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As was the case for the general population univariate and multivariate analyses, there was no evidence of a statistically significant decline in HCV prevalence over time for each high risk population subgroup (results not shown) and in the high risk population as a whole (p-value of 0.426).
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The Canadian Triage and Acuity Score (CTAS) has been structured using narrative description of the patient's medical condition including alarming signs and symptoms for each high-risk complaint [2].
Final models for each high-risk HPV type included the individual high-risk HPV type, age, CD4+ lymphocyte count, and presence of more than one high-risk HPV.
The planed sample of 1000 women for each high-risk group and 2000 low-risk women will be adequate to detect differences for genotypes with a prevalence of 1%1.5%% in the control group.
In adjusted models adjusting for multiple comparisons, the prevalence ratio point estimates for each high-risk HPV type were attenuated and no longer statistically significant, based upon a Bonferroni corrected p value of <0.004 for significance.
Definitions for high-risk variables were determined based on a combination of one or more factors, including validated International Classification of Diseases, 9th Revision (ICD-9) codes (8), laboratory data, and clinical information, with detailed criteria for each high-risk factor listed previously (6).
A recent study of >59,000 cervical cytology specimens and HPV genotypes from New Mexico demonstrated that, with the exception of HPV 16, HPV infection by more than one high-risk type conferred additional risk of HSIL compared to monoinfection for each high-risk type examined.
These cut-off values were applied to the patients to create two groups for each predictor - high risk and low risk.
Each item is scored 'yes' for high risk, 'no' for low risk and 'unclear' if not specified in the paper.
The article suggested a numbered system of one for low risk to five for high risk.
We calculated beta coefficients and standard errors for high risk associated with each pre-specified Medicare variable from a multivariable logistic regression model.
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