ArticleContemporary clinical trials2008
Hierarchical models to evaluate translational research: Connecticut collaboration for fall prevention.
Article in Contemporary clinical trials, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Association between treatment or usual care region and hospitalization for fall-related traumatic brain injury in the Connecticut Collaboration for Fall Prevention.Journal of the American Geriatrics Society · 2013Trial
- The epidemiologic data on falls, 1998-2010: more older Americans report falling.JAMA internal medicine · 2015Article
- Integration of fall prevention into state policy in Connecticut.The Gerontologist · 2013Article
- Bayesian hierarchical modeling for a non-randomized, longitudinal fall prevention trial with spatially correlated observations.Statistics in medicine · 2011Article
- It takes a village to prevent falls: reconceptualizing fall prevention and management for older adults.Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention · 2008Review
- Effect of dissemination of evidence in reducing injuries from falls.The New England journal of medicine · 2008Article
- An examination of effect estimation in factorial and standardly-tailored designs.Clinical trials (London, England) · 2008Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
BACKGROUND AND
objectiveEvidence-based second stage translational studies are necessary and difficult to evaluate. A quasi-experimental design is used to compare the rate of fall-related health care utilization of two geographically disparate areas in Connecticut, a small state in the northeastern United States, to evaluate an intervention designed to reduce fall-related injuries among older persons. This evaluation examines the two years immediately prior to intervention.
methodsThe experimental units are postal (i.e., zip) code tabulation areas (ZCTAs) in which counts of fall-related health care utilization and demographic characteristics can be gathered from local and federal public health sources. We employ hierarchical modeling to determine whether there was a difference in fall-related health care utilization between the study arms prior to initiating the intervention. Geographic information systems are used to characterize neighboring ZCTAs and to graph model-adjusted rates of fall-related utilization.
resultsAfter adjustment for covariates and spatial variation, we observed no significant difference between rates or temporal trends of fall-related health care utilization in the study arms over the two year pre-intervention period.
conclusionThe study arms of the Connecticut Collaboration for Falls Prevention have equivalent rates and temporal trends of fall-related utilization over the two year pre-intervention period.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.