Forest Recreational Services in the Face of COVID-19 Pandemic Stress

Forest ecosystems provide numerous services and benefits to both humans and biodiversity. Similarly, urban forests services play a vital role by providing urban dwellers with recreational and leisure space, mental health relief, and meditation. In the wake of the COVID-19 epidemic, many people living in the urban areas could benefit from the forest and park recreational services to relieve psychological stress due to lockdown rules. The study examined existing literature simultaneously; however, very few studies have presented the relationships between forest services’ role on COVID-19 stress relief. Furthermore, we examined forest visitors’ frequency at the Training Forest Enterprise (TFE) Masaryk Forest Křtiny in the outskirts of Brno City in the Czech Republic. The study collected data using a TRAFx infrared trail counter before the pandemic (2015–2018) and during the COVID-19 period (2021). As in other studies of the subject, we observed an increasing trend in forest visits during the COVID-19 lockdown in 2021, compared to the same months before the pandemic in 2016 and 2017. We recommend further research to focus on scientific analysis of the relationship between forest ecosystem services and COVID-19 stress and mental health. Moreover, given the spike in visitors during the COVID-19 lockdown in 2021 in March and April, our data provide evidence regarding the role of nature for relieving stress and supporting mental and physical health. Policy, decision-makers and medical advisors could use such data and study to guide future lockdowns and pandemic situations regarding nature and forest recreational use and importance.

Nanopore sequencing of SARS-CoV-2: Comparison of short and long PCR-tiling amplicon protocols

Surveillance of the SARS-CoV-2 variants including the quickly spreading mutants by rapid and near real-time sequencing of the viral genome provides an important tool for effective health policy decision making in the ongoing COVID-19 pandemic. Here we evaluated PCR-tiling of short (~400-bp) and long (~2 and ~2.5-kb) amplicons combined with nanopore sequencing on a MinION device for analysis of the SARS-CoV-2 genome sequences. Analysis of several sequencing runs demonstrated that using the long amplicon schemes outperforms the original protocol based on the 400-bp amplicons. It also illustrated common artefacts and problems associated with PCR-tiling approach, such as uneven genome coverage, variable fraction of discarded sequencing reads, including human and bacterial contamination, as well as the presence of reads derived from the viral sub-genomic RNAs.

Latent class growth modeling of depression and anxiety in older adults: an 8-year follow-up of a population-based study

Background Depression and anxiety are common mental health conditions in the older adult population. Understanding the trajectories of these will help implement treatments and interventions. Aims This study aims to identify depression and anxiety trajectories in older adults, evaluate the interrelationship of these conditions, and recognize trajectory-predicting characteristics. Methods Group-based dual trajectory modeling (GBDTM) was applied to the data of 3983 individuals, aged 65 years or older who participated in the Korean Health Panel Study between 2008 and 2015. Logistic regression was used to identify the association between characteristics and trajectory groups. Results Four trajectory groups from GBDTM were identified within both depression and anxiety outcomes. Depression outcome fell into “low-flat (87.0%)”, “low-to-middle (8.8%)”, “low-to-high (1.3%)” and “high-stable (2.8%)” trajectory groups. Anxiety outcome fell into “low-flat (92.5%)”, “low-to-middle (4.7%)”, “high-to-low (2.2%)” and “high-curve (0.6%)” trajectory groups. Interrelationships between depression and anxiety were identified. Members of the high-stable depression group were more likely to have “high-to-low” or “high-curved” anxiety trajectories. Female sex, the presence of more than three chronic diseases, and being engaged in income-generating activity were significant predictors for depression and anxiety. Conclusions Dual trajectory analysis of depression and anxiety in older adults shows that when one condition is present, the probability of the other is increased. Sex, having more than three chronic diseases, and not being involved in income-generating activity might increase risks for both depression and anxiety. Health policy decision-makers may use our findings to develop strategies for preventing both depression and anxiety in older adults.

System Architecture of A European Platform for Health Policy Decision Making: MIDAS (Preprint)

BACKGROUND Healthcare data is a rich yet underutilized resource due to its disconnected, heterogeneous nature. A means of connecting healthcare data and integrating it with additional open and social data in a secure way can support the monumental challenge policy-makers face in safely accessing all relevant data to assist in managing the health and wellbeing of all. OBJECTIVE The goal of this study was to develop a novel health data platform within the MIDAS (Meaningful Integration of Data Analytics and Services) project, that harnesses the potential of latent healthcare data in combination with open and social data to support evidence-based health policy decision-making in a privacy-preserving manner. METHODS The MIDAS platform was developed in an iterative and collaborative way with close involvement of academia, industry, healthcare staff and policy-makers, to solve tasks including data storage, data harmonization, data analytics and visualizations, and open and social data analytics. The platform has been piloted and tested by health departments in four European countries, each focusing on different region-specific health challenges and related data sources. RESULTS A novel health data platform solving the needs of Public Health decision-makers was successfully implemented within the four pilot regions connecting heterogeneous healthcare datasets and open datasets and turning large amounts of previously isolated data into actionable information allowing for evidence-based health policy-making and risk stratification through the application and visualization of advanced analytics. CONCLUSIONS The MIDAS platform delivers a secure, effective and integrated solution to deal with health data, providing support for health policy decision-making, planning of public health activities and the implementation of the Health in All Policies approach. The platform has proven transferable, sustainable and scalable across policies, data and regions.

Prioritisation and design of clinical trials

Clinical trials require participation of numerous patients, enormous research resources and substantial public funding. Time-consuming trials lead to delayed implementation of beneficial interventions and to reduced benefit to patients. This manuscript discusses two methods for the allocation of research resources and reviews a framework for prioritisation and design of clinical trials. The traditional error-driven approach of clinical trial design controls for type I and II errors. However, controlling for those statistical errors has limited relevance to policy makers. Therefore, this error-driven approach can be inefficient, waste research resources and lead to research with limited impact on daily practice. The novel value-driven approach assesses the currently available evidence and focuses on designing clinical trials that directly inform policy and treatment decisions. Estimating the net value of collecting further information, prior to undertaking a trial, informs a decision maker whether a clinical or health policy decision can be made with current information or if collection of extra evidence is justified. Additionally, estimating the net value of new information guides study design, data collection choices, and sample size estimation. The value-driven approach ensures the efficient use of research resources, reduces unnecessary burden to trial participants, and accelerates implementation of beneficial healthcare interventions.

Governing by Polling Public Opinion: A Potentially Dangerous Idea for Our Healthcare System

Kaplan and Baron-Epel advanced the notion that findings from public surveys should inform health policy decision making with respect to funding allocation. This approach to governing can draw large support from the populace, legislators, and the academic community alike. Yet, it has the potential to undermine evidence-based health policy decision making. In this paper, I delineate six drawbacks and several related corollaries drawn from historical events that have occurred during the recent coronavirus pandemic. These examples illustrate the dire downstream consequences (e.g., disregard for the needs of minority groups; diminution of critical services not broadly supported by the public; promotion of fringe group or foreign actor agendas; advancement of poorly informed opinions; shift from a forward-thinking, proactive perspective to a retroactive one; and reliance on potentially biased estimates) that may follow if public surveys become embedded in healthcare policy decision making. Without solutions to the drawbacks delineated in this paper, health policy driven by public opinion is likely to cause more harm than good.

Impact of public health interventions to curb SARS-CoV-2 spread assessed by an evidence-educated Delphi panel and tailored SEIR model

Aim To use a Delphi-panel-based assessment of the effectiveness of different non-pharmaceutical interventions (NPI) in order to retrospectively approximate and to prospectively predict the SARS-CoV-2 pandemic progression via a SEIR model (susceptible, exposed, infectious, removed). Methods We applied an evidence-educated Delphi-panel approach to elicit the impact of NPIs on the SARS-CoV-2 transmission rate R0 in Germany. Effectiveness was defined as the product of efficacy and compliance. A discrete, deterministic SEIR model with time step of 1 day, a latency period of 1.8 days, duration of infectiousness of 5 days, and a share of the total population of 15% assumed to be protected by immunity was developed in order to estimate the impact of selected NPI measures on the course of the pandemic. The model was populated with the Delphi-panel results and varied in sensitivity analyses. Results Efficacy and compliance estimates for the three most effective NPIs were as follows: test and isolate 49% (efficacy)/78% (compliance), keeping distance 42%/74%, personal protection masks (cloth masks or other face masks) 33%/79%. Applying all NPI effectiveness estimates to the SEIR model resulted in a valid replication of reported occurrence of the German SARS-CoV-2 pandemic. A combination of four NPIs at consented compliance rates might curb the CoViD-19 pandemic. Conclusion Employing an evidence-educated Delphi-panel approach can support SARS-CoV-2 modelling. Future curbing scenarios require a combination of NPIs. A Delphi-panel-based NPI assessment and modelling might support public health policy decision making by informing sequence and number of needed public health measures.

Nanopore Sequencing of SARS-CoV-2: Comparison of Short and Long PCR-tiling Amplicon Protocols

Surveillance of the SARS-CoV-2 variants including the quickly spreading mutants by rapid and near real-time sequencing of the viral genome provides an important tool for effective health policy decision making in the ongoing COVID-19 pandemic. Here we evaluated PCR-tiling of short (~400-bp) and long (~2 and ~2.5-kb) amplicons combined with nanopore sequencing on a MinION device for analysis of the SARS-CoV-2 genome sequences. Analysis of several sequencing runs demonstrated that using the long amplicon schemes outperforms the original protocol based on the 400-bp amplicons. It also illustrated common artefacts and problems associated with this approach, such as uneven genome coverage, variable fraction of discarded sequencing reads, as well as the reads derived from the viral sub-genomic RNAs and/or human and bacterial contamination.

Introduction to the Special Issue on "Analysing the Politics of Health Policy Change in Low- and Middle-Income Countries: The HPA Fellowship Programme 2017-2019"

This special issue presents a set of seven Health Policy Analysis (HPA) papers that offer new perspectives on health policy decision-making and implementation. They present primary empirical work from four countries in Asia and Africa, as well as reviews of literature about a wider range of low- and middle-income country (LMIC) experience.

A Group-based Dual Trajectory of Depression and Anxiety in Older Adults: An 8-year Follow-up of Population-based Study

Background: Depression and anxiety are common mental health conditions for elderly population. Understanding the trajectory developments of them will help us implementing treatments and interventions.Aims: This study aims to identify depression and anxiety trajectories in the elderly, evaluate the interrelationship of these conditions, and recognize trajectory-predicting characteristics.Methods: Group-based dual trajectory modeling (GBDTM) was applied to the data of 3,983 individuals, aged 65 years or older who participated in the Korean Health Panel Study between 2008 and 2015. Logistic regression was used to identify the association between characteristics and trajectory groups.Results: Four trajectory groups from GBDTM were identified in both the depression and anxiety outcomes. Depression has: “low-flat (87.0%)”, “low-to-middle (8.8%)”, “low-to-high (1.3%)” and “high-stable (2.8%)” trajectory groups. Anxiety has: “low-flat (92.5%)”, “low-to-middle (4.7%)”, “high-to-low (2.2%)” and “high-curve (0.6%)” trajectory groups. Interrelationship between depression and anxiety were identified. Members of the high-stable depression group were more likely to have “high-to-low” or “high-curved” anxiety trajectories. Female sex, the presence of more than three chronic diseases, and having income generating activity were significant factors in depression and anxiety.Conclusions: Dual trajectory analysis of depression and anxiety in older adults shows that when one condition is present, the probability of the other is increased. Sex, having more chronic disease, and income generating activity might be at increased risks for both depression and anxiety. Health policy decision-makers can use our findings in developing strategies for prevention of both depression and anxiety in older adults.