ID3: PRELIMINARY EVALUATION OF THE CLINICAL AND ECONOMIC BENEFITS OF UNIVERSAL VARICELLA VACCINATION OF CHILDREN IN GERMANY

According to WHO estimates, varicella disease is responsible of a worldwide significant burden in terms of hospitalizations, complications, and deaths, with more than 90% of cases under 12 years old. This study aims at evaluating the clinical, epidemiological, and economic burden of varicella in Ligurian children, about comorbidities, organizational variables, and vaccination coverages from 2010 to 2017, in terms of Emergency Department accesses and hospitalizations. The overall hospitalization rate was 179.76 (per 100,000 inhab.), with a gradual but significant decline since 2015, when universal varicella vaccination was introduced in Liguria (p < 0.0001). The risk of being hospitalized for complicated varicella in subjects with at least one comorbidity was significantly higher than in subjects without comorbidities (p = 0.0016). The economic analysis showed higher costs in subjects with complicated varicella who were 0–3 years old. This age group showed higher costs also considering extra-hospital costs for both outpatient procedures and pharmaceutical costs (p < 0.0001). The results confirm the relevant burden of varicella, especially in the 0–3 age group and in children with comorbidities. Thus, vaccination with the achievement of adequate vaccination coverages is confirmed to be a necessary control strategy to reduce hospitalizations and associated complications with important economic benefits.

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Smartphone Movement Sensors for Remote Monitoring of Respiratory Rate: Observational Study (Preprint)

10.2196/preprints.31453 ◽

2021 ◽

Author(s):

Sophie Valentine ◽

Benjamin Klasmer ◽

Mohammad Dabbah ◽

Marko Balabanovic ◽

DAVID PLANS

Keyword(s):

Respiratory Rate ◽

Reference Method ◽

Absolute Error ◽

Smartphone Application ◽

Healthcare Systems ◽

Economic Benefits ◽

Preliminary Evaluation ◽

The Novel ◽

In The Wild ◽

Movement Sensors

BACKGROUND Mobile health (mHealth) offers notable potential clinical and economic benefits to patients and healthcare systems alike. Although respiratory rate (RR) is of great clinical significance, existing remote technologies to measure RR suffer from limitations, such as cost, accessibility and reliability. Using smartphone movement sensors to measure RR may offer a potential solution to these shortcomings. OBJECTIVE The aim of this study was to conduct a comprehensive and ecologically valid assessment of a novel mHealth smartphone application designed to measure RR using movement sensors. METHODS Study 1 offered a preliminary evaluation, in which RR measurements from 15 participants generated via the mHealth app were compared to simultaneous measurements from a reference device cleared by the US Food and Drug Administration (FDA). Participants’ ability to successfully operate the app was also determined. Finally, a novel reference method, that would allow accuracy of the mHealth app to be investigated ‘in the wild’, was assessed for validity against the FDA-cleared reference. In Study 2, 165 participants of balanced demographics remotely downloaded the mHealth app and measured their RR. Measures from the mHealth app were compared to the novel reference that was assessed in Study 1. Usability was quantified based on the proportion of participants that were able to successfully use the app to measure their RR and standardised usability scales. RESULTS Outcomes from Study 1 supported further assessment of the mHealth app, including as assessed by the novel reference. The mHealth app, when compared to the FDA-cleared and novel references, respectively, showed a mean absolute error (MAE) of 1.65 (standard deviation (SD) = 1.49) and 1.14 (1.44), relative MAE of 12.2 (9.23) and 9.5 (18.70) and bias of 0.81 (limits of agreement (LoA) = -3.27-4.89) and 0.08 (-3.68-3.51). Pearson Product Moment Correlation (PPMC) coefficients were 0.700 and 0.885. 93% of participants could successfully operate the device on their first use and standardised usability scores were above industry averages. CONCLUSIONS The accuracy and usability of the mHealth app demonstrated in this research hold promise for the use of mHealth solutions employing smartphone movement sensors to remotely monitor RR. Considering methodological limitations, further research should be undertaken to more holistically validate the benefits that this technology may offer patients and healthcare systems.

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Preliminary Evaluation of a Weibull Function for Fitting Slow-Component Eye Velocity over the Time Course of Caloric-Induced Nystagmus

Journal of Speech Language and Hearing Research ◽

10.1044/jshr.3203.681 ◽

1989 ◽

Vol 32 (3) ◽

pp. 681-687 ◽

Cited By ~ 1

Author(s):

C. Formby ◽

B. Albritton ◽

I. M. Rivera

Keyword(s):

Time Course ◽

Slow Component ◽

Weibull Function ◽

Preliminary Evaluation ◽

Mathematical Function ◽

Before And After ◽

Best Fitting ◽

Eye Velocity ◽

Fitting Parameters ◽

Weibull Equation

We describe preliminary attempts to fit a mathematical function to the slow-component eye velocity (SCV) over the time course of caloric-induced nystagmus. Initially, we consider a Weibull equation with three parameters. These parameters are estimated by a least-squares procedure to fit digitized SCV data. We present examples of SCV data and fitted curves to show how adjustments in the parameters of the model affect the fitted curve. The best fitting parameters are presented for curves fit to 120 warm caloric responses. The fitting parameters and the efficacy of the fitted curves are compared before and after the SCV data were smoothed to reduce response variability. We also consider a more flexible four-parameter Weibull equation that, for 98% of the smoothed caloric responses, yields fits that describe the data more precisely than a line through the mean. Finally, we consider advantages and problems in fitting the Weibull function to caloric data.

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