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Objectives: Some of the most pressing health problems are found in rural America. However, the surveillance needed to track and prevent disease in these regions is lacking. Our objective was to perform a comprehensive health survey of a single rural county to assess the validity of using emergency claims data to estimate rural disease prevalence at a sub-county level., Design: We performed a cross-sectional study of chronic disease prevalence estimates using emergency department (ED) claims data versus mailed health surveys designed to capture a substantial proportion of residents in New York's rural Sullivan County., Setting: Sullivan County, a rural county ranked second-to-last for health outcomes in New York State., Participants: Adult residents of Sullivan County aged 25 years and older who responded to the health survey in 2017-2018 or had at least one ED visit in 2011-2015., Outcome Measures: We compared age and gender-adjusted prevalence of hypertension, hyperlipidaemia, diabetes, cancer, asthma and chronic obstructive pulmonary disease/emphysema among nine sub-county areas., Results: Our county-wide mailed survey obtained 6675 completed responses for a response rate of 30.4%. This sample represented more than 12% of the estimated 53 020 adults in Sullivan County. Using emergency claims data, we identified 34 576 adults from Sullivan County who visited an ED at least once during 2011-2015. At a sub-county level, prevalence estimates from mailed surveys and emergency claims data correlated especially well for diabetes (r=0.90) and asthma (r=0.85). Other conditions were not well correlated (range: 0.23-0.46). Using emergency claims data, we created more geographically detailed maps of disease prevalence using geocoded addresses., Conclusions: For select conditions, emergency claims data may be useful for tracking disease prevalence in rural areas and providing more geographically detailed estimates. For rural regions lacking robust health surveillance, emergency claims data can inform how to geographically target efforts to prevent chronic disease., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Dialysis patients have reduced moderate to vigorous physical activity, and light physical activity. This has been shown in self-reported surveys and objective accelerometer studies. Less attention has been directed toward sedentary behavior, which is characterized by low energy expenditure (≤1.5 metabolic equivalents). Furthermore, locations where physical activity and sedentary behavior occur are largely unknown for dialysis patients., Objectives: The objectives of this study were (1) to determine the minutes per day of moderate to vigorous physical activity, light physical activity, and sedentary behavior for hemodialysis patients; (2) to describe differences in moderate to vigorous physical activity, light physical activity, and sedentary behavior comparing dialysis versus nondialysis days; and (3) to describe the locations where moderate to vigorous physical activity, light physical activity, and sedentary behavior occur using global positioning system (GPS) data., Design: Cross-sectional study., Setting: The study was performed at a tertiary care hospital in Nova Scotia, Canada., Patients: A total of 50 adult in-center hemodialysis patients consented to the study., Measurements: Physical activity and sedentary behavior were measured with an Actigraph-GT3X accelerometer. Location was determined using a Qstarz BT-Q1000X GPS receiver., Methods: Minutes of daily activity were described as was percentage of wear time for each activity level across different locations during waking hours. Physical activity intensity, quantity, and location were also analyzed according to dialysis vs nondialysis days., Results: Forty-three patients met requirements for accelerometer analysis, of whom 42 had GPS data. Median wear time was 836.5 min/day (interquartile range [IQR]: 788.3-918.3). Median minutes of daily wear time spent in sedentary behavior, light physical activity, and moderate to vigorous physical activity was 636 minutes (IQR: 594.1-730.1), 178 minutes (IQR: 144-222.1), and 1.6 minutes (IQR: 0.6-7.7), respectively. Proportion of daily wear time spent in sedentary behavior, light physical activity, and moderate to vigorous physical activity was 78.4% (IQR: 70.7-84.0), 21.5% (IQR: 16.0-26.9), and 0.2% (IQR: 0.1-1.1), respectively. Home was the dominant location for total linked accelerometer-GPS time (59.4%, IQR: 46.9-69.5) as well as for each prespecified level of activity. Significantly more sedentary behavior and less light physical activity occurred on dialysis days compared with nondialysis days ( P ≤ .01, respectively). Moderate to vigorous physical activity did not differ significantly between dialysis and nondialysis days., Limitations: Small sample size from a single academic center may limit generalizability. Difficult to engage population as less than half of eligible dialysis patients provided consent. Physical activity may have been underestimated as devices were not worn for all waking hours or aquatic activities, and hip-based accelerometers may not capture stationary exercise., Conclusions: Ambulatory, in-center hemodialysis patients exhibit substantial sedentary behavior and minimal physical activity across a limited range of locations. Given the sedentary tendencies of this population, focus should be directed on increasing physical activity at any location frequented. Home-based exercise programs may serve as a potential adjunct to established intradialytic-based therapies given the amount of time spent in the home environment., Competing Interests: Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Introduction: Although screening for diabetes is recommended at age 45, some populations may be at greater risk at earlier ages. Our objective was to quantify age disparities among patients with type 2 diabetes in New York City., Methods: Using all-payer hospital claims data for New York City, we performed a cross-sectional analysis of patients with type 2 diabetes identified from emergency department visits during the 5-year period 2011-2015. We estimated type 2 diabetes prevalence at each year of life, the age distribution of patients stratified by decade, and the average age of patients by sex, race/ethnicity, and geographic location., Results: We identified 576,306 unique patients with type 2 diabetes. These patients represented more than half of all people with type 2 diabetes in New York City. Patients in racial/ethnic minority groups were on average 5.5 to 8.4 years younger than non-Hispanic white patients. At age 45, type 2 diabetes prevalence was 10.9% among non-Hispanic black patients and 5.2% among non-Hispanic white patients. In our geospatial analyses, patients with type 2 diabetes were on average 6 years younger in hotspots of diabetes-related emergency department use and inpatient hospitalizations. The average age of patients with type 2 diabetes was also 1 to 2 years younger in hotspots of microvascular diabetic complications., Conclusion: We identified profound age disparities among patients with type 2 diabetes in racial/ethnic minority groups and in neighborhoods with poor health outcomes. The younger age of these patients may be due to earlier onset of diabetes and/or earlier death from diabetic complications. Our findings demonstrate the need for geographically targeted interventions that promote earlier diagnosis and better glycemic control.

Purpose of Review: Many observational studies have demonstrated a survival benefit with home dialysis compared with in-center dialysis; however, results have been conflicting. The purpose of this review was to identify pitfalls and limitations in existing literature and examine the challenges of studying home and in-center dialysis populations., Sources of Information: Original research articles were identified from MEDLINE using search terms "in-center hemodialysis," "home hemodialysis," "conventional hemodialysis," "nocturnal hemodialysis," and "short daily hemodialysis.", Methods: A focused review and critical appraisal of existing home versus in-center hemodialysis survival literature was conducted to identify potential causes for variability in the observed survival outcomes., Key Findings: The controversy in existing literature stems from the challenges of randomizing patients to home versus in-center hemodialysis modalities, and therefore a reliance on observational comparisons for study. In many cases, these observational analyses have been limited by selection bias (variabilities in populations included, inclusion of both incident and prevalent cohorts, and variabilities in dialysis intensity), as well as residual confounding. Furthermore, the studies that do exist lack generalizability in many cases., Limitations: There are few randomized controlled trials examining the survival benefit of home versus in-center hemodialysis and existing observational studies are often limited by bias and reduced generalizability. These limitations comprise the body of this review., Implications: This review examines challenges surrounding survival comparisons with home versus in-center hemodialysis and identify important directions for future study., Competing Interests: Declaration of Conflicting Interests: The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: J.P. has received speaking honoraria from Baxter Healthcare, Amgen Canada, and DaVita Healthcare Partners and has consulting fees from Amgen, Boehringer Ingelheim Canada, Baxter Healthcare, Otsuka, Janssen Ortho Shire, and Takeda, as well as research support from Baxter Healthcare and salary support from Arbor Research Collaborative for Health. K.K.T. has received unrestricted grant funding from Astellas, Canada, and Otsuka, Canada, and has done consulting work for Otsuka and Astra Zeneca.

Background: Dialysis patients who require ambulance transport to the emergency department ("ambulance-ED") may subsequently require timely dialysis in a monitored setting ("urgent dialysis")., Objective: The purpose of this study was to develop and internally validate a risk prediction model for urgent dialysis based on patient characteristics at the time of paramedic assessment before ambulance-ED., Design: Cohort Study., Setting: Region of Nova Scotia, Canada, covered by a single emergency medical services provider., Patients: Thrice-weekly hemodialysis patients who initiated dialysis between 2009 and 2013 (follow-up to 2015) and experienced one or more ambulance-ED events., Measurements: The primary outcome ("urgent dialysis") was defined as dialysis within 24 hours of an ambulance-ED in a monitored setting or dialysis within 24 hours of an ambulance-ED with an initial ED potassium of >6.5 mmol/L. Predictors of urgent dialysis based on paramedic assessment before ambulance-ED included presenting complaint, vital signs and time from last dialysis to ambulance dispatch., Methods: Associations with urgent dialysis were analyzed using logistic regression from which a risk prediction model was created. The model was internally validated using bootstrapping and model performance was assessed by discrimination and calibration., Results: Among 197 patients, there were 624 ambulance-ED events and 87 episodes of urgent dialysis. Weakness as a presenting complaint (odds ratio [OR]: 4.62, 95% confidence interval [CI]: 1.23-17.29), >24 hours since last dialysis (OR: 2.09, 95% CI: 1.15-3.81), and vital signs, including heart rate <60 beats/minute (OR: 3.06, 95% CI: 1.09-8.61), oxygen saturation <90% (OR: 3.04, 95% CI: 1.55-5.94), elevated respiratory rate (≥20 breaths/min), and systolic blood pressure>160 mmHg, were associated with urgent dialysis after ambulance-ED. A risk prediction model incorporating these variables had very good discrimination (C-statistic: 0.81, 95% CI: 0.76-0.86). The negative predictive value was 93.6% using the optimal cut point. Of patients who were predicted to need urgent dialysis but were transported to a facility incapable of providing it, 31% were re-transported for urgent dialysis., Limitations: Findings of our study may not be generalizable to other centers where the practice of ambulance transfer and availability of monitored dialysis may differ, and data were lacking for potential missed dialysis sessions or changes in routine dialysis scheduling., Conclusions: Patient characteristics at the time of paramedic assessment are associated with urgent dialysis after ambulance-ED. This risk prediction model has the potential to guide dialysis patient transport to dialysis-capable facilities when needed., Competing Interests: Declaration of Conflicting Interests: The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: KT has no direct conflicts of interest relevant to the current area of research. KT has done advisory work for AstraZeneca and Otsuka, Canada. All other authors have no conflicts of interest to declare.

Background: Because of the challenges with organ scarcity, many centers performing simultaneous liver-kidney transplant (SLKT) are opting to accept donation after circulatory death (DCD) organs as a means of facilitating earlier transplant and reducing death rates on the waitlist. It has been suggested, however, that DCD organs may have inferior graft and patient survival posttransplant compared with donation after neurologic death (DND) organs., Methods: We created a Markov model to compare the overall outcomes of accepting a DCD SLKT now versus waiting for a DND SLKT in patients waitlisted for SLKT, stratified by base Model for End-Stage Liver Disease (MELD) score (≤20, 21-30, >30)., Results: Waiting for DND SLKT was the preferred treatment strategy for patients with a MELD score of 30 or less (incremental value of 0.54 and 0.36 quality-adjusted life years for MELD score of 20 or less and MELD score of 21 to 30 with DND versus DCD SLKT, respectively). The option to accept a DCD SLKT became the preferred choice for those with a MELD score greater than 30 (incremental value of 0.31 quality-adjusted life years for DCD versus DND SLKT). This finding was confirmed in a probabilistic sensitivity analysis and persisted when analyzing total life years obtained for accept DCD versus do not accept DCD., Conclusions: There is a benefit to accepting DCD SLKT for patients with MELD score greater than 30. Although not accepting DCD SLKT and waiting for DND SLKT is the preferred option for patients with MELD of 30 or less, the incremental value is small.

Background: In kidney transplantation, nonimmunologic donor-recipient (D-R) pairing is generally not given the same consideration as immunologic matching. The aim of this study was to determine how nonimmunologic D-R pairing relates to independent donor and recipient factors, and to immunologic HLA match for predicting graft loss., Methods: Seven D-R pairings (race, sex, age, weight, height, cytomegalovirus serostatus, and HLA match) were assessed for their association with the composite outcome of death or kidney graft loss using a Cox regression-based forward stepwise selection model. The best model for predicting graft loss (including nonimmunologic D-R pairings, independent D-R factors, and/or HLA match status) was determined using the Akaike Information Criterion., Results: Twenty three thousand two hundred sixty two (29.9%) people in the derivation data set and 9892 (29.7%) in the validation data set developed the composite outcome of death or graft loss. A model that included both independent and D-R pairing variables best predicted graft loss. The c-indices for the derivation and validation models were 0.626 and 0.629, respectively. Size mismatch (MM) between donor and recipient (>30 kg [D < R} and >15 cm [D < R]) was associated with poor patient and graft survival even with 0 HLA MM, and conversely, an optimal D-R size pairing mitigated the risk of graft loss seen with 6 HLA MM., Conclusions: D-R pairing is valuable in predicting patient and graft outcomes after kidney transplant. D-R size matching could offset the benefit and harm seen with 0 and 6 HLA MM, respectively. This is a novel finding., Competing Interests: The authors declare no funding or conflicts of interest.

Currently many but not all centers transplant hepatitis C virus (HCV) viremic positive (+) donor kidneys into HCV+ recipients. Directed donation of HCV+ organs reduces the wait time to transplantation for HCV+ patients. Direct-acting antiviral (DAA) therapy can cure HCV in virtually all who are infected. Some have suggested that treatment of HCV+ waitlisted patients be deferred with the hope that earlier transplantation will provide better outcomes than early DAA therapy. However, there are not enough organs to guarantee prompt transplantation for the current waitlist of infected candidates. A Markov medical decision analysis model was created to compare the overall outcomes of delayed DAA therapy (Option 1) to immediate DAA therapy (Option 2) in waitlisted HCV+ patients. Option 1 patients were modeled to be transplanted 1 year earlier, with a higher cumulative transplant incidence (54% at 5 years post-listing vs 45% for Option 2). Despite this, Option 2 provided 0.43 (95% confidence interval [CI] 0.38-0.49) more life years than Option 1. However, Option 1 was preferred for regions with much greater access to HCV+ organs or in patients with very low HCV+-associated mortality. The best option from an individual patient's perspective will differ by region and candidate., (© 2018 The American Society of Transplantation and the American Society of Transplant Surgeons.)

Traditional methods of health surveillance often under-represent racial and ethnic minorities. Our objective was to use geospatial analysis and emergency claims data to estimate local chronic disease prevalence separately for specific racial and ethnic groups. We also performed a regression analysis to identify associations between median household income and local disease prevalence among Black, Hispanic, Asian, and White adults in New York City. The study population included individuals who visited an emergency department at least once from 2009 to 2013. Our main outcomes were geospatial estimates of diabetes, hypertension, and asthma prevalence by Census tract as stratified by race and ethnicity. Using emergency claims data, we identified 4.9 million unique New York City adults with 28.5% of identifying as Black, 25.2% Hispanic, and 6.1% Asian. Age-adjusted disease prevalence was highest among Black and Hispanic adults for diabetes (13.4 and 13.1%), hypertension (28.7 and 24.1%), and asthma (9.9 and 10.1%). Correlation between disease prevalence maps demonstrated moderate overlap between Black and Hispanic adults for diabetes (0.49), hypertension (0.57), and asthma (0.58). In our regression analysis, we found that the association between low income and high disease prevalence was strongest for Hispanic adults, whereas increases in income had more modest reductions in disease prevalence for Black adults, especially for diabetes. Our geographically detailed maps of disease prevalence generate actionable evidence that can help direct health interventions to those communities with the highest health disparities. Using these novel geographic approaches, we reveal the underlying epidemiology of chronic disease for a racially and culturally diverse population.

Background: Prolonged warm ischemia time (WIT) is associated with graft failure and mortality, however less is known about factors associated with prolonged WIT., Methods: In a cohort of United States deceased donor kidney transplant recipients identified using the Scientific Registry of Transplant Recipients (Jan 2005-Dec 2013), we identified factors associated with prolonged WIT (defined as ≥ 30 minutes versus 10-30 minutes) using hierarchical multilevel models adjusting for center effect, and WIT as a continuous variable using multiple linear regression of log-transformed data., Results: Among 55 829 patients, potentially modifiable risk factors associated with prolonged WIT included increased recipient body mass index (BMI) (odds ratio [OR], 1.57; 95% confidence interval [CI], 1.44-1.72 for BMI > 35), right donor kidney (OR, 1.14; 95% CI, 1.08-1.19), and a prolonged cold ischemic time (OR, 1.23; 95% CI, 1.13-1.33 for cold ischemia time > 24 hours). Transplanting a right kidney into an obese recipient further prolonged WIT (OR, 1.75; 95% CI, 1.55-1.98; for BMI > 35), increasing overall WIT by 11.0%. There was no correlation between median WIT for a given center and annual center transplant rate (pairwise correlation coefficient, 0.0898)., Conclusions: In conclusion, several modifiable factors are associated with prolonged WIT and may represent strategies to improve WIT and subsequent posttransplant outcomes., Competing Interests: The authors declare no funding or conflicts of interest.

Geographic surveillance can identify hotspots of disease and reveal associations between health and the environment. Our study used emergency department surveillance to investigate geographic disparities in type 1 and type 2 diabetes prevalence among adults and children. Using all-payer emergency claims data from 2009 to 2013, we identified unique New York City residents with diabetes and geocoded their location using home addresses. Geospatial analysis was performed to estimate diabetes prevalence by New York City Census tract. We also used multivariable regression to identify neighborhood-level factors associated with higher diabetes prevalence. We estimated type 1 and type 2 diabetes prevalence at 0.23% and 10.5%, respectively, among adults and 0.20% and 0.11%, respectively, among children in New York City. Pediatric type 1 diabetes was associated with higher income ( P = 0.001), whereas adult type 2 diabetes was associated with lower income ( P < 0.001). Areas with a higher proportion of nearby restaurants categorized as fast food had a higher prevalence of all types of diabetes ( P < 0.001) except for pediatric type 2 diabetes. Type 2 diabetes among children was only higher in neighborhoods with higher proportions of African American residents ( P < 0.001). Our findings identify geographic disparities in diabetes prevalence that may require special attention to address the specific needs of adults and children living in these areas. Our results suggest that the food environment may be associated with higher type 1 diabetes prevalence. However, our analysis did not find a robust association with the food environment and pediatric type 2 diabetes, which was predominantly focused in African American neighborhoods., Competing Interests: The authors have nothing to disclose.

Given the inequalities in the distribution of disease burden, geographically detailed methods of disease surveillance are needed to identify local hot spots of chronic disease. However, few data sources include the patient-level addresses needed to perform these studies. Given that individual hospitals would have access to this geographically granular data, this study assessed the reliability of estimating chronic disease prevalence using emergency department surveillance at specific hospitals. Neighborhood-level diabetes, hypertension, and asthma prevalence were estimated using emergency claims data from each individual hospital in New York City from 2009-2012. Estimates were compared to prevalence obtained from a traditional health survey. A multivariable analysis also was performed to identify which individual hospitals were more accurate at estimating citywide disease prevalence. Among 52 hospitals, variation was found in the accuracy of disease prevalence estimates using emergency department surveillance. Estimates at some hospitals, such as NYU Langone Medical Center, had strong correlations for all diseases studied (diabetes: 0.81, hypertension: 0.84, and asthma: 0.84). Hospitals with patient populations geographically distributed throughout New York City had better accuracy in estimating citywide disease prevalence. For diabetes and hypertension, hospitals with racial/ethnic patient distributions similar to Census estimates and higher fidelity of diagnosis coding also had more accurate prevalence estimates. This study demonstrated how citywide chronic disease surveillance can be performed using emergency data from specific sentinel hospitals. The findings may provide an alternative means of mapping chronic disease burden by using existing data, which may be critical in regions without resources for geographically detailed health surveillance.