Remission of Long-Term Conditions Among Adults with Multimorbidity in UK Primary Care: A Retrospective Cohort Study of 4.7 Million People

Background Multimorbidity is increasingly prevalent among UK adults, yet the extent to which remission of long-term conditions is recorded in routine primary care remains poorly understood. Accurate documentation of remission has implications for clinical management, disease trajectories, and interpretation of electronic health records for research. We aimed to describe the frequency and variation of recorded remission across multiple long-term conditions among adults with multimorbidity. Methods We conducted a retrospective cohort study using linked primary care, hospital, and CPRD Aurum data. Adults aged ≥18 with multimorbidity (≥2 long-term conditions) between Jan 1, 1987, and Dec 31, 2020, were included. Remission was defined using condition-specific criteria based on diagnostic or resolution codes, clinical measurements, treatment patterns, and relevant clinical events. We examined remission for 11 conditions with operationalizable definitions. Analyses were descriptive, summarising remission frequencies and sociodemographic patterns. Findings Among 4,745,099 adults followed for a median of 8 years, 900,609 (18.1%) had recorded remission of at least one condition. Remission varied substantially by condition and was highest for endometriosis (90.36%), anaemia (63.0%), asthma (44.71%), and diabetes (15.51%), calculated among individuals with an ever-recorded diagnosis of each condition. Individuals with remission were slightly younger and more often male; Asian and Black ethnic groups were under-represented, while deprivation patterns were broadly similar. Interpretation Recorded remission in multimorbidity is highly heterogeneous and often rare. Variation between conditions and low rates of sustained remission likely reflect coding practices and clinical recording behaviours. Standardised remission definitions and consistent Systematized Nomenclature of Medicine (SNOMED) coding could improve data quality and support more reliable research on remission and disease trajectories.