ArticleWiener klinische Wochenschrift2012
Correction factor for the analysis of the hip fracture incidence--differences between age, sex, region, and calendar year.
Article in Wiener klinische Wochenschrift, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Interleukin-6 as possible early marker of stress response after femoral fracture.Molecular and cellular biochemistry · 2017Trial
- Epidemiology of distal radius fractures in Germany - incidence rates and trends based on inpatient and outpatient data.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2024Observational
- Extremely low frequency pulsed electromagnetic fields cause antioxidative defense mechanisms in human osteoblasts via induction of •OScientific reports · 2017Article
- Increased oxidative stress response in granulocytes from older patients with a hip fracture may account for slow regeneration.Oxidative medicine and cellular longevity · 2014Article
- Reliability of school surveys in estimating geographic variation in malaria transmission in the western Kenyan highlands.PloS one · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Several studies evaluated hip fracture incidences and its predictors and trends using hospital discharge registries. However, this source does not provide patient-related data, therefore the hospital changes or re-hospitalisations cannot be identified as "double counting". If double counting differs with age, sex, region, and time, the estimates may be biased. Aim of our study was to evaluate the magnitude of multiple counting and, in particular, its variation with age, sex, region, and calendar year. We used data of a German-wide health insurance (1.6 million members). Between 1998 and 2009, we assessed all hip fractures (ICD 9: 820, ICD 10: S.72.0-2) in individuals aged 50 years or older and calculated the probability to be a patient's "first" fracture in each calendar year. Using multiple logistic regressions, we estimated the influence of age, sex, region, and calendar year. The probabilities of a "first fracture" per patient and year varied between 86.7 % (95 % confidence interval 83.9-89.2 %, year 2003) and 93.9 % (90.9-96.2 %, year 1998). Age (odds ratio per 5 years 0.89; 95 % CI 0.86-0.92), region (East vs. West Germany: 0.65; 0.52-0.81), and calendar year (per year 0.97; 0.95-0.99) were significantly associated in the multiple regression. The probability to have multiple counting of hip fracture events varied significantly with age, region, and calendar year. It should be discussed that analyses which do not account for this may provide invalid estimates and conclusions when differences between age groups and regions or trends are analyzed.
Indexed as
Identifiers
22696226What 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.