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Wand H, Iversen J, Wilson D et al. Developing and validating a scoring tool for identifying people who inject drugs at increased risk of hepatitis C virus infection. BMJ Open 2012;2: e000387.
The following references were omitted from the paper:
20. Kshirsagar AV, Bang H, Bomback AS et al. A simple algorithm to predict incident kidney disease. Arch Intern Med 2008;168:2466–73.
25. Iversen J, Wand H, Gonnermann A, et al. Gender differences in hepatitis C antibody prevalence and risk behaviours amongst people who inject drugs in Australia 1998–2008. Int J Drug Policy 2010;21:471–76.
26. Bang H, Edwards AM, Bornback AS, et al. Development and validation of a patient self-assessment score for diabetes risk. Ann Intern Med 2009;151:775–83.
27. Bang H, Vupputuri S, Shoham DA, et al. SCreening for Occult REnal Disease (SCORED): a simple prediction model for chronic kidney disease. Arch Intern Med 2007;167:374–81.
36. Trooskin SB, Navarro VJ, Winn RJ, et al. Hepatitis C risk assessment, testing and referral for treatment in urban primary care: Role of race and ethnicity. World J Gastroenterol 2007;13:1074–1078.
References in the manuscript should be re-numbered accordingly.
‘INTRODUCTION’, fourth paragraph should have read:
‘…in the development of the new prediction algorithm”: (1) the use of routinely available and minimally intrusive variables and (2) estimation of the cumulative effect of concurrent risk factors on the likelihood of”20 HCV prevalence. “We are unaware of any studies”20 to date “that have quantified the cumulative effect of concurrent risk factors on the”20 acquisition of…’
‘STUDY POPULATION’, second paragraph, should have read:
“A modified, third generation enzyme immunoassay (Abbott hepatitis C 3.0, Chicago, IL, USA) was used to test for HCV antibody. A modified cutoff value for optical density was calculated to capture greater than 95% of the seronegative population. Specimens were considered positive for HCV antibody if the optical density to cutoff ratio was greater than or equal to one on initial and subsequent testing”.25
‘STATISTICAL ANALYSES’, third paragraph, should have read:
‘We used descriptive statistics to characterize the groups according to antibody HCV serostatus: mean and standard deviation (SD) “for continuous variables and percentages for categorical variables”.26 Logistic regression was used to create a predictive model based on the development data set. We used all non-missing observations available in the relevant analyses as only a small proportion of observations had any missing data (except for the variable “imprisonment history”).’26
‘Derivation of a Screening Score’, first paragraph, should have read:
‘Using the development data set (n = 10 662), “we included a comprehensive list of predictors known to be associated” with HCV antibody seropositivity “in an initial model”.26 “Specifically, we included the main effects of all variables listed in Table 1”.26
‘“We first analyzed the univariate associations between the independent variables and”27 HCV seropositivity. Backward elimination was used to reach the final multivariate “model, in which factors with the largest P value”’.20,27
‘We then “created a weighted scoring system by rounding all regression coefficients”26 up to the nearest integer (ie, “the smallest integer greater than”27 the estimate). “This method was based on the β-coefficients (or log of the odds ratios) rather than odds ratios, which can be excessively influenced by only a few factors”.27 Once the final model was defined, we created integer weights for each variable. We calculated these weights “by multiplying the model coefficients by 10. Using the rounded weights in the risk function, we estimated the participant-specific”27 probabilities of HCV seropositivity and characterized the “degrees of risk based on cut-off points of the probability distribution”’.20,27
‘Cross-sectional internal validation’, second paragraph, should have read:
‘We used the cross-sectional dataset to check the sensitivity and robustness of the new screening score. We computed standard validation measures: the proportion of antibody HCV seropositive specimens, sensitivity, specificity, positive likelihood and negative likelihood ratio and the area “under the receiver operating characteristic curve (AUC) as discrimination statistics. We also assessed the diagnostic characteristics of different cut-points based on the total score in the development as well as validation’.26
‘Prospective external validation’, first paragraph, should have read:
‘Using the Cox regression coefficients in the risk function, the participant-specific probability of HIV seroconversion was estimated. A rule to characterize different degrees of risk based on cut- off points of the probability distribution was then established. We also assessed the diagnostic features and characterized “different degrees of risk based on cut-off points of the probability distribution”’.20
‘RESULTS’, second paragraph, should have read:
‘Table 2 presents the final multivariate logistic regression model derived from the development data set by age groups’.26
‘…multiple categories “to capture the risk gradient, whereas other risk factors were binary”’.26
‘DISCUSSION’, second paragraph, should have read:
‘…of current HCV diagnoses and not incident HCV in the future, strong consistency in risk factors for the prediction incident events of HCV was shown in prospective validation of the tool.26 Therefore, “we expect that the same set of risk factors in our model plays an important role in the prediction of future”26….’
‘DISCUSSION’, fifth paragraph, should have read:
‘Risk calculation approaches have been extensively “used in decision making about public health and clinical care and have even been proposed as an alternative to diagnosis for some diseases”.26,33 Our risk calculation was based on a statistical method that yielded a systematic scoring system for carefully selected predictors, guided not only by numerical and scientific evidence but also feasibility perspectives.26 We “chose categorized variables”26 which highlighted the “important risk factors to motivate high-risk persons to be screened or to modify behaviors. This combination of factors may explain the enhanced properties of our”26 scoring tool’.
‘DISCUSSION’, sixth paragraph, should have read:
“Ideal risk assessment methods or prediction models should be derived from large representative samples”.26
‘DISCUSSION’, seventh paragraph, should have read:
“Risk factor screening and identification allows for patients to be educated regarding the risks of injection drug use and needle sharing. Appropriate testing and diagnosis of HCV allows for the patient to be evaluated for treatment and receive counseling regarding”36 HCV prevention. “In addition to physician education, patient education campaigns must also be developed to increase patient compliance with testing recommendations made by their physicians”.36
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