Climate change increases cross-species viral transmission risk.

At least 10,000 virus species have the ability to infect humans but, at present, the vast majority are circulating silently in wild mammals1,2. However, changes in climate and land use will lead to opportunities for viral sharing among previously geographically isolated species of wildlife3,4. In some cases, this will facilitate zoonotic spillover-a mechanistic link between global environmental change and disease emergence. Here we simulate potential hotspots of future viral sharing, using a phylogeographical model of the mammal-virus network, and projections of geographical range shifts for 3,139 mammal species under climate-change and land-use scenarios for the year 2070. We predict that species will aggregate in new combinations at high elevations, in biodiversity hotspots, and in areas of high human population density in Asia and Africa, causing the cross-species transmission of their associated viruses an estimated 4,000 times. Owing to their unique dispersal ability, bats account for the majority of novel viral sharing and are likely to share viruses along evolutionary pathways that will facilitate future emergence in humans. Notably, we find that this ecological transition may already be underway, and holding warming under 2 °C within the twenty-first century will not reduce future viral sharing. Our findings highlight an urgent need to pair viral surveillance and discovery efforts with biodiversity surveys tracking the range shifts of species, especially in tropical regions that contain the most zoonoses and are experiencing rapid warming.

Comparative approaches in social network ecology.

Social systems vary enormously across the animal kingdom, with important implications for ecological and evolutionary processes such as infectious disease dynamics, anti-predator defence, and the evolution of cooperation. Comparing social network structures between species offers a promising route to help disentangle the ecological and evolutionary processes that shape this diversity. Comparative analyses of networks like these are challenging and have been used relatively little in ecology, but are becoming increasingly feasible as the number of empirical datasets expands. Here, we provide an overview of multispecies comparative social network studies in ecology and evolution. We identify a range of advancements that these studies have made and key challenges that they face, and we use these to guide methodological and empirical suggestions for future research. Overall, we hope to motivate wider publication and analysis of open social network datasets in animal ecology.

Revisiting resistant hypertension in kidney disease.

PURPOSE OF REVIEW: As compared to controlled or uncontrolled hypertension, resistant hypertension in patients with chronic kidney disease (CKD) poses a significantly increased healthcare burden due to greater target end-organ damage including cardiovascular disease and CKD progression. Patients with CKD have two to three times higher risk of developing resistant hypertension. True resistant hypertension needs to be distinguished from apparent treatment resistant hypertension (aTRH); however, it is usually not possible in epidemiological studies. Moreover, impact of contemporary guidelines changes in the target blood pressure (BP) goal to less than 130/80 mmHg remains to be determined. RECENT FINDINGS: Up to half of patients with CKD meet aTRH criteria using 2017 ACC/AHA target BP less than 130/80 mmHg. Excess sodium retention in extracellular and tissue compartment remains the cornerstone cause of resistance to the treatment in CKD. Maximizing and optimizing the diuretic regimen in addition to dietary sodium restriction plays a critical role in these patients. Management requires a trustworthy provider-patient relationship facilitating identification and intervention for the barriers restricting the uptake of lifestyle modifications and medications. Recently, renal denervation has been approved and many other novel agents are on the horizon for treatment of true resistant hypertension associated with CKD. SUMMARY: This review discusses the latest in the pathophysiology, definition, identification and treatment strategies of resistant hypertension in individuals with CKD. Further investigations are required to identify the prevalence, future implication and treatment outcome data for true resistant hypertension associated with CKD.

Charting the spatial dynamics of early SARS-CoV-2 transmission in Washington state.

The spread of SARS-CoV-2 has been geographically uneven. To understand the drivers of this spatial variation in SARS-CoV-2 transmission, in particular the role of stochasticity, we used the early stages of the SARS-CoV-2 invasion in Washington state as a case study. We analysed spatially-resolved COVID-19 epidemiological data using two distinct statistical analyses. The first analysis involved using hierarchical clustering on the matrix of correlations between county-level case report time series to identify geographical patterns in the spread of SARS-CoV-2 across the state. In the second analysis, we used a stochastic transmission model to perform likelihood-based inference on hospitalised cases from five counties in the Puget Sound region. Our clustering analysis identifies five distinct clusters and clear spatial patterning. Four of the clusters correspond to different geographical regions, with the final cluster spanning the state. Our inferential analysis suggests that a high degree of connectivity across the region is necessary for the model to explain the rapid inter-county spread observed early in the pandemic. In addition, our approach allows us to quantify the impact of stochastic events in determining the subsequent epidemic. We find that atypically rapid transmission during January and February 2020 is necessary to explain the observed epidemic trajectories in King and Snohomish counties, demonstrating a persisting impact of stochastic events. Our results highlight the limited utility of epidemiological measures calculated over broad spatial scales. Furthermore, our results make clear the challenges with predicting epidemic spread within spatially extensive metropolitan areas, and indicate the need for high-resolution mobility and epidemiological data.

The impact of spatial connectivity on NPIs effectiveness.

BACKGROUND: France implemented a combination of non-pharmaceutical interventions (NPIs) to manage the COVID-19 pandemic between September 2020 and June 2021. These included a lockdown in the fall 2020 - the second since the start of the pandemic - to counteract the second wave, followed by a long period of nighttime curfew, and by a third lockdown in the spring 2021 against the Alpha wave. Interventions have so far been evaluated in isolation, neglecting the spatial connectivity between regions through mobility that may impact NPI effectiveness. METHODS: Focusing on September 2020-June 2021, we developed a regionally-based epidemic metapopulation model informed by observed mobility fluxes from daily mobile phone data and fitted the model to regional hospital admissions. The model integrated data on vaccination and variants spread. Scenarios were designed to assess the impact of the Alpha variant, characterized by increased transmissibility and risk of hospitalization, of the vaccination campaign and alternative policy decisions. RESULTS: The spatial model better captured the heterogeneity observed in the regional dynamics, compared to models neglecting inter-regional mobility. The third lockdown was similarly effective to the second lockdown after discounting for immunity, Alpha, and seasonality (51% vs 52% median regional reduction in the reproductive number R0, respectively). The 6pm nighttime curfew with bars and restaurants closed, implemented in January 2021, substantially reduced COVID-19 transmission. It initially led to 49% median regional reduction of R0, decreasing to 43% reduction by March 2021. In absence of vaccination, implemented interventions would have been insufficient against the Alpha wave. Counterfactual scenarios proposing a sequence of lockdowns in a stop-and-go fashion would have reduced hospitalizations and restriction days for low enough thresholds triggering and lifting restrictions. CONCLUSIONS: Spatial connectivity induced by mobility impacted the effectiveness of interventions especially in regions with higher mobility rates. Early evening curfew with gastronomy sector closed allowed authorities to delay the third wave. Stop-and-go lockdowns could have substantially lowered both healthcare and societal burdens if implemented early enough, compared to the observed application of lockdown-curfew-lockdown, but likely at the expense of several labor sectors. These findings contribute to characterize the effectiveness of implemented strategies and improve pandemic preparedness.

Dissecting recurrent waves of pertussis across the boroughs of London.

Pertussis has resurfaced in the UK, with incidence levels not seen since the 1980s. While the fundamental causes of this resurgence remain the subject of much conjecture, the study of historical patterns of pathogen diffusion can be illuminating. Here, we examined time series of pertussis incidence in the boroughs of Greater London from 1982 to 2013 to document the spatial epidemiology of this bacterial infection and to identify the potential drivers of its percolation. The incidence of pertussis over this period is characterized by 3 distinct stages: a period exhibiting declining trends with 4-year inter-epidemic cycles from 1982 to 1994, followed by a deep trough until 2006 and the subsequent resurgence. We observed systematic temporal trends in the age distribution of cases and the fade-out profile of pertussis coincident with increasing national vaccine coverage from 1982 to 1990. To quantify the hierarchy of epidemic phases across the boroughs of London, we used the Hilbert transform. We report a consistent pattern of spatial organization from 1982 to the early 1990s, with some boroughs consistently leading epidemic waves and others routinely lagging. To determine the potential drivers of these geographic patterns, a comprehensive parallel database of borough-specific features was compiled, comprising of demographic, movement and socio-economic factors that were used in statistical analyses to predict epidemic phase relationships among boroughs. Specifically, we used a combination of a feed-forward neural network (FFNN), and SHapley Additive exPlanations (SHAP) values to quantify the contribution of each covariate to model predictions. Our analyses identified a number of predictors of a borough's historical epidemic phase, specifically the age composition of households, the number of agricultural and skilled manual workers, latitude, the population of public transport commuters and high-occupancy households. Univariate regression analysis of the 2012 epidemic identified the ratio of cumulative unvaccinated children to the total population and population of Pakistan-born population to have moderate positive and negative association, respectively, with the timing of epidemic. In addition to providing a comprehensive overview of contemporary pertussis transmission in a large metropolitan population, this study has identified the characteristics that determine the spatial spread of this bacterium across the boroughs of London.

Spatial clustering in vaccination hesitancy: The role of social influence and social selection.

The phenomenon of vaccine hesitancy behavior has gained ground over the last three decades, jeopardizing the maintenance of herd immunity. This behavior tends to cluster spatially, creating pockets of unprotected sub-populations that can be hotspots for outbreak emergence. What remains less understood are the social mechanisms that can give rise to spatial clustering in vaccination behavior, particularly at the landscape scale. We focus on the presence of spatial clustering, and aim to mechanistically understand how different social processes can give rise to this phenomenon. In particular, we propose two hypotheses to explain the presence of spatial clustering: (i) social selection, in which vaccine-hesitant individuals share socio-demographic traits, and clustering of these traits generates spatial clustering in vaccine hesitancy; and (ii) social influence, in which hesitant behavior is contagious and spreads through neighboring societies, leading to hesitant clusters. Adopting a theoretical spatial network approach, we explore the role of these two processes in generating patterns of spatial clustering in vaccination behaviors under a range of spatial structures. We find that both processes are independently capable of generating spatial clustering, and the more spatially structured the social dynamics in a society are, the higher spatial clustering in vaccine-hesitant behavior it realizes. Together, we demonstrate that these processes result in unique spatial configurations of hesitant clusters, and we validate our models of both processes with fine-grain empirical data on vaccine hesitancy, social determinants, and social connectivity in the US. Finally, we propose, and evaluate the effectiveness of two novel intervention strategies to diminish hesitant behavior. Our generative modeling approach informed by unique empirical data provides insights on the role of complex social processes in driving spatial heterogeneity in vaccine hesitancy.

Rapid range shifts in African Anopheles mosquitoes over the last century.

Facing a warming climate, many tropical species-including the arthropod vectors of several infectious diseases-will be displaced to higher latitudes and elevations. These shifts are frequently projected for the future, but rarely documented in the present day. Here, we use one of the most comprehensive datasets ever compiled by medical entomologists to track the observed range limits of African malaria mosquito vectors (Anopheles spp.) from 1898 to 2016. Using a simple regression approach, we estimate that these species' ranges gained an average of 6.5 m of elevation per year, and the southern limits of their ranges moved polewards 4.7 km per year. These shifts would be consistent with the local velocity of recent climate change, and might help explain the incursion of malaria transmission into new areas over the past few decades. Confirming that climate change underlies these shifts, and applying similar methods to other disease vectors, are important directions for future research.

Spatial parasitology and the unmapped human helminthiases.

Helminthiases are a class of neglected tropical diseases that affect at least 1 billion people worldwide, with a disproportionate impact on resource-poor areas with limited disease surveillance. Geospatial methods can offer valuable insights into the burden of these infections, particularly given that many are subject to strong ecological influences on the environmental, vector-borne or zoonotic stages of their life cycle. In this study, we screened 6829 abstracts and analysed 485 studies that use maps to document, infer or predict transmission patterns for over 200 species of parasitic worms. We found that quantitative mapping methods are increasingly used in medical parasitology, drawing on One Health surveillance data from the community scale to model geographic distributions and burdens up to the regional or global scale. However, we found that the vast majority of the human helminthiases may be entirely unmapped, with research effort focused disproportionately on a half-dozen infections that are targeted by mass drug administration programmes. Entire regions were also surprisingly under-represented in the literature, particularly southern Asia and the Neotropics. We conclude by proposing a shortlist of possible priorities for future research, including several neglected helminthiases with a burden that may be underestimated.

Characterizing the Spatiotemporal Heterogeneity of the COVID-19 Vaccination Landscape.

As variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have emerged throughout 2021-2022, the need to maximize vaccination coverage across the United States to minimize severe outcomes of coronavirus disease 2019 (COVID-19) has been critical. Maximizing vaccination requires that we track vaccination patterns to measure the progress of the vaccination campaign and target locations that may be undervaccinated. To improve efforts to track and characterize COVID-19 vaccination progress in the United States, we integrated Centers for Disease Control and Prevention and state-provided vaccination data, identifying and rectifying discrepancies between these data sources. We found that COVID-19 vaccination coverage in the United States exhibits significant spatial heterogeneity at the county level, and we statistically identified spatial clusters of undervaccination, all with foci in the southern United States. We also identified vaccination progress at the county level as variable through summer 2021; the progress of vaccination in many counties stalled in June 2021, and few had recovered by July, with transmission of the SARS-CoV-2 delta variant rapidly rising. Using a comparison with a mechanistic growth model fitted to our integrated data, we classified vaccination dynamics across time at the county scale. Our findings underline the importance of curating accurate, fine-scale vaccination data and the continued need for widespread vaccination in the United States, especially with the continued emergence of highly transmissible SARS-CoV-2 variants.

Pathogen transmission modes determine contact network structure, altering other pathogen characteristics.

Pathogen traits can vary greatly and heavily impact the ability of a pathogen to persist in a population. Although this variation is fundamental to disease ecology, little is known about the evolutionary pressures that drive these differences, particularly where they interact with host behaviour. We hypothesized that host behaviours relevant to different transmission routes give rise to differences in contact network structure, constraining the space over which pathogen traits can evolve to maximize fitness. Our analysis of 232 contact networks across mammals, birds, reptiles, amphibians, arthropods, fish and molluscs found that contact network topology varies by contact type, most notably in networks that are representative of fluid-exchange transmission. Using infectious disease model simulations, we showed that these differences in network structure suggest pathogens transmitted through fluid-exchange contact types will need traits associated with high transmissibility to successfully proliferate, compared to pathogens that transmit through other types of contact. These findings were supported through a review of known traits of pathogens that transmit in humans. Our work demonstrates that contact network structure may drive the evolution of compensatory pathogen traits according to transmission strategy, providing essential context for understanding pathogen evolution and ecology.

Health inequities in influenza transmission and surveillance.

The lower an individual's socioeconomic position, the higher their risk of poor health in low-, middle-, and high-income settings alike. As health inequities grow, it is imperative that we develop an empirically-driven mechanistic understanding of the determinants of health disparities, and capture disease burden in at-risk populations to prevent exacerbation of disparities. Past work has been limited in data or scope and has thus fallen short of generalizable insights. Here, we integrate empirical data from observational studies and large-scale healthcare data with models to characterize the dynamics and spatial heterogeneity of health disparities in an infectious disease case study: influenza. We find that variation in social and healthcare-based determinants exacerbates influenza epidemics, and that low socioeconomic status (SES) individuals disproportionately bear the burden of infection. We also identify geographical hotspots of influenza burden in low SES populations, much of which is overlooked in traditional influenza surveillance, and find that these differences are most predicted by variation in susceptibility and access to sickness absenteeism. Our results highlight that the effect of overlapping factors is synergistic and that reducing this intersectionality can significantly reduce inequities. Additionally, health disparities are expressed geographically, and targeting public health efforts spatially may be an efficient use of resources to abate inequities. The association between health and socioeconomic prosperity has a long history in the epidemiological literature; addressing health inequities in respiratory-transmitted infectious disease burden is an important step towards social justice in public health, and ignoring them promises to pose a serious threat.

Revealing mechanisms of infectious disease spread through empirical contact networks.

The spread of pathogens fundamentally depends on the underlying contacts between individuals. Modeling the dynamics of infectious disease spread through contact networks, however, can be challenging due to limited knowledge of how an infectious disease spreads and its transmission rate. We developed a novel statistical tool, INoDS (Identifying contact Networks of infectious Disease Spread) that estimates the transmission rate of an infectious disease outbreak, establishes epidemiological relevance of a contact network in explaining the observed pattern of infectious disease spread and enables model comparison between different contact network hypotheses. We show that our tool is robust to incomplete data and can be easily applied to datasets where infection timings of individuals are unknown. We tested the reliability of INoDS using simulation experiments of disease spread on a synthetic contact network and find that it is robust to incomplete data and is reliable under different settings of network dynamics and disease contagiousness compared with previous approaches. We demonstrate the applicability of our method in two host-pathogen systems: Crithidia bombi in bumblebee colonies and Salmonella in wild Australian sleepy lizard populations. INoDS thus provides a novel and reliable statistical tool for identifying transmission pathways of infectious disease spread. In addition, application of INoDS extends to understanding the spread of novel or emerging infectious disease, an alternative approach to laboratory transmission experiments, and overcoming common data-collection constraints.

Low-dose prophylaxis and its impact on the health of haemophilia patients.

BACKGROUND AND OBJECTIVES: There is convincing evidence to show that low-dose prophylaxis (LDP) results in reduction in annualized bleeding rate (ABR) and better health-related quality of life (HRQoL) compared with on-demand or episodic treatment (ET) in haemophilia patients. The aim is to review various LDP protocols practised for the treatment of haemophilia, specifically in resource-limited countries. METHODS: A literature survey was made of articles published in English language in PubMed and EMBASE without any time limit using keywords 'low dose', 'prophylaxis' and 'haemophilia' in different combinations. RESULTS: A total of 19 reports involving LDP in patients with haemophilia were included in this review. Almost all studies reported reduction in ABR, improvement in joint function, pain and HRQoL compared with ET, but this did not fully translate into significant improvement in structural arthropathy already caused by earlier bleeds, suggesting that LDP may be less or ineffective in either stopping or reversing the damage. Individualized dose escalation protocols based on pharmacokinetic (PK) or clinical parameters were found to be superior to fixed LDP protocols and cost-effective compared with standard dose protocols. CONCLUSION: The developing countries can initiate LDP as the first step of prophylaxis, but certainly this should not be the final goal of the health care system in any country. Due to the complex pathophysiological mechanisms underlying haemophilic arthropathy, long-term data on LDP in haemophilia patients are warranted.

Mortality, Kidney Failure, and Hospitalization Among Medicare Beneficiaries With CKD and Pulmonary Hypertension.

RATIONALE & OBJECTIVE: Pulmonary hypertension (PH) is highly prevalent among patients with chronic kidney disease (CKD) not requiring kidney replacement therapy. We studied the associations of PH with mortality, kidney failure, as well as cardiovascular (CV) and non-CV hospitalization among Medicare beneficiaries with a CKD diagnosis. STUDY DESIGN: Retrospective, observational study using a matched cohort design. SETTING & PARTICIPANTS: Patients with PH (based on 2 claims within 2 years) and patients without PH matched on CKD stage from the Medicare 5% CKD sample (1996-2016). PREDICTOR: Presence of pulmonary hypertension. OUTCOME: Mortality, kidney failure, and all-cause, CV, and non-CV hospitalization. ANALYTICAL APPROACH: Cox proportional hazards models to assess the association between PH and mortality, adjusting for age, sex, race, and comorbidities. Death was considered as a competing event in Fine-Gray models to assess the association between PH and kidney failure. Negative binomial model was used to evaluate the relationship between PH and all-cause, CV, and non-CV hospitalizations. RESULTS: 30,052 patients with PH and CKD and 150,260 CKD stage-matched patients without diagnosed PH were studied. The median age of the study population was 80.7 years, 57.8% were women, and 10.3% were African Americans. The presence of PH was associated with an increased risk of mortality after 1 (HR, 2.87 [95% CI, 2.79-2.95]), 2-3 (HR, 1.56 [95% CI, 1.51-1.61]), and 4-5 (HR, 1.47 [95% CI, 1.40-1.53]) years of follow-up, and a higher risk of all-cause, CV, and non-CV hospitalization during the same period. PH was also associated with kidney failure in after 1 and 2-3 years but not after 4-5 years of follow-up evaluation. Patients with PH also experienced higher rates of acute kidney injury (AKI), and AKI requiring dialysis support within 30 and 90 days of AKI. LIMITATIONS: Reliance on billing codes and lack of echocardiogram or right heart catheterization data CONCLUSIONS: Among older Medicare beneficiaries with a CKD diagnosis not requiring kidney replacement therapy, the presence of PH was associated with an increased risk of mortality, kidney failure, and hospitalization. Understanding of the mechanism of these associations, especially the increased risk of kidney failure, requires further study.

Acute kidney injury in cardiogenic shock: A comprehensive review.

Acute kidney injury (AKI) is an ominous predictor of mortality in cardiogenic shock. The present review examines the pathophysiology of AKI in cardiogenic shock (CS), summarizes the pertinent literature including the diagnostic criteria/definitions for AKI and possible role of biomarkers, and identifies risk factors and possible therapeutic interventions for AKI in CS. Our review finds that AKI is common in patients with CS and is associated with increased morbidity and mortality. Urinary biomarkers of renal tubular injury appear more sensitive for detection of AKI but have yet to be incorporated into daily practice. Emerging data would suggest vasopressor choices, mechanical circulatory support, and renal replacement therapy may have important therapeutic roles in the management of CS.

Meta-analysis of intravascular volume expansion strategies to prevent contrast-associated acute kidney injury following invasive angiography.

OBJECTIVE: To perform a detailed analysis of published data regarding intravascular volume expansion to prevent contrast-associated acute kidney injury (CA-AKI) and to determine if an ideal dose of IV fluids can be recommended. BACKGROUND: Administration of contrast media during invasive angiography is associated with CA-AKI. Intravascular volume expansion is the most effective intervention to prevent CA-AKI, yet evidenced based protocols are lacking. METHODS: Literature review and meta-analysis of randomized controlled trials (RCT) of patients receiving IV volume expansion as prophylaxis for CA-AKI was performed. Normal saline, Lactated Ringer's and sodium bicarbonate were included. The primary outcome was incidence of CA-AKI. RESULTS: 37 RCTs studying 12,166 patients were included. Mean age was 67 ± 5 years, 70% of the patients were male. 68% had chronic kidney disease, 41% diabetes, and 30% heart failure. The incidence of CA-AKI was 9.5% (95% CI: 8-12%). IV expansion versus no volume administration was associated with a lower risk of CA-AKI (RR:0.62; 95% CI: 0.49-0.77, p < .001). Intensive IV volume expansion was associated with a reduced risk of CA-AKI(RR: 0.66; 95%CI: 0.52-0.85, p < .01). The intensive IV volume expansion arm received significantly more fluids than the standard protocols: 1,574(1,123 - 1,913) ml versus 849(558-1,067) ml (p = .03) without significant difference in the duration of infusion (median of 12 vs. 17 hr, p = .1) or pulmonary edema (1.7% vs 1.3%, p = .7). CONCLUSIONS: Despite high variability in protocols used, IV volume expansion is effective in preventing CA-AKI. Intensive IVF expansion (median 1.6 L over 17 hr) was associated with decreased risk of CA-AKI.

Unifying spatial and social network analysis in disease ecology.

Social network analysis has achieved remarkable popularity in disease ecology, and is sometimes carried out without investigating spatial heterogeneity. Many investigations into sociality and disease may nevertheless be subject to cryptic spatial variation, so ignoring spatial processes can limit inference regarding disease dynamics. Disease analyses can gain breadth, power and reliability from incorporating both spatial and social behavioural data. However, the tools for collecting and analysing these data simultaneously can be complex and unintuitive, and it is often unclear when spatial variation must be accounted for. These difficulties contribute to the scarcity of simultaneous spatial-social network analyses in disease ecology thus far. Here, we detail scenarios in disease ecology that benefit from spatial-social analysis. We describe procedures for simultaneous collection of both spatial and social data, and we outline statistical approaches that can control for and estimate spatial-social covariance in disease ecology analyses. We hope disease researchers will expand social network analyses to more often include spatial components and questions. These measures will increase the scope of such analyses, allowing more accurate model estimates, better inference of transmission modes, susceptibility effects and contact scaling patterns, and ultimately more effective disease interventions.

Fatigue characteristics on dialysis and non-dialysis days in patients with chronic kidney failure on maintenance hemodialysis.

BACKGROUND: Fatigue is prevalent in hemodialysis patients who for survival follow a strict dialysis treatment regimen - dialysis and non-dialysis days. As a result, the daily activities, symptom burden, and clinical outcomes of hemodialysis patients vary significantly between dialysis and non-dialysis days. Fatigue is one of the most reported debilitating symptoms by hemodialysis patients with profound negative impact on their quality of life. Prior studies assessed fatigue during the preceding 7 or 30 days and did not discriminate fatigue characteristics between dialysis and non-dialysis days. We aimed to characterize and compare fatigue severity and fatigue interference with daily activities between dialysis and non-dialysis days. METHODS: Hemodialysis patients self-reported fatigue on consecutive dialysis and non-dialysis days using the 9-item Brief Fatigue Inventory. The differences in fatigue characteristics between dialysis and non-dialysis days were analyzed using one-way ANCOVA. RESULTS: Global fatigue burden was worse on a dialysis day compared to a non-dialysis day (P for all < 0.001). Age and education were associated with fatigue, but hemodialysis-related variables were not. A significant inverse association of physical activity with fatigue severity observed on non-dialysis day; there was also a negative association between the normalized protein catabolic rate and fatigue severity on both dialysis and non-dialysis days. The positive association of depression with fatigue severity and fatigue interference were consistent on both dialysis and non-dialysis days. None of these factors, however, explained differences in fatigue characteristics between dialysis and non-dialysis days. CONCLUSIONS: Fatigue, measured in severity and interference, was more pronounced on a dialysis day relative to a non-dialysis day. These differences were not explained by age, sex, education, hemodialysis-related variables, habitual exercise, nutritional status, and or depression. The quantitative measures of fatigue characteristics may facilitate future interventional trials design and better fatigue management for hemodialysis patients.

Socioeconomic Disparities in Social Distancing During the COVID-19 Pandemic in the United States: Observational Study.

BACKGROUND: Eliminating disparities in the burden of COVID-19 requires equitable access to control measures across socio-economic groups. Limited research on socio-economic differences in mobility hampers our ability to understand whether inequalities in social distancing are occurring during the SARS-CoV-2 pandemic. OBJECTIVE: We aimed to assess how mobility patterns have varied across the United States during the COVID-19 pandemic and to identify associations with socioeconomic factors of populations. METHODS: We used anonymized mobility data from tens of millions of devices to measure the speed and depth of social distancing at the county level in the United States between February and May 2020, the period during which social distancing was widespread in this country. Using linear mixed models, we assessed the associations between social distancing and socioeconomic variables, including the proportion of people in the population below the poverty level, the proportion of Black people, the proportion of essential workers, and the population density. RESULTS: We found that the speed, depth, and duration of social distancing in the United States are heterogeneous. We particularly show that social distancing is slower and less intense in counties with higher proportions of people below the poverty level and essential workers; in contrast, we show that social distancing is intensely adopted in counties with higher population densities and larger Black populations. CONCLUSIONS: Socioeconomic inequalities appear to be associated with the levels of adoption of social distancing, potentially resulting in wide-ranging differences in the impact of the COVID-19 pandemic in communities across the United States. These inequalities are likely to amplify existing health disparities and must be addressed to ensure the success of ongoing pandemic mitigation efforts.