Pandemics and socio-economic status. Evidence from the plague of 1630 in northern Italy.

This paper investigates the biological, socio-economic, and institutional factors shaping the individual risk of death during a major pre-industrial epidemic. We use a micro-demographic database for an Italian city (Carmagnola) during the 1630 plague to explore in detail the survival dynamics of the population admitted to the isolation hospital (lazzaretto). We develop a theoretical model of admissions to the lazzaretto, for better interpretation of the observational data. We explore how age and sex shaped the individual risk of death, and we provide a one-of-a-kind study of the impact of socio-economic status. We report an inversion of the normal mortality gradient by status for those interned at the lazzaretto. The rich enjoyed a greater ability to make decisions about their hospitalization, but this backfired. Instead, the poor sent to the lazzaretto faced a relatively low risk of death because they enjoyed better conditions than they would have experienced outside the hospital.

Approximate Bayesian computation for the natural history of breast cancer, with application to data from a Milan cohort study.

We explore models for the natural history of breast cancer, where the main events of interest are the start of asymptomatic detectability of the disease (through screening) and the time of symptomatic detection (through symptoms). We develop several parametric specifications based on a cure rate structure, and present the results of the analysis of data collected as part of a motivating study from Milan. Participants in the study were part of a regional breast cancer screening program, and their ten-year trajectories were obtained from administrative data available from the Italian national health care system. We first present a tractable model for which we develop the likelihood contributions of the observed trajectories and perform maximum likelihood inference on the latent process. Likelihood based inference is not feasible for more flexible models, and we implement approximate Bayesian computation (ABC) for inference. Issues that arise from the use of ABC for model choice and parameter estimation are discussed, including the problem of choosing appropriate summary statistics. The estimated parameters of the underlying disease process allow for the study of the effect of different examination schedules (age range and frequency of screening examinations) on a population of asymptomatic subjects.

Isochronal superpositioning in the equilibrium regime of superpressed propylene carbonate to ∼ 1.8 GPa: A study by diffusivity measurement of the fluorescent probe Coumarin 1.

We address the problem of glass-forming of liquids by superpressing. We study the pressure-induced dynamic change of the fragile van der Waals liquid propylene carbonate towards the glassy state in the equilibrium regime by measuring the diffusivity of the fluorescent probe Coumarin 1 embedded in the host liquid. The probe diffusivity is measured by the fluorescence recovery after photobleaching (FRAP) technique across a bleached volume generated by the near-field diffracted pattern of a laser beam. The recovered fluorescence intensity fits to a stretched exponential with the diffusive time [Formula: see text] and the stretched exponent [Formula: see text] as free parameters. In the pressure range [0.3-1.0]GPa the diffusivity decouples from the Stokes-Einstein relation. The decoupling correlates well to a decrease of [Formula: see text]. The variation of [Formula: see text] is non-monotonous with [Formula: see text] showing a minimum at [Formula: see text] s. We evidence an isochronal superpositioning over about 3 decades of [Formula: see text] between ∼ 10 s and [Formula: see text] s and a density scaling in the whole investigated pressure range. The pressure at which [Formula: see text] is minimum coincides to the dynamical crossover pressure measured by other authors. This crossover pressure is compatible with the critical point of MCT theory. As our studied pressure range encompasses the critical pressure, the non-monotonous variation of [Formula: see text] opens new insight in the approach to the critical point.

A survival analysis of the last great European plagues: The case of Nonantola (Northern Italy) in 1630.

This paper develops the first survival analysis of a large-scale mortality crisis caused by plague. For the time-to-event analyses we used the Cox proportional hazards regression model. Our case study is the town of Nonantola during the 1630 plague, which was probably the worst to affect Italy since the Black Death. Individual risk of death did not depend on sex, grew with age (peaking at ages 40-60 and then declining), was not affected by socio-economic status, and was positively associated with household size. We discuss these findings in light of the historical-demographic and palaeo-demographic literature on medieval and early modern plagues. Our results are compatible with the debated idea that ancient plague was able to spread directly from human to human. Our methods could be replicated in other studies of European plagues to nuance and integrate the findings of recent palaeo-biological and palaeo-demographic research on plague.

Effect of the number of removed lymph nodes on prostate cancer recurrence and survival: evidence from an observational study.

BACKGROUND: The aim of this article is to analyze the effect on biochemical recurrence and on overall survival of removing an extensive number of pelvic lymph nodes during prostate cancer surgery. The lack of evidence from randomized clinical trials to address this specific question has hampered the ability to determine the true effect of the number of nodes removed. RESULTS: Our analysis is based on a large observational study, and this can lead unadjusted estimates to be very sensitive to confounding bias due to the different prognosis of individuals. We assess the effect of the number of lymph nodes removed by means of an Inverse Probability Weighting adjustment based on a Poisson regression model, and by a Doubly-robust adjustment. CONCLUSIONS: Our findings suggest that a large number of nodes removed is associated with a significant improvement in time to biochemical recurrence. However, it appears to have no impact on overall survival.

Can charged colloidal particles increase the thermoelectric energy conversion efficiency?

Currently, liquid thermocells are receiving increasing attention as an inexpensive alternative to conventional solid-state thermoelectrics for low-grade waste heat recovery applications. Here we present a novel path to increase the Seebeck coefficient of liquid thermoelectric materials using charged colloidal suspensions; namely, ionically stabilized magnetic nanoparticles (ferrofluids) dispersed in aqueous potassium ferro-/ferri-cyanide electrolytes. The dependency of thermoelectric potential on experimental parameters such as nanoparticle concentration and types of solute ions (lithium citrate and tetrabutylammonium citrate) is examined to reveal the relative contributions from the thermogalvanic potential of redox couples and the entropy of transfer of nanoparticles and ions. The results show that under specific ionic conditions, the inclusion of magnetic nanoparticles can lead to an enhancement of the ferrofluid's initial Seebeck coefficient by 15% (at a nanoparticle volume fraction of ∼1%). Based on these observations, some practical directions are given on which ionic and colloidal parameters to adjust for improving the Seebeck coefficients of liquid thermoelectric materials.

Longevity and concentration in survival times: the log-scale-location family of failure time models.

Evidence suggests that the increasing life expectancy levels at birth witnessed over the past centuries are associated with a decreasing concentration of the survival times. The purpose of this work is to study the relationships that exist between longevity and concentration measures for some regression models for the evolution of survival. In particular, we study a family of survival models that can be used to capture the observed trends in longevity and concentration over time. The parametric family of log-scale-location models is shown to allow for modeling different trends of expected value and concentration of survival times. An extension towards mixture models is also described in order to take into account scenarios where a fraction of the population experiences short term survival. Some results are also presented for such framework. The use of both the log-scale-location family and the mixture model is illustrated through an application to period life tables from the Human Mortality Database.

Meta-STEPP: subpopulation treatment effect pattern plot for individual patient data meta-analysis.

We have developed a method, called Meta-STEPP (subpopulation treatment effect pattern plot for meta-analysis), to explore treatment effect heterogeneity across covariate values in the meta-analysis setting for time-to-event data when the covariate of interest is continuous. Meta-STEPP forms overlapping subpopulations from individual patient data containing similar numbers of events with increasing covariate values, estimates subpopulation treatment effects using standard fixed-effects meta-analysis methodology, displays the estimated subpopulation treatment effect as a function of the covariate values, and provides a statistical test to detect possibly complex treatment-covariate interactions. Simulation studies show that this test has adequate type-I error rate recovery as well as power when reasonable window sizes are chosen. When applied to eight breast cancer trials, Meta-STEPP suggests that chemotherapy is less effective for tumors with high estrogen receptor expression compared with those with low expression. Copyright © 2016 John Wiley & Sons, Ltd.

Identifying treatment effect heterogeneity in clinical trials using subpopulations of events: STEPP.

BACKGROUND: Investigators conducting randomized clinical trials often explore treatment effect heterogeneity to assess whether treatment efficacy varies according to patient characteristics. Identifying heterogeneity is central to making informed personalized healthcare decisions. Treatment effect heterogeneity can be investigated using subpopulation treatment effect pattern plot (STEPP), a non-parametric graphical approach that constructs overlapping patient subpopulations with varying values of a characteristic. Procedures for statistical testing using subpopulation treatment effect pattern plot when the endpoint of interest is survival remain an area of active investigation. METHODS: A STEPP analysis was used to explore patterns of absolute and relative treatment effects for varying levels of a breast cancer biomarker, Ki-67, in the phase III Breast International Group 1-98 randomized clinical trial, comparing letrozole to tamoxifen as adjuvant therapy for postmenopausal women with hormone receptor-positive breast cancer. Absolute treatment effects were measured by differences in 4-year cumulative incidence of breast cancer recurrence, while relative effects were measured by the subdistribution hazard ratio in the presence of competing risks using O-E (observed-minus-expected) methodology, an intuitive non-parametric method. While estimation of hazard ratio values based on O-E methodology has been shown, a similar development for the subdistribution hazard ratio has not. Furthermore, we observed that the subpopulation treatment effect pattern plot analysis may not produce results, even with 100 patients within each subpopulation. After further investigation through simulation studies, we observed inflation of the type I error rate of the traditional test statistic and sometimes singular variance-covariance matrix estimates that may lead to results not being produced. This is due to the lack of sufficient number of events within the subpopulations, which we refer to as instability of the subpopulation treatment effect pattern plot analysis. We introduce methodology designed to improve stability of the subpopulation treatment effect pattern plot analysis and generalize O-E methodology to the competing risks setting. Simulation studies were designed to assess the type I error rate of the tests for a variety of treatment effect measures, including subdistribution hazard ratio based on O-E estimation. This subpopulation treatment effect pattern plot methodology and standard regression modeling were used to evaluate heterogeneity of Ki-67 in the Breast International Group 1-98 randomized clinical trial. RESULTS: We introduce methodology that generalizes O-E methodology to the competing risks setting and that improves stability of the STEPP analysis by pre-specifying the number of events across subpopulations while controlling the type I error rate. The subpopulation treatment effect pattern plot analysis of the Breast International Group 1-98 randomized clinical trial showed that patients with high Ki-67 percentages may benefit most from letrozole, while heterogeneity was not detected using standard regression modeling. CONCLUSION: The STEPP methodology can be used to study complex patterns of treatment effect heterogeneity, as illustrated in the Breast International Group 1-98 randomized clinical trial. For the subpopulation treatment effect pattern plot analysis, we recommend a minimum of 20 events within each subpopulation.

Subpopulation Treatment Effect Pattern Plot (STEPP) analysis for continuous, binary, and count outcomes.

BACKGROUND: For the past few decades, randomized clinical trials have provided evidence for effective treatments by comparing several competing therapies. Their successes have led to numerous new therapies to combat many diseases. However, since their conclusions are based on the entire cohort in the trial, the treatment recommendation is for everyone, and may not be the best option for an individual. Medical research is now focusing more on providing personalized care for patients, which requires investigating how patient characteristics, including novel biomarkers, modify the effect of current treatment modalities. This is known as heterogeneity of treatment effects. A better understanding of the interaction between treatment and patient-specific prognostic factors will enable practitioners to expand the availability of tailored therapies, with the ultimate goal of improving patient outcomes. The Subpopulation Treatment Effect Pattern Plot (STEPP) approach was developed to allow researchers to investigate the heterogeneity of treatment effects on survival outcomes across values of a (continuously measured) covariate, such as a biomarker measurement. METHODS: Here, we extend the Subpopulation Treatment Effect Pattern Plot approach to continuous, binary, and count outcomes, which can be easily modeled using generalized linear models. With this extension of Subpopulation Treatment Effect Pattern Plot, these additional types of treatment effects within subpopulations defined with respect to a covariate of interest can be estimated, and the statistical significance of any observed heterogeneity of treatment effect can be assessed using permutation tests. The desirable feature that commonly used models are applied to well-defined patient subgroups to estimate treatment effects is retained in this extension. RESULTS: We describe a simulation study to confirm that the proper Type I error rate is maintained when there is no treatment heterogeneity, and a power study to show that the statistics have power to detect treatment heterogeneity under alternative scenarios. As an illustration, we apply the methods to data from the Aspirin/Folate Polyp Prevention Study, a clinical trial evaluating the effect of oral aspirin, folic acid, or both as a chemoprevention agent against colorectal adenomas. The pre-existing R software package stepp has been extended to handle continuous, binary, and count data using Gaussian, Bernoulli, and Poisson models, and it is available on the Comprehensive R Archive Network. CONCLUSION: The extension of the method and the availability of new software now permit STEPP to be applied to the full range of clinical trial end points.

Thermoelectric energy recovery at ionic-liquid/electrode interface.

A thermally chargeable capacitor containing a binary solution of 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)-imide in acetonitrile is electrically charged by applying a temperature gradient to two ideally polarisable electrodes. The corresponding thermoelectric coefficient is -1.7 mV/K for platinum foil electrodes and -0.3 mV/K for nanoporous carbon electrodes. Stored electrical energy is extracted by discharging the capacitor through a resistor. The measured capacitance of the electrode/ionic-liquid interface is 5 µF for each platinum electrode while it becomes four orders of magnitude larger, ≈36 mF, for a single nanoporous carbon electrode. Reproducibility of the effect through repeated charging-discharging cycles under a steady-state temperature gradient demonstrates the robustness of the electrical charging process at the liquid/electrode interface. The acceleration of the charging by convective flows is also observed. This offers the possibility to convert waste-heat into electric energy without exchanging electrons between ions and electrodes, in contrast to what occurs in most thermogalvanic cells.

Evaluation of age-specific causes of death in the context of the Italian longevity transition.

In many low-mortality countries, life expectancy at birth increased steadily over the last century. In particular, both Italian females and males benefited from faster improvements in mortality compared to other high-income countries, especially from the 1960s, leading to an exceptional increase in life expectancy. However, Italy has not become the leader in longevity. Here, we investigate life expectancy trends in Italy during the period 1960-2015 for both sexes. Additionally, we contribute to the existing literature by complementing life expectancy with an indicator of dispersion in ages at death, also known as lifespan inequality. Lifespan inequality underlies heterogeneity over age in populating health improvements and is a marker of uncertainty in the timing of death. We further quantify the contributions of different age groups and causes of death to recent trends in life expectancy and lifespan inequality. Our findings highlight the contributions of cardiovascular diseases and neoplasms to the recent increase in life expectancy but not necessarily to the decrease in lifespan inequality. Our results also uncover a more recent challenge across Italy: worsening mortality from infectious diseases and mortality at older age.

Exploring the Association between Misinformation Endorsement, Opinions on the Government Response, Risk Perception, and COVID-19 Vaccine Hesitancy in the US, Canada, and Italy.

The COVID-19 pandemic has highlighted the adverse consequences created by an infodemic, specifically bringing attention to compliance with public health guidance and vaccine uptake. COVID-19 vaccine hesitancy is a complex construct that is related to health beliefs, misinformation exposure, and perceptions of governmental institutions. This study draws on theoretical models and current data on the COVID-19 infodemic to explore the association between the perceived risk of COVID-19, level of misinformation endorsement, and opinions about the government response on vaccine uptake. We surveyed a sample of 2697 respondents from the US, Canada, and Italy using a mobile platform between 21-28 May 2021. Using multivariate regression, we found that country of residence, risk perception of contracting and spreading COVID-19, perception of government response and transparency, and misinformation endorsement were associated with the odds of vaccine hesitancy. Higher perceived risk was associated with lower odds of hesitancy, while lower perceptions of government response and higher misinformation endorsement were associated with higher hesitancy.

Cross-National Vaccine Concerns and Predictors of Vaccine Hesitancy in Not-Fully Vaccinated Individuals: Findings from USA, Canada, Sweden, and Italy.

Vaccine hesitancy is a key contributor to reduced COVID-19 vaccine uptake and remains a threat to COVID-19 mitigation strategies as many countries are rolling out the campaign for booster shots. The goal of our study is to identify and compare the top vaccine concerns in four countries: Canada, Italy, Sweden, and the USA and how these concerns relate to vaccine hesitancy. While most individuals in these countries are now vaccinated, we expect our results to be helpful in guiding vaccination efforts for additional doses, and more in general for other vaccines in the future. We sought to empirically test whether vaccine related concerns followed similar thematic issues in the four countries included in this study, and then to see how these themes related to vaccine hesitancy using data from a cross-sectional survey conducted in May 2021. We applied CFA and created vaccine concern scales for analysis. We then utilized these results in regression-based modeling to determine how concerns related to vaccine hesitancy and whether there were similar or different concerns by country. The results quantitatively highlight that the same vaccine related concerns permeated multiple countries at the same point in time. This implies that COVID-19 vaccination communications could benefit from global collaboration.

Freedom of Choice to Vaccinate and COVID-19 Vaccine Hesitancy in Italy.

Despite the availability of effective vaccines that lower mortality and morbidity associated with COVID-19, many countries including Italy have adopted strict vaccination policies and mandates to increase the uptake of the COVID-19 vaccine. Such mandates have sparked debates on the freedom to choose whether or not to get vaccinated. In this study, we examined the people's belief in vaccine choice as a predictor of willingness to get vaccinated among a sample of unvaccinated individuals in Italy. An online cross-sectional survey was conducted in Italy in May 2021. The survey collected data on respondents' demographics and region of residence, socioeconomic factors, belief in the freedom to choose to be vaccinated or not, risk perception of contracting and transmitting the disease, previous vaccine refusal, opinion on adequacy of government measures to address the pandemic, experience in requesting and being denied government aid during the pandemic, and intent to accept COVID-19 vaccination. The analysis employed binary logistic regression models using a hierarchical model building approach to assess the association between intent to accept vaccination and belief in the freedom to choose to vaccinate, while adjusting for other variables of interest. 984 unvaccinated individuals were included in the study. Respondents who agreed that people should be free to decide whether or not to vaccinate with no restrictions on their personal life had 85% lower odds of vaccine acceptance (OR = 0.15; 95% CI, 0.09,0.23) after adjusting for demographic and socioeconomic factors and their risk perception of contracting and transmitting COVID-19. Belief in the freedom to choose whether or not to accept vaccinations was a major predictor of COVID-19 vaccine acceptance among a sample of unvaccinated individuals in Italy in May 2021. This understanding of how individuals prioritize personal freedoms and the perceived benefits and risks of vaccines, when making health care decisions can inform the development of public health outreach, educational programs, and messaging.

COVID-19 Vaccine Concerns about Safety, Effectiveness, and Policies in the United States, Canada, Sweden, and Italy among Unvaccinated Individuals.

Despite the effectiveness of the COVID-19 vaccine, global vaccination distribution efforts have thus far had varying levels of success. Vaccine hesitancy remains a threat to vaccine uptake. This study has four objectives: (1) describe and compare vaccine hesitancy proportions by country; (2) categorize vaccine-related concerns; (3) rank vaccine-related concerns; and (4) compare vaccine-related concerns by country and hesitancy status in four countries-the United States, Canada, Sweden, and Italy. Using the Pollfish survey platform, we sampled 1000 respondents in Canada, Sweden, and Italy and 750 respondents in the United States between 21-28 May 2021. Results showed vaccine-related concerns varied across three topical areas-vaccine safety and government control, vaccine effectiveness and population control, and freedom. For each thematic area, the top concern was statistically significantly different in each country and among the hesitant and non-hesitant subsamples within each county. Concerns related to freedom were the most universal. Understanding the specific concerns among individuals when it comes to the COVID-19 vaccine can help to inform public communications and identify which, if any, salient narratives are global.

Linking excess mortality to mobility data during the first wave of COVID-19 in England and Wales.

Non-pharmaceutical interventions have been implemented worldwide to curb the spread of COVID-19. However, the effectiveness of such governmental measures in reducing the mortality burden remains a key question of scientific interest and public debate. In this study, we leverage digital mobility data to assess the effects of reduced human mobility on excess mortality, focusing on regional data in England and Wales between February and August 2020. We estimate a robust association between mobility reductions and lower excess mortality, after adjusting for time trends and regional differences in a mixed-effects regression framework and considering a five-week lag between the two measures. We predict that, in the absence of mobility reductions, the number of excess deaths could have more than doubled in England and Wales during this period, especially in the London area. The study is one of the first attempts to quantify the effects of mobility reductions on excess mortality during the COVID-19 pandemic.

CEF is superior to CMF for tumours with TOP2A aberrations: a Subpopulation Treatment Effect Pattern Plot (STEPP) analysis on Danish Breast Cancer Cooperative Group Study 89D.

The aim of this study was to examine TOP2A gene copy number changes as a means to identify groups of breast cancer patients with superior benefit from treatment with anthracyclines. Tumour tissue was retrospectively collected and successfully analysed for TOP2A in 773 of 980 Danish patients randomly assigned to receive intravenous CMF (cyclophosphamide, methotrexate and fluorouracil) or CEF (cyclophosphamide, epirubicin and fluorouracil) in DBCG trial 89D. Subgroup analyses on this material published by Knoop et al. (J Clin Oncol 23:7483-7490, 2005) and updated by Nielsen et al. (Acta Oncol 47:725-734, 2008) demonstrated that superiority of CEF over CMF is limited to patients with TOP2A aberrations, defined as patients whose tumours have TOP2A ratio below 0.8 or above 2.0. The Subpopulation Treatment Effect Pattern Plot (STEPP) technique was applied to these data to explore the pattern of treatment effect relative to TOP2A and to compare that pattern to the ranges previously used to define 'aberrations'. The pattern of treatment effect illustrated by the STEPP analysis confirmed that the superiority of CEF over CMF is indeed limited to patients whose tumours have high or low TOP2A ratios. The hypothesis of no treatment effect-covariate interaction was rejected (P = 0.02). Furthermore, results indicated that the interval of TOP2A ratios hitherto denoted as 'normal' could be narrower than previously assumed. A more optimal separation of TOP2A subgroups could be obtained by altering cut-points currently used to define TOP2A amplified and TOP2A deleted tumours by narrowing the TOP2A normal interval, and consequently enlarging the population with TOP2A aberrated tumours.

Pressure effect on the kinetic of fluorescence photobleaching.

We investigate by fluorescence microscopy the bulk fluorescence photobleaching of coumarin 1 and fluorescein in different solvents as a function of pressure up to 3.5 kbar. We show that for coumarin 1, the decrease in the fluorescence intensity is well described by a single exponential function whose characteristic time decreases with pressure following a power law with exponent close to -0.29. Fluorescein photobleaching follows a double exponential function, the shorter time of which seems to follow a similar behavior as a function of pressure.

The Choice of the Number of Bins for the M Statistic.

Methods to monitor spatial patterns of disease in populations are of interest in public health practice. The M statistic uses interpoint distances between cases to detect abnormalities in the spatial patterns of diseases. This statistic compares the observed distribution of interpoint distances with that which is expected when no unusual spatial patterns exist. We show the relationship of M to Pearson's Chi Square statistic, xn2. Both statistics require the discretization of continuous data into bins and then are formed by creating a quadratic form, scaled by an appropriate variance covariance matrix. We seek to choose the number and type of these bins for the M statistic so as to maximize the power to detect spatial anomalies. By showing the relationship between M to xn2, we argue for the extension of the theory that has been developed for the selection of the number and type of bins for xn2 to M. We further show that spatial data provides a unique insight into the problem through examples with simulated data and spatial data from a health care provider. In the spatial setting, these indicate that the optimal number of bins depends on the size of the cluster. For large clusters, a smaller number of bins appears to be preferrable, however for small clusters having many bins increases the power. Further, results indicate that the number of bins does not appear to vary with m, the number of spatial locations. We discuss the implications of this result for further work.