Detta dokument tillhandahåller en analys av miljöhälsoindikatorer baserat på data från Miljöhälsoenkäten 2023. Det inkluderar definitioner, databehandlingssteg och visualiseringar för olika indikatorer såsom luftkvalitet, trafikbuller, tillgång till grönområden och frekvens av vistelser i grönområden. Syftet är att presentera viktade och sammanfattade data för Stockholmsregionen för FHK-plattformen.

The create_period_exposures() function provides: Aggregation of exposures into standardized time periods (month, quarter, half-year, year) Support for both point-in-time data and period-based exposure data Special handling for logarithmic values (e.g., decibel-scale noise measurements) Time-weighted averaging to account for varying exposure durations Data quality metrics to assess completeness of exposure information Flexible parameter options to customize the aggregation process

Time-weighted exposure metrics are essential in environmental epidemiology for capturing cumulative or lagged effects of exposures such as noise or air pollution. The calculate_tw_exposure() function provides a flexible way to compute time-weighted averages, minimum/maximum values, and missingness metrics for configurable time windows around each observation.

The document provides a detailed description of the data sources and methods used for population attribution of noise exposure in Stockholm County, as applied in the BMHE2019 and MHpK2024 projects. It outlines the methodology for estimating the number of people exposed to road traffic, railway, and aircraft noise above 55 dB, using 2019 population data stratified by age groups and administrative classifications. The analysis incorporates data from SCB, Trafikverket, Trafikförvaltningen, and Swedavia, along with GIS-based spatial assessments.

grönområden i angränsande kommuner till Solna

In epidemiological studies of environmental noise, the general term time-weighted average is often used to describe energy-based averaging of noise levels over defined periods of time.

GIND-VGH is a regional study aimed at assessing NDVI/EVI greenness data for Västra Götaland and Halland to support environmental and epidemiological research.

No description

To conduct the dose-response meta-analysis (i.e. all illustrations and results presented here are based on non-linear input data from corresponding papers), the dosresmeta package in R was employed, focusing on the association between exposure and response.

In this session, we will talk about why and how we can use dynamic plots in our work. Dynamic plots help researchers look at complex data interactively, making it easier to find relationships, patterns, and unusual data points that might be missed in regular, static charts.

1. Restrict curves to noise levels 75 dB or below 2. Exclude studies with very high “lower level” – Decide on 60 dB.