Quantitative Assessment of High School Students' Poly-drug Use: Related Social Aspects and School Dropout

INTRODUCTION

Poly-drug use has increased in recent decades, especially in young drug-using groups. Classic epidemiological indicators of drug use, such as prevalence and incidence of users of specific substances, are not adequate as measures of the possible harms of poly-drug use. We applied poly-drug use indicators, based on substance-specific harm scores reported by van Amsterdam and Nutt in 2015, to data from high school student surveys, showing their usefulness in identifying high-risk drug consumption. Analysing the 'correlation' between high-risk drug use of high school students and school dropout allows the evaluation of adopted prevention policies and may suggest more suitable approaches.

METHODS

Each drug user is characterized by two specific scores: overall frequency of use of substances during the period of interest (FUS) and poly-drug use score (PDS). The poly-drug use score is a weighted average of the harm scores of the individual substances used multiplied by their respective frequencies of use. The PDS increases with the frequency of use, with the number of substances used, and with the specific harm scores of each substance. This indicator consists of two components, one representing the health harm score toward self and the other the social harm score toward others.

RESULTS

The indicators have been applied to sample data involving youth population, specifically the ESPAD®Italia survey data on high school students conducted annually in Italy. The trends of poly-drug use at different ages of students, 15-19 years, over time, and gender have been studied. The results have been linked to educational outcomes, early school leaving and social aspects, making it possible to assess present prevention interventions and suggest appropriate planning of future prevention interventions.

CONCLUSION

Poly-drug use indicators allow a comprehensive quantitative evaluation of the risks of drug use. The analysis of the links between heavy use of drugs, school performance and dropout, and the social variables that influence them, shown in this work, suggests how best to plan secondary or indicative prevention interventions at school. The problem of including "new" NPS in analyses is also briefly discussed.

Commentary on the use of the reproduction number R during the COVID-19 pandemic

Since the beginning of the COVID-19 pandemic, the reproduction number [Formula: see text] has become a popular epidemiological metric used to communicate the state of the epidemic. At its most basic, [Formula: see text] is defined as the average number of secondary infections caused by one primary infected individual. [Formula: see text] seems convenient, because the epidemic is expanding if [Formula: see text] and contracting if [Formula: see text]. The magnitude of [Formula: see text] indicates by how much transmission needs to be reduced to control the epidemic. Using [Formula: see text] in a naïve way can cause new problems. The reasons for this are threefold: (1) There is not just one definition of [Formula: see text] but many, and the precise definition of [Formula: see text] affects both its estimated value and how it should be interpreted. (2) Even with a particular clearly defined [Formula: see text], there may be different statistical methods used to estimate its value, and the choice of method will affect the estimate. (3) The availability and type of data used to estimate [Formula: see text] vary, and it is not always clear what data should be included in the estimation. In this review, we discuss when [Formula: see text] is useful, when it may be of use but needs to be interpreted with care, and when it may be an inappropriate indicator of the progress of the epidemic. We also argue that careful definition of [Formula: see text], and the data and methods used to estimate it, can make [Formula: see text] a more useful metric for future management of the epidemic.

Modelling: Understanding pandemics and how to control them

New disease challenges, societal demands and better or novel types of data, drive innovations in the structure, formulation and analysis of epidemic models. Innovations in modelling can lead to new insights into epidemic processes and better use of available data, yielding improved disease control and stimulating collection of better data and new data types. Here we identify key challenges for the structure, formulation, analysis and use of mathematical models of pathogen transmission relevant to current and future pandemics.

Modelling preventive measures and their effect on generation times in emerging epidemics

We present a stochastic epidemic model to study the effect of various preventive measures, such as uniform reduction of contacts and transmission, vaccination, isolation, screening and contact tracing, on a disease outbreak in a homogeneously mixing community. The model is based on an infectivity process, which we define through stochastic contact and infectiousness processes, so that each individual has an independent infectivity profile. In particular, we monitor variations of the reproduction number and of the distribution of generation times. We show that some interventions, i.e. uniform reduction and vaccination, affect the former while leaving the latter unchanged, whereas other interventions, i.e. isolation, screening and contact tracing, affect both quantities. We provide a theoretical analysis of the variation of these quantities, and we show that, in practice, the variation of the generation time distribution can be significant and that it can cause biases in the estimation of reproduction numbers. The framework, because of its general nature, captures the properties of many infectious diseases, but particular emphasis is on COVID-19, for which numerical results are provided.

Drug Use Changes at the Individual Level: Results from a Longitudinal, Multisite Survey in Young Europeans Frequenting the Nightlife Scene

BACKGROUND

Monitoring emerging trends in the increasingly dynamic European drug market is vital; however, information on change at the individual level is scarce. In the current study, we investigated changes in drug use over 12 months in European nightlife attendees.

METHOD

In this longitudinal online survey, changes in substances used, use frequency in continued users, and relative initiation of use at follow-up were assessed for 20 different substances. To take part, participants had to be aged 18-34 years; be from Belgium, Italy, the Netherlands, Sweden, or the UK; and have attended at least 6 electronic music events in the past 12 months at baseline. Of 8,045 volunteers at baseline, 2,897 completed the survey at both time points (36% follow-up rate), in 2017 and 2018.

RESULTS

The number of people using ketamine increased by 21% (p < 0.001), and logarithmized frequency of use in those continuing use increased by 15% (p < 0.001; 95% CI: 0.07-0.23). 4-Fluoroamphetamine use decreased by 27% (p < 0.001), and logarithmized frequency of use in continuing users decreased by 15% (p < 0.001, 95% CI: -0.48 to -0.23). The drugs with the greatest proportion of relative initiation at follow-up were synthetic cannabinoids (73%, N = 30), mephedrone (44%, N = 18), alkyl nitrites (42%, N = 147), synthetic dissociatives (41%, N = 15), and prescription opioids (40%, N = 48).

CONCLUSIONS

In this European nightlife sample, ketamine was found to have the biggest increase in the past 12 months, which occurred alongside an increase in frequency of use in continuing users. The patterns of uptake and discontinuation of alkyl nitrates, novel psychoactive substances, and prescription opioids provide new information that has not been captured by existing cross-sectional surveys. These findings demonstrate the importance of longitudinal assessments of drug use and highlight the dynamic nature of the European drug landscape.

Cost-effectiveness of varicella and herpes zoster vaccination in Sweden: An economic evaluation using a dynamic transmission model

OBJECTIVES

Comprehensive cost-effectiveness analyses of introducing varicella and/or herpes zoster vaccination in the Swedish national vaccination programme.

DESIGN

Cost-effectiveness analyses based on epidemiological results from a specifically developed transmission model.

SETTING

National vaccination programme in Sweden, over an 85- or 20-year time horizon depending on the vaccination strategy.

PARTICIPANTS

Hypothetical cohorts of people aged 12 months and 65-years at baseline.

INTERVENTIONS

Four alternative vaccination strategies; 1, not to vaccinate; 2, varicella vaccination with one dose of the live attenuated vaccine at age 12 months and a second dose at age 18 months; 3, herpes zoster vaccination with one dose of the live attenuated vaccine at 65 years of age; and 4, both vaccine against varicella and herpes zoster with the before-mentioned strategies.

MAIN OUTCOME MEASURES

Accumulated cost and quality-adjusted life years (QALY) for each strategy, and incremental cost-effectiveness ratios (ICER).

RESULTS

It would be cost-effective to vaccinate against varicella (dominant), but not to vaccinate against herpes zoster (ICER of EUR 200,000), assuming a cost-effectiveness threshold of EUR 50,000 per QALY. The incremental analysis between varicella vaccination only and the combined programme results in a cost per gained QALY of almost EUR 1.6 million.

CONCLUSIONS

The results from this study are central components for policy-relevant decision-making, and suggest that it was cost-effective to introduce varicella vaccination in Sweden, whereas herpes zoster vaccination with the live attenuated vaccine for the elderly was not cost-effective-the health effects of the latter vaccination cannot be considered reasonable in relation to its costs. Future observational and surveillance studies are needed to make reasonable predictions on how boosting affects the herpes zoster incidence in the population, and thus the cost-effectiveness of a vaccination programme against varicella. Also, the link between herpes zoster and sequelae need to be studied in more detail to include it suitably in health economic evaluations.

On realized serial and generation intervals given control measures: The COVID-19 pandemic case

The SARS-CoV-2 pathogen is currently spreading worldwide and its propensity for presymptomatic and asymptomatic transmission makes it difficult to control. The control measures adopted in several countries aim at isolating individuals once diagnosed, limiting their social interactions and consequently their transmission probability. These interventions, which have a strong impact on the disease dynamics, can affect the inference of the epidemiological quantities. We first present a theoretical explanation of the effect caused by non-pharmaceutical intervention measures on the mean serial and generation intervals. Then, in a simulation study, we vary the assumed efficacy of control measures and quantify the effect on the mean and variance of realized generation and serial intervals. The simulation results show that the realized serial and generation intervals both depend on control measures and their values contract according to the efficacy of the intervention strategies. Interestingly, the mean serial interval differs from the mean generation interval. The deviation between these two values depends on two factors. First, the number of undiagnosed infectious individuals. Second, the relationship between infectiousness, symptom onset and timing of isolation. Similarly, the standard deviations of realized serial and generation intervals do not coincide, with the former shorter than the latter on average. The findings of this study are directly relevant to estimates performed for the current COVID-19 pandemic. In particular, the effective reproduction number is often inferred using both daily incidence data and the generation interval. Failing to account for either contraction or mis-specification by using the serial interval could lead to biased estimates of the effective reproduction number. Consequently, this might affect the choices made by decision makers when deciding which control measures to apply based on the value of the quantity thereof.

The generation and serial intervals are epidemiological quantities used to describe and predict an ongoing epidemic outbreak. These quantities are related to the contact pattern of individuals, since infection events can take place if infectious and susceptible individuals have a contact. Therefore, intervention measures that reduce the interactions between members of the population are expected to affect both the realized generation and serial intervals. For the current COVID-19 pandemic unprecedented interventions have been adopted worldwide, e.g. strict lockdown, isolation and quarantine, which influence the realized value of generation and serial intervals. The extent of the effect thereof depends on the efficacy of the control measure in place, on the relationship between symptom onset and infectiousness and on the proportion of infectious individuals that can be detected. To get more insight on this, we present an investigation that highlights the effect of quarantine and isolation on realized generation and serial intervals. In particular, we show that not only their variances but also their mean values can differ, suggesting that the use of the mean serial interval as a proxy for the mean generation time can lead to biased estimates of epidemiological quantities.

Key questions for modelling COVID-19 exit strategies

Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.

Estimation in emerging epidemics: biases and remedies

When analysing new emerging infectious disease outbreaks, one typically has observational data over a limited period of time and several parameters to estimate, such as growth rate, the basic reproduction number R₀, the case fatality rate and distributions of serial intervals, generation times, latency and incubation times and times between onset of symptoms, notification, death and recovery/discharge. These parameters form the basis for predicting a future outbreak, planning preventive measures and monitoring the progress of the disease outbreak. We study inference problems during the emerging phase of an outbreak, and point out potential sources of bias, with emphasis on: contact tracing backwards in time, replacing generation times by serial intervals, multiple potential infectors and censoring effects amplified by exponential growth. These biases directly affect the estimation of, for example, the generation time distribution and the case fatality rate, but can then propagate to other estimates such as R₀ and growth rate. We propose methods to remove or at least reduce bias using statistical modelling. We illustrate the theory by numerical examples and simulations.

Evaluating costs and health consequences of sick leave strategies against pandemic and seasonal influenza in Norway using a dynamic model

OBJECTIVES

To quantify population-level health and economic consequences of sick leave among workers with influenza symptoms.

INTERVENTIONS

Compared with current sick leave practice (baseline), we evaluated the health and cost consequences of: (1) increasing the proportion of workers on sick leave from 65% (baseline) to 80% or 90%; (2) shortening the maximum duration from symptom onset to sick leave from 4 days (baseline) to 2 days, 1.5 days, 1 day and 0.5 days; and (3) combinations of 1 and 2.

METHODS

A dynamic compartmental influenza model was developed using Norwegian population data and survey data on employee sick leave practices. The sick leave interventions were simulated under 12 different seasonal epidemic and 36 different pandemic influenza scenarios. These scenarios varied in terms of transmissibility, the proportion of symptomatic cases and illness severity (risk of primary care consultations, hospitalisations and deaths). Using probabilistic sensitivity analyses, a net health benefit approach was adopted to assess the cost-effectiveness of the interventions from a societal perspective.

RESULTS

Compared with current sick leave practice, sick leave interventions were cost-effective for 31 (65%) of the pandemic scenarios, and 11 (92%) of the seasonal scenarios. Economic benefits from sick leave interventions were greatest for scenarios with low transmissibility, high symptomatic proportions and high illness severity. Overall, the health and economic benefits were greatest for the intervention involving 90% of sick workers taking sick leave within one-half day of symptoms. Depending on the influenza scenario, this intervention resulted in a 44.4%-99.7% reduction in the attack rate. Interventions involving sick leave onset beginning 2 days or later, after the onset of symptoms, resulted in economic losses.

CONCLUSIONS

Prompt sick leave onset and a high proportion of sick leave among workers with influenza symptoms may be cost-effective, particularly during influenza epidemics and pandemics with low transmissibility or high morbidity.

Modeling the impact of combined vaccination programs against varicella and herpes zoster in Norway

BACKGROUND

Adoption of varicella immunization in Europe is limited due to a predicted increase in the incidence of herpes zoster (HZ) resulting from a removal of exogenous boosting by varicella vaccination. Most available assessments of immunization strategies only considered universal varicella vaccination (alone or in combination with HZ by the live vaccine). The development of a new subunit recombinant zoster vaccine may provide new perspectives of HZ control.

METHODS

We used a mathematical model for VZV in Norway based on the progressive immunity formulation of exogenous boosting. We evaluated a complete range of alternative immunization options against varicella and HZ including both universal and targeted varicella vaccination, either alone or with zoster immunization, and zoster immunization alone. We considered all values of the boosting intensity consistent with the Norwegian HZ incidence and compared the performance of the currently available live vaccine vs. a new recombinant vaccine.

RESULTS

Universal varicella vaccination alone resulted in a marked increase in the incidence of HZ under all scenarios considered. Even under the most favorable hypotheses on the magnitude of the boosting intensity, this increase could be mitigated only by a parallel HZ immunization with a recombinant vaccine, assuming a long duration of protection. Targeted varicella immunization of adolescents resulted in a modest increase in the HZ incidence which could be counterbalanced by both the live and, especially, the recombinant vaccine.

CONCLUSIONS

Given current knowledge on HZ pathogenesis and exogenous boosting, targeted varicella vaccination of adolescents was the only strategy that was not predicted to impact the epidemiology of HZ, and therefore it may represent a suitable alternative to universal vaccination. These results are aimed to support vaccine policy decisions in Norway and other countries with a similar VZV epidemiology.

Estimating Age-Specific Immunity and Force of Infection of Varicella Zoster Virus in Norway Using Mixture Models

This study applies mixture modelling to examine age-specific immunity to varicella zoster virus (VZV) infection in Norway based on the first large-scale serological study in the general population. We estimated the seropositive proportions at different ages and calculated the underlying force of infection by using a sample of 2103 residual sera obtained from patients seeking primary and hospital care. A rapid increase in the VZV-associated immunity is observed in the first years of life with 63% of children being immune by age 5. The increase in the immunity levels slows down thereafter, with a large proportion of adults still susceptible by age 20 (around 14.5%), thus at risk of serious sequelae of varicella infection. The corresponding force of infection peaks during the preschool period, subsequently declines to a minimum between ages 10 and 20 years, and afterwards moderately increases to reach a plateau lasting throughout the childbearing period. In comparison with the traditional cut-off approach, mixture modelling used the whole data without producing any inconclusive cases, led to an unbiased classification of individuals between susceptible and immune, and provided a smoother immune profile by age. These findings represent an important step towards any decision about the introduction of varicella vaccination in Norway, as they are a primary input for mathematical transmission models aimed at evaluating potential vaccination scenarios.

Influenza in workplaces: transmission, workers' adherence to sick leave advice and European sick leave recommendations

Background: Knowledge about influenza transmission in the workplace and whether staying home from work when experiencing influenza-like illness can reduce the spread of influenza is crucial for the design of efficient public health initiatives. Aim: This review synthesizes current literature on sickness presenteeism and influenza transmission in the workplace and provides an overview of sick leave recommendations in Europe for influenza. Methods: A search was performed on Medline, Embase, PsychINFO, Cinahl, Web of Science, Scopus and SweMed to identify studies related to workplace contacts, -transmission, -interventions and compliance with recommendations to take sick leave. A web-based survey on national recommendations and policies for sick leave during influenza was issued to 31 European countries. Results: Twenty-two articles (9 surveys; 13 modelling articles) were eligible for this review. Results from social mixing studies suggest that 20–25% of weekly contacts are made in the workplace, while modelling studies suggest that on average 16% (range 9–33%) of influenza transmission occurs in the workplace. The effectiveness of interventions to reduce workplace presenteeism is largely unknown. Finally, estimates from studies reporting expected compliance with sick leave recommendations ranged from 71 to 95%. Overall, 18 countries participated in the survey of which nine (50%) had issued recommendations encouraging sick employees to stay at home during the 2009 A(H1N1) pandemic, while only one country had official recommendations for seasonal influenza. Conclusions: During the 2009 A(H1N1) pandemic, many European countries recommended ill employees to take sick leave. Further research is warranted to quantify the effect of reduced presenteeism during influenza illness.

Four key challenges in infectious disease modelling using data from multiple sources

Public health-related decision-making on policies aimed at controlling epidemics is increasingly evidence-based, exploiting multiple sources of data. Policy makers rely on complex models that are required to be robust, realistically approximating epidemics and consistent with all relevant data. Meeting these requirements in a statistically rigorous and defendable manner poses a number of challenging problems. How to weight evidence from different datasets and handle dependence between them, efficiently estimate and critically assess complex models are key challenges that we expound in this paper, using examples from influenza modelling.

Seven challenges for metapopulation models of epidemics, including households models

This paper considers metapopulation models in the general sense, i.e. where the population is partitioned into sub-populations (groups, patches,...), irrespective of the biological interpretation they have, e.g. spatially segregated large sub-populations, small households or hosts themselves modelled as populations of pathogens. This framework has traditionally provided an attractive approach to incorporating more realistic contact structure into epidemic models, since it often preserves analytic tractability (in stochastic as well as deterministic models) but also captures the most salient structural inhomogeneity in contact patterns in many applied contexts. Despite the progress that has been made in both the theory and application of such metapopulation models, we present here several major challenges that remain for future work, focusing on models that, in contrast to agent-based ones, are amenable to mathematical analysis. The challenges range from clarifying the usefulness of systems of weakly-coupled large sub-populations in modelling the spread of specific diseases to developing a theory for endemic models with household structure. They include also developing inferential methods for data on the emerging phase of epidemics, extending metapopulation models to more complex forms of human social structure, developing metapopulation models to reflect spatial population structure, developing computationally efficient methods for calculating key epidemiological model quantities, and integrating within- and between-host dynamics in models.

Methods for estimating incidence of drug use: a review

Several methods for estimating drug use incidence, that have been used in the literature or that could be used, having been used in a different framework, are described and commented. The applicability of the different methods depends on available data and knowledge about relevant parameters. The many similarities between drug use incidence estimation and estimation of disease incidence are highlighted, but also the distinguishing aspects that make drug use incidence estimation a challenge to standard statistical methods.

Alternative splicing tends to avoid partial removals of protein-protein interaction sites

BACKGROUND

Anecdotal evidence of the involvement of alternative splicing (AS) in the regulation of protein-protein interactions has been reported by several studies. AS events have been shown to significantly occur in regions where a protein interaction domain or a short linear motif is present. Several AS variants show partial or complete loss of interface residues, suggesting that AS can play a major role in the interaction regulation by selectively targeting the protein binding sites. In the present study we performed a statistical analysis of the alternative splicing of a non-redundant dataset of human protein-protein interfaces known at molecular level to determine the importance of this way of modulation of protein-protein interactions through AS.

RESULTS

Using a Cochran-Mantel-Haenszel chi-square test we demonstrated that the alternative splicing-mediated partial removal of both heterodimeric and homodimeric binding sites occurs at lower frequencies than expected, and this holds true even if we consider only those isoforms whose sequence is less different from that of the canonical protein and which therefore allow to selectively regulate functional regions of the protein. On the other hand, large removals of the binding site are not significantly prevented, possibly because they are associated to drastic structural changes of the protein. The observed protection of the binding sites from AS is not preferentially directed towards putative hot spot interface residues, and is widespread to all protein functional classes.

CONCLUSIONS

Our findings indicate that protein-protein binding sites are generally protected from alternative splicing-mediated partial removals. However, some cases in which the binding site is selectively removed exist, and here we discuss one of them.

Effect of vaccines and antivirals during the major 2009 A(H1N1) pandemic wave in Norway--and the influence of vaccination timing

To evaluate the impact of mass vaccination with adjuvanted vaccines (eventually 40% population coverage) and antivirals during the 2009 influenza pandemic in Norway, we fitted an age-structured SEIR model using data on vaccinations and sales of antivirals in 2009/10 in Norway to Norwegian ILI surveillance data from 5 October 2009 to 4 January 2010. We estimate a clinical attack rate of approximately 30% (28.7-29.8%), with highest disease rates among children 0-14 years (43-44%). Vaccination started in week 43 and came too late to have a strong influence on the pandemic in Norway. Our results indicate that the countermeasures prevented approximately 11-12% of potential cases relative to an unmitigated pandemic. Vaccination was found responsible for roughly 3 in 4 of the avoided infections. An estimated 50% reduction in the clinical attack rate would have resulted from vaccination alone, had the campaign started 6 weeks earlier. Had vaccination been prioritized for children first, the intervention should have commenced approximately 5 weeks earlier in order to achieve the same 50% reduction. In comparison, we estimate that a non-adjuvanted vaccination program should have started 8 weeks earlier to lower the clinical attack rate by 50%. In conclusion, vaccination timing was a critical factor in relation to the spread of the 2009 A(H1N1) influenza. Our results also corroborate the central role of children for the transmission of A(H1N1) pandemic influenza.

Sudden unexpected deaths and vaccinations during the first two years of life in Italy: a case series study

BACKGROUND

The signal of an association between vaccination in the second year of life with a hexavalent vaccine and sudden unexpected deaths (SUD) in the two days following vaccination was reported in Germany in 2003. A study to establish whether the immunisation with hexavalent vaccines increased the short term risk of SUD in infants was conducted in Italy.

METHODOLOGY/PRINCIPAL FINDINGS

The reference population comprises around 3 million infants vaccinated in Italy in the study period 1999-2004 (1.5 million received hexavalent vaccines). Events of SUD in infants aged 1-23 months were identified through the death certificates. Vaccination history was retrieved from immunisation registries. Association between immunisation and death was assessed adopting a case series design focusing on the risk periods 0-1, 0-7, and 0-14 days after immunisation. Among the 604 infants who died of SUD, 244 (40%) had received at least one vaccination. Four deaths occurred within two days from vaccination with the hexavalent vaccines (RR = 1.5; 95% CI 0.6 to 4.2). The RRs for the risk periods 0-7 and 0-14 were 2.0 (95% CI 1.2 to 3.5) and 1.5 (95% CI 0.9 to 2.4). The increased risk was limited to the first dose (RR = 2.2; 95% CI 1.1 to 4.4), whereas no increase was observed for the second and third doses combined.

CONCLUSIONS

The RRs of SUD for any vaccines and any risk periods, even when greater than 1, were almost an order of magnitude lower than the estimates in Germany. The limited increase in RRs found in Italy appears confined to the first dose and may be partly explained by a residual uncontrolled confounding effect of age.

Estimating the incidence, prevalence and clinical burden of hepatitis C over time in Italy

Our objective was to estimate HCV clinical burden over time in Italy. A national age-specific HCV prevalence in 1995 was obtained from studies conducted in general population samples and intravenous drug users. Age profile of new HCV infections and trend of incidence since 1985 were derived from a database of reported acute HCV infections. These incidence and prevalence data were used to estimate HCV burden from 1950 to 2030 by mathematical modelling. Different rates of HCV related liver disease progression were tested to assess the robustness of estimates. It is estimated that HCV had a major spread in Italy in 1945-1969. HCV RNA-positive subjects peaked around 1970; their prevalence in 2005 was 3.2%, 58% of them being >65 y of age. The number of individuals with HCV related cirrhosis and that of HCV liver related deaths peaked in 1980-1985. In 2005, they were approximately 230,000 (range 150,000-240,000, according to lower or higher disease progression rates) and approximately 7,000 (range 2200-12,300), respectively: both will be halved by 2025. In conclusion, unlike other industrialized countries, the burden of clinically relevant HCV-positive cases in Italy is already on the decline and will further reduce in the future. This is due to differences in the age-specific prevalence, most of HCV-positive Italians currently being >65 y of age.

Some model based considerations on observing generation times for communicable diseases

The generation time of an infectious disease is usually defined as the time from the moment one person becomes infected until that person infects another person. The concept is similar to "generation gap" in demography, with new infections replacing births in a population. Originally applied to diseases such as measles where at least the first generations are clearly discernible, the concept has recently been extended to other diseases, such as influenza, where time order of infections is usually much less apparent. By formulating the relevant statistical questions within a simple yet basic mathematical model for infection spread, it is possible to derive theoretical properties of observations in various situations e.g. in "isolation", in households, or during large outbreaks. In each case, it is shown that the sampling distribution of observations depends on a number of factors, usually not considered in the literature and that must be taken into account in order to achieve unbiased inference about the generation time distribution. Some implications of these findings for statistical inference methods in epidemic spread models are discussed.

Social contacts and mixing patterns relevant to the spread of infectious diseases

BACKGROUND

Mathematical modelling of infectious diseases transmitted by the respiratory or close-contact route (e.g., pandemic influenza) is increasingly being used to determine the impact of possible interventions. Although mixing patterns are known to be crucial determinants for model outcome, researchers often rely on a priori contact assumptions with little or no empirical basis. We conducted a population-based prospective survey of mixing patterns in eight European countries using a common paper-diary methodology.

METHODS AND FINDINGS

7,290 participants recorded characteristics of 97,904 contacts with different individuals during one day, including age, sex, location, duration, frequency, and occurrence of physical contact. We found that mixing patterns and contact characteristics were remarkably similar across different European countries. Contact patterns were highly assortative with age: schoolchildren and young adults in particular tended to mix with people of the same age. Contacts lasting at least one hour or occurring on a daily basis mostly involved physical contact, while short duration and infrequent contacts tended to be nonphysical. Contacts at home, school, or leisure were more likely to be physical than contacts at the workplace or while travelling. Preliminary modelling indicates that 5- to 19-year-olds are expected to suffer the highest incidence during the initial epidemic phase of an emerging infection transmitted through social contacts measured here when the population is completely susceptible.

CONCLUSIONS

To our knowledge, our study provides the first large-scale quantitative approach to contact patterns relevant for infections transmitted by the respiratory or close-contact route, and the results should lead to improved parameterisation of mathematical models used to design control strategies.

Mitigation measures for pandemic influenza in Italy: an individual based model considering different scenarios

Background

Individual-based models can provide the most reliable estimates of the spread of infectious diseases. In the present study, we evaluated the diffusion of pandemic influenza in Italy and the impact of various control measures, coupling a global SEIR model for importation of cases with an individual based model (IBM) describing the Italian epidemic.

Methodology/Principal Findings

We co-located the Italian population (57 million inhabitants) to households, schools and workplaces and we assigned travel destinations to match the 2001 census data. We considered different R₀ values (1.4; 1.7; 2), evaluating the impact of control measures (vaccination, antiviral prophylaxis -AVP-, international air travel restrictions and increased social distancing). The administration of two vaccine doses was considered, assuming that first dose would be administered 1-6 months after the first world case, and different values for vaccine effectiveness (VE). With no interventions, importation would occur 37–77 days after the first world case. Air travel restrictions would delay the importation of the pandemic by 7–37 days. With an R₀ of 1.4 or 1.7, the use of combined measures would reduce clinical attack rates (AR) from 21–31% to 0.3–4%. Assuming an R₀ of 2, the AR would decrease from 38% to 8%, yet only if vaccination were started within 2 months of the first world case, in combination with a 90% reduction in international air traffic, closure of schools/workplaces for 4 weeks and AVP of household and school/work close contacts of clinical cases. Varying VE would not substantially affect the results.

Conclusions

This IBM, which is based on country-specific demographic data, could be suitable for the real-time evaluation of measures to be undertaken in the event of the emergence of a new pandemic influenza virus. All preventive measures considered should be implemented to mitigate the pandemic.

Epidemiology and costs of herpes zoster: background data to estimate the impact of vaccination

In 2004, we conducted a study in Piemonte (Italy), in order to describe incidence, treatment, hospitalizations and costs of herpes zoster (HZ), in population over 14 years of age. Twenty-four regional general practitioners, with 26,394 patients >14 years in charge (0.71% of the regional population), reported prospectively all diagnosed HZ cases. In addition, all regional hospital discharge records were reviewed. Forty-six HZ cases treated at home were reported, accounting for a total incidence of 1.74 cases/1000 population >14 years per year. HZ rate standardized by age on regional population 14 years older is 1.59/1000. The cost per observed home case was 136.06 euros. The incidence of hospital admissions was 0.12/1000 inhabitants. The mean cost of hospitalized cases was 4082.59 euros. These results contribute to depict the impact of HZ in the general population, and to provide background data for setting-up either mathematical models aimed to estimate the impact of vaccination on HZ, and the cost-benefit analyses of various preventive and therapeutic scenarios.

Changes in the expression of hypothalamic lipid sensing genes in rat model of intrauterine growth retardation (IUGR)

Intrauterine growth retardation (IUGR) has been linked to the development of type 2 diabetes in later life. The mechanisms underlying this phenomenon are unknown. Recent data suggest that some of the molecular defects underlying type 2 diabetes reside in the CNS. The enzyme carnitine palmitoyltransferase-1 (CPT1) regulates long-chain fatty acid (LCFA) entry into mitochondria, where LCFA undergo beta-oxidation. Hypothalamic inhibition of CPT1 decreases food intake and suppresses endogenous glucose production. Our aim was to investigate the effects of uterine artery ligation, a procedure that mimics uteroplacental insufficiency, on the CNS expression of CPT1 and other key enzymes of LCFA metabolism. Bilateral uterine artery ligation was performed on d 19 of gestation in the pregnant rat; sham-operated pregnant rats served as controls. Hypothalamus, cerebellum, hippocampus, and cortex were dissected and analyzed at birth by real-time PCR. Nonesterified fatty acid (NEFA) serum levels were significantly higher in IUGR pups (p<0.0001). In IUGR rats, the hypothalamic expression of CPT1 isoform C (p=0.005) and acetyl-CoA carboxylase (ACC) isoforms alpha (p<0.05) and beta (p=0.005) were significantly decreased. The data presented here support the hypothesis that an abnormal intrauterine milieu can induce changes in hypothalamic lipid sensing.

False occurrences of functional motifs in protein sequences highlight evolutionary constraints

Background

False occurrences of functional motifs in protein sequences can be considered as random events due solely to the sequence composition of a proteome. Here we use a numerical approach to investigate the random appearance of functional motifs with the aim of addressing biological questions such as: How are organisms protected from undesirable occurrences of motifs otherwise selected for their functionality? Has the random appearance of functional motifs in protein sequences been affected during evolution?

Results

Here we analyse the occurrence of functional motifs in random sequences and compare it to that observed in biological proteomes; the behaviour of random motifs is also studied. Most motifs exhibit a number of false positives significantly similar to the number of times they appear in randomized proteomes (=expected number of false positives). Interestingly, about 3% of the analysed motifs show a different kind of behaviour and appear in biological proteomes less than they do in random sequences. In some of these cases, a mechanism of evolutionary negative selection is apparent; this helps to prevent unwanted functionalities which could interfere with cellular mechanisms.

Conclusion

Our thorough statistical and biological analysis showed that there are several mechanisms and evolutionary constraints both of which affect the appearance of functional motifs in protein sequences.

[Prevalence of the use of old and new drugs among new entrants in Italian prisons]

An epidemiological survey was conducted to evaluate the prevalence of drug use among new entrants in Italian prisons. Overall, 1267 inmates were recruited by 9 prisons. Drug use before imprisonment was reported by 55.8% of the participants. Cocaine was the most commonly used drug (42%), followed by heroin (34%), marijuana/cannabis (33%), ecstasy (7%), hallucinogens (6%), amphetamines (5%); more than one drug was reported by 68% of abusers. Recent use (1 month before imprisonment) was admitted by up to 27% of inmates. Alcohol or tobacco use was reported by 38 and 77% of the inmates, respectively. Our findings indicate that a high proportion of inmates has ever used drugs; adequate intervention is needed to reduce the risk of addictive behaviour in this population group.

[Assessment of the potential impact in Italy of extensive varicella vaccination programs based on a mathematical model]

A mathematical model has been developed in order to assess the effect of extended programs of varicella vaccination on the epidemiology of the disease in Italy. The effect of different vaccination options have been estimated by the change in incidence of the disease and age distribution of cases over a short and long period of time. The developed mathematical model reproduces chickenpox transmission and immunisation; five strategies different for target age of vaccination and/or proportion of vaccinated subjects have been considered. In all scenarios the model pointed out an initial decrease of case frequency observed in the first 3-5 years, followed by a series of epidemic peaks, variable in number and size by vaccination strategy. Moreover, as the number of cases among infants decreases, the number of cases among adults increases. Such event is minimised only by very high vaccination coverage (80% in the first year of life and 50% at 12 years of age). Extensive programmes of vaccination against chickenpox must reach a high coverage as soon as possible in order to avoid undesirable effects that may move forward the age of cases and therefore should be offered to target age groups easy to reach.