Production Losses Research Articles

Estimating the societal cost of heroin dependence in an Australian population engaged in treatment or harm reduction services

IntroductionHeroin dependence is a public health concern in Australia. High mortality rates, increased risk of physical/mental health comorbidities and increased risk of social issues contribute to a high personal and societal cost. The aim of this paper is to understand the societal cost of heroin dependence in an Australian population. MethodsA longitudinal cohort study of 600 people with heroin dependence were interviewed at five timepoints. Resource use was determined from interviews and was multiplied by unit costs to estimate the annual healthcare, productivity, crime and other costs (homelessness, heroin drug and prison costs). The monetary value of premature mortality was calculated using the value of a statistical life year method. ResultsThe annual mean societal cost of heroin dependence in Australia was A$120,599/person. This included healthcare costs (A$10,055), lost productivity costs (A$63,158), crime costs (A$7204) and other costs (A$40,182). Healthcare costs, lost productivity costs, crime costs and other costs trended downwards over the five waves. Lost productivity was the highest cost contributor (52 %), followed by heroin drug (25 %). The estimated number of life years lost due to heroin over the 11-year study period was 2703 years, which approximates to a monetary value of premature death of $213 million. Discussion and conclusionsThe significant societal cost emphasises the importance of providing resources to heroin dependence. The cost of lost productivity, crime and heroin contributed to over 80 % of the total costs, which highlights the illicit nature and reduced capacity to work contributes to the high costs to society.

347 Quantifying phenotypic and genetic variation for cow fertility phenotypes in American Simmentals using total herd reporting data

Abstract Inefficiencies in reproduction are the largest drivers of loss in United States beef production. Nearly all reproductive traits are at least modestly heritable, making genetic selection and improvement possible over the long term. Cow-centric reproductive traits present unique challenges, as they are not expressed in male animals and will not be observed until later in the productive life of a female, if at all. This makes cow-centric fertility traits ideal candidates for selection with Expected Progeny Differences (EPDs). We used a commercially generated dataset from the American Simmental Association (ASA) to explore the phenotypic and genetic variation in these traits. The dataset comprised Total Herd Enrollment (THE) records, which required unbiased reporting of enrollment, calving, and disposal for 303,158 females (132,403 cows and 170,755 heifers). Using the THE data, we calculated three continuous and two discrete traits that are indicators of heifer and cow fertility and rebreeding ability. These traits were: 1) calving date (calving date of the cow relative to the start of the calving season), 2) calving interval (days between calves), 3) first calving interval (calving interval observation between the first and second calving record for a female), 4) heifer pregnancy (did the animal calve as 2-yr-old?), and 5) discrete early calving (did animal calve in the first 30 d of the calving season?). We observed a significant difference in calving date in mature cows that calved in the first 30 d of the calving season of their contemporary group as heifers compared with those that calved later (Table 1). Early calving heifers averaged a nearly 10 d sooner calving date throughout their productive lives compared with those that calved after 30 d as heifers (29.9 d vs. 40.2 d, P-value < 0.01). We used a combined pedigree and genomic approach, implemented in GIBBSF90+, to estimate genetic, environmental, and permanent environmental variance components. We estimated heritabilities of 0.24 (± 0.037215) for heifer pregnancy, 0.06 (± 0.009265) for calving date, 0.07 (± 0.015451) for discrete early calving, 0.04 (± 0.003954) for calving interval, and 0.04 (± 0.009604) for first calving interval. We observed positive phenotypic and genetic correlations between these traits, ranging from 0.01 to 0.96 (Table 2). This work provides an extensive population survey of phenotypic trends in reproductive performance and management in a large seedstock population. The heritable nature of the inspected traits motivates further work in understanding their genetic architectures and how they are affected by heterosis in crossbreeding programs.

‘My research agenda may never recover’: pandemic work-life demands and obstacles to career advancement for faculty mothers

ABSTRACT Historically, university systems have maintained gender inequities that facilitate promotion and advancement for Caucasian men, while creating barriers to career advancement for women and marginalised groups. Significant obstacles have existed for faculty mothers who have attempted to fulfill responsibilities as both mother and worker, particularly amid the COVID-19 global pandemic. The purpose of this study is to explore how, during the health crisis, faculty mothers perceived their experiences to impact their research productivity. Based on demographic and open-ended questionnaire data from 134 study participants in the United States, the findings suggest that as supportive duties demanded participants’ prompt attention, research was pushed to the side, resulting in perceived threats to faculty mothers’ careers. Given the value many institutions place on publications in the hiring, tenure, and promotion process, decreased or stalled publications could result in career repercussions. Specifically, participants viewed losses in research productivity as hindering job mobility for adjuncts and instructors; creating barriers to tenure and promotion for tenure-track faculty; and stalling career advancement for tenured faculty.

Response to The Use of Liquid Organic Fertilizer on Vegetative Growth in Soybean Plants Black (Glycine Soja L. Merrill) with Two Different Cultivars

Soybean-based food business and population growth drive the increase in soybean consumption every year in Indonesia. Soybean varieties and liquid organic fertilizer applications can help overcome losses in soybean plant growth and productivity. This study used a randomized block design (RAK) with treatment consisting of 2 factors with 3 repetitions. Factor i liquid organic fertilizer (p), consists of 4 levels, namely: p0 = 0 ml, p1 = 20 ml, p2 = 40 ml, p3 = 60 ml. the 2nd factor cultivar (k), consists of 2 types, namely: k1 = cultivar detam 1, k2 = cultivar detam 3. The results showed that the response and effectiveness of the application of soybean peel poc and different cultivars did not significantly affect the vegetative growth of black soybean plants, but when viewed as a whole p3 (60 ml) gave the highest value on the parameters of leaf area index 21 hst, root nodules, chlorophyll a, and chlorophyll b content.

Inhibition of diastatic yeasts by Saccharomyces killer toxins to prevent hyperattenuation during brewing.

Secondary fermentation in beer can result in undesirable consequences, such as off-flavors, increased alcohol content, hyperattenuation, gushing, and the spontaneous explosion of packaging. Strains of Saccharomyces cerevisiae var. diastaticus are a major contributor to such spoilage due to their production of extracellular glucoamylase enzyme encoded by the STA1 gene. Saccharomyces yeasts can naturally produce antifungal proteins named "killer" toxins that inhibit the growth of competing yeasts. Challenging diastatic yeasts with killer toxins revealed that 91% of strains are susceptible to the K1 killer toxin produced by S. cerevisiae. Screening of 192 killer yeasts identified novel K2 toxins that could inhibit all K1-resistant diastatic yeasts. Variant K2 killer toxins were more potent than the K1 and K2 toxins, inhibiting 95% of diastatic yeast strains tested. Brewing trials demonstrated that adding killer yeast during a simulated diastatic contamination event could prevent hyperattenuation. Currently, most craft breweries can only safeguard against diastatic yeast contamination by good hygiene and monitoring for the presence of diastatic yeasts. The detection of diastatic yeasts will often lead to the destruction of contaminated products and the aggressive decontamination of brewing facilities. Using killer yeasts in brewing offers an approach to safeguard against product loss and potentially remediate contaminated beer.IMPORTANCEThe rise of craft brewing means that more domestic beer in the marketplace is being produced in facilities lacking the means for pasteurization, which increases the risk of microbial spoilage. The most damaging spoilage yeasts are "diastatic" strains of Saccharomyces cerevisiae that cause increased fermentation (hyperattenuation), resulting in unpalatable flavors such as phenolic off-flavor, as well as over-carbonation that can cause exploding packaging. In the absence of a pasteurizer, there are no methods available that would avert the loss of beer due to contamination by diastatic yeasts. This manuscript has found that diastatic yeasts are sensitive to antifungal proteins named "killer toxins" produced by Saccharomyces yeasts, and in industrial-scale fermentation trials, killer yeasts can remediate diastatic yeast contamination. Using killer toxins to prevent diastatic contamination is a unique and innovative approach that could prevent lost revenue to yeast spoilage and save many breweries the time and cost of purchasing and installing a pasteurizer.

A comparison of environmental impacts of three contrasting cropping systems for barley production under Mediterranean conditions

Agriculture is a key contributor to environmental degradation and to global change. Consequently, the design of sustainable agricultural systems and the assessment of their relevance is a major priority for European agriculture. Different cropping systems, with variable objectives and constraints, can be used in cereal production in Spain. This study focused in comparing three winter barley cropping systems, ranging from intensive no-till to organic approaches. To assess the environmental impacts of each system, a Life Cycle Assessment was conducted. The findings indicate that the impacts varied depending on the chosen functional unit. When land area was considered the functional unit, the lowest impacts were obtained in the organic system, while the no-till system showed the highest. This difference was primarily attributed to variations in N fertilization. Nitrogen use had a significant impact across various categories, primarily due to the energy demands for its production and transportation, as well as the emissions of NH3 and N2O. However, when evaluating agricultural goods production as the functional unit, the organic system exhibited the highest impacts in terms of energy demand, freshwater ecotoxicity and freshwater eutrophication. These differences are explained by the loss of production in the fallow year and the low yields of the legume crop. The middle-way option provided the lowest impacts when economic net revenues were considered. The main reason for this was its higher total revenues associated to high crop production and EU subsidies.

Analysis of disease burden in patients with hereditary angioedema from Japan by patient-reported outcomes.

Hereditary angioedema (HAE) symptoms can vary greatly. Disease burden evaluation is essential for providing adequate treatments for patients. Patient-reported outcome measures (PROMs), including the 12-Item Short Form Health Survey (SF-12), the Angioedema Quality of Life (AE-QoL), the Hospital Anxiety and Depression Scale (HADS), and the Work Productivity and Activity Impairment: Specific Health Problem (WPAI:SHP) questionnaires, were collected in 2021, before modern medications for long-term prophylaxis (LTP) of HAE were licensed in Japan. Patients also reported their HAE attack frequency as "annual" (several attacks annually), "monthly" (several attacks monthly) or "weekly" (several attacks weekly). Multiple linear regression analyses were conducted on the relationship between independent parameters (sex, age, attack frequency, HAE type, and HADS scores) and dependent parameters (AE-QoL and SF-12 scores). Fifty-four patients reported PROMs. All PROMs showed substantial health-related quality of life (HRQoL) impairment. Overall, the higher the attack frequencies, the greater the reported impairment in the PROMs tended to be. In multiple linear regression analyses, higher AE-QoL Fatigue/Mood and Fears/Shame domain scores (greater impairment) were associated with higher HADS anxiety subscale scores; higher AE-QoL total scores (greater HRQoL impairment) and lower SF-12 Physical and Mental Health Composite scores (greater general health impairment) were associated with higher HADS depression subscale scores. Patients with monthly or weekly HAE attacks reported numerically low absenteeism and numerically high presenteeism and work productivity loss as measured by the WPAI:SHP questionnaire. In this study, conducted before modern LTP options were available in Japan, patients with HAE reported notable impairment in HRQoL and work productivity. Weekly or monthly HAE attack frequencies were associated with a high disease burden. Furthermore, a substantial number of patients reported notable fatigue/mood impairment as measured by the AE-QoL and depression as measured by the HADS regardless of attack frequency. These results provide a basis for future studies evaluating the effect of LTP on the clinical manifestations and HRQoL in patients with HAE.

Applications of Artificial Intelligence for Heat Stress Management in Ruminant Livestock.

Heat stress impacts ruminant livestock production on varied levels in this alarming climate breakdown scenario. The drastic effects of the global climate change-associated heat stress in ruminant livestock demands constructive evaluation of animal performance bordering on effective monitoring systems. In this climate-smart digital age, adoption of advanced and developing Artificial Intelligence (AI) technologies is gaining traction for efficient heat stress management. AI has widely penetrated the climate sensitive ruminant livestock sector due to its promising and plausible scope in assessing production risks and the climate resilience of ruminant livestock. Significant improvement has been achieved alongside the adoption of novel AI algorithms to evaluate the performance of ruminant livestock. These AI-powered tools have the robustness and competence to expand the evaluation of animal performance and help in minimising the production losses associated with heat stress in ruminant livestock. Advanced heat stress management through automated monitoring of heat stress in ruminant livestock based on behaviour, physiology and animal health responses have been widely accepted due to the evolution of technologies like machine learning (ML), neural networks and deep learning (DL). The AI-enabled tools involving automated data collection, pre-processing, data wrangling, development of appropriate algorithms, and deployment of models assist the livestock producers in decision-making based on real-time monitoring and act as early-stage warning systems to forecast disease dynamics based on prediction models. Due to the convincing performance, precision, and accuracy of AI models, the climate-smart livestock production imbibes AI technologies for scaled use in the successful reducing of heat stress in ruminant livestock, thereby ensuring sustainable livestock production and safeguarding the global economy.

Public Health and Economic Impact of Periodic COVID-19 Vaccination with BNT162b2 for Old Adults and High-Risk Patients in an Illustrative Prefecture of Japan: A Budget Impact Analysis.

Japan will be transitioning from the free-of-charge COVID-19 vaccination program to annual periodic vaccination under a national immunization program for old adults and high-risk patients from 2024 fall/winter season. The policy transition including out-of-pocket payment requirement may discourage vaccination, leading to a lower vaccination rate. This study aimed to estimate the impact of varying vaccination rates with BNT162b2 COVID-19 mRNA vaccine on economics and public health in an illustrative prefecture which administers and promotes the periodic vaccination program, using budget impact analysis. A combined cohort Markov decision tree model estimated the public health outcomes of COVID-19-related symptomatic cases, hospitalizations and deaths; and the economic outcomes including vaccine-related cost, non-vaccine-related medical cost, and productivity loss from the societal perspective. The base case examined the impact on the outcomes when vaccination coverage changed from the reference value of 50% to upper and lower values, respectively. Scenario analyses were performed based on multiple scenarios. Increase in the vaccination rate demonstrated improvement in all public health outcomes. At 50% vaccination, the vaccine-related cost for 3years in a prefecture was estimated at JPY 7.58 billion (USD 57.67 million), the non-vaccine-related medical cost at JPY 79.22 billion (USD 602.48 million), the productivity loss at JPY 253.11 billion (USD 1.92 billion), and the total cost at JPY 339.92 billion (USD 2.59 billion). When the vaccination rate increased to 90%, the total cost decreased by JPY 4.88 billion (USD 37.11 million) (1.4%). When the vaccination rate decreased to 10%, the total cost increased by JPY 5.73 billion (USD 43.58 million) (1.7%). Results were consistent across almost all scenario analyses. Maintaining a high vaccination rate with BNT162b2 is important from both public health and economic perspectives in Japan. The findings highlight to local governments the importance of continued effort to promote vaccination.

Exploration of a model driven by climate to simulate pond water temperature in aquaculture systems

IntroductionInterannual climate variability in the Asian mega deltas has been posing a wide range of climate risks in the aquaculture systems of the region. Water temperature variation is one of the key risks related to disease outbreak, fish health, and loss and damage in fish production. However, Climate information can improve the ability to predict changes in pond water quality parameters at the farm level using publicly available weather and climate data. Little research has been done to translate weather data into water temperature forecasts using mechanistic models in order to provide farmers with relevant forecasting information in the context of climate services.MethodsThe advantage of mechanistic models over statistical models is that they are based on physical processes and can therefore be used in a wider range of environmental conditions. In this study, we used an energy balance model to investigate its ability to simulate pond water temperature at daily and seasonal timescales in the southwest and northeast regions of Bangladesh. The model was able to adequately simulate pond water temperature at a daily timescale using publicly available weather data, and the accuracy of the model was lower at the study site with very heavy rainfall events.ResultsSensitivity analyses showed that the model was also able to simulate the impact of air temperature cold and hot spells on the pond water temperature. Connecting the model with seasonal air temperature forecasts resulted in very small variations in the forecasted seasonal pond water temperature, in large part due to the low variability observed in water temperature at seasonal scale in the study sites.DiscussionClimate information can improve the ability to predict changes in pond water quality parameters at the farm level using publicly available weather and climate data. Hence, these improved predictions are important to help fish-farmers make informed decisions for managing associated climate risks.

Research on origin-based cold storage location and routing optimization of fresh agricultural products based on hybrid whale algorithm

With the focus on the insufficient origin-based cold storage in China, this study investigates the location and routing problem (LRP) of origin-based cold storage for fresh agricultural products. This study considers the loss of fresh agricultural products in different environments during transportation and presents a cold storage LRP model. To address this issue, a hybrid whale algorithm with heuristic rules is designed, and the effectiveness of the algorithm is verified by standard instances. Finally, taking Chenggu County as a practical case, the influence of cold storage capacity and farmers’ demand for refrigeration are analysed. Experimental results show that the proposed algorithm has a good effect in solving medium-scale LRP. As the storage capacity increases, the total cost of the system can be increased by 0.086%. As farmers’ demand for refrigeration increases, the total cost of the system can be increased by 34.034%. Farmers’ demand has a greater impact on the system’s total costs than the cold storage capacity. When optimizing the cold storage layout, changes in fresh agricultural product output in the next few years can be roughly predicted, and the most economical optimization scheme can be obtained.

Management of Huanglongbing of Citrus: Lessons from São Paulo and Florida.

São Paulo, Brazil, and Florida, USA, were the two major orange production areas in the world until Huanglongbing (HLB) was discovered in São Paulo in 2004 and Florida in 2005. In the absence of resistant citrus varieties, HLB is the most destructive citrus disease known because of the lack of effective tools to reduce spread of the vector, Diaphorina citri (Asian citrus psyllid), and transmission of the associated pathogen, Candidatus Liberibacter asiaticus. In both countries, a three-pronged management approach was recommended and begun: planting only disease-free nursery trees, effective psyllid control, and removal of all symptomatic trees. In Brazil, these management procedures were continued and improved and resulted in relatively little overall loss of production. In contrast, in Florida the citrus industry has been devastated with annual production reduced by approximately 80%. This review compares and contrasts various cultural and pest management strategies that have been used to reduce infection by the pathogen and increase tolerance of HLB in the main orange-growing regions in the world.

Early Hotspot Detection in Photovoltaic Modules using Deep Learning Methods

Photovoltaic (PV) energy systems have been widely used in energy production especially in recent years due to their clean, reliable, environmentally friendly and resource continuity. The electrical energy to be obtained must be uninterrupted and of high quality. For this reason, faults that may cause production losses in PV power plants should be detected quickly and accurately. In this study, an approach that can automatically detect hotspot fault in solar PV modules is presented. In the proposed approach, firstly, Jet colormap based colored image transformation process is applied to gray scale infrared image data. Then, using three different deep learning models such as MobilNetV2, ResNet-18, InceptionV3, both original grey scale data and Jet colormap data are detected and evaluation metric values are obtained. The importance of this research lies in the potential of deep learning-based effective detection, especially in the early and rapid diagnosis of PV hotspot faults.

Upscaling ecological restoration by integrating with agriculture

Transformative change is needed to align common small‐scale ecological restoration approaches with expectations to restore millions of hectares of degraded lands globally. Currently, most restoration projects target small areas using costly manual methods that cannot be scaled up to meet global commitments. We propose that a judicious integration of agricultural practices into ecological restoration offers an opportunity to address this issue. This transformative process relies on three sequential and interconnected steps: (1) ensure that sufficient land is truly available for restoration; (2) compensate for the loss of agricultural production, income, or land value to encourage landholders to opt for restoration; and (3) develop scalable, affordable, and effective methods for restoring native ecosystems across the pledged hundreds of millions of hectares to deliver benefits to both nature and people. Large‐scale terrestrial restoration will require incorporating agronomic practices into the restoration toolbox to go beyond vague, ambitious promises and wishful thinking.

System dynamic model of water, energy and food nexus for policy implementation

In this study, a spatiotemporal disaggregated simulation model was developed based on water–food–energy (WFE) nexus approach to assess water and food supply security considering ecosystem provisioning services. The main components of the developed model in this study (SD-WFE model) are population, water, agriculture, and energy modules. The model, which was developed using system dynamics (SD) approach, was utilized to simulate effectiveness of sectoral municipal, industrial, and agricultural water and energy consumption management and environmental protection policies in improving ecosystem provisioning services during a 20-year period. Through sensitivity analysis utilizing the Monte Carlo model, the study addresses the formulation of sustainable water resource policies across four main categories: water demand management, water supply management, food resource management, and energy resource demand management. Additionally, it explores the integration of policies within an optimal framework. The simulation of proposed solutions revealed that a combination of water demand management and food resource management yielded the most promising outcome. Specifically, the recommended solution entails enhancing water irrigation efficiency by 18% through the expansion of pressurized irrigation network coverage, adjusting cropping patterns by 14%, reducing agricultural product losses by 8% via improved food supply management, minimizing food demand by 9% due to reduced food consumption losses, and achieving an annual 10% increase in crop performance. These selected policies form the foundation for sustainable water resource management strategies.

Synthesis of 6-bromo-7-arylaminoisoquinoline-5,8-quinones and its effects on Piscirickettsia salmonis infection invitro.

Among the most important aquaculture resources for our country, salmon and trout stand out. Their production has increased significantly in recent decades, making them two of the most valuable resources in economic terms. However, high aquaculture production has allowed many pathogens to proliferate, causing infectious diseases and significant production losses. Piscirickettsia salmonis is a gram-negative, facultative intracellular bacterium that is responsible for causing severe disease in a variety of salmonid fish species. Despite the significant impact of P. salmonis on aquaculture, effective treatments for this disease remain limited. Current prevention and control strategies often include antibiotics and vaccines. However, these treatments have shown varying degrees of efficacy. A promising approach involves synthesizing bioactive analog compounds with antibacterial properties. Quinones, secondary metabolites that are abundant in nature, have become a focal point of interest due to their diverse physiological activities, including antibiotic, insecticidal, antifungal, and anticancer properties. In this study, it is shown the synthesis of series 6-bromo-7-arylaminoisoquinoline-5,8-quinones, the characterization of these compounds using classical spectroscopic methods such as one-dimensional nuclear magnetic resonance (NMR), FT-IR (infrared), mass spectrometry, and the biological activity against Piscirickettsia salmonis. The brominated derivative compounds showed no cytotoxicity at any concentration evaluated. Furthermore, the infectivity of P. salmonis after treatment with the analog compounds indicated that derivatives methyl 6-bromo-7-((4-methoxyphenyl)amino)-1,3-dimethy-5,8-dioxo-5,8-dihydroisoquinoline-4-carboxylate (4b) and methyl 7-((4'-amino-[1,1'-biphenyl]-4-yl)amino)-6-bromo-1,3-dimethy-5,8-dioxo-5,8-dihydroisoquinoline-4-carboxylate (4g) reduced the bacterial load at 25 μg/mL concentration.

Contribution of Fractional Cover Analysis for Monitoring Degraded Ecosystems at the Watershed Level

The purpose of the research was to use the data generated on degradation and perform spectral mixture analysis to determine the changes in photosynthetic, non-photosynthetic vegetation and soil fractions, in tactically selected sites, verifying degradation and on the other hand to use this analysis to verify the recovery of degraded ecosystem surfaces that have had recovery projects. Fractional coverages were determined through Normalized Difference Fraction Index (NDFI) by analyzing spectral mixtures using Google Earth Engine. In this sense, the degradation identified in the Ponasa watershed is related to specific degradation values indicated by the NDFI values, to prove that in effect the degradation of these ecosystems is linked to loss of photosynthetic activity, increase in bare soil values and non-photosynthetic activity derived from the process of disturbance of the vegetation cover. In the case of the present study, the NDFI finally explains the degradation processes produced by the loss of vegetation cover and the loss of net primary productivity of the ecosystem, but it is not related in the same way in the case of fragmentation. Ongoing monitoring of the NDFI is key to understanding ecosystem degradation and recovery and to making informed decisions.

Foslevodopa/foscarbidopa (LDp/CDp) in advanced Parkinson’s Disease (aPD): demonstration of savings from a societal perspective in the UK

Aims In advanced Parkinson’s disease (aPD), adequate 24-hour control of OFF-time may not be achievable using oral/transdermal therapies. Clinical trials of foslevodopa/foscarbidopa (LDp/CDP) demonstrate meaningful reductions in OFF-time and OFF-related sleep disturbance in aPD. Previous analyses have only considered direct medical costs: this analysis considers a broader societal perspective (direct non-medical costs, informal care, loss of earnings, productivity, and tax). Methods Inputs for the societal impact model were taken from a cost-utility model comparing LDp/CDp with best medical treatment (BMT), accepted by the UK National Institute of Health and Care Excellence (NICE). Quintiles of normalized OFF-time across a 16-hour waking day in each treatment group were applied to literature-based estimates for direct medical, non-medical, and indirect costs. The resulting state-specific cost estimates were applied to the modelled aPD patient population. Results The model estimates the potential UK population for LDp/CDp at 17,505. Continuous 24-hour delivery of LDp/CDp results in greater time spent in OFF-time states 0–1 (0–4 hours of OFF-time/16-hour waking day) vs BMT alone. Net savings if all eligible patients receive LDp/CDp are £79.1 M in year 1, £235.4 M in year 2, rising to £262.2 M in year 3, declining to £222.9 M in year 4 and £153.7 M in year 5 as disease progresses and the efficacy of LDp/CDp declines. The estimated total net savings are £953 M after 5 years. Results are robust in scenario analyses (excluding costs of excessive sleepiness, earnings loss, productivity, and tax loss). Limitations A NICE-accepted model was used as the economic modelling basis for the societal impact model, however, much of the data was derived from Adelphi datasets, with the potential for inconsistent definitions. Conclusion When considered from a societal perspective, the use of LDp/CDp in aPD patients inadequately controlled on oral therapy is associated with net healthcare and societal annual savings of over £79.1 M vs BMT.

Effects of autism spectrum disorder on parents labour market: Productivity loss and policy evaluation in Kazakhstan.

Kazakhstan has witnessed a significant increase in the number of Autism Spectrum Disorder cases due to the implementation of mechanisms for early detection. However, despite these efforts, accessing quality services and effective interventions for individuals with Autism Spectrum Disorder remains challenging. While the government has implemented various policies to address the impact of Autism Spectrum Disorder on the labour market, especially for those with disabilities, the effectiveness of these policies needs to be evaluated. Therefore, this article aims to estimate the loss of productivity by calculating the cost of non-working for parents of children with Autism Spectrum Disorder. To achieve this goal, we combine data from official sources with data from our own survey to estimate the loss of productivity using human capital models. In addition, we conduct policy simulations to assess the impact of the existing policy implemented in Kazakhstan that recognises the time of caring for children with Autism Spectrum Disorder and disability as working in the paid labour market. Our results reveal that the productivity loss is substantial, with mothers of children with Autism Spectrum Disorder being particularly affected. Furthermore, based on the outcomes of the policy simulations, it becomes evident that policies solely targeting parents of children with Autism Spectrum Disorder and disability are insufficient to address the labour market gaps and the consequent loss of productivity. To effectively mitigate the impact of Autism Spectrum Disorder in the labour market, a more comprehensive approach is needed. This approach should encompass a broader range of interventions and support mechanisms, including those for individuals without disabilities and parents of children with Autism Spectrum Disorder.

Reducing Phosphorus Loss in Short-Cycle Horticultural Production Using Activated Aluminum-Amended Substrates and Modified Fertigation Practices.

To support growth, short-cycle horticultural crops require readily available nutrients. However, this often leads to nutrient leaching. Implementing best management practices in production decisions like incorporating fertilizer retaining amendments to substrates or modifying fertilization programs can mitigate nutrient losses to the environment and associated costs. This study examined using an activated aluminum (AA) material as a substrate amendment to retain phosphorus (P) within containers while also assessing methods to reduce P fertilization in Tagetes production over a six-week production cycle. A commercial peat moss substrate (PL) pre-loaded with nutrients was amended with AA, enabling comparisons between substrates with and without AA. Enhanced fertilizer practices involved supplementing the initial nutrients by applying a weekly fertigation solution including nitrogen and potassium over the six weeks, but P for either 0, 2, 4, or 6 weeks. The incorporation of AA significantly reduced P leaching losses by 89.5-97.7%, compared to the PL substrates receiving P the entire six weeks. Regardless of substrate or fertilizer management, all Tagetes had equivalent sizes (growth index) and aboveground biomass. The results indicate that amending substrates with AA and/or reducing additional P inputs are effective strategies to minimize P leaching without compromising Tagetes quality.