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In September of 2020, the Iowa Department of Public Health released guidance stating that persons exposed to someone with coronavirus disease (COVID-19) need not quarantine if the case-patient and the contact wore face masks at the time of exposure. This guidance differed from that issued by the Centers for Disease Control and Prevention. To determine the best action, we matched exposure information from COVID-19 case investigations with reported test results and calculated the secondary attack rates (SARs) after masked and unmasked exposures. Mask use by both parties reduced the SAR by half, from 25.6% to 12.5%. Longer exposure duration significantly increased SARs. Masks significantly reduced virus transmission when worn by both the case-patient and the contact, but SARs for each group were higher than anticipated. This finding suggests that quarantine after COVID-19 exposure is beneficial even if parties wore masks.

Bayesian compartmental infectious disease models yield important inference on disease transmission by appropriately accounting for the dynamics and uncertainty of infection processes. In addition to estimating transition probabilities and reproductive numbers, these statistical models allow researchers to assess the probability of disease risk and quantify the effectiveness of interventions. These infectious disease models rely on data collected from all individuals classified as positive based on various diagnostic tests. In infectious disease testing, however, such procedures produce both false‐positives and false‐negatives at varying rates depending on the sensitivity and specificity of the diagnostic tests being used. We propose a novel Bayesian spatio‐temporal infectious disease modeling framework that accounts for the additional uncertainty in the diagnostic testing and classification process that provides estimates of the important transmission dynamics of interest to researchers. The method is applied to data on the 2006 mumps epidemic in Iowa, in which over 6,000 suspected mumps cases were tested using a buccal or oral swab specimen, a urine specimen, and/or a blood specimen. Although all procedures are believed to have high specificities, the sensitivities can be low and vary depending on the timing of the test as well as the vaccination status of the individual being tested.

Compartmental models are commonly used to describe the spread of infectious diseases by estimating the probabilities of transitions between important disease states. A significant challenge in fitting Bayesian compartmental models lies in the need to estimate the duration of the infectious period, based on limited data providing only symptom onset date or another proxy for the start of infectiousness. Commonly, the exponential distribution is used to describe the infectious duration, an overly simplistic approach, which is not biologically plausible. More flexible distributions can be used, but parameter identifiability and computational cost can worsen for moderately sized or large epidemics. In this article, we present a novel approach, which considers a curve of transmissibility over a fixed infectious duration. The incorporation of infectious duration-dependent (IDD) transmissibility, which decays to zero during the infectious period, is biologically reasonable for many viral infections and fixing the length of the infectious period eases computational complexity in model fitting. Through simulation, we evaluate different functional forms of IDD transmissibility curves and show that the proposed approach offers improved estimation of the time-varying reproductive number. We illustrate the benefit of our approach through a new analysis of the 1995 outbreak of Ebola Virus Disease in the Democratic Republic of the Congo.

While many Bayesian state-space models for infectious disease processes focus on population infection dynamics (e.g., compartmental models), in this work we examine the evolution of infection processes and the complexities of the immune responses within the host using these techniques. We present a joint Bayesian state-space model to better understand how the immune system contributes to the control ofLeishmania infantuminfections over the disease course. We use longitudinal molecular diagnostic and clinical data of a cohort of dogs to describe population progression rates and present evidence for important drivers of clinical disease. Among these results, we find evidence for the importance of co-infection in disease progression. We also show that as dogs progress through the infection, parasite load is influenced by their age, ectoparasiticide treatment status, and serology. Furthermore, we present evidence that pathogen load information from an earlier point in time influences its future value and that the size of this effect varies depending on the clinical stage of the dog. In addition to characterizing the processes driving disease progression, we predict individual and aggregate patterns of Canine Leishmaniasis progression. Both our findings and the application to individual-level predictions are of direct clinical relevance, presenting possible opportunities for application in veterinary practice and motivating lines of additional investigation to better understand and predict disease progression. Finally, as an important zoonotic human pathogen, these results may support future efforts to prevent and treat human Leishmaniosis.1AUTHOR SUMMARYThe immune system is a complex network that involves organs, cells, and proteins working together with the main purpose of protecting the body against harmful microorganisms such as bacteria, viruses, fungi, and toxins. To explore and study the responses of the host immune system during the course of a disease, we modeled the interaction between pathogen load, antibody responses, and the clinical presentation of this complex system. Specifically, we focused onCanine Leishmaniasis(CanL), a vector-borne disease caused by a parasite that affects internal organs of the body and is known to be fatal if patients remain untreated. In addition, we also considered the impact of possible co-infections with other diseases, which could potentially interact with many disease processes and contribute to different outcomes for infected subjects. With CanL specifically, we consider the presence ofBorrelia, Anaplasma, Ehrlichia,, and Heartworm. In general, one limitation in vaccination strategies is a focus on neutralizing antibodies, without incorporating the broader complexities of immune responses. Here, we explore this complexity by jointly considering the interaction between pathogen and antibody development with the purpose of improving our understanding of the processes of disease progression and natural immunity.In this paper, we present a Bayesian model specification for immune responses to aLeishmaniainfection considering a tick-borne co-infection study. The model implementation is based on the general vector autoregressive (VAR) model, adapted to the problem under study. While the methodology around Bayesian VAR models is not new in the literature, in this work we adapt the more general VAR approach in a parsimonious way to a particular subclass of longitudinal problems. We believe our defined Bayesian model is useful to clinicians and veterinarians to better understand the immune responses andLeishmaniainfection control over time, which makes this work a novel application of Bayesian VAR models. We present evidence that pathogen load information from an earlier point in time influences its future value and that the size of this effect varies depending on the CanL clinical stage of the dog. In addition to characterizing evidence for the processes driving disease progression, we predict individual and aggregate patterns of CanL progression.The structure of this paper starts in Section 2 with an introduction to CanL infection as well as a discussion of possible co-infection with other pathogens. In Section 3, we include a description of the motivating prospective study along with the measured individual-level variables, a definition of the clinical signs of leishmaniosis infection, and a description of the available data coming from the study. In addition, this section explains the dynamic process and corresponding model specification via Bayesian methodology and a statement of contribution. A summary of prior distributions for model parameters, model implementation details, and convergence diagnostics are also included. In Section 4, we provide summary results from the posterior distribution as well as a summary of the corresponding disease progression forecasts. In Section 5, we discuss the results and describe future considerations to improve and extend the model.

Globally, norovirus is the most common gastroenteritis causing pathogen. Annually, norovirus causes 685 million cases of acute gastroenteritis and 200,000 deaths, worldwide. Recent evidence has suggested that norovirus can also be spread via aerosolization; however, an indoor generation source has yet to be determined. We optimized a sampling method for the collection of aerosolized norovirus using murine norovirus (MNV) as a surrogate. Optimization of the sampling method was performed using two bioaerosol samplers (SKC BioSampler and the NIOSH Bioaerosol Cyclone Sampler 251) and two sampling media (Hanks Balanced Salt Solution [HBSS] and Phosphate Buffered Saline [PBS]). Murine norovirus was aerosolized in a bioaerosol chamber and later collected using each sampler/media combination. Collected MNV was quantified using quantitative polymerase chain reaction (qPCR). Intact capsids of MNV were assessed using propidium monoazide dye in combination with qPCR and confirmed with transmission electron microscopy. Ten trials were conducted, with each trial lasting for 30 min. The SKC BioSampler collected a significantly higher concentration of MNV than the NIOSH-251 sampler did (p-value

Background Both incidence and geographical range of tick-borne disease has increased across the USA. Similar to people, dogs are hosts for Anaplasma spp., Babesia spp., Ehrlichia spp. and Borrelia burgdorferi. Dogs also share our homes and beds, making them both a sentinel for the ticks in our backyards but also increasing our exposure to ticks. Measures to better track, prevent, and/or treat tick-borne diseases in companion animals can lead to better control and prevention of human tick-borne disease. This study identifies demographic and co-infection risk factors for canine seropositivity to tick-borne infections in a cohort of hunting dogs across the USA. Results Human patterns of tick-borne disease co-infection in the USA have been predominantly driven by the geographical distribution of the tick vector. Dogs who tested seropositive for Anaplasma spp. were 1.40 times more likely (P = 0.0242) to also test seropositive for Babesia spp. and vice versa (1.60 times more likely, P = 0.0014). Dogs living in the West had 5% lower risk (P = 0.0001) for Ehrlichia spp. seropositivity compared to other regions. Controlling for age and Anaplasma spp. seroprevalence, dogs in all three other regions were 2.30 times more likely (P = 0.0216) to test seropositive for B. burgdorferi than dogs in the West. Dogs seropositive for B. burgdorferi were 1.60 times more likely (P = 0.0473) to be seropositive for Anaplasma spp. Conclusions Tick geographical distributions have a prominent impact on the regional distribution of hunting dog exposure to tick-borne diseases. Education concerning regional tick prevalence and disease risk is important for everyone, but particularly dog owners, regarding ticks in their region and protection from infection and co-infection of tick-borne pathogens as they travel or move with their dogs. Dogs are sentinel species for human exposure to ticks, and as such surveillance of canine tick-borne infections and understanding the probability that these infections might be seen together as co-infections helps predict emerging areas where people are more likely to be exposed as well.

Mutations in the spike glycoprotein of SARS-CoV-2 allow the virus to probe the sequence space in search of higher-fitness states. New sublineages of SARS-CoV-2 variants-of-concern (VOCs) continuously emerge with such mutations. Interestingly, the sites of mutation in these sublineages vary between the VOCs. Whether such differences reflect the random nature of mutation appearance or distinct evolutionary spaces of spike in the VOCs is unclear. Here we show that each position of spike has a lineage-specific likelihood for mutations to appear and dominate descendent sublineages. This likelihood can be accurately estimated from the lineage-specific mutational profile of spike at a protein-wide level. The mutability environment of each position, including adjacent sites on the protein structure and neighboring sites on the network of comutability, accurately forecast changes in descendent sublineages. Mapping of imminent changes within the VOCs can contribute to the design of immunogens and therapeutics that address future forms of SARS-CoV-2.

Visceral leishmaniasis (VL) is a neglected tropical disease that is globally distributed and has the potential to cause very serious illness. Prior literature highlights the emergence and spread of VL is influenced by multiple factors, such as socioeconomic status, sanitation levels or animal and human reservoirs. The study aimed to retrospectively investigate the presence and infectiousness of VL in Rio Grande do Norte (RN), Brazil between 2007 and 2020. We applied a hierarchical Bayesian approach to estimate municipality-specific relative risk of VL across space and time. The results show evidence that lower socioeconomic status is connected to higher municipality-specific VL risk. Overall, estimates reveal spatially heterogeneous VL risks in RN, with a high probability that VL risk for municipalities within the West Potiguar mesoregion are more than double the expected VL risk. Additionally, given the data available, results indicate there is a high probability of increasing VL risk in the municipalities of Natal, Patu and Pau dos Ferros. These findings demonstrate opportunities for municipality-specific public health policy interventions and warrant future research on identifying epidemiological drivers in at-risk regions.

Background The potential impact of the COVID-19 pandemic on recovery from alcohol use disorder (AUD) has received scant attention to date. In response, we investigated the stability of recovery and identified correlates of relapse, with particular interest in differences between women and men. Methods Data were obtained in a national survey of adults with resolved alcohol use disorder who were not drinking heavily (n = 1492). We calculated summary statistics and modeled odds of mild relapse (i.e., resolved at the time of data collection), overall and stratified by gender. Results Equivalent large majorities of women and men reported that the COVID-19 pandemic had not affected their recovery at all (88.9% and 88.8%, respectively). Mild relapse events were infrequent, with only 45 participants (3.1%) reporting a resumption of drinking after being abstinent and 35 participants (2.7%) reporting an increase from previously moderated drinking, with no differences in prevalence between men and women. Recovery capital showed consistent and comparable protective effects for both women and men (adjusted odds ratio [aOR] 0.90; 95% confidence interval [95% CI] 0.84, 0.97; and aOR 0.93; 95% CI 0.88, 0.98, respectively). We did not find any effect of pandemic-related stressors; however, there were a number of distinct correlates of mild relapse for women and men. Conclusions Recovery capital showed a consistently protective effect and may serve as a highly suitable intervention target as it is modifiable. Given gender differences, assessments of other key factors and tailored interventions targeting women and men may be necessary to ensure stable recovery.

Purpose Scientists in the speech, language, and hearing sciences rely on statistical analyses to help reveal complex relationships and patterns in the data collected from their research studies. However, data from studies in the fields of communication sciences and disorders rarely conform to the underlying assumptions of many traditional statistical methods. Fortunately, the field of statistics provides many mature statistical techniques that can be used to meet today's challenges involving complex studies of behavioral data from humans. In this review article, we highlight several techniques and general approaches with promising application to analyses in the speech and hearing sciences. Method The goal of this review article is to provide an overview of potentially underutilized statistical methods with promising application in the speech, language, and hearing sciences. Results We offer suggestions to identify when alternative statistical approaches might be advantageous when analyzing proportion data and repeated measures data. We also introduce the Bayesian paradigm and statistical learning and offer suggestions for when a scientist might consider those methods. Conclusion Modern statistical techniques provide more flexibility and enable scientists to ask more direct and informative research questions.

While a wide variety of machine-learning techniques have been productively applied to diverse prediction tasks, characterizing the nature of patterns and rules learned by these techniques has remained a difficult problem. Often called ‘black-box’ models for this reason, visualization has become a prominent area of research in understanding their behavior. One powerful tool for summarizing complex models, partial dependence plots (PDPs), offers a low-dimensional graphical interpretation by evaluating the effect of modifying individual predictors on fitted/predicted values. Nevertheless, in high-dimensional settings, PDPs may not capture more complex associations between groups of related variables and the outcome of interest. We propose an extension of PDPs based on the idea of grouping covariates, and interpreting the total effects of the groups. The method utilizes principal components analysis to explore the structure of the covariates, and offers several plots for assessing the approximation function. In conjunction with our diagnostic plot, totalvis gives insight into the total effect a group of covariates has on the prediction and can be used in situations where PDPs may not be appropriate. These tools provide a useful approach for pattern exploration, as well as a natural mechanism to reason about potential causal effects embedded in black-box models.

Lyme Disease presents unique challenges for public health. Transfer of protective antibodies between mothers and offspring should occur after vaccination of mice. We present new evidence for maternal transfer of oral vaccine induced neutralizing anti-OspA IgG antibodies to mouse pups mainly through ingestion of colostrum. We found a strong statistical correlation of antibody transfer between mothers that produced the most robust IgG response to OspA and their respective pups. OspA-specific antibody was detected as early as 24 h after birth and protective levels of antibodies lasted until ~5 weeks of age in the majority of pups but persisted in some mice until 9 weeks. This was further supported by detection of neutralizing antibodies in serum of all pups at 2–3 weeks after birth and in some offspring adult mice at 9 weeks of age. A clear association was found between robust antibody responses in mothers and the length of time antibody persisted in the respective pups using a novel longitudinal Bayesian model. These factors are likely to impact the enzootic cycle of B. burgdorferi if reservoir targeted OspA-based vaccination interventions are implemented.

Lyme Disease presents unique challenges for public health efforts. We hypothesized that transfer of protective antibodies between mothers and offspring should occur after oral vaccination of C3H-HeN mice with E. coli overexpressing OspA. We present new evidence for maternal transfer of vaccine induced neutralizing anti-OspA IgG antibodies to mouse pups through ingestion of colostrum. Protective levels of OspA antibodies in pups were present from 2-5 weeks after birth and they persisted in some mice until 9 weeks of age. This was corroborated by detection of neutralizing antibodies in the serum of all pups at 2-3 weeks after birth and in some mice at 9 weeks of age. A clear association was found between robust antibody responses in mothers and the length of antibody persistence in the respective pups using a novel longitudinal Bayesian model. These factors are likely to impact the enzootic cycle of B. burgdorferi when reservoir targeted OspA-based vaccination interventions are implemented.

Background Like many infectious diseases, there is no practical gold standard for diagnosing clinical visceral leishmaniasis (VL). Latent class modeling has been proposed to estimate a latent gold standard for identifying disease. These proposed models for VL have leveraged information from diagnostic tests with dichotomous serological and PCR assays, but have not employed continuous diagnostic test information. Methods/Principal findings In this paper, we employ Bayesian latent class models to improve the identification of canine visceral leishmaniasis using the dichotomous PCR assay and the Dual Path Platform (DPP) serology test. The DPP test has historically been used as a dichotomous assay, but can also yield numerical information via the DPP reader. Using data collected from a cohort of hunting dogs across the United States, which were identified as having either negative or symptomatic disease, we evaluate the impact of including numerical DPP reader information as a proxy for immune response. We find that inclusion of DPP reader information allows us to illustrate changes in immune response as a function of age. Conclusions/Significance Utilization of continuous DPP reader information can improve the correct discrimination between individuals that are negative for disease and those with clinical VL. These models provide a promising avenue for diagnostic testing in contexts with multiple, imperfect diagnostic tests. Specifically, they can easily be applied to human visceral leishmaniasis when diagnostic test results are available. Also, appropriate diagnosis of canine visceral leishmaniasis has important consequences for curtailing spread of disease to humans.

Population-level proportions of individuals that fall at different points in the spectrum [of disease severity], from asymptomatic infection to severe disease, are often difficult to observe, but estimating these quantities can provide information about the nature and severity of the disease in a particular population. Logistic and multinomial regression techniques are often applied to infectious disease modeling of large populations and are suited to identifying variables associated with a particular disease or disease state. However, they are less appropriate for estimating infection state prevalence over time because they do not naturally accommodate known disease dynamics like duration of time an individual is infectious, heterogeneity in the risk of acquiring infection, and patterns of seasonality. We propose a Bayesian compartmental model to estimate latent infection state prevalence over time that easily incorporates known disease dynamics. We demonstrate how and why a stochastic compartmental model is a better approach for determining infection state proportions than multinomial regression is by using a novel method for estimating Bayes factors for models with high-dimensional parameter spaces. We provide an example using visceral leishmaniasis in Brazil and present an empirically-adjusted reproductive number for the infection.

Approximate Bayesia n Computation (ABC) provides an attractive approach to estimation in complex Bayesian inferential problems for which evaluation of the kernel of the posterior distribution is impossible or computationally expensive. These highly parallelizable techniques have been successfully applied to many fields, particularly in cases where more traditional approaches such as Markov chain Monte Carlo (MCMC) are impractical. In this work, we demonstrate the application of approximate Bayesian inference to spatially heterogeneous Susceptible-Exposed-Infectious-Removed (SEIR) stochastic epidemic models. These models have a tractable posterior distribution, however MCMC techniques nevertheless become computationally infeasible for moderately sized problems. We discuss the practical implementation of these techniques via the open source ABSEIR package for R. The performance of ABC relative to traditional MCMC methods in a small problem is explored under simulation, as well as in the spatially heterogeneous context of the 2014 epidemic of Chikungunya in the Americas.

HIV-1 group M was transmitted to humans nearly one century ago. The virus has since diversified to form distinct clades, which spread to multiple regions worldwide. Of the different proteins encoded by HIV-1, the envelope glycoproteins (Envs) have diversified most rapidly in all infected populations. We compared the range of variants that emerged during the AIDS pandemic in diverse HIV-1 clades and distinct geographic regions. Our analyses focused on two components of Env that contain multiple epitopes of broadly-neutralizing antibodies: the glycan shield and apex domain. Interestingly, at each Env position, the amino acid in the inferred clade ancestor was replaced by a unique combination of emerging variants. Key antigenic sites and genetic signatures of vaccine protection have gradually evolved toward conserved frequency distributions (FDs) of all amino acids. FDs are specific for position and clade and are highly conserved in populations from different regions. Remarkably, founder effects of Env mutations in distinct clades and recently-infected regions were significantly reduced during the epidemic by evolution of each site toward the position-specific FD. These findings suggest that the selective pressures that guide evolution of Env are conserved in different populations. They are sufficiently strong to reduce founder effects at the clade and regional levels and have significantly altered the distribution of Env forms that circulate worldwide. Consequently, the intra-population diversity of the Env protein continues to increase whereas the inter-population diversity is gradually decreasing.ImportanceThe Env protein of HIV-1 is the primary target in AIDS vaccine design. Due to frequent mutations, new Env variants continuously emerge in the population. The increasing number of Env forms and apparent randomness of the changes limit our ability to design broadly-effective vaccines. We examined the populations-level changes that occurred in Env during the AIDS epidemic. Each position of the molecule has evolved toward a specific combination of amino acids. Similar changes occurred in different HIV-1 subtypes and geographic regions toward the same sets of forms, often from distinct ancestral sequences. Such conserved patterns of evolution define a new framework for designing vaccines that are tailored to the unique combination of Env variants expected to circulate in each population.

Purpose Clinicians depend on the accuracy of research in the speech, language, and hearing sciences to improve assessment and treatment of patients with communication disorders. Although this work has contributed to great advances in clinical care, common statistical misconceptions remain, which deserve closer inspection in the field. Challenges in applying and interpreting traditional statistical methods with behavioral data from humans have led to difficulties with replication and reproducibility in other allied scientific fields, including psychology and medicine. The importance of research in our fields of study for advancing science and clinical care for our patients means that the choices of statistical methods can have far-reaching, real-world implications. Method The goal of this article is to provide an overview of fundamental statistical concepts and methods that are used in the speech, language, and hearing sciences. Results We reintroduce basic statistical terms such as the p value and effect size, as well as recommended procedures for model selection and multiple comparisons. Conclusions Research in the speech, language, and hearing sciences can have a profound positive impact on the lives of individuals with communication disorders, but the validity of scientific findings in our fields is enhanced when data are analyzed using sound statistical methods. Misunderstanding or misinterpretation of basic statistical principles may erode public trust in research findings. Recommendations for practices that can help minimize the likelihood of errors in statistical inference are provided. Supplemental Material https://doi.org/10.23641/asha.7849223

Zoonotic visceral leishmaniasis (ZVL) is a serious neglected tropical disease that is endemic in 98 countries. ZVL is primarily transmitted via a sand fly vector. In the United States, it is enzootic in some canine populations; it is transmitted from infectious mother to pup transplacentally, and vector-borne transmission is absent. This absence affords a unique opportunity to study (1) vertical transmission dynamics in dogs and (2) the importance of vertical transmission in maintaining an infectious reservoir in the presence of a vector. In this paper, we present Bayesian compartmental models and reproductive number formulations to examine (1) and (2), providing a mechanism to plan and evaluate interventions in regions where both transmission modes are present. First, we propose an individual-level SIR model to study the effect of maternal infection status during pregnancy on pup infection progression. We provide evidence that pups born to diagnostically positive mothers during pregnancy are more likely to become diagnostically positive both earlier in life, and at some point during their lifetime, than those born to diagnostically negative mothers. Second, we propose a population-level SIR model to study the impact of a vertically-maintained reservoir on propagating infection in a naive canine population through emergent vector transmission using simulation studies. We also present reproductive numbers to quantify contributions of vertically-infected and vector-infected dogs to maintaining infection in the population. We show that a vertically-maintained canine reservoir can propagate infection in a theoretical naive population in the presence of a vector. Key words: Empirically-adjusted reproductive number; SIR; vertical transmission; visceral leishmaniasis.

Background Patients heterozygous for mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene may be more susceptible to respiratory infections than the general population. Methods. We conducted a retrospective case–control study using health insurance claims. We identified patients as either highly likely to be CFTR heterozygotes (CF carriers diagnosed during genetic counseling, parents of children with a diagnosis of CF, and children of mothers diagnosed with CF) or likely CFTR heterozygotes (children of CF carriers diagnosed during genetic counseling and parents of CF carriers diagnosed during genetic counseling). Next, we examined the rates of respiratory infections and antimicrobial prescriptions between both groups of CFTR patients and only the highly likely subcohort, compared with age/sex-matched controls. We examined the presence of any claim using McNemar’s test and the number of claims using the sign test. Results CFTR heterozygotes (the pooled highly likely and likely heterozygotes) were more prone to have at least 1 claim for a respiratory infection (odds ratio [OR], 1.28; P = .020) and to have a greater number of claims for respiratory infections (53.5%; P = .043) than controls. Patients in the highly likely cohort were also more prone to have at least 1 claim for a respiratory infection (OR, 1.30; P = .028) and more claims (54.3%; P = .039) than controls. In addition, the highly likely CFTR heterozygotes were more prone to be prescribed an antibiotic used to treat respiratory infections (OR, 1.34; P = .018) and to have more of these prescriptions (54.3%; P = .035) than controls. Conclusions Patients heterozygous for CFTR mutations are at higher risk for respiratory infections. Future work to describe clinical outcomes for CFTR heterozygotes is needed.

Quantification of aerosolized influenza virus is used for determining inhalation exposure. Several bioaerosol samplers and analytical methods have been used; however, the detection and quantification of influenza virus among aerosol samples remains challenging. Therefore, improved viral aerosol measurement methods are needed. This study evaluated influenza virus recovery among three filter types polytetrafluoroethylene, polyvinylchloride and polystyrene. Polytetrafluoroethylene, polyvinylchloride are fabricated filter materials and commonly used in the scientific literature to sample for viral aerosols. A novel, electrospun polystyrene filter material may improve viral aerosol recovery during filter-based air sampling. The filter materials were compared across the following conditions: treated with or without air, filter wash buffer (HBSS or PBS), and viral RNA extraction method (QIAamp Viral RNA Mini Kit or Trizol). Twenty trials were completed in a chamber and samples were analyzed using RT-qPCR. Viral recovery was significantly different (p-value < .0001) by filter type. Polystyrene filter use resulted in recovery of the most viral RNA. Air sampling did not affect the recovery of viral RNA from the filter materials (p-values > 0.05). Viral RNA concentrations were significantly different across extraction methods for all comparisons (p-values < 0.05). Our results demonstrated that the novel polystyrene filter material resulted in the highest concentration of extracted RNA compared to the commonly used polytetrafluoroethylene and polyvinylchloride, which we speculate may be related to the chemical composition of the filter material (e.g., polystyrene is an aromatic hydrocarbon whereas polytetrafluoroethylene and polyvinylchloride contain more polar, and thus potentially reactive, carbon-halogen bonds). Air sampling did not have an effect on viral RNA recovery. Using Hanks Balanced Salt Solution with QIAamp Viral RNA Mini Kit, and Phosphate-buffered saline with the Trizol extraction, resulted in the most viral RNA recovery.

BACKGROUND The majority of adults with alcohol use disorders do not obtain help, and women are less likely to utilize alcohol services than men. We sought to quantify gender differences in alcohol services utilization, overall and by type, using national longitudinal data and to explore potential gender differences in perceived need for help and reasons for not seeking help. METHODS We analyzed data from the National Epidemiologic Survey on Alcohol and Related Conditions from White, African American, and Hispanic adults (n = 2,592) who met DSM-IV criteria for alcohol abuse or dependence at Wave 1 (2000 to 2001). We tested gender differences in Wave 2 (2004 to 2005) services utilization, perceived need for help, and treatment barriers using Rao-Scott chi-square tests and assessed predictors of outcomes in multivariable logistic regression, adjusting for problem severity, co-occurring disorders, and demographics. RESULTS Women had much lower odds than men of utilizing any alcohol service (adjusted odds ratio [aOR] 0.53; 95% confidence interval [95% CI]: 0.33, 0.86), specialty services (aOR 0.41; 95% CI 0.19, 0.87), and 12-step groups (aOR 0.39; 95% CI 0.21, 0.71). Perceived need for help among those who had not used any services was very low (5%), with no gender difference. Further, men and women reported equivalent numbers of treatment barriers and the same rank order for the most frequently endorsed barriers. However, women were twice as likely as men to think a problem would get better by itself-the most frequent reason for not seeking help (47% vs. 24%, p

OBJECTIVE To evaluate the impact of Iowa's prescription monitoring program (PMP), implemented in 2009, on opioid pain reliever (OPR) prescribing patterns. METHODS We conducted interrupted time series analyses using 2003-2014 health insurance claims from a private health insurer in Iowa. OPR prescriptions for all beneficiaries were included. Another data set included only OPR prescription for new opioid users required to have six months of insurance coverage. We evaluate four OPR prescribing patterns: 1) average daily dosage in morphine milligrams equivalents (MME), 2) MME per prescription, 3) average days' supply per prescription, and 4) prescription rate per 1,000 insured person-years. We examined confounding and effect measure modification of the relationship between PMP and prescribing patterns by age and sex. RESULTS During the 12 years of follow-up, 1,512,388 insured Iowans contributed 6,169,634.92 person-years of follow-up. Of these, 505,274 patients filled 2,401,818 OPR prescriptions and 360,688 new OPR users filled as many first OPR prescriptions. The increasing trend of OPR prescription rates from 2003 to 2009 declined post-PMP. Similarly, there was a large decline in MME per day and MME per prescription. The OPR days' supply kept increasing post-PMP implementation, albeit at a slightly slower rate than pre-PMP implementation. There was no confounding by age and sex; however, we observed heterogeneity by age and sex; patients aged ≥50 years and females received higher doses and more prescriptions pre-PMP and experienced the greatest declines post-PMP. CONCLUSIONS Our study suggests that Iowa PMP implementation may have resulted in declines in OPR prescribing, and this impact varies by patient age and sex.

The large number of activities contributing to zoonoses surveillance and control capability, on both human and animal domains, and their likely heterogeneous implementation across administrative units make assessment and comparisons of capability performance between such units a complex task. Such comparisons are important to identify gaps in capability development, which could lead to clusters of vulnerable areas, and to rank and subsequently prioritize resource allocation toward the least capable administrative units. Area-level preparedness is a multidimensional entity and, to the best of our knowledge, there is no consensus on a single comprehensive indicator, or combination of indicators, in a summary metric. We use Bayesian spatial factor analysis models to jointly estimate and rank disease control and surveillance capabilities against visceral leishmaniasis (VL) at the municipality level in Brazil. The latent level of joint capability is informed by four variables at each municipality, three reflecting efforts to monitor and control the disease in humans, and one variable informing surveillance capability on the reservoir, the domestic dog. Because of the large volume of missing data, we applied imputation techniques to allow production of comprehensive rankings. We were able to show the application of these models to this sparse dataset and present a ranked list of municipalities based on their overall VL capability. We discuss improvements to our models, and additional applications.

The various thresholding quantities grouped under the "Basic Reproductive Number" umbrella are often confused, but represent distinct approaches to estimating epidemic spread potential, and address different modeling needs. Here, we contrast several common reproduction measures applied to stochastic compartmental models, and introduce a new quantity dubbed the "empirically adjusted reproductive number" with several advantages. These include: more complete use of the underlying compartmental dynamics than common alternatives, use as a potential diagnostic tool to detect the presence and causes of intensity process underfitting, and the ability to provide timely feedback on disease spread. Conceptual connections between traditional reproduction measures and our approach are explored, and the behavior of our method is examined under simulation. Two illustrative examples are developed: First, the single location applications of our method are established using data from the 1995 Ebola outbreak in the Democratic Republic of the Congo and a traditional stochastic SEIR model. Second, a spatial formulation of this technique is explored in the context of the ongoing Ebola outbreak in West Africa with particular emphasis on potential use in model selection, diagnosis, and the resulting applications to estimation and prediction. Both analyses are placed in the context of a newly developed spatial analogue of the traditional SEIR modeling approach.

In multiple fields of study, time series measured at high frequencies are used to estimate population curves that describe the temporal evolution of some characteristic of interest. These curves are typically nonlinear, and the deviations of each series from the corresponding curve are highly autocorrelated. In this scenario, we propose a procedure to compare the response curves for different groups at specific points in time. The method involves fitting the curves, performing potentially hundreds of serially correlated tests, and appropriately adjusting the overall alpha level of the tests. Our motivating application comes from psycholinguistics and the visual world paradigm. We describe how the proposed technique can be adapted to compare fixation curves within subjects as well as between groups. Our results lead to conclusions beyond the scope of previous analyses.

Introduction Antipsychotics are used for managing behavioral and psychological symptoms of dementia (BPSD) but have risks. Anticholinergics can worsen outcomes in dementia. The Improving Antipsychotic Appropriateness in Dementia Patients educational program (IA-ADAPT) and Centers for Medicare and Medicaid Services Partnership to Improve Dementia Care (CMS Partnership) promote improved care for BPSD. The purpose of this study was to evaluate the impact of these programs on medication use and BPSD among nursing home residents. Methods This quasi-experimental longitudinal study used Medicare and assessment data for Iowa nursing home residents from April 2011 to December 2012. Residents were required to be eligible for six continuous months for inclusion. Antipsychotic use and anticholinergic use were evaluated on a monthly basis, and changes in BPSD were tracked using assessment data. Results are presented as odds ratios (ORs) per month after exposure to the IA-ADAPT or the start of the CMS Partnership. Results Of 426 eligible Iowa nursing homes, 114 were exposed to the IA-ADAPT in 2012. Nursing home exposure to the IA-ADAPT was associated with reduced antipsychotic use (OR [95% CI] = 0.92 [0.89–0.95]) and anticholinergic use (OR [95% CI] = 0.95 [0.92–0.98]), reduced use of excessive antipsychotic doses per CMS guidance (OR [95% CI] = 0.80 [0.75–0.86]), increased odds of a potentially appropriate indication among antipsychotic users (OR [95% CI] = 1.04 [1.00–1.09]), and decreased documentation of verbal aggression (OR [95% CI] = 0.96 [0.94–0.99]). Facilities with two or more IA-ADAPT exposures had greater reductions in antipsychotic and anticholinergic use than those with only one. The CMS Partnership was associated with reduced antipsychotic use (OR [95% CI] = 0.96 [0.94–0.98]) and decreased documentation of any measured BPSD (OR [95% CI] = 0.98 [0.97–0.99]) as well as delirium specifically (OR [95% CI] = 0.98 [0.96–0.99]). Discussion This study suggests that the IA-ADAPT and the CMS Partnership improved medication use with no adverse impact on BPSD., Highlights • IA-ADAPT is a training program with decision aids to improve dementia care. • The CMS Partnership is a national initiative with similar goals. • IA-ADAPT training was associated with reduced antipsychotic and anticholinergic use. • The CMS Partnership was associated with reduced antipsychotic use. • No adverse effects on behavioral or psychological symptoms were detected.

In an increasingly connected world, epidemic modeling techniques are an important tool for understanding epidemic phenomenon and forecasting future pathogen spread. While an extensive literature concerning the application of these techniques to a variety of pathogen life cycles and population contexts has arisen, general-purpose epidemic modeling software has not been forthcoming, especially for computationally challenging stochastic epidemic models. We introduce a general-purpose spatial epidemic modeling framework applicable to many pathogens and describe an open source package for the R statistical computing environment designed to perform such analyses.

The various thresholding quantities grouped under the "Basic Reproductive Number" umbrella are often confused, but represent distinct approaches to estimating epidemic spread potential, and address different modeling needs. Here, we contrast several common reproduction measures applied to stochastic compartmental models, and introduce a new quantity dubbed the "empirically adjusted reproductive number" with several advantages. These include: more complete use of the underlying compartmental dynamics than common alternatives, use as a potential diagnostic tool to detect the presence and causes of intensity process underfitting, and the ability to provide timely feedback on disease spread. Conceptual connections between traditional reproduction measures and our approach are explored, and the behavior of our method is examined under simulation. Two illustrative examples are developed: First, the single location applications of our method are established using data from the 1995 Ebola outbreak in the Democratic Republic of the Congo and a traditional stochastic SEIR model. Second, a spatial formulation of this technique is explored in the context of the ongoing Ebola outbreak in West Africa with particular emphasis on potential use in model selection, diagnosis, and the resulting applications to estimation and prediction. Both analyses are placed in the context of a newly developed spatial analogue of the traditional SEIR modeling approach.