MDM Policy and Practice最新文献

Background. Modeled evidence is a proven useful tool for decision makers in making evidence-based policies and plans that will ensure the best possible health system outcomes. Thus, we sought to understand constraints to the use of models in making decisions in Nigeria's health system and how such constraints can be addressed. Method. We adopted a mixed-methods study for the research and relied on the evidence to policy and Knowledge-to-Action (KTA) frameworks to guide the conceptualization of the study. An online survey was administered to 34 key individuals in health organizations that recognize modeling, which was followed by in-depth interviews with 24 of the 34 key informants. Analysis was done using descriptive analytic methods and thematic arrangements of narratives. Results. Overall, the data revealed poor use of modeled evidence in decision making within the health sector, despite reporting that modeled evidence and modelers are available in Nigeria. However, the disease control agency in Nigeria was reported to be an exception. The complexity of models was a top concern. Thus, suggestions were made to improve communication of models in ways that are easily comprehensible and to improve overall research culture within Nigeria's health sector. Conclusion. Modeled evidence plays a crucial role in evidence-based health decisions. Therefore, it is imperative to strengthen and sustain in-country capacity to value, produce, interpret, and use modeled evidence for decision making in health. To overcome limitations in the usage of modeled evidence, decision makers, modelers/researchers, and knowledge brokers should forge viable relationships that regard and promote evidence translation.

Highlights: Despite the use of modeling by Nigeria's disease control agency in containing the COVID-19 pandemic, modeling remains poorly used in the country's overall health sector.Although policy makers recognize the importance of evidence in making decisions, there are still pertinent concerns about the poor research culture of policy-making institutions and communication gaps that exist between researchers/modelers and policy makers.Nigeria's health system can be strengthened by improving the value and usage of scientific evidence generation through conscious efforts to institutionalize research culture in the health sector and bridge gaps between researchers/modelers and decision makers.

Background. Infectious diseases constitute a significant concern worldwide due to their increasing prevalence, associated health risks, and the socioeconomic costs. Machine learning (ML) models and epidemic models formulated using deterministic differential equations are the most dominant tools for analyzing and modeling the transmission of infectious diseases. However, ML models can be inconsistent in extracting the dynamics of a disease in the presence of data drifts. Likewise, the capability of epidemic models is constrained to parameter dimensions and estimation. We aimed at creating a framework of informed ML that integrates a random forest (RF) with an adapted susceptible infectious recovered (SIR) model to account for accuracy and consistency in stochasticity within the dynamics of coronavirus disease 2019 (COVID-19). Methods. An adapted SIR model was used to inform a default RF on predicting new COVID-19 cases (NCCs) at given intervals. We validated the performance of the informed RF (IRF) using real data. We used Botswana's pharmaceutical interventions (PIs) and non-PIs (NPIs) adopted between February 2020 and August 2022. The discrepancy between predictions and observations is modeled using loss functions, which are minimized, interpreted, and used to assess the IRF. Results. The findings on the real data have revealed the effectiveness of the default RF in modeling and predicting NCCs. The use of the effective reproductive rate to inform the RF yielded an excellent predictive power (84%) compared with 75% by the default RF. Conclusion. This research has potential to inform policy and decision makers in developing systems to evaluate interventions for infectious diseases.

Highlights: This framework is initiated by incorporating model outputs from an epidemic model to a machine learning model.An informed random forest (RF) is instantiated to model government and public responses to the COVID-19 pandemic.This framework does not require data transformations, and the epidemic model is shown to boost the RF's performance.This is a baseline knowledge-informed learning framework for assessing public health interventions in Botswana.

Background. This study aimed to estimate the health care expenditure for managing type 2 diabetes (T2D) in the community setting of Nepal. Methods. This is a baseline cross-sectional study of a heath behavior intervention that was conducted between September 2021 and February 2022 among patients with T2D (N = 481) in the Kavrepalanchok and Nuwakot districts of Nepal. Bottom-up and micro-costing approaches were used to estimate the health care costs and were stratified according to residential status and the presence of comorbid conditions. A generalized linear model with a log-link and gamma distribution was applied for modeling the continuous right-skewed costs, and 95% confidence intervals were obtained from 10,000 bootstrapping resampling techniques. Results. Over 6 months the mean health care resource cost to manage T2D was US $22.87 per patient: 61% included the direct medical cost (US $14.01), 15% included the direct nonmedical cost (US $3.43), and 24% was associated with productivity losses (US $5.44). The mean health care resource cost per patient living in an urban community (US $24.65) was about US $4.95 higher than patients living in the rural community (US $19.69). The health care costs per patient with comorbid conditions was US $22.93 and was US $22.81 for those without comorbidities. Patients living in rural areas had 16% lower health care expenses compared with their urban counterparts. Conclusion. T2D imposes a substantial financial burden on both the health care system and individuals. There is a need to establish high-value care treatment strategies for the management of T2D to reduce the high health care expenses.

Highlights: More than 60% of health care expenses comprise the direct medical cost, 15% direct nonmedical cost, and 24% patient productivity losses. The costs of diagnosis, hospitalization, and recommended foods were the main drivers of health care costs for managing type 2 diabetes.Health care expenses among patients living in urban communities and patients with comorbid conditions was higher compared with those in rural communities and those with without comorbidities.The results of this study are expected to help integrate diabetes care within the existing primary health care systems, thereby reducing health care expenses and improving the quality of diabetes care in Nepal.

Objective. To conduct cost-utility analyses for Computed Tomography To Strength (CT2S), a novel osteoporosis screening service, compared with dual-energy X-ray absorptiometry (DXA), treat all without screening, and no screening methods for Dutch postmenopausal women referred to fracture liaison service (FLS). CT2S uses CT scans to generate femur models and simulate sideways fall scenarios for bone strength assessment. Methods. Early health technology assessment (HTA) was adopted to evaluate CT2S as a novel osteoporosis screening tool for secondary fracture prevention. We constructed a 2-dimensional simulation model considering 4 strategies (no screening, treat all without screening, DXA, CT2S) together with screening intervals (5 y, 2 y), treatments (oral alendronate, zoledronic acid), and discount rate scenarios among Dutch women in 3 age groups (60s, 70s, and 80s). Strategy comparisons were based on incremental cost-effectiveness ratios (ICERs), considering an ICER below €20,000 per QALY gained as cost-effective in the Netherlands. Results. Under the base-case scenario, CT2S versus DXA had estimated ICERs of €41,200 and €14,083 per QALY gained for the 60s and 70s age groups, respectively. For the 80s age group, CT2S was more effective and less costly than DXA. Changing treatment from weekly oral alendronate to annual zoledronic acid substantially decreased CT2S versus DXA ICERs across all age groups. Setting the screening interval to 2 y increased CT2S versus DXA ICERs to €100,333, €55,571, and €15,750 per QALY gained for the 60s, 70s, and 80s age groups, respectively. In all simulated populations and scenarios, CT2S was cost-effective (in some cases dominant) compared with the treat all strategy and cost-saving (more effective and less costly) compared with no screening. Conclusion. CT2S was estimated to be potentially cost-effective in the 70s and 80s age groups considering the willingness-to-pay threshold of the Netherlands. This early HTA suggests CT2S as a potential novel osteoporosis screening tool for secondary fracture prevention.

Highlights: For postmenopausal Dutch women who have been referred to the FLS, direct access to CT2S may be cost-effective compared with DXA for age groups 70s and 80s, when considering the ICER threshold of the Netherlands. This study positions CT2S as a potential novel osteoporosis-screening tool for secondary fracture prevention in the clinical setting.A shorter screening interval of 2 y increases the effectiveness of both screening strategies, but the ICER of CT2S compared with DXA also increased substantially, which made CT2S no longer cost-effective for the 70s age group; however, it remains cost-effective for individuals in their 80s.Annual zoledronic acid treatment with better adherence may contribute to a lower cost-effectiveness ratio when comparing CT2S to DXA screening and the treat all strategies for all age groups.

Background. While nerve and tendon transfer surgery can restore upper extremity function and independence after midcervical spinal cord injury, few individuals (∼14%) undergo surgery. There is limited information regarding these complex and time-sensitive treatment options. Patient decision aids (PtDAs) convey complex health information and help individuals make informed, preference-consistent choices. The purpose of this study is to evaluate a newly created PtDA for people with spinal cord injury who are considering options to optimize upper extremity function. Methods. The PtDA was developed by our multidisciplinary group based on clinical evidence and the Ottawa Decision Support Framework. A prospective pilot study enrolled adults with midcervical spinal cord injury to evaluate the PtDA. Participants completed surveys about knowledge and decisional conflict before and after viewing the PtDA. Acceptability measures and suggestions for further improvement were also solicited. Results. Forty-two individuals were enrolled and completed study procedures. Participants had a 20% increase in knowledge after using the PtDA (P < 0.001). The number of participants experiencing decisional conflict decreased after viewing the PtDA (33 v. 18, P = 0.001). Acceptability was high. To improve the PtDA, participants suggested adding details about specific surgeries and outcomes. Limitations. Due to the COVID-19 pandemic, we used an entirely virtual study methodology and recruited participants from national networks and organizations. Most participants were older than the general population with a new spinal cord injury and may have different injury causes than typical surgical candidates. Conclusions. A de novo PtDA improved knowledge of treatment options and reduced decisional conflict about reconstructive surgery among people with cervical spinal cord injury. Future work should explore PtDA use for improving knowledge and decisional conflict in the nonresearch, clinical setting.

Highlights: People with cervical spinal cord injury prioritize gaining upper extremity function after injury, but few individuals receive information about treatment options.A newly created patient decision aid (PtDA) provides information about recovery after spinal cord injury and the role of traditional tendon and newer nerve transfer surgery to improve upper extremity upper extremity function.The PtDA improved knowledge and decreased decisional conflict in this pilot study.Future work should focus on studying dissemination and implementation of the ptDA into clinical practice.

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In the context of historic reckoning with the role of the criminal-legal system as a structural driver of health harms, there is mounting evidence that punitive drug policies have failed to prevent problematic drug use while fueling societal harms. In this explainer article, we discuss how simulation modeling provides a methodological framework to explore the potential outcomes (beneficial and harmful) of various drug policy alternatives, from incremental to radical. We discuss potential simulation modeling opportunities while calling for a more active role of simulation modeling in visioning and operationalizing transformative change.

Highlights: This article discusses opportunities for simulation modeling in projecting health and economic impacts (beneficial and harmful) of drug-related criminal justice reforms.We call on modelers to explore radical interventions to reduce drug-related harm and model grand alternative futures in addition to more probable scenarios, with a goal of opening up policy discourse to these options.

Background. To support proactive decision making during the COVID-19 pandemic, mathematical models have been leveraged to identify surveillance indicator thresholds at which strengthening nonpharmaceutical interventions (NPIs) is necessary to protect health care capacity. Understanding tradeoffs between different adaptive COVID-19 response components is important when designing strategies that balance public preference and public health goals. Methods. We considered 3 components of an adaptive COVID-19 response: 1) the threshold at which to implement the NPI, 2) the time needed to implement the NPI, and 3) the effectiveness of the NPI. Using a compartmental model of SARS-CoV-2 transmission calibrated to Minnesota state data, we evaluated different adaptive policies in terms of the peak number of hospitalizations and the time spent with the NPI in force. Scenarios were compared with a reference strategy, in which an NPI with an 80% contact reduction was triggered when new weekly hospitalizations surpassed 8 per 100,000 population, with a 7-day implementation period. Assumptions were varied in sensitivity analysis. Results. All adaptive response scenarios substantially reduced peak hospitalizations relative to no response. Among adaptive response scenarios, slower NPI implementation resulted in somewhat higher peak hospitalization and a longer time spent under the NPIs than the reference scenario. A stronger NPI response resulted in slightly less time with the NPIs in place and smaller hospitalization peak. A higher trigger threshold resulted in greater peak hospitalizations with little reduction in the length of time under the NPIs. Conclusions. An adaptive NPI response can substantially reduce infection circulation and prevent health care capacity from being exceeded. However, population preferences as well as the feasibility and timeliness of compliance with reenacting NPIs should inform response design.

Highlights: This study uses a mathematical model to compare different adaptive nonpharmaceutical intervention (NPI) strategies for COVID-19 management across 3 dimensions: threshold when the NPI should be implemented, time it takes to implement the NPI, and the effectiveness of the NPI.All adaptive NPI response scenarios considered substantially reduced peak hospitalizations compared with no response.Slower NPI implementation results in a somewhat higher peak hospitalization and longer time spent with the NPI in place but may make an adaptive strategy more feasible by allowing the population sufficient time to prepare for changing restrictions.A stronger, more effective NPI response results in a modest reduction in the time spent under the NPIs and slightly lower peak hospitalizations.A higher threshold for triggering the NPI delays the time at which the NPI starts but results in a higher peak hospitalization and does not substantially reduce the time the NPI remains in force.

Background. It is a patient's right to be included in decisions about their health care. Implementing shared decision making (SDM) is important to enable active communication between clinicians and patients. Although health policy makers are increasingly mandating SDM implementation, SDM adoption has been slow. This study explored stakeholders' organizational- and system-level barriers and facilitators to implementing policy mandated SDM in maternity care in Victoria, Australia. Method. Twenty-four semi-structured interviews were conducted with participants including clinicians, health service administrators and decision makers, and government policy makers. Data were mapped to the Theoretical Domains Framework to identify barriers and facilitators to SDM implementation. Results. Factors identified as facilitating SDM implementation included using a whole-of-system approach, providing additional implementation resources, correct documentation facilitated by electronic medical records, and including patient outcomes in measurement. Barriers included health service lack of capacity, unclear policy definitions of SDM, and policy makers' lack of resources to track implementation. Conclusion. This is the first study to our knowledge to explore barriers and facilitators to SDM implementation from the perspective of multiple actors following policy mandating SDM in tertiary health services in Australia. The primary finding was that there are concerns that SDM implementation policy is outpacing practice. Nonclinical staff play a crucial role translating policy to practice. Addressing organizational- and system-level barriers and facilitators to SDM implementation should be a key concern of health policy makers, health services, and staff.

Highlights: New government policies require shared decision making (SDM) implementation in hospitals.There is limited evidence for how to implement SDM in hospital settings.There are concerns SDM implementation policy is outpacing practice.Understanding and capacity for SDM varies considerably among stakeholders.Whole of system approaches and electronic medical records are seen to facilitate SDM.