sub-programme which played a vital role in the increase of institutional delivery in public facilities. Therefore, this paper aims to provide an understanding of the JSY coverage at the district level in India. Further, it tries to carve out the factors responsible for the regional disparity of JSY coverage at district levels. Methods The study used the National Family Health Survey data, which is a cross-sectional survey conducted in 2015–16, India. The sample size of this study was 148,145 women aged 15–49 years who gave last birth in the institution during 5 years preceding the survey. Bivariate and multivariate regression analysis was used to fulfill the study objectives. Additionally, Moran’s I statistics and bivariate Local Indicator for Spatial Association (LISA) maps were used to understand spatial dependence and clustering of JSY coverage. Ordinary least square, spatial lag and spatial error models were used to examine the correlates of JSY utilization. Results The value of spatial-autocorrelation for JSY was 0.71 which depicts the high dependence of the JSY coverage over districts of India. The overall coverage of JSY in India is 36.4% and it highly varied across different regions, districts, and even socioeconomic groups. The spatial error model depicts that if in a district the women with no schooling status increase by 10% then the benefits of JSY get increased by 2.3%. Similarly, if in a district the women from poor wealth quintile, it increases by 10% the benefits of JSY also increased by 4.6%. However, the coverage of JSY made greater imperative to understand it due to its clustering among districts of specific states only. Conclusion It is well reflected in the EAGs states in terms of spatial-inequality in service coverage. There is a need to universalize the JSY programme at a very individual level. And, it is required to revisit the policy strategy and the implementation plans at regional or district levels.

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“…Moreover, it also varies across geographic regions and states in India [6,20,24]. Spatial disparity matters in the MCH services coverage and its utilization [23,[39][40][41]45]. Studies show that there is a strong correlation between the proximate determinants of spatial clustering and service coverage [40,41,45].…”

Section: Introductionmentioning

confidence: 99%

“…Where x and y are the variables of interest; ̅ is the mean of x; ̅ is the mean of y; n is the number of spatial units;is the standardized weight matrix between observation i and j with zeroes on the diagonal; and is the aggregate of all spatial weights, i.e. Positive autocorrelation indicates that points with similar attribute values are closely distributed in space, whereas negative spatial autocorrelation indicates that closely associated points are more dissimilar[44][45][46][47].Univariate LISA calculates the spatial-correlation of neighborhood values around the specific spatial location[47]. It determines the extent of spatial randomness and clustering present in the data.…”

mentioning

confidence: 99%

Regional inequality in the Janani Suraksha Yojana coverage in India: A Geo-spatial Analysis

Mishra

Kumar

Srivastava

2020

Preprint

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Introduction Although India has made significant progress in institutional delivery after the implementation of the National Rural Health Mission under which the Janani Suraksha Yojana (JSY) is a sub-programme which played a vital role in the increase of institutional delivery in public facilities. Therefore, this paper aims to provide an understanding of the JSY coverage at the district level in India. Further, it tries to carve out the factors responsible for the regional disparity of JSY coverage at district levels. Methods The study used the National Family Health Survey data, which is a cross-sectional survey conducted in 2015-16, India. The sample size of this study was148,145 women aged 15-49 years who gave last birth in the institution during five years preceding the survey. Bivariate and multivariate regression analysis was used to fulfill the study objectives. Additionally, Moran’s I statistics and bivariate Local Indicator for Spatial Association (LISA) maps were used to understand spatial dependence and clustering of JSY coverage. Ordinary least square, spatial lag and spatial error models were used to examine the correlates of JSY utilization.Results The value of spatial-autocorrelation for JSY was 0.71 which depicts the high dependence of the JSY coverage over districts of India. The overall coverage of JSY in India is 36.4% and it highly varied across different regions, districts, and even socioeconomic groups. The spatial error model depicts that if in a district the women with no schooling status increase by 10% then the benefits of JSY get increased by 2.3%. Similarly, if in a district the women from poor wealth quintile, it increases by 10% the benefits of JSY also increased by 4.6%. However, the coverage of JSY made greater imperative to understand it due to its clustering among districts of specific states only.Conclusion It is well reflected in the EAGs states in terms of spatial-inequality in service coverage. There is a need to universalize the JSY programme at a very individual level. And, it is required to revisit the policy strategy and the implementation plans at regional or district levels.

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

Cited by 21 publications

References 59 publications

Regional Disparities and Determinants of Caesarean Deliveries in India

Regional Disparities and Determinants of Caesarean Deliveries in India

Regional inequality in the Janani Suraksha Yojana coverage in India: a geo-spatial analysis

Regional inequality in the Janani Suraksha Yojana coverage in India: A Geo-spatial Analysis

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