Time trends in new diagnoses of 19 long-term conditions: a population-level cohort study in England using OpenSAFELY

Lay summary

In this study, we used data in OpenSAFELY to describe how the COVID-19 pandemic impacted the number of people with new diagnoses of 19 long-term health conditions in England. We showed that the number of new diagnoses for all conditions fell sharply in the first year of the pandemic. For many conditions, the number of diagnoses recovered by the second year of the pandemic; however, persistent deficits in diagnoses remained for several conditions by November 2024, including depression, chronic obstructive pulmonary disease (COPD), asthma, psoriasis and osteoporosis. In contrast, new diagnoses of chronic kidney disease have increased since 2022, coinciding with the publication of updated national guidelines.

Importantly, this study demonstrates how routinely collected health data can be used to monitor inequity across diseases and for under-served groups, and inform strategies to increase case capture and reduce diagnostic delay.

Abstract

Objectives: To evaluate temporal changes in the incidence and prevalence of 19 long-term conditions in England, quantifying the impact of the COVID-19 pandemic on diagnosis rates by disease, age group, sex, socioeconomic status, and ethnicity.

Design: Observational cohort study.

Setting: Primary care and hospital admission data, with the approval of NHS England.

Participants: 27,132,190 individuals registered with general practices in England contributing data to the OpenSAFELY-TPP platform.

Main outcomes measures: Temporal trends in age and sex-standardised incidence and prevalence were evaluated for 19 long-term conditions between April 1, 2016, and November 30, 2024. Differences between expected and observed incidence rates after the onset of the COVID-19 pandemic were compared using seasonal autoregressive integrated moving-average models.

Results: Between March 2020 and November 2024, persistent large deficits in incident diagnoses were evident for depression (738,068 [28.0%] fewer diagnoses than expected; 95% CI 701,452 to 774,685), asthma (150,708 [16.0%] fewer diagnoses; 95% CI 133,300 to 168,117), COPD (84,084 [15.1%] fewer diagnoses; 95% CI 74,342 to 93,827), osteoporosis (78,891 [16.5%] fewer diagnoses; 95% CI 72,804 to 84,978) and psoriasis (56,231 [17.6%] fewer diagnoses; 95% CI 51,054 to 61,407). Conversely, post-pandemic diagnoses of chronic kidney disease (CKD) have increased by 32.7% above expected levels, corresponding to 325,996 additional diagnoses (95% CI 252,212 to 399,779). Dementia diagnoses have rebounded above pre-pandemic levels for individuals of White ethnicity and less deprived socioeconomic quintiles, but remain lower than expected for individuals from other ethnicities and more deprived communities.

Conclusions: There has been a lasting and disproportionate impact of the pandemic on conditions including depression, asthma, COPD and osteoporosis, contrasting a post-pandemic surge in CKD diagnoses. Analyses stratified by ethnicity and socioeconomic status reveal inequity in the recovery from the pandemic, particularly for individuals with dementia. Importantly, this study demonstrates the potential for near real-time monitoring of disease epidemiology using routinely collected health data, informing strategies to enhance case detection and address healthcare disparities.