A novel online survey approach designed to measure consumer sunscreen application thickness-implications for estimating environmental emissions.

BACKGROUND: The effects of ultraviolet (UV) filters in the aquatic environment have been well studied, but environmental exposures remain unclear and understudied. Consumer usage directly influences the amount of sunscreen products, and subsequently UV filters, potentially released into the environment. OBJECTIVE: To conduct a literature review of previous research into sunscreen application thickness, develop a questionnaire protocol designed to semi-quantify sunscreen usage by US consumers, and conduct a large-scale survey to determine a sunscreen application thickness (to face and body) that is more refined than conservative defaults. The United States Food & Drug Administration (US FDA) recommends a sunscreen application rate of 2 mg/cm2. This value is typically used as a worst-case assumption in environmental exposure assessments of UV filters. METHODS: Designed a novel approach to estimate lotion sunscreen application thickness using an online questionnaire protocol employing visual references and self-reported height and weight of the respondents. A literature review was also conducted to collect historical sunscreen usage. RESULTS: Over 9000 people were surveyed in the US, and after the dataset was refined, their sunscreen application thickness was estimated based on calculated body surface area and reported sunscreen amounts. The mean and median values for survey respondents are 3.00 and 1.78 mg/cm2, respectively, for facial application thickness and 1.52 and 1.35 mg/cm2, respectively, for body application thickness. Earlier research from 1985-2020 reported 36 of the 38 values are below the US FDA's recommended application thickness of 2 mg/cm2 (range 0.2-5 mg/cm2). IMPACT STATEMENT: This web-based survey is the first of its kind, designed specifically to quantify sunscreen application in a large and diverse set of consumers. This method provides a greater reach to larger populations thus enabling more granular data analysis and understanding. Exposure assessments of sunscreen ingredients typically use conservative parameters. These data can refine those assessments and allow for more informed and science-based risk management decisions.

Hydrologic disturbance reduces biological integrity in urban streams.

The impact of urbanization on stream ecosystems is linked by land cover changes to the alteration of the natural hydrology and subsequent physical disruption of stream biota and habitat. Seasonal floods are part of the natural disturbance regime of many streams, but urbanization increases their frequency and magnitude. This study evaluated the impact of hydrologic disturbance on fish and aquatic macroinvertebrates in 81 (56 urban/25 reference) Ohio streams. Hydrologic variables included annual and monthly 24-h rainfall maxima and computed annual peak discharge, with computation supported by GIS-based drainage area delineation and land cover characterization. Ohio biological criteria for fish and macroinvertebrates measured during the late spring and summer were negatively impacted by annual peak discharge in urban streams as compared to reference streams. Results support the application of stormwater best management practices as part of stream restoration efforts to mitigate urbanization impacts to fish and macroinvertebrates.

The use of logistic regression to enhance risk assessment and decision making by mental health administrators.

Development of policies and procedures to contend with the risks presented by elopement, aggression, and suicidal behaviors are long-standing challenges for mental health administrators. Guidance in making such judgments can be obtained through the use of a multivariate statistical technique known as logistic regression. This procedure can be used to develop a predictive equation that is mathematically formulated to use the best combination of predictors, rather than considering just one factor at a time. This paper presents an overview of logistic regression and its utility in mental health administrative decision making. A case example of its application is presented using data on elopements from Missouri's long-term state psychiatric hospitals. Ultimately, the use of statistical prediction analyses tempered with differential qualitative weighting of classification errors can augment decision-making processes in a manner that provides guidance and flexibility while wrestling with the complex problem of risk assessment and decision making.