A probability-based approach for predicting HIV infection in a low prevalent population of injection drug users

This article proposes a method for estimating HIV risk in low-HIV- prevalent populations. Allard's risk probability model was used to compute individual risk scores. Based on a sample of 3854 injection drug users (IDUs) who were confidentially tested for HIV at five methadone treatment clinics in Los Angeles County, the following self-reported risk behaviors were used to derive an individual IDU risk score: (i) frequency of injection, (ii) frequency of using uncleaned needles, (iii) number of people sharing a needle, (iv) frequency of needle sharing, and (v) type of needle sharing practice. The overall HIV prevalence for the IDU sample was 2%. The risk score was strongly associated with HIV seropositivity (chi-square = 16.1, p < 0.0001), but only one of the individual IDU risk behaviors (needle cleaning) was significantly associated with HIV seropositivity (chi-square = 10.9, P < 0.001). In addition, the risk score was strongly associated with HIV serostatus for both males and females. For females, however, none of the individual IDU risk behaviors were associated with HIV serostatus. Our findings indicate that when predicting HIV infection in a low-prevalence population, the probability-based risk score makes a statistically significant over individual IDU risk behaviors

Occupational risk of acquiring HIV infection through needlestick injuries.

Accidental needlestick exposures occur frequently among hospital personnel and account for most incidents of percutaneous injuries. Even if universal precautions were followed routinely, it is unlikely that multiple needlestick exposures could be avoided completely. Despite the likelihood of persons incurring multiple needlestick exposures, relatively little information is available on the cumulative risk of human immunodeficiency virus (HIV) infection for health care workers attending unrecognized HIV-infected patients. A quantitative method to estimate annual cumulative risk from multiple exposures is offered, and the risk of HIV infection is estimated by use of a probability model for health care workers in both hospital and emergency department settings