Demand and supply factors affecting the rising overmedicalization of birth in India

Leone, Tiziana

doi:10.1016/j.ijgo.2014.05.018

Cited by 18 publications

(20 citation statements)

References 16 publications

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“…Some studies indicated that the caesarean section rate is higher in the private sector as is in our study. 4,6,7,9,16 The vaginal birth after caesarean rate in our study was 16.8% similar to those in the private sector of the same region 7 in contrast to other studies, which had success rates between 60 and 78%. 10,17,18 This is due to a very high rate of repeat caesarean section on maternal request (33.8% -36.07%) in both the studies.…”

Section: Fig 1: Flow Of Study Participantssupporting

confidence: 60%

“…Rates of caesarean section for maternal request appear to correspond with age, higher socioeconomic status and educational attainment. 16,19,20 This group is underreported in most studies and not represented adequately in any of the Robson or modified Robson groups. 14,21 Systematic reviews of randomised control trials, which provided various forms of decision support interventions including prenatal information, support and cognitive therapy failed to show any significant benefit in decision making about mode of delivery in women with a previous caesarean section.…”

Section: Fig 1: Flow Of Study Participantsmentioning

confidence: 99%

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Usefulness of a Simple Prediction Model for Vaginal Birth After Caesarean Section in a Low Resource Setting

Tahmina¹,

Kuruvila²,

Daniel³

2016

jebmh

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BACKGROUNDThe rising caesarean section rates can be curtailed by increasing the rates of Vaginal Birth After Caesarean (VBAC). Ability to predict vaginal birth after caesarean with certainty may enable better counselling and decision making regarding mode of delivery for pregnant women with a previously scarred uterus. AIMTo assess the accuracy of a simple validated vaginal birth after caesarean score in predicting the mode of delivery in pregnant women with previous one caesarean section. MATERIALS AND METHODSData for all patients who delivered at this institution between November 2011 and October 2013 were reviewed. Data of women who delivered after one caesarean section were analysed and details of the index pregnancy, labour and mode of delivery were noted. A vaginal birth after caesarean prediction model was applied to all women who underwent a Trial of Labour After Caesarean (TOLAC) using data at admission. Women who delivered after two or more caesarean sections were excluded from the study. The primary outcome assessed was the mode of delivery. SETTINGS AND DESIGNRetrospective Cohort study at a 650-bedded tertiary care hospital in South India. STATISTICAL ANALYSISFor categorical variables, data was compiled as frequency and percent. For continuous variables, data was calculated as mean±SD. Performance of the vaginal birth after caesarean score was assessed by receiver operating characteristic curve analysis. RESULTSSix hundred and eighty-seven women were delivered by caesarean section. Among them, 280 women who had a previous caesarean section were included in the study. Vaginal birth after caesarean scores were computed for 82 women who underwent a trial of labour after caesarean. 57.3% had a successful vaginal birth after caesarean and the remaining had a repeat caesarean section after a failed trial of labour. The score performed fairly well with an area under receiver operating characteristic curve of 0.75. CONCLUSIONThe vaginal birth after caesarean score maybe useful tool in counselling eligible women in favour of a trial of labour after caesarean thereby reducing the number of elective repeat caesarean sections. KEYWORDSVaginal Birth After Caesarean, Prediction Score, Trial of Labour, Repeat Caesarean. HOW TO CITE THIS ARTICLE: Tahmina S, Kuruvila SK, Daniel M. Usefulness of a simple prediction model for vaginal birth after caesarean section in a low resource setting.

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Section: Fig 1: Flow Of Study Participantssupporting

confidence: 60%

Section: Fig 1: Flow Of Study Participantsmentioning

confidence: 99%

Usefulness of a Simple Prediction Model for Vaginal Birth After Caesarean Section in a Low Resource Setting

Tahmina¹,

Kuruvila²,

Daniel³

2016

jebmh

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“…The present study found a significant relationship between induced demand and the related factors of knowledge and awareness of service recipients, sensitivity of clients toward their own health, monitoring and planning of insurance companies, personal interests of service providers, industrialization of the healthcare sector, increases in the variety and number of specialists, knowledge and awareness of service providers, quality of education models and training methods for service providers. Leone conducted a study in three provinces of India in 2014, which indicated the role of several factors in induced supply and demand 30 . The present study is also in line with a study by Keyvanara et al, which demonstrated factors such as the role of supplemental health insurance, lack of strict supervision on insurance companies, increased profit of equipment companies, diagnostic centers, pharmaceutical companies, the over-trusting of physicians, incorrect demands of patient from the physicians, lack of awareness, patients' free access to physicians, patients' willingness to make greater use of free services and franchising affect the incidence of induced demand 4 .…”

Section: Discussionmentioning

confidence: 99%

Factors associated with induced demand for services in Iran’s healthcare system

et al. 2018

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Background: One of the most important subjects in health economics and healthcare management is the theory of induced demand; that is, caring for or providing and selling unnecessary services to users of healthcare systems, which is accompanied by the exercising of power by the service providers. Methods: This study was performed on physicians, nurses, and laboratory and radiology technicians working in Medical Science universities. Random sampling was conducted from five areas: the center, north, west, east and south of Iran. Data were gathered by a questionnaire, with a Cronbach's alpha of >0.7, consisting of nine dimensions on existence of induced demand and its associated factors. Results: The results showed that overall, 65.2% of the participants agreed with the existence of induced demand. Chi-squared test showed there was no difference in the level of induced demand between the regions of the country, education level and occupation. However, there was a significant difference in terms of gender (P<0.005). The Kruskal-Wallis test indicated a significant relationship between the associated factors and induced demand (P<0.005). Conclusions: Results showed that induced demand was influenced by factors including service recipients’ awareness, personal benefits of service providers, the extent they cared about health, supervision of insurance companies, industrialization of the health sector, diversity and increased number of trained experts and the quality of methods of training the service providers. Therefore, policymakers and planners should consider raising awareness of health service recipients, supervising insurance companies, reforming teaching methods, social culture making and changing the beliefs of society.

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“…Another research described that many factors are involved in C-Section [14]. Major factors for C-Section delivery showed the relationship between section birth, wealth, education, age, ultrasonography, pregnancy completions.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comparative Study of Supervised Machine Learning Techniques for Diagnosing Mode of Delivery in Medical Sciences

Hussain¹,

Riaz²,

Fatima³

et al. 2019

IJACSA

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The uses of machine learning techniques in medical diagnosis are very helpful tools now-a-days. By using machine learning algorithms and techniques, many complex medical problems can be solved easily and quickly. Without these techniques, it was a difficult task to find the causes of a problem or to suggest most appropriate solution for the problem with high accuracy. The machine learning techniques are used in almost every field of medical sciences such as heart diseases, diabetes, cancer prediction, blood transfusion, gender prediction and many more. Both supervised and unsupervised machine learning techniques are applied in the field of medical and health sciences to find the best solution for any medical illness. In this paper, the implementation of supervised machine learning techniques is performed for classifying the data of the pregnant women on the basis of mode of delivery either it will be a C-Section or a normal delivery. This analysis allows classifying the subjects into caesarean and normal delivery cases, hence providing the insight to physician to take precautionary measures to ensure the health of an expecting mother and an expected child.

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Demand and supply factors affecting the rising overmedicalization of birth in India

Cited by 18 publications

References 16 publications

Usefulness of a Simple Prediction Model for Vaginal Birth After Caesarean Section in a Low Resource Setting

Usefulness of a Simple Prediction Model for Vaginal Birth After Caesarean Section in a Low Resource Setting

Factors associated with induced demand for services in Iran’s healthcare system

A Comparative Study of Supervised Machine Learning Techniques for Diagnosing Mode of Delivery in Medical Sciences

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