A Dynamic Model of Opioid Overdose Deaths in Canada during the Co-Occurring Opioid Overdose Crisis and COVID-19 Pandemic.

With over 40,000 opioid-related overdose deaths between January 2016 and June 2023, the opioid-overdose crisis is a significant public health concern for Canada. The opioid crisis arose from a complex system involving prescription opioid use, the use of prescription opioids not as prescribed, and non-medical opioid use. The increasing presence of fentanyl and its analogues in the illegal drugs supply has been an important driver of the crisis. In response to the overdose crisis, governments at the municipal, provincial/territorial, and federal levels have increased actions to address opioid-related harms. At the onset of the COVID-19 pandemic, concerns emerged over how the pandemic context may impact the opioid overdose crisis. Using evidence from a number of sources, we developed a dynamic mathematical model of opioid overdose death to simulate possible trajectories of overdose deaths during the COVID-19 pandemic. This model incorporates information on prescription opioid use, opioid use not as prescribed, non-medical opioid use, the level of fentanyl in the drug supply, and a measure of the proportion deaths preventable by new interventions. The simulated scenarios provided decision makers with insight into possible trajectories of the opioid crisis in Canada during the COVID-19 pandemic, highlighting the potential of the crisis to take a turn for the worse under certain assumptions, and thus, informing planning during a period when surveillance data were not yet available. This model provides a starting point for future models, and through its development, we have identified important data and evidence gaps that need to be filled in order to inform future action.

Comparing Contact Tracing Through Bluetooth and GPS Surveillance Data: Simulation-Driven Approach.

BACKGROUND: Accurate and responsive epidemiological simulations of epidemic outbreaks inform decision-making to mitigate the impact of pandemics. These simulations must be grounded in quantities derived from measurements, among which the parameters associated with contacts between individuals are notoriously difficult to estimate. Digital contact tracing data, such as those provided by Bluetooth beaconing or GPS colocating, can provide more precise measures of contact than traditional methods based on direct observation or self-reporting. Both measurement modalities have shortcomings and are prone to false positives or negatives, as unmeasured environmental influences bias the data. OBJECTIVE: We aim to compare GPS colocated versus Bluetooth beacon-derived proximity contact data for their impacts on transmission models' results under community and types of diseases. METHODS: We examined the contact patterns derived from 3 data sets collected in 2016, with participants comprising students and staff from the University of Saskatchewan in Canada. Each of these 3 data sets used both Bluetooth beaconing and GPS localization on smartphones running the Ethica Data (Avicenna Research) app to collect sensor data about every 5 minutes over a month. We compared the structure of contact networks inferred from proximity contact data collected with the modalities of GPS colocating and Bluetooth beaconing. We assessed the impact of sensing modalities on the simulation results of transmission models informed by proximate contacts derived from sensing data. Specifically, we compared the incidence number, attack rate, and individual infection risks across simulation results of agent-based susceptible-exposed-infectious-removed transmission models of 4 different contagious diseases. We have demonstrated their differences with violin plots, 2-tailed t tests, and Kullback-Leibler divergence. RESULTS: Both network structure analyses show visually salient differences in proximity contact data collected between GPS colocating and Bluetooth beaconing, regardless of the underlying population. Significant differences were found for the estimated attack rate based on distance threshold, measurement modality, and simulated disease. This finding demonstrates that the sensor modality used to trace contact can have a significant impact on the expected propagation of a disease through a population. The violin plots of attack rate and Kullback-Leibler divergence of individual infection risks demonstrated discernible differences for different sensing modalities, regardless of the underlying population and diseases. The results of the t tests on attack rate between different sensing modalities were mostly significant (P<.001). CONCLUSIONS: We show that the contact networks generated from these 2 measurement modalities are different and generate significantly different attack rates across multiple data sets and pathogens. While both modalities offer higher-resolution portraits of contact behavior than is possible with most traditional contact measures, the differential impact of measurement modality on the simulation outcome cannot be ignored and must be addressed in studies only using a single measure of contact in the future.

Real-Time Epidemiology and Acute Care Need Monitoring and Forecasting for COVID-19 via Bayesian Sequential Monte Carlo-Leveraged Transmission Models.

COVID-19 transmission models have conferred great value in informing public health understanding, planning, and response. However, the pandemic also demonstrated the infeasibility of basing public health decision-making on transmission models with pre-set assumptions. No matter how favourably evidenced when built, a model with fixed assumptions is challenged by numerous factors that are difficult to predict. Ongoing planning associated with rolling back and re-instituting measures, initiating surge planning, and issuing public health advisories can benefit from approaches that allow state estimates for transmission models to be continuously updated in light of unfolding time series. A model being continuously regrounded by empirical data in this way can provide a consistent, integrated depiction of the evolving underlying epidemiology and acute care demand, offer the ability to project forward such a depiction in a fashion suitable for triggering the deployment of acute care surge capacity or public health measures, and support quantitative evaluation of tradeoffs associated with prospective interventions in light of the latest estimates of the underlying epidemiology. We describe here the design, implementation, and multi-year daily use for public health and clinical support decision-making of a particle-filtered COVID-19 compartmental model, which served Canadian federal and provincial governments via regular reporting starting in June 2020. The use of the Bayesian sequential Monte Carlo algorithm of particle filtering allows the model to be regrounded daily and adapt to new trends within daily incoming data-including test volumes and positivity rates, endogenous and travel-related cases, hospital census and admissions flows, daily counts of dose-specific vaccinations administered, measured concentration of SARS-CoV-2 in wastewater, and mortality. Important model outputs include estimates (via sampling) of the count of undiagnosed infectives, the count of individuals at different stages of the natural history of frankly and pauci-symptomatic infection, the current force of infection, effective reproductive number, and current and cumulative infection prevalence. Following a brief description of the model design, we describe how the machine learning algorithm of particle filtering is used to continually reground estimates of the dynamic model state, support a probabilistic model projection of epidemiology and health system capacity utilization and service demand, and probabilistically evaluate tradeoffs between potential intervention scenarios. We further note aspects of model use in practice as an effective reporting tool in a manner that is parameterized by jurisdiction, including the support of a scripting pipeline that permits a fully automated reporting pipeline other than security-restricted new data retrieval, including automated model deployment, data validity checks, and automatic post-scenario scripting and reporting. As demonstrated by this multi-year deployment of the Bayesian machine learning algorithm of particle filtering to provide industrial-strength reporting to inform public health decision-making across Canada, such methods offer strong support for evidence-based public health decision-making informed by ever-current articulated transmission models whose probabilistic state and parameter estimates are continually regrounded by diverse data streams.

Qualitative Interviews to Add Patient Perspectives in Colorectal Cancer Screening: Improvements in a Learning Health System.

Health systems are interested in increasing colorectal cancer (CRC) screening rates as CRC is a leading cause of preventable cancer death. Learning health systems are ones that use data to continually improve care. Data can and should include qualitative local perspectives to improve patient and provider education and care. This study sought to understand local perspectives on CRC screening to inform future strategies to increase screening rates across our integrated health system. Health insurance plan members who were eligible for CRC screening were invited to participate in semi-structured phone interviews. Qualitative content analysis was conducted using an inductive approach. Forty member interviews were completed and analyzed. Identified barriers included ambivalence about screening options (e.g., "If it had the same performance, I'd rather do home fecal sample test. But I'm just too skeptical [so I do the colonoscopy]."), negative prior CRC screening experiences, and competing priorities. Identified facilitators included a positive general attitude towards health (e.g., "I'm a rule follower. There are certain things I'll bend rules. But certain medical things, you just got to do."), social support, a perceived risk of developing CRC, and positive prior CRC screening experiences. Study findings were used by the health system leaders to inform the selection of CRC screening outreach and education strategies to be tested in a future simulation model. For example, the identified barrier related to ambivalence about screening options led to a proposed revision of outreach materials that describe screening types more clearly.

Unemployment and underemployment are causes of suicide.

Epidemiological studies indicate that labor underutilization and suicide are associated, yet it remains unclear whether this association is causal. We applied convergent cross mapping to test for causal effects of unemployment and underemployment on suicidal behavior, using monthly data on labor underutilization and suicide rates in Australia for the period 2004-2016. Our analyses provide evidence that rates of unemployment and underemployment were significant drivers of suicide mortality in Australia over the 13-year study period. Predictive modeling indicates that 9.5% of the ~32,000 suicides reported between 2004 and 2016 resulted directly from labor underutilization, including 1575 suicides attributable to unemployment and 1496 suicides attributable to underemployment. We conclude that economic policies prioritizing full employment should be considered integral to any comprehensive national suicide prevention strategy.

Comparison of pretrained transformer-based models for influenza and COVID-19 detection using social media text data in Saskatchewan, Canada.

Background: The use of social media data provides an opportunity to complement traditional influenza and COVID-19 surveillance methods for the detection and control of outbreaks and informing public health interventions. Objective: The first aim of this study is to investigate the degree to which Twitter users disclose health experiences related to influenza and COVID-19 that could be indicative of recent plausible influenza cases or symptomatic COVID-19 infections. Second, we seek to use the Twitter datasets to train and evaluate the classification performance of Bidirectional Encoder Representations from Transformers (BERT) and variant language models in the context of influenza and COVID-19 infection detection. Methods: We constructed two Twitter datasets using a keyword-based filtering approach on English-language tweets collected from December 2016 to December 2022 in Saskatchewan, Canada. The influenza-related dataset comprised tweets filtered with influenza-related keywords from December 13, 2016, to March 17, 2018, while the COVID-19 dataset comprised tweets filtered with COVID-19 symptom-related keywords from January 1, 2020, to June 22, 2021. The Twitter datasets were cleaned, and each tweet was annotated by at least two annotators as to whether it suggested recent plausible influenza cases or symptomatic COVID-19 cases. We then assessed the classification performance of pre-trained transformer-based language models, including BERT-base, BERT-large, RoBERTa-base, RoBERT-large, BERTweet-base, BERTweet-covid-base, BERTweet-large, and COVID-Twitter-BERT (CT-BERT) models, on each dataset. To address the notable class imbalance, we experimented with both oversampling and undersampling methods. Results: The influenza dataset had 1129 out of 6444 (17.5%) tweets annotated as suggesting recent plausible influenza cases. The COVID-19 dataset had 924 out of 11939 (7.7%) tweets annotated as inferring recent plausible COVID-19 cases. When compared against other language models on the COVID-19 dataset, CT-BERT performed the best, supporting the highest scores for recall (94.8%), F1(94.4%), and accuracy (94.6%). For the influenza dataset, BERTweet models exhibited better performance. Our results also showed that applying data balancing techniques such as oversampling or undersampling method did not lead to improved model performance. Conclusions: Utilizing domain-specific language models for monitoring users' health experiences related to influenza and COVID-19 on social media shows improved classification performance and has the potential to supplement real-time disease surveillance.

Opioid-related harms and care impacts of conventional and AI-based prescription management strategies: insights from leveraging agent-based modeling and machine learning.

Introduction: Like its counterpart to the south, Canada ranks among the top five countries with the highest rates of opioid prescriptions. With many suffering from opioid use disorder first having encountered opioids via prescription routes, practitioners and health systems have an enduring need to identify and effectively respond to the problematic use of opioid prescription. There are strong challenges to successfully addressing this need: importantly, the patterns of prescription fulfillment that signal opioid abuse can be subtle and difficult to recognize, and overzealous enforcement can deprive those with legitimate pain management needs the appropriate care. Moreover, injudicious responses risk shifting those suffering from early-stage abuse of prescribed opioids to illicitly sourced street alternatives, whose varying dosage, availability, and the risk of adulteration can pose grave health risks. Methods: This study employs a dynamic modeling and simulation to evaluate the effectiveness of prescription regimes employing machine learning monitoring programs to identify the patients who are at risk of opioid abuse while being treated with prescribed opioids. To this end, an agent-based model was developed and implemented to examine the effect of reduced prescribing and prescription drug monitoring programs on overdose and escalation to street opioids among patients, and on the legitimacy of fulfillments of opioid prescriptions over a 5-year time horizon. A study released by the Canadian Institute for Health Information was used to estimate the parameter values and assist in the validation of the existing agent-based model. Results and discussion: The model estimates that lowering the prescription doses exerted the most favorable impact on the outcomes of interest over 5 years with a minimum burden on patients with a legitimate need for pharmaceutical opioids. The accurate conclusion about the impact of public health interventions requires a comprehensive set of outcomes to test their multi-dimensional effects, as utilized in this research. Finally, combining machine learning and agent-based modeling can provide significant advantages, particularly when using the latter to gain insights into the long-term effects and dynamic circumstances of the former.

The impact of alternate level of care on access block and operational strategies to reduce emergency wait times: a multi-center simulation study.

OBJECTIVES: Lengthy emergency department (ED) wait times caused by hospital access block is a growing concern for the Canadian health care system. Our objective was to quantify the impact of alternate-level-of-care on hospital access block and evaluate the likely effects of multiple interventions on ED wait times. METHODS: Discrete-event simulation models were developed to simulate patient flows in EDs and acute care of six Canadian hospitals. The model was populated with administrative data from multiple sources (April 2017-March 2018). We simulated and assessed six different intervention scenarios' impact on three outcome measures: (1) time waiting for physician initial assessment, (2) time waiting for inpatient bed, and (3) patients who leave without being seen. We compared each scenario's outcome measures to the baseline scenario for each ED. RESULTS: Eliminating 30% of medical inpatients' alternate-level-of-care days reduced the mean time waiting for inpatient bed by 0.25 to 4.22 h. Increasing ED physician coverage reduced the mean time waiting for physician initial assessment (∆ 0.16-0.46 h). High-quality care transitions targeting medical patients lowered the mean time waiting for inpatient bed for all EDs (∆ 0.34-6.85 h). Reducing ED visits for family practice sensitive conditions or improving continuity of care resulted in clinically negligible reductions in wait times and patients who leave without being seen rates. CONCLUSIONS: A moderate reduction in alternate-level-of-care hospital days for medical patients could alleviate access block and reduce ED wait times, although the magnitude of reduction varies by site. Increasing ED physician staffing and aligning physician capacity with inflow demand could also decrease wait time. Operational strategies for reducing ED wait times should prioritize resolving output and throughput factors rather than input factors.

ABSTRAIT: OBJECTIF: Les longs temps d'attente dans les services d'urgence (SU) à cause de blocage de l'accès à l'hôpital sont une préoccupation croissante pour le système de santé canadien. Notre objectif était de quantifier l'impact d'un autre niveau de soins sur le bloc d'accès à l'hôpital et d'évaluer les effets probables d'interventions multiples sur les temps d'attente aux départements d'urgences. MéTHODES: Des modèles de simulation aux événements discrets ont été développés pour simuler les flux de patients dans les urgences et les soins aigus de six hôpitaux canadiens. Le mod èle a été rempli de données administratives ayant plusieurs sources (avril 2017 à mars 2018). Nous avons simulé et évalué l'impact de six scénarios d'intervention différents sur trois mesures de résultats : 1) le temps d'attente pour l'évaluation initiale du médecin, 2) le temps d'attente pour un lit pour des patients hospitalisés et 3) les patients qui partent sans être vus. Nous avons comparé chaque mesure de résultats de ce scénario au scénario de référence pour chaque département d'urgences. RéSULTATS: L'élimination de 30 % des jours d'hospitalisation à un autre niveau de soins des patients médicaux a réduit le temps moyen d'attente pour un patient hospitalisé de 0,25 à 4,22 heures. L'augmentation du nombre des médecins des urgences a réduit le temps moyen d'attente pour l'évaluation initiale du médecin (∆ 0,16 à 0,46 heures). Les transitions de soins de haute qualité ciblant les patients médicaux ont réduit la période moyen d'attente des patients hospitalisés pour tous les services d'urgence (∆ 0,34 à 6,85 heures). La réduction des visites à l'urgence pour des conditions sensibles à la médecine familiale ou l'augmentation de la continuité des soins ont entraîné des réductions cliniquement insignifiantes des temps d'attente et des taux de patients qui quittent sans être vus. CONCLUSIONS: Une réduction modérée du nombre d'un autre niveau de soins pour les patients médicaux pourrait non seulement soulager le blocage de l'accès mais aussi réduire les temps d'attente aux urgences, afin de l'ampleur de la réduction varie selon le site. L'augmentation du nombre de médecins des urgences et l'harmonisation de la capacité des médecins avec la demande d'afflux pourraient également réduire le temps d'attente. Les stratégies opérationnelles destinées à réduire les temps d'attente aux urgences devraient accorder la priorité à la résolution des facteurs de sortie et de débit plutôt qu'aux facteurs d'entrée.

The Contribution of Gestational Diabetes to Diabetes Risk Among First Nations and Non-First Nations Women in Saskatchewan: Results From the DIP: ORRIIGENSS Project.

OBJECTIVES: Our aim in this study was to determine the risk for diabetes mellitus (DM) among Saskatchewan First Nations (FN) and non-FN women with prior gestational DM (GDM). METHODS: Using Ministry of Health administrative databases, we conducted a retrospective cohort study of DM risk by GDM occurrence among FN and non-FN women giving birth from 1980 to 2009 and followed to March 31, 2013. We determined frequencies and odds ratios (ORs) of DM in women with/without prior GDM after stratifying by FN status, while adjusting for other DM determinants. Survival curves of women until DM diagnosis were obtained by prior GDM occurrence and stratified by ethnicity and total parity. RESULTS: De-identified data were obtained for 202,588 women. Of those who developed DM, 2,074 of 10,114 (20.5%) had previously experienced GDM (811 of 3,128 [25.9%]) FN and 1,263 of 6,986 [18.1%] non-FN). Cumulative survival of women with prior GDM until DM was higher for FN than for non-FN women (82% vs 46%), but prior GDM was a stronger predictor of DM within the non-FN cohort (prior GDM vs no GDM: OR, 9.64 for non-FN; OR, 7.05 for FN). Finally, higher total parity interacted with prior GDM to increase DM risk in both groups. With prior GDM and parity ≥3, 93% of FN and 57% of non-FN women subsequently developed DM. CONCLUSIONS: GDM is a leading determinant of T2DM among FN and non-FN women, amplified by higher parity. This contributes to earlier onset diabetes, affecting subsequent pregnancies and increasing risk for chronic diabetic complications. It may also factor into higher type 2 DM rates observed in FN women compared with men.

Impacts of observation frequency on proximity contact data and modeled transmission dynamics.

Transmission of many communicable diseases depends on proximity contacts among humans. Modeling the dynamics of proximity contacts can help determine whether an outbreak is likely to trigger an epidemic. While the advent of commodity mobile devices has eased the collection of proximity contact data, battery capacity and associated costs impose tradeoffs between the observation frequency and scanning duration used for contact detection. The choice of observation frequency should depend on the characteristics of a particular pathogen and accompanying disease. We downsampled data from five contact network studies, each measuring participant-participant contact every 5 minutes for durations of four or more weeks. These studies included a total of 284 participants and exhibited different community structures. We found that for epidemiological models employing high-resolution proximity data, both the observation method and observation frequency configured to collect proximity data impact the simulation results. This impact is subject to the population's characteristics as well as pathogen infectiousness. By comparing the performance of two observation methods, we found that in most cases, half-hourly Bluetooth discovery for one minute can collect proximity data that allows agent-based transmission models to produce a reasonable estimation of the attack rate, but more frequent Bluetooth discovery is preferred to model individual infection risks or for highly transmissible pathogens. Our findings inform the empirical basis for guidelines to inform data collection that is both efficient and effective.

Evaluation of the effectiveness of maternal immunization against pertussis in Alberta using agent-based modeling: A Canadian immunization research network study.

INTRODUCTION: The re-emergence of pertussis has occurred in the past two decades in developed countries. The highest morbidity and mortality is seen among infants. Vaccination in pregnancy is recommended to reduce the pertussis burden in infants. METHODS: We developed and validated an agent-based model to characterize pertussis epidemiology in Alberta. We computed programmatic effectiveness of pertussis vaccination during pregnancy (PVE) in relation to maternal vaccine coverage and pertussis disease reporting thresholds. We estimated the population preventable fraction (PFP) of different levels of maternal vaccine coverage against counterfactual "no-vaccination" scenario. We modeled the effect of immunological blunting and measured protection through interruption of exposure pathways. RESULTS: PVE was inversely related to duration of passive immunity from maternal immunization across most simulations. In the scenario of 50% maternal vaccine coverage, PVE was 87% (95% quantiles 82-91%), with PFP of 44% (95% quantiles 41-45%). For monthly age intervals of 0-2, 2-4, 4-6 and 6-12, PVE ranged between 82 and 99%, and PFP ranged between 41 and 49%. At 75% maternal vaccine coverage, PVE and PFP were 90% (95% quantiles 86-92%) and 68% (95% quantiles 65-69%), respectively. At 50% maternal vaccine coverage and 10% blunting, PVE and PFP were 86% (95% quantiles 77-87%) and 43% (95% quantiles 39-44%), respectively, while at 50% blunting, the corresponding values of PVE and PFP were 76% (95% quantiles 70-81%) and 38% (95% quantiles 35-40%). PVE attributable to interruption of exposure pathways was 54-57%. CONCLUSIONS: Our model predicts significant reduction in future pertussis cases in infants due to maternal vaccination, with immunological blunting slightly moderating its effectiveness. The model is most sensitive to maternal vaccination coverage. The interruption of exposure pathways plays a role in the reduction of pertussis burden in infants due to maternal immunization. The effect of maternal immunization on population other than infants remains to be elucidated.

Health Literacy and Serious or Persistent Mental Illness: A Mixed Methods Study.

BACKGROUND: Health literacy is increasingly recognized as a major determinant of health; however, our insights into the health literacy strengths and needs of adults living with serious or persistent mental illness remain limited by a notable lack of research in this area. Improving our understanding is important because people in this group are especially vulnerable to numerous negative health outcomes, many preventable. OBJECTIVE: To assess the health literacy strengths and needs of people living with serious or persistent mental illness in terms of their ability to acquire, understand, and use information about their illness and the health services they require. METHODS: A cross-sectional convergent mixed methods design guided by the Ophelia Access and Equity Framework. People diagnosed with serious or persistent mental illness were offered participation. Quantitative and qualitative data was collected using questionnaires (Health Literacy Questionnaire [HLQ], World Health Organization [WHO-5]) and semi-structured interviews. Hierarchical cluster analysis identified and grouped participants with similar health literacy scores into mutually exclusive groups, for the development of clinical vignettes. KEY RESULTS: Participants struggled most with the appraisal of health information (HLQ mean 2.72, standard deviation [SD] .63 [scale 1-4]) and navigating what they often perceived to be a confusing health care system (HLQ mean 3.29, SD .79 [scale 1-5]). On the other hand, most participants reported positive experiences with their health care providers (HLQ mean 3.19, SD .62 [scale 1-4]) and generally felt understood and supported. The cluster analysis suggests we should not assume people living with serious or persistent mental illness have homogeneous HL strengths and needs, meaning a one-size-fits-all solution for improving health literacy in this diverse group will likely not be a successful strategy. It will be important to explore solutions that embrace patient-centered care approaches. CONCLUSIONS: This study is one of only a handful assessing the health literacy strengths and needs of people living with serious or persistent mental illness. By collecting both quantitative and qualitative data, then analyzing the results using sophisticated cluster analysis methods, the authors were able to develop clinical vignettes per the Ophelia Framework that offer results in a practical way that can be readily understood and acted upon by stakeholders. We found that the HLQ is a measure of HL that is acceptable to mental health clients, and our findings provide preliminary data on the use of this instrument in the mental health population. [HLRP: Health Literacy Research and Practice. 2023;7(1):e2-e13.] Plain Language Summary: This study explored the health literacy strengths and needs of people living with serious or persistent mental illness. The results showed a mix of strengths and needs among our participants, though several consistent themes emerged. Most of our participants felt understood and supported by their health care providers, but many often struggle with judging the quality of health information and finding their way through the health care system.

Investigating effective testing strategies for the control of Johne's disease in western Canadian cow-calf herds using an agent-based simulation model.

Johne's disease is an insidious infectious disease of ruminants caused by Mycobacterium avium subspecies paratuberculosis (MAP). Johne's disease can have important implications for animal welfare and risks causing economic losses in affected herds due to reduced productivity, premature culling and replacement, and veterinary costs. Despite the limited accuracy of diagnostic tools, testing and culling is the primary option for controlling Johne's disease in beef herds. However, evidence to inform specific test and cull strategies is lacking. In this study, a stochastic, continuous-time agent-based model was developed to investigate Johne's disease and potential control options in a typical western Canadian cow-calf herd. The objective of this study was to compare different testing and culling scenarios that included varying the testing method and frequency as well as the number and risk profile of animals targeted for testing using the model. The relative effectiveness of each testing scenario was determined by the simulated prevalence of cattle shedding MAP after a 10-year testing period. A second objective was to compare the direct testing costs of each scenario to identify least-cost options that are the most effective at reducing within-herd disease prevalence. Whole herd testing with individual PCR at frequencies of 6 or 12 months were the most effective options for reducing disease prevalence. Scenarios that were also effective at reducing prevalence but with the lowest total testing costs included testing the whole herd with individual PCR every 24 months and testing the whole herd with pooled PCR every 12 months. The most effective method with the lowest annual testing cost per unit of prevalence reduction was individual PCR on the whole herd every 24 months. Individual PCR testing only cows that had not already been tested 4 times also ranked well when considering both final estimated prevalence at 10 years and cost per unit of gain. A more in-depth economic analysis is needed to compare the cost of testing to the cost of disease, taking into account costs of culling, replacements and impacts on calf crops, and to determine if testing is an economically attractive option for commercial cow-calf operations.

Using simulation modelling and systems science to help contain COVID-19: A systematic review.

This study systematically reviews applications of three simulation approaches, that is, system dynamics model (SDM), agent-based model (ABM) and discrete event simulation (DES), and their hybrids in COVID-19 research and identifies theoretical and application innovations in public health. Among the 372 eligible papers, 72 focused on COVID-19 transmission dynamics, 204 evaluated both pharmaceutical and non-pharmaceutical interventions, 29 focused on the prediction of the pandemic and 67 investigated the impacts of COVID-19. ABM was used in 275 papers, followed by 54 SDM papers, 32 DES papers and 11 hybrid model papers. Evaluation and design of intervention scenarios are the most widely addressed area accounting for 55% of the four main categories, that is, the transmission of COVID-19, prediction of the pandemic, evaluation and design of intervention scenarios and societal impact assessment. The complexities in impact evaluation and intervention design demand hybrid simulation models that can simultaneously capture micro and macro aspects of the socio-economic systems involved.

Evaluating the impact of community interventions on childhood obesity in populations living in low-income households in Los Angeles: A simulation study.

BACKGROUND: The complex multifactorial nature of childhood obesity makes community interventions difficult to evaluate using traditional approaches; innovative methods are needed. OBJECTIVE: To evaluate the impact of various interventions targeting childhood obesity-related behaviours, and classified as using a micro-level (e.g., home visitation programs) or macro-level (e.g., business practices) strategy, on obesity among children enrolled in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC). METHODS: We simulated a population of 1500 children enrolled in WIC, with specific diet, physical activity, breastfeeding behaviours and body mass index z-scores (BMIz), following them from age 2 to 5 years. RESULTS: Combined interventions targeting breastfeeding appeared to be moderately effective, reducing BMIz by 0.03 (95% CI -005, -0.01). Two strategy-specific interventions, home visitation programs and business practices targeting obesity-related behaviours, appeared to be moderately effective at reducing BMIz by 0.04 (95% CI -0.06, -0.02) and 0.02 (95% CI -0.04, 0.00), respectively. Contrary to expectation, combining all micro and macro interventions appeared to have no impact or moderately increased the proportion of obesity/overweight among children. CONCLUSION: Interventions targeting breastfeeding behaviour were most effective when both micro and macro strategies were implemented. Interventions targeting obesity-related behaviours in general were effective for two strategies, home visitation and business practices.

Point-of-sale cigarette pricing strategies and young adult smokers' intention to purchase cigarettes: an online experiment.

BACKGROUND: Point-of-sale tobacco marketing has been shown to be related to tobacco use behaviours; however, specific influences of cigarette price discounts, price tiers and pack/carton availability on cigarette purchasing intention are less understood by the tobacco control community. METHODS: We conducted discrete choice experiments among an online sample of US young adult smokers (aged 18-30 years; n=1823). Participants were presented scenarios depicting their presence at a tobacco retail outlet with varying availability of cigarette price discounts, price tiers and pack/carton. At each scenario, participants were asked whether they would purchase cigarettes. Generalised linear regression models were used to examine the associations between of cigarette price discounts, price tiers and pack/carton with intention to purchase cigarettes overall and stratified by educational attainment. RESULTS: Participants chose to purchase cigarettes in 70.9% of the scenarios. Offering price discounts were associated with higher odds of choosing to purchase cigarettes. Reducing the number of cigarette price tiers available in the store was associated with lower odds of choosing to purchase cigarettes. Stratified analysis showed that offering discounts on high-tier cigarette packs increased odds of choosing to purchase cigarettes among young adult smokers with at least some college education, while offering discounts on medium-tier cigarette packs increased odds of choosing to purchase cigarettes among those with some college education or less (eg, with a 10% discount, adjusted odds ratio [AOR]some college=1.62, 95% confidence interval [CI] 1.21 to 2.16; AOR≤high school=1.44, 95% CI 1.08 to 1.93). CONCLUSIONS: Availability of cigarette price discounts, price tiers and pack/carton could potentially influence cigarette purchasing behaviours among young adult smokers. Regulating these marketing strategies may, therefore, reduce education-related smoking disparities.

Instability of Suicidal Ideation in Patients Hospitalized for Depression: An Exploratory Study Using Smartphone Ecological Momentary Assessment.

This study used ecological momentary assessment (EMA) to explore the correlates of suicidal ideation (SI) instability in patients hospitalized for depression and SI. Thirty-nine adult inpatients were given smartphones with visual analogue scales to rate current depressed mood, anger/irritability, feeling socially connected, and SI three times a day throughout hospitalization. Affective Lability Scales (ALS) were also completed at baseline. SI instability was correlated with SI intensity, depressed mood instability, and social connection instability. Social connection instability was not associated with SI instability after controlling for depressed mood instability. ALS scores were not associated with EMA-derived SI instability. Participants with multiple past suicide attempts experienced greater SI instability. More research examining the clinical significance of SI instability is warranted.

Rural vs urban inequalities in stage at diagnosis for lung cancer.

OBJECTIVES: Early diagnosis of lung cancer increases the chance of survival. The aim of this study was to measure the relationship between geographic residence in Saskatchewan and stage of lung cancer at the time of diagnosis. MATERIALS AND METHODS: Retrospective cohort analysis of 2,972 patients with a primary diagnosis of either non-small cell cancer (NSCLC) or small cell lung cancer (SCLC) between 2007 and 2012 was performed. Incidence proportion of early and advanced stage cancer, and relative risk of being diagnosed with advanced-stage lung cancer relative to early-stage was calculated. RESULTS: Compared to urban Saskatchewan, rural Saskatchewan lung cancer patients had a higher relative risk of advanced stage NSCLC (relative risk [RR] = 1.11, 95% confidence interval [CI]: 1.01-1.22). Rural Saskatchewan was further subdivided into north and south. The relative risk of advanced stage NSCLC in rural north Saskatchewan compared to urban Saskatchewan was even greater (RR = 1.17, 95% CI: 1.03-1.31). Although not statistically significant, there was a trend for a higher incidence of advanced stage SCLC in rural and rural north vs urban Saskatchewan (RR = 1.16, 95% CI: 0.95-1.43 and RR = 1.22; 95% CI: 0.94-1.58, respectively). There was a higher incidence proportion of advanced stage NSCLC in rural areas relative to urban (31.6-34.4 vs 29.5 per 10,000 people). CONCLUSION: Patients living in rural Saskatchewan have higher incidence proportion of and were more likely to present with advanced stage NSCLC in comparison to urban Saskatchewan patients at time of diagnosis. This inequality was even greater in rural north Saskatchewan.

An App-Based Mindfulness-Based Self-compassion Program to Support Caregivers of People With Dementia: Participatory Feasibility Study.

BACKGROUND: The number of persons with dementia is steadily growing, as is the number of individuals supporting persons with dementia. Primary caregivers of persons with dementia are most often family members or spouses of the persons with dementia, and they are more likely to experience increased stress and other negative effects than individuals who are not primary caregivers. Although in-person support groups have been shown to help buffer the negative impacts of caregiving, some caregivers live in isolated or rural communities and are unable to make the burdensome commitment of traveling to cities. Using an interdisciplinary approach, we developed a mobile smartphone support app designed for primary caregivers of persons with dementia, with the goal of reducing caregiver burden and easing stress. The app features a 12-week intervention, largely rooted in mindfulness-based self-compassion (MBSC), because MBSC has been linked to minimizing stress, depression, and anxiety. OBJECTIVE: The primary objectives of our program are twofold: to explore the feasibility of a 12-week mobile support program and to conduct an initial efficacy evaluation of changes in perceived caregiver burden, coping styles, and emotional well-being of caregivers before and after the program. METHODS: Our feasibility study used a 2-phase participatory pretest and posttest design, focusing on acceptability, demand, practicality, implementation, and efficacy. At phase I, we recruited 57 primary caregivers of persons with dementia (mean age 76.3, SD 12.9 years), comprising spouses (21/57, 37%), children (21/57, 37%), and friends or relatives (15/57, 26%) of persons with dementia, of whom 29 (51%) completed all measures at both pre- and postprogram. The content of the program featured a series of MBSC podcasts. Our primary outcome measure was caregiver burden, with secondary outcome measures including coping styles and emotional well-being. Daily ecological momentary assessments enabled us to ask participants, "How are you feeling today?" Phase II of our study involved semistructured follow-up interviews with most participants (n=21) who completed phase I. RESULTS: Our findings suggest that our app or program meets the feasibility criteria examined. Notably, participants generally accepted the program and believed it could be a useful resource. Emotional well-being increased significantly (P=.04), and emotion-based coping significantly decreased (P=.01). Participants generally considered the app or program to be a helpful resource. CONCLUSIONS: Although there were no significant changes in caregiver burden, we were encouraged by the increased emotional well-being of our participants following the completion of our program. We also conclude that our app or program demonstrated feasibility (ie, acceptability, practicality, implementation, and efficacy) and can provide a much-needed resource for primary caregivers of persons with dementia. In the subsequent version of the program, we will respond to participant feedback by incorporating web-based weekly sessions and incorporating an outcome measure of self-compassion.

Tobacco messages encountered in real-time among low socio-economic position groups: a descriptive study.

BACKGROUND: Tobacco advertising disproportionately targets low socio-economic position (SEP) groups, causing higher rates of tobacco use in this population. Anti-tobacco public health education campaigns persuade against use. This study measured real-time exposure of pro- and anti-tobacco messages from low SEP groups in two American cities. METHODS: Individuals in low SEP groups (N = 95), aged 18-34 years old, who were smokers and non-smokers, from the Boston and Houston areas, took part in a mobile health study. They submitted images of tobacco-related messages they encountered via a mobile application for a 7-week period. Two coders analyzed the images for message characteristics. Intercoder reliability was established using Krippendorff's alpha and data were analyzed descriptively. RESULTS: Of the submitted images (N = 131), 83 were pro-tobacco and 53 were anti-tobacco. Of the pro-tobacco messages, the majority were cigarette ads (80.7%) seen outside (36.1%) or inside (30.1%) a convenience store or gas station and used conventional themes (e.g., price promotion; 53.2%). Of the anti-tobacco messages, 56.6% were sponsored by public health campaigns or were signage prohibiting smoking in a public area (39.6%). Most focused on the health harms of smoking (28.3%). CONCLUSION: Low SEP groups in this study encountered more pro-tobacco than anti-tobacco messages at places that were point-of-sale using price promotions to appeal to this group. Anti-tobacco messages at point-of-sale and/or advertising regulations may help combat tobacco use.