Making sense of health data, research and public health
Blank ethnicity fields are often treated as background noise in NHS datasets. This article explains how 'not stated', 'unknown' and genuinely absent data differ, why completeness varies across settings, and how missingness can reshape Britain's health inequality statistics in 2026.
A single urine sample sent from a GP surgery can end up as part of a national antimicrobial resistance percentage. Tracing that journey shows why denominators, sampling and laboratory practice matter as much as the headline figure. In 2026, reading AMR surveillance well means asking what was tested, what was counted and what was left out.
Pain scores are now a routine digital entry in many NHS records, from emergency departments to community nursing. But a 0–10 rating is a request for communication, not a measurement of suffering. Reading pain data well means asking who was asked, how the question was put, and what happens next.
Cancer screening statistics are often read as a verdict on public behaviour, but they are better understood as a record of an administrative process. This article explains how invitation coverage, uptake and data lag shape what the NHS figures can and cannot tell us in 2026.
NHS genomic testing figures are often reported as if every finding were a diagnosis. This guide separates laboratory detection from clinical explanation, explains why variants of uncertain significance belong in the data, and shows how to read Genomic Medicine Service counts in 2026 with the right denominators and categories.
NHS dental access is frequently reported as a simple story of shortage, but the figures behind it are built from activity counts, contract currencies and rolling time windows. This guide explains what the data actually records, where regional variation comes from, and why a headline about disappearing dentists can obscure more than it reveals.
NHS 111 produces a detailed record of how calls are routed, not a diagnosis of the nation’s health. In 2026, reading call volumes, dispositions and emergency-department referrals means distinguishing demand from capacity, and unmet need from the absence of a record.
A measles alert looks like a simple count, but every figure on a UK dashboard has passed through clinical notification, laboratory confirmation, local investigation and outbreak definitions. In 2026, reading those layers carefully matters more than reacting to every cluster as if it were a crisis.
Digitised fit-note records are now a substantial public data source, routinely cited in debates about work, welfare and illness. But a fit note is an administrative document, shaped by GP workload, employer practice and benefit rules, not a clean register of disease. This article sets out which claims the fit-note record can support in 2026, and which it quietly distorts.
Virtual wards move the observation of illness from a hospital bay into a living room, and with it the point at which data is created. This piece examines what remote-monitoring records capture, which patients quietly fall out of the feed, and why a stable dashboard is not the same thing as a stable patient.
NHS imaging waiting-list figures compress a complex journey—referral, triage, appointment, report—into a single number. This guide explains how modality, referral route and community diagnostic centres shape the queue, and why the interval between referral and result deserves more attention than the headline count.
Missed appointments are often framed as waste or patient failure. But NHS no-show figures are operational records shaped by definitions, digital systems and local circumstances. Read carefully, they can point towards access barriers rather than apathy.
ICD-10 codes in the R00–R99 chapter are often read as a sign of medical mystery. In NHS hospital statistics and ONS mortality data, they more often record a familiar administrative and clinical reality: a patient needed a code before a firm diagnosis was available.
Death certificates become public health statistics through a chain of clinical judgement, legal process and coding. This guide explains how cause-of-death data is produced in the UK, what underlying and contributing causes mean, and why mortality figures deserve careful reading rather than immediate conclusions.
As winter 2026 approaches, NHS bed occupancy and discharge delays return to the headlines. But flow data are operational instruments, not a verdict on care. This analysis explains what the numbers record, where they mislead and how to read them with winter in mind.
The MHRA Yellow Card scheme turns individual suspicions about medicines into a national safety signal. But a report is not a proven harm, and a rise in entries is not automatically a rise in disease. This guide explains how to read the data with the caution the system itself requires.
Most alarming UK health statistics are arithmetically correct and analytically shaky. This explainer unpicks incidence, prevalence, testing rates and population bases, and offers a five-question checklist for reading the next scare story with a cooler head.
The national data opt-out is a right, and a small but consequential one. This deep dive examines who withholds their GP data, why missing records are rarely missing at random, and how researchers are trying to keep UK disease estimates honest without eroding the trust the system depends on.
Open prescribing data offer one of the most granular views of long-term illness in the UK, but they record treatment rather than diagnosis. This guide explains how to read repeat prescriptions for statins, inhalers, antidepressants and diabetes drugs without mistaking the pharmacy ledger for a disease register.
Rare disease registries are among the UK's most ambitious attempts to make small patient populations visible to clinicians and researchers. They also expose hard questions about consent, standardisation, linkage and statistical disclosure—questions that determine whether the data can be trusted and used well.