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Uses of real-world data to evaluate vaccine safety and effectiveness are often challenged by unmeasured confounding. The study aimed to review the application of methods to address unmeasured confounding in observational vaccine safety and effectiveness research. We conducted a systematic review (PROSPERO: CRD42024519882), and searched PubMed, Web of Science, Embase, and Scopus for epidemiological studies investigating influenza and COVID-19 vaccines as exposures, and respiratory and cardiovascular diseases as outcomes, published between January 1, 2017, and December 31, 2023. Data on study design and statistical analyses were extracted from eligible articles. A total of 913 studies were included, of which 42 (4.6%, 42/913) accounted for unmeasured confounding through statistical correction (31.0%, 13/42) or confounding detection or quantification (78.6%, 33/42). Negative control was employed in 24 (57.1%, 24/42) studies—2 (8.3%, 2/24) for confounding correction and 22 (91.7%, 22/24) for confounding detection or quantification—followed by E-value (31.0%, 13/42), prior event rate ratio (11.9%, 5/42), regression discontinuity design (7.1%, 3/42), instrumental variable (4.8%, 2/42), and difference-in-differences (2.4%, 1/42). A total of 871 (95.4%, 871/913) studies did not address unmeasured confounding, but 38.9% (355/913) reported it as study limitation. Unmeasured confounding in real-world vaccine safety and effectiveness studies remains underexplored. Current research primarily employed confounding detection or quantification, notably negative control and E-value, which did not yield adjusted effect estimates. While some studies used correction methods like instrumental variable, regression discontinuity design, and negative control, challenges arise from the stringent assumptions. Future efforts should prioritize developing valid methodologies to mitigate unmeasured confounding. [Display omitted] •Unmeasured confounding in vaccine safety and effectiveness studies remains seldom addressed.•Negative control is the most frequently employed method for unmeasured confounding.•Current studies primarily address unmeasured confounding by confounding detection or quantification, for example, negative control and E-value.•Identified methods for confounding correction include negative control, PERR, IV, RDD, and DiD.•The review stresses the need to develop valid methodologies for unmeasured confounding.

Motivated by recent calls to use electronic health records for research, we reviewed the application and development of methods for addressing the bias from unmeasured confounding in longitudinal data. Methodological review of existing literature. We searched MEDLINE and EMBASE for articles addressing the threat to causal inference from unmeasured confounding in nonrandomized longitudinal health data through quasi-experimental analysis. Among the 121 studies included for review, 84 used instrumental variable analysis (IVA), of which 36 used lagged or historical instruments. Difference-in-differences (DiD) and fixed effects (FE) models were found in 29 studies. Five of these combined IVA with DiD or FE to try to mitigate for time-dependent confounding. Other less frequently used methods included prior event rate ratio adjustment, regression discontinuity nested within pre-post studies, propensity score calibration, perturbation analysis, and negative control outcomes. Well-established econometric methods such as DiD and IVA are commonly used to address unmeasured confounding in nonrandomized longitudinal studies, but researchers often fail to take full advantage of available longitudinal information. A range of promising new methods have been developed, but further studies are needed to understand their relative performance in different contexts before they can be recommended for widespread use.

Observational studies have consistently reported large reductions in COVID-19 risk among vaccinated individuals. However, critics have raised concerns that unmeasured confounding may entirely explain these associations. We combined the classical Cornfield inequality with a Monte Carlo sensitivity analysis to evaluate whether unmeasured confounding alone could plausibly account for the observed effectiveness of COVID-19 vaccines. The Cornfield inequality provides a lower bound on the strength of confounding required to explain a given association. The Monte Carlo analysis simulates uncertainty over possible confounder-exposure and confounder-outcome relationships by drawing from weakly informative prior distributions, allowing us to estimate the frequency with which such confounding would be sufficient. For an observed risk ratio of 0.08-consistent with early estimates for the Pfizer-BioNTech vaccine-the confounder would need to be both highly imbalanced (e.g., 10 times more prevalent among vaccinated individuals) and strongly protective (e.g., reducing disease risk by 99%). Simulation results showed that, under the specified assumptions, fewer than 2% of draws satisfied this condition. Even in the more moderate case of a risk ratio of 0.25 (e.g., AstraZeneca), the proportion remained below 6%. Our findings suggest that while residual confounding may attenuate effect estimates, it is statistically and epidemiologically implausible that unmeasured confounding alone could fully account for the magnitude of observed vaccine effectiveness. This framework combines the falsificatory logic of Cornfield bounds with the flexibility of simulation-based sensitivity analysis, providing a transparent tool for evaluating confounding-based explanations in observational research.

Obtaining accurate estimates of the causal effects of socioeconomic position (SEP) on health is important for public health interventions. To do this, researchers must identify and adjust for all potential confounding variables, while avoiding inappropriate adjustment for mediator variables on a causal pathway between the exposure and outcome. Unfortunately, ‘overadjustment bias’ remains a common and under-recognized problem in social epidemiology. This paper offers an introduction on selecting appropriate variables for adjustment when examining effects of SEP on health, with a focus on overadjustment bias. We discuss the challenges of estimating different causal effects including overadjustment bias, provide guidance on overcoming them, and consider specific issues including the timing of variables across the life-course, mutual adjustment for socioeconomic indicators, and conducting systematic reviews. We recommend three key steps to select the most appropriate variables for adjustment. First, researchers should be clear about their research question and causal effect of interest. Second, using expert knowledge and theory, researchers should draw causal diagrams representing their assumptions about the interrelationships between their variables of interest. Third, based on their causal diagram(s) and causal effect(s) of interest, researchers should select the most appropriate set of variables, which maximizes adjustment for confounding while minimizing adjustment for mediators. [Display omitted]

Epidemiologic studies are increasingly used to investigate the safety and effectiveness of medical products and interventions. Appropriate adjustment for confounding in such studies is challenging because exposure is determined by a complex interaction of patient, physician, and healthcare system factors. The challenges of confounding control are particularly acute in studies using healthcare utilization databases where information on many potential confounding factors is lacking and the meaning of variables is often unclear. We discuss advantages and disadvantages of different approaches to confounder control in healthcare databases. In settings where considerable uncertainty surrounds the data or the causal mechanisms underlying the treatment assignment and outcome process, we suggest that researchers report a panel of results under various specifications of statistical models. Such reporting allows the reader to assess the sensitivity of the results to model assumptions that are often not supported by strong subject-matter knowledge.

Examining covariate balance is the prescribed method for determining the degree to which propensity score methods should be successful at reducing bias. This study assessed the performance of various balance measures, including a proposed balance measure based on the prognostic score (similar to a disease risk score), to determine which balance measures best correlate with bias in the treatment effect estimate. The correlations of multiple common balance measures with bias in the treatment effect estimate produced by weighting by the odds, subclassification on the propensity score, and full matching on the propensity score were calculated. Simulated data were used, based on realistic data settings. Settings included both continuous and binary covariates and continuous covariates only. The absolute standardized mean difference (ASMD) in prognostic scores, the mean ASMD (in covariates), and the mean t-statistic all had high correlations with bias in the effect estimate. Overall, prognostic scores displayed the highest correlations with bias of all the balance measures considered. Prognostic score measure performance was generally not affected by model misspecification, and the prognostic score measure performed well under a variety of scenarios. Researchers should consider using prognostic score–based balance measures for assessing the performance of propensity score methods for reducing bias in nonexperimental studies.

Age-associated motor and cognitive deficits increase the risk of falls, a major cause of morbidity and mortality. Because of the significant ramifications of falls, many interventions have been proposed, but few have aimed to prevent falls via an integrated approach targeting both motor and cognitive function. We aimed to test the hypothesis that an intervention combining treadmill training with non-immersive virtual reality (VR) to target both cognitive aspects of safe ambulation and mobility would lead to fewer falls than would treadmill training alone. We carried out this randomised controlled trial at five clinical centres across five countries (Belgium, Israel, Italy, the Netherlands, and the UK). Adults aged 60–90 years with a high risk of falls based on a history of two or more falls in the 6 months before the study and with varied motor and cognitive deficits were randomly assigned by use of computer-based allocation to receive 6 weeks of either treadmill training plus VR or treadmill training alone. Randomisation was stratified by subgroups of patients (those with a history of idiopathic falls, those with mild cognitive impairment, and those with Parkinson's disease) and sex, with stratification per clinical site. Group allocation was done by a third party not involved in onsite study procedures. Both groups aimed to train three times per week for 6 weeks, with each session lasting about 45 min and structured training progression individualised to the participant's level of performance. The VR system consisted of a motion-capture camera and a computer-generated simulation projected on to a large screen, which was specifically designed to reduce fall risk in older adults by including real-life challenges such as obstacles, multiple pathways, and distracters that required continual adjustment of steps. The primary outcome was the incident rate of falls during the 6 months after the end of training, which was assessed in a modified intention-to-treat population. Safety was assessed in all patients who were assigned a treatment. This study is registered with ClinicalTrials.gov, NCT01732653. Between Jan 6, 2013, and April 3, 2015, 302 adults were randomly assigned to either the treadmill training plus VR group (n=154) or treadmill training alone group (n=148). Data from 282 (93%) participants were included in the prespecified, modified intention-to-treat analysis. Before training, the incident rate of falls was similar in both groups (10·7 [SD 35·6] falls per 6 months for treadmill training alone vs 11·9 [39·5] falls per 6 months for treadmill training plus VR). In the 6 months after training, the incident rate was significantly lower in the treadmill training plus VR group than it had been before training (6·00 [95% CI 4·36–8·25] falls per 6 months; p<0·0001 vs before training), whereas the incident rate did not decrease significantly in the treadmill training alone group (8·27 [5·55–12·31] falls per 6 months; p=0·49). 6 months after the end of training, the incident rate of falls was also significantly lower in the treadmill training plus VR group than in the treadmill training group (incident rate ratio 0·58, 95% CI 0·36–0·96; p=0·033). No serious training-related adverse events occurred. In a diverse group of older adults at high risk for falls, treadmill training plus VR led to reduced fall rates compared with treadmill training alone. European Commission.

Immobilisation predicts adverse outcomes in patients in the surgical intensive care unit (SICU). Attempts to mobilise critically ill patients early after surgery are frequently restricted, but we tested whether early mobilisation leads to improved mobility, decreased SICU length of stay, and increased functional independence of patients at hospital discharge. We did a multicentre, international, parallel-group, assessor-blinded, randomised controlled trial in SICUs of five university hospitals in Austria (n=1), Germany (n=1), and the USA (n=3). Eligible patients (aged 18 years or older, who had been mechanically ventilated for <48 h, and were expected to require mechanical ventilation for ≥24 h) were randomly assigned (1:1) by use of a stratified block randomisation via restricted web platform to standard of care (control) or early, goal-directed mobilisation using an inter-professional approach of closed-loop communication and the SICU optimal mobilisation score (SOMS) algorithm (intervention), which describes patients’ mobilisation capacity on a numerical rating scale ranging from 0 (no mobilisation) to 4 (ambulation). We had three main outcomes hierarchically tested in a prespecified order: the mean SOMS level patients achieved during their SICU stay (primary outcome), and patient's length of stay on SICU and the mini-modified functional independence measure score (mmFIM) at hospital discharge (both secondary outcomes). This trial is registered with ClinicalTrials.gov (NCT01363102). Between July 1, 2011, and Nov 4, 2015, we randomly assigned 200 patients to receive standard treatment (control; n=96) or intervention (n=104). Intention-to-treat analysis showed that the intervention improved the mobilisation level (mean achieved SOMS 2·2 [SD 1·0] in intervention group vs 1·5 [0·8] in control group, p<0·0001), decreased SICU length of stay (mean 7 days [SD 5–12] in intervention group vs 10 days [6–15] in control group, p=0·0054), and improved functional mobility at hospital discharge (mmFIM score 8 [4–8] in intervention group vs 5 [2–8] in control group, p=0·0002). More adverse events were reported in the intervention group (25 cases [2·8%]) than in the control group (ten cases [0·8%]); no serious adverse events were observed. Before hospital discharge 25 patients died (17 [16%] in the intervention group, eight [8%] in the control group). 3 months after hospital discharge 36 patients died (21 [22%] in the intervention group, 15 [17%] in the control group). Early, goal-directed mobilisation improved patient mobilisation throughout SICU admission, shortened patient length of stay in the SICU, and improved patients’ functional mobility at hospital discharge. Jeffrey and Judy Buzen.

Cardiovascular risk factors are associated with an increased risk of dementia. We assessed whether a multidomain intervention targeting these factors can prevent dementia in a population of community-dwelling older people. In this open-label, cluster-randomised controlled trial, we recruited individuals aged 70–78 years through participating general practices in the Netherlands. General practices within each health-care centre were randomly assigned (1:1), via a computer-generated randomisation sequence, to either a 6-year nurse-led, multidomain cardiovascular intervention or control (usual care). The primary outcomes were cumulative incidence of dementia and disability score (Academic Medical Center Linear Disability Score [ALDS]) at 6 years of follow-up. The main secondary outcomes were incident cardiovascular disease and mortality. Outcome assessors were masked to group assignment. Analyses included all participants with available outcome data. This trial is registered with ISRCTN, number ISRCTN29711771. Between June 7, 2006, and March 12, 2009, 116 general practices (3526 participants) within 26 health-care centres were recruited and randomly assigned: 63 (1890 participants) were assigned to the intervention group and 53 (1636 participants) to the control group. Primary outcome data were obtained for 3454 (98%) participants; median follow-up was 6·7 years (21 341 person-years). Dementia developed in 121 (7%) of 1853 participants in the intervention group and in 112 (7%) of 1601 participants in the control group (hazard ratio [HR] 0·92, 95% CI 0·71–1·19; p=0·54). Mean ALDS scores measured during follow-up did not differ between groups (85·7 [SD 6·8] in the intervention group and 85·7 [7·1] in the control group; adjusted mean difference −0·02, 95% CI −0·38 to 0·42; p=0·93). 309 (16%) of 1885 participants died in the intervention group, compared with 269 (16%) of 1634 participants in the control group (HR 0·98, 95% CI 0·80–1·18; p=0·81). Incident cardiovascular disease did not differ between groups (273 [19%] of 1469 participants in the intervention group and 228 [17%] of 1307 participants in the control group; HR 1·06, 95% CI 0·86–1·31; p=0·57). A nurse-led, multidomain intervention did not result in a reduced incidence of all-cause dementia in an unselected population of older people. This absence of effect might have been caused by modest baseline cardiovascular risks and high standards of usual care. Future studies should assess the efficacy of such interventions in selected populations. Dutch Ministry of Health, Welfare and Sport; Dutch Innovation Fund of Collaborative Health Insurances; and Netherlands Organisation for Health Research and Development.