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One in four Indian births is now by caesarean section: a state-level analysis of public–private sector differences and ecological drivers using NFHS-6 (2023– 24)

This ecological analysis of NFHS-6 (2023–24) data reveals that women's educational attainment is the dominant predictor of India's rising caesarean section rates, which have reached 27.2% nationally with a stark 3.2-fold disparity between private and public sectors, prompting calls for targeted clinical audits and pre-natal education to curb demand-side medicalisation.

Original authors: Mahendra G., Ravindra S. Pukale, Kaveri Potaraj Potarad, Suresh Bangla

Published 2026-07-08

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Mahendra G., Ravindra S. Pukale, Kaveri Potaraj Potarad, Suresh Bangla

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

The Big Picture: A Rising Tide of C-Sections

Imagine India's childbirth landscape as a massive ocean. A few years ago, the water level (the rate of C-sections) was already high, sitting at about 21.5%. Now, based on the newest data (NFHS-6 from 2023–24), the water has risen significantly to 27.2%.

To put this in perspective, the World Health Organization (WHO) suggests that the "perfect" water level for safety is between 10% and 15%. India is now swimming at a level 82% higher than that recommended ceiling. The study finds that one in every four babies in India is now being born via C-section.

The Great Divide: The "Private vs. Public" Gap

The researchers didn't just look at the total water level; they looked at where the water is rising. They discovered a massive gap between government hospitals (public) and private clinics (private).

The Analogy: Imagine two grocery stores in the same town. Store A (Government) sells apples (C-sections) to 17 out of every 100 customers. Store B (Private) sells them to 54 out of every 100 customers.
The "Market-Driven Care Index" (MDCI): The authors invented a new ruler called the MDCI to measure this gap. It's like a "price tag" on how much more likely you are to get a C-section in a private hospital compared to a government one.
- In Kerala, the gap is small (the ruler reads 1.08). Both stores are selling apples at similar rates.
- In Bihar, the gap is enormous (the ruler reads 18.26). This means a woman is 18 times more likely to get a C-section in a private hospital than in a government one. In Bihar, the government hospitals are almost never doing C-sections (only 2.7%), while private ones are doing them frequently (49.3%).

The Three Types of "Water Levels"

The study groups Indian states into three distinct patterns, like different types of weather systems:

The "Private Monopoly" (e.g., Bihar, Jharkhand): Here, C-sections are a luxury item found mostly in private shops. The government shops barely stock them. The overall rate is low, but the gap between rich (private) and poor (public) access is huge.
The "System-Wide Flood" (e.g., Telangana, Andhra Pradesh): Here, both government and private hospitals are doing C-sections at very high rates. The gap between them is small, but the water level is dangerously high everywhere.
The "Equity in Excess" (e.g., Kerala): Both sectors are doing C-sections at high rates, and the rates are almost identical. Everyone is getting the procedure at the same high frequency, regardless of whether they go to a government or private hospital.

The Real Drivers: What's Pushing the Water Up?

The researchers asked: "What causes these rates to go up?" They tested several theories, like health insurance or how many people use government hospitals.

The Surprising Answer: The strongest predictor wasn't money or insurance. It was women's education.

The Analogy: Think of education as a "magnet." The more educated the women in a state are, the stronger the magnet pulls toward C-sections.
Why? The study suggests this is "demand-side medicalization." Educated women may be more receptive to medical advice, more aware of options, or more likely to choose the perceived "safety" of a C-section.
The Counter-Intuitive Finding: Having more births in government hospitals did not lower the C-section rate. In fact, in states like Telangana, government hospitals are doing C-sections at very high rates too. So, simply telling women to go to government hospitals doesn't fix the problem if the doctors there are also performing too many C-sections.

Another Factor: The Total Fertility Rate (TFR) (how many children a woman has on average) was linked to lower C-section rates.

The Analogy: In states where women have many children (high TFR), the "old ways" of natural birth are still very common, keeping the C-section rate lower. In states where families are smaller (low TFR), the shift toward medical interventions (C-sections) is stronger.

What Should Be Done? (The Paper's Recommendations)

The authors don't just point out the problem; they suggest four specific "life rafts" to stop the water from rising further:

The "Rulebook" Check: Private hospitals that take government insurance (PMJAY) must be forced to follow a strict "Rulebook" (called the Robson classification) to prove they have a good medical reason for every C-section.
Payment with Strings Attached: The government should only pay for a C-section if the hospital provides a clear record of why it was medically necessary.
Quality Control: Expand the "LaQshya" program (a government initiative to improve labor rooms) but add a specific goal: reduce the number of C-sections, not just improve the building.
Talk Before the Birth: In states with high education and high C-section rates, doctors need to have honest conversations with pregnant women before they give birth, explaining that C-sections carry risks and aren't always the safest choice.

The Bottom Line

This study is like a map showing that India's C-section rates are rising fast, driven largely by educated women choosing (or being offered) medical births, and by a massive gap where private hospitals do the procedure far more often than government ones. The solution isn't just building more government hospitals, but changing the culture of why and when these surgeries are performed.

Technical Summary: State-Level Analysis of Caesarean Section Rates in India (NFHS-6)

Problem Statement
India's caesarean section (CS) rate has escalated from 21.5% during the NFHS-5 survey (2019–21) to 27.2% in NFHS-6 (2023–24). This figure now stands 82% above the World Health Organization's (WHO) optimal population-level ceiling of 15%. While previous research has linked private-sector excesses to financial incentives and weak regulation, no published study had utilized the newly released NFHS-6 data to map CS rates across all 36 Indian states and union territories (UTs). Furthermore, there was a lack of analysis regarding the "private–public sector gap" and the identification of ecological determinants driving these rates at the state level.

Methodology
This study is an ecological cross-sectional analysis utilizing publicly available state fact sheets from NFHS-6 (2023–24).

Data Source: State-level data for 35 states/UTs (the national aggregate was excluded from regression analysis).
Primary Outcome: Overall state CS rate (% of institutional births).
Secondary Outcomes: Private-sector CS rate, public-sector CS rate, and the Market-Driven Care Index (MDCI), defined as the ratio of private-sector CS rate to public-sector CS rate ( $MDCI = I.39 \div I.40$ ).
Predictors: Four ecological variables were examined:
1. Health insurance coverage (% households).
2. Women's educational attainment (% aged 15–49 with $\ge$ 10 years of schooling).
3. Proportion of institutional births at public facilities.
4. Total Fertility Rate (TFR).
Statistical Analysis:
- Correlation: Spearman rank correlations were used to assess pairwise associations, with a Bonferroni-adjusted significance threshold of $p < 0.0125$ to control for multiple testing.
- Regression: Ordinary Least Squares (OLS) multiple regression with HC3 robust standard errors was employed to model state-level CS rates. Two parsimonious models were constructed to avoid overfitting given the sample size ( $n=35$ $n = 35$ ):
  - Model 1: Health insurance + Public facility birth share.
  - Model 2: Women's education + TFR.
- Software: Analyses were performed using Python 3.12.

Key Contributions

Comprehensive Mapping: The study provides the first state-level documentation of CS rates for all 35 reporting states/UTs using NFHS-6 data.
Introduction of MDCI: It introduces the Market-Driven Care Index (MDCI) as a composite measure to quantify the divergence between private and public sector CS rates. The authors note that MDCI is sensitive to low public-sector denominators and should be interpreted alongside absolute rates.
Ecological Predictors: The study identifies and quantifies the ecological drivers of state-level CS rates, distinguishing between demand-side factors (education) and structural factors (sectoral access).

Results

National Trends: The national CS rate increased by 5.7 percentage points (26.5% relative increase) in approximately five years. The national private-sector rate is 54.1%, compared to 16.9% in government hospitals, yielding a national MDCI of 3.20.
State Variability: Rates ranged from 6.4% (Meghalaya) to 62.2% (Telangana). Four states (Telangana, Andhra Pradesh, Sikkim, Jammu & Kashmir) exceeded 50%. Only three states (Nagaland, Mizoram, Meghalaya) fell within the WHO optimal range of 10–15%.
Sectoral Divergence (MDCI):
- High MDCI (Inequity): States like Bihar (MDCI 18.26) and Jharkhand (MDCI 8.87) exhibited massive gaps where private hospitals performed CS at rates 8 to 18 times higher than government facilities. In these regions, CS access is effectively a private commodity.
- Low MDCI (Systemic Medicalization): States like Kerala (MDCI 1.08) showed high CS rates in both sectors, indicating "equity-in-excess."
- Telangana/Andhra Pradesh: These states showed high overall rates but lower MDCI (1.74), suggesting system-wide medicalization rather than just private-sector excess.
Predictive Modeling:
- Women's Education: The strongest predictor of state CS rates. A Spearman correlation of $\rho = +0.532$ ( $p=0.001$ ) and an OLS coefficient of $\beta = +0.377$ ( $p=0.0001$ ) indicate that for every 1 percentage-point increase in women with $\ge$ 10 years of schooling, the state CS rate increases by 0.377 percentage points.
- Total Fertility Rate (TFR): Inversely correlated with CS rates ( $\rho = -0.428$ , $p=0.010$ ). In Model 2, TFR explained a significant portion of the variance, with a coefficient of $\beta = -12.101$ ( $p=0.035$ ).
- Non-Significant Factors: The proportion of births in public facilities and health insurance coverage showed no significant correlation with state CS rates.
- Model Performance: Model 2 (Education + TFR) explained 30.7% of the variance in state-level CS rates ( $R^2 = 0.307$ ).

Significance and Claims
The paper asserts that women's educational attainment is the dominant ecological predictor of India's state-level CS rates, reflecting "demand-side medicalisation" where educated women are more receptive to medical recommendations, potentially driving elective CS.

The study highlights a critical equity crisis masked by aggregate statistics. In high-fertility, low-income states (EAG states), low overall CS rates do not necessarily indicate appropriate care; rather, they often reflect severe under-provision in the public sector, where CS is accessible only as a private commodity (high MDCI). Conversely, in low-fertility southern states, high CS rates reflect system-wide medicalization.

The authors conclude that policy interventions must move beyond simply routing births to public facilities. Instead, they propose:

Mandatory CS audits using the Robson classification in private hospitals.
Conditioning PMJAY (Ayushman Bharat) reimbursement on documented clinical indications.
Scaling up the LaQshya initiative with specific CS-reduction targets.
Pre-natal education campaigns targeting high-CS and high-education states.

Limitations and Modesty
The authors explicitly state that these findings are ecological and do not imply individual-level causal relationships. The positive association between education and CS rates may capture broader development, urbanization, and private-sector dependence rather than education alone. The study acknowledges limitations including self-reported data, the inability to assess medical indications from NFHS data, and the potential for unmeasured confounders (e.g., obstetrician density, private hospital concentration). The MDCI is presented as a descriptive monitoring tool rather than a validated causal metric.