Frequency of Bacteremia and Urinary Tract Infection in Pediatric Renal Transplant Recipients.

BACKGROUND: Our primary goal was to determine the frequency of bacteremia and urinary tract infections (UTI) in pediatric renal transplant recipients presenting with suspected infection within 2 years of transplant and to identify clinical and laboratory factors associated with bacteremia. METHODS: We conducted a retrospective cross-sectional study for all pediatric ( < 18 years old) renal transplant recipients seen at 3 large children's hospitals from 2011 to 2018 for suspected infection within 2 years of transplant date, defined as pyrexia ( > 38°C) or a blood culture being ordered. Patients with primary immunodeficiencies, nontransplant immunosuppression, intestinal failure, and patients who had moved out of the local area were excluded. The primary outcome was bacteremia or UTI; secondary outcomes included pneumonia, bacterial or fungal meningitis, respiratory viral infections, and antibiotic resistance. The unit of analysis was the visit. RESULTS: One hundred fifteen children had 267 visits for infection evaluation within 2 years of transplant. Bacteremia (with or without UTI) was diagnosed in 9/213 (4.2%) and UTIs in 63/189 (33.3%). Tachycardia and hypotension were present in 66.7% and 0% of visits with documented bacteremia, respectively. White blood cell (12,700 cells/mm 3 vs. 10,900 cells/mm 3 ; P = 0.43) and absolute neutrophil count (10,700 vs. 8200 cells/mm 3 ; P = 0.24) were no different in bacteremic and nonbacteremic patients. The absolute band count was higher in children with bacteremia (1900 vs. 600 cells/mm 3 ; P = 0.02). Among Gram-negative pathogens, antibiotic resistance was seen to 3rd (14.5%) and 4th (3.6%) generation cephalosporins, 12.7% to semisynthetic penicillins, and 3.6% to carbapenems. CONCLUSIONS: Bacteremia or UTIs were diagnosed in one-quarter of all pediatric renal transplant recipients presenting with suspected infection within 2 years of transplant. Evaluations were highly variable, with one-third of visits not having urine cultures obtained. No single demographic, clinical or laboratory variable accurately identified patients with bacteremia, although combinations of findings may identify a high-risk population.

Identifying impacts of air pollution on subacute asthma symptoms using digital medication sensors.

BACKGROUND: Objective tracking of asthma medication use and exposure in real-time and space has not been feasible previously. Exposure assessments have typically been tied to residential locations, which ignore exposure within patterns of daily activities. METHODS: We investigated the associations of exposure to multiple air pollutants, derived from nearest air quality monitors, with space-time asthma rescue inhaler use captured by digital sensors, in Jefferson County, Kentucky. A generalized linear mixed model, capable of accounting for repeated measures, over-dispersion and excessive zeros, was used in our analysis. A secondary analysis was done through the random forest machine learning technique. RESULTS: The 1039 participants enrolled were 63.4% female, 77.3% adult (>18) and 46.8% White. Digital sensors monitored the time and location of over 286 980 asthma rescue medication uses and associated air pollution exposures over 193 697 patient-days, creating a rich spatiotemporal dataset of over 10 905 240 data elements. In the generalized linear mixed model, an interquartile range (IQR) increase in pollutant exposure was associated with a mean rescue medication use increase per person per day of 0.201 [95% confidence interval (CI): 0.189-0.214], 0.153 (95% CI: 0.136-0.171), 0.131 (95% CI: 0.115-0.147) and 0.113 (95% CI: 0.097-0.129), for sulphur dioxide (SO2), nitrogen dioxide (NO2), fine particulate matter (PM2.5) and ozone (O3), respectively. Similar effect sizes were identified with the random forest model. Time-lagged exposure effects of 0-3 days were observed. CONCLUSIONS: Daily exposure to multiple pollutants was associated with increases in daily asthma rescue medication use for same day and lagged exposures up to 3 days. Associations were consistent when evaluated with the random forest modelling approach.

Improved asthma outcomes observed in the vicinity of coal power plant retirement, retrofit, and conversion to natural gas.

Coal-fired power plants release substantial air pollution, including over 60% of U.S. sulfur dioxide (SO2) emissions in 2014. Such air pollution may exacerbate asthma however direct studies of health impacts linked to power plant air pollution are rare. Here, we take advantage of a natural experiment in Louisville, Kentucky, where one coal-fired power plant retired and converted to natural gas, and three others installed SO2 emission control systems between 2013 and 2016. Dispersion modeling indicated exposure to SO2 emissions from these power plants decreased after the energy transitions. We used several analysis strategies, including difference-in-differences, first-difference, and interrupted time-series modeling to show that the emissions control installations and plant retirements were associated with reduced asthma disease burden related to ZIP code-level hospitalizations and emergency room visits, and individual-level medication use as measured by digital medication sensors.

Passive Monitoring of Short-Acting Beta-Agonist Use via Digital Platform in Patients With Chronic Obstructive Pulmonary Disease: Quality Improvement Retrospective Analysis.

BACKGROUND: Digital health programs assist patients with chronic obstructive pulmonary disease (COPD) to better manage their disease. Technological and adoption barriers have been perceived as a limitation. OBJECTIVE: The aim of the research was to evaluate a digital quality improvement pilot in Medicare-eligible patients with COPD. METHODS: COPD patients were enrolled in a digital platform to help manage their medications and symptoms as part of their routine clinical care. Patients were provided with electronic medication monitors (EMMs) to monitor short-acting beta-agonist (SABA) use passively and a smartphone app to track use trends and receive feedback. Providers also had access to data collected via a secure website and were sent email notifications if a patient had a significant change in their prescribed inhaler use. Providers then determined if follow-up was needed. Change in SABA use and feasibility outcomes were evaluated at 3, 6, and 12 months. RESULTS: A total of 190 patients enrolled in the pilot. At 3, 6, and 12 months, patients recorded significant reductions in daily and nighttime SABA use and increases in SABA-free days (all P<.001). Patient engagement, as measured by the ratio of daily active use to monthly active use, was >90% at both 6 and 12 months. Retention at 6 months was 81% (154/190). Providers were sent on average two email notifications per patient during the 12-month program. CONCLUSIONS: A digital health program integrated as part of standard clinical practice was feasible and had low provider burden. The pilot demonstrated significant reduction in SABA use and increased SABA-free days among Medicare-eligible COPD patients. Further, patients readily adopted the digital platform and demonstrated strong engagement and retention rates at 6 and 12 months.

Association of influenza outbreaks with advanced pediatric medical support.

Retrospective data evaluated increases in advanced medical support for children with medically attended acute respiratory illness (MAARI) during influenza outbreak periods (IOP). Advanced support included hospitalisation, intensive care unit admission, or mechanical ventilation, for children aged 0-17 years hospitalised in Maryland's 50 acute-care hospitals over 12 influenza seasons. Weekly numbers of positive influenza tests in the Maryland area defined IOP for each season as the fewest consecutive weeks, including the peak week containing at least 85% of positive tests with a 2-week buffer on either side of the IOP. Peak IOP (PIOP) was defined as four consecutive weeks containing the peak week with the most number of positive influenza tests. Off-PIOP was defined as the 'shoulder' weeks during each IOP. Non-influenza season (NIS) was the remaining weeks of that study season. Rate ratios of mean daily MAARI-related admissions resulting in advanced medical support outcomes during PIOP or Off-PIOP were compared with the NIS and were significantly elevated for all 12 study seasons combined. The results suggest that influenza outbreaks are associated with increased advanced medical support utilisation by children with MAARI. We feel that this data may help preparedness for severe influenza epidemics or pandemic.

AIR Louisville: Addressing Asthma With Technology, Crowdsourcing, Cross-Sector Collaboration, And Policy.

Cross-sector partnerships benefit public health by leveraging ideas, resources, and expertise from a wide range of partners. In this study we documented the process and impact of AIR Louisville (a collaboration forged among the Louisville Metro Government, a nonprofit institute, and a technology company) in successfully tackling a complex public health challenge: asthma. We enrolled residents of Louisville, Kentucky, with asthma and used electronic inhaler sensors to monitor where and when they used medication. We found that the use of the digital health platform achieved positive clinical outcomes, including a 78 percent reduction in rescue inhaler use and a 48 percent improvement in symptom-free days. Moreover, the crowdsourced real-world data on inhaler use, combined with environmental data, led to policy recommendations including enhancing tree canopy, tree removal mitigation, zoning for air pollution emission buffers, recommended truck routes, and developing a community asthma notification system. AIR Louisville represents a model that can be replicated to address many public health challenges by simultaneously guiding individual, clinical, and policy decisions.