There is nothing like obstetrics in medicine. A patient diagnosed with cancer does not walk into hospital with a philosophy, a volume of personal research, and a preference for the intervention. In maternity, the mother often does. Natural birth, ideally drug-free. A philosophy supported by classes, networks, and midwives trained to know that pregnancy is not an illness but a natural process.
Natural birth is a real thing. It has been so for several hundred thousand years. What is unnatural is sticking with natural birth at all costs in a modern medical setting. It is the "at all costs" that turns a reasonable preference into an ideology. And ideologies cost lives.
Leeds Teaching Hospitals NHS Trust had the lowest rates of caesarean sections in the country between 2012 and 2023. Over the same period, its rate of stillbirths and newborn baby deaths soared to become the worst nationally. Its own 2015 maternity strategy expected all parts of the service to actively promote normal birth. The home birth team's language emphasised that "birth is a natural and physiological process."
Who knew being wedded to an ideology could cost lives. Modern science and medicine solved the historical killers: sepsis, haemorrhage, obstructed labour. What remains is whether timely midwife and obstetrician interventions happen when the natural stalls and the unnatural is needed.
Mrs Farrow and Ball vs Mrs Benson and Hedges
At STRASYS, the Decision Intelligence engine for healthcare, we looked at all England maternity units over 29 months: 3,451 trust-months of activity. We categorised maternal health into five quintiles, from Q1 (poorest health) to Q5 (best health). A generalisation to set an image against the data: Mrs Benson and Hedges at one end, Mrs Farrow and Ball at the other.
The finding was striking. Maternal health does not meaningfully affect how frequently certain interventions are applied. Mrs Benson and Hedges, with all her health risks, experiences intervention rates roughly comparable to Mrs Farrow and Ball.
This suggests one of three things. Maternal health is not being used to draw out risks and inform interventions. Or it is assessed but forgotten when the moment comes. Or mothers with the resources and confidence to advocate for a caesarean section get one, regardless of clinical need, while those without do not.
Some units serve a largely healthy, well-informed population. Others experience a growing number of mothers who are obese, smoke, eat poorly, and are not engaged in antenatal preparation. Despite this variation, the high-level intervention data does not show the lean towards more or fewer interventions that population health differences would predict.
The alpha in the data
The Strasys Maternity Index reveals something regulators currently miss. A unit dealing with mothers in generally poor health can be doing a really good job in terms of outcomes relative to the disease burden it starts with. It would love to be above the England average, but look at what it is achieving given its starting point.
Conversely, a unit whose maternal population is healthy from the outset can be underperforming against expected outcomes. Why? That question is worth the exploration.
Do regulators account for the health of the mothers when deciding to intervene? No. They do not have the decision intelligence to enable risk-based, population-adjusted judgement. The Maternity Task Force choosing its "terrible ten" based on raw outcome data without adjusting for maternal health will punish units serving the most deprived populations and reward those with the healthiest mothers.
A service underpinned by data, decision intelligence, and learning is more likely to get the balance between natural and unnatural right. The SMI provides that infrastructure: 58 measures, triangulating current performance, 12-month trends, and birth volume, adjusted for the population the unit actually serves.
Naeem Younis, STRASYS CEO, argues that every maternity unit should be assessed against the delta it achieves for its population, not against a national average that ignores where it starts. Fair judgement. Fair resource allocation. Fair chance for the humans with the greatest burden of inequalities.
See the Maternity Index in Action
How we use predictive analytics to support maternity governance.
Key Definitions
- Strasys Maternity Index (SMI)
- A STRASYS product benchmarking maternity safety risk across all NHS trusts using 58 measures from national data. Combines current performance, 12-month trend, and birth volume. Uniquely, the SMI can be read against maternal population health to reveal which units are achieving the greatest delta for their population.
- Decision Intelligence
- The discipline of converting complex healthcare data into structured, actionable decisions for NHS leaders. STRASYS coined and owns this category in UK healthcare.
- Maternal Health Quintiles
- A STRASYS analytical framework categorising maternal populations from Q1 (poorest health) to Q5 (best health). Reveals whether intervention rates and outcomes correlate with population health, exposing where ideology rather than evidence drives clinical practice.
Frequently Asked Questions
Leeds Teaching Hospitals had the lowest caesarean rate in England between 2012 and 2023. Over the same period, its stillbirth and newborn death rate became the worst nationally. The trust's own strategy promoted normal birth across all settings. This pattern, where ideology suppresses timely intervention, has been identified in multiple maternity inquiries.
STRASYS analysis of 3,451 trust-months of data across all England maternity units found that maternal health does not meaningfully affect how frequently interventions are applied. This suggests that risk assessment either is not informing clinical decisions at the point of delivery, or that other factors (patient advocacy, unit culture, philosophy) are overriding clinical need.
The SMI benchmarks each unit's outcomes against the maternal population it serves. A unit dealing with high-risk mothers achieving outcomes close to the national average is performing well relative to its starting point. A unit with healthy mothers underperforming the average warrants investigation. This population-adjusted view is not used by regulators, who judge on raw outcomes regardless of starting conditions.
Yes. Judging maternity units on raw outcome data without accounting for the health of the mothers they serve punishes units in deprived areas and rewards those with the healthiest populations. STRASYS's position is that fair regulation requires population-adjusted assessment, measuring the delta achieved rather than the absolute outcome.
A philosophy (natural birth at all costs) sets a predetermined approach regardless of individual circumstances. An evidence-based model uses data, risk assessment, and decision intelligence to determine the right intervention for each mother at each stage. The SMI provides the infrastructure for the latter, enabling units to track whether their system of care is producing reliable outcomes adjusted for the population they serve.
This article is adapted from the Friday Fish and Chip Paper, Dr Nadeem Moghal's weekly newsletter on LinkedIn.
Dr Nadeem Moghal
Chief Medical and Innovation Officer