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.

Integrating Patient Priorities with Science by Community Engagement in the Kidney Precision Medicine Project.

The Kidney Precision Medicine Project (KPMP) is a multisite study designed to improve understanding of CKD attributed to diabetes or hypertension and AKI by performing protocol-driven kidney biopsies. Study participants and their kidney tissue samples undergo state-of-the-art deep phenotyping using advanced molecular, imaging, and data analytical methods. Few patients participate in research design or concepts for discovery science. A major goal of the KPMP is to include patients as equal partners to inform the research for clinically relevant benefit. The purpose of this report is to describe patient and community engagement and the value they bring to the KPMP. Patients with CKD and AKI and clinicians from the study sites are members of the Community Engagement Committee, with representation on other KPMP committees. They participate in KPMP deliberations to address scientific, clinical, logistic, analytic, ethical, and community engagement issues. The Community Engagement Committee guides KPMP research priorities from perspectives of patients and clinicians. Patients led development of essential study components, including the informed consent process, no-fault harm insurance coverage, the ethics statement, return of results plan, a "Patient Primer" for scientists and the public, and Community Advisory Boards. As members across other KPMP committees, the Community Engagement Committee assures that the science is developed and conducted in a manner relevant to study participants and the clinical community. Patients have guided the KPMP to produce research aligned with their priorities. The Community Engagement Committee partnership has set new benchmarks for patient leadership in precision medicine research.

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.

Socio-economic disparities in social distancing during the COVID-19 pandemic in the United States.

Importance: 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: To assess how mobility patterns have varied across the United States during the COVID-19 pandemic, and identify associations with socio-economic factors of populations. Design Setting and Participants: We used anonymized mobility data from tens of millions of devices to measure the speed and depth of social distancing at the county level between February and May 2020. Using linear mixed models, we assessed the associations between social distancing and socio-economic variables, including the proportion of people below the poverty level, the proportion of Black people, the proportion of essential workers, and the population density. Main outcomes and Results: We find that the speed, depth, and duration of social distancing in the United States is 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; and in contrast, that social distancing is intense in counties with higher population densities and larger Black populations. Conclusions and relevance: Socio-economic inequalities appear to be associated with the levels of adoption of social distancing, potentially resulting in wide-ranging differences in the impact of COVID-19 in communities across the United States. This is likely to amplify existing health disparities, and needs to be addressed to ensure the success of ongoing pandemic mitigation efforts.

Ant colonies maintain social homeostasis in the face of decreased density.

Interactions lie at the heart of social organization, particularly in ant societies. Interaction rates are presumed to increase with density, but there is little empirical evidence for this. We manipulated density within carpenter ant colonies of the species Camponotus pennsylvanicus by quadrupling nest space and by manually tracking 6.9 million ant locations and over 3200 interactions to study the relationship between density, spatial organization and interaction rates. Colonies divided into distinct spatial regions on the basis of their underlying spatial organization and changed their movement patterns accordingly. Despite a reduction in both overall and local density, we did not find the expected concomitant reduction in interaction rates across all colonies. Instead, we found divergent effects across colonies. Our results highlight the remarkable organizational resilience of ant colonies to changes in density, which allows them to sustain two key basic colony life functions, that is food and information exchange, during environmental change.

Disease implications of animal social network structure: A synthesis across social systems.

The disease costs of sociality have largely been understood through the link between group size and transmission. However, infectious disease spread is driven primarily by the social organization of interactions in a group and not its size. We used statistical models to review the social network organization of 47 species, including mammals, birds, reptiles, fish and insects by categorizing each species into one of three social systems, relatively solitary, gregarious and socially hierarchical. Additionally, using computational experiments of infection spread, we determined the disease costs of each social system. We find that relatively solitary species have large variation in number of social partners, that socially hierarchical species are the least clustered in their interactions, and that social networks of gregarious species tend to be the most fragmented. However, these structural differences are primarily driven by weak connections, which suggest that different social systems have evolved unique strategies to organize weak ties. Our synthetic disease experiments reveal that social network organization can mitigate the disease costs of group living for socially hierarchical species when the pathogen is highly transmissible. In contrast, highly transmissible pathogens cause frequent and prolonged epidemic outbreaks in gregarious species. We evaluate the implications of network organization across social systems despite methodological challenges, and our findings offer new perspective on the debate about the disease costs of group living. Additionally, our study demonstrates the potential of meta-analytic methods in social network analysis to test ecological and evolutionary hypotheses on cooperation, group living, communication and resilience to extrinsic pressures.

Mind the Scales: Harnessing Spatial Big Data for Infectious Disease Surveillance and Inference.

Spatial big data have the velocity, volume, and variety of big data sources and contain additional geographic information. Digital data sources, such as medical claims, mobile phone call data records, and geographically tagged tweets, have entered infectious diseases epidemiology as novel sources of data to complement traditional infectious disease surveillance. In this work, we provide examples of how spatial big data have been used thus far in epidemiological analyses and describe opportunities for these sources to improve disease-mitigation strategies and public health coordination. In addition, we consider the technical, practical, and ethical challenges with the use of spatial big data in infectious disease surveillance and inference. Finally, we discuss the implications of the rising use of spatial big data in epidemiology to health risk communication, and public health policy recommendations and coordination across scales.

Detecting signals of seasonal influenza severity through age dynamics.

BACKGROUND: Measures of population-level influenza severity are important for public health planning, but estimates are often based on case-fatality and case-hospitalization risks, which require multiple data sources, are prone to surveillance biases, and are typically unavailable in the early stages of an outbreak. To address the limitations of traditional indicators, we propose a novel severity index based on influenza age dynamics estimated from routine physician diagnosis data that can be used retrospectively and for early warning. METHODS: We developed a quantitative 'ground truth' severity benchmark that synthesizes multiple traditional severity indicators from publicly available influenza surveillance data in the United States. Observing that the age distribution of cases may signal severity early in an epidemic, we constructed novel retrospective and early warning severity indexes based on the relative risk of influenza-like illness (ILI) among working-age adults to that among school-aged children using weekly outpatient medical claims. We compared our relative risk-based indexes to the composite benchmark and estimated seasonal severity for flu seasons from 2001-02 to 2008-09 at the national and state levels. RESULTS: The severity classifications made by the benchmark were not uniquely captured by any single contributing metric, including pneumonia and influenza mortality; the influenza epidemics of 2003-04 and 2007-08 were correctly identified as the most severe of the study period. The retrospective index was well correlated with the severity benchmark and correctly identified the two most severe seasons. The early warning index performance varied, but it projected 2007-08 as relatively severe 10 weeks prior to the epidemic peak. Influenza severity varied significantly among states within seasons, and four states were identified as possible early warning sentinels for national severity. CONCLUSIONS: Differences in age patterns of ILI may be used to characterize seasonal influenza severity in the United States in real-time and in a spatially resolved way. Future research on antigenic changes among circulating viruses, pre-existing immunity, and changing contact patterns may better elucidate the mechanisms underlying these indexes. Researchers and practitioners should consider the use of composite or ILI-based severity metrics in addition to traditional severity measures to inform epidemiological understanding and situational awareness in future seasonal outbreaks.

Comparison of Nutrition Profile and Diet Record Between Veteran and Nonveteran End-Stage Renal Disease Patients Receiving Hemodialysis in Veterans Affairs and Community Clinics in Metropolitan South-Central Texas.

BACKGROUND: U.S. military veterans have high rates of chronic disease and social disadvantage, which are risk factors for protein-energy wasting (PEW). It is not known whether this translates into high prevalence of PEW in veterans with end-stage renal disease. METHODS: We compared the clinical, socioeconomic, and nutrition status and the diet of 33 veteran and 38 nonveteran clinically stable patients receiving maintenance hemodialysis (MHD) in south-central Texas. RESULTS: The whole cohort included 82% Mexican Americans (MAs), 72% type 2 diabetics, and 73% males. The body mass index was 28.9 ± 6.2, while energy intake was 21.5 ± 8.2 kcal/kg/d and protein intake was 1.0 ± 0.4 g/kg/d. Serum albumin (bromocresol purple) was 3.5 ± 0.4 g/dL, transferrin was 171.9 ± 27.8 mg/d, C-reactive protein was 2.9 (1.4-6.5) mg/L, interleukin-6 (IL-6) was 8.3 (4.2-17.9) pg/mL, neutrophil gelatinase-associated lipocalin was 729 (552-1256) ng/mL, and the malnutrition-inflammation score was 8.8 ± 3.0. In group comparison that adjusted for sex and ethnicity, the veterans had better household income, less MAs (60% vs 100%), more males (94% vs 55%), more use of a renin-angiotensin-aldosterone system blockade (66% vs 33%), and lower IL-6 levels (4.4 [3.1-5.8] vs 15.4 [8.3-20.5] pg/mL; P = .01) than nonveterans. In regression analysis, the lower serum IL-6 level in veterans was independently explained by dialysis clinic, sex, and, possibly, household income (intermediate significance). CONCLUSION: In a relatively small cohort of clinically stable MHD patients, the veterans showed equivalent nutrition status and dietary intake and less inflammation than the nonveterans, thus not supporting the possibility that veteran MHD patients may have worse nutrition than the nonveteran counterpart.

Exploring biological network structure with clustered random networks.

BACKGROUND: Complex biological systems are often modeled as networks of interacting units. Networks of biochemical interactions among proteins, epidemiological contacts among hosts, and trophic interactions in ecosystems, to name a few, have provided useful insights into the dynamical processes that shape and traverse these systems. The degrees of nodes (numbers of interactions) and the extent of clustering (the tendency for a set of three nodes to be interconnected) are two of many well-studied network properties that can fundamentally shape a system. Disentangling the interdependent effects of the various network properties, however, can be difficult. Simple network models can help us quantify the structure of empirical networked systems and understand the impact of various topological properties on dynamics. RESULTS: Here we develop and implement a new Markov chain simulation algorithm to generate simple, connected random graphs that have a specified degree sequence and level of clustering, but are random in all other respects. The implementation of the algorithm (ClustRNet: Clustered Random Networks) provides the generation of random graphs optimized according to a local or global, and relative or absolute measure of clustering. We compare our algorithm to other similar methods and show that ours more successfully produces desired network characteristics.Finding appropriate null models is crucial in bioinformatics research, and is often difficult, particularly for biological networks. As we demonstrate, the networks generated by ClustRNet can serve as random controls when investigating the impacts of complex network features beyond the byproduct of degree and clustering in empirical networks. CONCLUSION: ClustRNet generates ensembles of graphs of specified edge structure and clustering. These graphs allow for systematic study of the impacts of connectivity and redundancies on network function and dynamics. This process is a key step in unraveling the functional consequences of the structural properties of empirical biological systems and uncovering the mechanisms that drive these systems.