Privacy Attitudes toward Mouse-Tracking Paradata Collection.

Survey participants' mouse movements provide a rich, unobtrusive source of paradata, offering insight into the response process beyond the observed answers. However, the use of mouse tracking may require participants' explicit consent for their movements to be recorded and analyzed. Thus, the question arises of how its presence affects the willingness of participants to take part in a survey at all-if prospective respondents are reluctant to complete a survey if additional measures are recorded, collecting paradata may do more harm than good. Previous research has found that other paradata collection modes reduce the willingness to participate, and that this decrease may be influenced by the specific motivation provided to participants for collecting the data. However, the effects of mouse movement collection on survey consent and participation have not been addressed so far. In a vignette experiment, we show that reported willingness to participate in a survey decreased when mouse tracking was part of the overall consent. However, a larger proportion of the sample indicated willingness to both take part and provide mouse-tracking data when these decisions were combined, compared to an independent opt-in to paradata collection, separated from the decision to complete the study. This suggests that survey practitioners may face a trade-off between maximizing their overall participation rate and maximizing the number of participants who also provide mouse-tracking data. Explaining motivations for paradata collection did not have a positive effect and, in some cases, even reduced participants' reported willingness to take part in the survey.

Estimated Covid-19 burden in Spain: ARCH underreported non-stationary time series.

BACKGROUND: The problem of dealing with misreported data is very common in a wide range of contexts for different reasons. The current situation caused by the Covid-19 worldwide pandemic is a clear example, where the data provided by official sources were not always reliable due to data collection issues and to the high proportion of asymptomatic cases. In this work, a flexible framework is proposed, with the objective of quantifying the severity of misreporting in a time series and reconstructing the most likely evolution of the process. METHODS: The performance of Bayesian Synthetic Likelihood to estimate the parameters of a model based on AutoRegressive Conditional Heteroskedastic time series capable of dealing with misreported information and to reconstruct the most likely evolution of the phenomenon is assessed through a comprehensive simulation study and illustrated by reconstructing the weekly Covid-19 incidence in each Spanish Autonomous Community. RESULTS: Only around 51% of the Covid-19 cases in the period 2020/02/23-2022/02/27 were reported in Spain, showing relevant differences in the severity of underreporting across the regions. CONCLUSIONS: The proposed methodology provides public health decision-makers with a valuable tool in order to improve the assessment of a disease evolution under different scenarios.

New statistical model for misreported data with application to current public health challenges.

The main goal of this work is to present a new model able to deal with potentially misreported continuous time series. The proposed model is able to handle the autocorrelation structure in continuous time series data, which might be partially or totally underreported or overreported. Its performance is illustrated through a comprehensive simulation study considering several autocorrelation structures and three real data applications on human papillomavirus incidence in Girona (Catalonia, Spain) and Covid-19 incidence in two regions with very different circumstances: the early days of the epidemic in the Chinese region of Heilongjiang and the most current data from Catalonia.

Caretaker Score Reliability for Personality Assessment of Bottlenose Dolphin (Tursiops truncatus).

The evaluation of zoo animals' personalities can likely lead to a range of benefits, including improving breeding success, creating stable social groups, and designing and developing environmental enrichment programmes. The goal of this study was to use caretakers scores to evaluate personality in bottlenose dolphins and to assess the reliability of scores within each rater and among raters from each centre. To this end, 24 caretakers from 3 countries (Spain, France, and Argentina), including a total of 5 dolphinariums and 6 groups of dolphins, used a questionnaire based on the Five-Factor Model of Personality to score bottlenose dolphins on a number of personality traits in three different contexts. Each caretaker evaluated the animals under their care twice, ensuring that raters did not share thoughts nor impressions with other raters. Our findings showed a good degree of agreement between each rater's scores and a fair degree of agreement among scores of raters from the same centre. We also identified which raters and centres had significant mean score differences and detected that 4 out of 24 raters from two different centres showed such differences systematically. The evaluation of raters' reliability and the identification of particular inconsistent raters and centres is critical to make more appropriate and realistic management decisions that, in turn, directly impact animals' welfare.

Cumulated burden of COVID-19 in Spain from a Bayesian perspective.

BACKGROUND: The main goal of this work is to estimate the actual number of cases of COVID-19 in Spain in the period 31 January 2020 to 01 June 2020 by Autonomous Communities. Based on these estimates, this work allows us to accurately re-estimate the lethality of the disease in Spain, taking into account unreported cases. METHODS: A hierarchical Bayesian model recently proposed in the literature has been adapted to model the actual number of COVID-19 cases in Spain. RESULTS: The results of this work show that the real load of COVID-19 in Spain in the period considered is well above the data registered by the public health system. Specifically, the model estimates show that, cumulatively until 1 June 2020, there were 2 425 930 cases of COVID-19 in Spain with characteristics similar to those reported (95% credibility interval: 2 148 261-2 813 864), from which were actually registered only 518 664. CONCLUSIONS: Considering the results obtained from the second wave of the Spanish seroprevalence study, which estimates 2 350 324 cases of COVID-19 produced in Spain, in the period of time considered, it can be seen that the estimates provided by the model are quite good. This work clearly shows the key importance of having good quality data to optimize decision-making in the critical context of dealing with a pandemic.

Quantifying the under-reporting of uncorrelated longitudal data: the genital warts example.

BACKGROUND: Genital warts are a common and highly contagious sexually transmitted disease. They have a large economic burden and affect several aspects of quality of life. Incidence data underestimate the real occurrence of genital warts because this infection is often under-reported, mostly due to their specific characteristics such as the asymptomatic course. METHODS: Genital warts cases for the analysis were obtained from the Catalan public health system database (SIDIAP) for the period 2009-2016. People under 15 and over 94 years old were excluded from the analysis as the incidence of genital warts in this population is negligible. This work introduces a time series model based on a mixture of two distributions, capable of detecting the presence of under-reporting in the data. In order to identify potential differences in the magnitude of the under-reporting issue depending on sex and age, these covariates were included in the model. RESULTS: This work shows that only about 80% in average of genital warts incidence in Catalunya in the period 2009-2016 was registered, although the frequency of under-reporting has been decreasing over the study period. It can also be seen that this issue has a deeper impact on women over 30 years old. CONCLUSIONS: Although this study shows that the quality of the registered data has improved over the considered period of time, the Catalan public health system is underestimating genital warts real burden in almost 10,000 cases, around 23% of the registered cases. The total annual cost is underestimated in about 10 million Euros respect the 54 million Euros annually devoted to genital warts in Catalunya, representing 0.4% of the total budget.

Estimating the real burden of disease under a pandemic situation: The SARS-CoV2 case.

The present paper introduces a new model used to study and analyse the severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) epidemic-reported-data from Spain. This is a Hidden Markov Model whose hidden layer is a regeneration process with Poisson immigration, Po-INAR(1), together with a mechanism that allows the estimation of the under-reporting in non-stationary count time series. A novelty of the model is that the expectation of the unobserved process's innovations is a time-dependent function defined in such a way that information about the spread of an epidemic, as modelled through a Susceptible-Infectious-Removed dynamical system, is incorporated into the model. In addition, the parameter controlling the intensity of the under-reporting is also made to vary with time to adjust to possible seasonality or trend in the data. Maximum likelihood methods are used to estimate the parameters of the model.

Enhancing the monitoring of fallen stock at different hierarchical administrative levels: an illustration on dairy cattle from regions with distinct husbandry, demographical and climate traits.

BACKGROUND: The automated collection of non-specific data from livestock, combined with techniques for data mining and time series analyses, facilitates the development of animal health syndromic surveillance (AHSyS). An example of AHSyS approach relates to the monitoring of bovine fallen stock. In order to enhance part of the machinery of a complete syndromic surveillance system, the present work developed a novel approach for modelling in near real time multiple mortality patterns at different hierarchical administrative levels. To illustrate its functionality, this system was applied to mortality data in dairy cattle collected across two Spanish regions with distinct demographical, husbandry, and climate conditions. RESULTS: The process analyzed the patterns of weekly counts of fallen dairy cattle at different hierarchical administrative levels across two regions between Jan-2006 and Dec-2013 and predicted their respective expected counts between Jan-2014 and Jun- 2015. By comparing predicted to observed data, those counts of fallen dairy cattle that exceeded the upper limits of a conventional 95% predicted interval were identified as mortality peaks. This work proposes a dynamic system that combines hierarchical time series and autoregressive integrated moving average models (ARIMA). These ARIMA models also include trend and seasonality for describing profiles of weekly mortality and detecting aberrations at the region, province, and county levels (spatial aggregations). Software that fitted the model parameters was built using the R statistical packages. CONCLUSIONS: The work builds a novel tool to monitor fallen stock data for different geographical aggregations and can serve as a means of generating early warning signals of a health problem. This approach can be adapted to other types of animal health data that share similar hierarchical structures.

Untangling serially dependent underreported count data for gender-based violence.

Underreporting in gender-based violence data is a worldwide problem leading to the underestimation of the magnitude of this social and public health concern. This problem deteriorates the data quality, providing poor and biased results that lead society to misunderstand the actual scope of this domestic violence issue. The present work proposes time series models for underreported counts based on a latent integer autoregressive of order 1 time series with Poisson distributed innovations and a latent underreporting binary state, that is, a first-order Markov chain. Relevant theoretical properties of the models are derived, and the moment-based and maximum-based methods are presented for parameter estimation. The new time series models are applied to the quarterly complaints of domestic violence against women recorded in some judicial districts of Galicia (Spain) between 2007 and 2017. The models allow quantifying the degree of underreporting. A comprehensive discussion is presented, studying how the frequency and intensity of underreporting in this public health concern are related to some interesting socioeconomic and health indicators of the provinces of Galicia (Spain).

AN EXACT GOODNESS-OF-FIT TEST BASED ON THE OCCUPANCY PROBLEMS TO STUDY ZERO-INFLATION AND ZERO-DEFLATION IN BIOLOGICAL DOSIMETRY DATA.

The goal in biological dosimetry is to estimate the dose of radiation that a suspected irradiated individual has received. For that, the analysis of aberrations (most commonly dicentric chromosome aberrations) in scored cells is performed and dose response calibration curves are built. In whole body irradiation (WBI) with X- and gamma-rays, the number of aberrations in samples is properly described by the Poisson distribution, although in partial body irradiation (PBI) the excess of zeros provided by the non-irradiated cells leads, for instance, to the Zero-Inflated Poisson distribution. Different methods are used to analyse the dosimetry data taking into account the distribution of the sample. In order to test the Poisson distribution against the Zero-Inflated Poisson distribution, several asymptotic and exact methods have been proposed which are focused on the dispersion of the data. In this work, we suggest an exact test for the Poisson distribution focused on the zero-inflation of the data developed by Rao and Chakravarti (Some small sample tests of significance for a Poisson distribution. Biometrics 1956; 12 : 264-82.), derived from the problems of occupancy. An approximation based on the standard Normal distribution is proposed in those cases where the computation of the exact test can be tedious. A Monte Carlo Simulation study was performed in order to estimate empirical confidence levels and powers of the exact test and other tests proposed in the literature. Different examples of applications based on in vitro data and also data recorded in several radiation accidents are presented and discussed. A Shiny application which computes the exact test and other interesting goodness-of-fit tests for the Poisson distribution is presented in order to provide them to all interested researchers.

Under-reported data analysis with INAR-hidden Markov chains.

In this work, we deal with correlated under-reported data through INAR(1)-hidden Markov chain models. These models are very flexible and can be identified through its autocorrelation function, which has a very simple form. A naïve method of parameter estimation is proposed, jointly with the maximum likelihood method based on a revised version of the forward algorithm. The most-probable unobserved time series is reconstructed by means of the Viterbi algorithm. Several examples of application in the field of public health are discussed illustrating the utility of the models. Copyright © 2016 John Wiley & Sons, Ltd.

Development of a cross-cultural deprivation index in five European countries.

BACKGROUND: Despite a concerted policy effort in Europe, social inequalities in health are a persistent problem. Developing a standardised measure of socioeconomic level across Europe will improve the understanding of the underlying mechanisms and causes of inequalities. This will facilitate developing, implementing and assessing new and more effective policies, and will improve the comparability and reproducibility of health inequality studies among countries. This paper presents the extension of the European Deprivation Index (EDI), a standardised measure first developed in France, to four other European countries-Italy, Portugal, Spain and England, using available 2001 and 1999 national census data. METHODS AND RESULTS: The method previously tested and validated to construct the French EDI was used: first, an individual indicator for relative deprivation was constructed, defined by the minimal number of unmet fundamental needs associated with both objective (income) poverty and subjective poverty. Second, variables available at both individual (European survey) and aggregate (census) levels were identified. Third, an ecological deprivation index was constructed by selecting the set of weighted variables from the second step that best correlated with the individual deprivation indicator. CONCLUSIONS: For each country, the EDI is a weighted combination of aggregated variables from the national census that are most highly correlated with a country-specific individual deprivation indicator. This tool will improve both the historical and international comparability of studies, our understanding of the mechanisms underlying social inequalities in health and implementation of intervention to tackle social inequalities in health.