Response Rates and Nonresponse Bias Among 20 Years of Pediatrician Surveys.

Objectives: Our objectives were to examine the following: physician survey response rates across a 20-year period; the impact of a token incentive on response rates; whether survey nonresponse bias is present and if it is associated with response rate; and the impact of a token incentive on nonresponse bias.

Methods: We utilized data from 68 American Academy of Pediatrics (AAP) pediatrician surveys from 2000 to 2019 and an AAP administrative database, which included information for both respondents and non-respondents (target sample). Linear regression examined response rates over time.

Early Childhood Screening Practices and Barriers: A National Survey of Primary Care Pediatricians.

Background And Objectives: The American Academy of Pediatrics recommends screening during the first 3 years of life for developmental risk/delay, maternal depression, and social determinants of health (SDOH) using standardized tools. Adoption of these guidelines has been gradual, and barriers to screening are as varied as pediatric practices are themselves.

Methods: We analyzed 2019 American Academy of Pediatrics Periodic Survey data.

An illustration of model agnostic explainability methods applied to environmental data.

Historically, two primary criticisms statisticians have of machine learning and deep neural models is their lack of uncertainty quantification and the inability to do inference (i.e., to explain what inputs are important).

A dependent Bayesian Dirichlet process model for source apportionment of particle number size distribution.

The relationship between particle exposure and health risks has been well established in recent years. Particulate matter (PM) is made up of different components coming from several sources, which might have different level of toxicity. Hence, identifying these sources is an important task in order to implement effective policies to improve air quality and population health.