Seasonality and geographical distribution of Kawasaki disease among Black children in the Southeast United States

IntroductionKawasaki Disease (KD) is a leading cause of pediatric acquired heart disease in the United States, affecting up to 7,000 children annually. Seasonal variation, an epidemiological characteristic of KD, has previously been reported predominantly among Asian children; however, little is known about the epidemiology and seasonality of KD of Black children within the U.S.MethodsElectronic medical records were abstracted from 529 hospitalized KD patients admitted to a single tertiary center in Alabama between 2005 and 2019. Medical charts were reviewed to confirm KD diagnosis following American Heart Association criteria. Cases were stratified by the month of diagnosis date to assess seasonality, and statewide distribution of incidence is reported at county level using geographical spatial analysis. Comparisons were performed between Black patients and White patients with KD.ResultsThe average number of KD cases per year was 35. Approximately, 60% were males and 44% were White children (N = 234), 45% were Black children (N = 240) and 11% were other races (N = 55). Black children were younger than White children at KD admission (median age 32 vs. 41 months respectively, p = 0.02). Overall, the highest rates of cases occurred between January and April. When stratifying by race, cases started to rise in December among White children with the highest rates between February and April with a peak in March. Among Black children cases were high during the winter season (January–April) with a peak in April. Similarly high rates also occurred in June, July and November. There were no differences in geographical distribution of cases by race.ConclusionKD incidence among White children in Alabama follows a seasonal cycle similar to other regions in the U.S. However, sustained incidence and additional peaks outside of the usual KD seasonality were seen among Black children with KD. Further studies are needed to investigate differential triggers between races

Clinical risk factors for admission with Pseudomonas and multidrug-resistant Pseudomonas community-acquired pneumonia

BACKGROUND: Microbial etiology for community-acquired pneumonia (CAP) is evolving with pathogens known for high CAP mortality e.g., Pseudomonas species. Chronic obstructive pulmonary disease (COPD) patients are at risk for hospitalization for CAP. Understanding regional patterns and risk factors for multidrug-resistant (MDR) Pseudomonas acquisition has implications for antimicrobial stewardship. OBJECTIVES: To evaluate the regional epidemiology of MDR Pseudomonas CAP and its association with COPD. METHODS: We queried the electronic medical records of the University of Alabama at Birmingham Healthcare System to identify patients hospitalized for CAP with Pseudomonas positive respiratory samples between 01/01/2013-12/31/2019. Log binomial regression models were used to examine associations between COPD diagnosis and risk of Pseudomonas/MDR Pseudomonas CAP. RESULTS: Cohort consisted of 913 culture positive CAP cases aged 59-year (IQR:48-68), 61% (560) male, 60% (547) white, 65% (580) current/past smokers, and 42% (384) COPD. Prevalence of Pseudomonas CAP in culture positive CAP was 18% (167), MDR Pseudomonas CAP in Pseudomonas CAP was 22% (36), and yearly incidence of MDR Pseudomonas CAP was stable (p = 0.169). COPD was associated with Pseudomonas CAP (RR 1.39; 95% CI 1.01, 1.91; p = 0.041) but not with MDR Pseudomonas CAP (0.71; 95% CI 0.35, 1.45; p = 0.349). Stroke (RR 2.64; 95% CI 1.51, 4.61; p = 0.0006) and use of supplemental oxygen (RR 2.31; 95% CI 1.30, 4.12; p = 0.005) were associated with MDR Pseudomonas CAP. CONCLUSION: Incidence of MDR Pseudomonas CAP was stable over time. COPD was associated with Pseudomonas CAP but not with MDR Pseudomonas CAP. Larger cohort studies are needed to confirm findings

Randomization to randomization probability: Estimating treatment effects under actual conditions of use

Blinded randomized controlled trials (RCT) require participants to be uncertain if they are receiving a treatment or placebo. Although uncertainty is ideal for isolating the treatment effect from all other potential effects, it is poorly suited for estimating the treatment effect under actual conditions of intended use—when individuals are certain that they are receiving a treatment. We propose an experimental design, Randomization to Randomization Probabilities (R2R), which significantly improves estimates of treatment effects under actual conditions of use by manipulating participant expectations about receiving treatment. In the R2R design, participants are first randomized to a value, π, denoting their probability of receiving treatment (vs placebo). Subjects are then told their value of π and randomized to either treatment or placebo with probabilities π and 1-π, respectively. Analysis of the treatment effect includes statistical controls for π (necessary for causal inference) and typically a π-by-treatment interaction. Random assignment of subjects to π and disclosure of its value to subjects manipulates subject expectations about receiving the treatment without deception. This method offers a better treatment effect estimate under actual conditions of use than does a conventional RCT. Design properties, guidelines for power analyses, and limitations of the approach are discussed. We illustrate the design by implementing an RCT of caffeine effects on mood and vigilance and show that some of the actual effects of caffeine differ by the expectation that one is receiving the active drug