Two teenagers. Same disease. Same city. Both crash-landed into advanced kidney failure without warning. Both needed emergency dialysis.
Green Girl was born into a middle-class family. Both parents professionals. Large house. Own garden. Supportive networks. She went home on peritoneal dialysis. She flourished. She attended school. A relative came forward as a kidney donor. Compatible. Surgery planned. Excellent outcome.
Blue Girl was born into poverty. A neglected Tyneside flat. No carpets. Damp. Dad absent. Mum struggling. She was poorly nourished, quiet, scared. Not a safe candidate for home dialysis. Hospital haemodialysis three times a week, four hours each time, plus travel. She missed school. She was isolated. She did not grow well. She was eventually fostered. She would have to wait for a stranger to die before she could be considered for a transplant.
Same disease. Same interventions. Same city. Different neighbourhoods. Different families. Different outcomes. The blue pill was baked in at birth.
The green-blue map the NHS ignores
The government published the 2025 England Indices of Deprivation. England cut into 33,755 small areas, each allocated a shade of blue (deprived) or green (affluent). The data reveals nothing we have not known for years. Which neighbourhoods are at the bottom of the pile, and why, is not a surprise.
NHS hospitals sit in this blue-green map. The latest hospital league table based on the NHS Outcomes Framework judges hospitals on waiting times, efficiency, and productivity. It does not account for the disease burden of the population around the hospital. Health outcomes are non-scoring.
At STRASYS, the Decision Intelligence engine for healthcare, Mark Jennings built the analysis that reveals what happens when you adjust for population disease burden.
Take SHMI, the Standardised Hospital Mortality Index. Predictably, the greater the disease burden in the area around a hospital, the higher the SHMI. A hospital in Blackpool starts with a sicker population than a hospital in Surrey. The raw SHMI penalises the hospital that starts furthest behind.
When we adjust SHMI for population disease burden, the picture changes. Some hospitals that look average on raw data are achieving a massive delta for their population. They are doing extraordinary work in the face of extraordinary disadvantage. Others that look respectable are underperforming against what their healthy population should produce. The difference matters.
Does the NHS account for this? No.
Maternity tells the same story
We applied the same logic to maternity through the Strasys Maternity Index. Does population disease burden surrounding a maternity unit correlate with outcomes? The correlation is minus 41.3%, with a P-value below 0.00001. Statistically, this is not debatable.
Do the regulators account for this as they judge units? No. They do not have the decision intelligence to enable population-adjusted assessment.
The Strasys Value Index was designed to fill this gap at the organisational level: measuring value relative to population need, not just raw activity. A trust serving a deprived population that achieves strong outcomes relative to its starting point should be recognised, resourced, and learned from. A trust serving a healthy population that underperforms should be challenged, regardless of what the unadjusted league table says.
Fair judgement, fair allocation, fair chance
Naeem Younis, STRASYS CEO, argues that if regulators are going to be fair in how they judge hospitals, they have to account for the blue-green map.
Fair judgement based on the delta achieved. Fair allocation of resources based on deprivation disease burden. Fair chance for those humans with the greatest burden of inequalities.
Population Need Segmentation provides the analytical infrastructure to make this real: understanding what each population actually needs, adjusted for deprivation, disease burden, and social determinants. Without it, the NHS will continue to judge and fund hospitals as though every population starts from the same baseline. They do not.
The data exists. The analytical tools exist. The question is whether the system has the will to use them.