Individual Participant Data Meta-Analysis (IPDMA) of long-term COVID-19 outcomes in a systematic review-informed, international, multidisciplinary database

Published Date: 14th August 2026

Publication Authors: Nune. A

Background
Identifying and addressing long-term health and societal challenges after COVID-19 is a research priority.

Objectives
To create an international, multidisciplinary COVID-19 database, and synthesise long-term outcomes, predictors and costs.

Design
Systematic identification of COVID-19 data sets and meta-analysis of individual participant data on long-term outcomes after COVID-19.

Setting
Contributed data were collected in clinical, community and research settings.

Interventions
Interventions from original studies were included as covariates in models.

Data sources
MEDLINE, Cochrane Central Register of Controlled Trials, EMBASE, Web of Science, PsycInfo® (American Psychological Association, Washington, DC, USA), Cumulative Index to Nursing and Allied Health Literature, World Health Organization Global Index Medicus, Epistemonikos, LitCOVID; World Health Organization International Clinical Trials Registry Platform; ClinicalTrials.gov and supplementary searches for studies (November 2019–November 2021) were searched for studies on > 10 people from cohort, case-control, survey or randomised controlled trial studies, across any setting, describing validated assessment instruments, symptoms, hospitalisation, discharge destination or mortality beyond 28-days after COVID-19 onset. Data were extracted by two independent reviewers.

Methods
Principal investigators contributed fully anonymised individual participant data. Demography, equity and symptoms were described. Assessment instruments were mapped to the International Classification of Functioning, Disability and Health. Factors associated with outcomes at 3–6 months, 9–12 months and beyond 12 months of index infection, for n > 500 individual participant data and > 1 data set were described using ratio of difference, point estimates, odds ratio and 95% confidence interval, as appropriate. The Mixed Methods Appraisal Tool described study quality; models were appraised using a Grading of Recommendations Assessment, Development and Evaluation-informed approach; heterogeneity was described using I2.

Outcome measures
Included overall perception of health, multidomain cognitive function, anxiety, depression, stress, post-traumatic stress disorder, fatigue, strength, walking ability, mobility, coping with daily life, breathlessness, mortality, later hospitalisation and health-related quality of life.

Results
PRECIOUS collated 116 data sets from 40 countries (individual participant data = 62,849), comprising 20 randomised controlled trials, 13 case-control, 60 cohort 2 longitudinal, 1 survey and 20 other study types. Participants’ median age was 58 years interquartile range (45–68); 34,185 (54.4%) were female; 158 unique symptoms and 137 unique assessment instruments were captured, predominantly describing International Classification of Function, Disability and Health-body functions. Women had poorer outcomes across 30/37 models, compared with men. In 15/37 models, pre-existing lung disease and increasing age were associated with poorer outcomes; hospitalisation, diabetes and chronic kidney disease were each associated with poorer outcomes in 8/37 models. Initial hospitalisation resulted in lower health-related quality of life that did not recover for up to 2 years after initial infection. Heterogeneity was low in 34/37 models; 22/37 models were of moderate and 11/37 were of low quality.

Limitations
Use of secondary data limits available covariates, outcomes and time points to those included in primary data sets; evidence was primarily based on high-income countries. There was a lack of data on longer-term healthcare resource use to estimate the costs to the healthcare system.

Conclusions
PRECIOUS contributes to the overall picture of long-term COVID-19 outcomes beyond the long-COVID condition and highlights poorer long-term outcomes in women and people with pre-existing comorbidities.

Ali, M et al (Collaborator: Nune, A et al). (2026). Individual Participant Data Meta-Analysis (IPDMA) of long-term COVID-19 outcomes in a systematic review-informed, international, multidisciplinary database. Health and Social Care Delivery Research. 14(29). [Online]. Available at: https://doi.org/10.3310/GJMA0602 [Accessed 3 September 2026]