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    Area of Science:

    • Environmental Epidemiology
    • Biostatistics
    • Public Health

    Background:

    • Distributed lag models (DLMs) are crucial for analyzing time-lagged associations between environmental exposures and health outcomes.
    • Accurate estimation of critical exposure windows requires careful consideration of model parameters and data resolution.
    • Previous studies highlight potential biases in environmental epidemiology, but the interplay of spatial resolution and time-trend adjustment in DLMs needs further investigation.

    Purpose of the Study:

    • To investigate the impact of spatial resolution of exposure data on bias and variance in distributed lag models (DLMs).
    • To evaluate the performance of different time-trend adjustment methods in DLMs under varying spatial resolutions.
    • To assess the consequences of bias amplification and variance inflation on the estimated associations between prenatal nitrogen dioxide (NO2) exposure and birth weight.

    Main Methods:

    • Conducted a simulation study using prenatal nitrogen dioxide (NO2) exposure and birth weight data.
    • Compared DLM estimation approaches with different spatial resolutions (high, low, none) and time-trend adjustment methods.
    • Analyzed a real-world cohort in Massachusetts using high-, low-, and no-spatial-resolution NO2 data within DLMs.

    Main Results:

    • High-spatial-resolution exposure data in DLMs resulted in low bias and nominal coverage, irrespective of time-trend adjustment.
    • Low- or no-spatial-resolution exposures amplified time-trend bias across all adjustment methods.
    • Variance inflation was higher in low- or no-spatial-resolution DLMs, particularly with long-term splines for trend adjustment due to concurvity.
    • Real-world NO2-birth weight analyses showed negative associations with high-resolution data, but null and positive associations with low- and no-resolution data, respectively.

    Conclusions:

    • The spatial resolution of exposure data is a critical factor in DLM analyses, directly influencing bias and variance.
    • Low-spatial-resolution exposure data can lead to substantial bias amplification and variance inflation, potentially distorting estimated health associations.
    • DLM analyses necessitate a joint consideration of exposure data's spatial resolution, time-trend adjustment parameterization, and lag constraints to ensure reliable results.