Data visualisation for time series in environmental epidemiology

Time series has been widely used in environmental epidemiology; especially in identifying the short-term associations between ambient air pollution and health outcomes. For both exposure and outcome, data are available at regular time intervals (daily hospital admissions and pollution levels) to explore short-term associations between them. In this article, we described main steps to conduct time series regression and highlighted some key ideas when applying this technique. A sample data was used to investigate short-term association between PM10 and daily hospital admission among children in Hanoi between 2008 and 2016. This analysis was conducted with R software. Keywords: time series, air pollution, short-term effect

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Case Time Series: A Novel Study Design for Big Data Analyses in Environmental Epidemiology

ISEE Conference Abstracts ◽

10.1289/isesisee.2018.o01.04.15 ◽

2018 ◽

Vol 2018 (1) ◽

Author(s):

Antonio Gasparrini ◽

Ana Maria Vicedo-Cabrera ◽

Francesco Sera

Keyword(s):

Time Series ◽

Big Data ◽

Study Design ◽

Environmental Epidemiology ◽

Data Analyses

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Alternative adjustment for seasonality and long-term time-trend in time-series analysis for long-term environmental exposures and disease counts

BMC Medical Research Methodology ◽

10.1186/s12874-020-01199-1 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Honghyok Kim ◽

Jong-Tae Lee ◽

Kelvin C. Fong ◽

Michelle L. Bell

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Health Outcomes ◽

Time Trend ◽

Environmental Epidemiology ◽

Real Data ◽

Alternative Methods ◽

Individual Level ◽

Series Analysis

Abstract Background Time-series analysis with case-only data is a prominent method for the effect of environmental determinants on disease events in environmental epidemiology. In this analysis, adjustment for seasonality and long-term time-trend is crucial to obtain valid findings. When applying this analysis for long-term exposure (e.g., months, years) of which effects are usually studied via survival analysis with individual-level longitudinal data, unlike its application for short-term exposure (e.g., days, weeks), a standard adjustment method for seasonality and long-term time-trend can extremely inflate standard error of coefficient estimates of the effects. Given that individual-level longitudinal data are difficult to construct and often available to limited populations, if this inflation of standard error can be solved, rich case-only data over regions and countries would be very useful to test a variety of research hypotheses considering unique local contexts. Methods We discuss adjustment methods for seasonality and time-trend used in time-series analysis in environmental epidemiology and explain why standard errors can be inflated. We suggest alternative methods to solve this problem. We conduct simulation analyses based on real data for Seoul, South Korea, 2002–2013, and time-series analysis using real data for seven major South Korean cities, 2006–2013 to identify whether the association between long-term exposure and health outcomes can be estimated via time-series analysis with alternative adjustment methods. Results Simulation analyses and real-data analysis confirmed that frequently used adjustment methods such as a spline function of a variable representing time extremely inflate standard errors of estimates for associations between long-term exposure and health outcomes. Instead, alternative methods such as a combination of functions of variables representing time can make sufficient adjustment with efficiency. Conclusions Our findings suggest that time-series analysis with case-only data can be applied for estimating long-term exposure effects. Rich case-only data such as death certificates and hospitalization records combined with repeated measurements of environmental determinants across countries would have high potentials for investigating the effects of long-term exposure on health outcomes allowing for unique contexts of local populations.

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Identification and Estimation of Causal Effects Using a Negative-Control Exposure in Time-Series Studies With Applications to Environmental Epidemiology

American Journal of Epidemiology ◽

10.1093/aje/kwaa172 ◽

2020 ◽

Cited By ~ 1

Author(s):

Yuanyuan Yu ◽

Hongkai Li ◽

Xiaoru Sun ◽

Xinhui Liu ◽

Fan Yang ◽

...

Keyword(s):

Time Series ◽

Causal Effect ◽

Environmental Epidemiology ◽

R Package ◽

Negative Control ◽

Environmental Data ◽

Causal Effects ◽

Unbiased Estimation ◽

Data Sets ◽

Time Series Studies

Abstract The initial aim of environmental epidemiology is to estimate the causal effects of environmental exposures on health outcomes. However, due to lack of enough covariates in most environmental data sets, current methods without enough adjustments for confounders inevitably lead to residual confounding. We propose a negative-control exposure based on a time-series studies (NCE-TS) model to effectively eliminate unobserved confounders using an after-outcome exposure as a negative-control exposure. We show that the causal effect is identifiable and can be estimated by the NCE-TS for continuous and categorical outcomes. Simulation studies indicate unbiased estimation by the NCE-TS model. The potential of NCE-TS is illustrated by 2 challenging applications: We found that living in areas with higher levels of surrounding greenness over 6 months was associated with less risk of stroke-specific mortality, based on the Shandong Ecological Health Cohort during January 1, 2010, to December 31, 2018. In addition, we found that the widely established negative association between temperature and cancer risks was actually caused by numbers of unobserved confounders, according to the Global Open Database from 2003–2012. The proposed NCE-TS model is implemented in an R package (R Foundation for Statistical Computing, Vienna, Austria) called NCETS, freely available on GitHub.

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On the equivalence of case-crossover and time series methods in environmental epidemiology

Biostatistics ◽

10.1093/biostatistics/kxl013 ◽

2006 ◽

Vol 8 (2) ◽

pp. 337-344 ◽

Cited By ~ 146

Author(s):

Y. Lu ◽

S. L. Zeger

Keyword(s):

Time Series ◽

Environmental Epidemiology ◽

Case Crossover

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The LDE-Type Flare Occurrence (1969-1993)

International Astronomical Union Colloquium ◽

10.1017/s0252921100025458 ◽

1994 ◽

Vol 144 ◽

pp. 279-282

Author(s):

A. Antalová

Keyword(s):

Time Series ◽

Fourier Analysis ◽

Sunspot Cycle ◽

List Type ◽

Type Number ◽

Solar Cycles ◽

Type Iv ◽

Long Duration ◽

Cycle Maximum ◽

Flare Index

AbstractThe occurrence of LDE-type flares in the last three cycles has been investigated. The Fourier analysis spectrum was calculated for the time series of the LDE-type flare occurrence during the 20-th, the 21-st and the rising part of the 22-nd cycle. LDE-type flares (Long Duration Events in SXR) are associated with the interplanetary protons (SEP and STIP as well), energized coronal archs and radio type IV emission. Generally, in all the cycles considered, LDE-type flares mainly originated during a 6-year interval of the respective cycle (2 years before and 4 years after the sunspot cycle maximum). The following significant periodicities were found:• in the 20-th cycle: 1.4, 2.1, 2.9, 4.0, 10.7 and 54.2 of month,• in the 21-st cycle: 1.2, 1.6, 2.8, 4.9, 7.8 and 44.5 of month,• in the 22-nd cycle, till March 1992: 1.4, 1.8, 2.4, 7.2, 8.7, 11.8 and 29.1 of month,• in all interval (1969-1992):a)the longer periodicities: 232.1, 121.1 (the dominant at 10.1 of year), 80.7, 61.9 and 25.6 of month,b)the shorter periodicities: 4.7, 5.0, 6.8, 7.9, 9.1, 15.8 and 20.4 of month.Fourier analysis of the LDE-type flare index (FI) yields significant peaks at 2.3 - 2.9 months and 4.2 - 4.9 months. These short periodicities correspond remarkably in the all three last solar cycles. The larger periodicities are different in respective cycles.

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