A capture-recapture modeling framework emphasizing expert opinion in disease surveillance.

In disease surveillance, capture-recapture methods are commonly used to estimate the number of diseased cases in a defined target population. Since the number of cases never identified by any surveillance system cannot be observed, estimation of the case count typically requires at least one crucial assumption about the dependency between surveillance systems. However, such assumptions are generally unverifiable based on the observed data alone. In this paper, we advocate a modeling framework hinging on the choice of a key population-level parameter that reflects dependencies among surveillance streams. With the key dependency parameter as the focus, the proposed method offers the benefits of (a) incorporating expert opinion in the spirit of prior information to guide estimation; (b) providing accessible bias corrections, and (c) leveraging an adapted credible interval approach to facilitate inference. We apply the proposed framework to two real human immunodeficiency virus surveillance datasets exhibiting three-stream and four-stream capture-recapture-based case count estimation. Our approach enables estimation of the number of human immunodeficiency virus positive cases for both examples, under realistic assumptions that are under the investigator's control and can be readily interpreted. The proposed framework also permits principled uncertainty analyses through which a user can acknowledge their level of confidence in assumptions made about the key non-identifiable dependency parameter.

A Model Predicting the 6-Month Disability of Patients With Traumatic Brain Injury to Assess the Quality of Care in Intensive Care Units: Results from the CREACTIVE Study.

Assessing quality of care is essential for improving the management of patients experiencing traumatic brain injury (TBI). This study aimed at devising a rigorous framework to evaluate the quality of TBI care provided by intensive care units (ICUs) and applying it to the Collaborative Research on Acute Traumatic Brain Injury in Intensive Care Medicine in Europe (CREACTIVE) consortium, which involved 83 ICUs from seven countries. The performance of the centers was assessed in terms of patients' outcomes, as measured by the 6-month Glasgow Outcome Scale-Extended (GOS-E). To account for the between-center differences in the characteristics of the admitted patients, we developed a multinomial logistic regression model estimating the probability of a four-level categorization of the GOS-E: good recovery (GR), moderate disability (MD), severe disability (SD), and death or vegetative state (D/VS). A total of 5928 patients admitted to the participating ICUs between March 2014 and March 2019 were analyzed. The model included 11 predictors and demonstrated good discrimination (area under the receiver operating characteristic [ROC] curve in the validation set for GR: 0.836, MD: 0.802, SD: 0.706, D/VS: 0.890) and calibration, both overall (Hosmer-Lemeshow test p value: 0.87) and in several subgroups, defined by prognostically relevant variables. The model was used as a benchmark for assessing quality of care by comparing the observed number of patients experiencing GR, MD, SD, and D/VS to the corresponding numbers expected in each category by the model, computing observed/expected (O/E) ratios. The four center-specific ratios were assembled with polar representations and used to provide a multidimensional assessment of the ICUs, overcoming the loss of information consequent to the traditional dichotomizations of the outcome in TBI research. The proposed framework can help in identifying strengths and weaknesses of current TBI care, triggering the changes that are necessary to improve patient outcomes.

Remembering the truth or falsity of advertising claims: A preregistered model-based test of three competing theoretical accounts.

Given the large amount of information that people process daily, it is important to understand memory for the truth and falsity of information. The most prominent theoretical models in this regard are the Cartesian model and the Spinozan model. The former assumes that both "true" and "false" tags may be added to the memory representation of encoded information; the latter assumes that only falsity is tagged. In the present work, we contrasted these two models with an expectation-violation model hypothesizing that truth or falsity tags are assigned when expectations about truth or falsity must be revised in light of new information. An interesting implication of the expectation-violation model is that a context with predominantly false information leads to the tagging of truth whereas a context with predominantly true information leads to the tagging of falsity. To test the three theoretical models against each other, veracity expectations were manipulated between participants by varying the base rates of allegedly true and false advertising claims. Memory for the veracity of these claims was assessed using a model-based analysis. To increase methodological rigor and transparency in the specification of the measurement model, we preregistered, a priori, the details of the model-based analysis test. Despite a large sample size (N = 208), memory for truth and falsity did not differ, regardless of the base rates of true and false claims. The results thus support the Cartesian model and provide evidence against the Spinozan model and the expectation-violation model.

Eco-epidemiological study of seropositivity against Rickettsia and Leptospira agents in rural areas of Urabá, Colombia.

Rickettsioses and leptospirosis are infectious diseases that are often underdiagnosed due to a lack of knowledge about their epidemiology, pathophysiology, diagnosis, management, among others. Objetive: to characterize the seroprevalence and seroincidence of both Rickettsia and Leptospira agents and determine the risk factors for these outcomes in rural areas of Urabá, Antioquia. Methods: a secondary data analysis using information on Rickettsia and Leptospira exposure from a prior prospective study that explored sociocultural and ecological aspects of Rickettsia infection in rural Urabá, Colombia. A multinomial mixed logistic regression model was employed to analyze factors linked to seroprevalent cases of Rickettsia, Leptospira and both, along with descriptive analyses of seroincident cases. Results: the concomitant seroprevalence against Rickettsiaand Leptospira was 9.38% [95%CI 6.08%-13.37%] (56/597). The factors associated with this seroprevalence were age (ORa= 1.02 [95%CI 1.007-1.03]), male gender (ORa= 3.06 [95%CI 1.75-5.37]), fever history (ORa= 1.71 [95%CI 1.06-2.77]) the presence of breeding pigs (ORa= 2.29 [95%CI 1.36-3.88]), peridomicile yucca crops(ORa= 2.5 [95%CI 1.1-5.62]), and deforestation practices(ORa= 1.74 [95%CI 1.06-2.87]). The concomitant seroincidence against Rickettsia and Leptospira was 1.09% (3/274) [95%CI 0.29%-4.05%], three cases were female, with a median age of 31.83 years-old (IQR 8.69-56.99). At the household level, all the seroincident cases had households built partially or totally with soil floors, wooden walls, and zinc roofs. Two seroincident cases described the presence of equines, canines, and domestic chickens in intra or peri-domicile. Finally, two cases were exposed to synanthropic rodents, and one case to tick infestation. Conclusion: there is evidence of seroprevalent and seroincident cases of seropositivity against both Rickettsia and Leptospira in rural areas of Urabá, Colombia. These findings can help improve public health surveillance systems in preventing, detecting, and attending to the different clinical cases caused by these pathogens.

Pseudo-document simulation for comparing LDA, GSDMM and GPM topic models on short and sparse text using Twitter data.

Topic models are a useful and popular method to find latent topics of documents. However, the short and sparse texts in social media micro-blogs such as Twitter are challenging for the most commonly used Latent Dirichlet Allocation (LDA) topic model. We compare the performance of the standard LDA topic model with the Gibbs Sampler Dirichlet Multinomial Model (GSDMM) and the Gamma Poisson Mixture Model (GPM), which are specifically designed for sparse data. To compare the performance of the three models, we propose the simulation of pseudo-documents as a novel evaluation method. In a case study with short and sparse text, the models are evaluated on tweets filtered by keywords relating to the Covid-19 pandemic. We find that standard coherence scores that are often used for the evaluation of topic models perform poorly as an evaluation metric. The results of our simulation-based approach suggest that the GSDMM and GPM topic models may generate better topics than the standard LDA model.

Weight of authorship evidence with multiple categories of stylometric features: A multinomial-based discrete model.

This study empirically demonstrates the efficacy of a two-level Dirichlet-multinomial statistical model (the Multinomial system) for computing likelihood ratios (LR) for linguistic, textual evidence with multiple stylometric feature types with discrete values. The LRs are calculated separately for each feature type, namely, word, character and part of speech N-grams (N = 1,2,3), which are combined as overall LRs through logistic regression fusion. The Multinomial system's performance is compared with that of a previously proposed system with the cosine distance (the Cosine system) using the same data (i.e., documents collated from 2160 authors). The experimental results show that: (1) the Multinomial system outperforms the Cosine system with the fused feature types by a log-LR cost of ca. 0.01 âˆ¼ 0.05 bits; and (2) the Multinomial system is more advantageous in performance with longer documents than the Cosine system. Although the Cosine system is more robust overall against the sampling variability arising from the number of authors included in the reference and calibration databases, the Multinomial system can achieve reasonable stability in performance; for example, the standard deviation value of the log-LR cost becomes lower than 0.01 (10 random samplings of authors for the reference and calibration databases) with 60 or more authors in each database.

Modeling sign concordance of quantile regression residuals with multiple outcomes.

Quantile regression permits describing how quantiles of a scalar response variable depend on a set of predictors. Because a unique definition of multivariate quantiles is lacking, extending quantile regression to multivariate responses is somewhat complicated. In this paper, we describe a simple approach based on a two-step procedure: in the first step, quantile regression is applied to each response separately; in the second step, the joint distribution of the signs of the residuals is modeled through multinomial regression. The described approach does not require a multidimensional definition of quantiles, and can be used to capture important features of a multivariate response and assess the effects of covariates on the correlation structure. We apply the proposed method to analyze two different datasets.

Latent multinomial models for extended batch-mark data.

Batch marking is common and useful for many capture-recapture studies where individual marks cannot be applied due to various constraints such as timing, cost, or marking difficulty. When batch marks are used, observed data are not individual capture histories but a set of counts including the numbers of individuals first marked, marked individuals that are recaptured, and individuals captured but released without being marked (applicable to some studies) on each capture occasion. Fitting traditional capture-recapture models to such data requires one to identify all possible sets of capture-recapture histories that may lead to the observed data, which is computationally infeasible even for a small number of capture occasions. In this paper, we propose a latent multinomial model to deal with such data, where the observed vector of counts is a non-invertible linear transformation of a latent vector that follows a multinomial distribution depending on model parameters. The latent multinomial model can be fitted efficiently through a saddlepoint approximation based maximum likelihood approach. The model framework is very flexible and can be applied to data collected with different study designs. Simulation studies indicate that reliable estimation results are obtained for all parameters of the proposed model. We apply the model to analysis of golden mantella data collected using batch marks in Central Madagascar.

Bayesian spatial analysis of crash severity data with the INLA approach: Assessment of different identification constraints.

Multinomial logit models have been widely used in the analysis of categorical crash data. When the regional information of the data is available, the dependence structure needs to be incorporated into the model to accommodate for spatial heterogeneity. We consider a Bayesian multinomial structured additive regression model to analyze categorical spatial crash data and compare its performance with a fractional split multinomial model. We use the multinomial-Poisson transformation to apply the integrated nested Laplace approximation method for fitting the proposed model efficiently and fast. Moreover, we consider two different types of identifiability constraints to deal with the inherent identifiability problem of the multinomial models. The proposed models are studied through simulated examples and applied to a road traffic crash dataset from Mazandaran province, Iran.

Modelling spatiotemporal trends in the frequency of genetic mutations conferring insecticide target-site resistance in African mosquito malaria vector species.

BACKGROUND: Resistance in malaria vectors to pyrethroids, the most widely used class of insecticides for malaria vector control, threatens the continued efficacy of vector control tools. Target-site resistance is an important genetic resistance mechanism caused by mutations in the voltage-gated sodium channel (Vgsc) gene that encodes the pyrethroid target-site. Understanding the geographic distribution of target-site resistance, and temporal trends across different vector species, can inform strategic deployment of vector control tools. RESULTS: We develop a Bayesian statistical spatiotemporal model to interpret species-specific trends in the frequency of the most common resistance mutations, Vgsc-995S and Vgsc-995F, in three major malaria vector species Anopheles gambiae, An. coluzzii, and An. arabiensis over the period 2005-2017. The models are informed by 2418 observations of the frequency of each mutation in field sampled mosquitoes collected from 27 countries spanning western and eastern regions of Africa. For nine selected countries, we develop annual predictive maps which reveal geographically structured patterns of spread of each mutation at regional and continental scales. The results show associations, as well as stark differences, in spread dynamics of the two mutations across the three vector species. The coverage of ITNs was an influential predictor of Vgsc allele frequencies, with modelled relationships between ITN coverage and allele frequencies varying across species and geographic regions. We found that our mapped Vgsc allele frequencies are a significant partial predictor of phenotypic resistance to the pyrethroid deltamethrin in An. gambiae complex populations. CONCLUSIONS: Our predictive maps show how spatiotemporal trends in insecticide target-site resistance mechanisms in African An. gambiae vary across individual vector species and geographic regions. Molecular surveillance of resistance mechanisms will help to predict resistance phenotypes and track their spread.

Personal Determinants of Burden Among Indonesian Female Caregivers of Older Adults in Taiwan.

INTRODUCTION: Families increasingly employ foreign domestic workers (FDWs) to care for older loved ones. Caregiver burden reflects FDWs' difficulty adapting to work demands. We test hypothesized associations between burden and six personal characteristics: children, marriage, education, Chinese proficiency, eldercare experience, and non-eldercare experience. METHOD: In total, 299 Indonesian FDWs in Taiwan completed the Zarit Burden Interview. Exploratory factor analysis identified the dimensions of burden. Multiple and multinomial regressions related the variables to overall burden, burden dimensions, and burden severity. RESULTS: Four dimensions were found: personal strain, role strain, dependency, and guilt. Children were negatively associated with burden, role strain, dependency, and guilt. Chinese proficiency was negatively associated with severity and guilt. Eldercare experience was positively associated with severity and personal strain. Marriage was non-monotonically related to severity. CONCLUSION: Caregivers whose earnings benefit their children may be more likely to thrive in Taiwan. Language training may boost caregiver performance and host family satisfaction.

Population attributable fraction estimates for factors associated with different types of anaemia among women in Ethiopia: multilevel multinomial analysis.

OBJECTIVE: This study aimed to identify factors for different levels of anaemia among Ethiopian women and to estimate the population attributable fraction (PAF). DESIGN: This study was a detailed analysis of data of the 2016 Ethiopian Demographic and Health Survey data. Adjusted OR (AOR) with 95 % CI was computed using multilevel multinomial regression models, and the PAF were estimated using these AOR. SETTING: This study was conducted in Ethiopia. PARTICIPANTS: Women of reproductive age. RESULTS: The PAF showed that the proportion of mild anaemia cases attributable to having no formal education was 14·6 % (95 % CI 3·4, 24·5), high gravidity (≥4) was 11·2 % (95 % CI 1·2, 19·9) and currently breast-feeding was 5·2 % (95 % CI 0·0, 10·7). Similarly, the proportion of moderate-severe anaemia cases attributable to being in a rural residence was 38·1 % (95 % CI 15·9, 54·8); poorest wealth quantile, 12·6 % (95 % CI 2·9, 24·6); giving birth in the last 5 years, 10·5 % (95 % CI 2·9, 18·2) and unimproved latrine facilities, 17 % (95 % CI 0, 32·5). CONCLUSIONS: The PAF suggest that rural residency, low education, low wealth status, high parity, pregnancy and breast-feeding contribute substantially to the occurrence of anaemia among women in Ethiopia. Mild anaemia could be reduced by setting intervention strategies targeting women with low education, multigravida women and breast-feeding women, while preventing moderate-severe anaemia may require increasing income and improving living environments through the accessibility of hygienic latrines.

Management Practices Associated With Prevalence of Lameness in Lambs in 2012-2013 in 1,271 English Sheep Flocks.

The evidence base for management practices associated with low prevalence of lameness in ewes is robust. Current best practice is prompt treatment of even mildly lame sheep with parenteral and topical antibiotics with no routine or therapeutic foot trimming and avoiding routine footbathing. To date, comparatively little is known about management of lameness in lambs. Data came from a questionnaire completed by 1,271 English sheep farmers in 2013. Latent class (LC) analyses were used to investigate associations between treatment of footrot and geometric mean flock prevalence of lameness (GMPL) in lambs and ewes, with multinomial models used to investigate effects of flock management with treatment. Different flock typologies were identified for ewes and lambs. In both ewe and lamb models, there was an LC (1) with GMPL <2%, where infectious causes of lameness were rare, and farmers rarely treated lame animals. There was a second LC in ewes only (GMPL 3.2%) where infectious causes of lameness were present but farmers followed "best practice" and apparently controlled lameness. In other typologies, farmers did not use best practice and had higher GMPL than LC1 (3.9-4.2% and 2.8-3.5%, respectively). In the multinomial model, farmers were more likely to use parenteral antibiotics to treat lambs when more than 2-5% of lambs were lame compared with ≤2%. Once >10% of lambs were lame, while farmers were likely to use parenteral antibiotics, only sheep with locomotion score >2 were considered lame, leaving lame sheep untreated, potentially allowing spread of footrot. These farmers also used poor practices of routine foot trimming and footbathing, delayed culling, and poor biosecurity. We conclude there are no managements beneficial to manage lameness in lambs different from those for ewes; however, currently lameness in lambs is not treated using "best practice." In flocks with <2% prevalence of all lameness, where infectious causes of lameness were rare, farmers rarely treated lame animals but also did not practice poor managements of routine foot trimming or footbathing. If more farmers adopted "best practice" in ewes and lambs, the prevalence of lameness in lambs could be reduced to <2%, antibiotic use would be reduced, and sheep welfare would be improved.

Application of the MSAP Technique to Evaluate Epigenetic Changes in Plant Conservation.

Epigenetic variation, and particularly DNA methylation, is involved in plasticity and responses to changes in the environment. Conservation biology studies have focused on the measurement of this variation to establish demographic parameters, diversity levels and population structure to design the appropriate conservation strategies. However, in ex situ conservation approaches, the main objective is to guarantee the characteristics of the conserved material (phenotype and epi-genetic). We review the use of the Methylation Sensitive Amplified Polymorphism (MSAP) technique to detect changes in the DNA methylation patterns of plant material conserved by the main ex situ plant conservation methods: seed banks, in vitro slow growth and cryopreservation. Comparison of DNA methylation patterns before and after conservation is a useful tool to check the fidelity of the regenerated plants, and, at the same time, may be related with other genetic variations that might appear during the conservation process (i.e., somaclonal variation). Analyses of MSAP profiles can be useful in the management of ex situ plant conservation but differs in the approach used in the in situ conservation. Likewise, an easy-to-use methodology is necessary for a rapid interpretation of data, in order to be readily implemented by conservation managers.

An exploratory analysis of factors associated with traffic crashes severity in Cartagena, Colombia.

Traffic fatalities are the second cause of violent deaths in Colombia. However, due to the signing of the peace agreement and the growing number of fatalities in road crashes, it is possible that soon traffic fatalities will be the primary cause of violent deaths in the country, particularly in urban areas. This study is an exploratory analysis focused on identifying the main factors associated with the severity of traffic crashes in urban areas, using Cartagena as a case study. We analyzed three levels of crash severity, namely fatal, injury, and property-damage-only, considering factors in several different dimensions: victim, vehicle, road infrastructure, traffic and control, day and time, and environmental factors. A modeling approach based on multinomial ordered discrete models was used to properly identify the main factors associated with the severity levels. We found that the probability of fatal accidents is higher on streets with speed limits over 40 km/h, and that males and people aged 60 years or older are the victims with the most significant risk of fatal crashes. Motorcycles were also identified as vehicles with the highest probability of fatal crashes in the city. We showed that the probability of fatal crashes occurring is higher on streets where pedestrian bridges, traffic lights, and crosswalks are present. These findings are worthy because, in Colombia and other developing countries, the authorities normally expect to reduce the probability of fatal accidents through investments in pedestrian bridges, signaling devices, and crosswalk markings. However, according to our results, it possibly will not occur unless further countermeasures are taken. Based on these findings, reducing speed limits, operational improvements at signalized intersections, zero tolerance for traffic violations related to pedestrians, an awareness campaign on pedestrian safety focused on males and people aged 60 or older, and improving motorcycle safety are the countermeasures we proposed. Furthermore, as the authorities make significant efforts to investing in pedestrian bridges, we propose a further investigation into the traffic crashes in streets where there is this infrastructure since more severe events occur near them.

Detecting Local Clusters of Under-5 Malnutrition in the Province of Marinduque, Philippines Using Spatial Scan Statistic.

Underweight and overweight among under-5 children continue to persist in the island Province of Marinduque, Philippines. Local spatial cluster detection provides a spatial perspective in understanding this phenomenon, specifically in which areas the double burden of malnutrition occurs. Using data from a province-wide census conducted in 2014-2016, we aimed to identify spatial clusters of different forms of malnutrition in the province and determine its relative risk. Weight-for-age z score was used to categorize the malnourished children into severely underweight, moderately underweight, and overweight. We used the multinomial model of Kulldorff's elliptical spatial scan statistic, adjusting for age and socioeconomic status. Four significant clusters across municipalities of Boac, Buenavista, Gasan, and Torrijos were found to have high risk of overweight and underweight simultaneously, indicating existence of double burden of malnutrition within these communities. These clusters should be targeted with tailored plans to respond to malnutrition, at the same time maximizing the resources and benefits.

Bayesian approach to discriminant problems for count data with application to multilocus short tandem repeat dataset.

Short Tandem Repeats (STRs) are a type of DNA polymorphism. This study considers discriminant analysis to determine the population of test individuals using an STR database containing the lengths of STRs observed at more than one locus. The discriminant method based on the Bayes factor is discussed and an improved method is proposed. The main issues are to develop a method that is relatively robust to sample size imbalance, identify a procedure to select loci, and treat the parameter in the prior distribution. A previous study achieved a classification accuracy of 0.748 for the g-mean (geometric mean of classification accuracies for two populations) and 0.867 for the AUC (area under the receiver operating characteristic curve). We improve the maximum values for the g-mean to 0.830 and the AUC to 0.935. Computer simulations indicate that the previous method is susceptible to sample size imbalance, whereas the proposed method is more robust while achieving almost identical classification accuracy. Furthermore, the results confirm that threshold adjustment is an effective countermeasure to sample size imbalance.

Temperature effects on egg and larval development rate in European smelt, Osmerus eperlanus, experiments and a 50 year hindcast.

This study investigates the effect of water temperature on the development rate of eggs and larvae, the duration of the endogenous feeding period and its consequences for recruitment of smelt (Osmerus eperlanus) in Dutch lakes IJsselmeer and Markermeer. This study measured temperature-dependent egg and larval development rates as well as mortality rates from fertilization till the moment of absorption of the yolk-sac and from yolk-sac depletion onwards in temperature-controlled indoor experiments. Using multinomial modelling the authors found significant differences in development time of egg development stages under different temperature regimes. Based on historic water temperatures, the model predicted that the larval endogenous feeding period has advanced at a rate of about 2.9 days per decade in a more than 50 year period since 1961, yet there was no change in the duration of the endogenous feeding period. As zooplankton is more responsive to daylight than water temperature cues, a mismatch between the peak of the onset of exogenous feeding of smelt and the peak of zooplankton blooms could lead to high mortality and therefore low recruitment of smelt. Such a mismatch might contribute to a decline in the smelt population in Lake IJsselmeer and Lake Markermeer.

What characterizes the reminiscence bump in autobiographical memory? New answers to an old question.

The reminiscence bump represents one of the most robust findings in autobiographical memory research. As such, it has led to a number of different explanatory accounts that aim to elucidate it. Because most of these accounts have received some empirical support, it has been assumed that they may equally contribute to the explanation of the reminiscence bump phenomenon. In the present study, we used a multilevel multinomial mixed-effects model to examine the predictive power of explanatory variables selected from different accounts simultaneously. Analyses were based on 2,813 autobiographical memories that 97 older adults aged between 60 and 88 years reported in response to 31 cue words. Overall, the predictor variables (i.e., first-time experience, importance and emotional valence) meaningfully distinguished memories from the reminiscence bump from memories from life periods before and after. These effects, however, did not always go into the hypothesized directions. In addition, results of a Commonality Analysis indicated that although the explanatory accounts considered in the present study draw on qualities of autobiographical memories (within-person effects), they might be more useful in explaining why individuals differ in the number of autobiographical memories reported from the reminiscence bump period (between-person effects). Taken together, our findings are in line with a more integrative view on the reminiscence bump that, additionally, emphasizes the individual (e.g., the life-story account).