Case-Crossover Method with a Short Time-Window

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 b...

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Main Author: Mieczysław Szyszkowicz
Format: Article
Language:English
Published: MDPI AG 2019-12-01
Series:International Journal of Environmental Research and Public Health
Subjects:
Online Access:https://www.mdpi.com/1660-4601/17/1/202
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spelling doaj-c26946ecd6674f93a30c8b6a3680f92f2020-11-25T02:03:25ZengMDPI AGInternational Journal of Environmental Research and Public Health1660-46012019-12-0117120210.3390/ijerph17010202ijerph17010202Case-Crossover Method with a Short Time-WindowMieczysław Szyszkowicz0Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON K1A 0K9, CanadaNumerous 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.https://www.mdpi.com/1660-4601/17/1/202air pollutioncase-crossoverclusterconcentrationcountstime-series
collection DOAJ
language English
format Article
sources DOAJ
author Mieczysław Szyszkowicz
spellingShingle Mieczysław Szyszkowicz
Case-Crossover Method with a Short Time-Window
International Journal of Environmental Research and Public Health
air pollution
case-crossover
cluster
concentration
counts
time-series
author_facet Mieczysław Szyszkowicz
author_sort Mieczysław Szyszkowicz
title Case-Crossover Method with a Short Time-Window
title_short Case-Crossover Method with a Short Time-Window
title_full Case-Crossover Method with a Short Time-Window
title_fullStr Case-Crossover Method with a Short Time-Window
title_full_unstemmed Case-Crossover Method with a Short Time-Window
title_sort case-crossover method with a short time-window
publisher MDPI AG
series International Journal of Environmental Research and Public Health
issn 1660-4601
publishDate 2019-12-01
description 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.
topic air pollution
case-crossover
cluster
concentration
counts
time-series
url https://www.mdpi.com/1660-4601/17/1/202
work_keys_str_mv AT mieczysławszyszkowicz casecrossovermethodwithashorttimewindow
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