Health economic modeling for Alzheimer's disease: Expert perspectives.

The successful development of an economic model for the evaluation of future Alzheimer's disease (AD) interventions is critical to accurately inform policy makers and payers. As our understanding of AD expands, this becomes an increasingly complex and challenging goal. Advances in diagnostic techniques for AD and the prospect of disease-modifying treatments raise an urgent need to define specifications for future economic models and to ensure that the necessary data to populate them are available. This Perspective article provides expert opinions from health economists and governmental agency representatives on how future economic models for AD might be structured, validated, and reported. We aim to stimulate much-needed discussion about the detailed specification of future health economic models for AD.

Optimal health and economic impact of non-pharmaceutical intervention measures prior and post vaccination in England: a mathematical modelling study.

Background. Even with good progress on vaccination, SARS-CoV-2 infections in the UK may continue to impose a high burden of disease and therefore pose substantial challenges for health policy decision makers. Stringent government-mandated physical distancing measures (lockdown) have been demonstrated to be epidemiologically effective, but can have both positive and negative economic consequences. The duration and frequency of any intervention policy could, in theory, be optimized to maximize economic benefits while achieving substantial reductions in disease. Methods. Here, we use a pre-existing SARS-CoV-2 transmission model to assess the health and economic implications of different strengths of control through time in order to identify optimal approaches to non-pharmaceutical intervention stringency in the UK, considering the role of vaccination in reducing the need for future physical distancing measures. The model is calibrated to the COVID-19 epidemic in England and we carry out retrospective analysis of the optimal timing of precautionary breaks in 2020 and the optimal relaxation policy from the January 2021 lockdown, considering the willingness to pay (WTP) for health improvement. Results. We find that the precise timing and intensity of interventions is highly dependent upon the objective of control. As intervention measures are relaxed, we predict a resurgence in cases, but the optimal intervention policy can be established dependent upon the WTP per quality adjusted life year loss avoided. Our results show that establishing an optimal level of control can result in a reduction in net monetary loss of billions of pounds, dependent upon the precise WTP value. Conclusion. It is vital, as the UK emerges from lockdown, but continues to face an on-going pandemic, to accurately establish the overall health and economic costs when making policy decisions. We demonstrate how some of these can be quantified, employing mechanistic infectious disease transmission models to establish optimal levels of control for the ongoing COVID-19 pandemic.

Health Insurance Literacy Levels of Information Intermediaries: How Prepared Are They to Address the Growing Health Insurance Access Needs of Consumers?

BACKGROUND: With rising unemployment rates brought on by coronavirus disease 2019 pandemic, the rates of underinsured and uninsured consumers are likely to rise. Health information intermediaries play a critical role in assisting consumers with navigating the complexities of the United States health care system and the ever-changing health care policy landscape. Not much is known about the health insurance literacy (HIL) levels of information intermediaries and their ability to assist consumers with making informed decisions about their health insurance. OBJECTIVE: This study aimed to examine the association between information intermediary levels of HIL, sociodemographic factors, and confidence and behaviors in assisting consumers with health insurance needs. METHODS: We surveyed 118 information intermediaries from various roles to assess objective and subjective HIL, frequency, and confidence in assisting consumers, and confidence in understanding changes in federal health reform policies and state Medicaid waiver programs. KEY RESULTS: Less than one-half (39%) of information intermediaries had high subjective HIL and much fewer (13%) had high objective HIL. The average frequency of assisting consumers with health insurance scores were somewhat low, and confidence in assisting consumers with health insurance scores and confidence with understanding state and federal policies were modest. Results from our logistic regression model indicated that confidence in assisting consumers was found to be the only significant contributor to high subjective HIL. For every one-point increase on the confidence assisting subscale, there was a 35% increase in the information intermediaries having high subjective HIL. CONCLUSIONS: Findings from this study, coupled with rising uninsured rates, indicate the need for tailored training programs and resources to equip our information intermediaries to provide timely and appropriate health insurance support for consumers. [HLRP: Health Literacy Research and Practice. 2022;6(1):e30-e36.] Plain Language Summary: In a sample of 118 information intermediaries, representing community health workers, navigators, and other people in outreach roles, the majority had low subjective and objective HIL. We also found that as confidence with assisting consumers with health insurance needs increases, HIL increased as well. These findings indicate that tailored training programs and resources are needed to equip information intermediaries to provide health insurance support for consumers.

Financial landscape of recovery housing in the United States.

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.

High infectiousness immediately before COVID-19 symptom onset highlights the importance of continued contact tracing.

Background: Understanding changes in infectiousness during SARS-COV-2 infections is critical to assess the effectiveness of public health measures such as contact tracing. Methods: Here, we develop a novel mechanistic approach to infer the infectiousness profile of SARS-COV-2-infected individuals using data from known infector-infectee pairs. We compare estimates of key epidemiological quantities generated using our mechanistic method with analogous estimates generated using previous approaches. Results: The mechanistic method provides an improved fit to data from SARS-CoV-2 infector-infectee pairs compared to commonly used approaches. Our best-fitting model indicates a high proportion of presymptomatic transmissions, with many transmissions occurring shortly before the infector develops symptoms. Conclusions: High infectiousness immediately prior to symptom onset highlights the importance of continued contact tracing until effective vaccines have been distributed widely, even if contacts from a short time window before symptom onset alone are traced. Funding: Engineering and Physical Sciences Research Council (EPSRC).

The risk of a person with COVID-19 spreading the SARS-CoV-2 virus that causes it to others varies over the course of their infection. Transmission depends both on how much virus is in the infected person's airway and their behaviors, such as whether they wear a mask and how many people they have contact with. Learning more about when people are most infectious would help public health officials stop the spread of the virus. For example, officials can then introduce policies that ensure that people are isolated when they are most infectious. The majority of studies assessing when people with COVID-19 are most infectious so far have assumed that transmission is not linked to when symptoms appear. But that may not be true. After people develop symptoms, they may be more likely to stay home, avoid others, or take other measures that prevent transmission. Using computer modeling and data from previous studies of individuals who infected others with SARS-CoV-2, Hart et al. show that about 65% of virus transmission occurs before symptoms develop. In fact, the computational experiments show the risk of transmission is highest immediately before symptoms develop. This highlights the importance of identifying people exposed to someone infected with the virus and isolating potential recipients before they develop symptoms. This information may help public health officials develop more effective strategies to prevent the spread of SARS-CoV-2. It may also help scientists develop more accurate models to predict the spread of the virus. However, the computational experiments used data on infections early in the pandemic that may not reflect the current situation. Changes in public health policy, the behavior of individuals and the appearance of new strains of SARS-CoV-2, all affect the timing of transmission. As more recent data become available, Hart et al. plan to explore how characteristics of transmission have changed as the pandemic has progressed.

The effect of the definition of 'pandemic' on quantitative assessments of infectious disease outbreak risk.

In the early stages of an outbreak, the term 'pandemic' can be used to communicate about infectious disease risk, particularly by those who wish to encourage a large-scale public health response. However, the term lacks a widely accepted quantitative definition. We show that, under alternate quantitative definitions of 'pandemic', an epidemiological metapopulation model produces different estimates of the probability of a pandemic. Critically, we show that using different definitions alters the projected effects of key parameters-such as inter-regional travel rates, degree of pre-existing immunity, and heterogeneity in transmission rates between regions-on the risk of a pandemic. Our analysis provides a foundation for understanding the scientific importance of precise language when discussing pandemic risk, illustrating how alternative definitions affect the conclusions of modelling studies. This serves to highlight that those working on pandemic preparedness must remain alert to the variability in the use of the term 'pandemic', and provide specific quantitative definitions when undertaking one of the types of analysis that we show to be sensitive to the pandemic definition.

Availability of Health Insurance Literacy Resources Fails to Meet Consumer Needs in Rural, Appalachian Communities: Implications for State Medicaid Waivers.

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.

Challenges in demonstrating the value of disease-modifying therapies for Alzheimer's disease.

INTRODUCTION: Alzheimer's disease (AD) is a complex neurodegenerative disease, affecting millions of people worldwide and imposing heavy economic burdens to societies. Currently, only symptomatic treatments are available for patients, but there is ongoing research on potential therapies that can modify the course of disease. The main objective of this work is to identify and explore the challenges surrounding decision modeling for economic evaluation of interventions for AD. AREAS COVERED: This article discusses the challenges in modeling the natural history of disease, particularly regarding the selection of disease progression and outcome measures, the inclusion of biomarker status in models, and the approach to model mortality. Challenges stemming from the use of long-term assumptions regarding treatment effects and the need for real-world evidence to fill data gaps are discussed. Lastly, the overwhelming economic impact of disease and the challenges in estimating these costs for modeling are addressed. EXPERT OPINION: Value assessment frameworks need to be reconsidered in order to demonstrate the full benefit of new disease-modifying therapies spanning beyond the scope of health systems. Data collection efforts that expand the evidence base, upon which economic models are based, will reduce the uncertainties surrounding the long-term outcomes of interventions in AD.

Alignment of European Regulatory and Health Technology Assessments: A Review of Licensed Products for Alzheimer's Disease.

Aims: To facilitate regulatory learning, we evaluated similarities and differences in evidence requirements between regulatory and health technology assessment (HTA) bodies of Alzheimer's disease (AD) approved products. Methods: The European marketing authorisation application dossiers and European public assessment reports (EPARs) of the licensed AD drugs were screened to identify the phase III randomised controlled trials (RCTs) and outcomes used. We also screened the assessment reports of the National Institute of Health and Care Excellence (NICE, England) and the National Health Care Institute (ZiN, the Netherlands) to identify the studies and outcomes used in HTA assessments. Results: The application dossiers of donepezil, galantamine, rivastigmine, and memantine contained 16 phase III RCTs in total. These trials were also included in HTA assessments except that NICE excluded studies that were not published (n = 2) or trials that included patients with other types of dementia (n = 3). In the regulatory assessments the focus was on cognitive and global outcomes, and to some extent on function. In the HTA assessments of clinical effectiveness other domains were also covered including: function, behaviour and mood, and, occasionally, quality of life. In the economic analyses of NICE the domains cognition, function, and quality of life were included. Conclusion: There was a large overlap in inclusion of trials in regulatory and HTA assessments, although the focus on specific outcomes slightly differed. Understanding the methods and perceptions of both authorities can stimulate regulatory and HTA cross-talk and further alignment, and therefore more rapid patient access to new treatments.

Cost Effectiveness of Nusinersen in the Treatment of Patients with Infantile-Onset and Later-Onset Spinal Muscular Atrophy in Sweden.

BACKGROUND: Spinal muscular atrophy is a rare neuromuscular disorder with a spectrum of severity related to age at onset and the number of SMN2 gene copies. Infantile-onset (≤ 6 months of age) is the most severe spinal muscular atrophy and is the leading monogenetic cause of infant mortality; patients with later-onset (> 6 months of age) spinal muscular atrophy can survive into adulthood. Nusinersen is a new treatment for spinal muscular atrophy. OBJECTIVE: The objective of this study was to evaluate the cost effectiveness of nusinersen for the treatment of patients with infantile-onset spinal muscular atrophy and later-onset spinal muscular atrophy in Sweden. METHODS: One Markov cohort health-state transition model was developed for each population. The infantile-onset and later-onset models were based on the efficacy results from the ENDEAR phase III trial and the CHERISH phase III trial, respectively. The cost effectiveness of nusinersen in both models was compared with standard of care in Sweden. RESULTS: For a time horizon of 40 years in the infantile-onset model and 80 years in the later-onset model, treatment with nusinersen resulted in 3.86 and 9.54 patient incremental quality-adjusted life-years and 0.02 and 2.39 caregiver incremental quality-adjusted life-years and an incremental cost of 21.9 and 38.0 million SEK (Swedish krona), respectively. These results translated into incremental cost-effectiveness ratios (including caregiver quality-adjusted life-years) of 5.64 million SEK (€551,300) and 3.19 million SEK (€311,800) per quality-adjusted life-year gained in the infantile-onset model and later-onset model, respectively. CONCLUSIONS: Treatment with nusinersen resulted in overall survival and quality-adjusted life-year benefits but with incremental costs above 21 million SEK (€2 million) [mainly associated with maintenance treatment with nusinersen over a patient's lifespan]. Nusinersen was not cost effective when using a willingness-to-pay threshold of 2 million SEK (€195,600), which has been considered in a recent discussion by the Dental and Pharmaceutical Benefits Agency as a reasonable threshold for rare disease. Nonetheless, nusinersen gained reimbursement in Sweden in 2017 for paediatric patients (below 18 years old) with spinal muscular atrophy type I-IIIa.

Challenges for Optimizing Real-World Evidence in Alzheimer's Disease: The ROADMAP Project.

ROADMAP is a public-private advisory partnership to evaluate the usability of multiple data sources, including real-world evidence, in the decision-making process for new treatments in Alzheimer's disease, and to advance key concepts in disease and pharmacoeconomic modeling. ROADMAP identified key disease and patient outcomes for stakeholders to make informed funding and treatment decisions, provided advice on data integration methods and standards, and developed conceptual cost-effectiveness and disease models designed in part to assess whether early treatment provides long-term benefit.

The Role of Telemedicine in Auditory Rehabilitation: A Systematic Review.

OBJECTIVE: The purpose of this study was to assess the feasibility and effectiveness of live telemedicine applications in hearing amplification and cochlear implantation. DATA SOURCES AND STUDY SELECTION: A systematic search was performed in PubMed, MEDLINE, PsychINFO, CINALH, and Web of Science to identify peer-reviewed research. Inclusion criteria were titles containing words from the search terms 1) audiology, otolaryngology, and hearing impairment, 2) rehabilitative methods, and 3) telemedicine. Exclusion criteria were: 1) non-English articles, and 2) non-original research. DATA EXTRACTION AND SYNTHESIS: Twelve eligible studies were identified. The studies employed a prospective design in nine of the articles and retrospective case series in three. The use of telemedicine for the provision of cochlear implant services was examined in eight of the articles and with hearing aids in four of the articles. The types of services include intraoperative cochlear implant telemetry; implant programming and assessment of electrode-specific measures and speech recognition after implantation. Hearing aid programming and remote gain assessments were also reported. Many studies assess patient and provider satisfaction along with encounter time comparison. The studies occurred from 2009 to 2014 and took place in seven countries. CONCLUSIONS: This review examined the feasibility of remote telemedicine connection to provide in auditory rehabilitation services through hearing aids and cochlear implants. There are significant concerns regarding Internet bandwidth limitations for remote clinics. There is a paucity of research examining reimbursement and cost-effectiveness for services. Further prospective research investigating cost-effectiveness and bandwidth limitations is warranted to assess long-term sustainability of remote audiological rehabilitative service delivery.

Management of invading pathogens should be informed by epidemiology rather than administrative boundaries.

Plant and animal disease outbreaks have significant ecological and economic impacts. The spatial extent of control is often informed solely by administrative geography - for example, quarantine of an entire county or state once an invading disease is detected - with little regard for pathogen epidemiology. We present a stochastic model for the spread of a plant pathogen that couples spread in the natural environment and transmission via the nursery trade, and use it to illustrate that control deployed according to administrative boundaries is almost always sub-optimal. We use sudden oak death (caused by Phytophthora ramorum) in mixed forests in California as motivation for our study, since the decision as to whether or not to deploy plant trade quarantine is currently undertaken on a county-by-county basis for that system. However, our key conclusion is applicable more generally: basing management of any disease entirely upon administrative borders does not balance the cost of control with the possible economic and ecological costs of further spread in the optimal fashion.

Employing a Monte Carlo algorithm in Newton-type methods for restricted maximum likelihood estimation of genetic parameters.

Estimation of variance components by Monte Carlo (MC) expectation maximization (EM) restricted maximum likelihood (REML) is computationally efficient for large data sets and complex linear mixed effects models. However, efficiency may be lost due to the need for a large number of iterations of the EM algorithm. To decrease the computing time we explored the use of faster converging Newton-type algorithms within MC REML implementations. The implemented algorithms were: MC Newton-Raphson (NR), where the information matrix was generated via sampling; MC average information(AI), where the information was computed as an average of observed and expected information; and MC Broyden's method, where the zero of the gradient was searched using a quasi-Newton-type algorithm. Performance of these algorithms was evaluated using simulated data. The final estimates were in good agreement with corresponding analytical ones. MC NR REML and MC AI REML enhanced convergence compared to MC EM REML and gave standard errors for the estimates as a by-product. MC NR REML required a larger number of MC samples, while each MC AI REML iteration demanded extra solving of mixed model equations by the number of parameters to be estimated. MC Broyden's method required the largest number of MC samples with our small data and did not give standard errors for the parameters directly. We studied the performance of three different convergence criteria for the MC AI REML algorithm. Our results indicate the importance of defining a suitable convergence criterion and critical value in order to obtain an efficient Newton-type method utilizing a MC algorithm. Overall, use of a MC algorithm with Newton-type methods proved feasible and the results encourage testing of these methods with different kinds of large-scale problem settings.

Dupuytren's contracture: a retrospective database analysis to assess clinical management and costs in England.

BACKGROUND: Dupuytren's disease is a fibro-proliferative disorder affecting ~3-5% of the UK population. Current surgical treatments for Dupuytren's contracture (DC) include fasciectomy and fasciotomy. We assessed the clinical management of DC in England over a 5-year period; associated NHS costs were assessed for a 1-year period. METHODS: Hospital Episode Statistics were extracted from April 2003 to March 2008 for patients with Palmar Fascial Fibromatosis (ICD10=M720) and DC-related procedures. Variables included demographics, OPCS, patient status and physician specialty. To estimate 2010-2011 costs, HRG4 codes and the National Schedule of Tariff 2010-11-NHS Trusts were applied to the 2007-2008 period. RESULTS: Over 5 years, 75,157 DC admissions were recorded; 64,506 were analyzed. Mean admissions per year were 12,901 and stable. Day cases increased from 42% (2003-2004) to 62% (2007-2008). The percent of patients having two or more admissions per year increased from 5.5% in 2003-2004 to 26.1% in 2007-2008. Between 2003 and 2007, 91% of procedures were Fasciectomy. Revision of Fasciectomy and Fasciotomy each accounted for ~4%; Amputation for 1%. In 2007, classification was extended to identify Digital Fasciectomy, its Revision and Dermofasciectomy. In 2007-2008, admissions were: 70% Palmar Fasciectomy, 16% Digital Fasciectomy, 1.3% Other Fasciectomy, 4.4% Revision of Palmar Fasciectomy, 1.3% Revision of Digital Fasciectomy, 3.8% Division of Palmar Fascia, 2.6% Dermofasciectomy and 1.1% Amputation. 79% of cases were overseen by trauma and orthopaedic surgeons, 19% by plastic surgeons. Mean (±SD) inpatient hospital length of stay was 1.5 (±1.4) days in 2003-2004 and 1.0 (±1.3) days in 2007-2008. Total estimated costs for 1 year (2010-2011) were £41,576,141. Per-patient costs were £2,885 (day case) and £3,534 (inpatient). Costs ranged from £2,736 (day-case Fasciectomy) to £9,210 (day-case Revision Digital). CONCLUSIONS: Between 2003 and 2008, fasciectomy was the most common surgical procedure for DC in England. While procedure rates and physician specialties varied little, there was a reversal in surgical venue: inpatient operations decreased as day-case procedures increased. The change is likely due to economic trends and changes to the healthcare system. Estimated costs for 2010-2011 varied by procedure type and patient status. These findings can be used to understand clinical management of DC and guide healthcare policy.

Estimation of prediction error variances via Monte Carlo sampling methods using different formulations of the prediction error variance.

Calculation of the exact prediction error variance covariance matrix is often computationally too demanding, which limits its application in REML algorithms, the calculation of accuracies of estimated breeding values and the control of variance of response to selection. Alternatively Monte Carlo sampling can be used to calculate approximations of the prediction error variance, which converge to the true values if enough samples are used. However, in practical situations the number of samples, which are computationally feasible, is limited. The objective of this study was to compare the convergence rate of different formulations of the prediction error variance calculated using Monte Carlo sampling. Four of these formulations were published, four were corresponding alternative versions, and two were derived as part of this study. The different formulations had different convergence rates and these were shown to depend on the number of samples and on the level of prediction error variance. Four formulations were competitive and these made use of information on either the variance of the estimated breeding value and on the variance of the true breeding value minus the estimated breeding value or on the covariance between the true and estimated breeding values.

Access to medicines and out of pocket payments for primary care: evidence from family medicine users in rural Tajikistan.

BACKGROUND: In Tajikistan it is estimated that out of pocket payments constitute two-thirds of all health spending with high proportions of these contributions through informal payments. As a consequence, access to basic care is a major concern particularly among the most needy and vulnerable groups. This article evaluates accessibility of prescription medicines and patient expenditures for primary care services in two rural districts of Tajikistan. METHODS: 901 patients aged 18 years or above who had accessed primary care facilities were interviewed, using a questionnaire based on questions regarding patient's experience of visiting the health facility. To group respondents by socio-economic status, an asset index was created using principal component analysis of the information included in the questionnaires. RESULTS: 76.7% of patients were prescribed a medicine during the visits and more than 83% of them managed to obtain it. Patients spent on average US$ 9.3 on medicines, with wide variation among socio-economic groups. Around 45% of patients paid the Family Doctor. Additionally, over 41% of patients in the highest socioeconomic quintile were referred to a specialist, while only 29% of the poorest 40%. CONCLUSION: This survey showed that there are financial barriers potentially inactivating utilization of basic services. These barriers can only be reduced by mobilizing more public resources to fund the health sector, providing incentives for family doctors to stop requiring payments from patients, and increasing the availability of prescription drugs in PHC facilities.

Health-seeking behaviour and rural/urban variation in Kazakhstan.

This paper seeks to analyse the findings of an extensive household survey, uncovering interesting evidence of variation in health-seeking behaviour across rural and urban areas due, it is suggested, to differences in real costs, quality of care, and perceptions of the value of health and health care. It is shown that, ceteris paribus, urban households in Kazakhstan are more likely to consult, to be admitted to hospital, to report illness, and will spend relatively more on health care. The data suggest the need for further qualitative study into the factors underlying these patterns leading to strategies to increase the quality, acceptability and affordability of rural health services. This is important given the decline in health indicators such as life expectancy in Kazakhstan and the increased burden on households of funding health care in a time of economic insecurity and deterioration of public services.