ArticleJournal of the Royal Statistical Society. Series C, Applied statistics2020
A spatially varying distributed lag model with application to an air pollution and term low birth weight study.
Article in Journal of the Royal Statistical Society. Series C, Applied statistics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- A Mixture of Distributed Lag Non-Linear Models to Account for Spatially Heterogeneous Exposure-Lag-Response Associations.Statistics in medicine · 2026Article
- Spatially varying coefficient generalized distributed lag models for binary response with MCEM estimation.BMC medical research methodology · 2026Article
- Air Pollution Exposure and Birth Weight in the ECHO Cohort.JAMA network open · 2025Article
- Assessing spatial variability in observed infectious disease spread in a prospective time-space series.International journal of health geographics · 2025Article
- Dynamic Single-Index Scalar-On-Function Model.Statistics in medicine · 2025Article
- Racial disparities in maternal exposure to ambient air pollution during pregnancy and prevalence of congenital heart defects.American journal of epidemiology · 2025Article
- The association of short-term increases in ambient PM2.5 and temperature exposures with stillbirth: racial/ethnic disparities among Medicaid recipients.American journal of epidemiology · 2024Article
- Penalized distributed lag interaction model: Air pollution, birth weight, and neighborhood vulnerability.Environmetrics · 2024Article
- An Approach to Identifying Spatial Variability in Observed Infectious Disease Spread in a Prospective Time-Space Series with Applications to COVID-19 and Dengue Incidence.Research square · 2024Article
- Heterogeneous Distributed Lag Models to Estimate Personalized Effects of Maternal Exposures to Air Pollution.Journal of the American Statistical Association · 2024Article
- Multiple exposure distributed lag models with variable selection.Biostatistics (Oxford, England) · 2023Article
- CRITICAL WINDOW VARIABLE SELECTION FOR MIXTURES: ESTIMATING THE IMPACT OF MULTIPLE AIR POLLUTANTS ON STILLBIRTH.The annals of applied statistics · 2022Article
- Bayesian spatio-temporal distributed lag modeling for delayed climatic effects on sparse malaria incidence data.BMC medical research methodology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Distributed lag models have been used to identify critical pregnancy periods of exposure (i.e. critical exposure windows) to air pollution in studies of pregnancy outcomes. However, much of the previous work in this area has ignored the possibility of spatial variability in the lagged health effect parameters that may result from exposure characteristics and/or residual confounding. We develop a spatially varying Gaussian process model for critical windows called 'SpGPCW' and use it to investigate geographic variability in the association between term low birth weight and average weekly concentrations of ozone and PM
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.