Prevalence Of Undiagnosed Infection Research Articles

Prevalence and detection of undiagnosed active hepatitis C virus infections in Navarre, Spain, 2017-2019.

undiagnosed active hepatitis C virus (HCV) infection remains an obstacle towards its eradication. This study aimed to estimate the prevalence of HCV infection and to describe the diagnostic advances in Navarre, Spain. HCV-infection diagnostic performance was analyzed in Navarre's primary and specialized health care between 2017 and 2019. The prevalence of undiagnosed infections was estimated for patients with programmed surgeries unrelated to HCV infection, who underwent a routine HCV antibody (anti-HCV) determination. HCV-RNA (viral load) was quantified in anti-HCV positive cases. The prevalence was standardized according to the sex and age distribution in the general population. from the 63,405 subjects examined for anti-HCV, 84 (five per 100,000 person-years) were diagnosed with an active infection. In Primary Health Care, 20,363 patients were analyzed and 47 active infections were detected, i.e. one case for every 433 people tested, implying 56 % of all identified active infections. On the other hand, 9,399 surgical patients were analyzed and 120 anti-HCV positive cases were detected (adjusted prevalence: 1.47 %; 95 % CI: 1.24-1.52). A positive viral load had been determined at any time in 66 cases (0.61 %), of which five were undiagnosed active infections (adjusted prevalence: 0.04 %; 95 % CI: 0.01-0.11). Preoperative screening allowed the detection of one undiagnosed infection per 795 people analyzed aged between 45 and 64 years. Primary Health Care efficiently contributes to the detection of undiagnosed HCV active infections. This may be speeded up by performing population screening, targeting subjects between 45 and 64 years of age.

Evaluation of hidden HIV infections in an urban ED with a rapid HIV screening program

BackgroundTo investigate the prevalence of undiagnosed HIV infections in an emergency department (ED) with an established screening program. MethodsEvaluation of the prevalence and risk factors for HIV from an 8-week (June 24, 2007–August 18, 2007) identity-unlinked HIV serosurvey, conducted at the same time as an ongoing opt-in rapid oral-fluid HIV screening program. Testing facilitators offering 24/7 bedside rapid testing to patients aged 18 to 64 years, with concordant collection of excess sera collected as part of routine clinical procedures. Known HIV positivity was determined by (1) medical record review or self-report from the screening program and/or (2) presence of antiretrovirals in serum specimens. ResultsAmong 3207 patients, 1165 (36.3%) patients were offered an HIV test. Among those offered, 567 (48.7%) consented to testing. Concordance identity-unlinked study revealed that the prevalence of undiagnosed infections was as follows: 2.3% in all patients, 1.0% in those offered testing vs 3.0% in those not offered testing (P < .001); and 1.3% in those who declined testing compared with 0.4% in those who were tested (P = .077). Higher median viral loads were observed in those not offered testing (14255 copies/mL; interquartile range, 1147-64354) vs those offered testing (1865 copies/mL; interquartile range, undetectable-21786), but the difference was not statistically significant. ConclusionsHigh undiagnosed HIV prevalence was observed in ED patients who were not offered HIV testing and those who declined testing, compared with those who were tested. This indicates that even with an intensive facilitator-based rapid HIV screening model, significant missed opportunities remain with regard to identifying undiagnosed infections in the ED.

Re: "Derivation and Validation of the Denver Human Immunodeficiency Virus (HIV) Risk Score for Targeted HIV Screening"

Haukoos et al. developed a commendable risk score for prescreening for human immunodeficiency virus (HIV) testing in clinical settings (1). Their thoughtfully constructed model contains a number of important predictors of HIV infection. Although the authors concluded the contrary, the predictive utility of the model, as displayed in the receiver operating characteristics curve, seemed to us to strongly endorse the United States Centers for Disease Control and Prevention (CDC) guidelines for general HIV screening with highly sensitive tests (2). The Denver sexually transmitted infection clinic data reflect a 0.5% prevalence of undiagnosed infection and an annual caseload of 10,000 patients (50 HIV-infected patients). Although values of sensitivity/specificity used to create Figure 2B in the article by Haukoos et al. were not reported, the joint maximum for both appears to be 80%/80% sensitivity/specificity on the curve for the derivation data. Applying the risk score at this cutpoint would result in referral of 2,030 patients for HIV-testing and detection of 40 HIV-positive patients, with 10 HIV-positive patients who received low risk scores not being tested. A value of the receiver operating characteristics curve that attains 95% sensitivity appears to occur at 20% specificity. Applying these properties, all but 2 HIV-infected patients would be detected, with the trade-off of conducting HIV testing on 8,008 patients. To identify all 50 infected patients, the figure suggests that one would need to screen nearly the entire population. The authors state as much, highlighting for the validation sample, “the top 3 risk groups represented 62.5%… of all patients diagnosed with HIV infection” (1, p 843), meaning that 37.5% of those infected would not be identified unless one also tested the lower-risk groups. The risk-score model appears to offer a very modest advantage over general HIV testing in the settings examined at the cost of obtaining extensive risk data to construct the score. This is aligned with a number of previous studies of targeted screening in clinical populations (3–5) and the CDC guidelines (2). We have additional concerns about the populations and methods used. First, in selecting a model derivation population of individuals reporting to a sexually transmitted infection clinic for HIV testing and a validation population of individuals clinically and behaviorally indicated for HIV testing, the authors have partially conditioned their analytic data on risk factors included in the final model. This likely biases observed associations with HIV infection and may help to explain why some traditionally strong predictors of undiagnosed HIV infection (e.g., partner number, condom use) were not retained in the final model. Because populations at increased risk were used for the score development, it is also unclear how this model would perform for more general healthcare settings with patients who have a broader distribution of risks and for which the CDC guidelines were intended. Second, it is unclear how the authors ascertained whether HIV-positive individuals were previously undiagnosed. Substantial numbers of individuals who tested HIV-positive may have had a previous diagnosis, and this may be an important consideration because risk factors for failure to disclose a previous diagnosis may differ from risk factors for an undiagnosed HIV infection (6–8). Last, we question the validity of constructing a risk score based on the addition of model slope parameter estimates from a logistic regression model (Table 2). This method is typically reserved for linear regression, whereas in the context of a logistic regression model, these parameters contribute multiplicatively to risk (9). Risk-score approaches to guide screening decisions are appealing, but in this case we feel that the data source limits the generalizability of the score to general populations. Even if it were generalizable, the results of the receiver operating characteristics curve indicate that a screening program, with higher costs required to identify patients for screening, would still require screening nearly everyone in a population to find those all of those living with HIV.

Improving Human Immunodeficiency Virus Testing Rates with an Electronic Clinical Reminder

Human immunodeficiency virus (HIV) is a major global public health problem. In the United States alone, 1 to 1.18 million people are affected, of whom approximately one quarter are unaware of their diagnosis. In the early stage of the epidemic, when effective therapy was not available, testing for HIV was done primarily to protect the nation’s blood supply. However, with the advent of highly active antiretroviral therapy, HIV is highly treatable, and those infected can live longer. Because more than one third of patients diagnosed with HIV are diagnosed with acquired immunodeficiency syndrome within 1 year after diagnosis, it is likely that many patients are infected for many years before diagnosis. Furthermore, beause approximately 20,000 new infections in the United tates are transmitted by people unaware of their HIV positive tatus, early detection of HIV has the potential to decrease ew infections and prevent complications from advanced disase and has been shown to be cost-effective. The Centers for Disease Control and Prevention recommends offering routine screening for HIV in health care settings for all patients aged 13 to 64 years, unless the prevalence of undiagnosed infection has been documented to be less than 0.1%.

Estimating Trends in Incidence, Time-to-Diagnosis and Undiagnosed Prevalence using a CD4-based Bayesian Back-calculation

Abstract There has been much recent speculation regarding the potential for HIV test-and-treat strategies to provide control of the HIV endemic. In the UK, despite advanced HIV surveillance and the implementation of a number of testing initiatives and attempts to widen access to antiretroviral drugs, the number of new diagnoses persists at a high level having risen considerably over the course of the last ten years. The extent to which this high level of diagnosis is attributable to levels of HIV transmission or improved rates of testing and diagnosis is unclear. To disentangle these competing factors, we use a Bayesian back-calculation based on HIV and AIDS diagnosis data augmented by observed CD4 count levels at diagnosis. The CD4 count acts as a prognostic marker indicative of the time-since-infection for any new diagnosis. In addition to estimating time-dependent rates of infection and diagnosis, we exploit the model structure to estimate posterior distributions for a number of key epidemiological quantities such as trends in the time-to-diagnosis and the time-since infection distributions as well as the prevalence of undiagnosed infection. These quantities are stratified by CD4 count where possible. The proposed methodology is applied to HIV/AIDS surveillance data from England &amp; Wales uncovering a decreasing trend in the time to diagnosis and stable levels of incidence in recent years.

Routine opt-out rapid HIV screening and detection of HIV infection in emergency department patients.

The Centers for Disease Control and Prevention (CDC) recommends routine (nontargeted) opt-out HIV screening in health care settings, including emergency departments (EDs), where the prevalence of undiagnosed infection is 0.1% or greater. The utility of this approach in EDs remains unknown. To determine whether nontargeted opt-out rapid HIV screening in the ED was associated with identification of more patients with newly diagnosed HIV infection than physician-directed diagnostic rapid HIV testing. Quasi-experimental equivalent time-samples design in an urban public safety-net hospital with an approximate annual ED census of 55,000 patient visits. Patients were 16 years or older and capable of providing consent for rapid HIV testing. Nontargeted opt-out rapid HIV screening and physician-directed diagnostic rapid HIV testing alternated in sequential 4-month time intervals between April 15, 2007, and April 15, 2009. Number of patients with newly identified HIV infection and the association between nontargeted opt-out rapid HIV screening and identification of HIV infection. In the opt-out phase, of 28,043 eligible ED patients, 6933 patients (25%) completed HIV testing (6702 patients were screened; 231 patients were diagnostically tested). Ten of 6702 patients (0.15%; 95% CI, 0.07%-0.27%) who did not decline HIV screening in the opt-out phase had new HIV diagnoses, and 5 of 231 patients (2.2%; 95% CI, 0.7%-5.0%) who were diagnostically tested during the opt-out phase had new HIV diagnoses. In the diagnostic phase, of 29,925 eligible patients, 243 (0.8%) completed HIV testing. Of these, 4 patients (1.6%; 95% CI, 0.5%-4.2%) had new diagnoses. The prevalence of new HIV diagnoses in the opt-out phase (including those diagnostically tested) and in the diagnostic phase was 15 in 28,043 (0.05%; 95% CI, 0.03%-0.09%) and 4 in 29,925 (0.01%; 95% CI, 0.004%-0.03%), respectively. Nontargeted opt-out HIV screening was independently associated with new HIV diagnoses (risk ratio, 3.6; 95% CI, 1.2-10.8) when adjusting for patient demographics, insurance status, and whether diagnostic testing was performed in the opt-out phase. The median CD4 cell count for those with new HIV diagnoses in the opt-out phase (including those diagnostically tested) and in the diagnostic phase was 69/microL (IQR, 17-430) and 13/microL (IQR, 11-15) , respectively (P = .02). Nontargeted opt-out rapid HIV screening in the ED, vs diagnostic testing, was associated with identification of a modestly increased number of patients with new HIV diagnoses, most of whom were identified late in the course of disease.