Adjusting for Berkson error in exposure in ordinary and conditional logistic regression and in Poisson regression.

BACKGROUND: INTEROCC is a seven-country cohort study of occupational exposures and brain cancer risk, including occupational exposure to electromagnetic fields (EMF). In the absence of data on individual exposures, a Job Exposure Matrix (JEM) may be used to construct likely exposure scenarios in occupational settings. This tool was constructed using statistical summaries of exposure to EMF for various occupational categories for a comparable group of workers. METHODS: In this study, we use the Canadian data from INTEROCC to determine the best EMF exposure surrogate/estimate from three appropriately chosen surrogates from the JEM, along with a fourth surrogate based on Berkson error adjustments obtained via numerical approximation of the likelihood function. In this article, we examine the case in which exposures are gamma-distributed for each occupation in the JEM, as an alternative to the log-normal exposure distribution considered in a previous study conducted by our research team. We also study using those surrogates and the Berkson error adjustment in Poisson regression and conditional logistic regression. RESULTS: Simulations show that the introduced methods of Berkson error adjustment for non-stratified analyses provide accurate estimates of the risk of developing tumors in case of gamma exposure model. Alternatively, and under some technical assumptions, the arithmetic mean is the best surrogate when a gamma-distribution is used as an exposure model. Simulations also show that none of the present methods could provide an accurate estimate of the risk in case of stratified analyses. CONCLUSION: While our previous study found the geometric mean to be the best exposure surrogate, the present study suggests that the best surrogate is dependent on the exposure model; the arithmetic means in case of gamma-exposure model and the geometric means in case of log-normal exposure model. However, we could present a better method of Berkson error adjustment for each of the two exposure models. Our results provide useful guidance on the application of JEMs for occupational exposure assessments, with adjustment for Berkson error.

Modeling the effect of observational social learning on parental decision-making for childhood vaccination and diseases spread over household networks.

In this paper, we introduce a novel model for parental decision-making about vaccinations against a childhood disease that spreads through a contact network. This model considers a bilayer network comprising two overlapping networks, which are either Erdos-Rényi (random) networks or Barabási-Albert networks. The model also employs a Bayesian aggregation rule for observational social learning on a social network. This new model encompasses other decision models, such as voting and DeGroot models, as special cases. Using our model, we demonstrate how certain levels of social learning about vaccination preferences can converge opinions, influencing vaccine uptake and ultimately disease spread. In addition, we explore how two different cultures of social learning affect the establishment of social norms of vaccination and the uptake of vaccines. In every scenario, the interplay between the dynamics of observational social learning and disease spread is influenced by the network's topology, along with vaccine safety and availability.

Prognostic values of myeloid differentiation factor 88 (MYD88) and transducin (ß)-like receptor 1 (TBLR1) expression in tissues of diffuse large B-cell non-Hodgkin lymphoma patients - an immunohistochemical study.

Introduction: Diffuse large B-cell non- Hodgkin lymphoma (DLBCL) is the largest common category of adult lymphoma. Recurrence and treatment resistance occurs in one-third of cases, triggering them to the progressive stage of DLBCL after treatment. Detection of novel predictive and prognostic biomarkers leads to improvement of its treatment and prognosis. Aim of the study: To assess the prognostic roles of protein expression of myeloid differentiation factor 88 (MYD88) and transducin (ß)-like receptor 1 (TBLR1) in tissues of DLBCL patients. Material and methods: In the current study we included tissues from 100 cases of DLBCL. For immunohistochemistry, tissues were stained with MYD88 and TBLR1. We followed patients for about 3 years, and then we correlated their expression with clinicopathological and prognostic parameters. Results: Higher MYD88 and TBLR1 expressions were associated with presence of B symptoms, fever, night sweat, advanced stage, bone marrow involvement and bulky nodal size, presence of extra-nodal extension, unfavourable relapse-free survival, and unfavourable overall survival rates (p < 0.001). Conclusions: overexpression of MYD88 and TBLR1 expression was present in DLBCL patients and was associated with unfavourable clinicopathological and prognostic parameters.

Using a stochastic continuous-time Markov chain model to examine alternative timing and duration of the COVID-19 lockdown in Kuwait: what can be done now?

BACKGROUND: Kuwait had its first COVID-19 in late February, and until October 6, 2020 it recorded 108,268 cases and 632 deaths. Despite implementing one of the strictest control measures-including a three-week complete lockdown, there was no sign of a declining epidemic curve. The objective of the current analyses is to determine, hypothetically, the optimal timing and duration of a full lockdown in Kuwait that would result in controlling new infections and lead to a substantial reduction in case hospitalizations. METHODS: The analysis was conducted using a stochastic Continuous-Time Markov Chain (CTMC), eight state model that depicts the disease transmission and spread of SARS-CoV 2. Transmission of infection occurs between individuals through social contacts at home, in schools, at work, and during other communal activities. RESULTS: The model shows that a lockdown 10 days before the epidemic peak for 90 days is optimal but a more realistic duration of 45 days can achieve about a 45% reduction in both new infections and case hospitalizations. CONCLUSIONS: In the view of the forthcoming waves of the COVID19 pandemic anticipated in Kuwait using a correctly-timed and sufficiently long lockdown represents a workable management strategy that encompasses the most stringent form of social distancing with the ability to significantly reduce transmissions and hospitalizations.

Modeling the Impact of Climate Change on Cervid Chronic Wasting Disease in Semi-Arid South Texas.

Chronic wasting disease (CWD) is a spongiform encephalopathy disease caused by the transmission of infectious prion agents. CWD is a fatal disease that affects wild and farmed cervids in North America with few cases reported overseas. Social interaction of cervids, feeding practices by wildlife keepers and climate effects on the environmental carrying capacity all can affect CWD transmission in deer. Wildlife deer game hunting is economically important to the semi-arid South Texas region and is affected by climate change. In this paper, we model and investigate the effect of climate change on the spread of CWD using typical climate scenarios. We use a system of impulsive differential equations to depict the transmission of CWD between different age groups and gender of cervids. The carrying capacity and contact rates are assumed to depend on climate. Due to the polygamy of bucks, we use mating rates that depend on the number of bucks and does. We analyze the stability of the model and use simulations to study the effect of harvesting (culling) on eradicating the disease, given the climate of South Texas. We use typical climate change scenarios based on published data and our assumptions. For the climate indicator, we calculated and utilized the Standard Precipitation Evapotranspiration Index (SPEI). We found that climate change might hinder the efforts to reduce and effectively manage CWD as it becomes endemic to South Texas. The model shows the extinction of the deer population from this region is a likely outcome.

Evaluation of the United States COVID-19 vaccine allocation strategy.

BACKGROUND: Anticipating an initial shortage of vaccines for COVID-19, the Centers for Disease Control (CDC) in the United States developed priority vaccine allocations for specific demographic groups in the population. This study evaluates the performance of the CDC vaccine allocation strategy with respect to multiple potentially competing vaccination goals (minimizing mortality, cases, infections, and years of life lost (YLL)), under the same framework as the CDC allocation: four priority vaccination groups and population demographics stratified by age, comorbidities, occupation and living condition (congested or non-congested). METHODS AND FINDINGS: We developed a compartmental disease model that incorporates key elements of the current pandemic including age-varying susceptibility to infection, age-varying clinical fraction, an active case-count dependent social distancing level, and time-varying infectivity (accounting for the emergence of more infectious virus strains). The CDC allocation strategy is compared to all other possibly optimal allocations that stagger vaccine roll-out in up to four phases (17.5 million strategies). The CDC allocation strategy performed well in all vaccination goals but never optimally. Under the developed model, the CDC allocation deviated from the optimal allocations by small amounts, with 0.19% more deaths, 4.0% more cases, 4.07% more infections, and 0.97% higher YLL, than the respective optimal strategies. The CDC decision to not prioritize the vaccination of individuals under the age of 16 was optimal, as was the prioritization of health-care workers and other essential workers over non-essential workers. Finally, a higher prioritization of individuals with comorbidities in all age groups improved outcomes compared to the CDC allocation. CONCLUSION: The developed approach can be used to inform the design of future vaccine allocation strategies in the United States, or adapted for use by other countries seeking to optimize the effectiveness of their vaccine allocation strategies.

Mitigating the externality of diseases of poverty through health aid.

Externality exists in healthcare when an individual benefits from others being healthy as it reduces the probability of getting sick from illness. Healthy workers are considered to be the more productive labourers leading to a country's positive economic growth over time. Several research studies have modelled disease transmission and its economic impact on a single country in isolation. We developed a two-country disease-economy model that explores disease transmission and cross-border infection of disease for its impacts. The model includes aspects of a worsening and rapid transmission of disease juxtaposed by positive impacts to the economy from tourism. We found that high friction affects the gross domestic product (GDP) of the lower-income country more than the higher-income country. Health aid from one country to another can substantially help grow the GDP of both countries due to the positive externality of disease reduction. Disease has less impact to both economies if the relative cost of treatment over an alternative (e.g. vaccination) is lower than the baseline value. Providing medical supplies to another country, adopting moderate friction between the countries, and finding treatments with lower costs result in the best scenario to preserve the GDP of both countries.

Trans-hyoid hyoidthyroidpexy: A modified technique for selected cases of obstructive sleep apnea.

OBJECTIVE: Hypopharyngeal collapse (HC) considered a challenge in surgery of obstructive sleep apnea (OSA). Several procedures were presented to deal with HC indirectly via providing support to the lateral walls of the hypopharynx preventing transverse collapse but hyoidthryoidpexy had gained more popularity. The procedure aimed to fix the mobile hyoid bone to a rigid mid-line neck structure, thus preventing the bone and its attached muscles from collapsing during sleep with the negative intrathoracic pressure on inspiration. STUDY DESIGN: A prospective case series study. METHODS: From April 2018 to January 2020, A Modified Technique of Trans hyoid hyoidthyroidpexy was applied for all included patients (24) patients with symptoms of OSA showing predominant lateral wall collapse of the hypopharynx (with retro-palatal collapse) with other OSA surgery. RESULTS: 6-8 months postoperatively, the Apnea Hypopnea index dropped from 43.75 ± 8.44 to 16.28 ± 7.35 (P < 0.0001; t = 10.6988). 14 patients (58.33%) were reported as successful while 7 patients (29.17%) were considered responders and three patients (12.5%) were considered non responders. The mean lowest oxygen desaturation elevated from 77.56 ± 5.64 to 92.38 ± 6.25 (p < 0.0001). Epworth Sleepiness Scale improved (P < 0.0001) from 16.85 ± 4.23 to 5.17 ± 3.89. CONCLUSION: Trans-hyoid hyoidthyroidpwxy is a modified technique of hyoidthyroidpexy. The procedure reported good outcomes in treating OSA. It is a simple, cost-effective and less traumatic technique. It could be combined with other multilevel surgical procedures.

Social distancing and testing as optimal strategies against the spread of COVID-19 in the Rio Grande Valley of Texas.

At the beginning of August 2020, the Rio Grande Valley (RGV) of Texas experienced a rapid increase of coronavirus disease 2019 (abbreviated as COVID-19) cases and deaths. This study aims to determine the optimal levels of effective social distancing and testing to slow the virus spread at the outset of the pandemic. We use an age-stratified eight compartment epidemiological model to depict COVID-19 transmission in the community and within households. With a simulated 120-day outbreak period data we obtain a post 180-days period optimal control strategy solution. Our results show that easing social distancing between adults by the end of the 180-day period requires very strict testing a month later and then daily testing rates of 5% followed by isolation of positive cases. Relaxing social distancing rates in adults from 50% to 25% requires both children and seniors to maintain social distancing rates of 50% for nearly the entire period while maintaining maximum testing rates of children and seniors for 150 of the 180 days considered in this model. Children have higher contact rates which leads to transmission based on our model, emphasizing the need for caution when considering school reopenings.

Analysis of intervention effectiveness using early outbreak transmission dynamics to guide future pandemic management and decision-making in Kuwait.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a World Health Organization designated pandemic that can result in severe symptoms and death that disproportionately affects older patients or those with comorbidities. Kuwait reported its first imported cases of COVID-19 on February 24, 2020. Analysis of data from the first three months of community transmission of the COVID-19 outbreak in Kuwait can provide important guidance for decision-making when dealing with future SARS-CoV-2 epidemic wave management. The analysis of intervention scenarios can help to evaluate the possible impacts of various outbreak control measures going forward which aim to reduce the effective reproduction number during the initial outbreak wave. Herein we use a modified susceptible-exposed-asymptomatic-infectious-removed (SEAIR) transmission model to estimate the outbreak dynamics of SARS-CoV-2 transmission in Kuwait. We fit case data from the first 96 days in the model to estimate the effective reproduction number and used Google mobility data to refine community contact matrices. The SEAIR modelled scenarios allow for the analysis of various interventions to determine their effectiveness. The model can help inform future pandemic wave management, not only in Kuwait but for other countries as well.

Modeling the effect of lockdown timing as a COVID-19 control measure in countries with differing social contacts.

The application, timing, and duration of lockdown strategies during a pandemic remain poorly quantified with regards to expected public health outcomes. Previous projection models have reached conflicting conclusions about the effect of complete lockdowns on COVID-19 outcomes. We developed a stochastic continuous-time Markov chain (CTMC) model with eight states including the environment (SEAMHQRD-V), and derived a formula for the basic reproduction number, R0, for that model. Applying the [Formula: see text] formula as a function in previously-published social contact matrices from 152 countries, we produced the distribution and four categories of possible [Formula: see text] for the 152 countries and chose one country from each quarter as a representative for four social contact categories (Canada, China, Mexico, and Niger). The model was then used to predict the effects of lockdown timing in those four categories through the representative countries. The analysis for the effect of a lockdown was performed without the influence of the other control measures, like social distancing and mask wearing, to quantify its absolute effect. Hypothetical lockdown timing was shown to be the critical parameter in ameliorating pandemic peak incidence. More importantly, we found that well-timed lockdowns can split the peak of hospitalizations into two smaller distant peaks while extending the overall pandemic duration. The timing of lockdowns reveals that a "tunneling" effect on incidence can be achieved to bypass the peak and prevent pandemic caseloads from exceeding hospital capacity.

Analysis of the Healthcare MERS-CoV Outbreak in King Abdulaziz Medical Center, Riyadh, Saudi Arabia, June-August 2015 Using a SEIR Ward Transmission Model.

Middle East respiratory syndrome coronavirus (MERS-CoV) is an emerging zoonotic coronavirus that has a tendency to cause significant healthcare outbreaks among patients with serious comorbidities. We analyzed hospital data from the MERS-CoV outbreak in King Abdulaziz Medical Center, Riyadh, Saudi Arabia, June-August 2015 using the susceptible-exposed-infectious-recovered (SEIR) ward transmission model. The SEIR compartmental model considers several areas within the hospital where transmission occurred. We use a system of ordinary differential equations that incorporates the following units: emergency department (ED), out-patient clinic, intensive care unit, and hospital wards, where each area has its own carrying capacity and distinguishes the transmission by three individuals in the hospital: patients, health care workers (HCW), or mobile health care workers. The emergency department, as parameterized has a large influence over the epidemic size for both patients and health care workers. Trend of the basic reproduction number (R0), which reached a maximum of 1.39 at the peak of the epidemic and declined to 0.92 towards the end, shows that until added hospital controls are introduced, the outbreak would continue with sustained transmission between wards. Transmission rates where highest in the ED, and mobile HCWs were responsible for large part of the outbreak.

Integer Versus Fractional Order SEIR Deterministic and Stochastic Models of Measles.

In this paper, we compare the performance between systems of ordinary and (Caputo) fractional differential equations depicting the susceptible-exposed-infectious-recovered (SEIR) models of diseases. In order to understand the origins of both approaches as mean-field approximations of integer and fractional stochastic processes, we introduce the fractional differential equations (FDEs) as approximations of some type of fractional nonlinear birth and death processes. Then, we examine validity of the two approaches against empirical courses of epidemics; we fit both of them to case counts of three measles epidemics that occurred during the pre-vaccination era in three different locations. While ordinary differential equations (ODEs) are commonly used to model epidemics, FDEs are more flexible in fitting empirical data and theoretically offer improved model predictions. The question arises whether, in practice, the benefits of using FDEs over ODEs outweigh the added computational complexities. While important differences in transient dynamics were observed, the FDE only outperformed the ODE in one of out three data sets. In general, FDE modeling approaches may be worth it in situations with large refined data sets and good numerical algorithms.

Berkson error adjustment and other exposure surrogates in occupational case-control studies, with application to the Canadian INTEROCC study.

Many epidemiological studies assessing the relationship between exposure and disease are carried out without data on individual exposures. When this barrier is encountered in occupational studies, the subject exposures are often evaluated with a job-exposure matrix (JEM), which consists of mean exposure for occupational categories measured on a comparable group of workers. One of the objectives of the seven-country case-control study of occupational exposure and brain cancer risk, INTEROCC, was to investigate the relationship of occupational exposure to electromagnetic fields (EMF) in different frequency ranges and brain cancer risk. In this paper, we use the Canadian data from INTEROCC to estimate the odds of developing brain tumours due to occupational exposure to EMF. The first step was to find the best EMF exposure surrogate among the arithmetic mean, the geometric mean, and the mean of log-normal exposure distribution for each occupation in the JEM, in comparison to Berkson error adjustments via numerical approximation of the likelihood function. Contrary to previous studies of Berkson errors in JEMs, we found that the geometric mean was the best exposure surrogate. This analysis provided no evidence that cumulative lifetime exposure to extremely low frequency magnetic fields increases brain cancer risk, a finding consistent with other recent epidemiological studies.

A stochastic model of the bovine spongiform encephalopathy epidemic in Canada.

Bovine spongiform encephalopathy (BSE) appeared in the United Kingdom in the mid 1980s, and has been attributed to the use of meat and bone meal (MBM) in cattle feed contaminated with a scrapie-like agent. Import of infectious materials from a country where BSE has occurred is believed to be the major factor underlying the spread of the BSE epidemic to other countries. This study presents a new stochastic model developed to estimate risk of BSE from importation of cattle infected with the BSE agent. The model describes the propagation of the BSE agent through the Canadian cattle herd through rendering and feeding processes, following importation of cattle with infectious prions. This model was used estimate the annual number of newly infected animals each year over the period 1980-2019. Model predictions suggested that the number of BSE infections in Canada might have been approximately 40-fold greater than the actual number of clinically diagnosed cases. Under complete compliance with the 2007 ban on feeding MBM, this model further predicts that BSE is disappearing from the Canadian cattle system. A series of sensitivity analyses was also conducted to test the robustness of model predictions to alternative assumptions about factors affecting the evolution of the Canadian BSE epidemic.

A Bayesian back-calculation method to estimate the risk of bovine spongiform encephalopathy (BSE) in Canada during the period 1996-2011.

Seventeen typical cases of bovine spongiform encephalopathy (BSE) were detected in Canada the period of 2003-2011. The clinical incidence of BSE was censored by early slaughter, death, or exportation of infected cattle due to the long incubation period of BSE disease. The aim of this study was to estimate the infection incidence of BSE in birth cohorts during 1996-2004 and project infection frequency through to 2007. An estimate of the number of asymptomatic infected cattle slaughtered for human consumption is also provided. The number of incident, asymptomatic cases was assumed to follow a Poisson process. A Bayesian back-calculation approach was used to project the risk of contracting BSE in those birth cohorts. Model parameters and inputs were taken from scientific literature and governmental data sources. The projected number of infected cattle in birth cohorts spanning the period 1996-2007 was 492, with median 95% credible interval 258-830. If the requirement to remove specified risk material (SRM) from cattle prior to entering the food chain was not in place, the predicted number of slaughtered infected in the human food chain from 1996-2010 was 298, with a 95% credible interval 156-500. The magnitude of the BSE epidemic in Canada for 1996-2007 birth cohorts was estimated to be approximately 28-fold higher than the number of clinical cases detected through to October 2011. Although some of those cattle were slaughtered for human consumption, the requirement of SRM removal may have prevented most of the infectious material from entering the food chain.

Using expert judgments to improve chronic wasting disease risk management in Canada.

ABSTARCT Chronic wasting disease (CWD) is a neurodegenerative, protein misfolding disease affecting cervids in North America in epidemic proportions. While the existence of CWD has been known for more than 40 years, risk management efforts to date have not been able to curtail the spread of this condition. An expert elicitation exercise was carried out in May 2011 to obtain the views of international experts on both the etiology of CWD and possible CWD risk management strategies. This study presents the results of the following three components of the elicitation exercise: (1) expert views of the most likely scenarios for the evolution of the CWD among cervid populations in Canada, (2) ranking analyses of the importance of direct and indirect transmission routes, and (3) rating analyses of CWD control measures in farmed and wild cervids. The implications of these findings for the development of CWD risk management strategies are described in a Canadian context.

Expert elicitation on the uncertainties associated with chronic wasting disease.

A high degree of uncertainty exists for chronic wasting disease (CWD) transmission factors in farmed and wild cervids. Evaluating the factors is important as it helps to inform future risk management strategies. Expert opinion is often used to assist decision making in a number of health, science, and technology domains where data may be sparse or missing. Using the "Classical Model" of elicitation, a group of experts was asked to estimate the most likely values for several risk factors affecting CWD transmission. The formalized expert elicitation helped structure the issues and hence provide a rational basis for estimating some transmission risk factors for which evidence is lacking. Considered judgments regarding environmental transmission, latency of CWD transmission, management, and species barrier were provided by the experts. Uncertainties for many items were determined to be large, highlighting areas requiring more research. The elicited values may be used as surrogate values until research evidence becomes available.

Mathematical Models for Estimating the Risks of Bovine Spongiform Encephalopathy (BSE).

When the bovine spongiform encephalopathy (BSE) epidemic first emerged in the United Kingdom in the mid 1980s, the etiology of animal prion diseases was largely unknown. Risk management efforts to control the disease were also subject to uncertainties regarding the extent of BSE infections and future course of the epidemic. As understanding of BSE increased, mathematical models were developed to estimate risk of BSE infection and to predict reductions in risk in response to BSE control measures. Risk models of BSE-transmission dynamics determined disease persistence in cattle herds and relative infectivity of cattle prior to onset of clinical disease. These BSE models helped in understanding key epidemiological features of BSE transmission and dynamics, such as incubation period distribution and age-dependent infection susceptibility to infection with the BSE agent. This review summarizes different mathematical models and methods that have been used to estimate risk of BSE, and discusses how such risk projection models have informed risk assessment and management of BSE. This review also provides some general insights on how mathematical models of the type discussed here may be used to estimate risks of emerging zoonotic diseases when biological data on transmission of the etiological agent are limited.