Prevalence of chronic HCV infection in EU/EEA countries in 2019 using multiparameter evidence synthesis.

Background: Epidemiological data are crucial to monitoring progress towards the 2030 Hepatitis C Virus (HCV) elimination targets. Our aim was to estimate the prevalence of chronic HCV infection (cHCV) in the European Union (EU)/European Economic Area (EEA) countries in 2019. Methods: Multi-parameter evidence synthesis (MPES) was used to produce national estimates of cHCV defined as: π = πrecρrec + πexρex + πnonρnon; πrec, πex, and πnon represent cHCV prevalence among recent people who inject drugs (PWID), ex-PWID, and non-PWID, respectively, while ρrec, ρex, and ρnon represent the proportions of these groups in the population. Information sources included the European Centre for Disease Prevention and Control (ECDC) national operational contact points (NCPs) and prevalence database, the European Monitoring Centre for Drugs and Drug Addiction databases, and the published literature. Findings: The cHCV prevalence in 29 of 30 EU/EEA countries in 2019 was 0.50% [95% Credible Interval (CrI): 0.46%, 0.55%]. The highest cHCV prevalence was observed in the eastern EU/EEA (0.88%; 95% CrI: 0.81%, 0.94%). At least 35.76% (95% CrI: 33.07%, 38.60%) of the overall cHCV prevalence in EU/EEA countries was associated with injecting drugs. Interpretation: Using MPES and collaborating with ECDC NCPs, we estimated the prevalence of cHCV in the EU/EEA to be low. Some areas experience higher cHCV prevalence while a third of prevalent cHCV infections was attributed to PWID. Further efforts are needed to scale up prevention measures and the diagnosis and treatment of infected individuals, especially in the east of the EU/EEA and among PWID. Funding: ECDC.

Issues with the expected information matrix of linear mixed models provided by popular statistical packages under missingness at random dropout.

Likelihood-based methods ignoring missingness at random (MAR) produce consistent estimates provided that the whole likelihood model is correct. However, the expected information matrix (EIM) depends on the missingness mechanism. It has been shown that calculating the EIM by considering the missing data pattern as fixed (naive EIM) is incorrect under MAR, but the observed information matrix (OIM) is valid under any MAR missingness mechanism. In longitudinal studies, linear mixed models (LMMs) are routinely applied, often without any reference to missingness. However, most popular statistical packages currently provide precision measures for the fixed effects by inverting only the corresponding submatrix of the OIM (naive OIM), which is effectively equivalent to the naive EIM. In this paper, we analytically derive the correct form of the EIM of LMMs under MAR dropout to compare its differences with the naive EIM, which clarifies why the naive EIM fails under MAR. The asymptotic coverage rate of the naive EIM is numerically calculated for two parameters (population slope and slope difference between two groups) under various dropout mechanisms. The naive EIM can severely underestimate the true variance, especially when the degree of MAR dropout is high. Similar trends emerge under misspecified covariance structure, where, even the full OIM may lead to incorrect inferences and sandwich/bootstrap estimators are generally required. Results from simulation studies and application to real data led to similar conclusions. In LMMs, the full OIM should be preferred to the naive EIM/OIM, though if misspecified covariance structure is suspected, robust estimators should be used.

The Effect of HIV Treatment Interruption on Subsequent Immunological Response.

Recovery of CD4-positive T lymphocyte count after initiation of antiretroviral therapy (ART) has been thoroughly examined among people with human immunodeficiency virus infection. However, immunological response after restart of ART following care interruption is less well studied. We compared CD4 cell-count trends before disengagement from care and after ART reinitiation. Data were obtained from the East Africa International Epidemiology Databases to Evaluate AIDS (IeDEA) Collaboration (2001-2011; n = 62,534). CD4 cell-count trends before disengagement, during disengagement, and after ART reinitiation were simultaneously estimated through a linear mixed model with 2 subject-specific knots placed at the times of disengagement and treatment reinitiation. We also estimated CD4 trends conditional on the baseline CD4 value. A total of 10,961 patients returned to care after disengagement from care, with the median gap in care being 2.7 (interquartile range, 2.1-5.4) months. Our model showed that CD4 cell-count increases after ART reinitiation were much slower than those before disengagement. Assuming that disengagement from care occurred 12 months after ART initiation and a 3-month treatment gap, CD4 counts measured at 3 years since ART initiation would be lower by 36.5 cells/µL than those obtained under no disengagement. Given that poorer CD4 restoration is associated with increased mortality/morbidity, specific interventions targeted at better retention in care are urgently required.

Joint modeling of longitudinal and competing-risk data using cumulative incidence functions for the failure submodels accounting for potential failure cause misclassification through double sampling.

Most of the literature on joint modeling of longitudinal and competing-risk data is based on cause-specific hazards, although modeling of the cumulative incidence function (CIF) is an easier and more direct approach to evaluate the prognosis of an event. We propose a flexible class of shared parameter models to jointly model a normally distributed marker over time and multiple causes of failure using CIFs for the survival submodels, with CIFs depending on the "true" marker value over time (i.e., removing the measurement error). The generalized odds rate transformation is applied, thus a proportional subdistribution hazards model is a special case. The requirement that the all-cause CIF should be bounded by 1 is formally considered. The proposed models are extended to account for potential failure cause misclassification, where the true failure causes are available in a small random sample of individuals. We also provide a multistate representation of the whole population by defining mutually exclusive states based on the marker values and the competing risks. Based solely on the assumed joint model, we derive fully Bayesian posterior samples for state occupation and transition probabilities. The proposed approach is evaluated in a simulation study and, as an illustration, it is fitted to real data from people with HIV.

HIV continuum of care: bridging cross-sectional and longitudinal analyses.

OBJECTIVE: The aim of this study was to propose a unified continuum-of-care (CoC) analysis combining cross-sectional and longitudinal elements, incorporating time spent between stages. DESIGN: The established 90-90-90 target follows a cross-sectional four-stage CoC analysis, lacking information on timing of diagnosis, antiretroviral therapy (ART) initiation, and viral suppression durability. METHODS: Data were derived from the Athens Multicenter AIDS Cohort Study (AMACS). In the cross-sectional CoC, we added stratification of diagnosed people with HIV (PWH) by estimated time from infection to diagnosis; of those who ever initiated ART or achieved viral suppression by corresponding current status (in 2018); and cumulative incidence function (CIF) of ART initiation and viral suppression, treating loss-to-follow-up (LTFU) as competing event. Viral suppression was defined as viral load less than 500 copies/ml. Viral suppression durability was assessed by the CIF of viral load rebound. FINDINGS: About 89.1% of PWH in 2018 were diagnosed (range of diagnoses: 1980-2018). Median time to diagnosis was 3.5 years (IQR: 1.1-7.0). Among diagnosed, 89.1% were ever treated, of whom 86.7% remained on ART. CIF of ART initiation and LTFU before ART initiation were 80.9 and 6.0% at 5 years since diagnosis, respectively. Among treated, 89.4% achieved viral suppression, of whom 87.4% were currently virally suppressed. The CIF of viral load rebound was 24.2% at 5 years since first viral suppression but substantially reduced in more recent years. INTERPRETATION: The proposed analysis highlights time gaps in CoC not evident by the standard cross-sectional approach. Our analysis highlights the need for early diagnosis and identifies late presenters as a key population for interventions that could decrease gaps in the CoC.

Misspecifying the covariance structure in a linear mixed model under MAR drop-out.

Misspecification of the covariance structure in a linear mixed model (LMM) can lead to biased population parameters' estimates under MAR drop-out. In our motivating example of modeling CD4 cell counts during untreated HIV infection, random intercept and slope LMMs are frequently used. In this article, we evaluate the performance of LMMs with specific covariance structures, in terms of bias in the fixed effects estimates, under specific MAR drop-out mechanisms, and adopt a Bayesian model comparison criterion to discriminate between the examined approaches in real-data applications. We analytically show that using a random intercept and slope structure when the true one is more complex can lead to seriously biased estimates, with the degree of bias depending on the magnitude of the MAR drop-out. Under misspecified covariance structure, we compare in terms of induced bias the approach of adding a fractional Brownian motion (BM) process on top of random intercepts and slopes with the approach of using splines for the random effects. In general, the performance of both approaches was satisfactory, with the BM model leading to smaller bias in most cases. A simulation study is carried out to evaluate the performance of the proposed Bayesian criterion in identifying the model with the correct covariance structure. Overall, the proposed method performs better than the AIC and BIC criteria under our specific simulation setting. The models under consideration are applied to real data from the CASCADE study; the most plausible model is identified by all examined criteria.

Determining the likely place of HIV acquisition for migrants in Europe combining subject-specific information and biomarkers data.

In most HIV-positive individuals, infection time is only known to lie between the time an individual started being at risk for HIV and diagnosis time. However, a more accurate estimate of infection time is very important in certain cases. For example, one of the objectives of the Advancing Migrant Access to Health Services in Europe (aMASE) study was to determine if HIV-positive migrants, diagnosed in Europe, were infected pre- or post-migration. We propose a method to derive subject-specific estimates of unknown infection times using information from HIV biomarkers' measurements, demographic, clinical, and behavioral data. We assume that CD4 cell count (CD4) and HIV-RNA viral load trends after HIV infection follow a bivariate linear mixed model. Using post-diagnosis CD4 and viral load measurements and applying the Bayes' rule, we derived the posterior distribution of the HIV infection time, whereas the prior distribution was informed by AIDS status at diagnosis and behavioral data. Parameters of the CD4-viral load and time-to-AIDS models were estimated using data from a large study of individuals with known HIV infection times (CASCADE). Simulations showed substantial predictive ability (e.g. 84% of the infections were correctly classified as pre- or post-migration). Application to the aMASE study (n = 2009) showed that 47% of African migrants and 67% to 72% of migrants from other regions were most likely infected post-migration. Applying a Bayesian method based on bivariate modeling of CD4 and viral load, and subject-specific information, we found that the majority of HIV-positive migrants in aMASE were most likely infected after their migration to Europe.

Longitudinal and time-to-drop-out joint models can lead to seriously biased estimates when the drop-out mechanism is at random.

Missing data are common in longitudinal studies. Likelihood-based methods ignoring the missingness mechanism are unbiased provided missingness is at random (MAR); under not-at-random missingness (MNAR), joint modeling is commonly used, often as part of sensitivity analyses. In our motivating example of modeling CD4 count trajectories during untreated HIV infection, CD4 counts are mainly censored due to treatment initiation, with the nature of this mechanism remaining debatable. Here, we evaluate the bias in the disease progression marker's change over time (slope) of a specific class of joint models, termed shared-random-effects-models (SREMs), under MAR drop-out and propose an alternative SREM model. Our proposed model relates drop-out to both the observed marker's data and the corresponding random effects, in contrast to most SREMs, which assume that the marker and the drop-out processes are independent given the random effects. We analytically calculate the asymptotic bias in two SREMs under specific MAR drop-out mechanisms, showing that the bias in marker's slope increases as the drop-out probability increases. The performance of the proposed model, and other commonly used SREMs, is evaluated under specific MAR and MNAR scenarios through simulation studies. Under MAR, the proposed model yields nearly unbiased slope estimates, whereas the other SREMs yield seriously biased estimates. Under MNAR, the proposed model estimates are approximately unbiased, whereas those from the other SREMs are moderately to heavily biased, depending on the parameterization used. The examined models are also fitted to real data and results are compared/discussed in the light of our analytical and simulation-based findings.

Psychotic (delusional) depression and completed suicide: a systematic review and meta-analysis.

BACKGROUND: It remains unclear whether psychotic features increase the risk of completed suicides in unipolar depression. The present systematic review coupled with a meta-analysis attempts to elucidate whether unipolar psychotic major depression (PMD) compared to non-PMD presents higher rates of suicides. METHODS: A systematic search was conducted in Scopus, PubMed, and "gray literature" for all studies providing data on completed suicides in PMD compared to non-PMD, and the findings were then subjected to meta-analysis. All articles were independently extracted by two authors using predefined data fields. RESULTS: Nine studies with 33,873 patients, among them 828 suicides, met our inclusion criteria. PMD compared to non-PMD presented a higher lifetime risk of completed suicides with fixed-effect pooled OR 1.21 (95% CI 1.04-1.40). In a sub-analysis excluding a very large study (weight = 86.62%), and comparing 681 PMD to 2106 non-PMD patients, an even higher pooled OR was found [fixed-effect OR 1.69 (95% CI 1.16-2.45)]. Our meta-analysis may provide evidence that the presence of psychosis increases the risk of suicide in patients suffering from severe depression. The data are inconclusive on the contribution of age, mood congruence, comorbidity, and suicide method on PMD's suicide risk. The lack of accurate diagnosis at the time of suicide, PMD's diagnostic instability, and the use of ICD-10 criteria constitute the main study limitations. CONCLUSIONS: The presence of psychosis in major depression should alert clinicians for the increased risk of completed suicide. Thus, the implementation of an effective treatment both for psychotic depression and patients' suicidality constitutes a supreme priority.

In-hospital dynamics of glucose, blood pressure and temperature predict outcome in patients with acute ischaemic stroke.

INTRODUCTION: We aimed to assess alterations in glucose, blood pressure and temperature in acute ischaemic stroke and investigate their association with early all-cause mortality and functional outcome. PATIENTS AND METHODS: We studied all consecutive acute ischaemic stroke patients admitted in 2001-2010 to the Acute Stroke Unit, at Alexandra University Hospital, in Athens. Serial measurements were performed in the first seven days post-stroke and different parameters have been estimated: mean daily values, variability, subject-specific baseline levels and rate of change in serial measurements. Cox-proportional-hazards-model analysis and logistic-regression analysis were applied to investigate the association between these parameters and all-cause mortality and functional outcome after adjustment for known confounders of stroke outcome. RESULTS: In 1271 patients (mean age 72.3 ± 11.2 years), after adjusting for confounders, baseline glucose levels (HR: 1.005, 95%CI: 1.001-1.01; p = 0.017), variability of systolic BP (SBP) as estimated by standard deviation (HR: 1.028, 95%CI: 1.01-1.048; p = 0.005), the baseline temperature (HR: 2.758, 95%CI: 2.067-3.68; p < 0.001) and the rate of temperature change (HR: 1.841, 95%CI: 1.616-2.908; p < 0.001) were independently associated with all-cause mortality within three months. Poor functional outcome was associated with subject-specific baseline values of temperature (OR: 1.743; 95%CI: 1.076-2.825; p = 0.024), the rate of SBP (OR: 1.159; 95% CI: 1.047-1.280; p = 0.004) and temperature change (OR: 1.402; 95% CI: 1.061-1.853; p = 0.018). DISCUSSION: The main strength of our study is that we analysed simultaneously three parameters and we used four different variables for each parameter of interest. CONCLUSION: Baseline glucose levels, variability of SBP and baseline temperature and its rate of change are independent predictors of all-cause mortality. Baseline values of temperature and the rate of changes in SBP and temperature are independent predictors of poor functional outcome.

High levels of postmigration HIV acquisition within nine European countries.

OBJECTIVE: We aimed to estimate the proportion of postmigration HIV acquisition among HIV-positive migrants in Europe. DESIGN: To reach HIV-positive migrants, we designed a cross-sectional study performed in HIV clinics. METHODS: The study was conducted from July 2013 to July 2015 in 57 clinics (nine European countries), targeting individuals over 18 years diagnosed in the preceding 5 years and born abroad. Electronic questionnaires supplemented with clinical data were completed in any of 15 languages. Postmigration HIV acquisition was estimated through Bayesian approaches combining extensive information on migration and patients' characteristics. CD4 cell counts and HIV-RNA trajectories from seroconversion were estimated by bivariate linear mixed models fitted to natural history data. Postmigration acquisition risk factors were investigated with weighted logistic regression. RESULTS: Of 2009 participants, 46% were MSM and a third originated from sub-Saharan Africa and Latin America & Caribbean, respectively. Median time in host countries was 8 years. Postmigration HIV acquisition was 63% (95% confidence interval: 57-67%); 72% among MSM, 58 and 51% in heterosexual men and women, respectively. Postmigration HIV acquisition was 71% for Latin America and Caribbean migrants and 45% for people from sub-Saharan Africa. Factors associated with postmigration HIV acquisition among heterosexual women and MSM were age at migration, length of stay in host country and HIV diagnosis year and among heterosexual men, length of stay in host country and HIV diagnosis year. CONCLUSION: A substantial proportion of HIV-positive migrants living in Europe acquired HIV postmigration. This has important implications for European public health policies.

Discontinuation of oral antivirals in chronic hepatitis B: A systematic review.

UNLABELLED: The possibility of safe discontinuation of therapy with nucleos(t)ide analogues (NAs) remains one of the most controversial topics in the management of chronic hepatitis B. Therefore, we systematically reviewed the existing data on NA discontinuation in this setting and tried to identify factors affecting the probability of posttherapy remission. A literature search was performed in order to identify all published studies including patients who discontinued NAs in virological remission (VR) and were followed for ≥12 months thereafter. Twenty-five studies with 1716 patients were included. The pooled rates of durable VR remission were 51.4%, 39.3%, and 38.2% at 12, 24, and 36 months, respectively, after NA discontinuation, being relatively higher in initially hepatitis B e antigen (HBeAg)-positive patients (62.5%, 53.4%, 51.5%) than HBeAg-negative patients (43.7%, 31.3%, 30.1%) (P = 0.064). The weighted probability of durable biochemical remission was 65.4%, being numerically higher in HBeAg-positive than HBeAg-negative patients (76.2% versus 56.7%, P = 0.130). The weighted probability of hepatitis B surface antigen loss was 2.0%. The rates of durable VR did not significantly differ according to the VR definition (hepatitis B virus DNA <200, < 2000, < 20,000 IU/mL) or duration of on-therapy VR in HBeAg-positive patients, but they were significantly higher in studies with HBeAg-negative patients and on-therapy VR > 24 than ≤ 24 months (VR at 12 months off-NAs: 75.0% versus 35.6%, P = 0.005). The weighted probability of durable HBeAg seroconversion was 91.9% and 88.0% at 12 and 24 months, respectively, after NA discontinuation without being affected by the duration of on-therapy VR or consolidation therapy (>6 months in all studies). CONCLUSION: Durable VR seems to be feasible in a substantial proportion of patients who discontinue long-term NA therapy; on-therapy VR > 24 months offers higher chances of off-NA VR in patients with HBeAg-negative chronic hepatitis B.