Machine learning models for prediction of nutrient concentrations in surface water in an agricultural watershed.

Prediction and quantification of nutrient concentrations in surface water has gained substantial attention during recent decades because excess nutrients released from agricultural and urban watersheds can significantly deteriorate surface water quality. Machine learning (ML) models are considered an effective tool for better understanding and characterization of nutrient release from agricultural fields to surface water. However, to date, no systematic investigations have examined the implementation of different classification and regression ML models in agricultural settings to predict nutrient concentrations in surface water using a group of input variables including climatological (e.

A cluster randomized trial of xylitol chewing gum for prevention of preterm birth: The PPaX trial.

Background: Maternal periodontal disease is associated with preterm and low-birthweight deliveries, but randomized trials of likely efficacious treatments (e.g., dental scaling and root planing) during pregnancy have not reduced these adverse outcomes.

Tailored vs. General COVID-19 prevention for adults with mental disabilities residing in group homes: a randomized controlled effectiveness-implementation trial.

Background: People with serious mental illness (SMI) and people with intellectual disabilities/developmental disabilities (ID/DD) are at higher risk for COVID-19 and more severe outcomes. We compare a tailored versus general best practice COVID-19 prevention program in group homes (GHs) for people with SMI or ID/DD in Massachusetts (MA).

Methods: A hybrid effectiveness-implementation cluster randomized control trial compared a four-component implementation strategy (Tailored Best Practices: TBP) to dissemination of standard prevention guidelines (General Best-Practices: GBP) in GHs across six MA behavioral health agencies.

Using simulation modeling to inform intervention and implementation selection in a rapid stakeholder-engaged hybrid effectiveness-implementation randomized trial.

Background: Implementation research generally assumes established evidence-based practices and prior piloting of implementation strategies, which may not be feasible during a public health emergency. We describe the use of a simulation model of the effectiveness of COVID-19 mitigation strategies to inform a stakeholder-engaged process of rapidly designing a tailored intervention and implementation strategy for individuals with serious mental illness (SMI) and intellectual/developmental disabilities (ID/DD) in group homes in a hybrid effectiveness-implementation randomized trial.

Methods: We used a validated dynamic microsimulation model of COVID-19 transmission and disease in late 2020/early 2021 to determine the most effective strategies to mitigate infections among Massachusetts group home staff and residents.