Definition of Local Labor Market Areas in Greece on the Basis of Travel-to-Work Flows

Labor does not move only between firms and occupations; labor moves also between geographic areas. The territorial dimension of labor markets, however, has been rather loosely conceptualized, suggesting a unity absent in practice, probably because spatial theories have been developed, to a great extent, separately from the economic ones. The recognition of the “multiplicity of sub-markets†in the real world – noticeable is the term “balkanization†– necessitates the definition of local labor market areas (LLMAs) since the geographical dimension of both production process and labor force breeds territorial partitions in the labor market, setting obstacles to – and creating opportunities for – the mobility of (potential) workers. The aim of the paper is the definition of LLMAs in Greece on the basis of travel-to-work flows (i.e. incoming and outgoing), towards the formation of better-targeted policy interventions. The definition of LLMAs is bound to establish a unit of locality which commands general acceptance as a reference for addressing issues of planning and development as well as issues of labor market, in a manner which is not possible through the conventional, administrative and/or statistical, territorial partitions. The identification of the functional linkages, under the prism of territorial structure and hierarchy, which exist within and between LLMAs is going to detect relations of interaction, overlapping and interdependence – and also discontinuities – in the Greek territory. The analysis is going to utilize the disaggregated travel-to-work data, among the 1,034 municipalities and communities, solicited in the 2001 Population Census and included in the “Panorama of Census Data 1991-2001â€Â. The aforementioned data are referred to permanent population and include both daily and seasonal travel-to-work flows.

Potential adjustment methodology for missing data and reporting delay in the HIV Surveillance System, European Union/European Economic Area, 2015

HIV remains one of the most important public health concerns in the European Union and European Economic Area (EU/EEA). Accurate data are therefore crucial to appropriately direct and evaluate public health response. The European Centre for Disease Prevention and Control (ECDC) and the World Health Organization Regional Office for Europe (WHO/Europe) have jointly coordinated enhanced HIV/AIDS surveillance in the European Region since 2008. The general objectives of the surveillance system in EU/EEA countries include monitoring of trends over time and across countries. Specific HIV-related objectives include the monitoring of testing patterns, late HIV diagnoses, defined by low CD4+ counts (<350 cells/mm3), and mortality, as well estimating HIV incidence and prevalence stratified by key populations, e.g. transmission category and migrant status [1]. To meet these objectives, the long-term strategy states that improving the quality of surveillance data is needed [2]. Achieving this in practice poses challenges, especially given the heterogeneous national surveillance systems in the EU/EEA and that the routinely collected data are known to suffer from important quality limitations. The limitations originating from national data collection systems may include under-reporting or duplication of cases, delays in reporting, incompleteness of data and misclassification. Accounting for some of these limitations (e.g. assessment of under-reporting) requires additional data such as cohort studies or registries, while other issues, such as incompleteness and reporting delay, may be addressed directly within the surveillance datasets. Missing data are a well-recognised problem within surveillance systems. When values for some variables are missing and cases with missing values are excluded from analysis, it may lead to biased and potentially less precise estimates [3,4]. In principle, whenever there are missing data or reporting delays, the accuracy of epidemiological distributions and trends should be interpreted with caution. Reporting delay, the time from case diagnosis to notification, can lead to problems when analysing the most recent years, given that the information on some cases or variables may not have been collected yet because of national reporting process characteristics. This phenomenon is common in disease surveillance and also applies to HIV [5-8]. Rough adjustments for reporting delay were already implemented in the past in Europe [8,9], but further refinement of the existing applied methodology is needed to address this issue across more countries’ data. The main purpose of this paper is to explore the issues of missing data and reporting delay in EU/EEA HIV surveillance data. We aim to quantify the extent to which these problems are present and to identify specific data characteristics that are relevant for data adjustments. Taking these characteristics into account, we also propose methods to adjust for missing data and reporting delay based on literature and existing national practices in EU/EEA countries.Peer Reviewe

Bayesian mixture models for phylogenetic source attribution from consensus sequences and time since infection estimates

In stopping the spread of infectious diseases, pathogen genomic data can be used to reconstruct transmission events and characterize population-level sources of infection. Most approaches for identifying transmission pairs do not account for the time that passed since divergence of pathogen variants in individuals, which is problematic in viruses with high within-host evolutionary rates. This is prompting us to consider possible transmission pairs in terms of phylogenetic data and additional estimates of time since infection derived from clinical biomarkers. We develop Bayesian mixture models with an evolutionary clock as signal component and additional mixed effects or covariate random functions describing the mixing weights to classify potential pairs into likely and unlikely transmission pairs. We demonstrate that although sources cannot be identified at the individual level with certainty, even with the additional data on time elapsed, inferences into the population-level sources of transmission are possible, and more accurate than using only phylogenetic data without time since infection estimates. We apply the approach to estimate age-specific sources of HIV infection in Amsterdam MSM transmission networks between 2010-2021. This study demonstrates that infection time estimates provide informative data to characterize transmission sources, and shows how phylogenetic source attribution can then be done with multi-dimensional mixture models

CASCADE protocol: exploring current viral and host characteristics, measuring clinical and patient-reported outcomes, and understanding the lived experiences and needs of individuals with recently acquired HIV infection through a multicentre mixed-methods observational study in Europe and Canada

Introduction: Despite the availability of pre-exposure prophylaxis (PrEP) and antiretroviral therapy (ART), 21 793 people were newly diagnosed with HIV in Europe in 2019. The Concerted action on seroconversion to AIDS and death in Europe study aims to understand current drivers of the HIV epidemic; factors associated with access to, and uptake of prevention methods and ART initiation; and the experiences, needs and outcomes of people with recently acquired HIV. / Methods and analysis: This longitudinal observational study is recruiting participants aged ≥16 years with documented laboratory evidence of HIV seroconversion from clinics in Canada and six European countries. We will analyse data from medical records, self-administered questionnaires, semistructured interviews and participatory photography. We will assess temporal trends in transmitted drug resistance and viral subtype and examine outcomes following early ART initiation. We will investigate patient-reported outcomes, well-being, and experiences of, knowledge of, and attitudes to HIV preventions, including PrEP. We will analyse qualitative data thematically and triangulate quantitative and qualitative findings. As patient public involvement is central to this work, we have convened a community advisory board (CAB) comprising people living with HIV. / Ethics and dissemination: All respective research ethics committees have approval for data to contribute to international collaborations. Written informed consent is required to take part. A dissemination strategy will be developed in collaboration with CAB and the scientific committee. It will include peer-reviewed publications, conference presentations and accessible summaries of findings on the study’s website, social media and via community organisations

Does rapid HIV disease progression prior to combination antiretroviral therapy hinder optimal CD4 + T-cell recovery once HIV-1 suppression is achieved?

This article compares trends in CD4 + T-cell recovery and proportions achieving optimal restoration (≥500 cells/μl) after viral suppression following combination antiretroviral therapy (cART) initiation between rapid and nonrapid progressors. We included HIV-1 seroconverters achieving viral suppression within 6 months of cART. Rapid progressors were individuals experiencing at least one CD4 + less than 200 cells/μl within 12 months of seroconverters before cART. We used piecewise linear mixed models and logistic regression for optimal restoration. Of 4024 individuals, 294 (7.3%) were classified as rapid progressors. At the same CD4 + T-cell count at cART start (baseline), rapid progressors experienced faster CD4 + T-cell increases than nonrapid progressors in first month [difference (95% confidence interval) in mean increase/month (square root scale): 1.82 (1.61; 2.04)], which reversed to slightly slower increases in months 1-18 [−0.05 (−0.06; −0.03)] and no significant differences in 18-60 months [−0.003 (−0.01; 0.01)]. Percentage achieving optimal restoration was significantly lower for rapid progressors than nonrapid progressors at months 12 (29.2 vs. 62.5%) and 36 (47.1 vs. 72.4%) but not at month 60 (70.4 vs. 71.8%). These differences disappeared after adjusting for baseline CD4 + T-cell count: odds ratio (95% confidence interval) 0.86 (0.61; 1.20), 0.90 (0.38; 2.17) and 1.56 (0.55; 4.46) at months 12, 36 and 60, respectively. Among people on suppressive antiretroviral therapy, rapid progressors experience faster initial increases of CD4 + T-cell counts than nonrapid progressors, but are less likely to achieve optimal restoration during the first 36 months after cART, mainly because of lower CD4 + T-cell counts at cART initiation

Differences in HIV Natural History among African and Non-African Seroconverters in Europe and Seroconverters in Sub-Saharan Africa

Introduction It is unknown whether HIV treatment guidelines, based on resource-rich country cohorts, are applicable to African populations. Methods We estimated CD4 cell loss in ART-naïve, AIDS-free individuals using mixed models allowing for random intercept and slope, and time from seroconversion to clinical AIDS, death and antiretroviral therapy (ART) initiation by survival methods. Using CASCADE data from 20 European and 3 sub-Saharan African (SSA) cohorts of heterosexually-infected individuals, aged ≥15 years, infected ≥2000, we compared estimates between non-African Europeans, Africans in Europe, and Africans in SSA. Results Of 1,959 (913 non-Africans, 302 Europeans - African origin, 744 SSA), two-thirds were female; median age at seroconversion was 31 years. Individuals in SSA progressed faster to clinical AIDS but not to death or non-TB AIDS. They also initiated ART later than Europeans and at lower CD4 cell counts. In adjusted models, Africans (especially from Europe) had lower CD4 counts at seroconversion and slower CD4 decline than non-African Europeans. Median (95% CI) CD4 count at seroconversion for a 15–29 year old woman was 607 (588–627) (non-African European), 469 (442–497) (European - African origin) and 570 (551–589) (SSA) cells/µL with respective CD4 decline during the first 4 years of 259 (228–289), 155 (110–200), and 199 (174–224) cells/µL (p<0.01). Discussion Despite differences in CD4 cell count evolution, death and non-TB AIDS rates were similar across study groups. It is therefore prudent to apply current ART guidelines from resource-rich countries to African populations

Causal relationships between frequency bands of extracellular signals in visual cortex revealed by an information theoretic analysis

Characterizing how different cortical rhythms interact and how their interaction changes with sensory stimulation is important to gather insights into how these rhythms are generated and what sensory function they may play. Concepts from information theory, such as Transfer Entropy (TE), offer principled ways to quantify the amount of causation between different frequency bands of the signal recorded from extracellular electrodes; yet these techniques are hard to apply to real data. To address the above issues, in this study we develop a method to compute fast and reliably the amount of TE from experimental time series of extracellular potentials. The method consisted in adapting efficiently the calculation of TE to analog signals and in providing appropriate sampling bias corrections. We then used this method to quantify the strength and significance of causal interaction between frequency bands of field potentials and spikes recorded from primary visual cortex of anaesthetized macaques, both during spontaneous activity and during binocular presentation of naturalistic color movies. Causal interactions between different frequency bands were prominent when considering the signals at a fine (ms) temporal resolution, and happened with a very short (ms-scale) delay. The interactions were much less prominent and significant at coarser temporal resolutions. At high temporal resolution, we found strong bidirectional causal interactions between gamma-band (40–100 Hz) and slower field potentials when considering signals recorded within a distance of 2 mm. The interactions involving gamma bands signals were stronger during movie presentation than in absence of stimuli, suggesting a strong role of the gamma cycle in processing naturalistic stimuli. Moreover, the phase of gamma oscillations was playing a stronger role than their amplitude in increasing causations with slower field potentials and spikes during stimulation. The dominant direction of causality was mainly found in the direction from MUA or gamma frequency band signals to lower frequency signals, suggesting that hierarchical correlations between lower and higher frequency cortical rhythms are originated by the faster rhythms

A Phylogenetic Analysis of Human Immunodeficiency Virus Type 1 Sequences in Kiev: Findings Among Key Populations

Background: The human immunodeficiency virus (HIV) epidemic in Ukraine has been driven by a rapid rise among people who inject drugs, but recent studies have shown an increase through sexual transmission. Methods: Protease and reverse transcriptase sequences from 876 new HIV diagnoses (April 2013–March 2015) in Kiev were linked to demographic data. We constructed phylogenetic trees for 794 subtype A1 and 64 subtype B sequences and identified factors associated with transmission clustering. Clusters were defined as ≥2 sequences, ≥80% local branch support, and maximum genetic distance of all sequence pairs in the cluster ≤2.5%. Recent infection was determined through the limiting antigen avidity enzyme immunoassay. Sequences were analyzed for transmitted drug resistance mutations. Results Thirty percent of subtype A1 and 66% of subtype B sequences clustered. Large clusters (maximum 11 sequences) contained mixed risk groups. In univariate analysis, clustering was significantly associated with subtype B compared to A1 (odds ratio [OR], 4.38 [95% confidence interval {CI}, 2.56–7.50]); risk group (OR, 5.65 [95% CI, 3.27–9.75]) for men who have sex with men compared to heterosexual males; recent, compared to long-standing, infection (OR, 2.72 [95% CI, 1.64–4.52]); reported sex work contact (OR, 1.93 [95% CI, 1.07–3.47]); and younger age groups compared with age ≥36 years (OR, 1.83 [95% CI, 1.10–3.05] for age ≤25 years). Females were associated with lower odds of clustering than heterosexual males (OR, 0.49 [95% CI, .31–.77]). In multivariate analysis, risk group, subtype, and age group were independently associated with clustering (P < .001, P = .007, and P = .033, respectively). Eighteen sequences (2.1%) indicated evidence of transmitted drug resistance. Conclusions Our findings suggest high levels of transmission and bridging between risk groups

Analyzing longitudinal data in the presence of informative drop-out: The jmre1 command

Many studies in various research areas have designs that involve repeated measurements over time of a continuous variable across a group of subjects. A frequent and serious problem in such studies is the occurrence of missing data. In many cases, missing data are caused by an event that leads to a premature termination of the series of repeated measurements on some subjects. When the probability of the occurrence of this event is related to the subject-specific underlying trend of the variable of interest, this missingness process is called informative censoring or informative drop-out. Standard likelihood-based methods (for example, linear mixed models) fail to give consistent estimates. In such cases, one needs to apply methods that simultaneously model the observed data and the missingness process. In this article, we review a method proposed by Touloumi et al. (1999, Statistics in Medicine 18: 1215–1233) to adjust for informative drop-out in longitudinal data analysis. We also present the jmre1 command, which can be used to fit the proposed model. The estimation method combines the restricted iterative generalized least-squares method with a nested expectation-maximization algorithm. The method is implemented mainly using Stata’s matrix programming language, Mata. Our example is derived from the epidemiology of the HIV infection

Robustness of a parametric model for informatively censored bivariate longitudinal data under misspecification of its distributional assumptions: A simulation study

Repeated measurements of surrogate markers are frequently used to track disease progression, but these series are often prematurely terminated due to disease progression or death. Analysing such data through standard likelihood-based approaches can yield severely biased estimates if the censoring mechanism is non-ignorable. Motivated by this problem, we have proposed the bivariate joint multivariate random effects (JMRE) model, which has shown that when correctly specified it performs well in terms of bias reduction and precision. The bivariate JMRE model is fully parametric and belongs to the class of shared parameters joint models where a survival model for the dropouts and a mixed model for the markers’ evolution are linked through a multivariate normal distribution of random effects. As in every parametric model, robustness under violations of its distributional assumptions is of great importance. In this study we generated 500 simulated data sets assuming that random effects jointly follow a heavy-tailed distribution, two skewed distributions or a mixture of two normal distributions. Moreover, we generated data where level-1 errors or residuals in the survival part of the model follow a skewed distribution. Further sensitivity analysis on the effects of reduced sample size, increased level-1 variances and altered fixed effects values was also performed. We found that fixed effects estimates are almost unaffected, but their standard errors (SEs) may be underestimated especially under heavily skewed distributions. The proposed model seems robust enough, but its performance on smaller data sets or under more extreme departures of its assumptions needs further investigation. Copyright (C) 2007 John Wiley &amp; Sons, Ltd