Importance of applying Mixed Generalized Additive Model (MGAM) as a method for assessing the environmental health impacts: Ambient temperature and Acute Myocardial Infarction (AMI), among elderly in Shanghai, China

Association between acute myocardial infarction (AMI) morbidity and ambient temperature has been examined with generalized linear model (GLM) or generalized additive model (GAM). However, the effect size by these two methods might be biased due to the autocorrelation of time series data and arbitrary selection of degree of freedom of natural cubic splines. The present study analyzed how the climatic factors affected AMI morbidity for older adults in Shanghai with Mixed generalized additive model (MGAM) that addressed these shortcomings mentioned. Autoregressive random effect was used to model the relationship between AMI and temperature, PM10, week days and time. The degree of freedom of time was chosen based on the seasonal pattern of temperature. The performance of MGAM was compared with GAM on autocorrelation function (ACF), partial autocorrelation function (PACF) and goodness of fit. One-year predictions of AMI counts in 2011 were conducted using MGAM with the moving average. Between 2007 and 2011, MGAM adjusted the autocorrelation of AMI time series and captured the seasonal pattern after choosing the degree of freedom of time at 5. Using MGAM, results were well fitted with data in terms of both internal (R2 = 0.86) and external validity (correlation coefficient = 0.85). The risk of AMI was relatively high in low temperature (Risk ratio = 0.988 (95% CI 0.984, 0.993) for under 12°C) and decreased as temperature increased and speeded up within the temperature zone from 12°C to 26°C (Risk ratio = 0.975 (95% CI 0.971, 0.979), but it become increasing again when it is 26°C although not significantly (Risk ratio = 0.999 (95% CI 0.986, 1.012). MGAM is more appropriate than GAM in the scenario of response variable with autocorrelation and predictors with seasonal variation. The risk of AMI was comparatively higher when temperature was lower than 12°C in Shanghai as a typical representative location of subtropical climate.

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Short-term association between ambient temperature and acute myocardial infarction hospitalizations for diabetes mellitus patients: A time series study

PLoS Medicine ◽

10.1371/journal.pmed.1002612 ◽

2018 ◽

Vol 15 (7) ◽

pp. e1002612 ◽

Cited By ~ 13

Author(s):

Holly Ching Yu Lam ◽

Juliana Chung Ngor Chan ◽

Andrea On Yan Luk ◽

Emily Ying Yang Chan ◽

William Bernard Goggins

Keyword(s):

Diabetes Mellitus ◽

Myocardial Infarction ◽

Acute Myocardial Infarction ◽

Time Series ◽

Ambient Temperature ◽

Short Term ◽

Time Series Study ◽

Series Study

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Impact of Influenza on Outpatient Visits, Hospitalizations, and Deaths by Using a Time Series Poisson Generalized Additive Model

PLoS ONE ◽

10.1371/journal.pone.0149468 ◽

2016 ◽

Vol 11 (2) ◽

pp. e0149468 ◽

Cited By ~ 6

Author(s):

Ru-ning Guo ◽

Hui-zhen Zheng ◽

Chun-quan Ou ◽

Li-qun Huang ◽

Yong Zhou ◽

...

Keyword(s):

Time Series ◽

Generalized Additive Model ◽

Additive Model ◽

Outpatient Visits

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Temporal Cross-Correlations between Ambient Air Pollutants and Seasonality of Tuberculosis: A Time-Series Analysis

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16091585 ◽

2019 ◽

Vol 16 (9) ◽

pp. 1585 ◽

Cited By ~ 6

Author(s):

Hua Wang ◽

Changwei Tian ◽

Wenming Wang ◽

Xiaoming Luo

Keyword(s):

Time Series ◽

Air Pollutants ◽

Quality Index ◽

Generalized Additive Model ◽

Additive Model ◽

Ambient Air ◽

Delayed Effect ◽

Incidence Data ◽

Ambient Air Pollutants ◽

Cross Correlations

The associations between ambient air pollutants and tuberculosis seasonality are unclear. We assessed the temporal cross-correlations between ambient air pollutants and tuberculosis seasonality. Monthly tuberculosis incidence data and ambient air pollutants (PM2.5, PM10, carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2)) and air quality index (AQI) from 2013 to 2017 in Shanghai were included. A cross-correlogram and generalized additive model were used. A 4-month delayed effect of PM2.5 (0.55), PM10 (0.52), SO2 (0.47), NO2 (0.40), CO (0.39), and AQI (0.45), and a 6-month delayed effect of O3 (−0.38) on the incidence of tuberculosis were found. The number of tuberculosis cases increased by 8%, 4%, 18%, and 14% for a 10 μg/m3 increment in PM2.5, PM10, SO2, and NO2; 4% for a 10 unit increment in AQI; 8% for a 0.1 mg/m3 increment in CO; and decreased by 4% for a 10 μg/m3 increment in O3. PM2.5 concentrations above 50 μg/m3, 70 μg/m3 for PM10, 16 μg/m3 for SO2, 47 μg/m3 for NO2, 0.85 mg/m3 for CO, and 85 for AQI, and O3 concentrations lower than 95 μg/m3 were positively associated with the incidence of tuberculosis. Ambient air pollutants were correlated with tuberculosis seasonality. However, this sort of study cannot prove causality.

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Association between Florida's smoke-free policy and acute myocardial infarction by race: A time series analysis, 2000–2013

Preventive Medicine ◽

10.1016/j.ypmed.2016.05.032 ◽

2016 ◽

Vol 92 ◽

pp. 169-175 ◽

Cited By ~ 5

Author(s):

Erin L. Mead ◽

Raul Cruz-Cano ◽

Debra Bernat ◽

Laurie Whitsel ◽

Jidong Huang ◽

...

Keyword(s):

Myocardial Infarction ◽

Acute Myocardial Infarction ◽

Time Series ◽

Time Series Analysis ◽

Series Analysis ◽

Smoke Free Policy

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A time-series analysis of the relation between unemployment rate and hospital admission for acute myocardial infarction and stroke in Brazil over more than a decade

International Journal of Cardiology ◽

10.1016/j.ijcard.2016.08.309 ◽

2016 ◽

Vol 224 ◽

pp. 33-36 ◽

Cited By ~ 3

Author(s):

Marcelo Katz ◽

Hayden B. Bosworth ◽

Renato D. Lopes ◽

Matthew E. Dupre ◽

Fernando Morita ◽

...

Keyword(s):

Myocardial Infarction ◽

Acute Myocardial Infarction ◽

Time Series ◽

Time Series Analysis ◽

Hospital Admission ◽

Unemployment Rate ◽

Series Analysis

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GENERALIZED ADDITIVE MODEL FOR COUNT TIME SERIES: AN APPLICATION TO QUANTIFY THE IMPACT OF AIR POLLUTANTS ON HUMAN HEALTH

Pesquisa Operacional ◽

10.1590/0101-7438.2021.041.00241120 ◽

2021 ◽

Vol 41 ◽

Author(s):

Ana Julia A. Camara ◽

Glaura C. Franco ◽

Valderio A. Reisen ◽

Pascal Bondon

Keyword(s):

Time Series ◽

Human Health ◽

Air Pollutants ◽

Generalized Additive Model ◽

Additive Model ◽

Count Time Series ◽

The Impact

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Association between tobacco control policies and hospital admissions for acute myocardial infarction in Thailand, 2006-2017: A time series analysis

PLoS ONE ◽

10.1371/journal.pone.0242570 ◽

2020 ◽

Vol 15 (12) ◽

pp. e0242570

Author(s):

Roengrudee Patanavanich ◽

Stanton A. Glantz

Keyword(s):

Myocardial Infarction ◽

Acute Myocardial Infarction ◽

Time Series ◽

Negative Binomial ◽

Hospital Admissions ◽

Age Groups ◽

Cigarette Price ◽

Tobacco Control Policies ◽

Cigarette Tax ◽

Icd 10

Introduction Studies in many countries have documented reductions of acute myocardial infarction (AMI) hospitalizations with smokefree policies. However, evidence on the association of cigarette tax with AMI events is unclear. There have been no studies of the associations between these two policies and AMI hospitalizations in Thailand. Methods We used negative binomial time series analyses of AMI hospitalizations (ICD-10 codes I21.0-I21.9), stratified by sex and age groups, from October 2006 to September 2017 to determine whether there was a change in AMI hospitalizations as a result of the changes in cigarette prices and the implementation of a 100% smokefree law. Results Cigarette price increases were associated with a significant 4.7% drop in AMI hospitalizations among adults younger than 45 (incidence rate ratio [IRR], 0.953; 95% confidence interval [CI], 0.914–0.993; p = 0.021). Implementation of the 100% smokefree law was followed by a significant 13.1% drop in AMI hospitalizations among adults younger than 45 (IRR, 0.869; 95% CI, 0.801–0.993; P = 0.001). There were not significant associations in older age groups. Conclusions The Thai cigarette tax policy and the smokefree law were associated with reduced AMI hospitalizations among younger adults. To improve effectiveness of the policies, taxes should be high enough to increase cigarette price above inflation rates, making cigarettes less likely to be purchased; smokefree laws should be strictly enforced.

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Abstract 345: Forecasting the Number of Out-Of-Hospital Cardiac Arrest in the Young Using Generalized Additive Model

Circulation ◽

10.1161/circ.140.suppl_2.345 ◽

2019 ◽

Vol 140 (Suppl_2) ◽

Author(s):

Alyssa Vermeulen ◽

Marina Del Rios ◽

Teri L Campbell ◽

Hai Nguyen ◽

Hoang H Nguyen

Keyword(s):

Time Series ◽

Cardiac Arrest ◽

Generalized Additive Model ◽

Moving Average ◽

Additive Model ◽

Resource Planning ◽

Model Framework ◽

Auto Regressive ◽

Hospital Cardiac Arrest ◽

Causality Hypothesis

Introduction: Accurate forecasting could help in resource planning and evaluation of intervention efforts to reduce out-of-hospital cardiac arrest. Hypothesis: Generalized additive model can rapidly and accurately forecast the number of OHCA in the young (1-35 years old) when compared to other auto regressive moving average (ARIMA) based models. Methods: Data were obtained from CARES in Chicago from May 2013 to December 2017. Monthly forecasts of the number of OHCA were performed using a generative additive model framework using the open source software Prophet and R. The first 50 months served as training and the last 6 months served as testing. Results: Figure 1 shows the distribution of the number of cases over the 3-year study period showing yearly seasonality and upward trend. Figure 2 shows the forecast of the number of arrest (middle line) along with the actual number of arrest (dots). The shaded band represents the 95% confidence interval of the prediction. Figure 3 show the comparison of the 6-month forecast and actual data. This model has low root mean square error score when compared with other ARIMA based models (2 vs. 4). Conclusion: Accurate time series forecasting of the number of OHCA arrests could be achieved. Time series analysis and forecasting are essential tools in evaluating the effectiveness of intervention efforts to reduce OHCA as they allow for causality hypothesis testing.

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