Search

Recovery housing provides substance-free living environments that use peer-support to empower individuals in recovery from substance use disorder. This study estimated the total revenue of the recovery housing industry from recovery houses, nationwide.Using survey data collected in June and July of 2020, we calculate the total revenue of the recovery housing industry and determine the share of revenue that comes from different sources for rural and nonrural houses.We find that individual recovery houses operate with an annual revenue of $250,000 and the whole industry accounts for $4.5 billion annually. COVID-19 has reduced industry revenue by 4%. Rural and nonrural houses differ significantly in their sources of revenue.Our results suggest that COVID-19 reduced the size of the recovery housing industry. Houses in nonrural regions may need more federal support due to the relatively high reliance on resident fees, which may be unreliable during COVID-19.

William Robin Thompson was born on 29 June 1887 in London, Ontario, Canada. He was the only child of William Thompson, schoolmaster and journalist of Derwent, Ontario, and of Alice (Morgan) Thompson of London, Ontario. His mother died within a fortnight of his birth. Thompson was descended on his father’s side from a pioneer Canadian family which migrated from Ulster, Ireland, in 1817 when his great grandfather Arthur and his brother Richard sold their land interests. This family can be traced through maps and deeds to Thomas Thompson (deceased 1717) of Ardunsheen Townland. Arthur Thompson’s son became Sir William John Thompson and was one of the pioneers in the fight against tuberculosis in Ireland. W. R. Thompson’s father, after a period as headmaster of a school, became the first editor of the (London) daily,The Advertiser, and finally editor-in-chief of the agricultural weekly,The Farmer's Advocate. W. R. Thompson’s great uncle, Thomas O’Hara, was a Moravian missionary in Labrador in the late 1860s.

Introduction Alzheimer’s disease (AD) is a chronic and progressive neurodegenerative disease that places a substantial burden on patients and caregivers. Aducanumab is the first AD therapy approved by the US Food and Drug Administration to reduce a defining pathophysiological feature of the disease, brain amyloid plaques. In the phase 3 clinical trial EMERGE (NCT02484547), aducanumab reduced clinical decline in patients with mild cognitive impairment (MCI) due to AD and mild AD dementia and confirmed amyloid pathology. Methods We used a Markov modeling approach to predict the long-term clinical benefits of aducanumab for patients with early AD based on EMERGE efficacy data. In the model, patients could transition between AD severity levels (MCI due to AD; mild, moderate, and severe AD dementia) and care settings (community vs. institution) or transition to death. The intervention was aducanumab added to standard of care (SOC), and the comparator was SOC alone. Data sources for base-case and scenario analyses included EMERGE, published National Alzheimer’s Coordinating Center analyses, and other published literature. Results Per patient over a lifetime horizon, aducanumab treatment corresponded to 0.65 incremental patient quality-adjusted life-years (QALYs) and 0.09 fewer caregiver QALYs lost compared with patients treated with SOC. Aducanumab treatment translated to a lower lifetime probability of transitioning to AD dementia, a lower lifetime probability of transitioning to institutionalization (25.2% vs. 29.4%), delays in the median time to transition to AD dementia (7.50 vs. 4.92 years from MCI to moderate AD dementia or worse), and an incremental median time in the community of 1.32 years compared with SOC. Conclusion The model predicted long-term benefits of aducanumab treatment in patients with MCI due to AD and mild AD dementia and their caregivers. The predicted outcomes provide a foundation for healthcare decision-makers and policymakers to understand the potential clinical and socioeconomic value of aducanumab. Supplementary Information The online version contains supplementary material available at 10.1007/s40120-021-00273-0.

Infectious diseases of plants present an ongoing and increasing threat to international biosecurity, with wide-ranging implications. An important challenge in plant disease management is achieving early detection of invading pathogens in new locations, which requires effective surveillance through the implementation of appropriate monitoring programs. However, when monitoring relies on visual inspection as a means of detection, surveillance is often hindered by a long incubation period (delay from infection to symptom onset) during which plants may be infectious but not displaying visible symptoms. ‘Sentinel’ plants – alternative susceptible host species that display visible symptoms of infection more rapidly – could be introduced to at-risk populations and included in monitoring programs to act as early warning beacons for infection. However, while sentinel hosts exhibit faster disease progression and so allow pathogens to be detected earlier, this often comes at a cost: faster disease progression typically promotes earlier onward transmission. Here, we construct a computational model of pathogen transmission to explore this trade-off and investigate how including sentinel plants in monitoring programmes could facilitate earlier detection of invasive plant pathogens. Using Xylella fastidiosa infection in Olea europaea (European olive) as a current high profile case study, for which Catharanthus roseus (Madagascan periwinkle) is a candidate sentinel host, we apply a Bayesian optimisation algorithm to determine the optimal number of sentinel hosts to introduce for a given sampling effort, as well as the optimal division of limited surveillance resources between crop and sentinel plants. Our results demonstrate that including sentinel plants in monitoring programmes can reduce the expected prevalence of infection upon outbreak detection substantially, increasing the feasibility of local outbreak containment.

Although new cases of monkeypox have been expected in the Western Pacific Region (WPR) since the virus emerged in Europe earlier this year, there have been only a few reported cases across the WPR (New Zealand 2, Singapore 6, South Korea 1, Taiwan 2), other than a limited number of cases (compared to numbers of cases seen elsewhere in the world) in Australia (33), as of July 15, 2022. In our short communication, we highlight two key reasons for this: i) international travel has still not fully resumed in the WPR following the COVID-19 pandemic, and ii) local public health measures to counter the spread of COVID-19 have not been completely relaxed. We provide supporting evidence for both of these reasons.\ud \ud

Defeating peripheral neuropathy The mechanisms underlying peripheral neuropathies are not well understood. Spaulding et al . studied mouse models of the inherited Charcot-Marie-Tooth (CMT) disease, which is caused by mutations in transfer RNA (tRNA) synthetases. Changes in gene expression and the rate of protein synthesis in neurons in the spinal cord triggered the cell stress response activated by the protein sensor GCN2. When GCN2 was genetically deleted or inhibited with drugs, the stress response was blocked, and the neuropathy was much milder. Zuko et al . found that mutant glycyl-tRNA synthetases bind tRNA Gly but fail to release it, thus depleting the cellular tRNA Gly pool. This process caused stalling of translating ribosomes on glycine codons and activated the integrated stress response. Transgenic tRNA Gly overexpression prevented peripheral neuropathy and protein synthesis defects in mouse and fruit fly models. Thus, elevating tRNA Gly levels or targeting GCN2 may have therapeutic potential for this currently untreatable disease (see the Perspective by Mellado and Willis). —SMH

Purpose With the impending changes to state Medicaid programs and other health reform policies, it is imperative to understand the factors at play in promoting consumer health insurance literacy and health system engagement. This study examines the availability of health system and community‐based programs promoting health insurance literacy and supporting informed consumer health care decision making in rural communities in Kentucky. Methods Forty‐six health systems, community‐based providers, and outreach workers participated in 4 focus groups and 10 semistructured interviews. Descriptive and analytic coding techniques were used to identify 5 major themes and subthemes from interview and focus group transcripts. Findings Consumers were generally identified as having low health insurance literacy, especially in rural communities, serving as a barrier to accessing health care insurance and services. Participants identified their own lack of knowledge and understanding around health systems, resulting from lack of training and challenges with staying updated on constant changes in health systems and policies. Overall, consumer demand or need for health insurance literacy resources and programs far exceeded supply or availability. Constant changes in the status of Kentucky's Medicaid program and the proposed changes to eligibility, specifically work requirements and copays, have caused increased confusion among both providers and consumers. Conclusions Findings indicate a pressing need for implementing programs that provide training, tools, and resources to outreach workers to help them better assist consumers with accessing and using health insurance, especially in low‐income, rural areas. Health reform policies need to be responsive to the health insurance literacy needs and abilities of consumers.

During the COVID-19 pandemic, mathematical modeling of disease transmission has become a cornerstone of key state decisions. To advance the state-of-the-art host viral modeling to handle future pandemics, many scientists working on related issues assembled to discuss the topics. These discussions exposed the reproducibility crisis that leads to inability to reuse and integrate models. This document summarizes these discussions, presents difficulties, and mentions existing efforts towards future solutions that will allow future model utility and integration. We argue that without addressing these challenges, scientists will have diminished ability to build, disseminate, and implement high-impact multi-scale modeling that is needed to understand the health crises we face.

The reproductive number is an important metric that has been widely used to quantify the infectiousness of communicable diseases. The time-varying instantaneous reproductive number is useful for monitoring the real time dynamics of a disease to inform policy making for disease control. Local estimation of this metric, for instance at a county or city level, allows for more targeted interventions to curb transmission. However, simultaneous estimation of local reproductive numbers must account for potential sources of heterogeneity in these time-varying quantities – a key element of which is human mobility. We develop a statistical method that incorporates human mobility between multiple regions for estimating region-specific instantaneous reproductive numbers. The model also can account for exogenous cases imported from outside of the regions of interest. We propose two approaches to estimate the reproductive numbers, with mobility data used to adjust incidence in the first approach and to inform a formal priori distribution in the second (Bayesian) approach. Through a simulation study, we show that region-specific reproductive numbers can be well estimated if human mobility is reasonably well approximated by available data. We use this approach to estimate the instantaneous reproductive numbers of COVID-19 for 14 counties in Massachusetts using CDC case report data and the human mobility data collected by SafeGraph. We found that, accounting for mobility, our method produces estimates of reproductive numbers that are distinct across counties. In contrast, independent estimation of county-level reproductive numbers tends to produce similar values, as trends in county case-counts for the state are fairly concordant. These approaches can also be used to estimate any heterogeneity in transmission, for instance, age-dependent instantaneous reproductive number estimates. As people are more mobile and interact frequently in ways that permit transmission, it is important to account for this in the estimation of the reproductive number.Author summaryTo control the spreading of an infectious disease, it is very important to understand the real-time infectiousness of the pathogen that causes the disease. An existing metric called instantaneous reproductive number is often used to quantify the average number of secondary cases generated by individuals who are infectious at a certain time point, assuming no changes to current conditions. In practice, we might be interested in using the metric to describe the infectiousness in multiple regions. However, this is challenging when there are visitors traveling between these regions, since this could lead to a misclassification of where an individual is actually infected and create biased estimates for the instantaneous reproductive numbers. We developed a method that takes account of human mobility to estimate the instantaneous reproductive numbers for multiple regions simultaneously, which could reveal the heterogeneity of the metric. This method aims to provide helpful information on region-specific infectiousness for disease control measures that focus on the region with higher pathogen infectiousness. This approach is also applicable for estimating the reproductive number in the presence of other sources of heterogeneity, including by age.

Background Spinal muscular atrophy (SMA) is a rare, genetic, progressive neuromuscular disorder characterized by severe muscle atrophy and weakness and is a leading genetic cause of death in infants and children. Nusinersen was the first treatment targeting the underlying cause of disease approved by the FDA, EMA and other countries for patients with SMA. There are currently very limited data available on the health-related quality of life (HRQoL) burden of SMA suitable for use in a cost-effectiveness analysis. Objective This study was designed to estimate quality of life weights or utilities for different SMA states. Methods SMA case studies were developed describing Type I (infantile onset) and Type II (later-onset) patients and different outcomes from treatment. These were developed so that quality of life weights or utilities (where the value of health ranges from 1 - full health to 0 - dead) could be estimated for cost-effectiveness analysis. Clinical experts (n=5) rated each of the case studies using standardized HRQoL instruments - the EQ-5D-Y and PedsQL-NMM (baseline states only). Results The SMA Type I utilities ranged from -0.33 (requires ventilation) to 0.71 (Type I patient reclassified as Type III following treatment), with quite substantial differences between some states. Most Type I states had a utility score below zero indicating the severity of the states. The SMA Type II utilities ranged from -0.13 (worsened) to 0.72 (stands/walks unaided). In general, the results showed HRQoL improved in line with better health states. Conclusion The utility scores obtained in this study highlight the very substantial burden experienced by SMA patients. Despite the limitations in the methods used, this study produced data with face validity and is a useful starting point for understanding the burden of SMA Types I and II in cost-effectiveness analysis.

We address the use of accelerometery to automatically monitor lying behaviour in free-farrowing sows; due to their freedom of movement and the consequent increased variety of movements the sows are able to exhibit, the challenges in automating this are greater than in sows housed in movement restricting farrowing environments. The methodology developed was applied to two salient applications: that of farrowing prediction through detection of nest building activity, and comparison of maternal lying behaviour in conventional movement-restricting and free-farrowing systems. Two sensors were attached at both the front and hind end to each of eight periparturient sows. Movement behaviour was recorded for a period of five days around parturition. Activity transitions were classified by a Support Vector Machine classifier, using data from both sensors individually, and combined; classifier output was validated against ground truth annotations collected from video data. We draw conclusions about the benefits of using multiple sensors over a single sensor, as well as the suitability of different sensor locations on the sow. Activity classification was found to improve through the use of multiple sensors, with a mean F 1 score (a measure of predictive performance between 0 and 1) of 0.84, compared to use of the front sensor alone (mean F 1 = 0.49) and the hind sensor alone (mean F 1 = 0.57). Activity transitions were classified using the dual sensor setup with a mean F 1 score of 0.77. Using a threshold-based approach, taking transition frequency as an indicator of nesting behaviour, we were able to detect the onset of nest building with an average latency to farrowing of 11.1 (±4.65) hours, and an average of 1 premature detection per sow; however, the majority of these premature were in a particular sow. We draw comparisons between the lying behaviour of free-farrowing and restricted sows. Using a mixed-design ANOVA we found a main effect of farrowing environment on transition duration ( p = 0.003 ), peak acceleration ( p = 0.007 ) and rate of change in pitch ( p = 0.009 ) . Improving the classification accuracy of sow activity transitions through the addition of multiple sensors allows for improved performance in applications such as farrowing prediction, which has the capacity to reduce piglet mortality through enabling farrowing supervision. Understanding how movement restriction affects the lying behaviour of farrowing sows has the potential to inform decisions regarding restriction of sows and development of free-farrowing environments.