Trends in caesarean delivery by country and wealth quintile: cross-sectional surveys in southern Asia and sub-Saharan Africa

Int J Reprod Contracept Obstet Gynecol

Jose

2017

Background: With Caesarean sections on the rise WHO proposes that health care facilities use the Robson's 10 group classification system to audit their C-sections rates. This classification would help understand the internal structure of the CS rates at individual health facilities identify key population groups, indications in each group and formulate strategies to reduce these rates.Methods: This was a cross sectional study for a period of 24 months at a tertiary care hospital in a tribal area of Kerala South India. Women who delivered during this period were included and classified into 10 Robson's classes and percentages were calculated for the overall rate, the representation of groups, contribution of groups and Caesarean percentage in each group.Results: Highest contribution was by Group 5 and Group 2. Together these two groups contributed to 38% of the total Caesareans. Followed by Group 8 and 10. All four added contributed to 63% of the section rate The least contribution was by Group 3. Groups 6, 7 and 9 by themselves did not contribute much but within their groups had a 100% C-Section rate.Conclusions: The contribution of the various Robson's Group to the absolute C-Section rates needs to be looked into. Reducing primary section rates, adequate counselling and encouraging for VBAC, changing the norms for dystocia and non-reassuring fetal status, training and encouraging obstetricians to perform versions when not contraindicated could reduce the contribution of Robson's groups towards the absolute C-Section rates.

Section: Introductionmentioning

confidence: 99%

Analysis of Caesarean-Section rates according to Robson's ten group classification system and evaluating the indications within the groups

Int J Reprod Contracept Obstet Gynecol

Jose

2017

“…14 Of particular interest are articles in which the outcomes were based on attitudes related to the perceived benefits or harms associated with certain interventions, even when these were not evidence-based. For example, the authors of articles on cesarean deliveries without medical indication 15,16 and on refusal to vaccinate children in the United Kingdom 17,18 quoted the inverse equity hypothesis to explain why these behaviors were initially adopted by the better-off within a population.…”

mentioning

confidence: 99%

The Inverse Equity Hypothesis: Analyses of Institutional Deliveries in 286 National Surveys

et al. 2018

Objectives. To test the inverse equity hypothesis, which postulates that new health interventions are initially adopted by the wealthy and thus increase inequalities-as population coverage increases, only the poorest will lag behind all other groups.Methods. We analyzed the proportion of births occurring in a health facility by wealth quintile in 286 surveys from 89 low-and middle-income countries and developed an inequality pattern index. Positive values indicate that inequality is driven by early adoption by the wealthy (top inequality), whereas negative values signal bottom inequality.Results. Absolute inequalities were widest when national coverage was around 50%.At low national coverage levels, top inequality was evident with coverage in the wealthiest quintile taking off rapidly; at 60% or higher national coverage, bottom inequality became the predominant pattern, with the poorest quintile lagging behind.Conclusions. Policies need to be tailored to inequality patterns. When top inequalities are present, barriers that limit uptake by most of the population must be identified and addressed. When bottom inequalities exist, interventions must be targeted at specific subgroups that are left behind. (Am J

“…Studies done in other countries spanning different time periods have shown the same trend in the both the developed and the developing world. 15,18 By way of contribution to the C-Section rate Group 5 followed by Group 3 had the highest contribution. Other such studies in the developed and developing world have found Group 5 to be the highest contributor followed by Group 1.…”

Section: Discussionmentioning

confidence: 99%

“…This though higher than the WHO recommended rate is quite in accordance with those found at tertiary care centres in both developed and developing countries. [15][16][17][18] This is probably because it is the biggest referral centre in the district and this rate reflects the hospital C-Section rate rather than the population C-Section rate. To be truly reflective of the population C-Section rate the live births in other hospitals which refer operative cases to our centre due to lack of facilities would have to be taken into account.…”

Section: Discussionmentioning

confidence: 99%

Comparing WHO C-Model generated C-Section probabilities to actual delivery outcomes in a tertiary care centre

Gopi

Int J Reprod Contracept Obstet Gynecol

2017

Background: A model which takes into account several relevant factors and gives the probability of C-Section in a woman would have the advantage of preparing for such an event. The identification of women at high risk of C-Section (>50% risk) would provide the opportunity for understanding risks involved in pursuing a vaginal delivery whereas if the risk of C-Section was less (<50%) it would prove useful in counselling for a vaginal delivery. We used the WHO C-Model with the aim to find the predictability of this model in our facility and the overuse of C-section in the 10 Robson’s Groups.Methods: A retrospective observational study in which all women who gave birth at our hospital from June 2016 to May 2017 were included and C-Section probability was calculated using the C-Model. Comparison with the actual mode of delivery was done to find the sensitivity, specificity, positive and negative predictive value of the model and the overuse of C-Section in the Robson’s Groups.Results: Out of the 314 C-Sections done only 45 women had a >50% probability, giving the model a sensitivity of 14.33%, specificity of 98.8%, positive predictive value of 90% and negative predictive value of 60.56%. Robson’s Groups 5 and 3 demonstrated the greatest overuse of C-Sections.Conclusions: The WHO Model when applied to this centre showed a high positive predictive value for C-Sections but the negative predictive value or the ability to correctly predict a vaginal delivery was much less.