The Health Foundation: Equal waiting for elective care? (17 July 2026)

Summary

New evidence on inequalities in NHS waiting times in England.

Content

Key points:

• Reducing the waiting list and improving waiting times for elective care are top priorities for the current government. There has been a commitment to inclusively reduce elective care waits since the COVID-19 pandemic, but little available data to track progress on tackling inequalities. This changed in July 2025, when NHS England began publishing statistics on waiting times split by key demographic factors.   
• While the proportion of people waiting less than 18 weeks has increased across all socioeconomic groups, inequalities in waiting times between people living in the most and least deprived areas persisted between June 2025 and April 2026. However, recent months have shown signs of these differences narrowing.
• There has been more progress reducing inequalities in very long waits for elective care. Differences in the proportion of people waiting for less than 52 weeks between the most and least deprived areas have narrowed substantially. This suggests that reducing inequalities is achievable while recovering overall performance.
• Progress has been uneven across the country. A small number of integrated care boards (ICBs), including Birmingham and Solihull, Lancashire and South Cumbria, and Greater Manchester, accounted for a large share of the reduction in inequalities for very long waits. 
• Inequalities in waiting times are not simply explained by geography or treatment specialties. Socioeconomic inequalities in waiting times are evident within most ICBs and specialties, indicating that broader barriers to accessing elective care continue to affect disadvantaged groups. 
• Inequalities in waiting times between ethnic groups are concentrated in particular specialties. People with Bangladeshi, Pakistani and Indian backgrounds consistently experience longer waits than average, especially for dermatology, plastic surgery and some surgical specialties.
• Learnings should be collected and shared from ICBs that have successfully reduced their number of very long waits. More granular waiting times data split by demographic groups would help also deepen our understanding of inequalities in waiting times.