Search

Abstract Polysomnography, the gold standard diagnostic tool in sleep medicine, is performed in an artificial environment. This might alter sleep and may not accurately reflect typical sleep patterns. While macro-structures are sensitive to environmental effects, micro-structures remain more stable. In this study we applied semi-automated algorithms to capture REM sleep without atonia (RSWA) and sleep spindles, comparing lab and home measurements. We analyzed 107 full-night recordings from 55 subjects: 24 healthy adults, 28 Parkinson’s disease patients (15 RBD), and three with isolated Rem sleep behavior disorder (RBD). Sessions were manually scored. An automatic algorithm for quantifying RSWA was developed and tested against manual scoring. RSWAi showed a 60% correlation between home and lab. RBD detection achieved 83% sensitivity, 79% specificity, and 81% balanced accuracy. The algorithm accurately quantified RSWA, enabling the detection of RBD patients. These findings could facilitate more accessible sleep testing, and provide a possible alternative for screening RBD.

Abstract Parkinson’s Disease is a progressive neurodegenerative disorder afflicting almost 12 million people. Increased understanding of its complex and heterogenous disease pathology, etiology and symptom manifestations has resulted in the need to design, capture and interrogate substantial clinical datasets. Herein we advocate how advances in the deployment of artificial intelligence models for Federated Data Analysis and Federated Learning can help spearhead coordinated and sustainable approaches to address this grand challenge.

Abstract Distinguishing Parkinson’s disease (PD) subgroups may be achieved by observing network responses to external stimuli. We compared TMS-evoked potential (TEP) measures from stimulation of bilateral motor cortex (M1), dorsolateral prefrontal cortex (DLPFC), and visual cortex (V1) between 62 PD patients (age: 69.9 ± 7.5) and 76 healthy controls (age: 69.2 ± 4.3) using a TMS–EEG protocol. TEP measures were analyzed using two-way ANCOVA adjusted for MOCA. PD patients were divided into tremor dominant (TD), non-tremor dominant (NTD) and rapid disease progression (RDP) subgroups. PD patients showed lower wide-waveform adherence (wWFA) (p = 0.025) and interhemispheric connectivity (IHCCONN) (p

Abstract Digital biomarkers that remotely monitor symptoms have the potential to revolutionize outcome assessments in future disease-modifying trials in Parkinson’s disease (PD), by allowing objective and recurrent measurement of symptoms and signs collected in the participant’s own living environment. This biomarker field is developing rapidly for assessing the motor features of PD, but the non-motor domain lags behind. Here, we systematically review and assess digital biomarkers under development for measuring non-motor symptoms of PD. We also consider relevant developments outside the PD field. We focus on technological readiness level and evaluate whether the identified digital non-motor biomarkers have potential for measuring disease progression, covering the spectrum from prodromal to advanced disease stages. Furthermore, we provide perspectives for future deployment of these biomarkers in trials. We found that various wearables show high promise for measuring autonomic function, constipation and sleep characteristics, including REM sleep behavior disorder. Biomarkers for neuropsychiatric symptoms are less well-developed, but show increasing accuracy in non-PD populations. Most biomarkers have not been validated for specific use in PD, and their sensitivity to capture disease progression remains untested for prodromal PD where the need for digital progression biomarkers is greatest. External validation in real-world environments and large longitudinal cohorts remains necessary for integrating non-motor biomarkers into research, and ultimately also into daily clinical practice.

Summary: The blood enzyme glutamate-oxaloacetate transaminase (GOT) has been postulated as an effective therapeutic to protect the brain during stroke. To demonstrate its potential clinical utility, a new human recombinant form of GOT (rGOT) was produced for medical use.We tested the pharmacokinetics and evaluated the protective efficacy of rGOT in rodent and non-human primate models that reflected clinical stroke conditions.We found that continuous intravenous administration of rGOT within the first 8 h after ischemic onset significantly reduced the infarct size in both severe (30%) and mild lesions (48%). Cerebrospinal fluid and proteomics analysis, in combination with positron emission tomography imaging, indicated that rGOT can reach the brain and induce cytoprotective autophagy and induce local protection by alleviating neuronal apoptosis.Our results suggest that rGOT can be safely used immediately in patients suspected of having a stroke. This study requires further validation in clinical stroke populations.

Abstract Step length is an important diagnostic and prognostic measure of health and disease. Wearable devices can estimate step length continuously (e.g., in clinic or real-world settings), however, the accuracy of current estimation methods is not yet optimal. We developed machine-learning models to estimate step length based on data derived from a single lower-back inertial measurement unit worn by 472 young and older adults with different neurological conditions, including Parkinson’s disease and healthy controls. Studying more than 80,000 steps, the best model showed high accuracy for a single step (root mean square error, RMSE = 6.08 cm, ICC(2,1) = 0.89) and higher accuracy when averaged over ten consecutive steps (RMSE = 4.79 cm, ICC(2,1) = 0.93), successfully reaching the predefined goal of an RMSE below 5 cm (often considered the minimal-clinically-important-difference). Combining machine-learning with a single, wearable sensor generates accurate step length measures, even in patients with neurologic disease. Additional research may be needed to further reduce the errors in certain conditions.

Abstract The term ‘perspective’ in the context of economic evaluations and costing studies in healthcare refers to the viewpoint that an analyst has adopted to define the types of costs and outcomes to consider in their studies. However, there are currently notable variations in terms of methodological recommendations, definitions, and applications of different perspectives, depending on the objective or intended user of the study. This can make it a complex area for stakeholders when interpreting these studies. Consequently, there is a need for a comprehensive overview regarding the different types of perspectives employed in such analyses, along with the corresponding implications of their use. This is particularly important, in the context of low-and-middle-income countries (LMICs), where practical guidelines may be less well-established and infrastructure for conducting economic evaluations may be more limited. This article addresses this gap by summarising the main types of perspectives commonly found in the literature to a broad audience (namely the patient, payer, health care providers, healthcare sector, health system, and societal perspectives), providing their most established definitions and outlining the corresponding implications of their uses in health economic studies, with examples particularly from LMIC settings. We then discuss important considerations when selecting the perspective and present key arguments to consider when deciding whether the societal perspective should be used. We conclude that there is no one-size-fits-all answer to what perspective should be used and the perspective chosen will be influenced by the context, policymakers'/stakeholders’ viewpoints, resource/data availability, and intended use of the analysis. Moving forward, considering the ongoing issues regarding the variation in terminology and practice in this area, we urge that more standardised definitions of the different perspectives and the boundaries between them are further developed to support future studies and guidelines, as well as to improve the interpretation and comparison of health economic evidence.

Alpha-synuclein (aSyn) aggregates in the central nervous system are the main pathological hallmark of Parkinson’s disease (PD). ASyn aggregates have also been detected in many peripheral tissues, including the skin, thus providing a novel and accessible target tissue for the detection of PD pathology. Still, a well-established validated quantitative biomarker for early diagnosis of PD that also allows for tracking of disease progression remains lacking. The main goal of this research was to characterize aSyn aggregates in skin biopsies as a comparative and quantitative measure for PD pathology. Using direct stochastic optical reconstruction microscopy (dSTORM) and computational tools, we imaged total and phosphorylated-aSyn at the single molecule level in sweat glands and nerve bundles of skin biopsies from healthy controls (HCs) and PD patients. We developed a user-friendly analysis platform that offers a comprehensive toolkit for researchers that combines analysis algorithms and applies a series of cluster analysis algorithms (i.e., DBSCAN and FOCAL) onto dSTORM images. Using this platform, we found a significant decrease in the ratio of the numbers of neuronal marker molecules to phosphorylated-aSyn molecules, suggesting the existence of damaged nerve cells in fibers highly enriched with phosphorylated-aSyn molecules. Furthermore, our analysis found a higher number of aSyn aggregates in PD subjects than in HC subjects, with differences in aggregate size, density, and number of molecules per aggregate. On average, aSyn aggregate radii ranged between 40 and 200 nm and presented an average density of 0.001–0.1 molecules/nm2. Our dSTORM analysis thus highlights the potential of our platform for identifying quantitative characteristics of aSyn distribution in skin biopsies not previously described for PD patients while offering valuable insight into PD pathology by elucidating patient aSyn aggregation status.

Abstract Background Creutzfeldt-Jakob Disease (CJD) is the most common prion disease in humans causing a rapidly progressive neurological decline and dementia and is invariably fatal. The familial forms (genetic CJD, gCJD) are caused by mutations in the PRNP gene encoding for the prion protein (PrP). In Israel, there is a large cluster of gCJD cases, carriers of an E200K mutation in the PRNP gene, and therefore the largest population of at-risk individuals in the world. The mutation is not necessarily sufficient for the formation and accumulation of the pathological prion protein (PrPsc), suggesting that other, genetic and non-genetic factors affect the age at symptoms onset. Here we present the protocol of a cross-sectional and longitudinal natural history study of gCJD patients and first-degree relatives of gCJD patients, aiming to identify biological markers of preclinical CJD and risk factors for phenoconversion. Methods The study has two groups: Patients diagnosed with gCJD, and first-degree healthy relatives (HR) (both carriers and non-carriers of the E200K mutation in the PRNP gene) of patients diagnosed with gCJD. At baseline, and at the end of every year, healthy participants are invited for an “in-depth” visit, which includes a clinical evaluation, blood and urine collection, gait assessment, brain MRI, lumbar puncture (LP), and Polysomnography (PSG). At 6 months from baseline, and then halfway through each year, participants are invited for a “brief” visit, which includes a clinical evaluation, short cognitive assessment, and blood and urine collection. gCJD patients will be invited for one “in-depth” visit, similar to the baseline visit of healthy relatives. Discussion This continuous follow-up of the participants and the frequent assessments will allow early identification and diagnosis in case of conversion into disease. The knowledge generated from this study is likely to advance the understanding of the underlying clinicopathological processes that occur at the very beginning of CJD, as well as potential genetic and environmental risk factors for the development of the disease, therefore advancing the development of safe and efficient interventions. Trial registration The study is an observational study. It has registered retrospectively in https://clinicaltrials.gov/ and has been assigned an identification number NCT05746715.

Abstract Treadmill training (TT) has been extensively used as an intervention to improve gait and mobility in patients with Parkinson’s disease (PD). Regional and global effects on brain activity could be induced through TT. Training effects can lead to a beneficial shift of interregional connectivity towards a physiological range. The current work investigates the effects of TT on brain activity and connectivity during walking and at rest by using both functional near-infrared spectroscopy and functional magnetic resonance imaging. Nineteen PD patients (74.0 ± 6.59 years, 13 males, disease duration 10.45 ± 6.83 years) before and after 6 weeks of TT, along with 19 age-matched healthy controls were assessed. Interregional effective connectivity (EC) between cortical and subcortical regions were assessed and its interrelation to prefrontal cortex (PFC) activity. Support vector regression (SVR) on the resting-state ECs was used to predict prefrontal connectivity. In response to TT, EC analysis indicated modifications in the patients with PD towards the level of healthy controls during walking and at rest. SVR revealed cerebellum related connectivity patterns that were associated with the training effect on PFC. These findings suggest that the potential therapeutic effect of training on brain activity may be facilitated via changes in compensatory modulation of the cerebellar interregional connectivity.

Abstract MRI was suggested as a promising method for the diagnosis and assessment of Parkinson’s Disease (PD). We aimed to assess the sensitivity of neuromelanin-MRI and T2* with radiomics analysis for detecting PD, identifying individuals at risk, and evaluating genotype-related differences. Patients with PD and non-manifesting (NM) participants [NM-carriers (NMC) and NM-non-carriers (NMNC)], underwent MRI and DAT-SPECT. Imaging-based metrics included 48 neuromelanin and T2* radiomics features and DAT-SPECT specific-binding-ratios (SBR), were extracted from several brain regions. Imaging values were assessed for their correlations with age, differences between groups, and correlations with the MDS-likelihood-ratio (LR) score. Several machine learning classifiers were evaluated for group classification. A total of 127 participants were included: 46 patients with PD (62.3 ± 10.0 years) [15:LRRK2-PD, 16:GBA-PD, and 15:idiopathic-PD (iPD)], 47 NMC (51.5 ± 8.3 years) [24:LRRK2-NMC and 23:GBA-NMC], and 34 NMNC (53.5 ± 10.6 years). No significant correlations were detected between imaging parameters and age. Thirteen MRI-based parameters and radiomics features demonstrated significant differences between PD and NMNC groups. Support-Vector-Machine (SVM) classifier achieved the highest performance (AUC = 0.77). Significant correlations were detected between LR scores and two radiomic features. The classifier successfully identified two out of three NMC who converted to PD. Genotype-related differences were detected based on radiomic features. SBR values showed high sensitivity in all analyses. In conclusion, neuromelanin and T2* MRI demonstrated differences between groups and can be used for the assessment of individuals at-risk in cases when DAT-SPECT can’t be performed. Combining neuromelanin and T2*-MRI provides insights into the pathophysiology underlying PD, and suggests that iron accumulation precedes neuromelanin depletion during the prodromal phase.

Background: The G2019S-LRRK2 gene mutation is a common cause of hereditary Parkinson’s disease (PD), associated with a higher frequency of the postural instability gait difficulty (PIGD) motor phenotype yet with preserved cognition. This study investigated neurophysiological changes during motor and cognitive tasks in PD patients with and without the G2019S-LRRK2 mutation. Methods: 33 iPD patients and 22 LRRK2-PD patients performed the visual Go/NoGo task (VGNG) during sitting (single-task) and walking (dual-task) while wearing a 64-channel EEG cap. Event-related potentials (ERP) from Fz and Pz, specifically N200 and P300, were extracted and analyzed to quantify brain activity patterns. Results: The LRRK2-PD group performed better in the VGNG than the iPD group (group*task; p = 0.05). During Go, the iPD group showed reduced N2 amplitude and prolonged N2 latency during walking, whereas the LRRK2-PD group showed only shorter latency (group*task p = 0.027). During NoGo, opposite patterns emerged; the iPD group showed reduced N2 and increased P3 amplitudes during walking while the LRRK2-PD group demonstrated increased N2 and reduced P3 (N2: group*task, p = 0.010, P3: group*task, p = 0.012). Conclusions: The LRRK2-PD group showed efficient early cognitive processes, reflected by N2, resulting in greater neural synchronization and prominent ERPs. These processes are possibly the underlying mechanisms for the observed better cognitive performance as compared to the iPD group. As such, future applications of intelligent medical sensing should be capable of capturing these electrophysiological patterns in order to enhance motor–cognitive functions.

Abstract Non-manifesting carriers (NMCs) of Parkinson’s disease (PD)-related mutations such as LRRK2 and GBA are at an increased risk for developing PD. Dopamine transporter (DaT)-spectral positron emission computed tomography is widely used for capturing functional nigrostriatal dopaminergic activity. However, it does not reflect other ongoing neuronal processes; especially in the prodromal stages of the disease. Resting-state fMRI (rs-fMRI) has been proposed as a mode for assessing functional alterations associated with PD, but its relation to dopaminergic deficiency remains unclear. We aimed to study the association between presynaptic striatal dopamine uptake and functional connectivity (FC) patterns among healthy first-degree relatives of PD patients with mutations in LRRK2 and GBA genes. N = 85 healthy first-degree subjects were enrolled and genotyped. All participants underwent DaT and rs-fMRI scans, as well as a comprehensive clinical assessment battery. Between-group differences in FC within striatal regions were investigated and compared with striatal binding ratios (SBR). N = 26 GBA-NMCs, N = 25 LRRK2-NMCs, and N = 34 age-matched nonmanifesting noncarriers (NM-NCs) were included in each study group based on genetic status. While genetically-defined groups were similar across clinical measures, LRRK2-NMCs demonstrated lower SBR in the right putamen compared with NM-NCs, and higher right putamen FC compared to GBA-NMCs. In this group, higher striatal FC was associated with increased risk for PD. The observed differential SBR and FC patterns among LRRK2-NMCs and GBA-NMCs indicate that DaTscan and FC assessments might offer a more sensitive prediction of the risk for PD in the pre-clinical stages of the disease.

Abstract Background Juvenile myoclonic epilepsy (JME) is characterized by generalized seizures. Nearly 30% of JME patients are drug‐resistant (DR‐JME), indicating a widespread cortical dysfunction. Walking is an important function that necessitates orchestrated coordination of frontocentral cortical regions. However, gait alterations in JME have been scarcely investigated. Our aim was to assess changes in gait and motor‐evoked responses in DR‐JME patients. Methods Twenty‐nine subjects (11 JME drug‐responder, 8 DR‐JME, and 10 healthy controls) underwent a gait analyses during usual walking and dual‐task walking. Later, subjects underwent 64‐channel EEG recordings while performing a simple motor task. We calculated the motor‐evoked current source densities (CSD) at a priori chosen cortical regions. Gait and CSD measures were compared between groups and tasks using mixed model analysis. Results DR‐JME patients demonstrated an altered gait pattern that included slower gait speed (p = .018), reduced cadence (p = .003), and smaller arm‐swing amplitude (p = .011). The DR‐JME group showed higher motor‐evoked CSD in the postcentral gyri compared to responders (p = .049) and both JME groups showed higher CSD in the superior frontal gyri compared to healthy controls (p |–0.494|, p

Stress tests, e.g., the cardiac stress test, are standard clinical screening tools aimed to unmask clinical pathology. As such stress tests indirectly measure physiological reserves. The term reserve has been developed to account for the dis-junction, often observed, between pathology and clinical manifestation. It describes a physiological capacity that is utilized in demanding situations. However, developing a new and reliable stress test based screening tool is complex, prolonged, and relies extensively on domain knowledge. We propose a novel distributional-free machine-learning framework, the Stress Test Performance Scoring (STEPS) framework, to model expected performance in a stress test. A performance scoring function is trained with measures taken during the performance in a given task while exploiting information regarding the stress test set-up and subjects' medical state. Multiple ways of aggregating performance scores at different stress levels are suggested and are examined with an extensive simulation study. When applied to a real-world data example, an AUC of 84.35[95%CI: 70.68 - 95.13] was obtained for the STEPS framework to distinguish subjects with neurodegeneration from controls. In summary, STEPS improved screening by exploiting existing domain knowledge and state-of-the-art clinical measures. The STEPS framework can ease and speed up the production of new stress tests.

Stress tests, e.g., the cardiac stress test, are standard clinical screening tools aimed to unmask clinical pathology. As such stress tests indirectly measure physiological reserves. The term reserve has been developed to account for the dis-junction, often observed, between pathology and clinical manifestation. It describes a physiological capacity that is utilized in demanding situations. However, developing a new and reliable stress test based screening tool is complex, prolonged, and relies extensively on domain knowledge. We propose a novel distributional-free machine–learning framework, the Stress Test Performance Scoring (STEPS) framework, to model expected performance in a stress test. A performance scoring function is trained with measures taken during the performance in a given task while exploiting information regarding the stress test set-up and subjects’ medical state. Multiple ways of aggregating performance scores at different stress levels are suggested and are examined with an extensive simulation study. When applied to a real-world data example, an AUC of 84.35[95%CI: 70.68 − 95.13] was obtained for the STEPS framework to distinguish subjects with neurodegeneration from controls. In summary, STEPS improved screening by exploiting existing domain knowledge and state-of-the-art clinical measures. The STEPS framework can ease and speed up the production of new stress tests.

Objectives: Disease transmission models are used in impact assessment and economic evaluations of infectious disease prevention and treatment strategies, prominently so in the COVID-19 response. These models rarely consider dimensions of equity relating to the differential health burden between individuals and groups. We describe concepts and approaches which are useful when considering equity in the priority setting process, and outline the technical choices concerning model structure, outputs, and data requirements needed to use transmission models in analyses of health equity. Methods: We reviewed the literature on equity concepts and approaches to their application in economic evaluation and undertook a technical consultation on how equity can be incorporated in priority setting for infectious disease control. The technical consultation brought together health economists with an interest in equity-informative economic evaluation, ethicists specialising in public health, mathematical modellers from various disease backgrounds, and representatives of global health funding and technical assistance organisations, to formulate key areas of consensus and recommendations. Results: We provide a series of recommendations for applying the Reference Case for Economic Evaluation in Global Health to infectious disease interventions, comprising guidance on 1) the specification of equity concepts; 2) choice of evaluation framework; 3) model structure; and 4) data needs. We present available conceptual and analytical choices, for example how correlation between different equity- and disease-relevant strata should be considered dependent on available data, and outline how assumptions and data limitations can be reported transparently by noting key factors for consideration. Conclusions: Current developments in economic evaluations in global health provide a wide range of methodologies to incorporate equity into economic evaluations. Those employing infectious disease models need to use these frameworks more in priority setting to accurately represent health inequities. We provide guidance on the technical approaches to support this goal and ultimately, to achieve more equitable health policies.

Parkinson’s disease (PD) is a complex neurodegenerative disorder characterized by motor symptoms such as bradykinesia, rigidity, and resting tremor. While the majority of PD cases are sporadic, approximately 15–20% of cases have a genetic component. Advances in neuroimaging techniques have provided valuable insights into the pathophysiology of PD, including the different genetic forms of the disease. This literature review aims to summarize the current state of knowledge regarding neuroimaging findings in genetic PD, focusing on the most prevalent known genetic forms: mutations in the GBA1, LRRK2, and Parkin genes. In this review, we will highlight the contributions of various neuroimaging modalities, including positron emission tomography (PET), single-photon emission computed tomography (SPECT), and magnetic resonance imaging (MRI), in elucidating the underlying pathophysiological mechanisms and potentially identifying candidate biomarkers for genetic forms of PD.

IntroductionAging affects the interplay between cognition and gait performance. Neuroimaging studies reported associations between gait performance and structural measures; however, functional connectivity (FC) analysis of imaging data can help to identify dynamic neural mechanisms underlying optimal performance. Here, we investigated the effects on divergent cognitive and inter-network FC patterns underlying gait performance during usual (UW) and dual-task (DT) walking.MethodsA total of 115 community-dwelling, healthy participants between 20 and 80 years were enrolled. All participants underwent comprehensive cognitive and gait assessments in two conditions and resting state functional MRI (fMRI) scans. Inter-network FC from motor-related to 6 primary cognitive networks were estimated. Step-wise regression models tested the relationships between gait parameters, inter-network FC, neuropsychological scores, and demographic variables. A threshold of p < 0.05 was adopted for all statistical analyses.ResultsUW was largely associated with FC levels between motor and sustained attention networks. DT performance was associated with inter-network FC between motor and divided attention, and processing speed in the overall group. In young adults, UW was associated with inter-network FC between motor and sustained attention networks. On the other hand, DT performance was associated with cognitive performance, as well as inter-network connectivity between motor and divided attention networks (VAN and SAL). In contrast, the older age group (> 65 years) showed increased integration between motor, dorsal, and ventral attention, as well as default-mode networks, which was negatively associated with UW gait performance. Inverse associations between motor and sustained attention inter-network connectivity and DT performance were observed.ConclusionWhile UW relies on inter-network FC between motor and sustained attention networks, DT performance relies on additional cognitive capacities, increased motor, and executive control network integration. FC analyses demonstrate that the decline in cognitive performance with aging leads to the reliance on additional neural resources to maintain routine walking tasks.

Objectives The implementation of COVID-19 vaccination globally poses unprecedented stress to health systems particularly for countries with persisting health workforce shortages prior the pandemic. The present paper estimates the workforce requirement to reach 70% COVID-19 vaccination coverage in all countries by mid-2022 using service target–based estimation.Methods Health workforce data from National Health Workforce Accounts and vaccination coverage reported to WHO as of January 2022 were used. Workload parameters were used to estimate the number of health workers needed with a service target–based approach, the gap and the scale-up required partially accounting for countries’ challenges, as well as the associated costs in human resources.Results As of 1 January 2022, only 34 countries achieved 70% COVID-19 vaccination coverage and 61 countries covered less than a quarter of their population. This analysis showed that 1 831 000 health workers working full time would be needed to reach a global coverage of 70% COVID-19 vaccination by mid-2022. To avoid severe disruptions to health system, 744 000 additional health workers should be added to domestic resources mostly (77%) in low-income countries. In a sensitivity analysis, allowing for vaccination over 12 months instead of 6 months would decrease the scale-up to 476 000 health workers. The costing for the employment of these 744 000 additional health workers is estimated to be US$2.5 billion. In addition to such a massive scale-up, it is estimated that 29 countries would have needed to redeploy more than 20% of their domestic workforce, placing them at serious risk of not achieving the mid-year target.Conclusion Reaching 70% global coverage with COVID-19 vaccination by mid-2022 requires extraordinary efforts not before witnessed in the history of immunisation programmes. COVID-19 vaccination programmes should receive rapid and sustainable investment in health workforce.

Abstract The cortical control of gait and mobility involves multiple brain regions. Therefore, one could speculate that the association between specific spatial patterns of cortical thickness may be differentially associated with different mobility domains. To test this possibility, 115 healthy participants aged 27–82 (mean 60.5 ± 13.8) underwent a mobility assessment (usual-walk, dual-task walk, Timed Up and Go) and MRI scan. Ten mobility domains of relatively simple (e.g., usual-walking) and complex tasks (i.e., dual task walking, turns, transitions) and cortical thickness of 68 ROIs were extracted. All associations between mobility and cortical thickness were controlled for age and gender. Scaled Subprofile Modelling (SSM), a PCA-regression, identified thickness patterns that were correlated with the individual mobility domains, controlling for multiple comparisons. We found that lower mean global cortical thickness was correlated with worse general mobility (r = − 0.296, p = 0.003), as measured by the time to complete the Timed Up and Go test. Three distinct patterns of cortical thickness were associated with three different gait domains during simple, usual-walking: pace, rhythm, and symmetry. In contrast, cortical thickness patterns were not related to the more complex mobility domains. These findings demonstrate that robust and topographically distinct cortical thickness patterns are linked to select mobility domains during relatively simple walking, but not to more complex aspects of mobility. Functional connectivity may play a larger role in the more complex aspects of mobility.

Abstract Background The performance of a secondary task while walking increases motor-cognitive interference and exacerbates fall risk in older adults. Previous studies have demonstrated that transcranial direct current stimulation (tDCS) may improve certain types of dual-task performance, and, that tDCS delivered during the performance of a task may augment the benefits of stimulation, potentially reducing motor-cognitive interference. However, it is not yet known if combining multi-target tDCS with the simultaneous performance of a task related to the tDCS targets reduces or increases dual-task walking costs among older adults. The objectives of the present work were (1) To examine whether tDCS applied during the performance of a task that putatively utilizes the brain networks targeted by the neuro-stimulation reduces dual-task costs, and (2) to compare the immediate after-effects of tDCS applied during walking, during seated-rest, and during sham stimulation while walking, on dual-task walking costs in older adults. We also explored the impact on postural sway and other measures of cognitive function. Methods A double-blind, ‘within-subject’ cross-over pilot study evaluated the effects of 20 min of anodal tDCS targeting both the primary motor cortex (M1) and the left dorsolateral prefrontal cortex (lDLPFC) in 25 healthy older adults (73.9 ± 5.2 years). Three stimulation conditions were assessed in three separate sessions: (1) tDCS while walking in a complex environment (tDCS + walking), (2) tDCS while seated (tDCS + seated), and (3) walking in a complex environment with sham tDCS (sham + walking). The complex walking condition utilized virtual reality to tax motor and cognitive abilities. During each session, usual-walking, dual-task walking, quiet standing sway, and cognitive function (e.g., Stroop test) were assessed before and immediately after stimulation. Dual-task costs to gait speed and other measures were computed. Results The dual-task cost to gait speed was reduced after tDCS + walking (p = 0.004) as compared to baseline values. Neither tDCS + seated (p = 0.173) nor sham + walking (p = 0.826) influenced this outcome. Similar results were seen for other gait measures and for Stroop performance. Sway was not affected by tDCS. Conclusions tDCS delivered during the performance of challenging walking decreased the dual-task cost to walking in older adults when they were tested just after stimulation. These results support the existence of a state-dependent impact of neuro-modulation that may set the stage for a more optimal neuro-rehabilitation. Trial registration: Clinical Trials Gov Registrations Number: NCT02954328.

IntroductionTranscranial direct current stimulation (tDCS) targeting the left dorsolateral prefrontal cortex (dlPFC) improves dual task walking in older adults, when tested just after stimulation. The acute effects of tDCS on the cortical physiology of walking, however, remains unknown.MethodsIn a previous study, older adults with slow gait and executive dysfunction completed a dual task walking assessment before and after 20 min of tDCS targeting the left dlPFC or sham stimulation. In a subset of seven participants per group, functional near-infrared spectroscopy (fNIRS) was used to quantify left and right prefrontal recruitment defined as the oxygenated hemoglobin response to usual and dual task walking (ΔHbO2), as well as the absolute change in this metric from usual to dual task conditions (i.e., ΔHbO2cost). Paired t-tests examined pre- to post-stimulation differences in each fNIRS metric within each group.ResultsThe tDCS group exhibited pre- to post-stimulation reduction in left prefrontal ΔHbO2cost (p = 0.03). This mitigation of dual task “cost” to prefrontal recruitment was induced primarily by a reduction in left prefrontal ΔHbO2 specifically within the dual task condition (p = 0.001), an effect that was observed in all seven participants within this group. Sham stimulation did not influence ΔHbO2cost or ΔHbO2 in either walking condition (p > 0.35), and neither tDCS nor sham substantially influenced right prefrontal recruitment (p > 0.16).DiscussionThis preliminary fNIRS data suggests that tDCS over the left dlPFC may modulate prefrontal recruitment, as reflected by a relative reduction in the oxygen consumption of this brain region in response to dual task walking.

Summary: Background: Investing in health emergency preparedness is critical to the safety, welfare and stability of communities and countries worldwide. Despite the global push to increase investments, questions remain around how much should be spent and what to focus on. We conducted a systematic review and analysis of studies that costed improvements to health emergency preparedness to help to answer these questions. Methods: We searched for studies that estimated the costs of improving health emergency preparedness and that were published between 1 January 2000 and 14 May 2021, using PubMed, Web of Science, Google Scholar, EconLit, and National Health Service Economic Evaluation Databases (PROSPERO CRD42021254428). We also searched grey literature repositories and contacted subject experts. We included studies that estimated the costs of improving preparedness at the global level and/or at the national level across at least ten countries, covered two or more technical areas in the WHO Benchmarks for International Health Regulations (IHR) Capacities, and included activities focused on human health. We mapped costs across technical areas in the WHO Benchmarks for IHR Capacities. Findings: Ten studies met our inclusion criteria. Costing methods varied substantially across included studies and cost estimates ranged from US$1·6 billion per year to improve capacities across 139 low- and middle-income countries (LMICs) to US$43 billion per year to support national-level activities worldwide and implement global-level initiatives, such as research and development for health technologies (diagnostics, therapeutics, and vaccines). Two recent studies estimated costs by drawing on IHR Monitoring and Evaluation Framework country capacity data, with one study estimating costs across 67 LMICs of US$15·4 billion per year (US$29·1 billion including upfront capital costs) and the other calculating costs for the 196 States Parties to the IHR of US$24·8 billion per year. Differences in included studies’ methods, and the characteristics of countries considered, mean it is difficult to make like–for–like comparisons of the absolute costs or per-capita costs estimated by studies. Interpretation: Improving health emergency preparedness worldwide will require substantial and sustained increases in investments. Further guidance on estimating the size of those investments can help to standardise methods, allowing greater interpretation and comparison across studies/countries. As well as greater transparency and detail in the reporting of methods by studies focused on this topic, this can help support estimates of global resource requirements and facilitate investments towards improving preparedness for future pandemics. Funding: None.

Juvenile myoclonic epilepsy (JME) is one of the most common epileptic syndromes; it is estimated to affect 1 in 1,000 people worldwide. Most people with JME respond well to medication, but up to 30% of them are drug-resistant. To date, there are no biomarkers for drug resistance in JME, and the poor response to medications is identified in retrospect. People with JME have frontal dysfunction manifested as impaired attention and difficulties in inhibiting habitual responses and these dysfunctions are more pronounced in drug-resistant individuals. Frontal networks play an important role in walking and therefore, gait can be used to overload the neural system and expose subtle changes between people with drug-responsive and drug-resistant JME. Electroencephalogram (EEG) is a promising tool to explore neural changes during real-time functions that combine a cognitive task while walking (dual tasking, DT). This exploratory study aimed to examine the alteration in electrical brain activity during DT in people with drug-responsive and drug-resistant JME. A total of 32 subjects (14 males and 18 females) participated: 11 drug-responsive (ages: 31.50 ± 1.50) and 8 drug-resistant (27.27 ± 2.30) people with JME, and 13 healthy controls (29.46 ± 0.69). The participants underwent EEG examination during the performance of the visual Go/NoGo (vGNG) task while sitting and while walking on a treadmill. We measured latencies and amplitudes of N2 and P3 event-related potentials, and the cognitive performance was assessed by accuracy rate and response time of Go/NoGo events. The results demonstrated that healthy controls had earlier N2 and P3 latencies than both JME groups (N2: p = 0.034 and P3: p = 0.011), however, a limited ability to adjust the N2 amplitude during walking was noticeable in the drug-resistant compared to drug-responsive. The two JME groups had lower success rates (drug-responsive p < 0.001, drug-resistant p = 0.004) than healthy controls, but the drug-resistant showed longer reaction times compared to both healthy controls (p = 0.033) and drug-responsive (p = 0.013). This study provides the first evidence that people with drug-resistant JME have changes in brain activity during highly demanding tasks that combine cognitive and motor functions compared to people with drug-responsive JME. Further research is needed to determine whether these alterations can be used as biomarkers to drug response in JME.

Background The development of optimal strategies to treat impaired mobility related to ageing and chronic disease requires better ways to detect and measure it. Digital health technology, including body worn sensors, has the potential to directly and accurately capture real-world mobility. Mobilise-D consists of 34 partners from 13 countries who are working together to jointly develop and implement a digital mobility assessment solution to demonstrate that real-world digital mobility outcomes have the potential to provide a better, safer, and quicker way to assess, monitor, and predict the efficacy of new interventions on impaired mobility. The overarching objective of the study is to establish the clinical validity of digital outcomes in patient populations impacted by mobility challenges, and to support engagement with regulatory and health technology agencies towards acceptance of digital mobility assessment in regulatory and health technology assessment decisions. Methods/design The Mobilise-D clinical validation study is a longitudinal observational cohort study that will recruit 2400 participants from four clinical cohorts. The populations of the Innovative Medicine Initiative-Joint Undertaking represent neurodegenerative conditions (Parkinson’s Disease), respiratory disease (Chronic Obstructive Pulmonary Disease), neuro-inflammatory disorder (Multiple Sclerosis), fall-related injuries, osteoporosis, sarcopenia, and frailty (Proximal Femoral Fracture). In total, 17 clinical sites in ten countries will recruit participants who will be evaluated every six months over a period of two years. A wide range of core and cohort specific outcome measures will be collected, spanning patient-reported, observer-reported, and clinician-reported outcomes as well as performance-based outcomes (physical measures and cognitive/mental measures). Daily-living mobility and physical capacity will be assessed directly using a wearable device. These four clinical cohorts were chosen to obtain generalizable clinical findings, including diverse clinical, cultural, geographical, and age representation. The disease cohorts include a broad and heterogeneous range of subject characteristics with varying chronic care needs, and represent different trajectories of mobility disability. Discussion The results of Mobilise-D will provide longitudinal data on the use of digital mobility outcomes to identify, stratify, and monitor disability. This will support the development of widespread, cost-effective access to optimal clinical mobility management through personalised healthcare. Further, Mobilise-D will provide evidence-based, direct measures which can be endorsed by regulatory agencies and health technology assessment bodies to quantify the impact of disease-modifying interventions on mobility. Trial registration ISRCTN12051706.

BackgroundThe development of optimal strategies to treat impaired mobility related to ageing and chronic disease requires better ways to detect and measure it. Digital health technology, including body worn sensors, has the potential to directly and accurately capture real-world mobility. Mobilise-D consists of 34 partners from 13 countries who are working together to jointly develop and implement a digital mobility assessment solution to demonstrate that real-world digital mobility outcomes have the potential to provide a better, safer, and quicker way to assess, monitor, and predict the efficacy of new interventions on impaired mobility. The overarching objective of the study is to establish the clinical validity of digital outcomes in patient populations impacted by mobility challenges, and to support engagement with regulatory and health technology agencies towards acceptance of digital mobility assessment in regulatory and health technology assessment decisions.Methods/designThe Mobilise-D clinical validation study is a longitudinal observational cohort study that will recruit 2400 participants from four clinical cohorts. The populations of the Innovative Medicine Initiative-Joint Undertaking represent neurodegenerative conditions (Parkinson's Disease), respiratory disease (Chronic Obstructive Pulmonary Disease), neuro-inflammatory disorder (Multiple Sclerosis), fall-related injuries, osteoporosis, sarcopenia, and frailty (Proximal Femoral Fracture). In total, 17 clinical sites in ten countries will recruit participants who will be evaluated every six months over a period of two years. A wide range of core and cohort specific outcome measures will be collected, spanning patient-reported, observer-reported, and clinician-reported outcomes as well as performance-based outcomes (physical measures and cognitive/mental measures). Daily-living mobility and physical capacity will be assessed directly using a wearable device. These four clinical cohorts were chosen to obtain generalizable clinical findings, including diverse clinical, cultural, geographical, and age representation. The disease cohorts include a broad and heterogeneous range of subject characteristics with varying chronic care needs, and represent different trajectories of mobility disability.DiscussionThe results of Mobilise-D will provide longitudinal data on the use of digital mobility outcomes to identify, stratify, and monitor disability. This will support the development of widespread, cost-effective access to optimal clinical mobility management through personalised healthcare. Further, Mobilise-D will provide evidence-based, direct measures which can be endorsed by regulatory agencies and health technology assessment bodies to quantify the impact of disease-modifying interventions on mobility.Trial registrationISRCTN12051706.

Treadmill training with virtual reality (TT + VR) has been shown to improve gait performance and to reduce fall risk in Parkinson’s disease (PD). However, there is no consensus on the optimal training duration. This study is a sub-study of the V-TIME randomized clinical trial (NCT01732653). In this study, we explored the effect of the duration of training based on the motor–cognitive interaction on motor and cognitive performance and on fall risk in subjects with PD. Patients in Hoehn and Yahr stages II–III, aged between 40 and 70 years, were included. In total, 96 patients with PD were assigned to 6 or 12 weeks of TT + VR intervention, and 77 patients completed the full protocol. Outcome measures for gait and cognitive performance were assessed at baseline, immediately after training, and at 1- and 6-month follow-up. The incident rate of falls in the 6-month pre-intervention was compared with that in the 6-month post-intervention. Dual-task gait performance (gait speed, gait speed variability and stride length under cognitive dual task and obstacle negotiation, and the leading foot clearance in obstacle negotiation) improved similarly in both groups with gains sustained at 6-month follow-up. A higher decrease in fall rate and fear of falling were observed in participants assigned to the 12-week intervention than the 6-week intervention. Improvements in cognitive functions (i.e., executive functions, visuospatial ability, and attention) were seen only in participants enrolled in 12-week training up to 1-month follow-up but vanished at the 6-month evaluation. Our results suggest that a longer TT + VR training leads to greater improvements in cognitive functions especially those directly addressed by the virtual environment.

Background: Parkinson’s disease (PD) is currently considered to be a multisystem neurodegenerative disease that involves cognitive alterations. EEG slowing has been associated with cognitive decline in various neurological diseases, such as PD, Alzheimer’s disease (AD), and epilepsy, indicating cortical involvement. A novel method revealed that this EEG slowing is composed of paroxysmal slow-wave events (PSWE) in AD and epilepsy, but in PD it has not been tested yet. Therefore, this study aimed to examine the presence of PSWE in PD as a biomarker for cortical involvement. Methods: 31 PD patients, 28 healthy controls, and 18 juvenile myoclonic epilepsy (JME) patients (served as positive control), underwent four minutes of resting-state EEG. Spectral analyses were performed to identify PSWEs in nine brain regions. Mixed-model analysis was used to compare between groups and brain regions. The correlation between PSWEs and PD duration was examined using Spearman’s test. Results: No significant differences in the number of PSWEs were observed between PD patients and controls (p > 0.478) in all brain regions. In contrast, JME patients showed a higher number of PSWEs than healthy controls in specific brain regions (p < 0.023). Specifically in the PD group, we found that a higher number of PSWEs correlated with longer disease duration. Conclusions: This study is the first to examine the temporal characteristics of EEG slowing in PD by measuring the occurrence of PSWEs. Our findings indicate that PD patients who are cognitively intact do not have electrographic manifestations of cortical involvement. However, the correlation between PSWEs and disease duration may support future studies of repeated EEG recordings along the disease course to detect early signs of cortical involvement in PD.

(1) Background: Directed functional connectivity (DFC) alterations within brain networks are described using fMRI. EEG has been scarcely used. We aimed to explore changes in DFC in the sensory-motor network (SMN), ventral-attention network (VAN), dorsal-attention network (DAN), and central-executive network (CEN) using an EEG-based mapping between PD patients and healthy controls (HCs). (2) Methods: Four-minutes resting EEG was recorded from 29 PD patients and 28 HCs. Network’s hubs were defined using fMRI-based binary masks and their electrical activity was calculated using the LORETA. DFC between each network’s hub-pairs was calculated for theta, alpha and beta bands using temporal partial directed coherence (tPDC). (3) Results: tPDCs percent was lower in the CEN and DAN in PD patients compared to HCs, while no differences were observed in the SMN and VAN (group*network: F = 5.943, p < 0.001) in all bands (group*band: F = 0.914, p = 0.401). However, in the VAN, PD patients showed greater tPDCs strength compared to HCs (p < 0.001). (4) Conclusions: Our results demonstrated reduced connectivity in the CEN and DAN, and increased connectivity in the VAN in PD patients. These results indicate a complex pattern of DFC alteration within major brain networks, reflecting the co-occurrence of impairment and compensatory mechanisms processes taking place in PD.

Background: Chemotherapy-induced peripheral neuropathy (CIPN) is a common and dosage-limited oxaliplatin-related toxicity. To date, there are no successful interventions for CIPN prevention or treatment. A therapeutic role for cannabis in diabetic and HIV-related peripheral neuropathy and a protective role in CIPN have been suggested. We examined the effect of cannabis on oncologic patients with CIPN. Methods: Medical records of 768 consecutive patients treated with oxaliplatin and 5-fluorouracil-based combinations at a tertiary medical center from October 2015 to January 2018 were reviewed. Excluded patients were those with pre-existing neuropathy or patients who received fewer than two cycles of oxaliplatin treatment. CIPN grade, oxaliplatin cumulative dose, and neuropathy-free survival were evaluated. The patients were divided based upon the exposure to cannabis: prior to oxaliplatin (cannabis-first), cannabis following the initiation of oxaliplatin treatment (oxaliplatin-first), and no exposure (control). Results: In total, 513 patients met the inclusion criteria, of whom 248 were treated with cannabis and 265 served as controls. The cannabis-first group included 116 (46.7%) patients and the oxaliplatin-first group included 132 (53.3%) patients. Demographic parameters were comparable between groups. There was a significant difference in CIPN grade 2–3 between cannabis-exposed patients and controls (15.3% and 27.9%, respectively, p

The question of whether and how changes to population health impact on economic growth has been actively studied in the literature, albeit with mixed results. We contribute to this debate by reassessing-and extending-[1], one of the most influential studies. We include a larger set of countries (135) and cover a more recent period (1990-2014). We also account for morbidity in addition to mortality and adopt the strategy of providing bounding sets for the effects of interest rather than point estimates. We find that reducing mortality and disability adjusted life years (DALYs), a measure which combines morbidity and mortality, promotes per capita GDP growth. The magnitude of the effect is moderate, but non negligible, and it is similar for mortality and DALYs.

ObjectiveT1-weighted MRI images are commonly used for volumetric assessment of brain structures. Magnetization prepared 2 rapid gradient echo (MP2RAGE) sequence offers superior gray (GM) and white matter (WM) contrast. This study aimed to quantitatively assess the agreement of whole brain tissue and deep GM (DGM) volumes obtained from MP2RAGE compared to the widely used MP-RAGE sequence.MethodsTwenty-nine healthy participants were included in this study. All subjects underwent a 3T MRI scan acquiring high-resolution 3D MP-RAGE and MP2RAGE images. Twelve participants were re-scanned after one year. The whole brain, as well as DGM segmentation, was performed using CAT12, volBrain, and FSL-FAST automatic segmentation tools based on the acquired images. Finally, contrast-to-noise ratio between WM and GM (CNRWG), the agreement between the obtained tissue volumes, as well as scan-rescan variability of both sequences were explored.ResultsSignificantly higher CNRWG was detected in MP2RAGE vs. MP-RAGE (Mean ± SD = 0.97 ± 0.04 vs. 0.8 ± 0.1 respectively; pConclusionsDue to its higher CNR, MP2RAGE resulted in reproducible brain tissue segmentation, and thus is a recommended method for volumetric imaging biomarkers for the monitoring of neurological diseases.

Abstract Introduction: The GBA‐N370S mutation is one of the most frequent risk factors for dementia with Lewy bodies (DLB) and Parkinson's disease (PD). We looked for genetic variations that contribute to the outcome in N370S‐carriers, whether PD or DLB. Methods Whole‐genome sequencing of 95 Ashkenazi‐N370S‐carriers affected with either DLB (n = 19) or PD (n = 76) was performed, and 564 genes related to dementia and PD analyzed. Results We identified enrichment of linked alleles in PINK1 locus in DLB patients (false discovery rate P = .0412). Haplotype analysis delineated 1.8 Mb interval encompassing 29 genes and 87 unique variants, of them, KIF17‐R869C received the highest functional prediction score (Combined Annotation Dependent Depletion = 34). Its frequency was significantly higher in 26 DLB‐N370S‐carriers compared to 140 PD‐N370S‐carriers (odds ratio [OR] = 33.4 P = .001, and OR = 70.2 when only heterozygotes were included). Discussion Because KIF17 was shown to be important for learning and memory in mice, our data further suggest, for the first time, its involvement in DLB, and possibly in human dementia.

Public payers around the world are increasingly using cost-effectiveness thresholds (CETs) to assess the value-for-money of an intervention and make coverage decisions. However, there is still much confusion about the meaning and uses of the CET, how it should be calculated, and what constitutes an adequate evidence base for its formulation. One widely referenced and used threshold in the last decade has been the 1-3 GDP per capita, which is often attributed to the Commission on Macroeconomics and WHO guidelines on Choosing Interventions that are Cost Effective (WHO-CHOICE). For many reasons, however, this threshold has been widely criticised; which has led experts across the world, including the WHO, to discourage its use. This has left a vacuum for policy-makers and technical staff at a time when countries are wanting to move towards Universal Health Coverage. This article seeks to address this gap by offering five practical options for decision-makers in low- and middle-income countries that can be used instead of the 1-3 GDP rule, to combine existing evidence with fair decision-rules or develop locally relevant CETs. It builds on existing literature as well as an engagement with a group of experts and decision-makers working in low, middle and high income countries.

BackgroundThere are many health benefits since 31 years after the foundation of the National Health Service (NHS) in Brazil, especially the increase in life expectancy. However, family-income inequalities, insufficient funding, and suboptimal private sector–public sector collaboration are still areas for improvement. The efforts of Brazil to achieve universal health coverage (UHC) for medicines have resulted in increased public financing of medicines and their availability, reducing avoidable hospitalization and mortality. However, lack of access to medicines still remains. Due to historical reasons, pharmaceutical service organization in developing countries may have important differences from high-income countries. In some cases, developing countries finance and promote medicine access by using the public infrastructure of health care/medical units as dispensing sites and cover all costs of medicines dispensed. In contrast, many high-income countries use private community pharmacies and cover the costs of medicines dispensed plus a fee, which includes all logistic costs. In this study, we will undertake an economic evaluation to understand the funding needs of the Brazilian NHS to reduce inequalities in access to medicines through adopting a pharmaceutical service organization similar to that seen in many high-income countries with hiring/accrediting private pharmacies.MethodsWe performed an economic evaluation of a model to provide access to medicines within public funds based on a decision tree model with two alternative scenarios public pharmacies (NHS, state-owned facilities) versus private pharmacies (NHS, agreements). The analysis assumed the perspective of the NHS. We identified the types of resources consumed, the amount, and costs in both scenarios. We also performed a budget impact forecast to estimate the incremental funding required to reduce inequalities in access to essential medicines in Brazil.FindingsThe model without rebates for medicines estimated an incremental cost of US$3.1 billion in purchasing power parity (PPP) but with an increase in the average availability of medicines from 65% to 90% for citizens across the country irrespective of family income. This amount places the NHS in a very good position to negotiate extensive rebates without the need for external reference pricing for government purchases. Forecast scenarios above 35% rebates place the alternative of hiring private pharmacies as dominant. Higher rebate rates are feasible and may lead to savings of more than US$1.3 billion per year (30%). The impact of incremental funding is related to medicine access improvement of 25% in the second year when paying by dispensing fee. The estimate of the incremental budget in five years would be US$4.8 billion PPP. We have yet to explore the potential reduction in hospital and outpatient costs, as well as in lawsuits, with increased availability with the yearly expenses for these at US$9 billion and US$1.4 billion PPP respectively in 2017.InterpretationThe results of the economic evaluation demonstrate potential savings for the NHS and society. Achieving UHC for medicines reduces household expenses with health costs, health litigation, outpatient care, hospitalization, and mortality. An optimal private sector–public sector collaboration model with private community pharmacy accreditation is economically dominant with a feasible medicine price negotiation. The results show the potential to improve access to medicines by 25% for all income classes. This is most beneficial to the poorest families, whose medicines account for 76% of their total health expenses, with potential savings of lives and public resources.

ObjectiveWe estimated the effect of an employer-sponsored health insurance (ESHI) scheme on healthcare utilisation of medically trained providers and reduction of out-of-pocket (OOP) expenditure among ready-made garment (RMG) workers.DesignWe used a case–control study design with cross-sectional preintervention and postintervention surveys.SettingsThe study was conducted among workers of seven purposively selected RMG factories in Shafipur, Gazipur in Bangladesh.ParticipantsIn total, 1924 RMG workers (480 from the insured and 482 from the uninsured, in each period) were surveyed from insured and uninsured RMG factories, respectively, in the preintervention (October 2013) and postintervention (April 2015) period.InterventionsWe tested the effect of a pilot ESHI scheme which was implemented for 1 year.Outcome measuresThe outcome measures were utilisation of medically trained providers and reduction of OOP expenditure among RMG workers. We estimated difference-in-difference (DiD) and applied two-part regression model to measure the association between healthcare utilisation, OOP payments and ESHI scheme membership while controlling for the socioeconomic characteristics of workers.ResultsThe ESHI scheme increased healthcare utilisation of medically trained providers by 26.1% (DiD=26.1; p

Introduction: The Montreal Cognitive Assessment (MoCA), an instrument widely used for cognitive screening in Parkinson's disease (PD), is validated in Hebrew and English. However, it remains unknown whether the scores are comparable. Methods: The MoCA was analyzed in 483 Ashkenazi Jewish PD patients in Tel-Aviv and New York who had MoCA ≥21. Each section of the MoCA was compared between English and Hebrew. Linear regression models were used to test the association between MoCA performance and language. Results: Total MoCA scores were lower in Hebrew than in English (25.4 versus 26.1; P = 0.007), even after adjustment for age, sex, PD duration, genotype, levodopa equivalent dose, the Unified Parkinson's Disease Rating Scale (UPDRS-III), and Geriatric Depression Scale score in a linear model (P

Abstract Background Chronic obstructive pulmonary disease (COPD) is the end result of a susceptible individual being exposed to sufficiently deleterious environmental stimuli. More than 90% of COPD-related deaths occur in low- and middle-income countries (LMICs). LMICs face unique challenges in managing COPD; for example, deficient primary care systems present challenges for proper diagnosis and management. Formal diagnosis of COPD requires quality-assured spirometry, which is often limited to urban health centres. Similarly, standard treatment options for COPD remain limited where few providers are trained to manage COPD. The Global Excellence in COPD Outcomes (GECo) studies aim to assess the performance of a COPD case-finding questionnaire with and without peak expiratory flow (PEF) to diagnose COPD, and inform the effectiveness and implementation of COPD self-management Action Plans in LMIC settings. The ultimate goal is to develop simple, low-cost models of care that can be implemented in LMICs. This study will be carried out in Nepal, Peru and Uganda, three distinct LMIC settings. Methods/design We aim to assess the diagnostic accuracy of a simple questionnaire with and without PEF to case-find COPD (GECo1), and examine the effectiveness, cost-effectiveness and implementation of a community-health-worker-supported self-management Action Plan strategy for managing exacerbations of COPD (GECo2). To achieve the first aim, we will enrol a randomly selected sample of up to 10,500 adults aged ≥ 40 years across our three sites, with the goal to enrol 240 participants with moderate-to-severe COPD in to GECo2. We will apply two case-finding questionnaires (Lung Function Questionnaire and CAPTURE) with and without PEF and compare performance against spirometry. We will report ROC areas, sensitivity and specificity. Individuals who are identified as having COPD grades B–D will be invited to enrol in an effectiveness-implementation hybrid randomised trial of a multi-faceted COPD self-management Action Plan intervention delivered by CHWs. The intervention group will receive (1) COPD education, (2) facilitated-self management Action Plans for COPD exacerbations and (3) monthly visits by community health workers. The control group will receive COPD education and standard of care treatment provided by local health providers. Beginning at baseline, we will measure quality of life with the EuroQol-5D (EQ-5D) and St. George’s Respiratory Questionnaire (SGRQ) every 3 months over a period of 1 year. The primary endpoint is SGRQ at 12 months. Quality-adjusted life years (QALYs) using the Short-Form 36 version 2 will also be calculated. We will additionally assess the acceptability and feasibility of implementing COPD Action Plans in each setting among providers and individuals with COPD. Discussion This study should provide evidence to inform the use of pragmatic models of COPD diagnosis and management in LMIC settings. Trial registration NCT03359915 (GECo1). Registered on 2 December 2017 and NCT03365713 (GECo2). Registered on 7 December 2017. Trial acronym: Global Excellence in COPD Outcomes (GECo1; GECo2).

Abstract Background Rapidly increasing healthcare costs and the growing burden of non-communicable diseases have increased the out-of-pocket (OOP) spending (63.3% of total health expenditure) in Bangladesh. This increasing OOP spending for healthcare has catastrophic economic impact on households. To reduce this burden, the Health Economics Unit (HEU) of the Ministry of Health and Family Welfare has developed the Shasthyo Surokhsha Karmasuchi (SSK) health protection scheme for the below-poverty line (BPL) population. The key actors in the scheme are HEU, contracted scheme operator and hospital. Under this scheme, each enrolled household is provided 50,000 BDT (620 USD) coverage per year for healthcare services against a government financed premium of 1000 BDT (12 USD). This initiative faces some challenges e.g., delays in scheme activities, registering the targeted population, low utilization of services, lack of motivation of the providers, and management related difficulties. It is also important to estimate the financial requirement for nationwide scale-up of this project. We aim to identify these implementation-related challenges and provide feedback to the project personnel. Methods This is a concurrent process documentation using mixed-method approaches. It will be conducted in the rural Kalihati Upazila where the SSK is being implemented. To validate the BPL population selection process, we will estimate the positive predictive value. A community survey will be conducted to assess the knowledge of the card holders about SSK services. From the SSK information management system, numbers of different services utilized by the card holders will be retrieved. Key-informant interviews with personnel from three key actors will be conducted to understand the barriers in the implementation of the project as per plan and gather their suggestions. To estimate the project costs, all inputs to be used will be identified, quantified and valued. The nationwide scale-up cost of the project will be estimated by applying economic modeling. Discussion SSK is the first ever government initiated health protection scheme in Bangladesh. The study findings will enable decision makers to gain a better understanding of the key challenges in implementation of such scheme and provide feedback towards the successful implementation of the program.

Abstract Background Impairments in biomechanics and neural control can disrupt the timing and muscle pattern activation necessary for smooth gait. Gait is one of the most affected motor characteristics in Parkinson’s disease (PD), but its smoothness has not been well-studied. This work applies the recently proposed spectral arc length measure (SPARC) to study, for the first time, gait in patients with PD. We hypothesized that the gait of patients with PD would be less smooth than that of healthy controls, as reflected in the SPARC measures. Methods The gait of 101 PD patients and 39 healthy controls was assessed using an inertial sensor. Smoothness of gait was estimated with SPARC (respectively from acceleration and angular velocity signals, SPARC-Acc and SPARC-Gyro) and harmonic ratios. Correlations between SPARC, traditional gait measures and the motor part of the Unified Parkinson’s Disease Rating Scale (UPDRS) were evaluated. Measurements and analysis were conducted with and without anti-PD medication. Results SPARC measures were lower (less smooth) in PD than in controls (SPARC-Acc: PD: − 6.11 ± 0.74; CO: -5.17 ± 0.79; p 1). SPARC measures were correlated with UPDRS motor score (r = − 0.65), while they were independent of other measures of gait smoothness. PD gait in the on state was smoother than in the off state (p

Abstract Background Cost-effectiveness analysis (CEA) is frequently used as an input for guiding priority setting in health. However, CEA seldom incorporates information about trade-offs between total health gains and equity impacts of interventions. This study investigates to what extent equity considerations have been taken into account in CEA in low- and middle-income countries (LMICs), using rotavirus vaccination as a case study. Methods Specific equity-related indicators for vaccination were first mapped to the Guidance on Priority Setting in Health Care (GPS-Health) checklist criteria. Economic evaluations of rotavirus vaccine in LMICs identified via a systematic review of the literature were assessed to explore the extent to which equity was considered in the research objectives and analysis, and whether it was reflected in the evaluation results. Results The mapping process resulted in 18 unique indicators. Under the ‘disease and intervention’ criteria, severity of illness was incorporated in 75% of the articles, age distribution of the disease in 70%, and presence of comorbidities in 5%. For the ‘social groups’ criteria, relative coverage reflecting wealth-based coverage inequality was taken into account in 30% of the articles, geographic location in 27%, household income level in 8%, and sex at birth in 5%. For the criteria of ‘protection against the financial and social effects of ill health’, age weighting was incorporated in 43% of the articles, societal perspective in 58%, caregiver’s loss of productivity in 45%, and financial risk protection in 5%. Overall, some articles incorporated the indicators in their model inputs (20%) while the majority (80%) presented results (costs, health outcomes, or incremental cost-effectiveness ratios) differentiated according to the indicators. Critically, less than a fifth (17%) of articles incorporating indicators did so due to an explicit study objective related to capturing equity considerations. Most indicators were increasingly incorporated over time, with a notable exception of age-weighting of DALYs. Conclusion Integrating equity criteria in CEA can help policy-makers better understand the distributional impact of health interventions. This study illustrates how equity considerations are currently being incorporated within CEA of rotavirus vaccination and highlights the components of equity that have been used in studies in LMICs. Areas for further improvement are identified.

Abstract Background The use of miniaturized magneto-inertial measurement units (MIMUs) allows for an objective evaluation of gait and a quantitative assessment of clinical outcomes. Spatial and temporal parameters are generally recognized as key metrics for characterizing gait. Although several methods for their estimate have been proposed, a thorough error analysis across different pathologies, multiple clinical centers and on large sample size is still missing. The aim of this study was to apply a previously presented method for the estimate of spatio-temporal parameters, named Trusted Events and Acceleration Direct and Reverse Integration along the direction of Progression (TEADRIP), on a large cohort (236 patients) including Parkinson, mildly cognitively impaired and healthy older adults collected in four clinical centers. Data were collected during straight-line gait, at normal and fast walking speed, by attaching two MIMUs just above the ankles. The parameters stride, step, stance and swing durations, as well as stride length and gait velocity, were estimated for each gait cycle. The TEADRIP performance was validated against data from an instrumented mat. Results Limits of agreements computed between the TEADRIP estimates and the reference values from the instrumented mat were − 27 to 27 ms for Stride Time, − 68 to 44 ms for Stance Time, − 31 to 31 ms for Step Time and − 67 to 52 mm for Stride Length. For each clinical center, the mean absolute errors averaged across subjects for the estimation of temporal parameters ranged between 1 and 4%, being on average less than 3% (

Background: The development of therapeutic interventions for Parkinson disease (PD) is challenged by disease complexity and subjectivity of symptom evaluation. A Parkinson's Disease Related Pattern (PDRP) of glucose metabolism via fluorodeoxyglucose positron emission tomography (FDG-PET) has been reported to correlate with motor symptom scores and may aid the detection of disease-modifying therapeutic effects. Objectives: We sought to independently evaluate the potential utility of the PDRP as a biomarker for clinical trials of early-stage PD. Methods: Two machine learning approaches (Scaled Subprofile Model (SSM) and NPAIRS with Canonical Variates Analysis) were performed on FDG-PET scans from 17 healthy controls (HC) and 23 PD patients. The approaches were compared regarding discrimination of HC from PD and relationship to motor symptoms. Results: Both classifiers discriminated HC from PD (p

Abstract When making recommendations about the public funding of new health technologies, policy makers typically pay close attention to quantitative evidence about the comparative effectiveness, cost effectiveness and budget impact of those technologies – what we might call “efficiency” criteria. Less attention is paid, however, to quantitative evidence about who gains and who loses from these public expenditure decisions, and whether those who gain are better or worse off than the rest of the population in terms of their health – what we might call “equity” criteria. Two studies recently published in this journal by Shmueli and colleagues suggest that this efficiency-oriented imbalance in the use of quantitative evidence may have unfortunate consequences – as the old adage goes: “what gets measured, gets done”. The first study, by Shmueli, Golan, Paolucci and Mentzakis, found that health policy makers in Israel think equity considerations are just as important as efficiency considerations – at least when it comes to making hypothetical technology funding decisions in a survey. By contrast, the second study – by Shmueli alone – found that efficiency rules the roost when it comes to making real decisions about health technology funding in Israel. Both studies have limitations and potential biases, and more research is needed using qualitative methods and more nuanced survey designs to determine precisely which kinds of equity consideration decision makers think are most important and why these considerations do not appear to be given much weight in decision making. However, the basic overall finding from the two studies seems plausible and important. It suggests that health technology funding bodies need to pay closer attention to equity considerations, and to start making equity a quantitative endpoint of health technology assessment using the methods of equity-informative economic evaluation that are now available.