longcovid {jarbes} | R Documentation |
Meta-analysis: Long-COVID Health Outcomes
Description
This dataset is based on a comprehensive meta-analysis of 33 studies, sourced from various databases, including the Cochrane COVID-19 Study Register (comprising the Cochrane Central Register of Controlled Trials, Medline, Embase, clinicaltrials.gov, the World Health Organization's International Clinical Trials Registry Platform, and medRxiv) and the World Health Organization’s COVID-19 research database. The analysis focused on evaluating health outcomes related to Long-COVID in controlled studies. Specifically, it examines the health outcomes in terms of incident medicinal diagnoses.
The dataset includes the assessment of risk of bias based on the Joanna Briggs Institute (JBI) tool for cohort studies, along with various participant and study details such as sample size, effect type, follow-up time, and disease severity.
Format
A data frame with 271 rows and 27 columns. Each row represents the results of a single study. The columns include:
- study
Name of the first author and publication year.
- category
Category of the health outcome.
- outcome_disease
Definition of the health outcome or disease.
- data_source
Type of data source: Administrative data, Health records, Patients claims, Survey, Combination of health records and claims.
- sample_size
Total number of participants.
- effect_type
Type of effect reported: RR (Relative Risk), HR (Hazard Ratio), or OR (Odds Ratio).
- effect
Estimated effect based on the effect type.
- TE
Logarithm of the estimated effect.
- seTE
Standard error of the logarithm of the estimated effect.
- rate_control
Event rate in the control group.
- follow_up_time
Follow-up time in weeks.
- mean_age
Mean age of the participants.
- disease_severity
Indicator for inclusion of severe or critical disease participants ("no" or "yes").
- reinfection
Indicator for inclusion of reinfected participants ("no" or "yes").
- no_of_confounders
Number of confounders for which adjustments were made in the study.
- uncertainty_of_confounders
high if ROB4 OR ROB5 is high or unclear or low otherwise.
- list_of_confounders
List of confounders considered in the study.
- ROB1
Were the two groups similar and recruited from the same population?
- ROB2
Were the exposures measured similarly to assign participants to exposed and unexposed groups?
- ROB3
Was the exposure measured in a valid and reliable way?
- ROB4
Were confounding factors identified?
- ROB5
Were strategies to address confounding factors stated?
- ROB6
Were the groups/participants free of the outcome at the start of the study (or at the moment of exposure)?
- ROB7
Were the outcomes measured in a valid and reliable way?
- ROB8
Was the follow-up time reported and sufficient to allow outcomes to occur?
- ROB9
Was follow-up complete, and if not, were the reasons for loss to follow-up described and explored?
- ROB10
Were strategies to address incomplete follow-up utilized?
- ROB11
Was appropriate statistical analysis used?
Source
Franco JVA, Garegnani LI, Metzendorf MI, Heldt K, Mumm R, Scheidt-Nave C. Post-COVID-19 conditions in adults: systematic review and meta-analysis of health outcomes in controlled studies. BMJ Medicine. 2024;3:e000723.