ArticleInternational journal of environmental research and public health2019
Case-Crossover Method with a Short Time-Window.
Article in International journal of environmental research and public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Short-term exposure to ambient air pollutants increases the mortality risk of chronic kidney disease: a time-stratified case-crossover study in Zhejiang Province.BMC nephrology · 2026Article
- Ambient PMBMC public health · 2025Article
- Association of short-term air pollution with risk of major adverse cardiovascular event mortality and modification effects of lifestyle in Chinese adults.Environmental health and preventive medicine · 2025Article
- A case-crossover analysis to quantify the impact of wildfire smoke on hospital respiratory admissions in the Rogue Valley, Oregon.Public health in practice (Oxford, England) · 2024Article
- Short-term air pollution exposure associated with death from kidney diseases: a nationwide time-stratified case-crossover study in China from 2015 to 2019.BMC medicine · 2023Article
- Air Pollution and Emergency Department Visits for Disease of the Genitourinary System.Environmental health insights · 2021Article
- Air Pollution and Emergency Department Visits for Mental Disorders among Youth.International journal of environmental research and public health · 2020Article
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Abstract
Numerous epidemiological studies have shown associations between short-term ambient air pollution exposure and various health problems. The time-stratified case-crossover design is a popular technique for estimating these associations. In the standard approach, the case-crossover model is realized by using a conditional logistic regression on data that are interpreted as a set of cases (i.e., individual health events) and controls. In statistical calculations, for each case record, three or four corresponding control records are considered. Here, the case-crossover model is realized as a conditional Poisson regression on counts with stratum indicators. Such an approach enables the reduction of the number of data records that are used in the numerical calculations. In this presentation, the method used analyzes daily counts on the shortest possible time-window, which is composed of two consecutive days. The proposed technique is positively tested on four challenging simulated datasets, for which classical time-series methods fail. The methodology presented here also suggests that the length of exposure (i.e., size of the time-window) may be associated with the severity of health conditions.
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